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Sample records for optimal signal selection

  1. Optimized multiple-quantum filter for robust selective excitation of metabolite signals

    NASA Astrophysics Data System (ADS)

    Holbach, Mirjam; Lambert, Jörg; Suter, Dieter

    2014-06-01

    The selective excitation of metabolite signals in vivo requires the use of specially adapted pulse techniques, in particular when the signals are weak and the resonances overlap with those of unwanted molecules. Several pulse sequences have been proposed for this spectral editing task. However, their performance is strongly degraded by unavoidable experimental imperfections. Here, we show that optimal control theory can be used to generate pulses and sequences that perform almost ideally over a range of rf field strengths and frequency offsets that can be chosen according to the specifics of the spectrometer or scanner being used. We demonstrate this scheme by applying it to lactate editing. In addition to the robust excitation, we also have designed the pulses to minimize the signal of unwanted molecular species.

  2. Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

    PubMed

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

  3. Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

    PubMed Central

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

  4. Optimal Prediction by Cellular Signaling Networks

    NASA Astrophysics Data System (ADS)

    Becker, Nils B.; Mugler, Andrew; ten Wolde, Pieter Rein

    2015-12-01

    Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.

  5. Selecting the optimal anti-aliasing filter for multichannel biosignal acquisition intended for inter-signal phase shift analysis.

    PubMed

    Keresnyei, Róbert; Megyeri, Péter; Zidarics, Zoltán; Hejjel, László

    2015-01-01

    The availability of microcomputer-based portable devices facilitates the high-volume multichannel biosignal acquisition and the analysis of their instantaneous oscillations and inter-signal temporal correlations. These new, non-invasively obtained parameters can have considerable prognostic or diagnostic roles. The present study investigates the inherent signal delay of the obligatory anti-aliasing filters. One cycle of each of the 8 electrocardiogram (ECG) and 4 photoplethysmogram signals from healthy volunteers or artificially synthesised series were passed through 100-80-60-40-20 Hz 2-4-6-8th order Bessel and Butterworth filters digitally synthesized by bilinear transformation, that resulted in a negligible error in signal delay compared to the mathematical model of the impulse- and step responses of the filters. The investigated filters have as diverse a signal delay as 2-46 ms depending on the filter parameters and the signal slew rate, which is difficult to predict in biological systems and thus difficult to compensate for. Its magnitude can be comparable to the examined phase shifts, deteriorating the accuracy of the measurement. As a conclusion, identical or very similar anti-aliasing filters with lower orders and higher corner frequencies, oversampling, and digital low pass filtering are recommended for biosignal acquisition intended for inter-signal phase shift analysis. PMID:25514627

  6. Methods to optimize selective hyperthermia

    NASA Astrophysics Data System (ADS)

    Cowan, Thomas M.; Bailey, Christopher A.; Liu, Hong; Chen, Wei R.

    2003-07-01

    Laser immunotherapy, a novel therapy for breast cancer, utilizes selective photothermal interaction to raise the temperature of tumor tissue above the cell damage threshold. Photothermal interaction is achieved with intratumoral injection of a laser absorbing dye followed by non-invasive laser irradiation. When tumor heating is used in combination with immunoadjuvant to stimulate an immune response, anti-tumor immunity can be achieved. In our study, gelatin phantom simulations were used to optimize therapy parameters such as laser power, laser beam radius, and dye concentration to achieve maximum heating of target tissue with the minimum heating of non-targeted tissue. An 805-nm diode laser and indocyanine green (ICG) were used to achieve selective photothermal interactions in a gelatin phantom. Spherical gelatin phantoms containing ICG were used to simulate the absorption-enhanced target tumors, which were embedded inside gelatin without ICG to simulate surrounding non-targeted tissue. Different laser powers and dye concentrations were used to treat the gelatin phantoms. The temperature distributions in the phantoms were measured, and the data were used to determine the optimal parameters used in selective hyperthermia (laser power and dye concentration for this case). The method involves an optimization coefficient, which is proportional to the difference between temperatures measured in targeted and non-targeted gel. The coefficient is also normalized by the difference between the most heated region of the target gel and the least heated region. A positive optimization coefficient signifies a greater temperature increase in targeted gelatin when compared to non-targeted gelatin, and therefore, greater selectivity. Comparisons were made between the optimization coefficients for varying laser powers in order to demonstrate the effectinvess of this method in finding an optimal parameter set. Our experimental results support the proposed use of an optimization

  7. Optimal Distinctiveness Signals Membership Trust.

    PubMed

    Leonardelli, Geoffrey J; Loyd, Denise Lewin

    2016-07-01

    According to optimal distinctiveness theory, sufficiently small minority groups are associated with greater membership trust, even among members otherwise unknown, because the groups are seen as optimally distinctive. This article elaborates on the prediction's motivational and cognitive processes and tests whether sufficiently small minorities (defined by relative size; for example, 20%) are associated with greater membership trust relative to mere minorities (45%), and whether such trust is a function of optimal distinctiveness. Two experiments, examining observers' perceptions of minority and majority groups and using minimal groups and (in Experiment 2) a trust game, revealed greater membership trust in minorities than majorities. In Experiment 2, participants also preferred joining minorities over more powerful majorities. Both effects occurred only when minorities were 20% rather than 45%. In both studies, perceptions of optimal distinctiveness mediated effects. Discussion focuses on the value of relative size and optimal distinctiveness, and when membership trust manifests. PMID:27140657

  8. Optimal investment in social signals.

    PubMed

    Dessalles, Jean-Louis

    2014-06-01

    This study is an attempt to determine how much individuals should invest in social communication, depending on the type of relationships they may form. Two simple models of social relationships are considered. In both models, individuals emit costly signals to advertise their "quality" as potential friends. Relationships are asymmetrical or symmetrical. In the asymmetrical condition (first model), we observe that low-quality individuals are discouraged from signaling. In the symmetrical condition (second model), all individuals invest in communication. In both models, high-quality individuals (elite) do not compete and signal uniformly. The level of this uniform signal and the size of the "elite" turn out to be controlled by the accuracy of signals. The two models may be relevant to several aspects of animal and human social communication. PMID:24495174

  9. Optimization of the signal selection of exclusively reconstructed decays of B0 and B/s mesons at CDF-II

    SciTech Connect

    Doerr, Christian

    2006-06-23

    The work presented in this thesis is mainly focused on the application in a Δms measurement. Chapter 1 starts with a general theoretical introduction on the unitarity triangle with a focus on the impact of a Δms measurement. Chapter 2 then describes the experimental setup, consisting of the Tevatron collider and the CDF II detector, that was used to collect the data. In chapter 3 the concept of parameter estimation using binned and unbinned maximum likelihood fits is laid out. In addition an introduction to the NeuroBayes{reg_sign} neural network package is given. Chapter 4 outlines the analysis steps walking the path from the trigger level selection to fully reconstructed B mesons candidates. In chapter 5 the concepts and formulas that form the ingredients to an unbinned maximum likelihood fit of Δms (Δmd) from a sample of reconstructed B mesons are discussed. Chapter 6 then introduces the novel method of using neural networks to achieve an improved signal selection. First the method is developed, tested and validated using the decay B0 → Dπ, D → Kππ and then applied to the kinematically very similar decay Bs → Dsπ, Ds→ Φπ, Φ → KK. Chapter 7 uses events selected by the neural network selection as input to an unbinned maximum likelihood fit and extracts the B0 lifetime and Δmd. In addition, an amplitude scan and an unbinned maximum likelihood fit of Δms is performed, applying the neural network selection developed for the decay channel Bs → Dsπ, Ds → Φπ, Φ → KK. Finally chapter 8 summarizes and gives an outlook.

  10. Optimal BLS: Optimizing transit-signal detection for Keplerian dynamics

    NASA Astrophysics Data System (ADS)

    Ofir, Aviv

    2015-08-01

    Transit surveys, both ground- and space-based, have already accumulated a large number of light curves that span several years. We optimize the search for transit signals for both detection and computational efficiencies by assuming that the searched systems can be described by Keplerian, and propagating the effects of different system parameters to the detection parameters. Importnantly, we mainly consider the information content of the transit signal and not any specific algorithm - and use BLS (Kovács, Zucker, & Mazeh 2002) just as a specific example.We show that the frequency information content of the light curve is primarily determined by the duty cycle of the transit signal, and thus the optimal frequency sampling is found to be cubic and not linear. Further optimization is achieved by considering duty-cycle dependent binning of the phased light curve. By using the (standard) BLS, one is either fairly insensitive to long-period planets or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3 yr long dataset). We also show how the physical system parameters, such as the host star's size and mass, directly affect transit detection. This understanding can then be used to optimize the search for every star individually.By considering Keplerian dynamics explicitly rather than implicitly one can optimally search the transit signal parameter space. The presented Optimal BLS enhances the detectability of both very short and very long period planets, while allowing such searches to be done with much reduced resources and time. The Matlab/Octave source code for Optimal BLS is made available.

  11. Personnel selection as a signaling game.

    PubMed

    Bangerter, Adrian; Roulin, Nicolas; König, Cornelius J

    2012-07-01

    Personnel selection involves exchanges of information between job market actors (applicants and organizations). These actors do not have an incentive to exchange accurate information about their ability and commitment to the employment relationship unless it is to their advantage. This state of affairs explains numerous phenomena in personnel selection (e.g., faking). Signaling theory describes a mechanism by which parties with partly conflicting interests (and thus an incentive for deception) can nevertheless exchange accurate information. We apply signaling theory to personnel selection, distinguishing between adaptive relationships between applicants and organizations, among applicants, and among organizations. In each case, repeated adaptations and counteradaptations between actors can lead to situations of equilibrium or escalation (arms races). We show that viewing personnel selection as a network of adaptive relationships among job market actors enables an understanding of both classic and underexplored micro- and macro-level selection phenomena and their dynamic interactions.

  12. Software for multimodal battlefield signal modeling and optimal sensor placement

    NASA Astrophysics Data System (ADS)

    Yamamoto, Kenneth K.; Vecherin, Sergey N.; Wilson, D. Keith; Borden, Christian T.; Bettencourt, Elizabeth; Pettit, Chris L.

    2012-05-01

    Effective use of passive and active sensors for surveillance, security, and intelligence must consider terrain and atmospheric effects on the sensor performance. Several years ago, U.S. Army ERDC undertook development of software for modeling environmental effects on target signatures, signal propagation, and battlefield sensors for many signal modalities (e.g., optical, acoustic, seismic, magnetic, radio-frequency, chemical, biological, and nuclear). Since its inception, the software, called Environmental Awareness for Sensor and Emitter Employment (EASEE), has matured and evolved significantly for simulating a broad spectrum of signal-transmission and sensing scenarios. The underlying software design involves a flexible, object-oriented approach to the various stages of signal modeling from emission through processing into inferences. A sensor placement algorithm has also been built in for optimizing sensor selections and placements based on specification of sensor supply limitations, coverage priorities, and wireless sensor communication requirements. Some recent and ongoing enhancements are described, including modeling of active sensing scenarios and signal reflections, directivity of signal emissions and sensors, improved handling of signal feature dependencies, extensions to realistically model additional signal modalities such as infrared and RF, and XML-based communication with other calculation and display engines.

  13. Pattern Selection by Dynamical Biochemical Signals

    PubMed Central

    Palau-Ortin, David; Formosa-Jordan, Pau; Sancho, José M.; Ibañes, Marta

    2015-01-01

    The development of multicellular organisms involves cells to decide their fate upon the action of biochemical signals. This decision is often spatiotemporally coordinated such that a spatial pattern arises. The dynamics that drive pattern formation usually involve genetic nonlinear interactions and positive feedback loops. These complex dynamics may enable multiple stable patterns for the same conditions. Under these circumstances, pattern formation in a developing tissue involves a selection process: why is a certain pattern formed and not another stable one? Herein we computationally address this issue in the context of the Notch signaling pathway. We characterize a dynamical mechanism for developmental selection of a specific pattern through spatiotemporal changes of the control parameters of the dynamics, in contrast to commonly studied situations in which initial conditions and noise determine which pattern is selected among multiple stable ones. This mechanism can be understood as a path along the parameter space driven by a sequence of biochemical signals. We characterize the selection process for three different scenarios of this dynamical mechanism that can take place during development: the signal either 1) acts in all the cells at the same time, 2) acts only within a cluster of cells, or 3) propagates along the tissue. We found that key elements for pattern selection are the destabilization of the initial pattern, the subsequent exploration of other patterns determined by the spatiotemporal symmetry of the parameter changes, and the speeds of the path compared to the timescales of the pattern formation process itself. Each scenario enables the selection of different types of patterns and creates these elements in distinct ways, resulting in different features. Our approach extends the concept of selection involved in cellular decision-making, usually applied to cell-autonomous decisions, to systems that collectively make decisions through cell

  14. Self-extinction through optimizing selection.

    PubMed

    Parvinen, Kalle; Dieckmann, Ulf

    2013-09-21

    Evolutionary suicide is a process in which selection drives a viable population to extinction. So far, such selection-driven self-extinction has been demonstrated in models with frequency-dependent selection. This is not surprising, since frequency-dependent selection can disconnect individual-level and population-level interests through environmental feedback. Hence it can lead to situations akin to the tragedy of the commons, with adaptations that serve the selfish interests of individuals ultimately ruining a population. For frequency-dependent selection to play such a role, it must not be optimizing. Together, all published studies of evolutionary suicide have created the impression that evolutionary suicide is not possible with optimizing selection. Here we disprove this misconception by presenting and analyzing an example in which optimizing selection causes self-extinction. We then take this line of argument one step further by showing, in a further example, that selection-driven self-extinction can occur even under frequency-independent selection. PMID:23583808

  15. Self-extinction through optimizing selection.

    PubMed

    Parvinen, Kalle; Dieckmann, Ulf

    2013-09-21

    Evolutionary suicide is a process in which selection drives a viable population to extinction. So far, such selection-driven self-extinction has been demonstrated in models with frequency-dependent selection. This is not surprising, since frequency-dependent selection can disconnect individual-level and population-level interests through environmental feedback. Hence it can lead to situations akin to the tragedy of the commons, with adaptations that serve the selfish interests of individuals ultimately ruining a population. For frequency-dependent selection to play such a role, it must not be optimizing. Together, all published studies of evolutionary suicide have created the impression that evolutionary suicide is not possible with optimizing selection. Here we disprove this misconception by presenting and analyzing an example in which optimizing selection causes self-extinction. We then take this line of argument one step further by showing, in a further example, that selection-driven self-extinction can occur even under frequency-independent selection.

  16. Feature Selection via Chaotic Antlion Optimization

    PubMed Central

    Zawbaa, Hossam M.; Emary, E.; Grosan, Crina

    2016-01-01

    Background Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used. Results We propose an optimization approach for the feature selection problem that considers a “chaotic” version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics. PMID:26963715

  17. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.

    PubMed

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  18. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    PubMed Central

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  19. Selection of AN Optimal FORCE STATE Map

    NASA Astrophysics Data System (ADS)

    Duym, S. W. R.; Schoukens, J. F. M.

    1996-11-01

    The restoring force method and the equivalent force-state mapping technique have been used to characterise non-linear mechanical systems. Both acquire a number of samples and process them to produce a non-parametric representation of the non-linear force as a function of two state variables. Errors are primarily introduced by an incomplete model and by measurement noise. By establishing a trade-off between both error sources it is possible to attain an optimal model. In this paper it is shown how such an optimal model is obtained by selecting the number of grid elements and their respective distribution. The optimal grid selection method is illustrated for automotive shock absorbers.

  20. Optimal Signal Processing of Frequency-Stepped CW Radar Data

    NASA Technical Reports Server (NTRS)

    Ybarra, Gary A.; Wu, Shawkang M.; Bilbro, Griff L.; Ardalan, Sasan H.; Hearn, Chase P.; Neece, Robert T.

    1995-01-01

    An optimal signal processing algorithm is derived for estimating the time delay and amplitude of each scatterer reflection using a frequency-stepped CW system. The channel is assumed to be composed of abrupt changes in the reflection coefficient profile. The optimization technique is intended to maximize the target range resolution achievable from any set of frequency-stepped CW radar measurements made in such an environment. The algorithm is composed of an iterative two-step procedure. First, the amplitudes of the echoes are optimized by solving an overdetermined least squares set of equations. Then, a nonlinear objective function is scanned in an organized fashion to find its global minimum. The result is a set of echo strengths and time delay estimates. Although this paper addresses the specific problem of resolving the time delay between the two echoes, the derivation is general in the number of echoes. Performance of the optimization approach is illustrated using measured data obtained from an HP-851O network analyzer. It is demonstrated that the optimization approach offers a significant resolution enhancement over the standard processing approach that employs an IFFT. Degradation in the performance of the algorithm due to suboptimal model order selection and the effects of additive white Gaussion noise are addressed.

  1. Optimal Signal Processing of Frequency-Stepped CW Radar Data

    NASA Technical Reports Server (NTRS)

    Ybarra, Gary A.; Wu, Shawkang M.; Bilbro, Griff L.; Ardalan, Sasan H.; Hearn, Chase P.; Neece, Robert T.

    1995-01-01

    An optimal signal processing algorithm is derived for estimating the time delay and amplitude of each scatterer reflection using a frequency-stepped CW system. The channel is assumed to be composed of abrupt changes in the reflection coefficient profile. The optimization technique is intended to maximize the target range resolution achievable from any set of frequency-stepped CW radar measurements made in such an environment. The algorithm is composed of an iterative two-step procedure. First, the amplitudes of the echoes are optimized by solving an overdetermined least squares set of equations. Then, a nonlinear objective function is scanned in an organized fashion to find its global minimum. The result is a set of echo strengths and time delay estimates. Although this paper addresses the specific problem of resolving the time delay between the first two echoes, the derivation is general in the number of echoes. Performance of the optimization approach is illustrated using measured data obtained from an HP-X510 network analyzer. It is demonstrated that the optimization approach offers a significant resolution enhancement over the standard processing approach that employs an IFFT. Degradation in the performance of the algorithm due to suboptimal model order selection and the effects of additive white Gaussion noise are addressed.

  2. Optimal signal processing for continuous qubit readout

    NASA Astrophysics Data System (ADS)

    Ng, Shilin; Tsang, Mankei

    2014-08-01

    The measurement of a quantum two-level system, or a qubit in modern terminology, often involves an electromagnetic field that interacts with the qubit, before the field is measured continuously and the qubit state is inferred from the noisy field measurement. During the measurement, the qubit may undergo spontaneous transitions, further obscuring the initial qubit state from the observer. Taking advantage of some well-known techniques in stochastic detection theory, here we propose a signal processing protocol that can infer the initial qubit state optimally from the measurement in the presence of noise and qubit dynamics. Assuming continuous quantum-nondemolition measurements with Gaussian or Poissonian noise and a classical Markov model for the qubit, we derive analytic solutions to the protocol in some special cases of interest using Itō calculus. Our method is applicable to multihypothesis testing for robust qubit readout and relevant to experiments on qubits in superconducting microwave circuits, trapped ions, nitrogen-vacancy centers in diamond, semiconductor quantum dots, or phosphorus donors in silicon.

  3. Multi objective SNP selection using pareto optimality.

    PubMed

    Gumus, Ergun; Gormez, Zeliha; Kursun, Olcay

    2013-04-01

    Biomarker discovery is a challenging task of bioinformatics especially when targeting high dimensional problems such as SNP (single nucleotide polymorphism) datasets. Various types of feature selection methods can be applied to accomplish this task. Typically, using features versus class labels of samples in the training dataset, these methods aim at selecting feature subsets with maximal classification accuracies. Although finding such class-discriminative features is crucial, selection of relevant SNPs for maximizing other properties that exist in the nature of population genetics such as the correlation between genetic diversity and geographical distance of ethnic groups can also be equally important. In this work, a methodology using a multi objective optimization technique called Pareto Optimal is utilized for selecting SNP subsets offering both high classification accuracy and correlation between genomic and geographical distances. In this method, discriminatory power of an SNP is determined using mutual information and its contribution to the genomic-geographical correlation is estimated using its loadings on principal components. Combining these objectives, the proposed method identifies SNP subsets that can better discriminate ethnic groups than those obtained with sole mutual information and yield higher correlation than those obtained with sole principal components on the Human Genome Diversity Project (HGDP) SNP dataset.

  4. Optimizing signal and image processing applications using Intel libraries

    NASA Astrophysics Data System (ADS)

    Landré, Jérôme; Truchetet, Frédéric

    2007-01-01

    This paper presents optimized signal and image processing libraries from Intel Corporation. Intel Performance Primitives (IPP) is a low-level signal and image processing library developed by Intel Corporation to optimize code on Intel processors. Open Computer Vision library (OpenCV) is a high-level library dedicated to computer vision tasks. This article describes the use of both libraries to build flexible and efficient signal and image processing applications.

  5. Optimal Sensor Selection for Health Monitoring Systems

    NASA Technical Reports Server (NTRS)

    Santi, L. Michael; Sowers, T. Shane; Aguilar, Robert B.

    2005-01-01

    Sensor data are the basis for performance and health assessment of most complex systems. Careful selection and implementation of sensors is critical to enable high fidelity system health assessment. A model-based procedure that systematically selects an optimal sensor suite for overall health assessment of a designated host system is described. This procedure, termed the Systematic Sensor Selection Strategy (S4), was developed at NASA John H. Glenn Research Center in order to enhance design phase planning and preparations for in-space propulsion health management systems (HMS). Information and capabilities required to utilize the S4 approach in support of design phase development of robust health diagnostics are outlined. A merit metric that quantifies diagnostic performance and overall risk reduction potential of individual sensor suites is introduced. The conceptual foundation for this merit metric is presented and the algorithmic organization of the S4 optimization process is described. Representative results from S4 analyses of a boost stage rocket engine previously under development as part of NASA's Next Generation Launch Technology (NGLT) program are presented.

  6. Optimal Portfolio Selection Under Concave Price Impact

    SciTech Connect

    Ma Jin; Song Qingshuo; Xu Jing; Zhang Jianfeng

    2013-06-15

    In this paper we study an optimal portfolio selection problem under instantaneous price impact. Based on some empirical analysis in the literature, we model such impact as a concave function of the trading size when the trading size is small. The price impact can be thought of as either a liquidity cost or a transaction cost, but the concavity nature of the cost leads to some fundamental difference from those in the existing literature. We show that the problem can be reduced to an impulse control problem, but without fixed cost, and that the value function is a viscosity solution to a special type of Quasi-Variational Inequality (QVI). We also prove directly (without using the solution to the QVI) that the optimal strategy exists and more importantly, despite the absence of a fixed cost, it is still in a 'piecewise constant' form, reflecting a more practical perspective.

  7. Separator profile selection for optimal battery performance

    NASA Astrophysics Data System (ADS)

    Whear, J. Kevin

    Battery performance, depending on the application, is normally defined by power delivery, electrical capacity, cycling regime and life in service. In order to meet the various performance goals, the Battery Design Engineer can vary things such as grid alloys, paste formulations, number of plates and methods of construction. Another design option available to optimize the battery performance is the separator profile. The goal of this paper is to demonstrate how separator profile selection can be utilized to optimize battery performance and manufacturing efficiencies. Also time will be given to explore novel separator profiles which may bring even greater benefits in the future. All major lead-acid application will be considered including automotive, motive power and stationary.

  8. Selected Isotopes for Optimized Fuel Assembly Tags

    SciTech Connect

    Gerlach, David C.; Mitchell, Mark R.; Reid, Bruce D.; Gesh, Christopher J.; Hurley, David E.

    2008-10-01

    In support of our ongoing signatures project we present information on 3 isotopes selected for possible application in optimized tags that could be applied to fuel assemblies to provide an objective measure of burnup. 1. Important factors for an optimized tag are compatibility with the reactor environment (corrosion resistance), low radioactive activation, at least 2 stable isotopes, moderate neutron absorption cross-section, which gives significant changes in isotope ratios over typical fuel assembly irradiation levels, and ease of measurement in the SIMS machine 2. From the candidate isotopes presented in the 3rd FY 08 Quarterly Report, the most promising appear to be Titanium, Hafnium, and Platinum. The other candidate isotopes (Iron, Tungsten, exhibited inadequate corrosion resistance and/or had neutron capture cross-sections either too high or too low for the burnup range of interest.

  9. Selectively-informed particle swarm optimization.

    PubMed

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-01-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315

  10. Selectively-informed particle swarm optimization

    PubMed Central

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-01-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315

  11. Selectively-informed particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-03-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.

  12. Quantum dot laser optimization: selectively doped layers

    NASA Astrophysics Data System (ADS)

    Korenev, Vladimir V.; Konoplev, Sergey S.; Savelyev, Artem V.; Shernyakov, Yurii M.; Maximov, Mikhail V.; Zhukov, Alexey E.

    2016-08-01

    Edge emitting quantum dot (QD) lasers are discussed. It has been recently proposed to use modulation p-doping of the layers that are adjacent to QD layers in order to control QD's charge state. Experimentally it has been proven useful to enhance ground state lasing and suppress the onset of excited state lasing at high injection. These results have been also confirmed with numerical calculations involving solution of drift-diffusion equations. However, deep understanding of physical reasons for such behavior and laser optimization requires analytical approaches to the problem. In this paper, under a set of assumptions we provide an analytical model that explains major effects of selective p-doping. Capture rates of elections and holes can be calculated by solving Poisson equations for electrons and holes around the charged QD layer. The charge itself is ruled by capture rates and selective doping concentration. We analyzed this self-consistent set of equations and showed that it can be used to optimize QD laser performance and to explain underlying physics.

  13. Intelligent Signal Processing for Detection System Optimization

    SciTech Connect

    Fu, C Y; Petrich, L I; Daley, P F; Burnham, A K

    2004-06-18

    A wavelet-neural network signal processing method has demonstrated approximately tenfold improvement in the detection limit of various nitrogen and phosphorus compounds over traditional signal-processing methods in analyzing the output of a thermionic detector attached to the output of a gas chromatograph. A blind test was conducted to validate the lower detection limit. All fourteen of the compound spikes were detected when above the estimated threshold, including all three within a factor of two above. In addition, two of six were detected at levels 1/2 the concentration of the nominal threshold. We would have had another two correct hits if we had allowed human intervention to examine the processed data. One apparent false positive in five nulls was traced to a solvent impurity, whose presence was identified by running a solvent aliquot evaporated to 1% residual volume, while the other four nulls were properly classified. We view this signal processing method as broadly applicable in analytical chemistry, and we advocate that advanced signal processing methods be applied as directly as possible to the raw detector output so that less discriminating preprocessing and post-processing does not throw away valuable signal.

  14. Intelligent Signal Processing for Detection System Optimization

    SciTech Connect

    Fu, C Y; Petrich, L I; Daley, P F; Burnham, A K

    2004-12-05

    A wavelet-neural network signal processing method has demonstrated approximately tenfold improvement over traditional signal-processing methods for the detection limit of various nitrogen and phosphorus compounds from the output of a thermionic detector attached to a gas chromatograph. A blind test was conducted to validate the lower detection limit. All fourteen of the compound spikes were detected when above the estimated threshold, including all three within a factor of two above the threshold. In addition, two of six spikes were detected at levels of 1/2 the concentration of the nominal threshold. Another two of the six would have been detected correctly if we had allowed human intervention to examine the processed data. One apparent false positive in five nulls was traced to a solvent impurity, whose presence was subsequently identified by analyzing a solvent aliquot evaporated to 1% residual volume, while the other four nulls were properly classified. We view this signal processing method as broadly applicable in analytical chemistry, and we advocate that advanced signal processing methods should be applied as directly as possible to the raw detector output so that less discriminating preprocessing and post-processing does not throw away valuable signal.

  15. Optimal Correlation Filters for Images with Signal-Dependent Noise

    NASA Technical Reports Server (NTRS)

    Downie, John D.; Walkup, John F.

    1994-01-01

    We address the design of optimal correlation filters for pattern detection and recognition in the presence of signal-dependent image noise sources. The particular examples considered are film-grain noise and speckle. Two basic approaches are investigated: (1) deriving the optimal matched filters for the signal-dependent noise models and comparing their performances with those derived for traditional signal-independent noise models and (2) first nonlinearly transforming the signal-dependent noise to signal-independent noise followed by the use of a classical filter matched to the transformed signal. We present both theoretical and computer simulation results that demonstrate the generally superior performance of the second approach in terms of the correlation peak signal-to-noise ratio.

  16. OPTIMAL TIME-SERIES SELECTION OF QUASARS

    SciTech Connect

    Butler, Nathaniel R.; Bloom, Joshua S.

    2011-03-15

    We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly et al., we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics-one describing the fit confidence and the other describing the probability of a false alarm-which can be tuned, a priori, to achieve high quasar detection fractions (99% completeness with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as {approx}<3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5 < z < 3, where selection by color is extremely inefficient. This represents 1875 new quasars in a 290 deg{sup 2} field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discuss the utility of the method at high redshift and in the regime of noisy and sparse data. Our time-series selection complements well-independent selection based on quasar colors and has strong potential for identifying high-redshift quasars for Baryon Acoustic Oscillations and other cosmology studies in the LSST era.

  17. Optimized wavelength selection for molecular absorption thermometry.

    PubMed

    An, Xinliang; Caswell, Andrew W; Lipor, John J; Sanders, Scott T

    2015-04-01

    A differential evolution (DE) algorithm is applied to a recently developed spectroscopic objective function to select wavelengths that optimize the temperature precision of water absorption thermometry. DE reliably finds optima even when many-wavelength sets are chosen from large populations of wavelengths (here 120 000 wavelengths from a spectrum with 0.002 cm(-1) resolution calculated by 16 856 transitions). Here, we study sets of fixed wavelengths in the 7280-7520 cm(-1) range. When optimizing the thermometer for performance within a narrow temperature range, the results confirm that the best temperature precision is obtained if all the available measurement time is split judiciously between the two most temperature-sensitive wavelengths. In the wide temperature range case (thermometer must perform throughout 280-2800 K), we find (1) the best four-wavelength set outperforms the best two-wavelength set by an average factor of 2, and (2) a complete spectrum (all 120 000 wavelengths from 16 856 transitions) is 4.3 times worse than the best two-wavelength set. Key implications for sensor designers include: (1) from the perspective of spectroscopic temperature sensitivity, it is usually sufficient to monitor two or three wavelengths, depending on the sensor's anticipated operating temperature range; and (2) although there is a temperature precision penalty to monitoring a complete spectrum, that penalty may be small enough, particularly at elevated pressure, to justify the complete-spectrum approach in many applications.

  18. Optimal and adaptive methods of processing hydroacoustic signals (review)

    NASA Astrophysics Data System (ADS)

    Malyshkin, G. S.; Sidel'nikov, G. B.

    2014-09-01

    Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.

  19. MaNGA: Target selection and Optimization

    NASA Astrophysics Data System (ADS)

    Wake, David

    2016-01-01

    The 6-year SDSS-IV MaNGA survey will measure spatially resolved spectroscopy for 10,000 nearby galaxies using the Sloan 2.5m telescope and the BOSS spectrographs with a new fiber arrangement consisting of 17 individually deployable IFUs. We present the simultaneous design of the target selection and IFU size distribution to optimally meet our targeting requirements. The requirements for the main samples were to use simple cuts in redshift and magnitude to produce an approximately flat number density of targets as a function of stellar mass, ranging from 1x109 to 1x1011 M⊙, and radial coverage to either 1.5 (Primary sample) or 2.5 (Secondary sample) effective radii, while maximizing S/N and spatial resolution. In addition we constructed a "Color-Enhanced" sample where we required 25% of the targets to have an approximately flat number density in the color and mass plane. We show how these requirements are met using simple absolute magnitude (and color) dependent redshift cuts applied to an extended version of the NASA Sloan Atlas (NSA), how this determines the distribution of IFU sizes and the resulting properties of the MaNGA sample.

  20. MaNGA: Target selection and Optimization

    NASA Astrophysics Data System (ADS)

    Wake, David

    2015-01-01

    The 6-year SDSS-IV MaNGA survey will measure spatially resolved spectroscopy for 10,000 nearby galaxies using the Sloan 2.5m telescope and the BOSS spectrographs with a new fiber arrangement consisting of 17 individually deployable IFUs. We present the simultaneous design of the target selection and IFU size distribution to optimally meet our targeting requirements. The requirements for the main samples were to use simple cuts in redshift and magnitude to produce an approximately flat number density of targets as a function of stellar mass, ranging from 1x109 to 1x1011 M⊙, and radial coverage to either 1.5 (Primary sample) or 2.5 (Secondary sample) effective radii, while maximizing S/N and spatial resolution. In addition we constructed a 'Color-Enhanced' sample where we required 25% of the targets to have an approximately flat number density in the color and mass plane. We show how these requirements are met using simple absolute magnitude (and color) dependent redshift cuts applied to an extended version of the NASA Sloan Atlas (NSA), how this determines the distribution of IFU sizes and the resulting properties of the MaNGA sample.

  1. Intramolecular conformational changes optimize protein kinase C signaling.

    PubMed

    Antal, Corina E; Violin, Jonathan D; Kunkel, Maya T; Skovsø, Søs; Newton, Alexandra C

    2014-04-24

    Optimal tuning of enzyme signaling is critical for cellular homeostasis. We use fluorescence resonance energy transfer reporters in live cells to follow conformational transitions that tune the affinity of a multidomain signal transducer, protein kinase C (PKC), for optimal response to second messengers. This enzyme comprises two diacylglycerol sensors, the C1A and C1B domains, that have a sufficiently high intrinsic affinity for ligand so that the enzyme would be in a ligand-engaged, active state if not for mechanisms that mask its domains. We show that both diacylglycerol sensors are exposed in newly synthesized PKC and that conformational transitions following priming phosphorylations mask the domains so that the lower affinity sensor, the C1B domain, is the primary diacylglycerol binder. The conformational rearrangements of PKC serve as a paradigm for how multimodule transducers optimize their dynamic range of signaling.

  2. Intramolecular conformational changes optimize protein kinase C signaling.

    PubMed

    Antal, Corina E; Violin, Jonathan D; Kunkel, Maya T; Skovsø, Søs; Newton, Alexandra C

    2014-04-24

    Optimal tuning of enzyme signaling is critical for cellular homeostasis. We use fluorescence resonance energy transfer reporters in live cells to follow conformational transitions that tune the affinity of a multidomain signal transducer, protein kinase C (PKC), for optimal response to second messengers. This enzyme comprises two diacylglycerol sensors, the C1A and C1B domains, that have a sufficiently high intrinsic affinity for ligand so that the enzyme would be in a ligand-engaged, active state if not for mechanisms that mask its domains. We show that both diacylglycerol sensors are exposed in newly synthesized PKC and that conformational transitions following priming phosphorylations mask the domains so that the lower affinity sensor, the C1B domain, is the primary diacylglycerol binder. The conformational rearrangements of PKC serve as a paradigm for how multimodule transducers optimize their dynamic range of signaling. PMID:24631122

  3. Neural network optimization, components, and design selection

    NASA Astrophysics Data System (ADS)

    Weller, Scott W.

    1990-07-01

    Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and noncontrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of muting and classification types of optimization problems. Neural Networks are constructed from neurons, which in electronics or software attempt to model but are not constrained by the real thing, i.e., neurons in our gray matter. Neurons are simple processing units connected to many other neurons over pathways which modify the incoming signals. A single synthetic neuron typically sums its weighted inputs, runs this sum through a non-linear function, and produces an output. In the brain, neurons are connected in a complex topology: in hardware/software the topology is typically much simpler, with neurons lying side by side, forming layers of neurons which connect to the layer of neurons which receive their outputs. This simplistic model is much easier to construct than the real thing, and yet can solve real problems. The information in a network, or its "memory", is completely contained in the weights on the connections from one neuron to another. Establishing these weights is called "training" the network. Some networks are trained by design -- once constructed no further learning takes place. Other types of networks require iterative training once wired up, but are not trainable once taught Still other types of networks can continue to learn after initial construction. The main benefit to using Neural Networks is their ability to work with conflicting or incomplete ("fuzzy") data sets. This ability and its usefulness will become evident in the following

  4. Radar antenna pointing for optimized signal to noise ratio.

    SciTech Connect

    Doerry, Armin Walter; Marquette, Brandeis

    2013-01-01

    The Signal-to-Noise Ratio (SNR) of a radar echo signal will vary across a range swath, due to spherical wavefront spreading, atmospheric attenuation, and antenna beam illumination. The antenna beam illumination will depend on antenna pointing. Calculations of geometry are complicated by the curved earth, and atmospheric refraction. This report investigates optimizing antenna pointing to maximize the minimum SNR across the range swath.

  5. Immunology (1955-1975): the natural selection theory, the two signal hypothesis and positive repertoire selection.

    PubMed

    Forsdyke, Donald R

    2012-01-01

    Observations suggesting the existence of natural antibody prior to exposure of an organism to the corresponding antigen, led to the natural selection theory of antibody formation of Jerne in 1955, and to the two signal hypothesis of Forsdyke in 1968. Aspects of these were not only first discoveries but also foundational discoveries in that they influenced contemporaries in a manner that, from our present vantage point, appears to have been constructive. Jerne's later hypothesis (1971, European Journal of Immunology 1: 1-9), that antibody-like receptors on lymphocytes were selected over evolutionary time for reactivity with the major histocompatibility complex (MHC) antigens of the species, was a first, but it was incorrect, and was foundational only to the extent that it emphasized the need to explain the Simonsen phenomenon. Although easily construed as derivative of Jerne (1971), the affinity/avidity model of Forsdyke (1975, Journal of Theoretical Biology 52: 187-198), which predicted that cell-surface components, including MHC antigens, would restrict antigen-reactivity by somatically shaping lymphocyte repertoires, was actually an extension of the two signal hypothesis. While presenting a mechanism for the positive selection of lymphocyte repertoires, and explaining the Simonsen phenomenon, the affinity/avidity model was not foundational in that it had to be independently rediscovered. For science to advance optimally we must seek to close temporal gaps so that first discoveries are also foundational. Listening to young scientists may be part of the solution. PMID:21336661

  6. On the application of optimal wavelet filter banks for ECG signal classification

    NASA Astrophysics Data System (ADS)

    Hadjiloucas, S.; Jannah, N.; Hwang, F.; Galvão, R. K. H.

    2014-03-01

    This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

  7. A criterion for signal-based selection of wavelets for denoising intrafascicular nerve recordings.

    PubMed

    Kamavuako, Ernest Nlandu; Jensen, Winnie; Yoshida, Ken; Kurstjens, Mathijs; Farina, Dario

    2010-02-15

    In this paper we propose a novel method for denoising intrafascicular nerve signals with the aim of improving action potential (AP) detection. The method is based on the stationary wavelet transform and thresholding of the wavelet coefficients. Since the choice of the mother wavelet substantially impact the performance, a criterion is proposed for selecting the optimal wavelet. The criterion for selection was based on the root mean square of the average of the output signal triggered by the detected APs. The mother wavelet was parameterized through the scaling filter, which allowed optimization through the proposed criterion. The method was tested on simulated signals and on experimental neural recordings. Experimental signals were recorded from the tibial branch of the sciatic nerve of three anaesthetized New Zealand white rabbits during controlled muscle stretches. The simulation results showed that the proposed method had an equivalent effect on AP detection performance (percentage of correct detection at 6 dB signal-to-noise ratio, mean+/-SD, 95.3+/-5.2%) to the a-posteriori choice of the best wavelet (96.1+/-3.6). Moreover, the AP detection after the proposed denoising method resulted in a correlation of 0.94+/-0.02 between the estimated spike rate and the muscle length. Therefore, the study proposes an effective method for selecting the optimal mother wavelet for denoising neural signals with the aim of improving AP detection.

  8. Vertical optimization procedure for an integrated micropower signal preprocessor

    NASA Astrophysics Data System (ADS)

    Harrison, T. R.

    1980-06-01

    The need for low power operation is characteristic of all electronic measurement systems that transmit physiological data across the intact skin. Custom integrated circuits and hybrid microcircuit assembly techniques leave battery volume (or power drain) as the limiting factor for implanted-system lifetime and animal model size. First generation totally implantable Doppler blood flowmeters, telemeter Doppler-shifted signals having a 40 kHz bandwidth. External electronics are then used to convert this information into a flow signal with a maximum bandwidth of approximately 100 Hz. A vertically optimized system circuit device procedure utilizes a digital data link for telemetry to reduce flowmeter transmitter power consumption. In this CW Doppler ultrasonic flowmeter, a potential 400:1 reduction in FM transmitter power is possible through additional signal preprocessing in the implanted package. A 10:1 savings in transmitter power drain is realized by a novel preprocessor developed to encode and telemeter pulsed digital data rather than Doppler signals.

  9. No-signaling principle can determine optimal quantum state discrimination.

    PubMed

    Bae, Joonwoo; Hwang, Won-Young; Han, Yeong-Deok

    2011-10-21

    We provide a general framework of utilizing the no-signaling principle in derivation of the guessing probability in the minimum-error quantum state discrimination. We show that, remarkably, the guessing probability can be determined by the no-signaling principle. This is shown by proving that, in the semidefinite programing for the discrimination, the optimality condition corresponds to the constraint that quantum theory cannot be used for a superluminal communication. Finally, a general bound to the guessing probability is presented in a closed form.

  10. Multidimensional optimization of signal space distance parameters in WLAN positioning.

    PubMed

    Brković, Milenko; Simić, Mirjana

    2014-01-01

    Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware. PMID:24757443

  11. Multidimensional Optimization of Signal Space Distance Parameters in WLAN Positioning

    PubMed Central

    Brković, Milenko; Simić, Mirjana

    2014-01-01

    Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware. PMID:24757443

  12. Multidimensional optimization of signal space distance parameters in WLAN positioning.

    PubMed

    Brković, Milenko; Simić, Mirjana

    2014-01-01

    Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware.

  13. Optimal Input Signal Design for Data-Centric Estimation Methods

    PubMed Central

    Deshpande, Sunil; Rivera, Daniel E.

    2013-01-01

    Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures. The proposed method specifically addresses the distribution of the regressor vectors. The design is examined for a linear time-invariant system under amplitude constraints on the input. The resulting optimization problem is solved using semidefinite relaxation methods. Numerical examples show the benefits in comparison to a classical PRBS input design. PMID:24317042

  14. On Optimal Input Design and Model Selection for Communication Channels

    SciTech Connect

    Li, Yanyan; Djouadi, Seddik M; Olama, Mohammed M

    2013-01-01

    In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

  15. Optimized tuner selection for engine performance estimation

    NASA Technical Reports Server (NTRS)

    Simon, Donald L. (Inventor); Garg, Sanjay (Inventor)

    2013-01-01

    A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

  16. Optically Modulated Fluorophores for Selective Fluorescence Signal Recovery

    PubMed Central

    Richards, Chris I.; Hsiang, Jung-Cheng; Senapati, Dulal; Patel, Sandeep; Yu, Junhua; Vosch, Tom; Dickson, Robert M.

    2009-01-01

    Fluorescence imaging in biological sciences is hindered by significant depth-dependent signal attenuation and high fluorescent backgrounds. We have developed optically modulated near-IR-emitting few-atom Ag nanodots that are selectively and dynamically photobrightened upon simultaneous excitation with a secondary laser enabling high sensitivity image extraction to reveal only the demodulated fluorophores. Image demodulation is demonstrated in high background environments to extract weak signals from completely obscuring background emission. PMID:19284790

  17. A review of channel selection algorithms for EEG signal processing

    NASA Astrophysics Data System (ADS)

    Alotaiby, Turky; El-Samie, Fathi E. Abd; Alshebeili, Saleh A.; Ahmad, Ishtiaq

    2015-12-01

    Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

  18. Optimization of ultrasonic transducers for selective guided wave actuation

    NASA Astrophysics Data System (ADS)

    Miszczynski, Mateusz; Packo, Pawel; Zbyrad, Paulina; Stepinski, Tadeusz; Uhl, Tadeusz; Lis, Jerzy; Wiatr, Kazimierz

    2016-04-01

    The application of guided waves using surface-bonded piezoceramic transducers for nondestructive testing (NDT) and Structural Health Monitoring (SHM) have shown great potential. However, due to difficulty in identification of individual wave modes resulting from their dispersive and multi-modal nature, selective mode excitement methods are highly desired. The presented work focuses on an optimization-based approach to design of a piezoelectric transducer for selective guided waves generation. The concept of the presented framework involves a Finite Element Method (FEM) model in the optimization process. The material of the transducer is optimized in topological sense with the aim of tuning piezoelectric properties for actuation of specific guided wave modes.

  19. Digital logic optimization using selection operators

    NASA Technical Reports Server (NTRS)

    Whitaker, Sterling R. (Inventor); Miles, Lowell H. (Inventor); Cameron, Eric G. (Inventor); Gambles, Jody W. (Inventor)

    2004-01-01

    According to the invention, a digital design method for manipulating a digital circuit netlist is disclosed. In one step, a first netlist is loaded. The first netlist is comprised of first basic cells that are comprised of first kernel cells. The first netlist is manipulated to create a second netlist. The second netlist is comprised of second basic cells that are comprised of second kernel cells. A percentage of the first and second kernel cells are selection circuits. There is less chip area consumed in the second basic cells than in the first basic cells. The second netlist is stored. In various embodiments, the percentage could be 2% or more, 5% or more, 10% or more, 20% or more, 30% or more, or 40% or more.

  20. Optimized Selective Coatings for Solar Collectors

    NASA Technical Reports Server (NTRS)

    Mcdonald, G.; Curtis, H. B.

    1967-01-01

    The spectral reflectance properties of black nickel electroplated over stainless steel and of black copper produced by oxidation of copper sheet were measured for various plating times of black nickel and for various lengths of time of oxidation of the copper sheet, and compared to black chrome over nickel and to converted zinc. It was determined that there was an optimum time for both plating of black nickel and for the oxidation of copper black. At this time the solar selective properties show high absorptance in the solar spectrum and low emittance in the infrared. The conditions are compared for production of optimum optical properties for black nickel, black copper, black chrome, and two black zinc conversions which at the same conditions had absorptances of 0.84, 0.90, 0.95, 0.84, and 0.92, respectively, and emittances of 0.18, 0.08, 0.09, 0.10, and 0.08, respectively.

  1. Efficient Simulation Budget Allocation for Selecting an Optimal Subset

    NASA Technical Reports Server (NTRS)

    Chen, Chun-Hung; He, Donghai; Fu, Michael; Lee, Loo Hay

    2008-01-01

    We consider a class of the subset selection problem in ranking and selection. The objective is to identify the top m out of k designs based on simulated output. Traditional procedures are conservative and inefficient. Using the optimal computing budget allocation framework, we formulate the problem as that of maximizing the probability of correc tly selecting all of the top-m designs subject to a constraint on the total number of samples available. For an approximation of this corre ct selection probability, we derive an asymptotically optimal allocat ion and propose an easy-to-implement heuristic sequential allocation procedure. Numerical experiments indicate that the resulting allocatio ns are superior to other methods in the literature that we tested, and the relative efficiency increases for larger problems. In addition, preliminary numerical results indicate that the proposed new procedur e has the potential to enhance computational efficiency for simulation optimization.

  2. Mode selective generation of guided waves by systematic optimization of the interfacial shear stress profile

    NASA Astrophysics Data System (ADS)

    Yazdanpanah Moghadam, Peyman; Quaegebeur, Nicolas; Masson, Patrice

    2015-01-01

    Piezoelectric transducers are commonly used in structural health monitoring systems to generate and measure ultrasonic guided waves (GWs) by applying interfacial shear and normal stresses to the host structure. In most cases, in order to perform damage detection, advanced signal processing techniques are required, since a minimum of two dispersive modes are propagating in the host structure. In this paper, a systematic approach for mode selection is proposed by optimizing the interfacial shear stress profile applied to the host structure, representing the first step of a global optimization of selective mode actuator design. This approach has the potential of reducing the complexity of signal processing tools as the number of propagating modes could be reduced. Using the superposition principle, an analytical method is first developed for GWs excitation by a finite number of uniform segments, each contributing with a given elementary shear stress profile. Based on this, cost functions are defined in order to minimize the undesired modes and amplify the selected mode and the optimization problem is solved with a parallel genetic algorithm optimization framework. Advantages of this method over more conventional transducers tuning approaches are that (1) the shear stress can be explicitly optimized to both excite one mode and suppress other undesired modes, (2) the size of the excitation area is not constrained and mode-selective excitation is still possible even if excitation width is smaller than all excited wavelengths, and (3) the selectivity is increased and the bandwidth extended. The complexity of the optimal shear stress profile obtained is shown considering two cost functions with various optimal excitation widths and number of segments. Results illustrate that the desired mode (A0 or S0) can be excited dominantly over other modes up to a wave power ratio of 1010 using an optimal shear stress profile.

  3. Estimation of motor unit conduction velocity from surface EMG recordings by signal-based selection of the spatial filters.

    PubMed

    Mesin, Luca; Tizzani, Francesca; Farina, Dario

    2006-10-01

    Muscle fiber conduction velocity (CV) can be estimated by the application of a pair of spatial filters to surface electromagnetic (EMG) signals and compensation of the spatial filter transfer function with equivalent temporal filters. This method integrates the selection of the spatial filters for signal detection to the estimation of CV. Using this approach, in this paper, we propose a novel technique for signal-based selection of the spatial filter pair that minimizes the effect of nonpropagating signal components (end-of-fiber effects) on CV estimates (optimal filters). The technique is applicable to signals with one propagating and one nonpropagating component, such as single motor unit action potentials. It is shown that the determination of the optimal filters also allows the identification of the propagating and nonpropagating signal components. The new method was applied to simulated and experimental EMG signals. Simulated signals were generated by a cylindrical, layered volume conductor model. Experimental signals were recorded from the abductor pollicis brevis with a linear array of 16 electrodes. In the simulations, the proposed approach provided CV estimates with lower bias due to nonpropagating signal components than previously proposed methods based on the entire signal waveform. In the experimental signals, the technique separated propagating and nonpropagating signal components with an average reconstruction error of 2.9 +/- 0.9% of the signal energy. The technique may find application in single motor unit studies for decreasing the variability and bias of CV estimates due to the presence and different weights of the nonpropagating components.

  4. Opposing selection and environmental variation modify optimal timing of breeding.

    PubMed

    Tarwater, Corey E; Beissinger, Steven R

    2013-09-17

    Studies of evolution in wild populations often find that the heritable phenotypic traits of individuals producing the most offspring do not increase proportionally in the population. This paradox may arise when phenotypic traits influence both fecundity and viability and when there is a tradeoff between these fitness components, leading to opposing selection. Such tradeoffs are the foundation of life history theory, but they are rarely investigated in selection studies. Timing of breeding is a classic example of a heritable trait under directional selection that does not result in an evolutionary response. Using a 22-y study of a tropical parrot, we show that opposing viability and fecundity selection on the timing of breeding is common and affects optimal breeding date, defined by maximization of fitness. After accounting for sampling error, the directions of viability (positive) and fecundity (negative) selection were consistent, but the magnitude of selection fluctuated among years. Environmental conditions (rainfall and breeding density) primarily and breeding experience secondarily modified selection, shifting optimal timing among individuals and years. In contrast to other studies, viability selection was as strong as fecundity selection, late-born juveniles had greater survival than early-born juveniles, and breeding later in the year increased fitness under opposing selection. Our findings provide support for life history tradeoffs influencing selection on phenotypic traits, highlight the need to unify selection and life history theory, and illustrate the importance of monitoring survival as well as reproduction for understanding phenological responses to climate change.

  5. Opposing selection and environmental variation modify optimal timing of breeding.

    PubMed

    Tarwater, Corey E; Beissinger, Steven R

    2013-09-17

    Studies of evolution in wild populations often find that the heritable phenotypic traits of individuals producing the most offspring do not increase proportionally in the population. This paradox may arise when phenotypic traits influence both fecundity and viability and when there is a tradeoff between these fitness components, leading to opposing selection. Such tradeoffs are the foundation of life history theory, but they are rarely investigated in selection studies. Timing of breeding is a classic example of a heritable trait under directional selection that does not result in an evolutionary response. Using a 22-y study of a tropical parrot, we show that opposing viability and fecundity selection on the timing of breeding is common and affects optimal breeding date, defined by maximization of fitness. After accounting for sampling error, the directions of viability (positive) and fecundity (negative) selection were consistent, but the magnitude of selection fluctuated among years. Environmental conditions (rainfall and breeding density) primarily and breeding experience secondarily modified selection, shifting optimal timing among individuals and years. In contrast to other studies, viability selection was as strong as fecundity selection, late-born juveniles had greater survival than early-born juveniles, and breeding later in the year increased fitness under opposing selection. Our findings provide support for life history tradeoffs influencing selection on phenotypic traits, highlight the need to unify selection and life history theory, and illustrate the importance of monitoring survival as well as reproduction for understanding phenological responses to climate change. PMID:24003118

  6. A Novel Variable Selection Method Based on a Partial KL Information Measure and Its Application to Channel Selection for Bioelectric Signal Classification

    NASA Astrophysics Data System (ADS)

    Shibanoki, Taro; Shima, Keisuke; Tsuji, Toshio; Takaki, Takeshi; Otsuka, Akira; Chin, Takaaki

    This paper proposes a novel variable selection method based on the KL information measure, and applies it to optimal channel selection for bioelectric signal classification. Generally, the accuracy of classifcation for bioelectric signals is greatly influenced by measuring positions of the signals as well as individual physical abilities of a user. Therefore, it is effective for classification to select optimal positions for each user in advance. In the proposed method, the probability density functions (pdfs) of measured data are estimated through learning of a multidimensional probabilistic neural network (PNN) based on the KL information theory. Then, a partial KL information measure is newly defined to evaluate contribution of each dimension in the data. The effective dimensions can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to EMG electrode selection with six subjects (including an amputee), and the effective channels were selected from all channels attached to each subject's forearm. Experimental results showed that the number of channels was reduced with 36.1±12.5 [%], and the average classification rate using selected channels by the proposed method was 98.99±1.31 [%]. These results indicated that the proposed method is capable to select effective channels (optimal or semi-optimal) for accurate classification.

  7. Novel genomic signals of recent selection in an Ethiopian population

    PubMed Central

    Tekola-Ayele, Fasil; Adeyemo, Adebowale; Chen, Guanjie; Hailu, Elena; Aseffa, Abraham; Davey, Gail; Newport, Melanie J; Rotimi, Charles N

    2015-01-01

    The recent feasibility of genome-wide studies of adaptation in human populations has provided novel insights into biological pathways that have been affected by adaptive pressures. However, only a few African populations have been investigated using these genome-wide approaches. Here, we performed a genome-wide analysis for evidence of recent positive selection in a sample of 120 individuals of Wolaita ethnicity belonging to Omotic-speaking people who have inhabited the mid- and high-land areas of southern Ethiopia for millennia. Using the 11 HapMap populations as the comparison group, we found Wolaita-specific signals of recent positive selection in several human leukocyte antigen (HLA) loci. Notably, the selected loci overlapped with HLA regions that we previously reported to be associated with podoconiosis–a geochemical lymphedema of the lower legs common in the Wolaita area. We found selection signals in PPARA, a gene involved in energy metabolism during prolonged food deficiency. This finding is consistent with the dietary use of enset, a crop with high-carbohydrate and low-fat and -protein contents domesticated in Ethiopia subsequent to food deprivation 10 000 years ago, and with metabolic adaptation to high-altitude hypoxia. We observed novel selection signals in CDKAL1 and NEGR1, well-known diabetes and obesity susceptibility genes. Finally, the SLC24A5 gene locus known to be associated with skin pigmentation was in the top selection signals in the Wolaita, and the alleles of single-nucleotide polymorphisms rs1426654 and rs1834640 (SLC24A5) associated with light skin pigmentation in Eurasian populations were of high frequency (47.9%) in this Omotic-speaking indigenous Ethiopian population. PMID:25370040

  8. Local and global strategies for optimal selective mass scaling

    NASA Astrophysics Data System (ADS)

    Tkachuk, Anton; Bischoff, Manfred

    2014-06-01

    The problem of optimal selective mass scaling for linearized elasto-dynamics is discussed. Optimal selective mass scaling should provide solutions for dynamical problems that are close to the ones obtained with a lumped mass matrix, but at much smaller computational costs. It should be equally applicable to all structurally relevant load cases. The three main optimality criteria, namely eigenmode preservation, small number of non-zero entries and good conditioning of the mass matrix are explicitly formulated in the article. An example of optimal mass scaling which relies on redistribution of mass on a global system level is constructed. Alternative local mass scaling strategies are proposed and compared with existing methods using one modal and two transient numerical examples.

  9. Low-power slice selective imaging of broad signals

    NASA Astrophysics Data System (ADS)

    Yang, Weiqi; Lee, Jae-Seung; Kharkov, Boris; Ilott, Andrew J.; Jerschow, Alexej

    2016-11-01

    One of the major challenges in using magnetic resonance imaging (MRI) to study immobile samples, such as solid materials or rigid tissues like bone or ligaments, is that the images appear dark due to these samples' short-lived signals. Although it is well known that narrowband signals can be excited in inhomogeneously-broadened lines, it is less well known that similar effects can be observed in dipolar-broadened systems. These long-lived signals have not been used much, mainly because their description frequently does not match intuition. While 3D imaging with these signals has previously been reported, here we focus on the demonstration of faster, 2D slice-selective imaging. The faster imaging provides more flexibility for visualizing these rigid objects. We also focus on the frequently-encountered regime wherein the maximum power achievable for rf pulses is significantly weaker than the linewidth. This regime is typically encountered in clinical MRI scans or large volume setups. When compared to UTE and conventional slice-selective spin echo methods, this technique provides better representations of the sample considered here (an eraser sample), and higher signal-to-noise ratios than spin-echo techniques in both the high and low power regimes.

  10. Optimum primary and supplementary signals optimizing the seismic data resolution

    NASA Astrophysics Data System (ADS)

    Tyapkin, Yuriy K.

    2001-03-01

    Often in practice, when generating seismic waves on a line, even with a wide-band source, numerous natural and technical obstacles cause a low resolution of reflection seismograms. In this case, the economy of the survey should be taken into consideration and rather than ignoring preexisting data, generating additional signal to complement the preexisting data should be tried. This paper describes how this can be done to optimize the resolution of the combined data. The new approach requires a fundamental change in the field technique such that records with different spectral characteristics (RDSC) are now generated from each source-receiver pair. These coincident records share a common reflectivity series, but differ from each other in wavelets and noise. A comprehensive theory for optimum processing (deconvolution) of any available suite of the RDSC is developed. The solution for the problem is a particular case of multichannel Wiener filtering. It can be thought of as two successive procedures. The first is optimum frequency-dependent weighted stacking of the RDSC. The second is single-channel zero-phase Wiener deconvolution filtering of the previous output. This representation enables suggested multichannel filtering to be easily implemented. The effectiveness of the method as well as its advantage over straight summing of the RDSC, followed by single-channel Wiener deconvolution filtering, are corroborated theoretically and demonstrated with field data. Furthermore, a solution is suggested for the problem to evaluate the spectrum of an optimum supplementary signal. The signal contributes to the available set of the RDSC and yields either maximum resolution with limited energy expenses or a certain desired resolution with minimum, but unrestricted energy expenses at the output of the optimum procedure. The optimum distribution of the spectral energy of a primary signal along the frequency axis is a particular case of the above problem with no preexisting data.

  11. Optimal neural network architecture selection: effects on computer-aided detection of mammographic microcalcifications

    NASA Astrophysics Data System (ADS)

    Gurcan, Metin N.; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Petrick, Nicholas; Helvie, Mark A.

    2002-05-01

    We evaluated the effectiveness of an optimal convolution neural network (CNN) architecture selected by simulated annealing for improving the performance of a computer-aided diagnosis (CAD) system designed for the detection of microcalcification clusters on digitized mammograms. The performances of the CAD programs with manually and optimally selected CNNs were compared using an independent test set. This set included 472 mammograms and contained 253 biopsy-proven malignant clusters. Free-response receiver operating characteristic (FROC) analysis was used for evaluation of the detection accuracy. At a false positive (FP) rate of 0.7 per image, the film-based sensitivity was 84.6% with the optimized CNN, in comparison with 77.2% with the manually selected CNN. If clusters having images in both craniocaudal and mediolateral oblique views were analyzed together and a cluster was considered to be detected when it was detected in one or both views, at 0.7 FPs/image, the sensitivity was 93.3% with the optimized CNN and 87.0% with the manually selected CNN. This study indicates that classification of true positive and FP signals is an important step of the CAD program and that the detection accuracy of the program can be considerably improved by optimizing this step with an automated optimization algorithm.

  12. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

    NASA Astrophysics Data System (ADS)

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-03-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

  13. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

    PubMed Central

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-01-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

  14. An adaptive locally optimal method detecting weak deterministic signals

    NASA Astrophysics Data System (ADS)

    Wang, C. H.

    1983-10-01

    A new method for detecting weak signals in interference and clutter in radar systems is presented. The detector which uses this method is adaptive for an environment varying with time and locally optimal for detecting targets and constant false-alarm ratio (CFAR) for the statistics of interference and clutter varying with time. The loss of CFAR is small, and the detector is also simple in structure. The statistical equivalent transfer characteristic of a rank quantizer which can be used as part of an adaptive locally most powerful detector (ALMP) is obtained. It is shown that the distribution-free Doppler processor of Dillard (1974) is not only a nonparameter detector, but also an ALMP detector under certain conditions.

  15. Training set optimization under population structure in genomic selection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The optimization of the training set (TRS) in genomic selection (GS) has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the Coefficient of D...

  16. Optimal Financial Aid Policies for a Selective University.

    ERIC Educational Resources Information Center

    Ehrenberg, Ronald G.; Sherman, Daniel R.

    1984-01-01

    This paper provides a model of optimal financial aid policies for a selective university. The model implies that the financial aid package to be offered to each category of admitted applicants depends on the elasticity of the fraction who accept offers of admission with respect to the financial aid package offered them. (Author/SSH)

  17. Multidimensional Adaptive Testing with Optimal Design Criteria for Item Selection

    ERIC Educational Resources Information Center

    Mulder, Joris; van der Linden, Wim J.

    2009-01-01

    Several criteria from the optimal design literature are examined for use with item selection in multidimensional adaptive testing. In particular, it is examined what criteria are appropriate for adaptive testing in which all abilities are intentional, some should be considered as a nuisance, or the interest is in the testing of a composite of the…

  18. Temporally selective processing of communication signals by auditory midbrain neurons

    PubMed Central

    Christensen-Dalsgaard, Jakob; Kelley, Darcy B.

    2011-01-01

    Perception of the temporal structure of acoustic signals contributes critically to vocal signaling. In the aquatic clawed frog Xenopus laevis, calls differ primarily in the temporal parameter of click rate, which conveys sexual identity and reproductive state. We show here that an ensemble of auditory neurons in the laminar nucleus of the torus semicircularis (TS) of X. laevis specializes in encoding vocalization click rates. We recorded single TS units while pure tones, natural calls, and synthetic clicks were presented directly to the tympanum via a vibration-stimulation probe. Synthesized click rates ranged from 4 to 50 Hz, the rate at which the clicks begin to overlap. Frequency selectivity and temporal processing were characterized using response-intensity curves, temporal-discharge patterns, and autocorrelations of reduplicated responses to click trains. Characteristic frequencies ranged from 140 to 3,250 Hz, with minimum thresholds of −90 dB re 1 mm/s at 500 Hz and −76 dB at 1,100 Hz near the dominant frequency of female clicks. Unlike units in the auditory nerve and dorsal medullary nucleus, most toral units respond selectively to the behaviorally relevant temporal feature of the rate of clicks in calls. The majority of neurons (85%) were selective for click rates, and this selectivity remained unchanged over sound levels 10 to 20 dB above threshold. Selective neurons give phasic, tonic, or adapting responses to tone bursts and click trains. Some algorithms that could compute temporally selective receptive fields are described. PMID:21289132

  19. Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

    NASA Technical Reports Server (NTRS)

    2004-01-01

    Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate

  20. Optimizing Ligand Efficiency of Selective Androgen Receptor Modulators (SARMs).

    PubMed

    Handlon, Anthony L; Schaller, Lee T; Leesnitzer, Lisa M; Merrihew, Raymond V; Poole, Chuck; Ulrich, John C; Wilson, Joseph W; Cadilla, Rodolfo; Turnbull, Philip

    2016-01-14

    A series of selective androgen receptor modulators (SARMs) containing the 1-(trifluoromethyl)benzyl alcohol core have been optimized for androgen receptor (AR) potency and drug-like properties. We have taken advantage of the lipophilic ligand efficiency (LLE) parameter as a guide to interpret the effect of structural changes on AR activity. Over the course of optimization efforts the LLE increased over 3 log units leading to a SARM 43 with nanomolar potency, good aqueous kinetic solubility (>700 μM), and high oral bioavailability in rats (83%).

  1. Optimization of Signal Decomposition Matched Filtering (SDMF) for Improved Detection of Copy-Number Variations.

    PubMed

    Stamoulis, Catherine; Betensky, Rebecca A

    2016-01-01

    We aim to improve the performance of the previously proposed signal decomposition matched filtering (SDMF) method [26] for the detection of copy-number variations (CNV) in the human genome. Through simulations, we show that the modified SDMF is robust even at high noise levels and outperforms the original SDMF method, which indirectly depends on CNV frequency. Simulations are also used to develop a systematic approach for selecting relevant parameter thresholds in order to optimize sensitivity, specificity and computational efficiency. We apply the modified method to array CGH data from normal samples in the cancer genome atlas (TCGA) and compare detected CNVs to those estimated using circular binary segmentation (CBS) [19], a hidden Markov model (HMM)-based approach [11] and a subset of CNVs in the Database of Genomic Variants. We show that a substantial number of previously identified CNVs are detected by the optimized SDMF, which also outperforms the other two methods. PMID:27295643

  2. Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

    PubMed Central

    Ozekes, Serhat; Gultekin, Selahattin; Tarhan, Nevzat

    2014-01-01

    Objective Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection. Methods Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects. Results BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset. Conclusion ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination. PMID:25110496

  3. Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

    PubMed Central

    Faltermeier, Rupert; Proescholdt, Martin A.; Bele, Sylvia; Brawanski, Alexander

    2015-01-01

    Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses. PMID:26693250

  4. Improved Clonal Selection Algorithm Combined with Ant Colony Optimization

    NASA Astrophysics Data System (ADS)

    Gao, Shangce; Wang, Wei; Dai, Hongwei; Li, Fangjia; Tang, Zheng

    Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.

  5. Monte Carlo optimization for site selection of new chemical plants.

    PubMed

    Cai, Tianxing; Wang, Sujing; Xu, Qiang

    2015-11-01

    Geographic distribution of chemical manufacturing sites has significant impact on the business sustainability of industrial development and regional environmental sustainability as well. The common site selection rules have included the evaluation of the air quality impact of a newly constructed chemical manufacturing site to surrounding communities. In order to achieve this target, the simultaneous consideration should cover the regional background air-quality information, the emissions of new manufacturing site, and statistical pattern of local meteorological conditions. According to the above information, the risk assessment can be conducted for the potential air-quality impacts from candidate locations of a new chemical manufacturing site, and thus the optimization of the final site selection can be achieved by minimizing its air-quality impacts. This paper has provided a systematic methodology for the above purpose. There are total two stages of modeling and optimization work: i) Monte Carlo simulation for the purpose to identify background pollutant concentration based on currently existing emission sources and regional statistical meteorological conditions; and ii) multi-objective (simultaneous minimization of both peak pollutant concentration and standard deviation of pollutant concentration spatial distribution at air-quality concern regions) Monte Carlo optimization for optimal location selection of new chemical manufacturing sites according to their design data of potential emission. This study can be helpful to both determination of the potential air-quality impact for geographic distribution of multiple chemical plants with respect to regional statistical meteorological conditions, and the identification of an optimal site for each new chemical manufacturing site with the minimal environment impact to surrounding communities. The efficacy of the developed methodology has been demonstrated through the case studies.

  6. Optimization of Parameter Selection for Partial Least Squares Model Development

    NASA Astrophysics Data System (ADS)

    Zhao, Na; Wu, Zhi-Sheng; Zhang, Qiao; Shi, Xin-Yuan; Ma, Qun; Qiao, Yan-Jiang

    2015-07-01

    In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection. In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the ratio of standard error of prediction to standard deviation (RPD), and the determination coefficient of calibration (Rcal2) and validation (Rpre2) were simultaneously assessed to optimize the best modeling path. We used three different near-infrared (NIR) datasets, which illustrated that there was more than one modeling path to ensure good modeling. The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

  7. Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security

    SciTech Connect

    Harmer, Paul K; Temple, Michael A; Buckner, Mark A; Farquhar, Ethan

    2011-01-01

    Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.

  8. Hyperopt: a Python library for model selection and hyperparameter optimization

    NASA Astrophysics Data System (ADS)

    Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.

    2015-01-01

    Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.

  9. Selection of optimal threshold to construct recurrence plot for structural operational vibration measurements

    NASA Astrophysics Data System (ADS)

    Yang, Dong; Ren, Wei-Xin; Hu, Yi-Ding; Li, Dan

    2015-08-01

    The structural health monitoring (SHM) involves the sampled operational vibration measurements over time so that the structural features can be extracted accordingly. The recurrence plot (RP) and corresponding recurrence quantification analysis (RQA) have become a useful tool in various fields due to its efficiency. The threshold selection is one of key issues to make sure that the constructed recurrence plot contains enough recurrence points. Different signals have in nature different threshold values. This paper is aiming at presenting an approach to determine the optimal threshold for the operational vibration measurements of civil engineering structures. The surrogate technique and Taguchi loss function are proposed to generate reliable data and to achieve the optimal discrimination power point where the threshold is optimum. The impact of selecting recurrence thresholds on different signals is discussed. It is demonstrated that the proposed method to identify the optimal threshold is applicable to the operational vibration measurements. The proposed method provides a way to find the optimal threshold for the best RP construction of structural vibration measurements under operational conditions.

  10. Selection of optimal measures of growth and reproduction for the sublethal Leptocheirus plumulosus sediment bioassay

    SciTech Connect

    Gray, B.R.; Wright, R.B.; Duke, B.M.; Farrar, J.D.; Emery, V.L. Jr.; Brandon, D.L.; Moore, D.W.

    1998-11-01

    This article describes the selection process used to identify optimal measures of growth and reproduction for the proposed 28-d sublethal sediment bioassay with the estuarine amphipod Leptocheirus plumulosus. The authors used four criteria (relevance of each measure to its respective endpoint, signal-to-noise ratio, redundancy relative to other measures of the same endpoint, and cost) to evaluate nine growth and seven reproductive measures. Optimal endpoint measures were identified as those receiving relatively high scores for all or most criteria. Measures of growth scored similarly on all criteria, except for cost. The cost of the pooled (female plus male) growth measures was substantially lower than the cost of the female and male growth measures because the latter required more labor (by approx. 25 min per replicate). Pooled dry weight was identified as the optimal growth measure over pooled length because the latter required additional labor and nonstandard software and equipment. Embryo and neonate measures of reproduction exhibited wide differences in labor costs but yielded similar scores for other criteria. In contrast, brooding measures of reproduction scored relatively low on endpoint relevance, signal-to-noise ratio, and redundancy criteria. The authors recommend neonates/survivor as the optimal measure of L. plumulosus reproduction because it exhibited high endpoint relevance and signal-to-noise ratios, was redundant to other reproductive measures, and required minimal time.

  11. Optimal band selection in hyperspectral remote sensing of aquatic benthic features: a wavelet filter window approach

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R., Jr.

    2006-09-01

    This paper describes a wavelet based approach to derivative spectroscopy. The approach is utilized to select, through optimization, optimal channels or bands to use as derivative based remote sensing algorithms. The approach is applied to airborne and modeled or synthetic reflectance signatures of environmental media and features or objects within such media, such as benthic submerged vegetation canopies. The technique can also applied to selected pixels identified within a hyperspectral image cube obtained from an board an airborne, ground based, or subsurface mobile imaging system. This wavelet based image processing technique is an extremely fast numerical method to conduct higher order derivative spectroscopy which includes nonlinear filter windows. Essentially, the wavelet filter scans a measured or synthetic signature in an automated sequential manner in order to develop a library of filtered spectra. The library is utilized in real time to select the optimal channels for direct algorithm application. The unique wavelet based derivative filtering technique makes us of a translating, and dilating derivative spectroscopy signal processing (TDDS-SP (R)) approach based upon remote sensing science and radiative transfer processes unlike other signal processing techniques applied to hyperspectral signatures.

  12. State-Selective Excitation of Quantum Systems via Geometrical Optimization.

    PubMed

    Chang, Bo Y; Shin, Seokmin; Sola, Ignacio R

    2015-09-01

    We lay out the foundations of a general method of quantum control via geometrical optimization. We apply the method to state-selective population transfer using ultrashort transform-limited pulses between manifolds of levels that may represent, e.g., state-selective transitions in molecules. Assuming that certain states can be prepared, we develop three implementations: (i) preoptimization, which implies engineering the initial state within the ground manifold or electronic state before the pulse is applied; (ii) postoptimization, which implies engineering the final state within the excited manifold or target electronic state, after the pulse; and (iii) double-time optimization, which uses both types of time-ordered manipulations. We apply the schemes to two important dynamical problems: To prepare arbitrary vibrational superposition states on the target electronic state and to select weakly coupled vibrational states. Whereas full population inversion between the electronic states only requires control at initial time in all of the ground vibrational levels, only very specific superposition states can be prepared with high fidelity by either pre- or postoptimization mechanisms. Full state-selective population inversion requires manipulating the vibrational coherences in the ground electronic state before the optical pulse is applied and in the excited electronic state afterward, but not during all times.

  13. Signal peptide optimization tool for the secretion of recombinant protein from Saccharomyces cerevisiae.

    PubMed

    Mori, Akihiro; Hara, Shoichi; Sugahara, Tomohiro; Kojima, Takaaki; Iwasaki, Yugo; Kawarasaki, Yasuaki; Sahara, Takehiko; Ohgiya, Satoru; Nakano, Hideo

    2015-11-01

    The secretion efficiency of foreign proteins in recombinant microbes is strongly dependent on the combination of the signal peptides (SPs) used and the target proteins; therefore, identifying the optimal SP sequence for each target protein is a crucial step in maximizing the efficiency of protein secretion in both prokaryotes and eukaryotes. In this study, we developed a novel method, named the SP optimization tool (SPOT), for the generation and rapid screening of a library of SP-target gene fusion constructs to identify the optimal SP for maximizing target protein secretion. In contrast to libraries generated in previous studies, SPOT fusion constructs are generated without adding the intervening sequences associated with restriction enzyme digestion sites. Therefore, no extra amino acids are inserted at the N-terminus of the target protein that might affect its function or conformational stability. As a model system, β-galactosidase (LacA) from Aspergillus oryzae was used as a target protein for secretion from Saccharomyces cerevisiae. In total, 60 SPs were selected from S. cerevisiae secretory proteins and utilized to generate the SP library. While many of the SP-LacA fusions were not secreted, several of the SPs, AGA2, CRH1, PLB1, and MF(alpha)1, were found to enhance LacA secretion compared to the WT sequence. Our results indicate that SPOT is a valuable method for optimizing the bioproduction of any target protein, and could be adapted to many host strains.

  14. Field of view selection for optimal airborne imaging sensor performance

    NASA Astrophysics Data System (ADS)

    Goss, Tristan M.; Barnard, P. Werner; Fildis, Halidun; Erbudak, Mustafa; Senger, Tolga; Alpman, Mehmet E.

    2014-05-01

    The choice of the Field of View (FOV) of imaging sensors used in airborne targeting applications has major impact on the overall performance of the system. Conducting a market survey from published data on sensors used in stabilized airborne targeting systems shows a trend of ever narrowing FOVs housed in smaller and lighter volumes. This approach promotes the ever increasing geometric resolution provided by narrower FOVs, while it seemingly ignores the influences the FOV selection has on the sensor's sensitivity, the effects of diffraction, the influences of sight line jitter and collectively the overall system performance. This paper presents a trade-off methodology to select the optimal FOV for an imaging sensor that is limited in aperture diameter by mechanical constraints (such as space/volume available and window size) by balancing the influences FOV has on sensitivity and resolution and thereby optimizing the system's performance. The methodology may be applied to staring array based imaging sensors across all wavebands from visible/day cameras through to long wave infrared thermal imagers. Some examples of sensor analysis applying the trade-off methodology are given that highlights the performance advantages that can be gained by maximizing the aperture diameters and choosing the optimal FOV for an imaging sensor used in airborne targeting applications.

  15. Some useful upper bounds for the selection of optimal profiles

    NASA Astrophysics Data System (ADS)

    Daripa, Prabir

    2012-08-01

    In enhanced oil recovery by chemical flooding within tertiary oil recovery, it is often necessary to choose optimal viscous profiles of the injected displacing fluids that reduce growth rates of hydrodynamic instabilities the most thereby substantially reducing the well-known fingering problem and improving oil recovery. Within the three-layer Hele-Shaw model, we show in this paper that selection of the optimal monotonic viscous profile of the middle-layer fluid based on well known theoretical upper bound formula [P. Daripa, G. Pasa, A simple derivation of an upper bound in the presence of a viscosity gradient in three-layer Hele-Shaw flows, Journal of Statistical Mechanics (2006) 11. http://dx.doi.org/10.1088/1742-5468/2006/01/P01014] agrees very well with that based on the computation of maximum growth rate of instabilities from solving the linearized stability problem. Thus, this paper proposes a very simple, fast method for selection of the optimal monotonic viscous profiles of the displacing fluids in multi-layer flows.

  16. Plastic scintillation dosimetry: Optimal selection of scintillating fibers and scintillators

    SciTech Connect

    Archambault, Louis; Arsenault, Jean; Gingras, Luc; Sam Beddar, A.; Roy, Rene; Beaulieu, Luc

    2005-07-15

    Scintillation dosimetry is a promising avenue for evaluating dose patterns delivered by intensity-modulated radiation therapy plans or for the small fields involved in stereotactic radiosurgery. However, the increase in signal has been the goal for many authors. In this paper, a comparison is made between plastic scintillating fibers and plastic scintillator. The collection of scintillation light was measured experimentally for four commercial models of scintillating fibers (BCF-12, BCF-60, SCSF-78, SCSF-3HF) and two models of plastic scintillators (BC-400, BC-408). The emission spectra of all six scintillators were obtained by using an optical spectrum analyzer and they were compared with theoretical behavior. For scintillation in the blue region, the signal intensity of a singly clad scintillating fiber (BCF-12) was 120% of that of the plastic scintillator (BC-400). For the multiclad fiber (SCSF-78), the signal reached 144% of that of the plastic scintillator. The intensity of the green scintillating fibers was lower than that of the plastic scintillator: 47% for the singly clad fiber (BCF-60) and 77% for the multiclad fiber (SCSF-3HF). The collected light was studied as a function of the scintillator length and radius for a cylindrical probe. We found that symmetric detectors with nearly the same spatial resolution in each direction (2 mm in diameter by 3 mm in length) could be made with a signal equivalent to those of the more commonly used asymmetric scintillators. With augmentation of the signal-to-noise ratio in consideration, this paper presents a series of comparisons that should provide insight into selection of a scintillator type and volume for development of a medical dosimeter.

  17. Plastic scintillation dosimetry: optimal selection of scintillating fibers and scintillators.

    PubMed

    Archambault, Louis; Arsenault, Jean; Gingras, Luc; Beddar, A Sam; Roy, René; Beaulieu, Luc

    2005-07-01

    Scintillation dosimetry is a promising avenue for evaluating dose patterns delivered by intensity-modulated radiation therapy plans or for the small fields involved in stereotactic radiosurgery. However, the increase in signal has been the goal for many authors. In this paper, a comparison is made between plastic scintillating fibers and plastic scintillator. The collection of scintillation light was measured experimentally for four commercial models of scintillating fibers (BCF-12, BCF-60, SCSF-78, SCSF-3HF) and two models of plastic scintillators (BC-400, BC-408). The emission spectra of all six scintillators were obtained by using an optical spectrum analyzer and they were compared with theoretical behavior. For scintillation in the blue region, the signal intensity of a singly clad scintillating fiber (BCF-12) was 120% of that of the plastic scintillator (BC-400). For the multiclad fiber (SCSF-78), the signal reached 144% of that of the plastic scintillator. The intensity of the green scintillating fibers was lower than that of the plastic scintillator: 47% for the singly clad fiber (BCF-60) and 77% for the multiclad fiber (SCSF-3HF). The collected light was studied as a function of the scintillator length and radius for a cylindrical probe. We found that symmetric detectors with nearly the same spatial resolution in each direction (2 mm in diameter by 3 mm in length) could be made with a signal equivalent to those of the more commonly used asymmetric scintillators. With augmentation of the signal-to-noise ratio in consideration, this paper presents a series of comparisons that should provide insight into selection of a scintillator type and volume for development of a medical dosimeter.

  18. Extended Salecker-Wigner formula for optimal accuracy in reading a clock via a massive signal particle

    SciTech Connect

    Kudaka, Shoju; Matsumoto, Shuichi

    2007-07-15

    In order to acquire an extended Salecker-Wigner formula from which to derive the optimal accuracy in reading a clock with a massive particle as the signal, von Neumann's classical measurement is employed, by which simultaneously both position and momentum of the signal particle can be measured approximately. By an appropriate selection of wave function for the initial state of the composite system (a clock and a signal particle), the formula is derived accurately. Valid ranges of the running time of a clock with a given optimal accuracy are also given. The extended formula means that contrary to the Salecker-Wigner formula there exists the possibility of a higher accuracy of time measurement, even if the mass of the clock is very small.

  19. Optimizing Hammermill Performance Through Screen Selection and Hammer Design

    SciTech Connect

    Neal A. Yancey; Tyler L. Westover; Christopher T. Wright

    2013-01-01

    Background: Mechanical preprocessing, which includes particle size reduction and mechanical separation, is one of the primary operations in the feedstock supply system for a lignocellulosic biorefinery. It is the means by which raw biomass from the field or forest is mechanically transformed into an on-spec feedstock with characteristics better suited for the fuel conversion process. Results: This work provides a general overview of the objectives and methodologies of mechanical preprocessing and then presents experimental results illustrating (1) improved size reduction via optimization of hammer mill configuration, (2) improved size reduction via pneumatic-assisted hammer milling, and (3) improved control of particle size and particle size distribution through proper selection of grinder process parameters. Conclusion: Optimal grinder configuration for maximal process throughput and efficiency is strongly dependent on feedstock type and properties, such moisture content. Tests conducted using a HG200 hammer grinder indicate that increasing the tip speed, optimizing hammer geometry, and adding pneumatic assist can increase grinder throughput as much as 400%.

  20. Optimal Selection of Threshold Value 'r' for Refined Multiscale Entropy.

    PubMed

    Marwaha, Puneeta; Sunkaria, Ramesh Kumar

    2015-12-01

    Refined multiscale entropy (RMSE) technique was introduced to evaluate complexity of a time series over multiple scale factors 't'. Here threshold value 'r' is updated as 0.15 times SD of filtered scaled time series. The use of fixed threshold value 'r' in RMSE sometimes assigns very close resembling entropy values to certain time series at certain temporal scale factors and is unable to distinguish different time series optimally. The present study aims to evaluate RMSE technique by varying threshold value 'r' from 0.05 to 0.25 times SD of filtered scaled time series and finding optimal 'r' values for each scale factor at which different time series can be distinguished more effectively. The proposed RMSE was used to evaluate over HRV time series of normal sinus rhythm subjects, patients suffering from sudden cardiac death, congestive heart failure, healthy adult male, healthy adult female and mid-aged female groups as well as over synthetic simulated database for different datalengths 'N' of 3000, 3500 and 4000. The proposed RMSE results in improved discrimination among different time series. To enhance the computational capability, empirical mathematical equations have been formulated for optimal selection of threshold values 'r' as a function of SD of filtered scaled time series and datalength 'N' for each scale factor 't'.

  1. Optimal subinterval selection approach for power system transient stability simulation

    SciTech Connect

    Kim, Soobae; Overbye, Thomas J.

    2015-10-21

    Power system transient stability analysis requires an appropriate integration time step to avoid numerical instability as well as to reduce computational demands. For fast system dynamics, which vary more rapidly than what the time step covers, a fraction of the time step, called a subinterval, is used. However, the optimal value of this subinterval is not easily determined because the analysis of the system dynamics might be required. This selection is usually made from engineering experiences, and perhaps trial and error. This paper proposes an optimal subinterval selection approach for power system transient stability analysis, which is based on modal analysis using a single machine infinite bus (SMIB) system. Fast system dynamics are identified with the modal analysis and the SMIB system is used focusing on fast local modes. An appropriate subinterval time step from the proposed approach can reduce computational burden and achieve accurate simulation responses as well. As a result, the performance of the proposed method is demonstrated with the GSO 37-bus system.

  2. Optimal subinterval selection approach for power system transient stability simulation

    DOE PAGESBeta

    Kim, Soobae; Overbye, Thomas J.

    2015-10-21

    Power system transient stability analysis requires an appropriate integration time step to avoid numerical instability as well as to reduce computational demands. For fast system dynamics, which vary more rapidly than what the time step covers, a fraction of the time step, called a subinterval, is used. However, the optimal value of this subinterval is not easily determined because the analysis of the system dynamics might be required. This selection is usually made from engineering experiences, and perhaps trial and error. This paper proposes an optimal subinterval selection approach for power system transient stability analysis, which is based on modalmore » analysis using a single machine infinite bus (SMIB) system. Fast system dynamics are identified with the modal analysis and the SMIB system is used focusing on fast local modes. An appropriate subinterval time step from the proposed approach can reduce computational burden and achieve accurate simulation responses as well. As a result, the performance of the proposed method is demonstrated with the GSO 37-bus system.« less

  3. Tests of variable-band multilayers designed for investigating optimal signal-to-noise vs artifact signal ratios in Dual-Energy Digital Subtraction Angiography (DDSA) imaging systems

    SciTech Connect

    Boyers, D.; Ho, A.; Li, Q.; Piestrup, M.; Rice, M.; Tatchyn, R.

    1993-08-01

    In recent work, various design techniques were applied to investigate the feasibility of controlling the bandwidth and bandshape profiles of tungsten/boron-carbon (W/B{sub 4}C) and tungsten/silicon (W/Si) multilayers for optimizing their performance in synchrotron radiation based angiographical imaging systems at 33 keV. Varied parameters included alternative spacing geometries, material thickness ratios, and numbers of layer pairs. Planar optics with nominal design reflectivities of 30%--94% and bandwidths ranging from 0.6%--10% were designed at the Stanford Radiation Laboratory, fabricated by the Ovonic Synthetic Materials Company, and characterized on Beam Line 4-3 at the Stanford Synchrotron Radiation Laboratory, in this paper we report selected results of these tests and review the possible use of the multilayers for determining optimal signal to noise vs. artifact signal ratios in practical Dual-Energy Digital Subtraction Angiography systems.

  4. Optimal experiment design for model selection in biochemical networks

    PubMed Central

    2014-01-01

    Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. PMID:24555498

  5. An improved response surface methodology algorithm with an application to traffic signal optimization for urban networks

    SciTech Connect

    Joshi, S.S.; Rathi, A.K.; Tew, J.D.

    1995-12-31

    This paper illustrates the use of the simulation-optimization technique of response surface methodology (RSM) in traffic signal optimization of urban networks. It also quantifies the gains of using the common random number (CRN) variance reduction strategy in such an optimization procedure. An enhanced RSM algorithm which employs conjugate gradient search techniques and successive second-order models is presented instead of the conventional approach. An illustrative example using an urban traffic network exhibits the superiority of using the CRN strategy ovr direct simulation in performing traffic signal optimization. Relative performance of the two strategies is quantified with computational results using the total network-wide delay as the measure of effectivness.

  6. Optimal band selection for dimensionality reduction of hyperspectral imagery

    NASA Technical Reports Server (NTRS)

    Stearns, Stephen D.; Wilson, Bruce E.; Peterson, James R.

    1993-01-01

    Hyperspectral images have many bands requiring significant computational power for machine interpretation. During image pre-processing, regions of interest that warrant full examination need to be identified quickly. One technique for speeding up the processing is to use only a small subset of bands to determine the 'interesting' regions. The problem addressed here is how to determine the fewest bands required to achieve a specified performance goal for pixel classification. The band selection problem has been addressed previously Chen et al., Ghassemian et al., Henderson et al., and Kim et al.. Some popular techniques for reducing the dimensionality of a feature space, such as principal components analysis, reduce dimensionality by computing new features that are linear combinations of the original features. However, such approaches require measuring and processing all the available bands before the dimensionality is reduced. Our approach, adapted from previous multidimensional signal analysis research, is simpler and achieves dimensionality reduction by selecting bands. Feature selection algorithms are used to determine which combination of bands has the lowest probability of pixel misclassification. Two elements required by this approach are a choice of objective function and a choice of search strategy.

  7. Optimal signal-to-noise ratio in stochastic time-delayed bistable systems

    NASA Astrophysics Data System (ADS)

    Gao, Shilong

    2016-04-01

    We study the optimal signal-to-noise ratio in a stochastic time-delayed bistable system. By using the small delay approximation, we transform the time-delayed system into stochastic nondelayed differential equations to obtain the analytic expressions of the signal-to-noise ratio in different mechanisms. In the valid range of small delay approximation, we compare the peak values of signal-to-noise ratio curves and obtain the optimal signal-to-noise ratio. From the results, we find that the interplay of time delay and noise has a great influence on time-delayed bistable systems.

  8. Simultaneous seismic random noise attenuation and signal preservation by optimal spatiotemporal TFPF

    NASA Astrophysics Data System (ADS)

    Lin, Hongbo; Li, Yue; Ma, Haitao; Xu, Liping

    2016-05-01

    The time-frequency peak filtering (TFPF) algorithm has been successfully applied to seismic random noise attenuation. However, the time-frequency peak filtering with fixed-type spatiotemporal filtering trajectories fails to preserve reflected signals in seismic events which have complex geometric structure. An optimal spatiotemporal TFPF (OST-TFPF) is proposed here combining the Shapiro-Francia (S-F) statistic to reduce random noise and preserve seismic signals simultaneously. In the novel algorithm, the S-F statistic is first calculated for seismic data to detect seismic events based on the fact that the non-Gaussian seismic signals lead to smaller values of the S-F statistic comparing to seismic random noise which is general Gaussian. Then, optimal spatiotemporal filtering trajectory can be constructed based on the S-F statistic to coincide with the shape of each event. Finally, the optimal spatiotemporal TFPF de-noises seismic data along the optimal trajectories. Since the resampled signals along the trajectories matching the geometric structures of seismic events become more linear compared to signals in time, the OST-TFPF gives better signal estimation while attenuating random noise. Synthetic and field data examples demonstrate that the optimal spatiotemporal TFPF is effective in the denoising and signal-preserving of the seismic data with low signal-to-noise ratio. Moreover, the OST-TFPF also obtains good performance in preservation of seismic event with complex geometric structure.

  9. Influenza B vaccine lineage selection--an optimized trivalent vaccine.

    PubMed

    Mosterín Höpping, Ana; Fonville, Judith M; Russell, Colin A; James, Sarah; Smith, Derek J

    2016-03-18

    Epidemics of seasonal influenza viruses cause considerable morbidity and mortality each year. Various types and subtypes of influenza circulate in humans and evolve continuously such that individuals at risk of serious complications need to be vaccinated annually to keep protection up to date with circulating viruses. The influenza vaccine in most parts of the world is a trivalent vaccine, including an antigenically representative virus of recently circulating influenza A/H3N2, A/H1N1, and influenza B viruses. However, since the 1970s influenza B has split into two antigenically distinct lineages, only one of which is represented in the annual trivalent vaccine at any time. We describe a lineage selection strategy that optimizes protection against influenza B using the standard trivalent vaccine as a potentially cost effective alternative to quadrivalent vaccines.

  10. Selective optimization of side activities: the SOSA approach.

    PubMed

    Wermuth, Camille G

    2006-02-01

    Selective optimization of side activities of drug molecules (the SOSA approach) is an intelligent and potentially more efficient strategy than HTS for the generation of new biological activities. Only a limited number of highly diverse drug molecules are screened, for which bioavailability and toxicity studies have already been performed and efficacy in humans has been confirmed. Once the screening has generated a hit it will be used as the starting point for a drug discovery program. Using traditional medicinal chemistry as well as parallel synthesis, the initial 'side activity' is transformed into the 'main activity' and, conversely, the initial 'main activity' is significantly reduced or abolished. This strategy has a high probability of yielding safe, bioavailable, original and patentable analogues. PMID:16533714

  11. Optimal grid point selection for improved nonrigid medical image registration

    NASA Astrophysics Data System (ADS)

    Fookes, Clinton; Maeder, Anthony

    2004-05-01

    Non-rigid image registration is an essential tool required for overcoming the inherent local anatomical variations that exist between medical images acquired from different individuals or atlases, among others. This type of registration defines a deformation field that gives a translation or mapping for every pixel in the image. One popular local approach for estimating this deformation field, known as block matching, is where a grid of control points are defined on an image and are each taken as the centre of a small window. These windows are then translated in the second image to maximise a local similarity criterion. This generates two corresponding sets of control points for the two images, yielding a sparse deformation field. This sparse field can then be propagated to the entire image using well known methods such as the thin-plate spline warp or simple Gaussian convolution. Previous block matching procedures all utilise uniformly distributed grid points. This results in the generation of a sparse deformation field containing displacement estimates at uniformly spaced locations. This neglects to make use of the evidence that block matching results are dependent on the amount of local information content. That is, results are better in regions of high information when compared to regions of low information. Consequently, this paper presents a solution to this drawback by proposing the use of a Reversible Jump Markov Chain Monte Carlo (RJMCMC) statistical procedure to optimally select grid points of interest. These grid points have a greater concentration in regions of high information and a lower concentration in regions of small information. Results show that non-rigid registration can by improved by using optimally selected grid points of interest.

  12. Optimal quantum state estimation with use of the no-signaling principle

    SciTech Connect

    Han, Yeong-Deok; Bae, Joonwoo; Wang Xiangbin; Hwang, Won-Young

    2010-12-15

    A simple derivation of the optimal state estimation of a quantum bit was obtained by using the no-signaling principle. In particular, the no-signaling principle determines a unique form of the guessing probability independent of figures of merit, such as the fidelity or information gain. This proves that the optimal estimation for a quantum bit can be achieved by the same measurement for almost all figures of merit.

  13. Optimized bioregenerative space diet selection with crew choice.

    PubMed

    Vicens, Carrie; Wang, Carolyn; Olabi, Ammar; Jackson, Peter; Hunter, Jean

    2003-01-01

    Previous studies on optimization of crew diets have not accounted for choice. A diet selection model with crew choice was developed. Scenario analyses were conducted to assess the feasibility and cost of certain crew preferences, such as preferences for numerous-desserts, high-salt, and high-acceptability foods. For comparison purposes, a no-choice and a random-choice scenario were considered. The model was found to be feasible in terms of food variety and overall costs. The numerous-desserts, high-acceptability, and random-choice scenarios all resulted in feasible solutions costing between 13.2 and 17.3 kg ESM/person-day. Only the high-sodium scenario yielded an infeasible solution. This occurred when the foods highest in salt content were selected for the crew-choice portion of the diet. This infeasibility can be avoided by limiting the total sodium content in the crew-choice portion of the diet. Cost savings were found by reducing food variety in scenarios where the preference bias strongly affected nutritional content.

  14. Optimized bioregenerative space diet selection with crew choice

    NASA Technical Reports Server (NTRS)

    Vicens, Carrie; Wang, Carolyn; Olabi, Ammar; Jackson, Peter; Hunter, Jean

    2003-01-01

    Previous studies on optimization of crew diets have not accounted for choice. A diet selection model with crew choice was developed. Scenario analyses were conducted to assess the feasibility and cost of certain crew preferences, such as preferences for numerous-desserts, high-salt, and high-acceptability foods. For comparison purposes, a no-choice and a random-choice scenario were considered. The model was found to be feasible in terms of food variety and overall costs. The numerous-desserts, high-acceptability, and random-choice scenarios all resulted in feasible solutions costing between 13.2 and 17.3 kg ESM/person-day. Only the high-sodium scenario yielded an infeasible solution. This occurred when the foods highest in salt content were selected for the crew-choice portion of the diet. This infeasibility can be avoided by limiting the total sodium content in the crew-choice portion of the diet. Cost savings were found by reducing food variety in scenarios where the preference bias strongly affected nutritional content.

  15. Optimization of killer assays for yeast selection protocols.

    PubMed

    Lopes, C A; Sangorrín, M P

    2010-01-01

    A new optimized semiquantitative yeast killer assay is reported for the first time. The killer activity of 36 yeast isolates belonging to three species, namely, Metschnikowia pulcherrima, Wickerhamomyces anomala and Torulaspora delbrueckii, was tested with a view to potentially using these yeasts as biocontrol agents against the wine spoilage species Pichia guilliermondii and Pichia membranifaciens. The effectiveness of the classical streak-based (qualitative method) and the new semiquantitative techniques was compared. The percentage of yeasts showing killer activity was found to be higher by the semiquantitative technique (60%) than by the qualitative method (45%). In all cases, the addition of 1% NaCl into the medium allowed a better observation of the killer phenomenon. Important differences were observed in the killer capacity of different isolates belonging to a same killer species. The broadest spectrum of action was detected in isolates of W. anomala NPCC 1023 and 1025, and M. pulcherrima NPCC 1009 and 1013. We also brought experimental evidence supporting the importance of the adequate selection of the sensitive isolate to be used in killer evaluation. The new semiquantitative method proposed in this work enables to visualize the relationship between the number of yeasts tested and the growth of the inhibition halo (specific productivity). Hence, this experimental approach could become an interesting tool to be taken into account for killer yeast selection protocols.

  16. When is an optimization not an optimization? Evaluation of clinical implications of information content (signal-to-noise ratio) in optimization of cardiac resynchronization therapy, and how to measure and maximize it.

    PubMed

    Pabari, Punam A; Willson, Keith; Stegemann, Berthold; van Geldorp, Irene E; Kyriacou, Andreas; Moraldo, Michela; Mayet, Jamil; Hughes, Alun D; Francis, Darrel P

    2011-05-01

    Impact of variability in the measured parameter is rarely considered in designing clinical protocols for optimization of atrioventricular (AV) or interventricular (VV) delay of cardiac resynchronization therapy (CRT). In this article, we approach this question quantitatively using mathematical simulation in which the true optimum is known and examine practical implications using some real measurements. We calculated the performance of any optimization process that selects the pacing setting which maximizes an underlying signal, such as flow or pressure, in the presence of overlying random variability (noise). If signal and noise are of equal size, for a 5-choice optimization (60, 100, 140, 180, 220 ms), replicate AV delay optima are rarely identical but rather scattered with a standard deviation of 45 ms. This scatter was overwhelmingly determined (ρ = -0.975, P < 0.001) by Information Content, [Formula: see text], an expression of signal-to-noise ratio. Averaging multiple replicates improves information content. In real clinical data, at resting, heart rate information content is often only 0.2-0.3; elevated pacing rates can raise information content above 0.5. Low information content (e.g. <0.5) causes gross overestimation of optimization-induced increment in VTI, high false-positive appearance of change in optimum between visits and very wide confidence intervals of individual patient optimum. AV and VV optimization by selecting the setting showing maximum cardiac function can only be accurate if information content is high. Simple steps to reduce noise such as averaging multiple replicates, or to increase signal such as increasing heart rate, can improve information content, and therefore viability, of any optimization process.

  17. Considerations on the Optimal and Efficient Processing of Information-Bearing Signals

    ERIC Educational Resources Information Center

    Harms, Herbert Andrew

    2013-01-01

    Noise is a fundamental hurdle that impedes the processing of information-bearing signals, specifically the extraction of salient information. Processing that is both optimal and efficient is desired; optimality ensures the extracted information has the highest fidelity allowed by the noise, while efficiency ensures limited resource usage. Optimal…

  18. Optimized signal processing for FMCW interrogated reflective delay line-type SAW sensors.

    PubMed

    Viikari, Ville; Kokkonen, Kimmo; Meltaus, Johanna

    2008-11-01

    This correspondence presents an optimized frequency modulated continuous-wave (FMCW) interrogation procedure for reflective delay line-type SAW sensors. In this method, the time delays between reflections are obtained with Fourier transform from optimally windowed frequency response. Optimal window functions maximize the signal-tointerference ratio at chosen temporal points of interest. The method is experimentally verified and its accuracy is compared with that of a Fourier transform from Hamming-windowed frequency response. PMID:19049933

  19. Socially selected ornaments influence hormone titers of signalers and receivers.

    PubMed

    Tibbetts, Elizabeth A; Crocker, Katherine; Huang, Zachary Y

    2016-07-26

    Decades of behavioral endocrinology research have shown that hormones and behavior have a bidirectional relationship; hormones both influence and respond to social behavior. In contrast, hormones are often thought to have a unidirectional relationship with ornaments. Hormones influence ornament development, but little empirical work has tested how ornaments influence hormones throughout life. Here, we experimentally alter a visual signal of fighting ability in Polistes dominulus paper wasps and measure the behavioral and hormonal consequences of signal alteration in signalers and receivers. We find wasps that signal inaccurately high fighting ability receive more aggression than controls and receiving aggression reduces juvenile hormone (JH) titers. As a result, immediately after contests, inaccurate signalers have lower JH titers than controls. Ornaments also directly influence rival JH titers. Three hours after contests, wasps who interacted with rivals signaling high fighting ability have higher JH titers than wasps who interacted with rivals signaling low fighting ability. Therefore, ornaments influence hormone titers of both signalers and receivers. We demonstrate that relationships between hormones and ornaments are flexible and bidirectional rather than static and unidirectional. Dynamic relationships among ornaments, behavior, and physiology may be an important, but overlooked factor in the evolution of honest communication. PMID:27402762

  20. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    PubMed

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification. PMID:27652153

  1. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    PubMed

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

  2. Near optimal energy selective x-ray imaging system performance with simple detectors

    SciTech Connect

    Alvarez, Robert E.

    2010-02-15

    Purpose: This article describes a method to achieve near optimal performance with low energy resolution detectors. Tapiovaara and Wagner [Phys. Med. Biol. 30, 519-529 (1985)] showed that an energy selective x-ray system using a broad spectrum source can produce images with a larger signal to noise ratio (SNR) than conventional systems using energy integrating or photon counting detectors. They showed that there is an upper limit to the SNR and that it can be achieved by measuring full spectrum information and then using an optimal energy dependent weighting. Methods: A performance measure is derived by applying statistical detection theory to an abstract vector space of the line integrals of the basis set coefficients of the two function approximation to the x-ray attenuation coefficient. The approach produces optimal results that utilize all the available energy dependent data. The method can be used with any energy selective detector and is applied not only to detectors using pulse height analysis (PHA) but also to a detector that simultaneously measures the total photon number and integrated energy, as discussed by Roessl et al. [Med. Phys. 34, 959-966 (2007)]. A generalization of this detector that improves the performance is introduced. A method is described to compute images with the optimal SNR using projections in a ''whitened'' vector space transformed so the noise is uncorrelated and has unit variance in both coordinates. Material canceled images with optimal SNR can also be computed by projections in this space. Results: The performance measure is validated by showing that it provides the Tapiovaara-Wagner optimal results for a detector with full energy information and also a conventional detector. The performance with different types of detectors is compared to the ideal SNR as a function of x-ray tube voltage and subject thickness. A detector that combines two bin PHA with a simultaneous measurement of integrated photon energy provides near ideal

  3. Optimal Design of Calibration Signals in Space Borne Gravitational Wave Detectors

    NASA Technical Reports Server (NTRS)

    Nofrarias, Miquel; Karnesis, Nikolaos; Gibert, Ferran; Armano, Michele; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo; Dolesi, Rita; Ferraioli, Luigi; Thorpe, James I.

    2014-01-01

    Future space borne gravitational wave detectors will require a precise definition of calibration signals to ensure the achievement of their design sensitivity. The careful design of the test signals plays a key role in the correct understanding and characterization of these instruments. In that sense, methods achieving optimal experiment designs must be considered as complementary to the parameter estimation methods being used to determine the parameters describing the system. The relevance of experiment design is particularly significant for the LISA Pathfinder mission, which will spend most of its operation time performing experiments to characterize key technologies for future space borne gravitational wave observatories. Here we propose a framework to derive the optimal signals in terms of minimum parameter uncertainty to be injected to these instruments during its calibration phase. We compare our results with an alternative numerical algorithm which achieves an optimal input signal by iteratively improving an initial guess. We show agreement of both approaches when applied to the LISA Pathfinder case.

  4. Parametric optimization of selective laser melting for forming Ti6Al4V samples by Taguchi method

    NASA Astrophysics Data System (ADS)

    Sun, Jianfeng; Yang, Yongqiang; Wang, Di

    2013-07-01

    In this study, a selective laser melting experiment was carried out with Ti6Al4V alloy powders. To produce samples with maximum density, selective laser melting parameters of laser power, scanning speed, powder thickness, hatching space and scanning strategy were carefully selected. As a statistical design of experimental technique, the Taguchi method was used to optimize the selected parameters. The results were analyzed using analyses of variance (ANOVA) and the signal-to-noise (S/N) ratios by design-expert software for the optimal parameters, and a regression model was established. The regression equation revealed a linear relationship among the density, laser power, scanning speed, powder thickness and scanning strategy. From the experiments, sample with density higher than 95% was obtained. The microstructure of obtained sample was mainly composed of acicular martensite, α phase and β phase. The micro-hardness was 492 HV0.2.

  5. Applications of Optimal Building Energy System Selection and Operation

    SciTech Connect

    Marnay, Chris; Stadler, Michael; Siddiqui, Afzal; DeForest, Nicholas; Donadee, Jon; Bhattacharya, Prajesh; Lai, Judy

    2011-04-01

    Berkeley Lab has been developing the Distributed Energy Resources Customer Adoption Model (DER-CAM) for several years. Given load curves for energy services requirements in a building microgrid (u grid), fuel costs and other economic inputs, and a menu of available technologies, DER-CAM finds the optimum equipment fleet and its optimum operating schedule using a mixed integer linear programming approach. This capability is being applied using a software as a service (SaaS) model. Optimisation problems are set up on a Berkeley Lab server and clients can execute their jobs as needed, typically daily. The evolution of this approach is demonstrated by description of three ongoing projects. The first is a public access web site focused on solar photovoltaic generation and battery viability at large commercial and industrial customer sites. The second is a building CO2 emissions reduction operations problem for a University of California, Davis student dining hall for which potential investments are also considered. And the third, is both a battery selection problem and a rolling operating schedule problem for a large County Jail. Together these examples show that optimization of building u grid design and operation can be effectively achieved using SaaS.

  6. Electrosensory optimization to conspecific phasic signals for mating.

    PubMed

    Tricas, T C; Michael, S W; Sisneros, J A

    1995-12-29

    Ampullary electroreceptor systems in fishes and aquatic amphibians are known to function in prey localization by the movement of the animal through a weak dc field produced by their prey. The round stingray produces an electric field with a complex geometry that is modulated rhythmically by movements of the spiracles and gill slits during ventilation. This weak stimulus is used in the field by reproductively active male stingrays to locate mates, and also by female rays to locate buried consexuals. Electrosensory primary afferent neurons are most sensitive to stimuli that vary sinusoidally at the same frequency as the natural respiratory movements. The match between primary afferent frequency sensitivity and the ventilatory phasic signals produced by conspecifics indicates that the electrosensory system serves an important biological function in the social behavior of elasmobranchs. PMID:8787848

  7. Optimizing Electromagnetically Induced Transparency Signals with Laguerre-Gaussian Beams

    NASA Astrophysics Data System (ADS)

    Holtfrerich, Matthew; Akin, Tom; Krzyzewski, Sean; Marino, Alberto; Abraham, Eric

    2016-05-01

    We have performed electromagnetically induced transparency in ultracold Rubidium atoms using a Laguerre-Gaussian laser mode as the control beam. Laguerre-Gaussian modes are characterized by a ring type transverse intensity profile and carry intrinsic orbital angular momentum. This angular momentum carried by the control beam can be utilized in optical computing applications which is unavailable to the more common Gaussian laser field. Specifically, we use a Laguerre-Gaussian control beam with a Gaussian probe to show that the linewidth of the transmission spectrum can be narrowed when compared to a Gaussian control beam that has the same peak intensity. We present data extending this work to compare control fields in both the Gaussian and Laguerre-Gaussian modes with constant total power. We have made efforts to find the optical overlap that best minimizes the transmission linewidth while also maintaining signal contrast. This was done by changing the waist size of the control beam with respect to the probe. The best results were obtained when the waist of a Laguerre-Gaussian control beam is equal to the waist of the Gaussian probe resulting in narrow linewidth features.

  8. Genome-wide signals of positive selection in human evolution

    PubMed Central

    Enard, David; Messer, Philipp W.; Petrov, Dmitri A.

    2014-01-01

    The role of positive selection in human evolution remains controversial. On the one hand, scans for positive selection have identified hundreds of candidate loci, and the genome-wide patterns of polymorphism show signatures consistent with frequent positive selection. On the other hand, recent studies have argued that many of the candidate loci are false positives and that most genome-wide signatures of adaptation are in fact due to reduction of neutral diversity by linked deleterious mutations, known as background selection. Here we analyze human polymorphism data from the 1000 Genomes Project and detect signatures of positive selection once we correct for the effects of background selection. We show that levels of neutral polymorphism are lower near amino acid substitutions, with the strongest reduction observed specifically near functionally consequential amino acid substitutions. Furthermore, amino acid substitutions are associated with signatures of recent adaptation that should not be generated by background selection, such as unusually long and frequent haplotypes and specific distortions in the site frequency spectrum. We use forward simulations to argue that the observed signatures require a high rate of strongly adaptive substitutions near amino acid changes. We further demonstrate that the observed signatures of positive selection correlate better with the presence of regulatory sequences, as predicted by the ENCODE Project Consortium, than with the positions of amino acid substitutions. Our results suggest that adaptation was frequent in human evolution and provide support for the hypothesis of King and Wilson that adaptive divergence is primarily driven by regulatory changes. PMID:24619126

  9. Sexual selection accelerates signal evolution during speciation in birds

    PubMed Central

    Seddon, Nathalie; Botero, Carlos A.; Tobias, Joseph A.; Dunn, Peter O.; MacGregor, Hannah E. A.; Rubenstein, Dustin R.; Uy, J. Albert C.; Weir, Jason T.; Whittingham, Linda A.; Safran, Rebecca J.

    2013-01-01

    Sexual selection is proposed to be an important driver of diversification in animal systems, yet previous tests of this hypothesis have produced mixed results and the mechanisms involved remain unclear. Here, we use a novel phylogenetic approach to assess the influence of sexual selection on patterns of evolutionary change during 84 recent speciation events across 23 passerine bird families. We show that elevated levels of sexual selection are associated with more rapid phenotypic divergence between related lineages, and that this effect is restricted to male plumage traits proposed to function in mate choice and species recognition. Conversely, we found no evidence that sexual selection promoted divergence in female plumage traits, or in male traits related to foraging and locomotion. These results provide strong evidence that female choice and male–male competition are dominant mechanisms driving divergence during speciation in birds, potentially linking sexual selection to the accelerated evolution of pre-mating reproductive isolation. PMID:23864596

  10. Sexual selection accelerates signal evolution during speciation in birds.

    PubMed

    Seddon, Nathalie; Botero, Carlos A; Tobias, Joseph A; Dunn, Peter O; Macgregor, Hannah E A; Rubenstein, Dustin R; Uy, J Albert C; Weir, Jason T; Whittingham, Linda A; Safran, Rebecca J

    2013-09-01

    Sexual selection is proposed to be an important driver of diversification in animal systems, yet previous tests of this hypothesis have produced mixed results and the mechanisms involved remain unclear. Here, we use a novel phylogenetic approach to assess the influence of sexual selection on patterns of evolutionary change during 84 recent speciation events across 23 passerine bird families. We show that elevated levels of sexual selection are associated with more rapid phenotypic divergence between related lineages, and that this effect is restricted to male plumage traits proposed to function in mate choice and species recognition. Conversely, we found no evidence that sexual selection promoted divergence in female plumage traits, or in male traits related to foraging and locomotion. These results provide strong evidence that female choice and male-male competition are dominant mechanisms driving divergence during speciation in birds, potentially linking sexual selection to the accelerated evolution of pre-mating reproductive isolation.

  11. Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

    NASA Astrophysics Data System (ADS)

    Naik, Ganesh R.; Kumar, Dinesh K.

    2010-03-01

    This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.

  12. Optimal secretion of alkali-tolerant xylanase in Bacillus subtilis by signal peptide screening.

    PubMed

    Zhang, Weiwei; Yang, Mingming; Yang, Yuedong; Zhan, Jian; Zhou, Yaoqi; Zhao, Xin

    2016-10-01

    Xylanases are industrially important enzymes for xylan digestion. We experimentally screened over 114 Sec and 24 Tat pathway signal peptides, with two different promoters, for optimal production of an alkaline active xylanase (XynBYG) from Bacillus pumilus BYG in a Bacillus subtilis host. Though both promoters yielded highly consistent secretion levels (0.97 Pearson correlation coefficient), the Sec pathway was found to be more efficient than the Tat pathway for XynBYG secretion. Furthermore, the optimal signal peptide (phoB) for XynBYG secretion was found to be different from the optimal peptides for cutinase and esterase reported in previous studies. A partial least squares regression analysis further identified several statistically important variables: helical properties, amino acid composition bias, and the discrimination score in Signal P. These variables explain the observed 23 % variance in the secretion yield of XynBYG by the different signal peptides. The results also suggest that the helical propensity of a signal peptide plays a significant role in the beta-rich xylanase, but not in the helix-rich cutinase, suggesting a coupling of the conformations between the signal peptide and its cargo protein for optimal secretion. PMID:27225471

  13. Analysis Methodology for Optimal Selection of Ground Station Site in Space Missions

    NASA Astrophysics Data System (ADS)

    Nieves-Chinchilla, J.; Farjas, M.; Martínez, R.

    2013-12-01

    Optimization of ground station sites is especially important in complex missions that include several small satellites (clusters or constellations) such as the QB50 project, where one ground station would be able to track several spatial vehicles, even simultaneously. In this regard the design of the communication system has to carefully take into account the ground station site and relevant signal phenomena, depending on the frequency band. To propose the optimal location of the ground station, these aspects become even more relevant to establish a trusted communication link due to the ground segment site in urban areas and/or selection of low orbits for the space segment. In addition, updated cartography with high resolution data of the location and its surroundings help to develop recommendations in the design of its location for spatial vehicles tracking and hence to improve effectiveness. The objectives of this analysis methodology are: completion of cartographic information, modelling the obstacles that hinder communication between the ground and space segment and representation in the generated 3D scene of the degree of impairment in the signal/noise of the phenomena that interferes with communication. The integration of new technologies of geographic data capture, such as 3D Laser Scan, determine that increased optimization of the antenna elevation mask, in its AOS and LOS azimuths along the horizon visible, maximizes visibility time with spatial vehicles. Furthermore, from the three-dimensional cloud of points captured, specific information is selected and, using 3D modeling techniques, the 3D scene of the antenna location site and surroundings is generated. The resulting 3D model evidences nearby obstacles related to the cartographic conditions such as mountain formations and buildings, and any additional obstacles that interfere with the operational quality of the antenna (other antennas and electronic devices that emit or receive in the same bandwidth

  14. Ultra-fast fluence optimization for beam angle selection algorithms

    NASA Astrophysics Data System (ADS)

    Bangert, M.; Ziegenhein, P.; Oelfke, U.

    2014-03-01

    Beam angle selection (BAS) including fluence optimization (FO) is among the most extensive computational tasks in radiotherapy. Precomputed dose influence data (DID) of all considered beam orientations (up to 100 GB for complex cases) has to be handled in the main memory and repeated FOs are required for different beam ensembles. In this paper, the authors describe concepts accelerating FO for BAS algorithms using off-the-shelf multiprocessor workstations. The FO runtime is not dominated by the arithmetic load of the CPUs but by the transportation of DID from the RAM to the CPUs. On multiprocessor workstations, however, the speed of data transportation from the main memory to the CPUs is non-uniform across the RAM; every CPU has a dedicated memory location (node) with minimum access time. We apply a thread node binding strategy to ensure that CPUs only access DID from their preferred node. Ideal load balancing for arbitrary beam ensembles is guaranteed by distributing the DID of every candidate beam equally to all nodes. Furthermore we use a custom sorting scheme of the DID to minimize the overall data transportation. The framework is implemented on an AMD Opteron workstation. One FO iteration comprising dose, objective function, and gradient calculation takes between 0.010 s (9 beams, skull, 0.23 GB DID) and 0.070 s (9 beams, abdomen, 1.50 GB DID). Our overall FO time is < 1 s for small cases, larger cases take ~ 4 s. BAS runs including FOs for 1000 different beam ensembles take ~ 15-70 min, depending on the treatment site. This enables an efficient clinical evaluation of different BAS algorithms.

  15. Wild Western Lowland Gorillas Signal Selectively Using Odor

    PubMed Central

    Klailova, Michelle; Lee, Phyllis C.

    2014-01-01

    Mammals communicate socially through visual, auditory and chemical signals. The chemical sense is the oldest sense and is shared by all organisms including bacteria. Despite mounting evidence for social chemo-signaling in humans, the extent to which it modulates behavior is debated and can benefit from comparative models of closely related hominoids. The use of odor cues in wild ape social communication has been only rarely explored. Apart from one study on wild chimpanzee sniffing, our understanding is limited to anecdotes. We present the first study of wild gorilla chemo-communication and the first analysis of olfactory signaling in relation to arousal levels and odor strength in wild apes. If gorilla scent is used as a signaling mechanism instead of only a sign of arousal or stress, odor emission should be context specific and capable of variation as a function of the relationships between the emitter and perceiver(s). Measured through a human pungency scale, we determined the factors that predicted extreme levels of silverback odor for one wild western lowland gorilla (Gorilla gorilla gorilla) group silverback. Extreme silverback odor was predicted by the presence and intensity of inter-unit interactions, silverback anger, distress and long-calling auditory rates, and the absence of close proximity between the silverback and mother of the youngest infant. Odor strength also varied according to the focal silverback's strategic responses during high intensity inter-unit interactions. Silverbacks appear to use odor as a modifiable form of communication; where odor acts as a highly flexible, context dependent signaling mechanism to group members and extra-group units. The importance of olfaction to ape social communication may be especially pertinent in Central African forests where limited visibility may necessitate increased reliance on other senses. PMID:25006973

  16. Wild western lowland gorillas signal selectively using odor.

    PubMed

    Klailova, Michelle; Lee, Phyllis C

    2014-01-01

    Mammals communicate socially through visual, auditory and chemical signals. The chemical sense is the oldest sense and is shared by all organisms including bacteria. Despite mounting evidence for social chemo-signaling in humans, the extent to which it modulates behavior is debated and can benefit from comparative models of closely related hominoids. The use of odor cues in wild ape social communication has been only rarely explored. Apart from one study on wild chimpanzee sniffing, our understanding is limited to anecdotes. We present the first study of wild gorilla chemo-communication and the first analysis of olfactory signaling in relation to arousal levels and odor strength in wild apes. If gorilla scent is used as a signaling mechanism instead of only a sign of arousal or stress, odor emission should be context specific and capable of variation as a function of the relationships between the emitter and perceiver(s). Measured through a human pungency scale, we determined the factors that predicted extreme levels of silverback odor for one wild western lowland gorilla (Gorilla gorilla gorilla) group silverback. Extreme silverback odor was predicted by the presence and intensity of inter-unit interactions, silverback anger, distress and long-calling auditory rates, and the absence of close proximity between the silverback and mother of the youngest infant. Odor strength also varied according to the focal silverback's strategic responses during high intensity inter-unit interactions. Silverbacks appear to use odor as a modifiable form of communication; where odor acts as a highly flexible, context dependent signaling mechanism to group members and extra-group units. The importance of olfaction to ape social communication may be especially pertinent in Central African forests where limited visibility may necessitate increased reliance on other senses.

  17. Experiments with repeating weighted boosting search for optimization in signal processing applications.

    PubMed

    Chen, Sheng; Wang, Xunxian; Harris, Chris J

    2005-08-01

    Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations, and optimization methods that require no gradient and can achieve a global optimal solution are highly desired to tackle these difficult problems. The paper proposes a guided global search optimization technique, referred to as the repeated weighted boosting search. The proposed optimization algorithm is extremely simple and easy to implement, involving a minimum programming effort. Heuristic explanation is given for the global search capability of this technique. Comparison is made with the two better known and widely used guided global search techniques, known as the genetic algorithm and adaptive simulated annealing, in terms of the requirements for algorithmic parameter tuning. The effectiveness of the proposed algorithm as a global optimizer are investigated through several application examples.

  18. Photoacoustic correlation signal-to-noise ratio enhancement by coherent averaging and optical waveform optimization.

    PubMed

    Telenkov, Sergey A; Alwi, Rudolf; Mandelis, Andreas

    2013-10-01

    Photoacoustic (PA) imaging of biological tissues using laser diodes instead of conventional Q-switched pulsed systems provides an attractive alternative for biomedical applications. However, the relatively low energy of laser diodes operating in the pulsed regime, results in generation of very weak acoustic waves, and low signal-to-noise ratio (SNR) of the detected signals. This problem can be addressed if optical excitation is modulated using custom waveforms and correlation processing is employed to increase SNR through signal compression. This work investigates the effect of the parameters of the modulation waveform on the resulting correlation signal and offers a practical means for optimizing PA signal detection. The advantage of coherent signal averaging is demonstrated using theoretical analysis and a numerical model of PA generation. It was shown that an additional 5-10 dB of SNR can be gained through waveform engineering by adjusting the parameters and profile of optical modulation waveforms.

  19. A Bayes optimal matrix-variate LDA for extraction of spatio-spectral features from EEG signals.

    PubMed

    Mahanta, Mohammad Shahin; Aghaei, Amirhossein S; Plataniotis, Konstantinos N

    2012-01-01

    Classification of mental states from electroencephalogram (EEG) signals is used for many applications in areas such as brain-computer interfacing (BCI). When represented in the frequency domain, the multichannel EEG signal can be considered as a two-directional spatio-spectral data of high dimensionality. Extraction of salient features using feature extractors such as the commonly used linear discriminant analysis (LDA) is an essential step for the classification of these signals. However, multichannel EEG is naturally in matrix-variate format, while LDA and other traditional feature extractors are designed for vector-variate input. Consequently, these methods require a prior vectorization of the EEG signals, which ignores the inherent matrix-variate structure in the data and leads to high computational complexity. A matrix-variate formulation of LDA have previously been proposed. However, this heuristic formulation does not provide the Bayes optimality benefits of LDA. The current paper proposes a Bayes optimal matrix-variate formulation of LDA based on a matrix-variate model for the spatio-spectral EEG patterns. The proposed formulation also provides a strategy to select the most significant features among the different rows and columns.

  20. A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

    NASA Astrophysics Data System (ADS)

    Pereyra, Marcelo; Schniter, Philip; Chouzenoux, Emilie; Pesquet, Jean-Christophe; Tourneret, Jean-Yves; Hero, Alfred O.; McLaughlin, Steve

    2016-03-01

    Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.

  1. Centrifugal selection of signal-directed pecking1

    PubMed Central

    Barrera, F. J.

    1974-01-01

    Pigeons were exposed to a schedule of stimulus-correlated food presentations. When key pecks terminated trial signals and cancelled the delivery of food, pecking was either gradually or rapidly redirected away from the keys, depending on whether the food-omission contingency was introduced from the outset or after exposure to a response-independent baseline. In all cases, the food-omission contingency substantially reduced or eliminated pecking at the keys. PMID:16811798

  2. Bicoid signal extraction with a selection of parametric and nonparametric signal processing techniques.

    PubMed

    Ghodsi, Zara; Silva, Emmanuel Sirimal; Hassani, Hossein

    2015-06-01

    The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.

  3. Bicoid Signal Extraction with a Selection of Parametric and Nonparametric Signal Processing Techniques

    PubMed Central

    Ghodsi, Zara; Silva, Emmanuel Sirimal; Hassani, Hossein

    2015-01-01

    The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average. PMID:26197438

  4. Neural network classifier with analytic translation and scaling capabilities for optimal signal viewing

    SciTech Connect

    Vilim, R.B.; Wegerich, S.W.

    1995-12-31

    A neural network originally proposed by Szu for performing pattern recognition has been modified for use in a noisy manufacturing environment. Signals from the factory floor are frequently affine transformed and, as a consequence, a signal may not be properly aligned with respect to the input node that corresponds to the signal leading edge or with respect to the number of nodes representing the time varying part. Rater than translate and scale the presented signal, an operation which because of noise can be prone to numerical error since the signal is not smoothly varying, the network in this paper has the capability to analytically translate and scale its internal representation of the signal so that it overlays the presented signal. A response surface in the neighborhood of the stored reference signal is built during, training, and covers the range of translate and scale parameter values expected. A genetic algorithm is used to search over this hilly terrain to find the optimal values of these parameters so that the reference signal overlays the presented signal. The procedure is repeated over all hypothesized pattern classes with the best fit identifying the class.

  5. Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

    NASA Astrophysics Data System (ADS)

    Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

    2016-06-01

    Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

  6. Stimulus design for model selection and validation in cell signaling.

    PubMed

    Apgar, Joshua F; Toettcher, Jared E; Endy, Drew; White, Forest M; Tidor, Bruce

    2008-02-01

    Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus-response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models. PMID

  7. To Eat or Not to Eat: An Easy Simulation of Optimal Diet Selection in the Classroom

    ERIC Educational Resources Information Center

    Ray, Darrell L.

    2010-01-01

    Optimal diet selection, a component of optimal foraging theory, suggests that animals should select a diet that either maximizes energy or nutrient consumption per unit time or minimizes the foraging time needed to attain required energy or nutrients. In this exercise, students simulate the behavior of foragers that either show no foraging…

  8. 75 FR 39437 - Optimizing the Security of Biological Select Agents and Toxins in the United States

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-07-08

    ..., 2010. [FR Doc. 2010-16864 Filed 7-7-10; 11:15 am] Billing code 3195-W0-P ... Executive Order 13546--Optimizing the Security of Biological Select Agents and Toxins in the United States... July 2, 2010 Optimizing the Security of Biological Select Agents and Toxins in the United States By...

  9. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    SciTech Connect

    Hinders, Mark K.; Miller, Corey A.

    2014-02-18

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

  10. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    NASA Astrophysics Data System (ADS)

    Hinders, Mark K.; Miller, Corey A.

    2014-02-01

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it's never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes "line up" in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

  11. Resonance Raman enhancement optimization in the visible range by selecting different excitation wavelengths

    NASA Astrophysics Data System (ADS)

    Wang, Zhong; Li, Yuee

    2015-09-01

    Resonance enhancement of Raman spectroscopy (RS) has been used to significantly improve the sensitivity and selectivity of detection for specific components in complicated environments. Resonance RS gives more insight into the biochemical structure and reactivity. In this field, selecting a proper excitation wavelength to achieve optimal resonance enhancement is vital for the study of an individual chemical/biological ingredient with a particular absorption characteristic. Raman spectra of three azo derivatives with absorption spectra in the visible range are studied under the same experimental conditions at 488, 532, and 633 nm excitations. Universal laws in the visible range have been concluded by analyzing resonance Raman (RR) spectra of samples. The long wavelength edge of the absorption spectrum is a better choice for intense enhancement and the integrity of a Raman signal. The obtained results are valuable for applying RR for the selective detection of biochemical constituents whose electronic transitions take place at energies corresponding to the visible spectra, which is much friendlier to biologial samples compared to ultraviolet.

  12. Polyhedral Interpolation for Optimal Reaction Control System Jet Selection

    NASA Technical Reports Server (NTRS)

    Gefert, Leon P.; Wright, Theodore

    2014-01-01

    An efficient algorithm is described for interpolating optimal values for spacecraft Reaction Control System jet firing duty cycles. The algorithm uses the symmetrical geometry of the optimal solution to reduce the number of calculations and data storage requirements to a level that enables implementation on the small real time flight control systems used in spacecraft. The process minimizes acceleration direction errors, maximizes control authority, and minimizes fuel consumption.

  13. Age-Related Differences in Goals: Testing Predictions from Selection, Optimization, and Compensation Theory and Socioemotional Selectivity Theory

    ERIC Educational Resources Information Center

    Penningroth, Suzanna L.; Scott, Walter D.

    2012-01-01

    Two prominent theories of lifespan development, socioemotional selectivity theory and selection, optimization, and compensation theory, make similar predictions for differences in the goal representations of younger and older adults. Our purpose was to test whether the goals of younger and older adults differed in ways predicted by these two…

  14. Methodology and method and apparatus for signaling with capacity optimized constellations

    NASA Technical Reports Server (NTRS)

    Barsoum, Maged F. (Inventor); Jones, Christopher R. (Inventor)

    2011-01-01

    Communication systems having transmitter, includes a coder configured to receive user bits and output encoded bits at an expanded output encoded bit rate, a mapper configured to map encoded bits to symbols in a symbol constellation, a modulator configured to generate a signal for transmission via the communication channel using symbols generated by the mapper. In addition, the receiver includes a demodulator configured to demodulate the received signal via the communication channel, a demapper configured to estimate likelihoods from the demodulated signal, a decoder that is configured to estimate decoded bits from the likelihoods generated by the demapper. Furthermore, the symbol constellation is a capacity optimized geometrically spaced symbol constellation that provides a given capacity at a reduced signal-to-noise ratio compared to a signal constellation that maximizes d.sub.min.

  15. Multicomponent floral signals elicit selective foraging in bumblebees

    NASA Astrophysics Data System (ADS)

    Gegear, Robert J.

    2005-06-01

    Flower constancy, or the tendency of individual pollinators to visit sequentially a single flower type even when other equally rewarding types are available, has important implications for animal-pollinated plants. Yet, the proximal reason for the behaviour still remains poorly understood. Here I show that bumblebees visiting equally rewarding flowers that differ in size and odour are more flower constant and less efficient (visited fewer flowers per minute) than bees visiting flowers that differ in size only and odour only. These results are consistent with the view that flower constancy in pollinators is related to their inability to perceive, process or recall multicomponent floral signals. I discuss these findings in the context of pollinator behavioural mechanisms and the evolution of floral diversity.

  16. Time and frequency constrained sonar signal design for optimal detection of elastic objects.

    PubMed

    Hamschin, Brandon; Loughlin, Patrick J

    2013-04-01

    In this paper, the task of model-based transmit signal design for optimizing detection is considered. Building on past work that designs the spectral magnitude for optimizing detection, two methods for synthesizing minimum duration signals with this spectral magnitude are developed. The methods are applied to the design of signals that are optimal for detecting elastic objects in the presence of additive noise and self-noise. Elastic objects are modeled as linear time-invariant systems with known impulse responses, while additive noise (e.g., ocean noise or receiver noise) and acoustic self-noise (e.g., reverberation or clutter) are modeled as stationary Gaussian random processes with known power spectral densities. The first approach finds the waveform that preserves the optimal spectral magnitude while achieving the minimum temporal duration. The second approach yields a finite-length time-domain sequence by maximizing temporal energy concentration, subject to the constraint that the spectral magnitude is close (in a least-squares sense) to the optimal spectral magnitude. The two approaches are then connected analytically, showing the former is a limiting case of the latter. Simulation examples that illustrate the theory are accompanied by discussions that address practical applicability and how one might satisfy the need for target and environmental models in the real-world.

  17. Symmetry breaking in optimal timing of traffic signals on an idealized two-way street

    NASA Astrophysics Data System (ADS)

    Panaggio, Mark J.; Ottino-Löffler, Bertand J.; Hu, Peiguang; Abrams, Daniel M.

    2013-09-01

    Simple physical models based on fluid mechanics have long been used to understand the flow of vehicular traffic on freeways; analytically tractable models of flow on an urban grid, however, have not been as extensively explored. In an ideal world, traffic signals would be timed such that consecutive lights turned green just as vehicles arrived, eliminating the need to stop at each block. Unfortunately, this “green-wave” scenario is generally unworkable due to frustration imposed by competing demands of traffic moving in different directions. Until now this has typically been resolved by numerical simulation and optimization. Here, we develop a theory for the flow in an idealized system consisting of a long two-way road with periodic intersections. We show that optimal signal timing can be understood analytically and that there are counterintuitive asymmetric solutions to this signal coordination problem. We further explore how these theoretical solutions degrade as traffic conditions vary and automotive density increases.

  18. Symmetry breaking in optimal timing of traffic signals on an idealized two-way street.

    PubMed

    Panaggio, Mark J; Ottino-Löffler, Bertand J; Hu, Peiguang; Abrams, Daniel M

    2013-09-01

    Simple physical models based on fluid mechanics have long been used to understand the flow of vehicular traffic on freeways; analytically tractable models of flow on an urban grid, however, have not been as extensively explored. In an ideal world, traffic signals would be timed such that consecutive lights turned green just as vehicles arrived, eliminating the need to stop at each block. Unfortunately, this "green-wave" scenario is generally unworkable due to frustration imposed by competing demands of traffic moving in different directions. Until now this has typically been resolved by numerical simulation and optimization. Here, we develop a theory for the flow in an idealized system consisting of a long two-way road with periodic intersections. We show that optimal signal timing can be understood analytically and that there are counterintuitive asymmetric solutions to this signal coordination problem. We further explore how these theoretical solutions degrade as traffic conditions vary and automotive density increases.

  19. Optimal Bandwidth Selection in Observed-Score Kernel Equating

    ERIC Educational Resources Information Center

    Häggström, Jenny; Wiberg, Marie

    2014-01-01

    The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent…

  20. Optimization of parameters affecting signal intensity in an LTQ-orbitrap in negative ion mode: A design of experiments approach.

    PubMed

    Lemonakis, Nikolaos; Skaltsounis, Alexios-Leandros; Tsarbopoulos, Anthony; Gikas, Evagelos

    2016-01-15

    A multistage optimization of all the parameters affecting detection/response in an LTQ-orbitrap analyzer was performed, using a design of experiments methodology. The signal intensity, a critical issue for mass analysis, was investigated and the optimization process was completed in three successive steps, taking into account the three main regions of an orbitrap, the ion generation, the ion transmission and the ion detection regions. Oleuropein and hydroxytyrosol were selected as the model compounds. Overall, applying this methodology the sensitivity was increased more than 24%, the resolution more than 6.5%, whereas the elapsed scan time was reduced nearly to its half. A high-resolution LTQ Orbitrap Discovery mass spectrometer was used for the determination of the analytes of interest. Thus, oleuropein and hydroxytyrosol were infused via the instruments syringe pump and they were analyzed employing electrospray ionization (ESI) in the negative high-resolution full-scan ion mode. The parameters of the three main regions of the LTQ-orbitrap were independently optimized in terms of maximum sensitivity. In this context, factorial design, response surface model and Plackett-Burman experiments were performed and analysis of variance was carried out to evaluate the validity of the statistical model and to determine the most significant parameters for signal intensity. The optimum MS conditions for each analyte were summarized and the method optimum condition was achieved by maximizing the desirability function. Our observation showed good agreement between the predicted optimum response and the responses collected at the predicted optimum conditions.

  1. Response Selection and Response Execution in Task Switching: Evidence from a Go-Signal Paradigm

    ERIC Educational Resources Information Center

    Philipp, Andrea M.; Jolicoeur, Pierre; Falkenstein, Michael; Koch, Iring

    2007-01-01

    The present study used a go/no-go signal delay (GSD) to explore the role of response-related processes in task switching. A go/no-go signal was presented at either 100 ms or 1,500 ms after the stimulus. Participants were encouraged to use the GSD for response selection and preparation. The data indicate that the opportunity to select and prepare a…

  2. Optimization of a digital lock-in algorithm with a square-wave reference for frequency-divided multi-channel sensor signal detection

    NASA Astrophysics Data System (ADS)

    Zhang, Shengzhao; Li, Gang; Lin, Ling; Zhao, Jing

    2016-08-01

    A digital lock-in detection technique is commonly used to measure the amplitude and phase of a selected frequency signal. A technique that uses a square wave as the reference signal has an advantage over the one using a sinusoidal wave due to its easier implementation and higher computational efficiency. However, demodulating multiple-frequency composite signals using square wave reference may result in interference between channels. To avoid interference between channels and reduce the computational complexity, we modify the calculations and determine the optimal parameter settings of the low-pass filter and carrier frequency, as detailed in this paper. The results of our analysis show that when the length of the average filter and carrier frequencies are properly set, the interference between the channels is removed. This optimization produces the digital lock-in detection suitable for measuring multi-channel sensor signals.

  3. Optimization of a digital lock-in algorithm with a square-wave reference for frequency-divided multi-channel sensor signal detection.

    PubMed

    Zhang, Shengzhao; Li, Gang; Lin, Ling; Zhao, Jing

    2016-08-01

    A digital lock-in detection technique is commonly used to measure the amplitude and phase of a selected frequency signal. A technique that uses a square wave as the reference signal has an advantage over the one using a sinusoidal wave due to its easier implementation and higher computational efficiency. However, demodulating multiple-frequency composite signals using square wave reference may result in interference between channels. To avoid interference between channels and reduce the computational complexity, we modify the calculations and determine the optimal parameter settings of the low-pass filter and carrier frequency, as detailed in this paper. The results of our analysis show that when the length of the average filter and carrier frequencies are properly set, the interference between the channels is removed. This optimization produces the digital lock-in detection suitable for measuring multi-channel sensor signals. PMID:27587155

  4. Optimization of a digital lock-in algorithm with a square-wave reference for frequency-divided multi-channel sensor signal detection.

    PubMed

    Zhang, Shengzhao; Li, Gang; Lin, Ling; Zhao, Jing

    2016-08-01

    A digital lock-in detection technique is commonly used to measure the amplitude and phase of a selected frequency signal. A technique that uses a square wave as the reference signal has an advantage over the one using a sinusoidal wave due to its easier implementation and higher computational efficiency. However, demodulating multiple-frequency composite signals using square wave reference may result in interference between channels. To avoid interference between channels and reduce the computational complexity, we modify the calculations and determine the optimal parameter settings of the low-pass filter and carrier frequency, as detailed in this paper. The results of our analysis show that when the length of the average filter and carrier frequencies are properly set, the interference between the channels is removed. This optimization produces the digital lock-in detection suitable for measuring multi-channel sensor signals.

  5. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit...

  6. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit...

  7. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit...

  8. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit...

  9. A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

    PubMed

    Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

    2016-01-01

    Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system. PMID:27631789

  10. A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface

    PubMed Central

    Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

    2016-01-01

    Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The “high quality” training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system. PMID:27631789

  11. An artificial system for selecting the optimal surgical team

    PubMed Central

    Saberi, Nahid; Mahvash, Mohsen; Zenati, Marco

    2016-01-01

    We introduce an intelligent system to optimize a team composition based on the team’s historical outcomes and apply this system to compose a surgical team. The system relies on a record of the procedures performed in the past. The optimal team composition is the one with the lowest probability of unfavorable outcome. We use the theory of probability and the inclusion exclusion principle to model the probability of team outcome for a given composition. A probability value is assigned to each person of database and the probability of a team composition is calculated from them. The model allows to determine the probability of all possible team compositions even if there is no recoded procedure for some team compositions. From an analytical perspective, assembling an optimal team is equivalent to minimizing the overlap of team members who have a recurring tendency to be involved with procedures of unfavorable results. A conceptual example shows the accuracy of the proposed system on obtaining the optimal team. PMID:26736239

  12. Self-Selection, Optimal Income Taxation, and Redistribution

    ERIC Educational Resources Information Center

    Amegashie, J. Atsu

    2009-01-01

    The author makes a pedagogical contribution to optimal income taxation. Using a very simple model adapted from George A. Akerlof (1978), he demonstrates a key result in the approach to public economics and welfare economics pioneered by Nobel laureate James Mirrlees. He shows how incomplete information, in addition to the need to preserve…

  13. Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN+

    PubMed Central

    Lopez, B. M.; Kang, H. S.; Kim, T. H.; Viterbo, V. S.; Kim, H. S.; Na, C. S.; Seo, K. S.

    2016-01-01

    The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit. PMID:26954222

  14. Identifying or measuring selected substances or toxins in a subject using resonant raman signals

    NASA Technical Reports Server (NTRS)

    Lambert, James L. (Inventor); Borchert, Mark S. (Inventor)

    2005-01-01

    Methods and systems of the present invention identify the presence of and/or the concentration of a selected analyte in a subject by: (a) illuminating a selected region of the eye of a subject with an optical excitation beam, wherein the excitation beam wavelength is selected to generate a resonant Raman spectrum of the selected analyte with a signal strength that is at least 100 times greater than Raman spectrums generated by non-resonant wavelengths and/or relative to signals of normal constituents present in the selected region of the eye; (b) detecting a resonant Raman spectrum corresponding to the selected illuminated region of the eye; and (c) identifying the presence, absence and/or the concentration of the selected analyte in the subject based on said detecting step. The apparatus may also be configured to be able to obtain biometric data of the eye to identify (confirm the identity of) the subject.

  15. Optimal design and selection of magneto-rheological brake types based on braking torque and mass

    NASA Astrophysics Data System (ADS)

    Nguyen, Q. H.; Lang, V. T.; Choi, S. B.

    2015-06-01

    In developing magnetorheological brakes (MRBs), it is well known that the braking torque and the mass of the MRBs are important factors that should be considered in the product’s design. This research focuses on the optimal design of different types of MRBs, from which we identify an optimal selection of MRB types, considering braking torque and mass. In the optimization, common types of MRBs such as disc-type, drum-type, hybrid-type, and T-shape types are considered. The optimization problem is to find an optimal MRB structure that can produce the required braking torque while minimizing its mass. After a brief description of the configuration of the MRBs, the MRBs’ braking torque is derived based on the Herschel-Bulkley rheological model of the magnetorheological fluid. Then, the optimal designs of the MRBs are analyzed. The optimization objective is to minimize the mass of the brake while the braking torque is constrained to be greater than a required value. In addition, the power consumption of the MRBs is also considered as a reference parameter in the optimization. A finite element analysis integrated with an optimization tool is used to obtain optimal solutions for the MRBs. Optimal solutions of MRBs with different required braking torque values are obtained based on the proposed optimization procedure. From the results, we discuss the optimal selection of MRB types, considering braking torque and mass.

  16. Selective Vulnerability of Synaptic Signaling and Metabolism to Nitrosative Stress

    PubMed Central

    Dohare, Preeti

    2012-01-01

    Abstract Significance: Nitric oxide (NO) plays diverse physiological roles in the central nervous system, where it modulates neuronal communication, regulates blood flow, and contributes to the innate immune responses. In a number of brain pathologies, the excessive production of NO also leads to the formation of reactive and toxic intermediates generically termed reactive nitrogen species (RNS). RNS cause irreversible or poorly reversible damage to brain cells. Recent Advances: Recent work in the field focused on the ability of NO and RNS to yield protein modifications, including the S-nitrosation of cysteine residues, which, in many instances, impact cellular functions and viability. Critical Issues: The vast majority of neuropathological studies focus on the loss of cell viability, but nitrosative stress may also strongly impair the functions of neuronal processes: axonal projections and dendritic trees. The functional integrity of axons and dendrites critically depends on local metabolism and effective delivery of metabolic enzymes and organelles. Here, we summarize the existing literature describing the effects of nitrosative stress on the major pathways of energetic metabolism: glycolysis, tricarboxylic acid cycle, and mitochondrial respiration, with the emphasis on modifications of protein thiols. Future Directions: We propose that axons and dendrites are highly vulnerable to nitrosative stress because of their low glycolytic capacity and high dependence on timely delivery of metabolic enzymes and organelles from the cell body. Thus, supplementation with the end products of glycolysis, pyruvate or lactate, may help preserve metabolism in distal neuronal processes and protect or restore synaptic function in the ailing brain. Antioxid. Redox Signal. 17, 992–1012. PMID:22339371

  17. Optimization of the signal processing in frequency modulated continuous wave laser ranging system

    NASA Astrophysics Data System (ADS)

    Meng, Xiangsong; Zhang, Fumin; Qu, Xinghua

    2015-02-01

    Based on a dual interferometry frequency modulated wave laser (FMCW) laser ranging system, three steps to optimize the signal processing is proposed in this paper. The first step is signal re-sampling, by which the sampling signal is turned to be equal optical frequency intervals. The second step is splicing the re-sampled signal, by which can break though the tuning range of the laser source limitation. The last step is the all-phase pretreatment of the signal, its means that the all-phase Fast Fourier Transformation (apFFT) is used to handle the re-sampled signal, which could reduce the phase error of the signal. The experiments shows that the noise effect due to the tuning nonlinearity of laser can be reduced by re-sampling the signal, 50μm range resolution can be easily obtained by this method, the apFFT is more reliable and effective than FFT in the processing to reduce the phase error and improve the speed of operation.

  18. Methodology and Method and Apparatus for Signaling with Capacity Optimized Constellations

    NASA Technical Reports Server (NTRS)

    Barsoum, Maged F. (Inventor); Jones, Christopher R. (Inventor)

    2016-01-01

    Design Methodology and Method and Apparatus for Signaling with Capacity Optimized Constellation Abstract Communication systems are described that use geometrically PSK shaped constellations that have increased capacity compared to conventional PSK constellations operating within a similar SNR band. The geometrically shaped PSK constellation is optimized based upon parallel decoding capacity. In many embodiments, a capacity optimized geometrically shaped constellation can be used to replace a conventional constellation as part of a firmware upgrade to transmitters and receivers within a communication system. In a number of embodiments, the geometrically shaped constellation is optimized for an Additive White Gaussian Noise channel or a fading channel. In numerous embodiments, the communication uses adaptive rate encoding and the location of points within the geometrically shaped constellation changes as the code rate changes.

  19. Optimizing drilling performance using a selected drilling fluid

    DOEpatents

    Judzis, Arnis; Black, Alan D.; Green, Sidney J.; Robertson, Homer A.; Bland, Ronald G.; Curry, David Alexander; Ledgerwood, III, Leroy W.

    2011-04-19

    To improve drilling performance, a drilling fluid is selected based on one or more criteria and to have at least one target characteristic. Drilling equipment is used to drill a wellbore, and the selected drilling fluid is provided into the wellbore during drilling with the drilling equipment. The at least one target characteristic of the drilling fluid includes an ability of the drilling fluid to penetrate into formation cuttings during drilling to weaken the formation cuttings.

  20. Parameter selection for the SSC trade-offs and optimization

    SciTech Connect

    Edwards, D.A.; Syphers, M.J.

    1991-10-14

    In November of 1988, a site was selected in the state of Texas for the SSC. In January of 1989, the SSC Laboratory was established in Texas to adapt the design of the collider to the site and to manage the construction of the project. This paper describes the evolution of the SSC design since site selection, notes the increased concentration on the injector system, and addresses the rationale for choice of parameters.

  1. Saccade target selection relies on feedback competitive signal integration.

    PubMed

    Kalisvaart, Joke P; Noest, André J; van den Berg, Albert V; Goossens, Jeroen

    2013-07-17

    It is often assumed that decision making involves neural competition, accumulation of evidence "scores" over time, and commitment to a particular alternative once its scores reach a critical decision threshold first. So far, however, neither the first-to-threshold rule nor the nature of competition (feedforward or feedback inhibition) has been revealed by experiments. Here, we presented two simultaneously flashed targets that reversed their intensity difference during each presentation and instructed human subjects to make a saccade toward the brightest target. All subjects preferentially chose the target that was brightest during the first stimulus phase. Unless this first phase lasted only 40 ms, this primacy effect persisted even if the second, reversed-intensity phase lasted longer. This effect did not result from premature commitment to the initially dominant target, because a strong target imbalance in the opposite direction later drove nearly all responses toward that location. Moreover, there was a nonmonotonic relation between target imbalance and primacy: increasing the target imbalance beyond 40 cd/m(2) caused an attenuation of primacy. These are the hallmarks of hysteresis, predicted by models in which target representations compete through strong feedback. Reaction times were independent of the choice probability. This dissociation suggests that target selection and movement initiation are distinct phenomena.

  2. Identifying Ensembles of Signal Transduction Models using Pareto Optimal Ensemble Techniques (POETs)

    PubMed Central

    Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D.

    2010-01-01

    Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass action kinetics within an ordinary differential equation (ODE) framework (64-ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information. PMID:20665647

  3. Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs).

    PubMed

    Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D

    2010-07-01

    Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.

  4. Optimizing promoters and secretory signal sequences for producing ethanol from inulin by recombinant Saccharomyces cerevisiae carrying Kluyveromyces marxianus inulinase.

    PubMed

    Hong, Soo-Jeong; Kim, Hyo Jin; Kim, Jin-Woo; Lee, Dae-Hee; Seo, Jin-Ho

    2015-02-01

    Inulin is a polyfructan that is abundant in plants such as Jerusalem artichoke, chicory and dahlia. Inulinase can easily hydrolyze inulin to fructose, which is consumed by microorganisms. Generally, Saccharomyces cerevisiae, an industrial workhorse strain for bioethanol production, is known for not having inulinase activity. The inulinase gene from Kluyveromyces marxianus (KmINU), with the ability of converting inulin to fructose, was introduced into S. cerevisiae D452-2. The inulinase gene was fused to three different types of promoter (GPD, PGK1, truncated HXT7) and secretory signal sequence (KmINU, MFα1, SUC2) to generate nine expression cassettes. The inulin fermentation performance of the nine transformants containing different promoter and signal sequence combinations for inulinase production were compared to select an optimized expression system for efficient inulin fermentation. Among the nine inulinase-producing transformants, the S. cerevisiae carrying the PGK1 promoter and MFα1 signal sequence (S. cerevisiae D452-2/p426PM) showed not only the highest specific KmINU activity, but also the best inulin fermentation capability. Finally, a batch fermentation of the selected S. cerevisiae D452-2/p426PM in a bioreactor with 188.2 g/L inulin was performed to produce 80.2 g/L ethanol with 0.43 g ethanol/g inulin of ethanol yield and 1.22 g/L h of ethanol productivity.

  5. Optimizing promoters and secretory signal sequences for producing ethanol from inulin by recombinant Saccharomyces cerevisiae carrying Kluyveromyces marxianus inulinase.

    PubMed

    Hong, Soo-Jeong; Kim, Hyo Jin; Kim, Jin-Woo; Lee, Dae-Hee; Seo, Jin-Ho

    2015-02-01

    Inulin is a polyfructan that is abundant in plants such as Jerusalem artichoke, chicory and dahlia. Inulinase can easily hydrolyze inulin to fructose, which is consumed by microorganisms. Generally, Saccharomyces cerevisiae, an industrial workhorse strain for bioethanol production, is known for not having inulinase activity. The inulinase gene from Kluyveromyces marxianus (KmINU), with the ability of converting inulin to fructose, was introduced into S. cerevisiae D452-2. The inulinase gene was fused to three different types of promoter (GPD, PGK1, truncated HXT7) and secretory signal sequence (KmINU, MFα1, SUC2) to generate nine expression cassettes. The inulin fermentation performance of the nine transformants containing different promoter and signal sequence combinations for inulinase production were compared to select an optimized expression system for efficient inulin fermentation. Among the nine inulinase-producing transformants, the S. cerevisiae carrying the PGK1 promoter and MFα1 signal sequence (S. cerevisiae D452-2/p426PM) showed not only the highest specific KmINU activity, but also the best inulin fermentation capability. Finally, a batch fermentation of the selected S. cerevisiae D452-2/p426PM in a bioreactor with 188.2 g/L inulin was performed to produce 80.2 g/L ethanol with 0.43 g ethanol/g inulin of ethanol yield and 1.22 g/L h of ethanol productivity. PMID:25142154

  6. Material selection and corresponding optimal surface relief height for multilayer diffractive optical elements

    NASA Astrophysics Data System (ADS)

    Dun, Xiong; Jin, Weiqi; Wang, Xia

    2015-11-01

    We present a model based on refractive index difference analysis for optimization of material selection for multilayer diffractive optical elements (MLDOEs). From the proposed model, two important relationships are derived: the relationship between material selection and the maximum polychromatic integral diffraction efficiency of MLDOEs, and between material selection and the surface relief heights of MLDOEs. The new relationships are more comprehensive and reliable than those discussed in previous papers. A theoretical expression of the optimal surface relief heights of MLDOEs is also presented, and its correctness is demonstrated through a comparison with the results of enumeration optimization.

  7. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

    PubMed Central

    Al-Qazzaz, Noor Kamal; Hamid Bin Mohd Ali, Sawal; Ahmad, Siti Anom; Islam, Mohd Shabiul; Escudero, Javier

    2015-01-01

    We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions. PMID:26593918

  8. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 49 Transportation 4 2013-10-01 2013-10-01 false Spring switch; selection of signal control... SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.13 Spring switch... facing movements over a main track spring switch shall be selected through the contacts of a...

  9. A signal integration model of thymic selection and natural regulatory T cell commitment.

    PubMed

    Khailaie, Sahamoddin; Robert, Philippe A; Toker, Aras; Huehn, Jochen; Meyer-Hermann, Michael

    2014-12-15

    The extent of TCR self-reactivity is the basis for selection of a functional and self-tolerant T cell repertoire and is quantified by repeated engagement of TCRs with a diverse pool of self-peptides complexed with self-MHC molecules. The strength of a TCR signal depends on the binding properties of a TCR to the peptide and the MHC, but it is not clear how the specificity to both components drives fate decisions. In this study, we propose a TCR signal-integration model of thymic selection that describes how thymocytes decide among distinct fates, not only based on a single TCR-ligand interaction, but taking into account the TCR stimulation history. These fates are separated based on sustained accumulated signals for positive selection and transient peak signals for negative selection. This spans up the cells into a two-dimensional space where they are either neglected, positively selected, negatively selected, or selected as natural regulatory T cells (nTregs). We show that the dynamics of the integrated signal can serve as a successful basis for extracting specificity of thymocytes to MHC and detecting the existence of cognate self-peptide-MHC. It allows to select a self-MHC-biased and self-peptide-tolerant T cell repertoire. Furthermore, nTregs in the model are enriched with MHC-specific TCRs. This allows nTregs to be more sensitive to activation and more cross-reactive than conventional T cells. This study provides a mechanistic model showing that time integration of TCR-mediated signals, as opposed to single-cell interaction events, is needed to gain a full view on the properties emerging from thymic selection. PMID:25392533

  10. Optimization of Metamaterial Selective Emitters for Use in Thermophotovoltaic Applications

    NASA Astrophysics Data System (ADS)

    Pfiester, Nicole A.

    The increasing costs of fossil fuels, both financial and environmental, has motivated many to look into sustainable energy sources. Thermophotovoltaics (TPVs), specialized photovoltaic cells focused on the infrared range, offer an opportunity to achieve both primary energy capture, similar to traditional photovoltaics, as well as secondary energy capture in the form of waste heat. However, to become a feasible energy source, TPV systems must become more efficient. One way to do this is through the development of selective emitters tailored to the bandgap of the TPV diode in question. This thesis proposes the use of metamaterial emitters as an engineerable, highly selective emitter that can withstand the temperatures required to collect waste heat. Metamaterial devices made of platinum and a dielectric such as alumina or silicon nitride were initially designed and tested as perfect absorbers. High temperature robustness testing demonstrates the device's ability to withstand the rigors of operating as a selective emitter.

  11. Optimal climate change signal detection using space-time empirical orthogonal functions

    NASA Astrophysics Data System (ADS)

    Wu, Qigang

    2000-10-01

    Space-time optimal filtering is used to estimate the response of the Earth's surface temperature to both natural and anthropogenic climate forcings over the past century. In this study, a variety of site/record-length configurations is used to represent the climate change signals, natural variability and observational data streams. The hypothesized space-time patterns of the response to the climate forcings are generated from an energy balance climate model (EBCM) and four coupled ocean/atmosphere general circulation models (GCMs). The natural variability statistics are estimated from 1000- year control runs from four coupled GCMs and then such natural variability is represented precisely in terms of the space-time empirical orthogonal functions (EOFs), obtained directly from the lagged space-time covariance matrix on the approximate time interval. Optimal filters are constructed using the hypothesized space-time patterns along with the EOF-represented natural variability. The constructed filters are then applied to the observational surface temperature data. With a proposed orthogonalization process, signals `not- of-interest' are optimally removed from the data stream in the estimation process. Comparisons show that the responses to the anthropogenic climate forcings generated from the EBCM and four GCMs are very similar to each other. A robust greenhouse-gas signal is detected in the observational surface temperature data, but with a weak amplitude only about 60-70% of that expected from the hypothesized signal patterns. An anthropogenic aerosol signal is very weak and not statistically significant. The estimated amplitude of aerosol indicates that four GCMs in this study might overpredict the response to greenhouse gas- plus-aerosol forcing by a factor of two to five. A volcanic signal is also robust but with about 65% expected amplitude. A solar-cycle signal is significant at a level of 90% and somewhat stronger than the amplitude of the EBCM

  12. Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

    NASA Astrophysics Data System (ADS)

    Mao, Lei; Jackson, Lisa

    2016-10-01

    In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.

  13. Selection of optimal auxiliary soil nutrient variables for Cokriging interpolation.

    PubMed

    Song, Genxin; Zhang, Jing; Wang, Ke

    2014-01-01

    In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes.

  14. Selection of Optimal Auxiliary Soil Nutrient Variables for Cokriging Interpolation

    PubMed Central

    Song, Genxin; Zhang, Jing; Wang, Ke

    2014-01-01

    In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes. PMID:24927129

  15. Optimizing visual comfort for stereoscopic 3D display based on color-plus-depth signals.

    PubMed

    Shao, Feng; Jiang, Qiuping; Fu, Randi; Yu, Mei; Jiang, Gangyi

    2016-05-30

    Visual comfort is a long-facing problem in stereoscopic 3D (S3D) display. In this paper, targeting to produce S3D content based on color-plus-depth signals, a general framework for depth mapping to optimize visual comfort for S3D display is proposed. The main motivation of this work is to remap the depth range of color-plus-depth signals to a new depth range that is suitable to comfortable S3D display. Towards this end, we first remap the depth range globally based on the adjusted zero disparity plane, and then present a two-stage global and local depth optimization solution to solve the visual comfort problem. The remapped depth map is used to generate the S3D output. We demonstrate the power of our approach on perceptually uncomfortable and comfortable stereoscopic images. PMID:27410090

  16. Optimization of integration limit in the charge comparison method based on signal shape function

    NASA Astrophysics Data System (ADS)

    Wang, Zhonghai; Zeng, Jun; Zhu, Tonghua; Wang, Yudong; Yang, Chaowen; Zhou, Rong

    2014-10-01

    A novel method is proposed to analyze neutron and gamma-ray signal shapes in liquid scintillation detectors. Specifically, the signal shape functions for a BC501 detector were characterized and a statistical model was used to analyze the discrimination of neutrons and gamma rays. The model varied the starting points of tail integration in the charge comparison method (CCM), and an optimized starting point was determined. Experimental measurements were performed to verify the model, and the results indicated good agreement. For a BC501 scintillator with 8.07 ns and 74.63 ns decay time constants we found optimal time to start the tail integration at 24 ns past the decay maximum.

  17. Wavelet transform based on the optimal wavelet pairs for tunable diode laser absorption spectroscopy signal processing.

    PubMed

    Li, Jingsong; Yu, Benli; Fischer, Horst

    2015-04-01

    This paper presents a novel methodology-based discrete wavelet transform (DWT) and the choice of the optimal wavelet pairs to adaptively process tunable diode laser absorption spectroscopy (TDLAS) spectra for quantitative analysis, such as molecular spectroscopy and trace gas detection. The proposed methodology aims to construct an optimal calibration model for a TDLAS spectrum, regardless of its background structural characteristics, thus facilitating the application of TDLAS as a powerful tool for analytical chemistry. The performance of the proposed method is verified using analysis of both synthetic and observed signals, characterized with different noise levels and baseline drift. In terms of fitting precision and signal-to-noise ratio, both have been improved significantly using the proposed method.

  18. Optimization for enhancement of signal effectiveness in three-dimensional (3D) cell based electrochemical biosensor.

    PubMed

    Jeong, Se Hoon; Ku, Bosung; Yi, Sang Hyun; Lee, Dong Woo; Lee, Hye Seon; Kim, Jhingook

    2011-01-01

    This study addresses the optimization for enhancement of signal effectiveness in 3D cell based electrochemical biosensor. While 2D culture has a structural limitation to mimic an in vivo, 3D culture can provide more similar cell responses. In addition, although 3D cultured cells have been applied to measure electrically, the intensity of electrical signal from cells on the electrode was extremely low. Thus, we have optimized and evaluated the condition of gelation between several types of sol-gel and cancer cells using the electrical measurement to make fine 3D cell structure on the electrode. These results show that our work can be an useful method for monitoring cell activity by compensating a limitation of 2D culture in real time. PMID:22256300

  19. Sexually selected UV signals in the tropical ornate jumping spider, Cosmophasis umbratica may incur costs from predation

    PubMed Central

    Bulbert, Matthew W; O'Hanlon, James C; Zappettini, Shane; Zhang, Shichang; Li, Daiqin

    2015-01-01

    Sexually selected ornaments and signals are costly to maintain if they are maladaptive in nonreproductive contexts. The jumping spider Cosmophasis umbratica exhibits distinct sexual dichromatism with males displaying elaborate UV body markings that signal male quality. Female C. umbratica respond favorably to UV-reflecting males and ignore males that have their UV masked. However, Portia labiata, a UV-sensitive spider-eating specialist and a natural predator of C. umbratica, is known to use UV reflectance as a cue when hunting prey. We investigated the cost of these UV signals in C. umbratica in terms of their predation risk. Under experimental conditions, three choice scenarios were presented to P. labiata individuals. Choices by P. labiata were made between male C. umbratica with and without the UV signal; a UV-reflecting male and non-UV-reflecting female; and a UV-masked male and female. The presence and absence of UV signals was manipulated using an optical filter. Portia labiata exhibited a strong bias toward UV+ individuals. These results suggest the sexually selected trait of UV reflectance increases the visibility of males to UV-sensitive predators. The extent of this male-specific UV signal then is potentially moderated by predation pressure. Interestingly though, P. labiata still preferred males to females irrespective of whether UV reflectance was present or not. This suggests P. labiata can switch cues when conditions to detect UV reflectance are not optimal. PMID:25750717

  20. Optimal selection of Orbital Replacement Unit on-orbit spares - A Space Station system availability model

    NASA Technical Reports Server (NTRS)

    Schwaab, Douglas G.

    1991-01-01

    A mathematical programing model is presented to optimize the selection of Orbital Replacement Unit on-orbit spares for the Space Station. The model maximizes system availability under the constraints of logistics resupply-cargo weight and volume allocations.

  1. Selection of Reserves for Woodland Caribou Using an Optimization Approach

    PubMed Central

    Schneider, Richard R.; Hauer, Grant; Dawe, Kimberly; Adamowicz, Wiktor; Boutin, Stan

    2012-01-01

    Habitat protection has been identified as an important strategy for the conservation of woodland caribou (Rangifer tarandus). However, because of the economic opportunity costs associated with protection it is unlikely that all caribou ranges can be protected in their entirety. We used an optimization approach to identify reserve designs for caribou in Alberta, Canada, across a range of potential protection targets. Our designs minimized costs as well as three demographic risk factors: current industrial footprint, presence of white-tailed deer (Odocoileus virginianus), and climate change. We found that, using optimization, 60% of current caribou range can be protected (including 17% in existing parks) while maintaining access to over 98% of the value of resources on public lands. The trade-off between minimizing cost and minimizing demographic risk factors was minimal because the spatial distributions of cost and risk were similar. The prospects for protection are much reduced if protection is directed towards the herds that are most at risk of near-term extirpation. PMID:22363702

  2. Optimizing selection of decentralized stormwater management strategies in urbanized regions

    NASA Astrophysics Data System (ADS)

    Yu, Z.; Montalto, F.

    2011-12-01

    A variety of decentralized stormwater options are available for implementation in urbanized regions. These strategies, which include bio-retention, porous pavement, green roof etc., vary in terms of cost, ability to reduce runoff, and site applicability. This paper explores the tradeoffs between different types of stormwater control meastures that could be applied in a typical urban study area. A nested optimization strategy first identifies the most cost-effective (e.g. runoff reduction / life cycle cost invested ) options for individual land parcel typologies, and then scales up the results with detailed attention paid to uncertainty in adoption rates, life cycle costs, and hydrologic performance. The study is performed with a custom built stochastic rainfall-runoff model (Monte Carlo techniques are used to quantify uncertainties associated with phased implementation of different strategies and different land parcel typologies under synthetic precipitation ensembles). The results are presented as a comparison of cost-effectiveness over the time span of 30 years, and state an optimized strategy on the cumulative cost-effectiveness over the period.

  3. Detection capability of a pulsed Ground Penetrating Radar utilizing an oscilloscope and Radargram Fusion Approach for optimal signal quality

    NASA Astrophysics Data System (ADS)

    Seyfried, Daniel; Schoebel, Joerg

    2015-07-01

    In scientific research pulsed radars often employ a digital oscilloscope as sampling unit. The sensitivity of an oscilloscope is determined in general by means of the number of digits of its analog-to-digital converter and the selected full scale vertical setting, i.e., the maximal voltage range displayed. Furthermore oversampling or averaging of the input signal may increase the effective number of digits, hence the sensitivity. Especially for Ground Penetrating Radar applications high sensitivity of the radar system is demanded since reflection amplitudes of buried objects are strongly attenuated in ground. Hence, in order to achieve high detection capability this parameter is one of the most crucial ones. In this paper we analyze the detection capability of our pulsed radar system utilizing a Rohde & Schwarz RTO 1024 oscilloscope as sampling unit for Ground Penetrating Radar applications, such as detection of pipes and cables in the ground. Also effects of averaging and low-noise amplification of the received signal prior to sampling are investigated by means of an appropriate laboratory setup. To underline our findings we then present real-world radar measurements performed on our GPR test site, where we have buried pipes and cables of different types and materials in different depths. The results illustrate the requirement for proper choice of the settings of the oscilloscope for optimal data recording. However, as we show, displaying both strong signal contributions due to e.g., antenna cross-talk and direct ground bounce reflection as well as weak reflections from objects buried deeper in ground requires opposing trends for the oscilloscope's settings. We therefore present our Radargram Fusion Approach. By means of this approach multiple radargrams recorded in parallel, each with an individual optimized setting for a certain type of contribution, can be fused in an appropriate way in order to finally achieve a single radargram which displays all

  4. Application’s Method of Quadratic Programming for Optimization of Portfolio Selection

    NASA Astrophysics Data System (ADS)

    Kawamoto, Shigeru; Takamoto, Masanori; Kobayashi, Yasuhiro

    Investors or fund-managers face with optimization of portfolio selection, which means that determine the kind and the quantity of investment among several brands. We have developed a method to obtain optimal stock’s portfolio more rapidly from twice to three times than conventional method with efficient universal optimization. The method is characterized by quadratic matrix of utility function and constrained matrices divided into several sub-matrices by focusing on structure of these matrices.

  5. Sensor Selection and Optimization for Health Assessment of Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Maul, William A.; Kopasakis, George; Santi, Louis M.; Sowers, Thomas S.; Chicatelli, Amy

    2008-01-01

    Aerospace systems are developed similarly to other large-scale systems through a series of reviews, where designs are modified as system requirements are refined. For space-based systems few are built and placed into service these research vehicles have limited historical experience to draw from and formidable reliability and safety requirements, due to the remote and severe environment of space. Aeronautical systems have similar reliability and safety requirements, and while these systems may have historical information to access, commercial and military systems require longevity under a range of operational conditions and applied loads. Historically, the design of aerospace systems, particularly the selection of sensors, is based on the requirements for control and performance rather than on health assessment needs. Furthermore, the safety and reliability requirements are met through sensor suite augmentation in an ad hoc, heuristic manner, rather than any systematic approach. A review of the current sensor selection practice within and outside of the aerospace community was conducted and a sensor selection architecture is proposed that will provide a justifiable, defendable sensor suite to address system health assessment requirements.

  6. Sensor Selection and Optimization for Health Assessment of Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Maul, William A.; Kopasakis, George; Santi, Louis M.; Sowers, Thomas S.; Chicatelli, Amy

    2007-01-01

    Aerospace systems are developed similarly to other large-scale systems through a series of reviews, where designs are modified as system requirements are refined. For space-based systems few are built and placed into service. These research vehicles have limited historical experience to draw from and formidable reliability and safety requirements, due to the remote and severe environment of space. Aeronautical systems have similar reliability and safety requirements, and while these systems may have historical information to access, commercial and military systems require longevity under a range of operational conditions and applied loads. Historically, the design of aerospace systems, particularly the selection of sensors, is based on the requirements for control and performance rather than on health assessment needs. Furthermore, the safety and reliability requirements are met through sensor suite augmentation in an ad hoc, heuristic manner, rather than any systematic approach. A review of the current sensor selection practice within and outside of the aerospace community was conducted and a sensor selection architecture is proposed that will provide a justifiable, dependable sensor suite to address system health assessment requirements.

  7. Alignment mark signal simulation system for the optimum mark feature selection

    NASA Astrophysics Data System (ADS)

    Sato, Takashi; Endo, Ayako; Higashiki, Tatsuhiko; Ishigo, Kazutaka; Kono, Takuya; Sakamoto, Takashi; Shioyama, Yoshiyuki; Tanaka, Satoshi

    2004-05-01

    Recently the overlay accuracy has got seriously severe. For the accurate overlay, signal intensity and waveform from the topographical alignment mark has been examined by signal simulation. Actually these results have given good agreements with actual signal profiles, but it is difficult to select particular alignment marks in each mask level by the signal simulation. Even after mass production, many mark candidates leave in kerf area. To help the selection, we propose a mark TCAD system. It is a useful system for the mark selection with the signal simulation in advance. In our system, alignment mark signal can be very easily simulated after input of some process parameters and process of record (POR). The POR is read into the system and a process simulator makes stacked films on a wafer. Topographical marks are simulated from the stacked films and the resist pattern. The topographical marks are illuminated and reflected beams are produced. It is simulated how the reflected beams are imaged through inspection optics. We show two applications. This system is not only to predict and show a signal waveform, but also helpful to find optimum marks.

  8. Optimal design of calibration signals in space-borne gravitational wave detectors

    NASA Astrophysics Data System (ADS)

    Nofrarias, Miquel; Karnesis, Nikolaos; Gibert, Ferran; Armano, Michele; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo; Dolesi, Rita; Ferraioli, Luigi; Ferroni, Valerio; Hewitson, Martin; Hueller, Mauro; Inchauspe, Henri; Jennrich, Oliver; Korsakova, Natalia; McNamara, Paul W.; Plagnol, Eric; Thorpe, James I.; Vetrugno, Daniele; Vitale, Stefano; Wass, Peter; Weber, William J.

    2016-05-01

    Future space-borne gravitational wave detectors will require a precise definition of calibration signals to ensure the achievement of their design sensitivity. The careful design of the test signals plays a key role in the correct understanding and characterization of these instruments. In that sense, methods achieving optimal experiment designs must be considered as complementary to the parameter estimation methods being used to determine the parameters describing the system. The relevance of experiment design is particularly significant for the LISA Pathfinder mission, which will spend most of its operation time performing experiments to characterize key technologies for future space-borne gravitational wave observatories. Here we propose a framework to derive the optimal signals—in terms of minimum parameter uncertainty—to be injected into these instruments during the calibration phase. We compare our results with an alternative numerical algorithm which achieves an optimal input signal by iteratively improving an initial guess. We show agreement of both approaches when applied to the LISA Pathfinder case.

  9. The plasticity of the 7TMR signaling machinery and the search for pharmacological selectivity.

    PubMed

    Flordellis, Christodoulos S

    2012-01-01

    Components of 7TMR signaling machinery once considered as rigid, fixed and inflexible entities, operating in a one-dimensional way, homogeneous spatially and temporally, are now proved to be structurally plastic, flexible and dynamic in space and time. 7TMRs are thought to exist as ensembles of multiple, inter-convertible, pre-existing conformations and this conformational diversity provides a structural plasticity and the molecular mechanism for the functional diversity of 7TMRs. Furthermore, 7TMRs appear to function not as monomers, but rather as higher order structures, within which allosteric mechanisms affect ligand binding, G protein selection and receptor mobility and signalling of GPCR protomers. Moreover, their function is regulated through compartmentalization of G protein receptor and effector molecules into specialized membrane microdomains, such as lipid rafts and caveolae. Different permutations of receptor localization and translocation in membrane microdomains and internalization patterns, employed by 7TMRs regulate their signalling. Finally, the previous clear distinction between cell signalling and endocytic membrane trafficking has been blurred by evidence that these processes are intertwined and bidirectionally linked. 7TMRs normally signaling from the plasma membrane can elicit signaling cascades from an intracellular location, with distinct biochemical outputs. All these developments have moved the search for selective drugs beyond the mere design of receptor subtype-selective ligands targeting their orthosteric site. Even the notion of ligand has been expanded, so as to include "superagonists" ", acting as "super" 7TMR agonists to confer sustained endosomal signaling and biased agonists targeting GPCRs signalling from intracellular sites, as well as pharmacochaperones restoring insufficiently folded or misfolded receptors. The plasticity of the 7TMR signaling machinery and its dynamics in space and time will most probably impact further on

  10. Multi-Bandwidth Frequency Selective Surfaces for Near Infrared Filtering: Design and Optimization

    NASA Technical Reports Server (NTRS)

    Cwik, Tom; Fernandez, Salvador; Ksendzov, A.; LaBaw, Clayton C.; Maker, Paul D.; Muller, Richard E.

    1999-01-01

    Frequency selective surfaces are widely used in the microwave and millimeter wave regions of the spectrum for filtering signals. They are used in telecommunication systems for multi-frequency operation or in instrument detectors for spectroscopy. The frequency selective surface operation depends on a periodic array of elements resonating at prescribed wavelengths producing a filter response. The size of the elements is on the order of half the electrical wavelength, and the array period is typically less than a wavelength for efficient operation. When operating in the optical region, diffraction gratings are used for filtering. In this regime the period of the grating may be several wavelengths producing multiple orders of light in reflection or transmission. In regions between these bands (specifically in the infrared band) frequency selective filters consisting of patterned metal layers fabricated using electron beam lithography are beginning to be developed. The operation is completely analogous to surfaces made in the microwave and millimeter wave region except for the choice of materials used and the fabrication process. In addition, the lithography process allows an arbitrary distribution of patterns corresponding to resonances at various wavelengths to be produced. The design of sub-millimeter filters follows the design methods used in the microwave region. Exacting modal matching, integral equation or finite element methods can be used for design. A major difference though is the introduction of material parameters and thicknesses tha_ may not be important in longer wavelength designs. This paper describes the design of multi-bandwidth filters operating in the I-5 micrometer wavelength range. This work follows on previous design [1,2]. In this paper extensions based on further optimization and an examination of the specific shape of the element in the periodic cell will be reported. Results from the design, manufacture and test of linear wedge filters built

  11. Multi-Bandwidth Frequency Selective Surfaces for Near Infrared Filtering: Design and Optimization

    NASA Technical Reports Server (NTRS)

    Cwik, Tom; Fernandez, Salvador; Ksendzov, A.; LaBaw, Clayton C.; Maker, Paul D.; Muller, Richard E.

    1998-01-01

    Frequency selective surfaces are widely used in the microwave and millimeter wave regions of the spectrum for filtering signals. They are used in telecommunication systems for multi-frequency operation or in instrument detectors for spectroscopy. The frequency selective surface operation depends on a periodic array of elements resonating at prescribed wavelengths producing a filter response. The size of the elements is on the order of half the electrical wavelength, and the array period is typically less than a wavelength for efficient operation. When operating in the optical region, diffraction gratings are used for filtering. In this regime the period of the grating may be several wavelengths producing multiple orders of light in reflection or transmission. In regions between these bands (specifically in the infrared band) frequency selective filters consisting of patterned metal layers fabricated using electron beam lithography are beginning to be developed. The operation is completely analogous to surfaces made in the microwave and millimeter wave region except for the choice of materials used and the fabrication process. In addition, the lithography process allows an arbitrary distribution of patterns corresponding to resonances at various wavelengths to be produced. The design of sub-millimeter filters follows the design methods used in the microwave region. Exacting modal matching, integral equation or finite element methods can be used for design. A major difference though is the introduction of material parameters and thicknesses that may not be important in longer wavelength designs. This paper describes the design of multi- bandwidth filters operating in the 1-5 micrometer wavelength range. This work follows on a previous design. In this paper extensions based on further optimization and an examination of the specific shape of the element in the periodic cell will be reported. Results from the design, manufacture and test of linear wedge filters built

  12. Selection of optimal composition-control parameters for friable materials

    SciTech Connect

    Pak, Yu.N.; Vdovkin, A.V.

    1988-05-01

    A method for composition analysis of coal and minerals is proposed which uses scattered gamma radiation and does away with preliminary sample preparation to ensure homogeneous particle density, surface area, and size. Reduction of the error induced by material heterogeneity has previously been achieved by rotation of the control object during analysis. A further refinement is proposed which addresses the necessity that the contribution of the radiation scattered from each individual surface to the total intensity be the same. This is achieved by providing a constant linear rate of travel for the irradiated spot through back-and-forth motion of the sensor. An analytical expression is given for the laws of motion for the sensor and test tube which provides for uniform irradiated area movement along a path analogous to the Archimedes spiral. The relationships obtained permit optimization of measurement parameters in analyzing friable materials which are not uniform in grain size.

  13. Automated selection of appropriate pheromone representations in ant colony optimization.

    PubMed

    Montgomery, James; Randall, Marcus; Hendtlass, Tim

    2005-01-01

    Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm. PMID:16053571

  14. Cortical membrane potential signature of optimal states for sensory signal detection

    PubMed Central

    McGinley, Matthew J.; David, Stephen V.; McCormick, David A.

    2015-01-01

    The neural correlates of optimal states for signal detection task performance are largely unknown. One hypothesis holds that optimal states exhibit tonically depolarized cortical neurons with enhanced spiking activity, such as occur during movement. We recorded membrane potentials of auditory cortical neurons in mice trained on a challenging tone-in-noise detection task while assessing arousal with simultaneous pupillometry and hippocampal recordings. Arousal measures accurately predicted multiple modes of membrane potential activity, including: rhythmic slow oscillations at low arousal, stable hyperpolarization at intermediate arousal, and depolarization during phasic or tonic periods of hyper-arousal. Walking always occurred during hyper-arousal. Optimal signal detection behavior and sound-evoked responses, at both sub-threshold and spiking levels, occurred at intermediate arousal when pre-decision membrane potentials were stably hyperpolarized. These results reveal a cortical physiological signature of the classically-observed inverted-U relationship between task performance and arousal, and that optimal detection exhibits enhanced sensory-evoked responses and reduced background synaptic activity. PMID:26074005

  15. Atomic library optimization for pulse ultrasonic sparse signal decomposition and reconstruction

    NASA Astrophysics Data System (ADS)

    Song, Shoupeng; Li, Yingxue; Dogandžić, Aleksandar

    2016-02-01

    Compressive sampling of pulse ultrasonic NDE signals could bring significant savings in the data acquisition process. Sparse representation of these signals using an atomic library is key to their interpretation and reconstruction from compressive samples. However, the obstacles to practical applicability of such representations are: large size of the atomic library and computational complexity of the sparse decomposition and reconstruction. To help solve these problems, we develop a method for optimizing the ranges of parameters of traditional Gabor-atom library to match a real pulse ultrasonic signal in terms of correlation. As a result of atomic-library optimization, the number of the atoms is greatly reduced. Numerical simulations compare the proposed approach with the traditional method. Simulation results show that both the time efficiency and signal reconstruction energy error are superior to the traditional one even with small-scale atomic library. The performance of the proposed method is also explored under different noise levels. Finally, we apply the proposed method to real pipeline ultrasonic testing data, and the results indicate that our reduced atomic library outperforms the traditional library.

  16. Themis sets the signal threshold for positive and negative selection in T-cell development.

    PubMed

    Fu, Guo; Casas, Javier; Rigaud, Stephanie; Rybakin, Vasily; Lambolez, Florence; Brzostek, Joanna; Hoerter, John A H; Paster, Wolfgang; Acuto, Oreste; Cheroutre, Hilde; Sauer, Karsten; Gascoigne, Nicholas R J

    2013-12-19

    Development of a self-tolerant T-cell receptor (TCR) repertoire with the potential to recognize the universe of infectious agents depends on proper regulation of TCR signalling. The repertoire is whittled down during T-cell development in the thymus by the ability of quasi-randomly generated TCRs to interact with self-peptides presented by major histocompatibility complex (MHC) proteins. Low-affinity TCR interactions with self-MHC proteins generate weak signals that initiate 'positive selection', causing maturation of CD4- or CD8αβ-expressing 'single-positive' thymocytes from CD4(+)CD8αβ(+) 'double-positive' precursors. These develop into mature naive T cells of the secondary lymphoid organs. TCR interaction with high-affinity agonist self-ligands results in 'negative selection' by activation-induced apoptosis or 'agonist selection' of functionally differentiated self-antigen-experienced T cells. Here we show that positive selection is enabled by the ability of the T-cell-specific protein Themis to specifically attenuate TCR signal strength via SHP1 recruitment and activation in response to low- but not high-affinity TCR engagement. Themis acts as an analog-to-digital converter translating graded TCR affinity into clear-cut selection outcome. By dampening mild TCR signals Themis increases the affinity threshold for activation, enabling positive selection of T cells with a naive phenotype in response to low-affinity self-antigens.

  17. Robust breathing signal extraction from cone beam CT projections based on adaptive and global optimization techniques.

    PubMed

    Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi

    2016-04-21

    We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.

  18. Robust breathing signal extraction from cone beam CT projections based on adaptive and global optimization techniques

    NASA Astrophysics Data System (ADS)

    Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E.; Lo, Yeh-Chi

    2016-04-01

    We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as  -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.

  19. Robust breathing signal extraction from cone beam CT projections based on adaptive and global optimization techniques.

    PubMed

    Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi

    2016-04-21

    We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients. PMID:27008349

  20. A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications

    PubMed Central

    Hernandez, Wilmar

    2007-01-01

    In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart) sensors that today's cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher's interest in the fusion of intelligent sensors and optimal signal processing techniques.

  1. Discrepancies between selected Pareto optimal plans and final deliverable plans in radiotherapy multi-criteria optimization.

    PubMed

    Kyroudi, Archonteia; Petersson, Kristoffer; Ghandour, Sarah; Pachoud, Marc; Matzinger, Oscar; Ozsahin, Mahmut; Bourhis, Jean; Bochud, François; Moeckli, Raphaël

    2016-08-01

    Multi-criteria optimization provides decision makers with a range of clinical choices through Pareto plans that can be explored during real time navigation and then converted into deliverable plans. Our study shows that dosimetric differences can arise between the two steps, which could compromise the clinical choices made during navigation.

  2. Warning signals are under positive frequency-dependent selection in nature.

    PubMed

    Chouteau, Mathieu; Arias, Mónica; Joron, Mathieu

    2016-02-23

    Positive frequency-dependent selection (FDS) is a selection regime where the fitness of a phenotype increases with its frequency, and it is thought to underlie important adaptive strategies resting on signaling and communication. However, whether and how positive FDS truly operates in nature remains unknown, which hampers our understanding of signal diversity. Here, we test for positive FDS operating on the warning color patterns of chemically defended butterflies forming multiple coexisting mimicry assemblages in the Amazon. Using malleable prey models placed in localities showing differences in the relative frequencies of warningly colored prey, we demonstrate that the efficiency of a warning signal increases steadily with its local frequency in the natural community, up to a threshold where protection stabilizes. The shape of this relationship is consistent with the direct effect of the local abundance of each warning signal on the corresponding avoidance knowledge of the local predator community. This relationship, which differs from purifying selection acting on each mimetic pattern, indicates that predator knowledge, integrated over the entire community, is saturated only for the most common warning signals. In contrast, among the well-established warning signals present in local prey assemblages, most are incompletely known to local predators and enjoy incomplete protection. This incomplete predator knowledge should generate strong benefits to life history traits that enhance warning efficiency by increasing the effective frequency of prey visible to predators. Strategies such as gregariousness or niche convergence between comimics may therefore readily evolve through their effects on predator knowledge and warning efficiency. PMID:26858416

  3. Warning signals are under positive frequency-dependent selection in nature

    PubMed Central

    Chouteau, Mathieu; Arias, Mónica; Joron, Mathieu

    2016-01-01

    Positive frequency-dependent selection (FDS) is a selection regime where the fitness of a phenotype increases with its frequency, and it is thought to underlie important adaptive strategies resting on signaling and communication. However, whether and how positive FDS truly operates in nature remains unknown, which hampers our understanding of signal diversity. Here, we test for positive FDS operating on the warning color patterns of chemically defended butterflies forming multiple coexisting mimicry assemblages in the Amazon. Using malleable prey models placed in localities showing differences in the relative frequencies of warningly colored prey, we demonstrate that the efficiency of a warning signal increases steadily with its local frequency in the natural community, up to a threshold where protection stabilizes. The shape of this relationship is consistent with the direct effect of the local abundance of each warning signal on the corresponding avoidance knowledge of the local predator community. This relationship, which differs from purifying selection acting on each mimetic pattern, indicates that predator knowledge, integrated over the entire community, is saturated only for the most common warning signals. In contrast, among the well-established warning signals present in local prey assemblages, most are incompletely known to local predators and enjoy incomplete protection. This incomplete predator knowledge should generate strong benefits to life history traits that enhance warning efficiency by increasing the effective frequency of prey visible to predators. Strategies such as gregariousness or niche convergence between comimics may therefore readily evolve through their effects on predator knowledge and warning efficiency. PMID:26858416

  4. Warning signals are under positive frequency-dependent selection in nature.

    PubMed

    Chouteau, Mathieu; Arias, Mónica; Joron, Mathieu

    2016-02-23

    Positive frequency-dependent selection (FDS) is a selection regime where the fitness of a phenotype increases with its frequency, and it is thought to underlie important adaptive strategies resting on signaling and communication. However, whether and how positive FDS truly operates in nature remains unknown, which hampers our understanding of signal diversity. Here, we test for positive FDS operating on the warning color patterns of chemically defended butterflies forming multiple coexisting mimicry assemblages in the Amazon. Using malleable prey models placed in localities showing differences in the relative frequencies of warningly colored prey, we demonstrate that the efficiency of a warning signal increases steadily with its local frequency in the natural community, up to a threshold where protection stabilizes. The shape of this relationship is consistent with the direct effect of the local abundance of each warning signal on the corresponding avoidance knowledge of the local predator community. This relationship, which differs from purifying selection acting on each mimetic pattern, indicates that predator knowledge, integrated over the entire community, is saturated only for the most common warning signals. In contrast, among the well-established warning signals present in local prey assemblages, most are incompletely known to local predators and enjoy incomplete protection. This incomplete predator knowledge should generate strong benefits to life history traits that enhance warning efficiency by increasing the effective frequency of prey visible to predators. Strategies such as gregariousness or niche convergence between comimics may therefore readily evolve through their effects on predator knowledge and warning efficiency.

  5. Determination of an Optimal Recruiting-Selection Strategy to Fill a Specified Quota of Satisfactory Personnel.

    ERIC Educational Resources Information Center

    Sands, William A.

    Managers of military and civilian personnel systems justifiably demand an estimate of the payoff in dollars and cents, which can be expected to result from the implementation of a proposed selection program. The Cost of Attaining Personnel Requirements (CAPER) Model provides an optimal recruiting-selection strategy for personnel decisions which…

  6. Sexual selection, multiple male ornaments, and age- and condition-dependent signaling in the common yellowthroat.

    PubMed

    Freeman-Gallant, Corey R; Taff, Conor C; Morin, Douglas F; Dunn, Peter O; Whittingham, Linda A; Tsang, Susan M

    2010-04-01

    In many animals, sexual selection has resulted in complex signaling systems in which males advertise aspects of their phenotypic or genetic quality through elaborate ornamentation and display behaviors. Different ornaments might convey different information or be directed at different receivers, but they might also be redundant signals of quality that function reliably at different times (ages) or in different contexts. We explored sexual selection and age- and condition-dependent signaling in the common yellowthroat (Geothlypis trichas), a sexually dichromatic warbler with two prominent plumage ornaments--a melanin-based, black facial "mask" and carotenoid-based, UV-yellow "bib." In a three-year study, variance among males in the number of social (M(w)) and extra-pair (M(e)) mates generated strong sexual selection on mask and bib attributes. Some traits (mask size, bib yellow brightness) were correlated with male age and did not experience selection beyond age-related increases in M(w) and M(e). Other traits showed age-specific (bib size) or age-reversed (ultraviolet brightness) patterns of selection that paralleled changes in the information-content of each ornament. The components of male fitness generating selection in young versus old males were distinct, reflecting different sources of variation in male fertilization success. Age- and context-dependent changes in the strength, direction, and target of selection may help explain the maintenance of multiple ornaments in this and other species.

  7. Reduced TCR signaling potential impairs negative selection but does not result in autoimmune disease.

    PubMed

    Hwang, Sujin; Song, Ki-Duk; Lesourne, Renaud; Lee, Jan; Pinkhasov, Julia; Li, Liqi; El-Khoury, Dalal; Love, Paul E

    2012-09-24

    Negative selection and regulatory T (T reg) cell development are two thymus-dependent processes necessary for the enforcement of self-tolerance, and both require high-affinity interactions between the T cell receptor (TCR) and self-ligands. However, it remains unclear if they are similarly impacted by alterations in TCR signaling potential. We generated a knock-in allele (6F) of the TCR ζ chain gene encoding a mutant protein lacking signaling capability whose expression is controlled by endogenous ζ regulatory sequences. Although negative selection was defective in 6F/6F mice, leading to the survival of autoreactive T cells, 6F/6F mice did not develop autoimmune disease. We found that 6F/6F mice generated increased numbers of thymus-derived T reg cells. We show that attenuation of TCR signaling potential selectively impacts downstream signaling responses and that this differential effect favors Foxp3 expression and T reg cell lineage commitment. These results identify a potential compensatory pathway for the enforcement of immune tolerance in response to defective negative selection caused by reduced TCR signaling capability. PMID:22945921

  8. Selection for Social Signalling Drives the Evolution of Chameleon Colour Change

    PubMed Central

    Stuart-Fox, Devi; Moussalli, Adnan

    2008-01-01

    Rapid colour change is a remarkable natural phenomenon that has evolved in several vertebrate and invertebrate lineages. The two principal explanations for the evolution of this adaptive strategy are (1) natural selection for crypsis (camouflage) against a range of different backgrounds and (2) selection for conspicuous social signals that maximise detectability to conspecifics, yet minimise exposure to predators because they are only briefly displayed. Here we show that evolutionary shifts in capacity for colour change in southern African dwarf chameleons (Bradypodion spp.) are associated with increasingly conspicuous signals used in male contests and courtship. To the chameleon visual system, species showing the most dramatic colour change display social signals that contrast most against the environmental background and amongst adjacent body regions. We found no evidence for the crypsis hypothesis, a finding reinforced by visual models of how both chameleons and their avian predators perceive chameleon colour variation. Instead, our results suggest that selection for conspicuous social signals drives the evolution of colour change in this system, supporting the view that transitory display traits should be under strong selection for signal detectability. PMID:18232740

  9. Variation in predator species abundance can cause variable selection pressure on warning signaling prey

    PubMed Central

    Valkonen, Janne K; Nokelainen, Ossi; Niskanen, Martti; Kilpimaa, Janne; Björklund, Mats; Mappes, Johanna

    2012-01-01

    Predation pressure is expected to drive visual warning signals to evolve toward conspicuousness. However, coloration of defended species varies tremendously and can at certain instances be considered as more camouflaged rather than conspicuous. Recent theoretical studies suggest that the variation in signal conspicuousness can be caused by variation (within or between species) in predators' willingness to attack defended prey or by the broadness of the predators' signal generalization. If some of the predator species are capable of coping with the secondary defenses of their prey, selection can favor reduced prey signal conspicuousness via reduced detectability or recognition. In this study, we combine data collected during three large-scale field experiments to assess whether variation in avian predator species (red kite, black kite, common buzzard, short-toed eagle, and booted eagle) affects the predation pressure on warningly and non-warningly colored artificial snakes. Predation pressure varied among locations and interestingly, if common buzzards were abundant, there were disadvantages to snakes possessing warning signaling. Our results indicate that predator community can have important consequences on the evolution of warning signals. Predators that ignore the warning signal and defense can be the key for the maintenance of variation in warning signal architecture and maintenance of inconspicuous signaling. PMID:22957197

  10. Selection for optimal crew performance - Relative impact of selection and training

    NASA Technical Reports Server (NTRS)

    Chidester, Thomas R.

    1987-01-01

    An empirical study supporting Helmreich's (1986) theoretical work on the distinct manner in which training and selection impact crew coordination is presented. Training is capable of changing attitudes, while selection screens for stable personality characteristics. Training appears least effective for leadership, an area strongly influenced by personality. Selection is least effective for influencing attitudes about personal vulnerability to stress, which appear to be trained in resource management programs. Because personality correlates with attitudes before and after training, it is felt that selection may be necessary even with a leadership-oriented training cirriculum.

  11. Socs36E attenuates STAT signaling to optimize motile cell specification in the Drosophila ovary.

    PubMed

    Monahan, Amanda J; Starz-Gaiano, Michelle

    2013-07-15

    The Janus kinase/Signal transducers and activators of transcription (JAK/STAT) pathway determines cell fates by regulating gene expression. One example is the specification of the motile cells called border cells during Drosophila oogenesis. It has been established that too much or too little STAT activity disrupts follicle cell identity and cell motility, which suggests the signaling must be precisely regulated. Here, we find that Suppressor of cytokine signaling at 36E (Socs36E) is a necessary negative regulator of JAK/STAT signaling during border cell specification. We find when STAT signaling is too low to induce migration in the presumptive border cell population, nearby follicle cells uncharacteristically become invasive to enable efficient migration of the cluster. We generated a genetic null allele that reveals Socs36E is required in the anterior follicle cells to limit invasive behavior to an optimal number of cells. We further show Socs36E genetically interacts with the required STAT feedback inhibitor apontic (apt) and APT's downstream target, mir-279, and provide evidence that suggests APT directly regulates Socs36E transcriptionally. Our work shows Socs36E plays a critical role in a genetic circuit that establishes a boundary between the motile border cell cluster and its non-invasive epithelial neighbors through STAT attenuation. PMID:23583584

  12. Experimental validation of an optimized signal processing method to handle non-linearity in swept-source optical coherence tomography.

    PubMed

    Vergnole, Sébastien; Lévesque, Daniel; Lamouche, Guy

    2010-05-10

    We evaluate various signal processing methods to handle the non-linearity in wavenumber space exhibited by most laser sources for swept-source optical coherence tomography. The following methods are compared for the same set of experimental data: non-uniform discrete Fourier transforms with Vandermonde matrix or with Lomb periodogram, resampling with linear interpolation or spline interpolation prior to fast-Fourier transform (FFT), and resampling with convolution prior to FFT. By selecting an optimized Kaiser-Bessel window to perform the convolution, we show that convolution followed by FFT is the most efficient method. It allows small fractional oversampling factor between 1 and 2, thus a minimal computational time, while retaining an excellent image quality. PMID:20588899

  13. Storage of human biospecimens: selection of the optimal storage temperature.

    PubMed

    Hubel, Allison; Spindler, Ralf; Skubitz, Amy P N

    2014-06-01

    Millions of biological samples are currently kept at low tempertures in cryobanks/biorepositories for long-term storage. The quality of the biospecimen when thawed, however, is not only determined by processing of the biospecimen but the storage conditions as well. The overall objective of this article is to describe the scientific basis for selecting a storage temperature for a biospecimen based on current scientific understanding. To that end, this article reviews some physical basics of the temperature, nucleation, and ice crystal growth present in biological samples stored at low temperatures (-20°C to -196°C), and our current understanding of the role of temperature on the activity of degradative molecules present in biospecimens. The scientific literature relevant to the stability of specific biomarkers in human fluid, cell, and tissue biospecimens is also summarized for the range of temperatures between -20°C to -196°C. These studies demonstrate the importance of storage temperature on the stability of critical biomarkers for fluid, cell, and tissue biospecimens.

  14. Optimizing landfill site selection by using land classification maps.

    PubMed

    Eskandari, M; Homaee, M; Mahmoodi, S; Pazira, E; Van Genuchten, M Th

    2015-05-01

    Municipal solid waste disposal is a major environmental concern throughout the world. Proper landfill siting involves many environmental, economic, technical, and sociocultural challenges. In this study, a new quantitative method for landfill siting that reduces the number of evaluation criteria, simplifies siting procedures, and enhances the utility of available land evaluation maps was proposed. The method is demonstrated by selecting a suitable landfill site near the city of Marvdasht in Iran. The approach involves two separate stages. First, necessary criteria for preliminary landfill siting using four constraints and eight factors were obtained from a land classification map initially prepared for irrigation purposes. Thereafter, the criteria were standardized using a rating approach and then weighted to obtain a suitability map for landfill siting, with ratings in a 0-1 domain and divided into five suitability classes. Results were almost identical to those obtained with a more traditional environmental landfill siting approach. Because of far fewer evaluation criteria, the proposed weighting method was much easier to implement while producing a more convincing database for landfill siting. The classification map also considered land productivity. In the second stage, the six best alternative sites were evaluated for final landfill siting using four additional criteria. Sensitivity analyses were furthermore conducted to assess the stability of the obtained ranking. Results indicate that the method provides a precise siting procedure that should convince all pertinent stakeholders.

  15. The Role of Sexual Selection in the Evolution of Chemical Signals in Insects

    PubMed Central

    Steiger, Sandra; Stökl, Johannes

    2014-01-01

    Chemical communication is the most ancient and widespread form of communication. Yet we are only beginning to grasp the complexity of chemical signals and the role they play in sexual selection. Focusing on insects, we review here the recent progress in the field of olfactory-based sexual selection. We will show that there is mounting empirical evidence that sexual selection affects the evolution of chemical traits, but form and strength of selection differ between species. Studies indicate that some chemical signals are expressed in relation to an individual’s condition and depend, for example, on age, immunocompetence, fertility, body size or degree of inbreeding. Males or females might benefit by choosing based on those traits, gaining resources or “good genes”. Other chemical traits appear to reliably reflect an individual’s underlying genotype and are suitable to choose a mating partner that matches best the own genotype. PMID:26462692

  16. Genetic association of marbling score with intragenic nucleotide variants at selection signals of the bovine genome.

    PubMed

    Ryu, J; Lee, C

    2016-04-01

    Selection signals of Korean cattle might be attributed largely to artificial selection for meat quality. Rapidly increased intragenic markers of newly annotated genes in the bovine genome would help overcome limited findings of genetic markers associated with meat quality at the selection signals in a previous study. The present study examined genetic associations of marbling score (MS) with intragenic nucleotide variants at selection signals of Korean cattle. A total of 39 092 nucleotide variants of 407 Korean cattle were utilized in the association analysis. A total of 129 variants were selected within newly annotated genes in the bovine genome. Their genetic associations were analyzed using the mixed model with random polygenic effects based on identical-by-state genetic relationships among animals in order to control for spurious associations produced by population structure. Genetic associations of MS were found (P<3.88×10-4) with six intragenic nucleotide variants on bovine autosomes 3 (cache domain containing 1, CACHD1), 5 (like-glycosyltransferase, LARGE), 16 (cell division cycle 42 binding protein kinase alpha, CDC42BPA) and 21 (snurportin 1, SNUPN; protein tyrosine phosphatase, non-receptor type 9, PTPN9; chondroitin sulfate proteoglycan 4, CSPG4). In particular, the genetic associations with CDC42BPA and LARGE were confirmed using an independent data set of Korean cattle. The results implied that allele frequencies of functional variants and their proximity variants have been augmented by directional selection for greater MS and remain selection signals in the bovine genome. Further studies of fine mapping would be useful to incorporate favorable alleles in marker-assisted selection for MS of Korean cattle. PMID:26621608

  17. Reduction of false arrhythmia alarms using signal selection and machine learning.

    PubMed

    Eerikäinen, Linda M; Vanschoren, Joaquin; Rooijakkers, Michael J; Vullings, Rik; Aarts, Ronald M

    2016-08-01

    In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. In addition, patient safety is compromised because reaction time of the caregivers to true alarms is reduced. The data for the algorithm development consisted of records of electrocardiogram (ECG), arterial blood pressure, and photoplethysmogram signals in which an alarm for either asystole, extreme bradycardia, extreme tachycardia, ventricular fibrillation or flutter, or ventricular tachycardia occurs. First, heart beats are extracted from every signal. Next, the algorithm selects the most reliable signal pair from the available signals by comparing how well the detected beats match between different signals based on [Formula: see text]-score and selecting the best match. From the selected signal pair, arrhythmia specific features, such as heart rate features and signal purity index are computed for the alarm classification. The classification is performed with five separate Random Forest models. In addition, information on the local noise level of the selected ECG lead is added to the classification. The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58. PMID:27454128

  18. Signal transmission between gap-junctionally coupled passive cables is most effective at an optimal diameter.

    PubMed

    Nadim, Farzan; Golowasch, Jorge

    2006-06-01

    We analyze simple morphological configurations that represent gap-junctional coupling between neuronal processes or between muscle fibers. Specifically, we use cable theory and simulations to examine the consequences of current flow from one cable to other gap-junctionally coupled passive cables. When the proximal end of the first cable is voltage clamped, the amplitude of the electrical signal in distal portions of the second cable depends on the cable diameter. However, this amplitude does not simply increase if cable diameter is increased, as expected from the larger length constant; instead, an optimal diameter exists. The optimal diameter arises because the dependency of voltage attenuation along the second cable on cable diameter follows two opposing rules. As cable diameter increases, the attenuation decreases because of a larger length constant yet increases because of a reduction in current density arising from the limiting effect of the gap junction on current flow into the second cable. The optimal diameter depends on the gap junction resistance and cable parameters. In branched cables, dependency on diameter is local and thus may serve to functionally compartmentalize branches that are coupled to other cells. Such compartmentalization may be important when periodic signals or action potentials cause the current flow across gap junctions.

  19. Biomass selection for optimal anaerobic treatment of olive mill wastewater.

    PubMed

    Sabbah, I; Yazbak, A; Haj, J; Saliba, A; Basheer, S

    2005-01-01

    This research was conducted to identify the most efficient biomass out of five different types of biomass sources for anaerobic treatment of Olive Mill Wastewater (OMW). This study was first focused on examining the selected biomass in anaerobic batch systems with sodium acetate solutions (control study). Then, the different types of biomass were tested with raw OMW (water-diluted) and with pretreated OMW by coagulation-flocculation using Poly Aluminum Chloride (PACl) combined with hydrated lime (Ca(OH)2). Two types of biomass from wastewater treatment systems of a citrus juice producing company "PriGat" and from a citric acid manufacturing factory "Gadot", were found to be the most efficient sources of microorganisms to anaerobically treat both sodium acetate solution and OMW. Both types of biomass were examined under different concentration ranges (1-40 g l(-1)) of OMW in order to detect the maximal COD tolerance for the microorganisms. The results show that 70-85% of COD removal was reached using Gadot biomass after 8-10 days when the initial concentration of OMW was up to 5 g l(-1), while a similar removal efficiency was achieved using OMW of initial COD concentration of 10 g l(-1) in 2-4 days of contact time with the PriGat biomass. The physico-chemical pretreatment of OMW was found to enhance the anaerobic activity for the treatment of OMW with initial concentration of 20 g l(-1) using PriGat biomass. This finding is attributed to reducing the concentrations of polyphenols and other toxicants originally present in OMW upon the applied pretreatment process. PMID:15747599

  20. Biomass selection for optimal anaerobic treatment of olive mill wastewater.

    PubMed

    Sabbah, I; Yazbak, A; Haj, J; Saliba, A; Basheer, S

    2005-01-01

    This research was conducted to identify the most efficient biomass out of five different types of biomass sources for anaerobic treatment of Olive Mill Wastewater (OMW). This study was first focused on examining the selected biomass in anaerobic batch systems with sodium acetate solutions (control study). Then, the different types of biomass were tested with raw OMW (water-diluted) and with pretreated OMW by coagulation-flocculation using Poly Aluminum Chloride (PACl) combined with hydrated lime (Ca(OH)2). Two types of biomass from wastewater treatment systems of a citrus juice producing company "PriGat" and from a citric acid manufacturing factory "Gadot", were found to be the most efficient sources of microorganisms to anaerobically treat both sodium acetate solution and OMW. Both types of biomass were examined under different concentration ranges (1-40 g l(-1)) of OMW in order to detect the maximal COD tolerance for the microorganisms. The results show that 70-85% of COD removal was reached using Gadot biomass after 8-10 days when the initial concentration of OMW was up to 5 g l(-1), while a similar removal efficiency was achieved using OMW of initial COD concentration of 10 g l(-1) in 2-4 days of contact time with the PriGat biomass. The physico-chemical pretreatment of OMW was found to enhance the anaerobic activity for the treatment of OMW with initial concentration of 20 g l(-1) using PriGat biomass. This finding is attributed to reducing the concentrations of polyphenols and other toxicants originally present in OMW upon the applied pretreatment process.

  1. Signaling thresholds and negative B cell selection in acute lymphoblastic leukemia

    PubMed Central

    Chen, Zhengshan; Shojaee, Seyedmehdi; Buchner, Maike; Geng, Huimin; Lee, Jae Woong; Klemm, Lars; Titz, Björn; Graeber, Thomas G.; Park, Eugene; Tan, Ying Xim; Satterthwaite, Anne; Paietta, Elisabeth; Hunger, Stephen P.; Willman, Cheryl L.; Melnick, Ari; Loh, Mignon L.; Jung, Jae U.; Coligan, John E.; Bolland, Silvia; Mak, Tak W.; Limnander, Andre; Jumaa, Hassan; Reth, Michael; Weiss, Arthur; Lowell, Clifford A.; Müschen, Markus

    2015-01-01

    B cells are selected for an intermediate level of B cell receptor (BCR) signaling strength: Attenuation below minimum (e.g. non-functional BCR)1 or hyperactivation above maximum (e.g. self-reactive BCR)2–3 thresholds of signaling strength causes negative selection. In ~25% of cases, acute lymphoblastic leukemia (ALL) cells carry the oncogenic BCR-ABL1 tyrosine kinase (Ph+), which mimics constitutively active pre-BCR signaling4,5. Current therapy approaches are largely focused on the development of more potent tyrosine kinase inhibitors to suppress oncogenic signaling below a minimum threshold for survival6. Here, we tested the hypothesis that targeted hyperactivation above a maximum threshold will engage a deletional checkpoint for removal of self-reactive B cells and selectively kill ALL cells. Testing various components of proximal pre-BCR signaling, we found that an incremental increase of Syk tyrosine kinase activity was required and sufficient to induce cell death. Hyperactive Syk was functionally equivalent to acute activation of a self-reactive BCR on ALL cells. Despite oncogenic transformation, this basic mechanism of negative selection was still functional in ALL cells. Unlike normal pre-B cells, patient-derived ALL cells express the inhibitory receptors PECAM1, CD300A and LAIR1 at high levels. Genetic studies revealed that Pecam1, Cd300a and Lair1 are critical to calibrate oncogenic signaling strength through recruitment of the inhibitory phosphatases Ptpn67 and Inpp5d8. Using a novel small molecule inhibitor of INPP5D9, we demonstrated that pharmacological hyperactivation of SYK and engagement of negative B cell selection represents a promising new strategy to overcome drug-resistance in human ALL. PMID:25799995

  2. Abstract and Effector-Selective Decision Signals Exhibit Qualitatively Distinct Dynamics before Delayed Perceptual Reports

    PubMed Central

    Twomey, Deirdre M.; Kelly, Simon P.

    2016-01-01

    Electrophysiological research has isolated neural signatures of decision formation in a variety of brain regions. Studies in rodents and monkeys have focused primarily on effector-selective signals that translate the emerging decision into a specific motor plan, but, more recently, research on the human brain has identified an abstract signature of evidence accumulation that does not appear to play any direct role in action preparation. The functional dissociations between these distinct signal types have only begun to be characterized, and their dynamics during decisions with deferred actions with or without foreknowledge of stimulus-effector mapping, a commonly studied task scenario in single-unit and functional imaging investigations, have not been established. Here we traced the dynamics of distinct abstract and effector-selective decision signals in the form of the broad-band centro-parietal positivity (CPP) and limb-selective β-band (8–16 and 18–30 Hz) EEG activity, respectively, during delayed-reported motion direction decisions with and without foreknowledge of direction-response mapping. With foreknowledge, the CPP and β-band signals exhibited a similar gradual build-up following evidence onset, but whereas choice-predictive β-band activity persisted up until the delayed response, the CPP dropped toward baseline after peaking. Without foreknowledge, the CPP exhibited identical dynamics, whereas choice-selective β-band activity was eliminated. These findings highlight qualitative functional distinctions between effector-selective and abstract decision signals and are of relevance to the assumptions founding functional neuroimaging investigations of decision-making. SIGNIFICANCE STATEMENT Neural signatures of evidence accumulation have been isolated in numerous brain regions. Although animal neurophysiology has largely concentrated on effector-selective decision signals that translate the emerging decision into a specific motor plan, recent research

  3. Info-gap robustness of an input signal optimization algorithm for damage detection

    NASA Astrophysics Data System (ADS)

    Pasquali, M.; Stull, C. J.; Farrar, C. R.

    2015-01-01

    Info-Gap Decision Theory is adopted to assess the robustness of a technique aimed at identifying the optimal excitation signal to be used for active sensing approaches to damage detection. Here the term "active sensing" refers to procedures where a known input is applied to the structure to enhance the damage detection process. Given limited system response measurements and ever-present physical limits on the level of excitation, the ultimate goal of the mentioned technique is to improve the detectability of damage by increasing the difference between measured outputs of the undamaged and damaged systems. In particular, a two degree-of-freedom mass-spring-damper system characterized by the presence of a nonlinear stiffness is considered. Uncertainty is introduced to the system in the form of deviations of its parameters (mass, stiffness, damping ratio) from their nominal values. Variations in the performance of the mentioned technique are then evaluated both in terms of changes in the estimated difference between the responses of the damaged and undamaged systems and in terms of deviations of the identified optimal input signal from its nominal estimation. Finally, plots of the performances of the analyzed algorithm for different levels of uncertainty are obtained, enabling a clear evaluation of the risks connected with designing excitation signals for damage detection, when the parameters that dictate system behavior (e.g. stiffness, mass) are poorly characterized or improperly modeled.

  4. Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

    PubMed Central

    Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

    2014-01-01

    This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system. PMID:24368701

  5. Design and optimization of a multi-element piezoelectric transducer for mode-selective generation of guided waves

    NASA Astrophysics Data System (ADS)

    Yazdanpanah Moghadam, Peyman; Quaegebeur, Nicolas; Masson, Patrice

    2016-07-01

    A novel multi-element piezoelectric transducers (MEPT) is designed, optimized, machined and experimentally tested to improve structural health monitoring systems for mode-selective generation of guided waves (GW) in an isotropic structure. GW generation using typical piezoceramics makes the signal processing and consequently damage detection very complicated because at any driving frequency at least two fundamental symmetric (S 0) and antisymmetric (A 0) modes are generated. To prevent this, mode selective transducer design is proposed based on MEPT. A numerical method is first developed to extract the interfacial stress between a single piezoceramic element and a host structure and then used as the input of an analytical model to predict the GW propagation through the thickness of an isotropic plate. Two novel objective functions are proposed to optimize the interfacial shear stress for both suppressing unwanted mode(s) and maximizing the desired mode. Simplicity and low manufacturing cost are two main targets driving the design of the MEPT. A prototype MEPT is then manufactured using laser micro-machining. An experimental procedure is presented to validate the performances of the MEPT as a new solution for mode-selective GW generation. Experimental tests illustrate the high capability of the MEPT for mode-selective GW generation, as unwanted mode is suppressed by a factor up to 170 times compared with the results obtained with a single piezoceramic.

  6. Separate visual signals for saccade initiation during target selection in the primate superior colliculus.

    PubMed

    White, Brian J; Munoz, Douglas P

    2011-02-01

    The primary function of the superior colliculus (SC) is to orient the visual system toward behaviorally relevant stimuli defined by features such as color. However, a longstanding view has held that visual activity in the SC arises exclusively from achromatic pathways. Recently, we reported evidence that the primate SC is highly sensitive to signals originating from chromatic pathways, but these signals are delayed relative to luminance signals (White et al., 2009). Here, we describe a functional consequence of this difference in visual arrival time on the processes leading to target selection and saccade initiation. Two rhesus monkeys performed a simple color-singleton selection task in which stimuli carried a chromatic component only (target and distractors were isoluminant with the background, but differed in chromaticity) or a combined chromatic-achromatic component (36% luminance contrast added equally to all stimuli). Although visual responses were delayed in the chromatic-only relative to the combined chromatic-achromatic condition, SC neurons discriminated the target from distractors at approximately the same time provided stimulus chromaticity was held constant. However, saccades were triggered sooner, and with more errors, with the chromatic-achromatic condition, suggesting that luminance signals associated with these stimuli increased the probability of triggering a saccade before the target color was adequately discriminated. These results suggest that separate mechanisms may independently influence the saccadic command in the SC, one linked to the arrival time of pertinent visual signals, and another linked to the output of the visual selection process. PMID:21289164

  7. Perceptually optimized gain function for cochlear implant signal-to-noise ratio based noise reduction.

    PubMed

    Mauger, Stefan J; Dawson, Pam W; Hersbach, Adam A

    2012-01-01

    Noise reduction in cochlear implants has achieved significant speech perception improvements through spectral subtraction and signal-to-noise ratio based noise reduction techniques. Current methods use gain functions derived through mathematical optimization or motivated by normal listening psychoacoustic experiments. Although these gain functions have been able to improve speech perception, recent studies have indicated that they are not optimal for cochlear implant noise reduction. This study systematically investigates cochlear implant recipients' speech perception and listening preference of noise reduction with a range of gain functions. Results suggest an advantageous gain function and show that gain functions currently used for noise reduction are not optimal for cochlear implant recipients. Using the cochlear implant optimised gain function, a 27% improvement over the current advanced combination encoder (ACE) stimulation strategy in speech weighted noise and a 7% improvement over current noise reduction strategies were observed in babble noise conditions. The optimized gain function was also most preferred by cochlear implant recipients. The CI specific gain function derived from this study can be easily incorporated into existing noise reduction strategies, to further improve listening performance for CI recipients in challenging environments.

  8. A comparison of signal detection between an echolocating dolphin and an optimal receiver.

    PubMed

    Au, W W; Pawloski, D A

    1989-01-01

    An electronic simulated target apparatus was used in a two-experiment study to compare the target detection performance of an echolocating bottlenose dolphin with an optimal receiver. Random Gaussian noise with a relatively flat spectrum from 20 to 160 kHz was used as a masking source. Experiment I was conducted to establish a technique for estimating the echo energy-to-noise ratio, Ee/N, at the dolphin's threshold of detection. Dolphins typically vary the amplitude of their emitted signal over a large range making it difficult to estimate Ee/N. In the first part of experiment I, the simulated echo was a double click, the pulses separated by 200 microseconds, with each pulse being a replica of the dolphin's transmitted signal. A staircase psychophysical procedure was used to obtain the detection threshold, and the echo energy-to-noise ratio based on the highest amplitude click emitted per trial, (Ee/N)max, was determined at each reversal point. The second echo type consisted of one of the animal's echolocation clicks, previously measured, digitized and stored in an erasable programmable read-only memory (EPROM). The electronic target simulator was modified so that every time the dolphin emitted an echolocation signal, the EPROM was triggered to produce two pulses separated by 200 microseconds. On any trial, the EPROM signal was played back at a fixed amplitude, regardless of the amplitude of the dolphin's emitted signal. The Ee/N obtained with the EPROM signal at threshold was found to be 2.9 dB lower than (Ee/N)max obtained with the normal phantom target. Therefore an estimate of Ee/N can be obtained by subtracting 2.9 dB from (Ee/N)max.(ABSTRACT TRUNCATED AT 250 WORDS)

  9. Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.

    PubMed

    Wang, Gang; Wang, Zhizhong; Chen, Weiting; Zhuang, Jun

    2006-10-01

    In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

  10. Network dynamics for optimal compressive-sensing input-signal recovery.

    PubMed

    Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David

    2014-10-01

    By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs.

  11. EEG data classification through signal spatial redistribution and optimized linear discriminants.

    PubMed

    Gutiérrez, David; Escalona-Vargas, Diana I

    2010-01-01

    This paper presents a preprocessing technique for improving the classification of electroencephalographic (EEG) data in brain-computer interfaces (BCI) for the case of realistic measuring conditions, such as low signal-to-noise ratio (SNR), reduced number of measuring electrodes, and reduced amount of data used to train the classifier. The proposed method is based on a linear minimum mean squared error (LMMSE) spatial filter specifically designed to improve the SNR of the signals before being classified. The design parameters of the spatial filter are obtained through an optimized version of Fisher's linear discriminant (FLD) whose area under the receiver operating characteristics (ROC) curve is maximized. The combination of the spatial filter and the optimized FLD increases the SNR and changes the spatial distribution of the measured signals. As a result, the signals can be more easily discriminated by means of a simple sign detector or threshold-based classifier. A series of experiments on simulated EEG data compare the performance of the proposed classification scheme to the performance of the Mahalanobis distance-based classifier, which is widely used in BCI systems. Numerical results show that the proposed preprocessing technique enhances the classifier's performance even for low SNR conditions and few measurements, while the Mahalanobis classifier is not reliable under such realistic operating conditions. Furthermore, real EEG data from a self-paced key typing experiment is used to demonstrate the applicability of the preprocessing technique. The proposed method has the potential of improving the efficiency of real-life BCI systems, as well as reducing the computational complexity associated with their implementation.

  12. The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

    PubMed Central

    Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

    2014-01-01

    Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

  13. Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

    PubMed Central

    Zhao, Yu-Xiang; Chou, Chien-Hsing

    2016-01-01

    In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

  14. Stronger signal of recent selection for lactase persistence in Maasai than in Europeans.

    PubMed

    Schlebusch, Carina M; Sjödin, Per; Skoglund, Pontus; Jakobsson, Mattias

    2013-05-01

    Continued ability to digest lactose after weaning provides a possible selective advantage to individuals who have access to milk as a food source. The lactase persistence (LP) phenotype exists at varying frequencies in different populations and SNPs that modulate the regulation of the LCT gene have been identified in many of these populations. Very strong positive selection for LP has been illustrated for a single SNP (rs4988235) in northwestern European populations, which has become a textbook example of the effect of recent selective sweeps on genetic variation and linkage disequilibrium. In this study, we employed two different methods to detect signatures of positive selection in an East African pastoralist population in the HapMap collection, the Maasai from Kenya, and compared results with other HapMap populations. We found that signatures of recent selection coinciding with the LCT gene are the strongest across the genome in the Maasai population. Furthermore, the genome-wide signal of recent positive selection on haplotypic variation and population differentiation around the LCT gene is greater in the Maasai than in the CEU population (northwestern European descent), possibly due to stronger selection pressure, but it could also be an indication of more recent selection in Maasai compared with the Central European group or more efficient selection in the Maasai due to less genetic drift for their larger effective population size. This signal of recent selection is driven by a putative East African LP haplotype that is different from the haplotype that contributes to the LP phenotype in northwestern Europe. PMID:22948027

  15. Integrated spectra extraction based on signal-to-noise optimization using integral field spectroscopy

    NASA Astrophysics Data System (ADS)

    Rosales-Ortega, F. F.; Arribas, S.; Colina, L.

    2012-03-01

    Aims: We explore the potential of a method to extract high signal-to-noise (S/N) integrated spectra of particular physical and/or morphological regions of a two-dimensional field using integral field spectroscopy (IFS) observations by applying an optimization procedure based on either continuum (stellar) or line (nebular) emission features. Methods: The optimization method is applied to a set of IFS VLT-VIMOS observations of (U)LIRG galaxies. We describe the advantages of the optimization by comparing the results with a fixed-aperture, single-spectrum case, and by implementing some statistical tests. Results: We demonstrate that the S/N of the IFS optimized integrated spectra is significantly higher than for the single-aperture unprocessed case. In some cases, the optimization based on the emission lines allows to characterize some of the source properties more reliably than with standard integration methods. We are able to clearly retrieve the weak continuum features, hence more precisely constrain the properties of the unresolved stellar population. The most suitable method for integrating spectra over (part of) the field-of-view ultimately depends on the science case, and may involve a trade off among the different variables (e.g. S/N, probe area, spatial resolution, etc.). we therefore provide an iterative user-friendly and versatile IDL algorithm that, in addition to the above-mentioned method, allows the user to spatially integrate spectra following more standard procedures. Our procedure is made available to the community as part of the PINGSoft IFS software package.

  16. Optimization of Residual Water Signal Removal by HLSVD on Simulated Short Echo Time Proton MR Spectra of the Human Brain

    NASA Astrophysics Data System (ADS)

    Cabanes, E.; Confort-Gouny, S.; Le Fur, Y.; Simond, G.; Cozzone, P. J.

    2001-06-01

    Suppression of the residual water signal from proton magnetic resonance (MR) spectra recorded in human brain is a prerequisite to an accurate quantification of cerebral metabolites. Several postacquisition methods of residual water signal suppression have been reported but none of them provide a complete elimination of the residual water signal, thereby preventing reliable quantification of brain metabolites. In the present study, the elimination of the residual water signal by the Hankel Lanczos singular value decomposition method has been evaluated and optimized to provide fast automated processing of spectra. Model free induction decays, reproducing the proton signal acquired in human brain localized MR spectroscopy at short echo times (e.g., 20 ms), have been generated. The optimal parameters in terms of number of components and dimension of the Hankel data matrix allowing complete elimination of the residual water signal are reported.

  17. A computerized traffic control algorithm to determine optimal traffic signal settings. Ph.D. Thesis - Toledo Univ.

    NASA Technical Reports Server (NTRS)

    Seldner, K.

    1977-01-01

    An algorithm was developed to optimally control the traffic signals at each intersection using a discrete time traffic model applicable to heavy or peak traffic. Off line optimization procedures were applied to compute the cycle splits required to minimize the lengths of the vehicle queues and delay at each intersection. The method was applied to an extensive traffic network in Toledo, Ohio. Results obtained with the derived optimal settings are compared with the control settings presently in use.

  18. Sexual selection and experimental evolution of chemical signals in Drosophila pseudoobscura.

    PubMed

    Hunt, J; Snook, R R; Mitchell, C; Crudgington, H S; Moore, A J

    2012-11-01

    Our expectations for the evolution of chemical signals in response to sexual selection are uncertain. How are chemical signals elaborated? Does sexual selection result in complexity of the composition or in altered quantities of expression? We addressed this in Drosophila pseudoobscura by examining male and female cuticular hydrocarbons (CHs) after 82 generations of elevated (E) sexual selection or relaxed sexual selection through monogamy (M). The CH profile consisted of 18 different components. We extracted three eigenvectors using principal component analysis that explained 72% of the variation. principal component (PC)1 described the amount of CHs produced, PC2 the trade-off between short- and long-chain CHs and PC3 the trade-off between apparently arbitrary CHs. In both sexes, the amount of CHs produced was greater in flies from the E treatment. PC3 was also higher, indicating that sexual selection also influenced the evolution of CH composition. The sexes differed in all three PCs, indicating substantial sexual dimorphism in this species, although the magnitude of this dimorphism was not increased as a result of our experimental evolution. Collectively, our work provides direct evidence that sexual selection plays an important role in the evolution of CHs in D. pseudoobscura and that both increased quantity and overall composition are targeted.

  19. Sexual dimorphism and directional sexual selection on aposematic signals in a poison frog

    PubMed Central

    Maan, Martine E.; Cummings, Molly E.

    2009-01-01

    It is commonly assumed that natural selection imposed by predators is the prevailing force driving the evolution of aposematic traits. Here, we demonstrate that aposematic signals are shaped by sexual selection as well. We evaluated sexual selection for coloration brightness in populations of the poison frog Oophaga [Dendrobates] pumilio in Panama's Bocas del Toro archipelago. We assessed female preferences for brighter males by manipulating the perceived brightness of spectrally matched males in two-way choice experiments. We found strong female preferences for bright males in two island populations and weaker or ambiguous preferences in females from mainland populations. Spectral reflectance measurements, coupled with an O. pumilio-specific visual processing model, showed that O. pumilio coloration was significantly brighter in island than in mainland morphs. In one of the island populations (Isla Solarte), males were significantly more brightly colored than females. Taken together, these results provide evidence for directional sexual selection on aposematic coloration and document sexual dimorphism in vertebrate warning coloration. Although aposematic signals have long been upheld as exemplars of natural selection, our results show that sexual selection should not be ignored in studies of aposematic evolution. PMID:19858491

  20. Selective waste collection optimization in Romania and its impact to urban climate

    NASA Astrophysics Data System (ADS)

    Mihai, Šercǎianu; Iacoboaea, Cristina; Petrescu, Florian; Aldea, Mihaela; Luca, Oana; Gaman, Florian; Parlow, Eberhard

    2016-08-01

    According to European Directives, transposed in national legislation, the Member States should organize separate collection systems at least for paper, metal, plastic, and glass until 2015. In Romania, since 2011 only 12% of municipal collected waste was recovered, the rest being stored in landfills, although storage is considered the last option in the waste hierarchy. At the same time there was selectively collected only 4% of the municipal waste. Surveys have shown that the Romanian people do not have selective collection bins close to their residencies. The article aims to analyze the current situation in Romania in the field of waste collection and management and to make a proposal for selective collection containers layout, using geographic information systems tools, for a case study in Romania. Route optimization is used based on remote sensing technologies and network analyst protocols. Optimizing selective collection system the greenhouse gases, particles and dust emissions can be reduced.

  1. Selecting danger signals: dissociable roles of nucleus accumbens shell and core glutamate in predictive fear learning.

    PubMed

    Li, Susan S Y; McNally, Gavan P

    2015-06-01

    Conditioned stimuli (CSs) vary in their reliability as predictors of danger. Animals must therefore select among CSs those that are appropriate to enter into an association with the aversive unconditioned stimulus (US). The actions of prediction error instruct this stimulus selection so that when prediction error is large, attention to the CS is maintained and learning occurs but when prediction is small attention to the CS is withdrawn and learning is prevented. Here we studied the role of glutamate acting at rat nucleus accumbens shell (AcbSh) and core (AcbC) α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in this selection of danger signals. Using associative blocking and unblocking designs in rats, we show that antagonizing AcbSh AMPA receptors via infusions of 2,3-dihydroxy-6-nitro-7-sulphamoyl-benzo[f]quinoxaline-2,3-dione (NBQX; 0.5 μg) prevents the unblocking of fear learning, whereas antagonizing AcbC AMPA receptors via infusions of NBQX (0.5 μg) prevents both the blocking and unblocking of fear learning. These results identify dissociable but complementary roles for AcbSh and AcbC glutamate acting at AMPA receptors in selecting danger signals: AcbSh AMPA receptors upregulate attention and learning to CSs that signal surprising USs, whereas AcbC AMPA receptors encode the predicted outcome of each trial.

  2. SHAPE Selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data.

    PubMed

    Poulsen, Line Dahl; Kielpinski, Lukasz Jan; Salama, Sofie R; Krogh, Anders; Vinther, Jeppe

    2015-05-01

    Selective 2' Hydroxyl Acylation analyzed by Primer Extension (SHAPE) is an accurate method for probing of RNA secondary structure. In existing SHAPE methods, the SHAPE probing signal is normalized to a no-reagent control to correct for the background caused by premature termination of the reverse transcriptase. Here, we introduce a SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling between the SHAPES reagent and a biotin molecule. We demonstrate that SHAPES-based selection of cDNA-RNA hybrids on streptavidin beads effectively removes the large majority of background signal present in SHAPE probing data and that sequencing-based SHAPES data contain the same amount of RNA structure data as regular sequencing-based SHAPE data obtained through normalization to a no-reagent control. Moreover, the selection efficiently enriches for probed RNAs, suggesting that the SHAPES strategy will be useful for applications with high-background and low-probing signal such as in vivo RNA structure probing.

  3. Signal design and perception in Hypocnemis antbirds: evidence for convergent evolution via social selection.

    PubMed

    Tobias, Joseph A; Seddon, Nathalie

    2009-12-01

    Natural selection is known to produce convergent phenotypes through mimicry or ecological adaptation. It has also been proposed that social selection--i.e., selection exerted by social competition--may drive convergent evolution in signals mediating interspecific communication, yet this idea remains controversial. Here, we use color spectrophotometry, acoustic analyses, and playback experiments to assess the hypothesis of adaptive signal convergence in two competing nonsister taxa, Hypocnemis peruviana and H. subflava (Aves: Thamnophilidae). We show that the structure of territorial songs in males overlaps in sympatry, with some evidence of convergent character displacement. Conversely, nonterritorial vocal and visual signals in males are strikingly diagnostic, in line with 6.8% divergence in mtDNA sequences. The same pattern of variation applies to females. Finally, we show that songs in both sexes elicit strong territorial responses within and between species, whereas songs of a third, allopatric and more closely related species (H. striata) are structurally divergent and elicit weaker responses. Taken together, our results provide compelling evidence that social selection can act across species boundaries to drive convergent or parallel evolution in taxa competing for space and resources. PMID:19659594

  4. Optimization of polarizer azimuth in improving signal-to-noise ratio in Kerr microscopy.

    PubMed

    Wang, X; Lian, J; Xu, X J; Li, X; Li, P; Li, M M; Wang, Y; Liu, Y X

    2016-03-01

    The magneto optical Kerr effect (MOKE) is a widely used technique in magnetic domain imaging for its high surface sensitivity and external magnetic compatibility. Optimization of Kerr microscopy will improve the detecting sensitivity and provide high-quality domain images. In this work, we provide a method to optimize the polarizer azimuth in improving the signal-to-noise ratio (S/N) in longitudinal Kerr microscopy with the generalized magneto optical ellipsometry. Detailed analysis of the MOKE signal and the noise components are provided to study the optimum polarizer and analyzer azimuth combinations. Results show that, for a fixed polarizer angle 1°, the laser intensity noise and the shot noise, which vary with the input laser power, have a similar amplitude and decline with the analyzer azimuth increasing. When the analyzer is set at the extinction place, the Johnson noise plays a dominate role in the total noise. Then, the S/N values are calculated to find the optimum polarizer and analyzer azimuth. Results show that the optimum polarizer and analyzer azimuth combination for Permalloy is (18.35°, 68.35°) under an incident angle of 45°. After that, the S/N of 200 nm Permalloy at different analyzer angles with the polarizer azimuth set at 18.35° is measured to verify the validity of the simulation results. At last, the S/N at different incident angles is calculated. Results show that the optimum incident angle of 200 nm Permalloy film to improve the S/N is 70.35° under the polarizer and analyzer angles set at the optimal combinations (18.35°, 68.35°).

  5. Optimized Signal-To Ratio with Shot Noise Limited Detection in Stimulated Raman Scattering Microscopy

    NASA Astrophysics Data System (ADS)

    Moester, M. J. B.; Ariese, F.; de Boer, J. F.

    2015-04-01

    We describe our set-up for Stimulated Raman Scattering (SRS) microscopy with shot noise limited detection for a broad window of biologically relevant laser powers. This set-up is used to demonstrate that the highest signal-to-noise ratio (SNR) in SRS with shot noise limited detection is achieved with a time-averaged laser power ratio of 1:2 of the unmodulated and modulated beam. In SRS, two different coloured laser beams are incident on a sample. If the energy difference between them matches a molecular vibration of a molecule, energy can be transferred from one beam to the other. By applying amplitude modulation to one of the beams, the modulation transfer to the other beam can be measured. The efficiency of this process is a direct measure for the number of molecules of interest in the focal volume. Combined with laser scanning microscopy, this technique allows for fast and sensitive imaging with sub-micrometre resolution. Recent technological advances have resulted in an improvement of the sensitivity of SRS applications, but few show shot noise limited detection. The dominant noise source in this SRS microscope is the shot noise of the unmodulated, detected beam. Under the assumption that photodamage is linear with the total laser power, the optimal SNR shifts away from equal beam powers, where the most signal is generated, to a 1:2 power ratio. Under these conditions the SNR is maximized and the total laser power that could induce photodamage is minimized. Compared to using a 1:1 laser power ratio, we show improved image quality and a signal-to-noise ratio improvement of 8 % in polystyrene beads and C. Elegans worms. Including a non-linear damage mechanism in the analysis, we find that the optimal power ratio converges to a 1:1 ratio with increasing order of the non-linear damage mechanism.

  6. An optimized controller for ARGOS: using multiple wavefront sensor signals for homogeneous correction over the field

    NASA Astrophysics Data System (ADS)

    Peter, D.

    2010-07-01

    ARGOS is the ground layer adaptive optics system planned for the LBT. The goal of such a ground layer adaptive optics system is to provide a maximum homogeneity of the point spread function over the full field of view. Controllers for optimized correction with an adaptive optics system with guide star and science target at different field angles are well known in the case of a single guide star. As ARGOS uses three laser guide stars and one auxiliary natural guide star a weighting scheme is required to optimize the homogeneity using all available information. Especially the tip and tilt modes measured by the one single off axis guide star and estimated thereof over the field will need to be improved by incorporation of the laser measurements. I will present the full scheme for an optimized controller for the ARGOS system. This controller uses the wavefront signals of the three lasers to additionally reconstruct the lower atmosphere. Information on the higher atmosphere will be provided by a DIMM-MASS instrument. The control scheme is tested analytically and the variation of the point spread function is then measured over the full field.

  7. Effect of codon-optimized E. coli signal peptides on recombinant Bacillus stearothermophilus maltogenic amylase periplasmic localization, yield and activity.

    PubMed

    Samant, Shalaka; Gupta, Gunja; Karthikeyan, Subbulakshmi; Haq, Saiful F; Nair, Ayyappan; Sambasivam, Ganesh; Sukumaran, Sunilkumar

    2014-09-01

    Recombinant proteins can be targeted to the Escherichia coli periplasm by fusing them to signal peptides. The popular pET vectors facilitate fusion of target proteins to the PelB signal. A systematic comparison of the PelB signal with native E. coli signal peptides for recombinant protein expression and periplasmic localization is not reported. We chose the Bacillus stearothermophilus maltogenic amylase (MA), an industrial enzyme widely used in the baking and brewing industry, as a model protein and analyzed the competence of seven, codon-optimized, E. coli signal sequences to translocate MA to the E. coli periplasm compared to PelB. MA fusions to three of the signals facilitated enhanced periplasmic localization of MA compared to the PelB fusion. Interestingly, these three fusions showed greatly improved MA yields and between 18- and 50-fold improved amylase activities compared to the PelB fusion. Previously, non-optimal codon usage in native E. coli signal peptide sequences has been reported to be important for protein stability and activity. Our results suggest that E. coli signal peptides with optimal codon usage could also be beneficial for heterologous protein secretion to the periplasm. Moreover, such fusions could even enhance activity rather than diminish it. This effect, to our knowledge has not been previously documented. In addition, the seven vector platform reported here could also be used as a screen to identify the best signal peptide partner for other recombinant targets of interest. PMID:25038884

  8. Low Emissions and Delay Optimization for an Isolated Signalized Intersection Based on Vehicular Trajectories.

    PubMed

    Lin, Ciyun; Gong, Bowen; Qu, Xin

    2015-01-01

    A traditional traffic signal control system is established based on vehicular delay, queue length, saturation and other indicators. However, due to the increasing severity of urban environmental pollution issues and the development of a resource-saving and environmentally friendly social philosophy, the development of low-carbon and energy-efficient urban transport is required. This paper first defines vehicular trajectories and the calculation of vehicular emissions based on VSP. Next, a regression analysis method is used to quantify the relationship between vehicular emissions and delay, and a traffic signal control model is established to reduce emissions and delay using the enumeration method combined with saturation constraints. Finally, one typical intersection of Changchun is selected to verify the model proposed in this paper; its performance efficiency is also compared using simulations in VISSIM. The results of this study show that the proposed model can significantly reduce vehicle delay and traffic emissions simultaneously.

  9. Low Emissions and Delay Optimization for an Isolated Signalized Intersection Based on Vehicular Trajectories

    PubMed Central

    2015-01-01

    A traditional traffic signal control system is established based on vehicular delay, queue length, saturation and other indicators. However, due to the increasing severity of urban environmental pollution issues and the development of a resource-saving and environmentally friendly social philosophy, the development of low-carbon and energy-efficient urban transport is required. This paper first defines vehicular trajectories and the calculation of vehicular emissions based on VSP. Next, a regression analysis method is used to quantify the relationship between vehicular emissions and delay, and a traffic signal control model is established to reduce emissions and delay using the enumeration method combined with saturation constraints. Finally, one typical intersection of Changchun is selected to verify the model proposed in this paper; its performance efficiency is also compared using simulations in VISSIM. The results of this study show that the proposed model can significantly reduce vehicle delay and traffic emissions simultaneously. PMID:26720095

  10. Optimal Wavelength Selection on Hyperspectral Data with Fused Lasso for Biomass Estimation of Tropical Rain Forest

    NASA Astrophysics Data System (ADS)

    Takayama, T.; Iwasaki, A.

    2016-06-01

    Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous large-area forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number of training samples is smaller than the dimensionality of the samples due to limitation of require time, cost, and human resources for field surveys. A common approach to addressing this problem is reducing the dimensionality of dataset. Also, acquired hyperspectral data usually have low signal-to-noise ratio due to a narrow bandwidth and local or global shifts of peaks due to instrumental instability or small differences in considering practical measurement conditions. In this work, we propose a methodology based on fused lasso regression that select optimal bands for the biomass prediction model with encouraging sparsity and grouping, which solves the small-sample-size problem by the dimensionality reduction from the sparsity and the noise and peak shift problem by the grouping. The prediction model provided higher accuracy with root-mean-square error (RMSE) of 66.16 t/ha in the cross-validation than other methods; multiple linear analysis, partial least squares regression, and lasso regression. Furthermore, fusion of spectral and spatial information derived from texture index increased the prediction accuracy with RMSE of 62.62 t/ha. This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests.

  11. Direct-aperture optimization applied to selection of beam orientations in intensity-modulated radiation therapy

    NASA Astrophysics Data System (ADS)

    Bedford, J. L.; Webb, S.

    2007-01-01

    Direct-aperture optimization (DAO) was applied to iterative beam-orientation selection in intensity-modulated radiation therapy (IMRT), so as to ensure a realistic segmental treatment plan at each iteration. Nested optimization engines dealt separately with gantry angles, couch angles, collimator angles, segment shapes, segment weights and wedge angles. Each optimization engine performed a random search with successively narrowing step sizes. For optimization of segment shapes, the filtered backprojection (FBP) method was first used to determine desired fluence, the fluence map was segmented, and then constrained direct-aperture optimization was used thereafter. Segment shapes were fully optimized when a beam angle was perturbed, and minimally re-optimized otherwise. The algorithm was compared with a previously reported method using FBP alone at each orientation iteration. An example case consisting of a cylindrical phantom with a hemi-annular planning target volume (PTV) showed that for three-field plans, the method performed better than when using FBP alone, but for five or more fields, neither method provided much benefit over equally spaced beams. For a prostate case, improved bladder sparing was achieved through the use of the new algorithm. A plan for partial scalp treatment showed slightly improved PTV coverage and lower irradiated volume of brain with the new method compared to FBP alone. It is concluded that, although the method is computationally intensive and not suitable for searching large unconstrained regions of beam space, it can be used effectively in conjunction with prior class solutions to provide individually optimized IMRT treatment plans.

  12. Optimal band selection for high dimensional remote sensing data using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Xianfeng; Sun, Quan; Li, Jonathan

    2009-06-01

    A 'fused' method may not be suitable for reducing the dimensionality of data and a band/feature selection method needs to be used for selecting an optimal subset of original data bands. This study examined the efficiency of GA in band selection for remote sensing classification. A GA-based algorithm for band selection was designed deliberately in which a Bhattacharyya distance index that indicates separability between classes of interest is used as fitness function. A binary string chromosome is designed in which each gene location has a value of 1 representing a feature being included or 0 representing a band being not included. The algorithm was implemented in MATLAB programming environment, and a band selection task for lithologic classification in the Chocolate Mountain area (California) was used to test the proposed algorithm. The proposed feature selection algorithm can be useful in multi-source remote sensing data preprocessing, especially in hyperspectral dimensionality reduction.

  13. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection.

    PubMed

    Sun, Jun; Fang, Wei; Wu, Xiaojun; Palade, Vasile; Xu, Wenbo

    2012-01-01

    Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This paper presents a comprehensive analysis of the QPSO algorithm. In the theoretical analysis, we analyze the behavior of a single particle in QPSO in terms of probability measure. Since the particle's behavior is influenced by the contraction-expansion (CE) coefficient, which is the most important parameter of the algorithm, the goal of the theoretical analysis is to find out the upper bound of the CE coefficient, within which the value of the CE coefficient selected can guarantee the convergence or boundedness of the particle's position. In the experimental analysis, the theoretical results are first validated by stochastic simulations for the particle's behavior. Then, based on the derived upper bound of the CE coefficient, we perform empirical studies on a suite of well-known benchmark functions to show how to control and select the value of the CE coefficient, in order to obtain generally good algorithmic performance in real world applications. Finally, a further performance comparison between QPSO and other variants of PSO on the benchmarks is made to show the efficiency of the QPSO algorithm with the proposed parameter control and selection methods.

  14. [The Near Infrared Spectral Bands Optimal Selection in the Application of Liquor Fermented Grains Composition Analysis].

    PubMed

    Xiong, Ya-ting; Li, Zong-peng; Wang, Jian; Zhang, Ying; Wang, Shu-jun; Yin, Jian-jun; Song, Quan-hou

    2016-01-01

    In order to improve the technical level of the rapid detection of liquor fermented grains, in this paper, use near infrared spectroscopy technology to quantitative analysis moisture, starch, acidity and alcohol of liquor fermented grains. Using CARS, iPLS and no information variable elimination method (UVE), realize the characteristics of spectral band selection. And use the multiple scattering correction (MSC), derivative and standard normal variable transformation (SNV) pretreatment method to optimize the models. Establish models of quantitative analysis of fermented grains by PLS, and in order to select the best modeling method, using R2, RMSEP and optimal number of main factors to evaluate models. The results showed that the band selection is vital to optimize the model and CARS is the best optimization of the most significant effect. The calculation results showed that R2 of moisture, starch, acidity and alcohol were 0.885, 0.915, 0.951, 0.885 respectively and RMSEP of moisture, starch, acidity and alcohol were 0.630, 0.519, 0.228, 0.234 respectively. After optimization, the model prediction effect is good, the models can satisfy the requirement of the rapid detection of liquor fermented grains, which has certain reference value in the practical. PMID:27228746

  15. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme

    PubMed Central

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-01-01

    Purpose: Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. Methods: An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Results: Among these four methods, SFFS has highest efficacy, which takes 3%–5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC

  16. On selection of the optimal data time interval for real-time hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Liu, J.; Han, D.

    2013-09-01

    With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is which suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall-runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet transform tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall-runoff modelling due to the catchment low-pass-filtering effect. To further investigate the impact of a data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the probability distributed model (PDM) with a real-time updating scheme, the autoregressive moving-average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15, 30, 45, 60, 90 and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications of the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the future is that higher sampling rates will become

  17. On selection of the optimal data time interval for real-time hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Liu, J.; Han, D.

    2012-09-01

    With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is what suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall-runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet decomposition tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall-runoff modelling due to the catchment low-pass filtering effect. To further investigate the impact of data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the Probability Distributed Model (PDM) with a real-time updating scheme, the autoregressive-moving average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15 min, 30 min, 45 min, 60 min, 90 min and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications on the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the trend in the future is that

  18. Jagged1-selective notch signaling induces smooth muscle differentiation via a RBP-Jkappa-dependent pathway.

    PubMed

    Doi, Hiroshi; Iso, Tatsuya; Sato, Hiroko; Yamazaki, Miki; Matsui, Hiroki; Tanaka, Toru; Manabe, Ichiro; Arai, Masashi; Nagai, Ryozo; Kurabayashi, Masahiko

    2006-09-29

    The Notch signaling pathway plays a crucial role in specifying cellular fates by interaction between cellular neighbors; however, the molecular mechanism underlying smooth muscle cell (SMC) differentiation by Notch signaling has not been well characterized. Here we demonstrate that Jagged1-Notch signaling promotes SMC differentiation from mesenchymal cells. Overexpression of the Notch intracellular domain, an activated form of Notch, up-regulates the expression of multiple SMC marker genes including SMC-myosin heavy chain (Sm-mhc) in mesenchymal 10T1/2 cells, but not in non-mesenchymal cells. Physiological Notch stimulation by its ligand Jagged1, but not Dll4, directly induces Sm-mhc expression in 10T1/2 cells without de novo protein synthesis, indicative of a ligand-selective effect. Jagged1-induced expression of SM-MHC was blocked bygamma-secretase inhibitor, N-(N-(3,5-difluorophenyl)-l-alanyl)-S-phenylglycine t-butyl ester, which impedes Notch signaling. Using Rbp-jkappa-deficient cells and site-specific mutagenesis of the SM-MHC gene, we show that such an induction is independent of the myocardin-serum response factor-CArG complex, but absolutely dependent on RBP-Jkappa, a major mediator of Notch signaling, and its cognate binding sequence. Of importance, Notch signaling and myocardin synergistically activate SM-MHC gene expression. Taken together, these data suggest that the Jagged1-Notch pathway constitutes an instructive signal for SMC differentiation through an RBP-Jkappa-dependent mechanism and augments gene expression mediated by the myocardin-SRF-CArG complex. Given that Notch pathway components are expressed in vascular SMC during normal development and disease, Notch signaling is likely to play a pivotal role in such situations to modulate the vascular smooth muscle cell phenotype. PMID:16867989

  19. Subjective Career Success and Emotional Well-Being: Longitudinal Predictive Power of Selection, Optimization, and Compensation.

    ERIC Educational Resources Information Center

    Wiese, Bettina S.; Freund, Alexandra M.; Baltes, Paul B.

    2002-01-01

    A 3-year study of 82 young professionals found that work-related well-being was predicted by selection (commitment to personal goals), optimization (application of goal-related skills), and compensation (maintaining goals in the face of loss). The degree of compensation predicted emotional well-being and job satisfaction 3 years later. (Contains…

  20. Selection, Optimization, and Compensation: An Action-Related Approach to Work and Partnership.

    ERIC Educational Resources Information Center

    Wiese, Bettina S.; Baltes, Paul B.; Freund, Alexandra M.

    2000-01-01

    Data from German professionals (n=206) were used to test selective optimization with compensation (SOC)--goal setting in career and partnership domains and use of means to achieve goals. A positive relationship was found between SOC behaviors and successful life management; it was more predictive for the partnership domain. (Contains 82…

  1. Optimization of a series of potent and selective ketone histone deacetylase inhibitors.

    PubMed

    Pescatore, Giovanna; Kinzel, Olaf; Attenni, Barbara; Cecchetti, Ottavia; Fiore, Fabrizio; Fonsi, Massimiliano; Rowley, Michael; Schultz-Fademrecht, Carsten; Serafini, Sergio; Steinkühler, Christian; Jones, Philip

    2008-10-15

    Histone deacetylase (HDAC) inhibitors offer a promising strategy for cancer therapy and the first generation HDAC inhibitors are currently in the clinic. Herein we describe the optimization of a series of ketone small molecule HDAC inhibitors leading to potent and selective class I HDAC inhibitors with good dog PK.

  2. Self-Regulatory Strategies in Daily Life: Selection, Optimization, and Compensation and Everyday Memory Problems

    ERIC Educational Resources Information Center

    Robinson, Stephanie A.; Rickenbach, Elizabeth H.; Lachman, Margie E.

    2016-01-01

    The effective use of self-regulatory strategies, such as selection, optimization, and compensation (SOC) requires resources. However, it is theorized that SOC use is most advantageous for those experiencing losses and diminishing resources. The present study explored this seeming paradox within the context of limitations or constraints due to…

  3. Selective secretion of annexin 1, a protein without a signal sequence, by the human prostate gland.

    PubMed

    Christmas, P; Callaway, J; Fallon, J; Jones, J; Haigler, H T

    1991-02-01

    Annexins are primarily intracellular proteins as would be predicted from their lack of hydrophobic signal sequences. However, we now report that the human prostate gland selectively secretes high concentrations of annexin 1 (also called lipocortin 1 and p35) and a proteolytic cleavage product, des1-29-annexin 1, into seminal plasma. Secreted annexin 1 had a blocked amino terminus and was structurally indistinguishable from intracellular annexin 1. Although annexin 1 and the structurally related protein, annexin 4, co-localized to many of the same cells of the ductal epithelium of the prostate, annexin 4 was not secreted. Thus, the secretion of annexin 1 appears to involve a highly selective mechanism that does not involve targeting to the endoplasmic reticulum by a hydrophobic signal sequence.

  4. Harmonic signal generation and frequency upconversion using selective sideband Brillouin amplification in single-mode fiber.

    PubMed

    Lee, Kwang-Hyun; Choi, Woo-Young

    2007-06-15

    Harmonic signal generation and frequency upconversion at millimeter-wave bands are experimentally demonstrated by using selective sideband Brillouin amplification induced by stimulated Brillouin scattering in a single-mode fiber. The harmonic signals and frequency upconverted signals are simultaneously generated by the beating of optical sidebands, one of which is Brillouin amplified. By using this method, we successfully demonstrate generation of third-harmonic millimeter waves at 32.55 GHz with f(LO) of 10.85 GHz and upconversion of 10 Mbps quadrature-shift keyed data at f(IF) of 1.55 GHz into a 30 GHz band with more than 17 dB RF power gain.

  5. Selective regulation of MAP kinase signaling by an endomembrane phosphatidylinositol 4-kinase.

    PubMed

    Cappell, Steven D; Dohlman, Henrik G

    2011-04-29

    Multiple MAP kinase pathways share components yet initiate distinct biological processes. Signaling fidelity can be maintained by scaffold proteins and restriction of signaling complexes to discreet subcellular locations. For example, the yeast MAP kinase scaffold Ste5 binds to phospholipids produced at the plasma membrane and promotes selective MAP kinase activation. Here we show that Pik1, a phosphatidylinositol 4-kinase that localizes primarily to the Golgi, also regulates MAP kinase specificity but does so independently of Ste5. Pik1 is required for full activation of the MAP kinases Fus3 and Hog1 and represses activation of Kss1. Further, we show by genetic epistasis analysis that Pik1 likely regulates Ste11 and Ste50, components shared by all three MAP kinase pathways, through their interaction with the scaffold protein Opy2. These findings reveal a new regulator of signaling specificity functioning at endomembranes rather than at the plasma membrane. PMID:21388955

  6. Implementation and optimization of ultrasound signal processing algorithms on mobile GPU

    NASA Astrophysics Data System (ADS)

    Kong, Woo Kyu; Lee, Wooyoul; Kim, Kyu Cheol; Yoo, Yangmo; Song, Tai-Kyong

    2014-03-01

    A general-purpose graphics processing unit (GPGPU) has been used for improving computing power in medical ultrasound imaging systems. Recently, a mobile GPU becomes powerful to deal with 3D games and videos at high frame rates on Full HD or HD resolution displays. This paper proposes the method to implement ultrasound signal processing on a mobile GPU available in the high-end smartphone (Galaxy S4, Samsung Electronics, Seoul, Korea) with programmable shaders on the OpenGL ES 2.0 platform. To maximize the performance of the mobile GPU, the optimization of shader design and load sharing between vertex and fragment shader was performed. The beamformed data were captured from a tissue mimicking phantom (Model 539 Multipurpose Phantom, ATS Laboratories, Inc., Bridgeport, CT, USA) by using a commercial ultrasound imaging system equipped with a research package (Ultrasonix Touch, Ultrasonix, Richmond, BC, Canada). The real-time performance is evaluated by frame rates while varying the range of signal processing blocks. The implementation method of ultrasound signal processing on OpenGL ES 2.0 was verified by analyzing PSNR with MATLAB gold standard that has the same signal path. CNR was also analyzed to verify the method. From the evaluations, the proposed mobile GPU-based processing method has no significant difference with the processing using MATLAB (i.e., PSNR<52.51 dB). The comparable results of CNR were obtained from both processing methods (i.e., 11.31). From the mobile GPU implementation, the frame rates of 57.6 Hz were achieved. The total execution time was 17.4 ms that was faster than the acquisition time (i.e., 34.4 ms). These results indicate that the mobile GPU-based processing method can support real-time ultrasound B-mode processing on the smartphone.

  7. An optimized method for the estimation of the respiratory rate from electrocardiographic signals: implications for estimating minute ventilation.

    PubMed

    Weiss, Eric H; Sayadi, Omid; Ramaswamy, Priya; Merchant, Faisal M; Sajja, Naveen; Foley, Lori; Laferriere, Shawna; Armoundas, Antonis A

    2014-08-01

    It is well-known that respiratory activity influences electrocardiographic (ECG) morphology. In this article we present a new algorithm for the extraction of respiratory rate from either intracardiac or body surface electrograms. The algorithm optimizes selection of ECG leads for respiratory analysis, as validated in a swine model. The algorithm estimates the respiratory rate from any two ECG leads by finding the power spectral peak of the derived ratio of the estimated root-mean-squared amplitude of the QRS complexes on a beat-by-beat basis across a 32-beat window and automatically selects the lead combination with the highest power spectral signal-to-noise ratio. In 12 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the right ventricle, coronary sinus, left ventricle, and epicardial surface, as well as body surface electrograms, while the ventilation rate was varied between 7 and 13 breaths/min at tidal volumes of 500 and 750 ml. We found excellent agreement between the estimated and true respiratory rate for right ventricular (R(2) = 0.97), coronary sinus (R(2) = 0.96), left ventricular (R(2) = 0.96), and epicardial (R(2) = 0.97) intracardiac leads referenced to surface lead ECGII. When applied to intracardiac right ventricular-coronary sinus bipolar leads, the algorithm exhibited an accuracy of 99.1% (R(2) = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% (R(2) = 0.93). In conclusion, the proposed algorithm provides an accurate estimation of the respiratory rate using either intracardiac or body surface signals without the need for additional hardware.

  8. The plant physical features selected by wildcats as signal posts: an economic approach to fecal marking

    NASA Astrophysics Data System (ADS)

    Piñeiro, Ana; Barja, Isabel

    2012-10-01

    The chemical signals of solitary and territorial felid species are essential for their intraspecific communication. We studied the selection of plant substrates during the fecal marking behavior of the European wildcat Felis silvestris from September 2008 to June 2009 in a protected area in Northwest Spain. The aim of the study was to examine the selection of plants as signal posts with respect to their physical characteristics. We hypothesized that wildcats deposit their fecal marks on plants with physical characteristics (e.g., size, species, and visual conspicuousness) that enhance the olfactory and visual effectiveness of the signal. Our results indicate that diameter, plant group, visual conspicuousness, and interaction between the diameter and plant group influence the decision of wildcats to deposit their fecal marks on plants. The wildcats chose plants with greater diameters and greater visual conspicuousness as scent-marking posts. Moreover, the wildcats chose woody and herbaceous plants, and certain plant species were marked more frequently than expected at random. Indeed, our results indicate that the fecal marks were not randomly distributed on the plants: the wildcats chose to place their marks on plants with certain physical characteristics that maximized the detectability of the signal by intruders and potential mates, thus facilitating the spatial distribution of the species.

  9. Exploring the optimal performances of irreversible single resonance energy selective electron refrigerators

    NASA Astrophysics Data System (ADS)

    Zhou, Junle; Chen, Lingen; Ding, Zemin; Sun, Fengrui

    2016-05-01

    Applying finite-time thermodynamics (FTT) and electronic transport theory, the optimal performances of irreversible single resonance energy selective electron (ESE) refrigerator are analyzed. The effects of heat leakage between two electron reservoirs on optimal performances are discussed. The influences of system operating parameters on cooling load, coefficient of performance (COP), figure of merit and ecological function are demonstrated using numerical examples. Comparative performance analyses among different objective functions show that performance characteristics at maximum ecological function and maximum figure of merit are of great practical significance. Combining the two optimization objectives of maximum ecological function and maximum figure of merit together, more specific optimal ranges of cooling load and COP are obtained. The results can provide some advices to the design of practical electronic machine systems.

  10. Selection of Thermal Worst-Case Orbits via Modified Efficient Global Optimization

    NASA Technical Reports Server (NTRS)

    Moeller, Timothy M.; Wilhite, Alan W.; Liles, Kaitlin A.

    2014-01-01

    Efficient Global Optimization (EGO) was used to select orbits with worst-case hot and cold thermal environments for the Stratospheric Aerosol and Gas Experiment (SAGE) III. The SAGE III system thermal model changed substantially since the previous selection of worst-case orbits (which did not use the EGO method), so the selections were revised to ensure the worst cases are being captured. The EGO method consists of first conducting an initial set of parametric runs, generated with a space-filling Design of Experiments (DoE) method, then fitting a surrogate model to the data and searching for points of maximum Expected Improvement (EI) to conduct additional runs. The general EGO method was modified by using a multi-start optimizer to identify multiple new test points at each iteration. This modification facilitates parallel computing and decreases the burden of user interaction when the optimizer code is not integrated with the model. Thermal worst-case orbits for SAGE III were successfully identified and shown by direct comparison to be more severe than those identified in the previous selection. The EGO method is a useful tool for this application and can result in computational savings if the initial Design of Experiments (DoE) is selected appropriately.

  11. A general method to select representative models for decision making and optimization under uncertainty

    NASA Astrophysics Data System (ADS)

    Shirangi, Mehrdad G.; Durlofsky, Louis J.

    2016-11-01

    The optimization of subsurface flow processes under geological uncertainty technically requires flow simulation to be performed over a large set of geological realizations for each function evaluation at every iteration of the optimizer. Because flow simulation over many permeability realizations (only permeability is considered to be uncertain in this study) may entail excessive computation, simulations are often performed for only a subset of 'representative' realizations. It is however challenging to identify a representative subset that provides flow statistics in close agreement with those from the full set, especially when the decision parameters (e.g., time-varying well pressures, well locations) are unknown a priori, as they are in optimization problems. In this work, we introduce a general framework, based on clustering, for selecting a representative subset of realizations for use in simulations involving 'new' sets of decision parameters. Prior to clustering, each realization is represented by a low-dimensional feature vector that contains a combination of permeability-based and flow-based quantities. Calculation of flow-based features requires the specification of a (base) flow problem and simulation over the full set of realizations. Permeability information is captured concisely through use of principal component analysis. By computing the difference between the flow response for the subset and the full set, we quantify the performance of various realization-selection methods. The impact of different weightings for flow and permeability information in the cluster-based selection procedure is assessed for a range of examples involving different types of decision parameters. These decision parameters are generated either randomly, in a manner that is consistent with the solutions proposed in global stochastic optimization procedures such as GA and PSO, or through perturbation around a base case, consistent with the solutions considered in pattern search

  12. DOA estimation for local scattered CDMA signals by particle swarm optimization.

    PubMed

    Chang, Jhih-Chung

    2012-01-01

    This paper deals with the direction-of-arrival (DOA) estimation of local scattered code-division multiple access (CDMA) signals based on a particle swarm optimization (PSO) search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison.

  13. Signal Analysis Algorithms for Optimized Fitting of Nonresonant Laser Induced Thermal Acoustics Damped Sinusoids

    NASA Technical Reports Server (NTRS)

    Balla, R. Jeffrey; Miller, Corey A.

    2008-01-01

    This study seeks a numerical algorithm which optimizes frequency precision for the damped sinusoids generated by the nonresonant LITA technique. It compares computed frequencies, frequency errors, and fit errors obtained using five primary signal analysis methods. Using variations on different algorithms within each primary method, results from 73 fits are presented. Best results are obtained using an AutoRegressive method. Compared to previous results using Prony s method, single shot waveform frequencies are reduced approx.0.4% and frequency errors are reduced by a factor of approx.20 at 303K to approx. 0.1%. We explore the advantages of high waveform sample rates and potential for measurements in low density gases.

  14. Efficient and Optimal Attitude Determination Using Recursive Global Positioning System Signal Operations

    NASA Technical Reports Server (NTRS)

    Crassidis, John L.; Lightsey, E. Glenn; Markley, F. Landis

    1998-01-01

    In this paper, a new and efficient algorithm is developed for attitude determination from Global Positioning System signals. The new algorithm is derived from a generalized nonlinear predictive filter for nonlinear systems. This uses a one time-step ahead approach to propagate a simple kinematics model for attitude determination. The advantages of the new algorithm over previously developed methods include: it provides optimal attitudes even for coplanar baseline configurations; it guarantees convergence even for poor initial conditions; it is a non-iterative algorithm; and it is computationally efficient. These advantages clearly make the new algorithm well suited to on-board applications. The performance of the new algorithm is tested on a dynamic hardware simulator. Results indicate that the new algorithm accurately estimates the attitude of a moving vehicle, and provides robust attitude estimates even when other methods, such as a linearized least-squares approach, fail due to poor initial starting conditions.

  15. Optimal Signal Filtration in Optical Sensors with Natural Squeezing of Vacuum Noises

    NASA Technical Reports Server (NTRS)

    Gusev, A. V.; Kulagin, V. V.

    1996-01-01

    The structure of optimal receiver is discussed for optical sensor measuring a small displacement of probe mass. Due to nonlinear interaction of the field and the mirror, a reflected wave is in squeezed state (natural squeezing), two quadratures of which are correlated and therefore one can increase signal-to-noise ratio and overcome the SQL. A measurement procedure realizing such correlation processing of two quadratures is clarified. The required combination of quadratures can be produced via mixing of pump field reflected from the mirror with local oscillator phase modulated field in duel-detector homodyne scheme. Such measurement procedure could be useful not only for resonant bar gravitational detector but for laser longbase interferometric detectors as well.

  16. A Novel Method of Failure Sample Selection for Electrical Systems Using Ant Colony Optimization

    PubMed Central

    Tian, Shulin; Yang, Chenglin; Liu, Cheng

    2016-01-01

    The influence of failure propagation is ignored in failure sample selection based on traditional testability demonstration experiment method. Traditional failure sample selection generally causes the omission of some failures during the selection and this phenomenon could lead to some fearful risks of usage because these failures will lead to serious propagation failures. This paper proposes a new failure sample selection method to solve the problem. First, the method uses a directed graph and ant colony optimization (ACO) to obtain a subsequent failure propagation set (SFPS) based on failure propagation model and then we propose a new failure sample selection method on the basis of the number of SFPS. Compared with traditional sampling plan, this method is able to improve the coverage of testing failure samples, increase the capacity of diagnosis, and decrease the risk of using. PMID:27738424

  17. Top down and bottom up selection drives variations in frequency and form of a visual signal

    PubMed Central

    Yeh, Chien-Wei; Blamires, Sean J.; Liao, Chen-Pan; Tso, I.-Min

    2015-01-01

    The frequency and form of visual signals can be shaped by selection from predators, prey or both. When a signal simultaneously attracts predators and prey, selection may favour a strategy that minimizes risks while attracting prey. Accordingly, varying the frequency and form of the silken decorations added to their web may be a way that Argiope spiders minimize predation while attracting prey. Nonetheless, the role of extraneous factors renders the influences of top down and bottom up selection on decoration frequency and form variation difficult to discern. Here we used dummy spiders and decorations to simulate four possible strategies that the spider Argiope aemula may choose and measured the prey and predator attraction consequences for each in the field. The strategy of decorating at a high frequency with a variable form attracted the most prey, while that of decorating at a high frequency with a fixed form attracted the most predators. These results suggest that mitigating the cost of attracting predators while maintaining prey attraction drives the use of variation in decoration form by many Argiope spp. when decorating frequently. Our study highlights the importance of considering top-down and bottom up selection pressure when devising evolutionary ecology experiments. PMID:25828030

  18. Top down and bottom up selection drives variations in frequency and form of a visual signal.

    PubMed

    Yeh, Chien-Wei; Blamires, Sean J; Liao, Chen-Pan; Tso, I-Min

    2015-01-01

    The frequency and form of visual signals can be shaped by selection from predators, prey or both. When a signal simultaneously attracts predators and prey selection may favour a strategy that minimizes risks while attracting prey. Accordingly, varying the frequency and form of the silken decorations added to their web may be a way that Argiope spiders minimize predation while attracting prey. Nonetheless, the role of extraneous factors renders the influences of top down and bottom up selection on decoration frequency and form variation difficult to discern. Here we used dummy spiders and decorations to simulate four possible strategies that the spider Argiope aemula may choose and measured the prey and predator attraction consequences for each in the field. The strategy of decorating at a high frequency with a variable form attracted the most prey, while that of decorating at a high frequency with a fixed form attracted the most predators. These results suggest that mitigating the cost of attracting predators while maintaining prey attraction drives the use of variation in decoration form by many Argiope spp. when decorating frequently. Our study highlights the importance of considering top-down and bottom up selection pressure when devising evolutionary ecology experiments.

  19. A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

    SciTech Connect

    Kim, Minsun Stewart, Robert D.; Phillips, Mark H.

    2015-11-15

    Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T{sub d}), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D{sub mean} ≤ 45 Gy), lungs (D{sub mean} ≤ 20 Gy), cord (D{sub max} ≤ 45 Gy), esophagus (D{sub max} ≤ 63 Gy), and unspecified tissues (D{sub 05} ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D{sub 95} of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T{sub d} (3–100 days), tumor lag-time (T{sub k} = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D{sub 95} were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T{sub d} and T{sub k} used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating

  20. A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

    PubMed

    Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao

    2014-10-01

    In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.

  1. Optimizing the Intrinsic Signal-to-Noise Ratio of MRI Strip Detectors

    PubMed Central

    Kumar, Ananda; Bottomley, Paul A.

    2007-01-01

    An MRI detector is formed from a conducting strip separated by a dielectric substrate from a ground plane, and tuned to a quarter-wavelength. By distributing discrete tuning elements along the strip, the geometric design may be adjusted to optimize the signal-to-noise ratio (SNR) for a given application. Here a numerical electromagnetic (EM) method of moments (MoM) is applied to determine the length, width, substrate thickness, dielectric constant, and number of tuning elements that yield the best intrinsic SNR (ISNR) of the strip detector at 1.5 Tesla. The central question of how strip performance compares with that of a conventional optimized loop coil is also addressed. The numerical method is validated against the known ISNR performance of loop coils, and its ability to predict the tuning capacitances and performance of seven experimental strip detectors of varying length, width, substrate thickness, and dielectric constant. We find that strip detectors with low-dielectric constant, moderately thin-substrate, and length about 1.3 (±0.2) times the depth of interest perform best. The ISNR of strips is comparable to that of loops (i.e., higher close to the detector but lower at depth). The SNR improves with two inherently-decoupled strips, whose sensitivity profile is well-suited to parallel MRI. The findings are summarized as design “rules of thumb.” PMID:16724302

  2. Optimization of near-infrared spectroscopic process monitoring at low signal-to-noise ratio.

    PubMed

    Schneider, Hendrik; Reich, Gabriele

    2011-03-15

    An approach for the optimization of near-infrared (NIR) spectroscopic process monitoring at low signal-to-noise ratio is presented. It compromises the combined adjustment of different measurement variables and data pretreatments considering the prediction error, economic aspects of the application, and process constraints. The integration time, light intensity, and number of averaged spectra were varied; their mutual influence on the prediction error of partial least squares (PLS) models (i.e., root-mean-square error of cross-validation (RMSECV)) was evaluated in the laboratory. At low signal levels, the spectral uncertainty had a strong impact on the prediction error. It leveled off with increasing values of all three parameters and was finally dominated by other sources of uncertainty. The experimental findings could be characterized and explained by a mathematical equation, which was deduced from theoretical principles. The knowledge about the interaction of the measurement variables allowed their combined adjustment resulting in a reduced impact of spectral uncertainty on the prediction error (i.e., root-mean-square error of prediction (RMSEP)) without additional costs or process modifications. Moreover, a convenient procedure to compensate the stray light caused by strongly absorbing windows was developed. The whole approach was successfully applied to a challenging process, namely, the NIR inline monitoring of the liquid content of two model substances in a rotating suspension dryer.

  3. Multiobjective Signal Processing Optimization: The way to balance conflicting metrics in 5G systems

    NASA Astrophysics Data System (ADS)

    Bjornson, Emil; Jorswieck, Eduard Axel; Debbah, Merouane; Ottersten, Bjorn

    2014-11-01

    The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: Any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved closer to this wireless dream; first by delivering wireless access to voice communications, then by providing wireless data services, and recently by delivering a WiFi-like experience with wide-area coverage and user mobility management. The support for high data rates has been the main objective in recent years, as seen from the academic focus on sum-rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in IMT-Advanced. In contrast, a variety of metrics/objectives are put forward in the technological preparations for 5G networks: higher peak rates, improved coverage with uniform user experience, higher reliability and lower latency, better energy efficiency, lower-cost user devices and services, better scalability with number of devices, etc. These multiple objectives are coupled, often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives. Hence, the design of future networks calls for new optimization tools that properly handle the existence and tradeoffs between multiple objectives. In this article, we provide a review of multi-objective optimization (MOO), which is a mathematical framework to solve design problems with multiple conflicting objectives. (...) We provide a survey of the basic definitions, properties, and algorithmic tools in MOO. This reveals how signal processing algorithms are used to visualize the inherent conflicts between 5G performance objectives, thereby allowing the network designer to understand the possible operating points and how to balance the objectives in an efficient and satisfactory way. For clarity, we provide a case study on massive MIMO.

  4. Space debris selection and optimal guidance for removal in the SSO with low-thrust propulsion

    NASA Astrophysics Data System (ADS)

    Olympio, J. T.; Frouvelle, N.

    2014-06-01

    The current paper deals with the mission design of a generic active space debris removal spacecraft. Considered space debris are all on sun-synchronous orbits. A perturbed Lambert's problem, modelling the transfer between two space debris is devised to take into account J2 perturbation, and to quickly evaluate mission scenarios. A robust approach, using techniques of global optimisation, is followed to find the optimal space debris sequence and mission strategy. Low-thrust optimisation is then performed to turn bi-impulse transfers into optimal low-thrust transfers, and refine the selected scenarios.

  5. Optimal decision rule with class-selective rejection and performance constraints.

    PubMed

    Grall-Maës, Edith; Beauseroy, Pierre

    2009-11-01

    The problem of defining a decision rule which takes into account performance constraints and class-selective rejection is formalized in a general framework. In the proposed formulation, the problem is defined using three kinds of criteria. The first is the cost to be minimized, which defines the objective function, the second are the decision options, determined by the admissible assignment classes or subsets of classes, and the third are the performance constraints. The optimal decision rule within the statistical decision theory framework is obtained by solving the stated optimization problem. Two examples are provided to illustrate the formulation and the decision rule is obtained.

  6. New Signal Readout Principle for Solid-Contact Ion-Selective Electrodes.

    PubMed

    Vanamo, Ulriika; Hupa, Elisa; Yrjänä, Ville; Bobacka, Johan

    2016-04-19

    A novel approach to signal transduction concerning solid-contact ion-selective electrodes (SC-ISE) with a conducting polymer (CP) as the solid contact is investigated. The method presented here is based on constant potential coulometry, where the potential of the SC-ISE vs the reference electrode is kept constant using a potentiostat. The change in the potential at the interface between the ion-selective membrane (ISM) and the sample solution, due to the change in the activity of the primary ion, is compensated with a corresponding but opposite change in the potential of the CP solid contact. This enforced change in the potential of the solid contact results in a transient reducing/oxidizing current flow through the SC-ISE. By measuring and integrating the current needed to transfer the CP to a new state of equilibrium, the total cumulated charge that is linearly proportional to the change of the logarithm of the primary ion activity is obtained. In this work, different thicknesses of poly(3,4-ethylenedioxythiophene) (PEDOT) doped with poly(styrenesulfonate) (PSS) were used as solid contact. Also, coated wire electrodes (CWEs) were included in the study to show the general validity of the new approach. The ISM employed was selective for K(+) ions, and the selectivity of the membrane under implementation of the presented transduction mechanism was confirmed by measurements performed with a constant background concentration of Na(+) ions. A unique feature of this signal readout principle is that it allows amplification of the analytical signal by increasing the capacitance (film thickness) of the solid contact of the SC-ISE.

  7. Optimum selection of mechanism type for heavy manipulators based on particle swarm optimization method

    NASA Astrophysics Data System (ADS)

    Zhao, Yong; Chen, Genliang; Wang, Hao; Lin, Zhongqin

    2013-07-01

    The mechanism type plays a decisive role in the mechanical performance of robotic manipulators. Feasible mechanism types can be obtained by applying appropriate type synthesis theory, but there is still a lack of effective and efficient methods for the optimum selection among different types of mechanism candidates. This paper presents a new strategy for the purpose of optimum mechanism type selection based on the modified particle swarm optimization method. The concept of sub-swarm is introduced to represent the different mechanisms generated by the type synthesis, and a competitive mechanism is employed between the sub-swarms to reassign their population size according to the relative performances of the mechanism candidates to implement the optimization. Combining with a modular modeling approach for fast calculation of the performance index of the potential candidates, the proposed method is applied to determine the optimum mechanism type among the potential candidates for the desired manipulator. The effectiveness and efficiency of the proposed method is demonstrated through a case study on the optimum selection of mechanism type of a heavy manipulator where six feasible candidates are considered with force capability as the specific performance index. The optimization result shows that the fitness of the optimum mechanism type for the considered heavy manipulator can be up to 0.578 5. This research provides the instruction in optimum selection of mechanism types for robotic manipulators.

  8. Velocity Selective Neural Signal Recording Using a Space-Time Electrode Array.

    PubMed

    Karimi, Fatemeh; Seydnejad, Saeid R

    2015-09-01

    Extracting the activity of a particular neural fiber from the extracellular recording of a peripheral nerve is quite important from different clinical perspectives. While traditional neural recording methods are unable to provide such granularity, new signal recording and processing techniques have offered promising solutions recently. A multi-electrode cuff in conjunction with a delay and sum beamforming structure has been used to detect the activity of different fibers in a nerve based on the propagation velocity of action potentials. However, as it is shown in this paper, simple delay and sum beamforming method encounters severe selectivity problem and signal distortion especially at higher velocities. In addition to scrutinizing the performance of the delay and sum beamformer and its inherent problems, here we propose a new beamforming method, based on broadband sensor array signal processing techniques, which exhibits much better selectivity with uniform frequency-velocity response. Our simulation results show how the new method can faithfully extract individual neural fiber activities and explore potential applications which could emerge from using such technique.

  9. PITPs as Targets for Selectively Interfering With Phosphoinositide Signaling in Cells

    PubMed Central

    Nile, Aaron H.; Tripathi, Ashutosh; Yuan, Peihua; Mousley, Carl J.; Suresh, Sundari; Wallace, Iain Michael; Shah, Sweety D.; Pohlhaus, Denise Teotico; Temple, Brenda; Nislow, Corey; Giaever, Guri; Tropsha, Alexander; Davis, Ronald W.; St Onge, Robert P.; Bankaitis, Vytas A.

    2013-01-01

    Sec14-like phosphatidylinositol transfer proteins (PITPs) integrate diverse territories of intracellular lipid metabolism with stimulated phosphatidylinositol-4-phosphate production, and are discriminating portals for interrogating phosphoinositide signaling. Yet, neither Sec14-like PITPs, nor PITPs in general, have been exploited as targets for chemical inhibition for such purposes. Herein, we validate the first small molecule inhibitors (SMIs) of the yeast PITP Sec14. These SMIs are nitrophenyl(4-(2-methoxyphenyl)piperazin-1-yl)methanones (NPPMs), and are effective inhibitors in vitro and in vivo. We further establish Sec14 is the sole essential NPPM target in yeast, that NPPMs exhibit exquisite targeting specificities for Sec14 (relative to related Sec14-like PITPs), propose a mechanism for how NPPMs exert their inhibitory effects, and demonstrate NPPMs exhibit exquisite pathway selectivity in inhibiting phosphoinositide signaling in cells. These data deliver proof-of-concept that PITP-directed SMIs offer new and generally applicable avenues for intervening with phosphoinositide signaling pathways with selectivities superior to those afforded by contemporary lipid kinase-directed strategies. PMID:24292071

  10. Tissue-Specific Gain of RTK Signalling Uncovers Selective Cell Vulnerability during Embryogenesis

    PubMed Central

    Audebert, Stéphane; Helmbacher, Françoise; Dono, Rosanna; Maina, Flavio

    2015-01-01

    The successive events that cells experience throughout development shape their intrinsic capacity to respond and integrate RTK inputs. Cellular responses to RTKs rely on different mechanisms of regulation that establish proper levels of RTK activation, define duration of RTK action, and exert quantitative/qualitative signalling outcomes. The extent to which cells are competent to deal with fluctuations in RTK signalling is incompletely understood. Here, we employ a genetic system to enhance RTK signalling in a tissue-specific manner. The chosen RTK is the hepatocyte growth factor (HGF) receptor Met, an appropriate model due to its pleiotropic requirement in distinct developmental events. Ubiquitously enhanced Met in Cre/loxP-based Rosa26 stopMet knock-in context (Del-R26 Met) reveals that most tissues are capable of buffering enhanced Met-RTK signalling thus avoiding perturbation of developmental programs. Nevertheless, this ubiquitous increase of Met does compromise selected programs such as myoblast migration. Using cell-type specific Cre drivers, we genetically showed that altered myoblast migration results from ectopic Met expression in limb mesenchyme rather than in migrating myoblasts themselves. qRT-PCR analyses show that ectopic Met in limbs causes molecular changes such as downregulation in the expression levels of Notum and Syndecan4, two known regulators of morphogen gradients. Molecular and functional studies revealed that ectopic Met expression in limb mesenchyme does not alter HGF expression patterns and levels, but impairs HGF bioavailability. Together, our findings show that myoblasts, in which Met is endogenously expressed, are capable of buffering increased RTK levels, and identify mesenchymal cells as a cell type vulnerable to ectopic Met-RTK signalling. These results illustrate that embryonic cells are sensitive to alterations in the spatial distribution of RTK action, yet resilient to fluctuations in signalling levels of an RTK when occurring

  11. Mitochondria-derived hydrogen peroxide selectively enhances T cell receptor-initiated signal transduction.

    PubMed

    Gill, Tejpal; Levine, Alan D

    2013-09-01

    T cell receptor (TCR)-initiated signal transduction is reported to increase production of intracellular reactive oxygen species, such as superoxide (O2˙(-)) and hydrogen peroxide (H2O2), as second messengers. Although H2O2 can modulate signal transduction by inactivating protein phosphatases, the mechanism and the subcellular localization of intracellular H2O2 as a second messenger of the TCR are not known. The antioxidant enzyme superoxide dismutase (SOD) catalyzes the dismutation of highly reactive O2˙(-) into H2O2 and thus acts as an intracellular generator of H2O2. As charged O2˙(-) is unable to diffuse through intracellular membranes, cells express distinct SOD isoforms in the cytosol (Cu,Zn-SOD) and mitochondria (Mn-SOD), where they locally scavenge O2˙(-) leading to production of H2O2. A 2-fold organelle-specific overexpression of either SOD in Jurkat T cell lines increases intracellular production of H2O2 but does not alter the levels of intracellular H2O2 scavenging enzymes such as catalase, membrane-bound peroxiredoxin1 (Prx1), and cytosolic Prx2. We report that overexpression of Mn-SOD enhances tyrosine phosphorylation of TCR-associated membrane proximal signal transduction molecules Lck, LAT, ZAP70, PLCγ1, and SLP76 within 1 min of TCR cross-linking. This increase in mitochondrial H2O2 specifically modulates MAPK signaling through the JNK/cJun pathway, whereas overexpressing Cu,Zn-SOD had no effect on any of these TCR-mediated signaling molecules. As mitochondria translocate to the immunological synapse during TCR activation, we hypothesize this translocation provides the effective concentration of H2O2 required to selectively modulate downstream signal transduction pathways.

  12. Analysis of the Impact of Variations on Signal Electro-Migration and Optimization of Interconnects in FinFET Designs.

    PubMed

    Ban, Yongchan

    2016-05-01

    The An AC current induced electro-migration (EM) on clock and logic signals becomes a significant problem even in the presence of reverse-recovery effect. Compared to power network, clock and logic signal interconnects are much narrower and suffer from fast switching and large driving current from FinFETs. Thus, the high current density on those signal interconnects can cause a serious failure. In this paper, we analyze EM on signal interconnects in 16 nm FinFET design, and characterize the impact of process variations, e.g., lithography and etch process, CMP (chemical-mechanical polishing) process, redundant via, etc. We also analyze signal-line EM with transistor-level PVT (process-voltage-temperature) variation corners. Then we optimize the signal lines with various design approaches to mitigate EM problem in 16 nm design.

  13. Analysis of the Impact of Variations on Signal Electro-Migration and Optimization of Interconnects in FinFET Designs.

    PubMed

    Ban, Yongchan

    2016-05-01

    The An AC current induced electro-migration (EM) on clock and logic signals becomes a significant problem even in the presence of reverse-recovery effect. Compared to power network, clock and logic signal interconnects are much narrower and suffer from fast switching and large driving current from FinFETs. Thus, the high current density on those signal interconnects can cause a serious failure. In this paper, we analyze EM on signal interconnects in 16 nm FinFET design, and characterize the impact of process variations, e.g., lithography and etch process, CMP (chemical-mechanical polishing) process, redundant via, etc. We also analyze signal-line EM with transistor-level PVT (process-voltage-temperature) variation corners. Then we optimize the signal lines with various design approaches to mitigate EM problem in 16 nm design. PMID:27483808

  14. Inhibition of mutant BRAF splice variant signaling by next-generation, selective RAF inhibitors.

    PubMed

    Basile, Kevin J; Le, Kaitlyn; Hartsough, Edward J; Aplin, Andrew E

    2014-05-01

    Vemurafenib and dabrafenib block MEK-ERK1/2 signaling and cause tumor regression in the majority of advanced-stage BRAF(V600E) melanoma patients; however, acquired resistance and paradoxical signaling have driven efforts for more potent and selective RAF inhibitors. Next-generation RAF inhibitors, such as PLX7904 (PB04), effectively inhibit RAF signaling in BRAF(V600E) melanoma cells without paradoxical effects in wild-type cells. Furthermore, PLX7904 blocks the growth of vemurafenib-resistant BRAF(V600E) cells that express mutant NRAS. Acquired resistance to vemurafenib and dabrafenib is also frequently driven by expression of mutation BRAF splice variants; thus, we tested the effects of PLX7904 and its clinical analog, PLX8394 (PB03), in BRAF(V600E) splice variant-mediated vemurafenib-resistant cells. We show that paradox-breaker RAF inhibitors potently block MEK-ERK1/2 signaling, G1/S cell cycle events, survival and growth of vemurafenib/PLX4720-resistant cells harboring distinct BRAF(V600E) splice variants. These data support the further investigation of paradox-breaker RAF inhibitors as a second-line treatment option for patients failing on vemurafenib or dabrafenib.

  15. An analysis of an optimal selection process for characteristics and technical performance of baseball pitchers.

    PubMed

    Lin, Wen-Bin; Tung, I-Wu; Chen, Mei-Jung; Chen, Mei-Yen

    2011-08-01

    Selection of a qualified pitcher has relied previously on qualitative indices; here, both quantitative and qualitative indices including pitching statistics, defense, mental skills, experience, and managers' recognition were collected, and an analytic hierarchy process was used to rank baseball pitchers. The participants were 8 experts who ranked characteristics and statistics of 15 baseball pitchers who comprised the first round of potential representatives for the Chinese Taipei National Baseball team. The results indicated a selection rate that was 91% consistent with the official national team roster, as 11 pitchers with the highest scores who were recommended as optimal choices to be official members of the Chinese Tai-pei National Baseball team actually participated in the 2009 Baseball World Cup. An analytic hierarchy can aid in selection of qualified pitchers, depending on situational and practical needs; the method could be extended to other sports and team-selection situations. PMID:21987928

  16. In Vitro Selection of Optimal DNA Substrates for Ligation by a Water-Soluble Carbodiimide

    NASA Technical Reports Server (NTRS)

    Harada, Kazuo; Orgel, Leslie E.

    1994-01-01

    We have used in vitro selection to investigate the sequence requirements for efficient template-directed ligation of oligonucleotides at 0 deg C using a water-soluble carbodiimide as condensing agent. We find that only 2 bp at each side of the ligation junction are needed. We also studied chemical ligation of substrate ensembles that we have previously selected as optimal by RNA ligase or by DNA ligase. As anticipated, we find that substrates selected with DNA ligase ligate efficiently with a chemical ligating agent, and vice versa. Substrates selected using RNA ligase are not ligated by the chemical condensing agent and vice versa. The implications of these results for prebiotic chemistry are discussed.

  17. An analysis of an optimal selection process for characteristics and technical performance of baseball pitchers.

    PubMed

    Lin, Wen-Bin; Tung, I-Wu; Chen, Mei-Jung; Chen, Mei-Yen

    2011-08-01

    Selection of a qualified pitcher has relied previously on qualitative indices; here, both quantitative and qualitative indices including pitching statistics, defense, mental skills, experience, and managers' recognition were collected, and an analytic hierarchy process was used to rank baseball pitchers. The participants were 8 experts who ranked characteristics and statistics of 15 baseball pitchers who comprised the first round of potential representatives for the Chinese Taipei National Baseball team. The results indicated a selection rate that was 91% consistent with the official national team roster, as 11 pitchers with the highest scores who were recommended as optimal choices to be official members of the Chinese Tai-pei National Baseball team actually participated in the 2009 Baseball World Cup. An analytic hierarchy can aid in selection of qualified pitchers, depending on situational and practical needs; the method could be extended to other sports and team-selection situations.

  18. Optoelectronic optimization of mode selective converter based on liquid crystal on silicon

    NASA Astrophysics Data System (ADS)

    Wang, Yongjiao; Liang, Lei; Yu, Dawei; Fu, Songnian

    2016-03-01

    We carry out comprehensive optoelectronic optimization of mode selective converter used for the mode division multiplexing, based on liquid crystal on silicon (LCOS) in binary mode. The conversion error of digital-to-analog (DAC) is investigated quantitatively for the purpose of driving the LCOS in the application of mode selective conversion. Results indicate the DAC must have a resolution of 8-bit, in order to achieve high mode extinction ratio (MER) of 28 dB. On the other hand, both the fast axis position error of half-wave-plate (HWP) and rotation angle error of Faraday rotator (FR) have negative influence on the performance of mode selective conversion. However, the commercial products provide enough angle error tolerance for the LCOS-based mode selective converter, taking both of insertion loss (IL) and MER into account.

  19. A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.

    PubMed

    Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

    2015-01-01

    This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme. PMID:26690571

  20. On the non-stationarity of financial time series: impact on optimal portfolio selection

    NASA Astrophysics Data System (ADS)

    Livan, Giacomo; Inoue, Jun-ichi; Scalas, Enrico

    2012-07-01

    We investigate the possible drawbacks of employing the standard Pearson estimator to measure correlation coefficients between financial stocks in the presence of non-stationary behavior, and we provide empirical evidence against the well-established common knowledge that using longer price time series provides better, more accurate, correlation estimates. Then, we investigate the possible consequences of instabilities in empirical correlation coefficient measurements on optimal portfolio selection. We rely on previously published works which provide a framework allowing us to take into account possible risk underestimations due to the non-optimality of the portfolio weights being used in order to distinguish such non-optimality effects from risk underestimations genuinely due to non-stationarities. We interpret such results in terms of instabilities in some spectral properties of portfolio correlation matrices.

  1. A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks

    PubMed Central

    Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

    2015-01-01

    This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme. PMID:26690571

  2. A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.

    PubMed

    Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

    2015-01-01

    This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.

  3. Gadolinium contrast agent selection and optimal use for body MR imaging.

    PubMed

    Guglielmo, Flavius F; Mitchell, Donald G; Gupta, Shiva

    2014-07-01

    Proper selection of a gadolinium-based contrast agent (GBCA) for body magnetic resonance imaging (MRI) cases requires understanding the indication for the MRI exam, the key features of the different GBCAs, and the effect that the GBCA has on the selected imaging protocol. The different categories of GBCAs require timing optimization on postcontrast sequences and adjusting imaging parameters to obtain the highest T1 contrast. Gadoxetate disodium has many advantages when evaluating liver lesions, although there are caveats and limitations that need to be understood. Gadobenate dimeglumine, a high-relaxivity GBCA, can be used for indications when stronger T1 relaxivity is needed.

  4. Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors.

    PubMed

    Miao, Jiacheng; Zhang, Tinglin; Wang, You; Li, Guang

    2015-07-03

    The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

  5. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy.

    PubMed

    Fu, Qinyi; Martin, Benjamin L; Matus, David Q; Gao, Liang

    2016-01-01

    Despite the progress made in selective plane illumination microscopy, high-resolution 3D live imaging of multicellular specimens remains challenging. Tiling light-sheet selective plane illumination microscopy (TLS-SPIM) with real-time light-sheet optimization was developed to respond to the challenge. It improves the 3D imaging ability of SPIM in resolving complex structures and optimizes SPIM live imaging performance by using a real-time adjustable tiling light sheet and creating a flexible compromise between spatial and temporal resolution. We demonstrate the 3D live imaging ability of TLS-SPIM by imaging cellular and subcellular behaviours in live C. elegans and zebrafish embryos, and show how TLS-SPIM can facilitate cell biology research in multicellular specimens by studying left-right symmetry breaking behaviour of C. elegans embryos. PMID:27004937

  6. Maximal area and conformal welding heuristics for optimal slice selection in splenic volume estimation

    NASA Astrophysics Data System (ADS)

    Gutenko, Ievgeniia; Peng, Hao; Gu, Xianfeng; Barish, Mathew; Kaufman, Arie

    2016-03-01

    Accurate estimation of splenic volume is crucial for the determination of disease progression and response to treatment for diseases that result in enlargement of the spleen. However, there is no consensus with respect to the use of single or multiple one-dimensional, or volumetric measurement. Existing methods for human reviewers focus on measurement of cross diameters on a representative axial slice and craniocaudal length of the organ. We propose two heuristics for the selection of the optimal axial plane for splenic volume estimation: the maximal area axial measurement heuristic and the novel conformal welding shape-based heuristic. We evaluate these heuristics on time-variant data derived from both healthy and sick subjects and contrast them to established heuristics. Under certain conditions our heuristics are superior to standard practice volumetric estimation methods. We conclude by providing guidance on selecting the optimal heuristic for splenic volume estimation.

  7. Techniques for optimal crop selection in a controlled ecological life support system

    NASA Technical Reports Server (NTRS)

    Mccormack, Ann; Finn, Cory; Dunsky, Betsy

    1993-01-01

    A Controlled Ecological Life Support System (CELSS) utilizes a plant's natural ability to regenerate air and water while being grown as a food source in a closed life support system. Current plant research is directed toward obtaining quantitative empirical data on the regenerative ability of each species of plant and the system volume and power requirements. Two techniques were adapted to optimize crop species selection while at the same time minimizing the system volume and power requirements. Each allows the level of life support supplied by the plants to be selected, as well as other system parameters. The first technique uses decision analysis in the form of a spreadsheet. The second method, which is used as a comparison with and validation of the first, utilizes standard design optimization techniques. Simple models of plant processes are used in the development of these methods.

  8. Techniques for optimal crop selection in a controlled ecological life support system

    NASA Technical Reports Server (NTRS)

    Mccormack, Ann; Finn, Cory; Dunsky, Betsy

    1992-01-01

    A Controlled Ecological Life Support System (CELSS) utilizes a plant's natural ability to regenerate air and water while being grown as a food source in a closed life support system. Current plant research is directed toward obtaining quantitative empirical data on the regenerative ability of each species of plant and the system volume and power requirements. Two techniques were adapted to optimize crop species selection while at the same time minimizing the system volume and power requirements. Each allows the level of life support supplied by the plants to be selected, as well as other system parameters. The first technique uses decision analysis in the form of a spreadsheet. The second method, which is used as a comparison with and validation of the first, utilizes standard design optimization techniques. Simple models of plant processes are used in the development of these methods.

  9. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy

    PubMed Central

    Fu, Qinyi; Martin, Benjamin L.; Matus, David Q.; Gao, Liang

    2016-01-01

    Despite the progress made in selective plane illumination microscopy, high-resolution 3D live imaging of multicellular specimens remains challenging. Tiling light-sheet selective plane illumination microscopy (TLS-SPIM) with real-time light-sheet optimization was developed to respond to the challenge. It improves the 3D imaging ability of SPIM in resolving complex structures and optimizes SPIM live imaging performance by using a real-time adjustable tiling light sheet and creating a flexible compromise between spatial and temporal resolution. We demonstrate the 3D live imaging ability of TLS-SPIM by imaging cellular and subcellular behaviours in live C. elegans and zebrafish embryos, and show how TLS-SPIM can facilitate cell biology research in multicellular specimens by studying left-right symmetry breaking behaviour of C. elegans embryos. PMID:27004937

  10. Selective detection of ordered sodium signals by a jump-and-return pulse sequence

    NASA Astrophysics Data System (ADS)

    Lee, Jae-Seung; Regatte, Ravinder R.; Jerschow, Alexej

    2009-09-01

    A simple pulse sequence, derived from the shaped pulse optimally exciting the central transition of a spin 3/2, can be used to selectively detect ordered sodium with a given quadrupolar coupling. The pulse sequence consists of two pulses with opposite phases and separated by a delay, called a quadrupolar jump-and-return (QJR) sequence. This QJR sequence is tested with a phantom made of sodium ions in bacteriophage and in aqueous solution and its feasibility for contrast modification based on the quadrupolar coupling is demonstrated.

  11. Rapid parameter optimization of low signal-to-noise samples in NMR spectroscopy using rapid CPMG pulsing during acquisition: application to recycle delays.

    PubMed

    Farooq, Hashim; Courtier-Murias, Denis; Soong, Ronald; Masoom, Hussain; Maas, Werner; Fey, Michael; Kumar, Rajeev; Monette, Martine; Stronks, Henry; Simpson, Myrna J; Simpson, André J

    2013-03-01

    A method is presented that combines Carr-Purcell-Meiboom-Gill (CPMG) during acquisition with either selective or nonselective excitation to produce a considerable intensity enhancement and a simultaneous loss in chemical shift information. A range of parameters can theoretically be optimized very rapidly on the basis of the signal from the entire sample (hard excitation) or spectral subregion (soft excitation) and should prove useful for biological, environmental, and polymer samples that often exhibit highly dispersed and broad spectral profiles. To demonstrate the concept, we focus on the application of our method to T(1) determination, specifically for the slowest relaxing components in a sample, which ultimately determines the optimal recycle delay in quantitative NMR. The traditional inversion recovery (IR) pulse program is combined with a CPMG sequence during acquisition. The slowest relaxing components are selected with a shaped pulse, and then, low-power CPMG echoes are applied during acquisition with intervals shorter than chemical shift evolution (RCPMG) thus producing a single peak with an SNR commensurate with the sum of the signal integrals in the selected region. A traditional (13)C IR experiment is compared with the selective (13)C IR-RCPMG sequence and yields the same T(1) values for samples of lysozyme and riverine dissolved organic matter within error. For lysozyme, the RCPMG approach is ~70 times faster, and in the case of dissolved organic matter is over 600 times faster. This approach can be adapted for the optimization of a host of parameters where chemical shift information is not necessary, such as cross-polarization/mixing times and pulse lengths.

  12. Ant-cuckoo colony optimization for feature selection in digital mammogram.

    PubMed

    Jona, J B; Nagaveni, N

    2014-01-15

    Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques. PMID:24783812

  13. Ant-cuckoo colony optimization for feature selection in digital mammogram.

    PubMed

    Jona, J B; Nagaveni, N

    2014-01-15

    Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques.

  14. Metal-binding sites are designed to achieve optimal mechanical and signaling properties.

    PubMed

    Dutta, Anindita; Bahar, Ivet

    2010-09-01

    Many proteins require bound metals to achieve their function. We take advantage of increasing structural data on metal-binding proteins to elucidate three properties: the involvement of metal-binding sites in the global dynamics of the protein, predicted by elastic network models, their exposure/burial to solvent, and their signal-processing properties indicated by Markovian stochastics analysis. Systematic analysis of a data set of 145 structures reveals that the residues that coordinate metal ions enjoy remarkably efficient and precise signal transduction properties. These properties are rationalized in terms of their physical properties: participation in hinge sites that control the softest modes collectively accessible to the protein and occupancy of central positions minimally exposed to solvent. Our observations suggest that metal-binding sites may have been evolutionary selected to achieve optimum allosteric communication. They also provide insights into basic principles for designing metal-binding sites, which are verified to be met by recently designed de novo metal-binding proteins.

  15. Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Armstrong, Jeffrey B.; Garg, Sanjay

    2012-01-01

    An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specific-ally addresses the under-determined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy.

  16. Enhancing Signal Output and Avoiding BOD/Toxicity Combined Shock Interference by Operating a Microbial Fuel Cell Sensor with an Optimized Background Concentration of Organic Matter

    PubMed Central

    Jiang, Yong; Liang, Peng; Liu, Panpan; Bian, Yanhong; Miao, Bo; Sun, Xueliang; Zhang, Helan; Huang, Xia

    2016-01-01

    In the monitoring of pollutants in an aquatic environment, it is important to preserve water quality safety. Among the available analysis methods, the microbial fuel cell (MFC) sensor has recently been used as a sustainable and on-line electrochemical microbial biosensor for biochemical oxygen demand (BOD) and toxicity, respectively. However, the effect of the background organic matter concentration on toxicity monitoring when using an MFC sensor is not clear and there is no effective strategy available to avoid the signal interference by the combined shock of BOD and toxicity. Thus, the signal interference by the combined shock of BOD and toxicity was systematically studied in this experiment. The background organic matter concentration was optimized in this study and it should be fixed at a high level of oversaturation for maximizing the signal output when the current change (ΔI) is selected to correlate with the concentration of a toxic agent. When the inhibition ratio (IR) is selected, on the other hand, it should be fixed as low as possible near the detection limit for maximizing the signal output. At least two MFC sensors operated with high and low organic matter concentrations and a response chart generated from pre-experiment data were both required to make qualitative distinctions of the four types of combined shock caused by a sudden change in BOD and toxicity. PMID:27563887

  17. Enhancing Signal Output and Avoiding BOD/Toxicity Combined Shock Interference by Operating a Microbial Fuel Cell Sensor with an Optimized Background Concentration of Organic Matter.

    PubMed

    Jiang, Yong; Liang, Peng; Liu, Panpan; Bian, Yanhong; Miao, Bo; Sun, Xueliang; Zhang, Helan; Huang, Xia

    2016-01-01

    In the monitoring of pollutants in an aquatic environment, it is important to preserve water quality safety. Among the available analysis methods, the microbial fuel cell (MFC) sensor has recently been used as a sustainable and on-line electrochemical microbial biosensor for biochemical oxygen demand (BOD) and toxicity, respectively. However, the effect of the background organic matter concentration on toxicity monitoring when using an MFC sensor is not clear and there is no effective strategy available to avoid the signal interference by the combined shock of BOD and toxicity. Thus, the signal interference by the combined shock of BOD and toxicity was systematically studied in this experiment. The background organic matter concentration was optimized in this study and it should be fixed at a high level of oversaturation for maximizing the signal output when the current change (ΔI) is selected to correlate with the concentration of a toxic agent. When the inhibition ratio (IR) is selected, on the other hand, it should be fixed as low as possible near the detection limit for maximizing the signal output. At least two MFC sensors operated with high and low organic matter concentrations and a response chart generated from pre-experiment data were both required to make qualitative distinctions of the four types of combined shock caused by a sudden change in BOD and toxicity.

  18. Enhancing Signal Output and Avoiding BOD/Toxicity Combined Shock Interference by Operating a Microbial Fuel Cell Sensor with an Optimized Background Concentration of Organic Matter.

    PubMed

    Jiang, Yong; Liang, Peng; Liu, Panpan; Bian, Yanhong; Miao, Bo; Sun, Xueliang; Zhang, Helan; Huang, Xia

    2016-01-01

    In the monitoring of pollutants in an aquatic environment, it is important to preserve water quality safety. Among the available analysis methods, the microbial fuel cell (MFC) sensor has recently been used as a sustainable and on-line electrochemical microbial biosensor for biochemical oxygen demand (BOD) and toxicity, respectively. However, the effect of the background organic matter concentration on toxicity monitoring when using an MFC sensor is not clear and there is no effective strategy available to avoid the signal interference by the combined shock of BOD and toxicity. Thus, the signal interference by the combined shock of BOD and toxicity was systematically studied in this experiment. The background organic matter concentration was optimized in this study and it should be fixed at a high level of oversaturation for maximizing the signal output when the current change (ΔI) is selected to correlate with the concentration of a toxic agent. When the inhibition ratio (IR) is selected, on the other hand, it should be fixed as low as possible near the detection limit for maximizing the signal output. At least two MFC sensors operated with high and low organic matter concentrations and a response chart generated from pre-experiment data were both required to make qualitative distinctions of the four types of combined shock caused by a sudden change in BOD and toxicity. PMID:27563887

  19. Structural basis and selectivity of tankyrase inhibition by a Wnt signaling inhibitor WIKI4.

    PubMed

    Haikarainen, Teemu; Venkannagari, Harikanth; Narwal, Mohit; Obaji, Ezeogo; Lee, Hao-Wei; Nkizinkiko, Yves; Lehtiö, Lari

    2013-01-01

    Recently a novel inhibitor of Wnt signaling was discovered. The compound, WIKI4, was found to act through tankyrase inhibition and regulate β-catenin levels in many cancer cell lines and human embryonic stem cells. Here we confirm that WIKI4 is a high potency tankyrase inhibitor and that it selectively inhibits tankyrases over other ARTD enzymes tested. The binding mode of the compound to tankyrase 2 was determined by protein X-ray crystallography to 2.4 Å resolution. The structure revealed a novel binding mode to the adenosine subsite of the donor NAD(+) binding groove of the catalytic domain. Our results form a structural basis for further development of potent and selective tankyrase inhibitors based on the WIKI4 scaffold. PMID:23762361

  20. Structural Basis and Selectivity of Tankyrase Inhibition by a Wnt Signaling Inhibitor WIKI4

    PubMed Central

    Haikarainen, Teemu; Venkannagari, Harikanth; Narwal, Mohit; Obaji, Ezeogo; Lee, Hao-Wei; Nkizinkiko, Yves; Lehtiö, Lari

    2013-01-01

    Recently a novel inhibitor of Wnt signaling was discovered. The compound, WIKI4, was found to act through tankyrase inhibition and regulate β-catenin levels in many cancer cell lines and human embryonic stem cells. Here we confirm that WIKI4 is a high potency tankyrase inhibitor and that it selectively inhibits tankyrases over other ARTD enzymes tested. The binding mode of the compound to tankyrase 2 was determined by protein X-ray crystallography to 2.4 Å resolution. The structure revealed a novel binding mode to the adenosine subsite of the donor NAD+ binding groove of the catalytic domain. Our results form a structural basis for further development of potent and selective tankyrase inhibitors based on the WIKI4 scaffold. PMID:23762361

  1. Development of response activation and inhibition in a selective stop-signal task.

    PubMed

    van de Laar, Maria C; van den Wildenberg, Wery P M; van Boxtel, Geert J M; van der Molen, Maurits W

    2014-10-01

    To gain more insight into the development of action control, the current brain potential study examined response selection, activation, and selective inhibition during choice- and stop-signal processing in three age groups (8-, 12-, and 21-year-olds). Results revealed that age groups differed in the implementation of proactive control; children slowed their go response and showed reduced cortical motor output compared to adults. On failed inhibition trials, children were less able than adults to suppress muscle output resulting in increased partial-inhibition rates. On invalid stop trials, all age groups initially activated, subsequently inhibited, and then reactivated the go response. Yet, children were less efficient in implementing this strategy. Then, older children recruit motor responses to a greater extent than younger children and adults, which reduced the efficiency of implementing response inhibition and proactive control. The results are discussed in relation to current notions of developmental change in proactive and reactive action control. PMID:25014630

  2. Statistically Optimal Approximations of Astronomical Signals: Implications to Classification and Advanced Study of Variable Stars

    NASA Astrophysics Data System (ADS)

    Andronov, I. L.; Chinarova, L. L.; Kudashkina, L. S.; Marsakova, V. I.; Tkachenko, M. G.

    2016-06-01

    We have elaborated a set of new algorithms and programs for advanced time series analysis of (generally) multi-component multi-channel observations with irregularly spaced times of observations, which is a common case for large photometric surveys. Previous self-review on these methods for periodogram, scalegram, wavelet, autocorrelation analysis as well as on "running" or "sub-interval" local approximations were self-reviewed in (2003ASPC..292..391A). For an approximation of the phase light curves of nearly-periodic pulsating stars, we use a Trigonometric Polynomial (TP) fit of the statistically optimal degree and initial period improvement using differential corrections (1994OAP.....7...49A). For the determination of parameters of "characteristic points" (minima, maxima, crossings of some constant value etc.) we use a set of methods self-reviewed in 2005ASPC..335...37A, Results of the analysis of the catalogs compiled using these programs are presented in 2014AASP....4....3A. For more complicated signals, we use "phenomenological approximations" with "special shapes" based on functions defined on sub-intervals rather on the complete interval. E. g. for the Algol-type stars we developed the NAV ("New Algol Variable") algorithm (2012Ap.....55..536A, 2012arXiv1212.6707A, 2015JASS...32..127A), which was compared to common methods of Trigonometric Polynomial Fit (TP) or local Algebraic Polynomial (A) fit of a fixed or (alternately) statistically optimal degree. The method allows determine the minimal set of parameters required for the "General Catalogue of Variable Stars", as well as an extended set of phenomenological and astrophysical parameters which may be used for the classification. Totally more that 1900 variable stars were studied in our group using these methods in a frame of the "Inter-Longitude Astronomy" campaign (2010OAP....23....8A) and the "Ukrainian Virtual Observatory" project (2012KPCB...28...85V).

  3. A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

    NASA Astrophysics Data System (ADS)

    Maringanti, C.; Chaubey, I.; Arabi, M.; Engel, B.

    2008-07-01

    Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs), such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solution that satisfies the given watershed management objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP selection and placement problem. Most optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC) watersheds. However, most previous works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. In this study, the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II). The limitation with the dynamic linkage with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine concentration reduction

  4. Structural Modifications of (Z)-3-(2-aminoethyl)-5-(4-ethoxybenzylidene)thiazolidine-2,4-dione that Improve Selectivity for the Inhibition of Melanoma Cells Containing Active ERK Signaling

    PubMed Central

    Jung, Kwan-Young; Samadani, Ramin; Chauhan, Jay; Nevels, Kerrick; Yap, Jeremy L.; Zhang, Jun; Worlikar, Shilpa; Lanning, Maryanna E.; Chen, Lijia; Ensey, Mary; Shukla, Sagar; Salmo, Rosene; Heinzl, Geoffrey; Gordon, Caryn; Dukes, Troy; MacKerell, Alexander D.; Shapiro, Paul; Fletcher, Steven

    2013-01-01

    Towards the development of potent and selective inhibitors of melanoma cells containing active ERK signaling, we herein report on the pharmacophore determination and optimization of the ERK docking domain inhibitor (Z)-3-(2-aminoethyl)-5-(4-ethoxybenzylidene)thiazolidine-2,4-dione. PMID:23624850

  5. Optimal part and module selection for synthetic gene circuit design automation.

    PubMed

    Huynh, Linh; Tagkopoulos, Ilias

    2014-08-15

    An integral challenge in synthetic circuit design is the selection of optimal parts to populate a given circuit topology, so that the resulting circuit behavior best approximates the desired one. In some cases, it is also possible to reuse multipart constructs or modules that have been already built and experimentally characterized. Efficient part and module selection algorithms are essential to systematically search the solution space, and their significance will only increase in the following years due to the projected explosion in part libraries and circuit complexity. Here, we address this problem by introducing a structured abstraction methodology and a dynamic programming-based algorithm that guaranties optimal part selection. In addition, we provide three extensions that are based on symmetry check, information look-ahead and branch-and-bound techniques, to reduce the running time and space requirements. We have evaluated the proposed methodology with a benchmark of 11 circuits, a database of 73 parts and 304 experimentally constructed modules with encouraging results. This work represents a fundamental departure from traditional heuristic-based methods for part and module selection and is a step toward maximizing efficiency in synthetic circuit design and construction.

  6. Optimal part and module selection for synthetic gene circuit design automation.

    PubMed

    Huynh, Linh; Tagkopoulos, Ilias

    2014-08-15

    An integral challenge in synthetic circuit design is the selection of optimal parts to populate a given circuit topology, so that the resulting circuit behavior best approximates the desired one. In some cases, it is also possible to reuse multipart constructs or modules that have been already built and experimentally characterized. Efficient part and module selection algorithms are essential to systematically search the solution space, and their significance will only increase in the following years due to the projected explosion in part libraries and circuit complexity. Here, we address this problem by introducing a structured abstraction methodology and a dynamic programming-based algorithm that guaranties optimal part selection. In addition, we provide three extensions that are based on symmetry check, information look-ahead and branch-and-bound techniques, to reduce the running time and space requirements. We have evaluated the proposed methodology with a benchmark of 11 circuits, a database of 73 parts and 304 experimentally constructed modules with encouraging results. This work represents a fundamental departure from traditional heuristic-based methods for part and module selection and is a step toward maximizing efficiency in synthetic circuit design and construction. PMID:24933033

  7. Automatic selection of optimal segmentation scales for high-resolution remote sensing images

    NASA Astrophysics Data System (ADS)

    Yin, Ruijuan; Shi, Runhe; Gao, Wei

    2013-09-01

    To extract information from high resolution images is a challenge work.Compared tothe traditional pixel-based approach, the advantages of object-oriented classification methods are well documented. However, the appropriate scale parametersofthese methods are difficult to be determined, andthe choices of scale parametersareof high importance, whichwill havea strong effect on the segmentation effectiveness. Whereas the evaluations of the quality of a segmentation method are still mainly based onsubjective judgment, which is a complicated process and lacksstability and reliability. Thus, an objective and unsupervised method needs to beestablished for selecting suitable parameters for a multi-scale segmentation to ensure the bestresults. In this work, a novicemethod is introduced to choose the optimal parameter for themulti-scale segmentation. For large information in band itself and weak relationship among multispectral bands, valuable bands should be selected from original data and weighed by the degreeofcorrelation. Then thresholds of all 3 selected bands ranging from 20 to 200 (intervals of 10)are created in Definiens Professional 8.7. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighborhood. Therefore, the global intra-segment and inter-segment heterogeneity indexes are taken into account to identify the optimal segmentation scale. Finally, cubic spline interpolation is applied to select the optimalsegmentation scale. As a result, the measure combining a spatial autocorrelation indicator and a variance indicator shows that the method can improve the precision in global segmentation.

  8. Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

    NASA Astrophysics Data System (ADS)

    Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

    2010-10-01

    To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.

  9. New approach for automatic recognition of melanoma in profilometry: optimized feature selection using genetic algorithms

    NASA Astrophysics Data System (ADS)

    Handels, Heinz; Ross, Th; Kreusch, J.; Wolff, H. H.; Poeppl, S. J.

    1998-06-01

    A new approach to computer supported recognition of melanoma and naevocytic naevi based on high resolution skin surface profiles is presented. Profiles are generated by sampling an area of 4 X 4 mm2 at a resolution of 125 sample points per mm with a laser profilometer at a vertical resolution of 0.1 micrometers . With image analysis algorithms Haralick's texture parameters, Fourier features and features based on fractal analysis are extracted. In order to improve classification performance, a subsequent feature selection process is applied to determine the best possible subset of features. Genetic algorithms are optimized for the feature selection process, and results of different approaches are compared. As quality measure for feature subsets, the error rate of the nearest neighbor classifier estimated with the leaving-one-out method is used. In comparison to heuristic strategies and greedy algorithms, genetic algorithms show the best results for the feature selection problem. After feature selection, several architectures of feed forward neural networks with error back-propagation are evaluated. Classification performance of the neural classifier is optimized using different topologies, learning parameters and pruning algorithms. The best neural classifier achieved an error rate of 4.5% and was found after network pruning. The best result in all with an error rate of 2.3% was obtained with the nearest neighbor classifier.

  10. Rotating disk potentiometry for inner solution optimization of low-detection-limit ion-selective electrodes.

    PubMed

    Radu, Aleksandar; Telting-Diaz, Martin; Bakker, Eric

    2003-12-15

    The extent of optimization of the lower detection limit of ion-selective electrodes (ISEs) can be assessed with an elegant new method. At the detection limit (i.e., in the absence of primary ions in the sample), one can observe a reproducible change in the membrane potential upon alteration of the aqueous diffusion layer thickness. This stir effect is predicted to depend on the composition of the inner solution, which is known to influence the lower detection limit of the potentiometric sensor dramatically. For an optimized electrode, the stir effect is calculated to be exactly one-half the value of the case when substantial coextraction occurs at the inner membrane side. In contrast, there is no stir effect when substantial ion exchange occurs at the inner membrane side. Consequently, this experimental method can be used to determine how well the inner filling solution has been optimized. A rotating disk electrode was used in this study because it provides adequate control of the aqueous diffusion layer thickness. Various ion-selective membranes with a variety of inner solutions that gave different calculated concentrations of the complex at the inner membrane side were studied to evaluate this principle. They contained the well-examined silver ionophore O,O' '-bis[2-(methylthio)ethyl]-tert-butylcalix[4]arene, the potassium ionophore valinomycin, or the iodide carrier [9]mercuracarborand-3. Stir effects were determined in different background solutions and compared to theoretical expectations. Correlations were good, and the results encourage the use of such stir-effect measurements to optimize ISE compositions for real-world applications. The technique was also found to be useful in estimating the level of primary ion impurities in the sample. For an iodide-selective electrode measured in phosphoric acid, for example, apparent iodide impurity levels were calculated as 5 x 10(-10) M.

  11. An electrostatic selection mechanism controls sequential kinase signaling downstream of the T cell receptor

    PubMed Central

    Shah, Neel H; Wang, Qi; Yan, Qingrong; Karandur, Deepti; Kadlecek, Theresa A; Fallahee, Ian R; Russ, William P; Ranganathan, Rama; Weiss, Arthur; Kuriyan, John

    2016-01-01

    The sequence of events that initiates T cell signaling is dictated by the specificities and order of activation of the tyrosine kinases that signal downstream of the T cell receptor. Using a platform that combines exhaustive point-mutagenesis of peptide substrates, bacterial surface-display, cell sorting, and deep sequencing, we have defined the specificities of the first two kinases in this pathway, Lck and ZAP-70, for the T cell receptor ζ chain and the scaffold proteins LAT and SLP-76. We find that ZAP-70 selects its substrates by utilizing an electrostatic mechanism that excludes substrates with positively-charged residues and favors LAT and SLP-76 phosphosites that are surrounded by negatively-charged residues. This mechanism prevents ZAP-70 from phosphorylating its own activation loop, thereby enforcing its strict dependence on Lck for activation. The sequence features in ZAP-70, LAT, and SLP-76 that underlie electrostatic selectivity likely contribute to the specific response of T cells to foreign antigens. DOI: http://dx.doi.org/10.7554/eLife.20105.001 PMID:27700984

  12. The Activity of Surface Electromyographic Signal of Selected Muscles during Classic Rehabilitation Exercise.

    PubMed

    Xiao, Jinzhuang; Sun, Jinli; Gao, Junmin; Wang, Hongrui; Yang, Xincai

    2016-01-01

    Objectives. Prone bridge, unilateral bridge, supine bridge, and bird-dog are classic rehabilitation exercises, which have been advocated as effective ways to improve core stability among healthy individuals and patients with low back pain. The aim of this study was to investigate the activity of seven selected muscles during rehabilitation exercises through the signal of surface electromyographic. Approaches. We measured the surface electromyographic signals of four lower limb muscles, two abdominal muscles, and one back muscle during rehabilitation exercises of 30 healthy students and then analyzed its activity level using the median frequency method. Results. Different levels of muscle activity during the four rehabilitation exercises were observed. The prone bridge and unilateral bridge caused the greatest muscle fatigue; however, the supine bridge generated the lowest muscle activity. There was no significant difference (P > 0.05) between left and right body side muscles in the median frequency slope during the four rehabilitation exercises of seven muscles. Conclusions. The prone bridge can affect the low back and lower limb muscles of most people. The unilateral bridge was found to stimulate muscles much more active than the supine bridge. The bird-dog does not cause much fatigue to muscles but can make most selected muscles active. PMID:27195151

  13. Selection of optimal artificial boundary condition (ABC) frequencies for structural damage identification

    NASA Astrophysics Data System (ADS)

    Mao, Lei; Lu, Yong

    2016-07-01

    In this paper, the sensitivities of artificial boundary condition (ABC) frequencies to the damages are investigated, and the optimal sensors are selected to provide the reliable structural damage identification. The sensitivity expressions for one-pin and two-pin ABC frequencies, which are the natural frequencies from structures with one and two additional constraints to its original boundary condition, respectively, are proposed. Based on the expressions, the contributions of the underlying mode shapes in the ABC frequencies can be calculated and used to select more sensitive ABC frequencies. Selection criteria are then defined for different conditions, and their performance in structural damage identification is examined with numerical studies. From the findings, conclusions are given.

  14. Regulated ADAM17-dependent EGF family ligand release by substrate-selecting signaling pathways.

    PubMed

    Dang, Michelle; Armbruster, Nicole; Miller, Miles A; Cermeno, Efrain; Hartmann, Monika; Bell, George W; Root, David E; Lauffenburger, Douglas A; Lodish, Harvey F; Herrlich, Andreas

    2013-06-11

    Ectodomain cleavage of cell-surface proteins by A disintegrin and metalloproteinases (ADAMs) is highly regulated, and its dysregulation has been linked to many diseases. ADAM10 and ADAM17 cleave most disease-relevant substrates. Broad-spectrum metalloprotease inhibitors have failed clinically, and targeting the cleavage of a specific substrate has remained impossible. It is therefore necessary to identify signaling intermediates that determine substrate specificity of cleavage. We show here that phorbol ester or angiotensin II-induced proteolytic release of EGF family members may not require a significant increase in ADAM17 protease activity. Rather, inducers activate a signaling pathway using PKC-α and the PKC-regulated protein phosphatase 1 inhibitor 14D that is required for ADAM17 cleavage of TGF-α, heparin-binding EGF, and amphiregulin. A second pathway involving PKC-δ is required for neuregulin (NRG) cleavage, and, indeed, PKC-δ phosphorylation of serine 286 in the NRG cytosolic domain is essential for induced NRG cleavage. Thus, signaling-mediated substrate selection is clearly distinct from regulation of enzyme activity, an important mechanism that offers itself for application in disease.

  15. Receiver discriminability drives the evolution of complex sexual signals by sexual selection.

    PubMed

    Cui, Jianguo; Song, Xiaowei; Zhu, Bicheng; Fang, Guangzhan; Tang, Yezhong; Ryan, Michael J

    2016-04-01

    A hallmark of sexual selection by mate choice is the evolution of exaggerated traits, such as longer tails in birds and more acoustic components in the calls of birds and frogs. Trait elaboration can be opposed by costs such as increased metabolism and greater predation risk, but cognitive processes of the receiver can also put a brake on trait elaboration. For example, according to Weber's Law traits of a fixed absolute difference will be more difficult to discriminate as the absolute magnitude increases. Here, we show that in the Emei music frog (Babina daunchina) increases in the fundamental frequency between successive notes in the male advertisement call, which increases the spectral complexity of the call, facilitates the female's ability to compare the number of notes between calls. These results suggest that female's discriminability provides the impetus to switch from enhancement of signaling magnitude (i.e., adding more notes into calls) to employing a new signal feature (i.e., increasing frequency among notes) to increase complexity. We suggest that increasing the spectral complexity of notes ameliorates some of the effects of Weber's Law, and highlights how perceptual and cognitive biases of choosers can have important influences on the evolution of courtship signals. PMID:26920078

  16. Belief about nicotine selectively modulates value and reward prediction error signals in smokers

    PubMed Central

    Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro; Baldwin, Philip R.; Soltani, Alireza; Kirk, Ulrich; Cinciripini, Paul M.; Montague, P. Read

    2015-01-01

    Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers’ prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers’ beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of “no nicotine in cigarette” (compared with “nicotine in cigarette”) strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers’ choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems. PMID:25605923

  17. EEG signals classification of epileptic patients via feature selection and voting criteria in intelligent method.

    PubMed

    Ghaffari, Ali; Ebrahimi Orimi, H

    2014-04-01

    Epileptic disease can be diagnosed by using intelligent methods on the Electroencephalograph (EEG) signals. In this paper, wavelet packet transform (WPT) was used in each of the frequency bands and wavelet coefficients were obtained, then the energy and entropy function was done on the wavelet coefficients and used as initial feature vectors. In the next step, eight and 15 features from 30 initial energy and entropy features were selected as the final features because their receiver operating characteristic (ROC) curve areas were higher than others. There were seven classifier inputs. These seven classifiers consisted of four artificial neural networks (ANN) with different structures, support vector machines (SVM), K-nearest neighbours (KNN) and a hybrid network. Each classifier was trained by 0.5, 0.8 and 0.9 EEG signals. After the training process, a fusion network based on a voting criteria was used to make the algorithm robust against the possible changes in each classifier and increase the classification accuracy. Finally, the algorithm was tested by other EEG signals. As a result, normal and epileptic classes were detected with total classification accuracy of 99-100%.

  18. Audio signal reconstruction based on adaptively selected seed points from laser speckle images

    NASA Astrophysics Data System (ADS)

    Chen, Ziyi; Wang, Cheng; Huang, Chaohong; Fu, Hongyan; Luo, Huan; Wang, Hanyun

    2014-11-01

    Speckle patterns, present in the laser reflection from an object, reflect the micro-structure of the object where the laser is illuminated on. When the object surface vibrates, the speckle patterns move accordingly, and this movement can be recovered with a high-speed camera system. Due to the low signal to noise ratio (SNR), it is a challenging task to recover the micro-vibration information and reconstruct the audio signal from the captured speckle image sequences fast and effectively. In this paper, we propose a novel method based on pixels' gray value variations in laser speckle patterns to work out with the challenging task. The major contribution of the proposed method relies on using the intensity variations of the adaptively selected seed points to recover the vibration information and the audio signal with a novel model that effectively fuses the multiple seed points' information together. Experiments show that our method not only recovers the vibration information with high quality but is also robust and runs fast. The SNR of the experimental results reach about 20 dB and 10 dB at the detection distances of 10 m and 50 m, respectively.

  19. Selection of Antibodies Interfering with Cell Surface Receptor Signaling Using Embryonic Stem Cell Differentiation.

    PubMed

    Melidoni, Anna N; Dyson, Michael R; McCafferty, John

    2016-01-01

    Antibodies able to bind and modify the function of cell surface signaling components in vivo are increasingly being used as therapeutic drugs. The identification of such "functional" antibodies from within large antibody pools is, therefore, the subject of intense research. Here we describe a novel cell-based expression and reporting system for the identification of functional antibodies from antigen-binding populations preselected with phage display. The system involves inducible expression of the antibody gene population from the Rosa-26 locus of embryonic stem (ES) cells, followed by secretion of the antibodies during ES cell differentiation. Target antigens are cell-surface signaling components (receptors or ligands) with a known effect on the direction of cell differentiation (FGFR1 mediating ES cell exit from self renewal in this particular protocol). Therefore, inhibition or activation of these components by functional antibodies in a few elite clones causes a shift in the differentiation outcomes of these clones, leading to their phenotypic selection. Functional antibody genes are then recovered from positive clones and used to produce the purified antibodies, which can be tested for their ability to affect cell fates exogenously. Identified functional antibody genes can be further introduced in different stem cell types. Inducible expression of functional antibodies has a temporally controlled protein-knockdown capability, which can be used to study the unknown role of the signaling pathway in different developmental contexts. Moreover, it provides a means for control of stem cell differentiation with potential in vivo applications.

  20. Belief about nicotine selectively modulates value and reward prediction error signals in smokers.

    PubMed

    Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro; Baldwin, Philip R; Soltani, Alireza; Kirk, Ulrich; Cinciripini, Paul M; Montague, P Read

    2015-02-24

    Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers' prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers' beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of "no nicotine in cigarette" (compared with "nicotine in cigarette") strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers' choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems.

  1. Optimal Sensor Placement for Modal Parameter Identification Using Signal Subspace Correlation Techniques

    NASA Astrophysics Data System (ADS)

    Cherng, An-Pan

    2003-03-01

    Placing vibration sensors at appropriate locations plays an important role in experimental modal analysis. It is known that maximising the determinant of Fisher information matrix (FIM) can result in an optimal configuration of sensors from a set of candidate locations. Some methods have already been proposed in the literature, such as maximising the determinant of the diagonal elements of mode shape correlation matrix, ranking the sensor contributions by Hankel singular values (HSVs), and using perturbation theory to achieve minimum variance of estimation, etc. The objectives of this work were to systematically analyse existing methods and to propose methods that either improve their performance or accelerate the searching process for modal parameter identification. The approach used in this article is based on the analytical formulation of singular value decomposition (SVD) for a candidate-blocked Hankel matrix using signal subspace correlation (SSC) techniques developed earlier by the author. The SSC accounts for factors that contribute to the estimated results, such as mode shapes, damping ratios, sampling rate and matrix size (or number of data used). With the aid of SSC, it will be shown that using information of mode shapes and that of singular values are equivalent under certain conditions. The results of this work are not only consistent with those of existing methods, but also demonstrate a more general viewpoint to the optimisation problem. Consequently, the insight of the sensor placement problem is clearly interpreted. Finally, two modified methods that inherit the merits of existing methods are proposed, and their effectiveness is demonstrated by numerical examples.

  2. Randomly organized lipids and marginally stable proteins: a coupling of weak interactions to optimize membrane signaling.

    PubMed

    Rice, Anne M; Mahling, Ryan; Fealey, Michael E; Rannikko, Anika; Dunleavy, Katie; Hendrickson, Troy; Lohese, K Jean; Kruggel, Spencer; Heiling, Hillary; Harren, Daniel; Sutton, R Bryan; Pastor, John; Hinderliter, Anne

    2014-09-01

    Eukaryotic lipids in a bilayer are dominated by weak cooperative interactions. These interactions impart highly dynamic and pliable properties to the membrane. C2 domain-containing proteins in the membrane also interact weakly and cooperatively giving rise to a high degree of conformational plasticity. We propose that this feature of weak energetics and plasticity shared by lipids and C2 domain-containing proteins enhance a cell's ability to transduce information across the membrane. We explored this hypothesis using information theory to assess the information storage capacity of model and mast cell membranes, as well as differential scanning calorimetry, carboxyfluorescein release assays, and tryptophan fluorescence to assess protein and membrane stability. The distribution of lipids in mast cell membranes encoded 5.6-5.8bits of information. More information resided in the acyl chains than the head groups and in the inner leaflet of the plasma membrane than the outer leaflet. When the lipid composition and information content of model membranes were varied, the associated C2 domains underwent large changes in stability and denaturation profile. The C2 domain-containing proteins are therefore acutely sensitive to the composition and information content of their associated lipids. Together, these findings suggest that the maximum flow of signaling information through the membrane and into the cell is optimized by the cooperation of near-random distributions of membrane lipids and proteins. This article is part of a Special Issue entitled: Interfacially Active Peptides and Proteins. Guest Editors: William C. Wimley and Kalina Hristova.

  3. Quorum Sensing Signal Synthesis May Represent a Selective Advantage Independent of Its Role in Regulation of Bioluminescence in Vibrio fischeri

    PubMed Central

    Chong, Grace; Kimyon, Önder; Manefield, Mike

    2013-01-01

    The evolution of biological signalling systems and apparently altruistic or cooperative traits in diverse organisms has required selection against the subversive tendencies of self-interested biological entities. The bacterial signalling and response system known as quorum sensing or Acylated Homoserine Lactone (AHL) mediated gene expression is thought to have evolved through kin selection. In this in vitro study on the model quorum sensing bioluminescent marine symbiont Vibrio fischeri, competition and long-term sub culturing experiments suggest that selection for AHL synthesis (encoded by the AHL synthase gene luxI) is independent of the quorum sensing regulated phenotype (bioluminescence encoded by luxCDABE). Whilst results support the hypothesis that signal response (AHL binding and transcriptional activation encoded by the luxR gene) is maintained through indirect fitness benefits (kin selection), signal synthesis is maintained in the V. fischeri genome over evolutionary time through direct fitness benefits at the individual level from an unknown function. PMID:23825662

  4. Computer-aided image geometry analysis and subset selection for optimizing texture quality in photorealistic models

    NASA Astrophysics Data System (ADS)

    Sima, Aleksandra Anna; Bonaventura, Xavier; Feixas, Miquel; Sbert, Mateu; Howell, John Anthony; Viola, Ivan; Buckley, Simon John

    2013-03-01

    Photorealistic 3D models are used for visualization, interpretation and spatial measurement in many disciplines, such as cultural heritage, archaeology and geoscience. Using modern image- and laser-based 3D modelling techniques, it is normal to acquire more data than is finally used for 3D model texturing, as images may be acquired from multiple positions, with large overlap, or with different cameras and lenses. Such redundant image sets require sorting to restrict the number of images, increasing the processing efficiency and realism of models. However, selection of image subsets optimized for texturing purposes is an example of complex spatial analysis. Manual selection may be challenging and time-consuming, especially for models of rugose topography, where the user must account for occlusions and ensure coverage of all relevant model triangles. To address this, this paper presents a framework for computer-aided image geometry analysis and subset selection for optimizing texture quality in photorealistic models. The framework was created to offer algorithms for candidate image subset selection, whilst supporting refinement of subsets in an intuitive and visual manner. Automatic image sorting was implemented using algorithms originating in computer science and information theory, and variants of these were compared using multiple 3D models and covering image sets, collected for geological applications. The image subsets provided by the automatic procedures were compared to manually selected sets and their suitability for 3D model texturing was assessed. Results indicate that the automatic sorting algorithms are a promising alternative to manual methods. An algorithm based on a greedy solution to the weighted set-cover problem provided image sets closest to the quality and size of the manually selected sets. The improved automation and more reliable quality indicators make the photorealistic model creation workflow more accessible for application experts

  5. [The spectral characteristic wavelength selection and parameter optimization based on Tikhonov regularization].

    PubMed

    Zhao, An-Xin; Tang, Xiao-Jun; Zhang, Zhong-Hua; Liu, Jun-Hua

    2014-07-01

    In the multicomponent mixture hydrocarbon gases Fourier transform infrared (FTIR) quantitative analysis, especially for light alkane gases, it is not easy to establish the quantitative analysis model because their IR spectra absorption peaks are seriously overlapped. Aiming at this problem, the Tikhonov regularization algorithm was used to select the characteristic wavelengths for seven kinds of light alkane mixture gases FTIR which are composed with methane, ethane, propane, iso-butane, n-butane, iso-pentane and n-pentane. And then the wavelength selection was used to establish the quantitative analysis model. By comparing the analysis characteristics wavelength selection and TR parameters optimization of the mixed gases in the infrared all wave band, the first absorption peak band and the second peak band, the characteristic wavelength of 7 kinds of gases were selected by Tikhonov algorithm. The wavelength selection and Tikhonov regularization parameters were used to test the actual measured methane spectral data, and then we got that with other gas components the max cross sensitivity was 11.153 7%, the minimum cross sensitivity was 1.239 7%, and the root mean square prediction error was 0.004 8. The Tikhonov regularization algorithm effectively enhanced the accuracy in the light alkane mixed gas quantitative analysis. The feasibility of alkane gases mixture Fourier transform infrared spectrum wavelength selection was preliminarily verified by using the Tikhonov regularization algorithm. PMID:25269291

  6. Optimal signal constellation design for ultra-high-speed optical transport in the presence of nonlinear phase noise.

    PubMed

    Liu, Tao; Djordjevic, Ivan B

    2014-12-29

    In this paper, we first describe an optimal signal constellation design algorithm suitable for the coherent optical channels dominated by the linear phase noise. Then, we modify this algorithm to be suitable for the nonlinear phase noise dominated channels. In optimization procedure, the proposed algorithm uses the cumulative log-likelihood function instead of the Euclidian distance. Further, an LDPC coded modulation scheme is proposed to be used in combination with signal constellations obtained by proposed algorithm. Monte Carlo simulations indicate that the LDPC-coded modulation schemes employing the new constellation sets, obtained by our new signal constellation design algorithm, outperform corresponding QAM constellations significantly in terms of transmission distance and have better nonlinearity tolerance.

  7. Band-pass processing in a GPCR signaling pathway selects for NFAT transcription factor activation.

    PubMed

    Sumit, M; Neubig, R R; Takayama, S; Linderman, J J

    2015-11-01

    Many biological processes are rhythmic and proper timing is increasingly appreciated as being critical for development and maintenance of physiological functions. To understand how temporal modulation of an input signal influences downstream responses, we employ microfluidic pulsatile stimulation of a G-protein coupled receptor, the muscarinic M3 receptor, in single cells with simultaneous real-time imaging of both intracellular calcium and NFAT nuclear localization. Interestingly, we find that reduced stimulation with pulses of ligand can give more efficient transcription factor activation, if stimuli are timed appropriately. Our experiments and computational analyses show that M3 receptor-induced calcium oscillations form a low pass filter while calcium-induced NFAT translocation forms a high pass filter. The combination acts as a band-pass filter optimized for intermediate frequencies of stimulation. We demonstrate that receptor desensitization and NFAT translocation rates determine critical features of the band-pass filter and that the band-pass may be shifted for different receptors or NFAT dynamics. As an example, we show that the two NFAT isoforms (NFAT4 and NFAT1) have shifted band-pass windows for the same receptor. While we focus specifically on the M3 muscarinic receptor and NFAT translocation, band-pass processing is expected to be a general theme that applies to multiple signaling pathways.

  8. Band-pass processing in a GPCR signaling pathway selects for NFAT transcription factor activation†

    PubMed Central

    Sumit, M.; Neubig, R. R.; Takayama, S.; Linderman, J. J.

    2015-01-01

    Many biological processes are rhythmic and proper timing is increasingly appreciated as being critical for development and maintenance of physiological functions. To understand how temporal modulation of an input signal influences downstream responses, we employ microfluidic pulsatile stimulation of a G-Protein coupled receptor, the muscarinic M3 receptor, in single cells with simultaneous real-time imaging of both intracellular calcium and NFAT nuclear localization. Interestingly, we find that reduced stimulation with pulses of ligand can give more efficient transcription factor activation, if stimuli are timed appropriately. Our experiments and computational analyses show that M3 receptor-induced calcium oscillations form a low pass filter while calcium-induced NFAT translocation forms a high pass filter. The combination acts as a band-pass filter optimized for intermediate frequencies of stimulation. We demonstrate that receptor desensitization and NFAT translocation rates determine critical features of the band-pass filter and that the band-pass may be shifted for different receptors or NFAT dynamics. As an example, we show that the two NFAT isoforms (NFAT4 and NFAT1) have shifted band-pass windows for the same receptor. While we focus specifically on the M3 muscarinic receptor and NFAT translocation, band-pass processing is expected to be a general theme that applies to multiple signaling pathways. PMID:26374065

  9. Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

    PubMed

    Engemann, Denis A; Gramfort, Alexandre

    2015-03-01

    Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.

  10. Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

    PubMed

    Engemann, Denis A; Gramfort, Alexandre

    2015-03-01

    Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals. PMID:25541187

  11. [Selection of back-ground electrolyte in capillary zone electrophoresis by triangle and tetrahedron optimization methods].

    PubMed

    Sun, Guoxiang; Song, Wenjing; Lin, Ting

    2008-03-01

    The triangle and tetrahedron optimization methods were developed for the selection of back-ground electrolyte (BGE) in capillary zone electrophoresis (CZE). Chromatographic fingerprint index F and chromatographic fingerprint relative index F(r) were used as the objective functions for the evaluation, and the extract of Saussurea involucrate by water was used as the sample. The BGE was composed of borax, boric acid, dibasic sodium phosphate and sodium dihydrogen phosphate solution with different concentrations using triangle and tetrahedron optimization methods. Re-optimization was carried out by adding organic modifier to the BGE and adjusting the pH value. In triangle method, when 50 mmol/L borax-150 mmol/L sodium dihydrogen phosphate (containing 3% acetonitrile) (1 : 1, v/v) was used as BGE, the isolation was considered to be satisfactory. In tetrahedron method, the best BGE was 50 mmol/L borax-150 mmol/L sodium dihydrogen phosphate-200 mmol/L boric acid (1 : 1 : 2, v/v/v; adjusting the pH value to 8.55 by 0.1 mol/L sodium hydroxide). There were 28 peaks and 25 peaks under the different conditions respectively. The results showed that the methods could be applied to the selection of BGE in CZE of the extract of traditional Chinese medicine by water or ethanol.

  12. Concerted evolution of life stage performances signals recent selection on yeast nitrogen use.

    PubMed

    Ibstedt, Sebastian; Stenberg, Simon; Bagés, Sara; Gjuvsland, Arne B; Salinas, Francisco; Kourtchenko, Olga; Samy, Jeevan K A; Blomberg, Anders; Omholt, Stig W; Liti, Gianni; Beltran, Gemma; Warringer, Jonas

    2015-01-01

    Exposing natural selection driving phenotypic and genotypic adaptive differentiation is an extraordinary challenge. Given that an organism's life stages are exposed to the same environmental variations, we reasoned that fitness components, such as the lag, rate, and efficiency of growth, directly reflecting performance in these life stages, should often be selected in concert. We therefore conjectured that correlations between fitness components over natural isolates, in a particular environmental context, would constitute a robust signal of recent selection. Critically, this test for selection requires fitness components to be determined by different genetic loci. To explore our conjecture, we exhaustively evaluated the lag, rate, and efficiency of asexual population growth of natural isolates of the model yeast Saccharomyces cerevisiae in a large variety of nitrogen-limited environments. Overall, fitness components were well correlated under nitrogen restriction. Yeast isolates were further crossed in all pairwise combinations and coinheritance of each fitness component and genetic markers were traced. Trait variations tended to map to quantitative trait loci (QTL) that were private to a single fitness component. We further traced QTLs down to single-nucleotide resolution and uncovered loss-of-function mutations in RIM15, PUT4, DAL1, and DAL4 as the genetic basis for nitrogen source use variations. Effects of SNPs were unique for a single fitness component, strongly arguing against pleiotropy between lag, rate, and efficiency of reproduction under nitrogen restriction. The strong correlations between life stage performances that cannot be explained by pleiotropy compellingly support adaptive differentiation of yeast nitrogen source use and suggest a generic approach for detecting selection.

  13. The Steppengrille (Gryllus spec./assimilis): selective filters and signal mismatch on two time scales.

    PubMed

    Rothbart, Matti Michael; Hennig, Ralf Matthias

    2012-01-01

    In Europe, several species of crickets are available commercially as pet food. Here we investigated the calling song and phonotactic selectivity for sound patterns on the short and long time scales for one such a cricket, Gryllus spec., available as "Gryllus assimilis", the Steppengrille, originally from Ecuador. The calling song consisted of short chirps (2-3 pulses, carrier frequency: 5.0 kHz) emitted with a pulse period of 30.2 ms and chirp rate of 0.43 per second. Females exhibited high selectivity on both time scales. The preference for pulse period peaked at 33 ms which was higher then the pulse period produced by males. Two consecutive pulses per chirp at the correct pulse period were already sufficient for positive phonotaxis. The preference for the chirp pattern was limited by selectivity for small chirp duty cycles and for chirp periods between 200 ms and 500 ms. The long chirp period of the songs of males was unattractive to females. On both time scales a mismatch between the song signal of the males and the preference of females was observed. The variability of song parameters as quantified by the coefficient of variation was below 50% for all temporal measures. Hence, there was not a strong indication for directional selection on song parameters by females which could account for the observed mismatch. The divergence of the chirp period and female preference may originate from a founder effect, when the Steppengrille was cultured. Alternatively the mismatch was a result of selection pressures exerted by commercial breeders on low singing activity, to satisfy customers with softly singing crickets. In the latter case the prominent divergence between male song and female preference was the result of domestication and may serve as an example of rapid evolution of song traits in acoustic communication systems. PMID:22970154

  14. The Steppengrille (Gryllus spec./assimilis): Selective Filters and Signal Mismatch on Two Time Scales

    PubMed Central

    Rothbart, Matti Michael; Hennig, Ralf Matthias

    2012-01-01

    In Europe, several species of crickets are available commercially as pet food. Here we investigated the calling song and phonotactic selectivity for sound patterns on the short and long time scales for one such a cricket, Gryllus spec., available as “Gryllus assimilis”, the Steppengrille, originally from Ecuador. The calling song consisted of short chirps (2–3 pulses, carrier frequency: 5.0 kHz) emitted with a pulse period of 30.2 ms and chirp rate of 0.43 per second. Females exhibited high selectivity on both time scales. The preference for pulse period peaked at 33 ms which was higher then the pulse period produced by males. Two consecutive pulses per chirp at the correct pulse period were already sufficient for positive phonotaxis. The preference for the chirp pattern was limited by selectivity for small chirp duty cycles and for chirp periods between 200 ms and 500 ms. The long chirp period of the songs of males was unattractive to females. On both time scales a mismatch between the song signal of the males and the preference of females was observed. The variability of song parameters as quantified by the coefficient of variation was below 50% for all temporal measures. Hence, there was not a strong indication for directional selection on song parameters by females which could account for the observed mismatch. The divergence of the chirp period and female preference may originate from a founder effect, when the Steppengrille was cultured. Alternatively the mismatch was a result of selection pressures exerted by commercial breeders on low singing activity, to satisfy customers with softly singing crickets. In the latter case the prominent divergence between male song and female preference was the result of domestication and may serve as an example of rapid evolution of song traits in acoustic communication systems. PMID:22970154

  15. An integrated approach of topology optimized design and selective laser melting process for titanium implants materials.

    PubMed

    Xiao, Dongming; Yang, Yongqiang; Su, Xubin; Wang, Di; Sun, Jianfeng

    2013-01-01

    The load-bearing bone implants materials should have sufficient stiffness and large porosity, which are interacted since larger porosity causes lower mechanical properties. This paper is to seek the maximum stiffness architecture with the constraint of specific volume fraction by topology optimization approach, that is, maximum porosity can be achieved with predefine stiffness properties. The effective elastic modulus of conventional cubic and topology optimized scaffolds were calculated using finite element analysis (FEA) method; also, some specimens with different porosities of 41.1%, 50.3%, 60.2% and 70.7% respectively were fabricated by Selective Laser Melting (SLM) process and were tested by compression test. Results showed that the computational effective elastic modulus of optimized scaffolds was approximately 13% higher than cubic scaffolds, the experimental stiffness values were reduced by 76% than the computational ones. The combination of topology optimization approach and SLM process would be available for development of titanium implants materials in consideration of both porosity and mechanical stiffness.

  16. Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design.

    PubMed

    Tian, Xiao-Jun; Zhang, Hang; Sannerud, Jens; Xing, Jianhua

    2016-05-24

    Multiple-objective optimization is common in biological systems. In the mammalian olfactory system, each sensory neuron stochastically expresses only one out of up to thousands of olfactory receptor (OR) gene alleles; at the organism level, the types of expressed ORs need to be maximized. Existing models focus only on monoallele activation, and cannot explain recent observations in mutants, especially the reduced global diversity of expressed ORs in G9a/GLP knockouts. In this work we integrated existing information on OR expression, and constructed a comprehensive model that has all its components based on physical interactions. Analyzing the model reveals an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a previously unidentified enhancer competition step. This model not only recapitulates monoallelic OR expression, but also elucidates how the olfactory system maximizes and maintains the diversity of OR expression, and has multiple predictions validated by existing experimental results. Through making an analogy to a physical system with thermally activated barrier crossing and comparative reverse engineering analyses, the study reveals that the olfactory receptor selection system is optimally designed, and particularly underscores cooperativity and synergy as a general design principle for multiobjective optimization in biology.

  17. An integrated approach of topology optimized design and selective laser melting process for titanium implants materials.

    PubMed

    Xiao, Dongming; Yang, Yongqiang; Su, Xubin; Wang, Di; Sun, Jianfeng

    2013-01-01

    The load-bearing bone implants materials should have sufficient stiffness and large porosity, which are interacted since larger porosity causes lower mechanical properties. This paper is to seek the maximum stiffness architecture with the constraint of specific volume fraction by topology optimization approach, that is, maximum porosity can be achieved with predefine stiffness properties. The effective elastic modulus of conventional cubic and topology optimized scaffolds were calculated using finite element analysis (FEA) method; also, some specimens with different porosities of 41.1%, 50.3%, 60.2% and 70.7% respectively were fabricated by Selective Laser Melting (SLM) process and were tested by compression test. Results showed that the computational effective elastic modulus of optimized scaffolds was approximately 13% higher than cubic scaffolds, the experimental stiffness values were reduced by 76% than the computational ones. The combination of topology optimization approach and SLM process would be available for development of titanium implants materials in consideration of both porosity and mechanical stiffness. PMID:23988713

  18. Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design.

    PubMed

    Tian, Xiao-Jun; Zhang, Hang; Sannerud, Jens; Xing, Jianhua

    2016-05-24

    Multiple-objective optimization is common in biological systems. In the mammalian olfactory system, each sensory neuron stochastically expresses only one out of up to thousands of olfactory receptor (OR) gene alleles; at the organism level, the types of expressed ORs need to be maximized. Existing models focus only on monoallele activation, and cannot explain recent observations in mutants, especially the reduced global diversity of expressed ORs in G9a/GLP knockouts. In this work we integrated existing information on OR expression, and constructed a comprehensive model that has all its components based on physical interactions. Analyzing the model reveals an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a previously unidentified enhancer competition step. This model not only recapitulates monoallelic OR expression, but also elucidates how the olfactory system maximizes and maintains the diversity of OR expression, and has multiple predictions validated by existing experimental results. Through making an analogy to a physical system with thermally activated barrier crossing and comparative reverse engineering analyses, the study reveals that the olfactory receptor selection system is optimally designed, and particularly underscores cooperativity and synergy as a general design principle for multiobjective optimization in biology. PMID:27162367

  19. Gas ultrasonic flow rate measurement through genetic-ant colony optimization based on the ultrasonic pulse received signal model

    NASA Astrophysics Data System (ADS)

    Hou, Huirang; Zheng, Dandan; Nie, Laixiao

    2015-04-01

    For gas ultrasonic flowmeters, the signals received by ultrasonic sensors are susceptible to noise interference. If signals are mingled with noise, a large error in flow measurement can be caused by triggering mistakenly using the traditional double-threshold method. To solve this problem, genetic-ant colony optimization (GACO) based on the ultrasonic pulse received signal model is proposed. Furthermore, in consideration of the real-time performance of the flow measurement system, the improvement of processing only the first three cycles of the received signals rather than the whole signal is proposed. Simulation results show that the GACO algorithm has the best estimation accuracy and ant-noise ability compared with the genetic algorithm, ant colony optimization, double-threshold and enveloped zero-crossing. Local convergence doesn’t appear with the GACO algorithm until -10 dB. For the GACO algorithm, the converging accuracy and converging speed and the amount of computation are further improved when using the first three cycles (called GACO-3cycles). Experimental results involving actual received signals show that the accuracy of single-gas ultrasonic flow rate measurement can reach 0.5% with GACO-3 cycles, which is better than with the double-threshold method.

  20. Novel screening assay for the selective detection of G-protein-coupled receptor heteromer signaling.

    PubMed

    van Rijn, Richard M; Harvey, Jessica H; Brissett, Daniela I; DeFriel, Julia N; Whistler, Jennifer L

    2013-01-01

    Drugs targeting G-protein-coupled receptors (GPCRs) make up more than 25% of all prescribed medicines. The ability of GPCRs to form heteromers with unique signaling properties suggests an entirely new and unexplored pool of drug targets. However, current in vitro assays are ill equipped to detect heteromer-selective compounds. We have successfully adapted an approach, using fusion proteins of GPCRs and chimeric G proteins, to create an in vitro screening assay (in human embryonic kidney cells) in which only activated heteromers are detectable. Here we show that this assay can demonstrate heteromer-selective G-protein bias as well as measure transinhibition. Using this assay, we reveal that the δ-opioid receptor agonist ADL5859, which is currently in clinical trials, has a 10-fold higher potency against δ-opioid receptor homomers than δ/μ-opioid receptor heteromers (pEC(50) = 6.7 ± 0.1 versus 5.8 ± 0.2). The assay enables the screening of large compound libraries to identify heteromer-selective compounds that could then be used in vivo to determine the functional role of heteromers and develop potential therapeutic agents.

  1. Complex Selection on Human Polyadenylation Signals Revealed by Polymorphism and Divergence Data

    PubMed Central

    Kainov, Yaroslav A.; Aushev, Vasily N.; Naumenko, Sergey A.; Tchevkina, Elena M.; Bazykin, Georgii A.

    2016-01-01

    Polyadenylation is a step of mRNA processing which is crucial for its expression and stability. The major polyadenylation signal (PAS) represents a nucleotide hexamer that adheres to the AATAAA consensus sequence. Over a half of human genes have multiple cleavage and polyadenylation sites, resulting in a great diversity of transcripts differing in function, stability, and translational activity. Here, we use available whole-genome human polymorphism data together with data on interspecies divergence to study the patterns of selection acting on PAS hexamers. Common variants of PAS hexamers are depleted of single nucleotide polymorphisms (SNPs), and SNPs within PAS hexamers have a reduced derived allele frequency (DAF) and increased conservation, indicating prevalent negative selection; at the same time, the SNPs that “improve” the PAS (i.e., those leading to higher cleavage efficiency) have increased DAF, compared to those that “impair” it. SNPs are rarer at PAS of “unique” polyadenylation sites (one site per gene); among alternative polyadenylation sites, at the distal PAS and at exonic PAS. Similar trends were observed in DAFs and divergence between species of placental mammals. Thus, selection permits PAS mutations mainly at redundant and/or weakly functional PAS. Nevertheless, a fraction of the SNPs at PAS hexamers likely affect gene functions; in particular, some of the observed SNPs are associated with disease. PMID:27324920

  2. SIGNALING EFFICACY DRIVES THE EVOLUTION OF LARGER SEXUAL ORNAMENTS BY SEXUAL SELECTION

    PubMed Central

    Tazzyman, Samuel J; Iwasa, Yoh; Pomiankowski, Andrew

    2014-01-01

    Why are there so few small secondary sexual characters? Theoretical models predict that sexual selection should lead to reduction as often as exaggeration, and yet we mainly associate secondary sexual ornaments with exaggerated features such as the peacock's tail. We review the literature on mate choice experiments for evidence of reduced sexual traits. This shows that reduced ornamentation is effectively impossible in certain types of ornamental traits (behavioral, pheromonal, or color-based traits, and morphological ornaments for which the natural selection optimum is no trait), but that there are many examples of morphological traits that would permit reduction. Yet small sexual traits are very rarely seen. We analyze a simple mathematical model of Fisher's runaway process (the null model for sexual selection). Our analysis shows that the imbalance cannot be wholly explained by larger ornaments being less costly than smaller ornaments, nor by preferences for larger ornaments being less costly than preferences for smaller ornaments. Instead, we suggest that asymmetry in signaling efficacy limits runaway to trait exaggeration. PMID:24099137

  3. SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

    PubMed Central

    Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W. M.; Li, R. K.; Jiang, Bo-Ru

    2014-01-01

    Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases. PMID:25295306

  4. Impact of cultivar selection and process optimization on ethanol yield from different varieties of sugarcane

    PubMed Central

    2014-01-01

    Background The development of ‘energycane’ varieties of sugarcane is underway, targeting the use of both sugar juice and bagasse for ethanol production. The current study evaluated a selection of such ‘energycane’ cultivars for the combined ethanol yields from juice and bagasse, by optimization of dilute acid pretreatment optimization of bagasse for sugar yields. Method A central composite design under response surface methodology was used to investigate the effects of dilute acid pretreatment parameters followed by enzymatic hydrolysis on the combined sugar yield of bagasse samples. The pressed slurry generated from optimum pretreatment conditions (maximum combined sugar yield) was used as the substrate during batch and fed-batch simultaneous saccharification and fermentation (SSF) processes at different solid loadings and enzyme dosages, aiming to reach an ethanol concentration of at least 40 g/L. Results Significant variations were observed in sugar yields (xylose, glucose and combined sugar yield) from pretreatment-hydrolysis of bagasse from different cultivars of sugarcane. Up to 33% difference in combined sugar yield between best performing varieties and industrial bagasse was observed at optimal pretreatment-hydrolysis conditions. Significant improvement in overall ethanol yield after SSF of the pretreated bagasse was also observed from the best performing varieties (84.5 to 85.6%) compared to industrial bagasse (74.5%). The ethanol concentration showed inverse correlation with lignin content and the ratio of xylose to arabinose, but it showed positive correlation with glucose yield from pretreatment-hydrolysis. The overall assessment of the cultivars showed greater improvement in the final ethanol concentration (26.9 to 33.9%) and combined ethanol yields per hectare (83 to 94%) for the best performing varieties with respect to industrial sugarcane. Conclusions These results suggest that the selection of sugarcane variety to optimize ethanol

  5. An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

    PubMed Central

    Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

    2013-01-01

    One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This

  6. Optimal Spectral Domain Selection for Maximizing Archaeological Signatures: Italy Case Studies

    PubMed Central

    Cavalli, Rosa Maria; Pascucci, Simone; Pignatti, Stefano

    2009-01-01

    Different landscape elements, including archaeological remains, can be automatically classified when their spectral characteristics are different, but major difficulties occur when extracting and classifying archaeological spectral features, as archaeological remains do not have unique shape or spectral characteristics. The spectral anomaly characteristics due to buried remains depend strongly on vegetation cover and/or soil types, which can make feature extraction more complicated. For crop areas, such as the test sites selected for this study, soil and moisture changes within near-surface archaeological deposits can influence surface vegetation patterns creating spectral anomalies of various kinds. In this context, this paper analyzes the usefulness of hyperspectral imagery, in the 0.4 to 12.8 μm spectral region, to identify the optimal spectral range for archaeological prospection as a function of the dominant land cover. MIVIS airborne hyperspectral imagery acquired in five different archaeological areas located in Italy has been used. Within these archaeological areas, 97 test sites with homogenous land cover and characterized by a statistically significant number of pixels related to the buried remains have been selected. The archaeological detection potential for all MIVIS bands has been assessed by applying a Separability Index on each spectral anomaly-background system of the test sites. A scatterplot analysis of the SI values vs. the dominant land cover fractional abundances, as retrieved by spectral mixture analysis, was performed to derive the optimal spectral ranges maximizing the archaeological detection. This work demonstrates that whenever we know the dominant land cover fractional abundances in archaeological sites, we can a priori select the optimal spectral range to improve the efficiency of archaeological observations performed by remote sensing data. PMID:22573985

  7. Optimal selection of space transportation fleet to meet multi-mission space program needs

    NASA Technical Reports Server (NTRS)

    Morgenthaler, George W.; Montoya, Alex J.

    1989-01-01

    A space program that spans several decades will be comprised of a collection of missions such as low earth orbital space station, a polar platform, geosynchronous space station, lunar base, Mars astronaut mission, and Mars base. The optimal selection of a fleet of several recoverable and expendable launch vehicles, upper stages, and interplanetary spacecraft necessary to logistically establish and support these space missions can be examined by means of a linear integer programming optimization model. Such a selection must be made because the economies of scale which comes from producing large quantities of a few standard vehicle types, rather than many, will be needed to provide learning curve effects to reduce the overall cost of space transportation if these future missions are to be affordable. Optimization model inputs come from data and from vehicle designs. Each launch vehicle currently in existence has a launch history, giving rise to statistical estimates of launch reliability. For future, not-yet-developed launch vehicles, theoretical reliabilities corresponding to the maturity of the launch vehicles' technology and the degree of design redundancy must be estimated. Also, each such launch vehicle has a certain historical or estimated development cost, tooling cost, and a variable cost. The cost of a launch used in this paper includes the variable cost plus an amortized portion of the fixed and development costs. The integer linear programming model will have several constraint equations based on assumptions of mission mass requirements, volume requirements, and number of astronauts needed. The model will minimize launch vehicle logistic support cost and will select the most desirable launch vehicle fleet.

  8. Efficient expression of nattokinase in Bacillus licheniformis: host strain construction and signal peptide optimization.

    PubMed

    Wei, Xuetuan; Zhou, Yinhua; Chen, Jingbang; Cai, Dongbo; Wang, Dan; Qi, Gaofu; Chen, Shouwen

    2015-02-01

    Nattokinase (NK) possesses the potential for prevention and treatment of thrombus-related diseases. In this study, high-level expression of nattokinase was achieved in Bacillus licheniformis WX-02 via host strain construction and signal peptides optimization. First, ten genes (mpr, vpr, aprX, epr, bpr, wprA, aprE, bprA, hag, amyl) encoding for eight extracellular proteases, a flagellin and an amylase were deleted to obtain B. licheniformis BL10, which showed no extracellular proteases activity in gelatin zymography. Second, the gene fragments of P43 promoter, Svpr, nattokinase and TamyL were combined into pHY300PLK to form the expression vector pP43SNT. In BL10 (pP43SNT), the fermentation activity and product activity per unit of biomass of nattokinase reached 14.33 FU/mL and 2,187.71 FU/g respectively, which increased by 39 and 156 % compared to WX-02 (pP43SNT). Last, Svpr was replaced with SsacC and SbprA, and the maximum fermentation activity (33.83 FU/mL) was achieved using SsacC, which was 229 % higher than that of WX-02 (pP43SNT). The maximum NK fermentation activity in this study reaches the commercial production level of solid state fermentation, and this study provides a promising engineered strain for industrial production of nattokinase, as well as a potential platform host for expression of other target proteins.

  9. Screening and selection of synthetic peptides for a novel and optimized endotoxin detection method.

    PubMed

    Mujika, M; Zuzuarregui, A; Sánchez-Gómez, S; Martínez de Tejada, G; Arana, S; Pérez-Lorenzo, E

    2014-09-30

    The current validated endotoxin detection methods, in spite of being highly sensitive, present several drawbacks in terms of reproducibility, handling and cost. Therefore novel approaches are being carried out in the scientific community to overcome these difficulties. Remarkable efforts are focused on the development of endotoxin-specific biosensors. The key feature of these solutions relies on the proper definition of the capture protocol, especially of the bio-receptor or ligand. The aim of the presented work is the screening and selection of a synthetic peptide specifically designed for LPS detection, as well as the optimization of a procedure for its immobilization onto gold substrates for further application to biosensors. PMID:25034430

  10. An optimized method for tremor detection and temporal tracking through repeated second order moment calculations on the surface EMG signal.

    PubMed

    De Marchis, Cristiano; Schmid, Maurizio; Conforto, Silvia

    2012-11-01

    In this study, the problem of detecting and tracking tremor from the surface myoelectric signal is addressed. A method based on the calculation of a Second Order Moment Function (SOMF) inside a window W sliding over the sEMG signal is here presented. An analytical formulation of the detector allows the extraction of the optimal parameters characterizing the algorithm. Performance of the optimized method is assessed on a set of synthetic tremor sEMG signals in terms of sensitivity, precision and accuracy through the use of a properly defined cost function able to explain the overall detector performance. The obtained results are compared to those emerging from the application of optimized versions of traditional detection techniques. Once tested on a database of synthetic tremor sEMG data, a quantitative assessment of the SOMF algorithm performance is carried out on experimental tremor sEMG signals recorded from two patients affected by Essential Tremor and from two patients affected by Parkinson's Disease. The SOMF algorithm outperforms the traditional techniques both in detecting (sensitivity and positive predictive value >99% for SNR higher than 3dB) and in estimating timings of muscular tremor bursts (bias and standard deviation on the estimation of the onset and offset time instants lower than 8ms). Its independence from the SNR level and its low computational cost make it suitable for real-time implementation and clinical use. PMID:22257701

  11. Optimal bispectrum estimator and simulations of the CMB lensing-integrated Sachs Wolfe non-Gaussian signal

    NASA Astrophysics Data System (ADS)

    Mangilli, A.; Wandelt, B.; Elsner, F.; Liguori, M.

    2013-07-01

    We present the tools to optimally extract the lensing-integrated Sachs Wolfe (L-ISW) bispectrum signal from future cosmic microwave background (CMB) data. We implemented two different methods to simulate the non-Gaussian CMB maps with the L-ISW signal: a non-perturbative method based on the FLINTS lensing code and the separable mode-expansion method. We implemented the Komatsu, Spergel, and Wandelt (KSW) optimal estimator analysis for the L-ISW bispectrum and tested it on the non-Gaussian simulations for realistic CMB experimental settings with an inhomogeneous sky coverage. We show that the estimator approaches the Cramer-Rao bound and that Wiener filtering the L-ISW simulations slightly improves the estimate of fNLL-ISW by ≤ 10%. For a realistic CMB experimental setting that accounts for anisotropic noise and masked sky, we show that the linear term of the estimator is highly correlated to the cubic term and it is necessary to recover the signal and the optimal error bars. We also show that the L-ISW bispectrum, if not correctly accounted for, yields an underestimation of the fNLlocal error bars of ≃ 4%. A joint analysis of the non-Gaussian shapes and/or L-ISW template subtraction is needed to recover unbiased results of the primordial non-Gaussian signal from ongoing and future CMB experiments.

  12. Signal of Interest Selection Standard for Ultrasonic Backscatter in Cancellous Bone Evaluation.

    PubMed

    Liu, Chengcheng; Tang, Tao; Xu, Feng; Ta, Dean; Matsukawa, Mami; Hu, Bo; Wang, Weiqi

    2015-10-01

    The aim of this study was to examine the effect of the backscattered signal of interest (SOI) on ultrasonic cancellous bone evaluation. In vitro backscatter measurements were performed using 16 bovine cancellous bone specimens and six different transducers with central frequencies of 0.5, 1, 2.25, 3.5, 5 and 10 MHz. The SOI for signal analysis was selected by a rectangular window. The delay (T1) and duration (T2) of the time window were varied, and the apparent integrated backscatter (AIB) and its correlation to bone volume fraction (BV/TV) were calculated. The results indicate that in addition to affecting the measured value of AIB, the SOI influences the observed correlation between AIB and BV/TV. Strong positive correlations were observed for short T1 (0.5 MHz: ≤6 μs, 1 MHz: ≤3 μs, 2.25 and 3.5 MHz: ≤2 μs, 5 and 10 MHz: ≤1 μs). However, strong negative correlations were observed when T1 was long (0.5 MHz: >9 μs, 1 MHz: >7 μs, 2.25 and 3.5 MHz: >3 μs, 5 and 10 MHz: >2 μs). The T2 value, especially low values (≤3 μs), also influenced the correlation coefficients. Positive correlations were more commonly observed at lower frequencies (i.e., 0.5-1 MHz), whereas negative correlations were more common at higher frequencies (i.e., 2.25-10 MHz). An explicit standard for in vitro SOI selection and cancellous bone assessment was proposed for a broad frequency range (0.5-10 MHz). Current conflicting findings are explained, and constructive suggestions for ultrasonic backscatter cancellous bone evaluation are provided. PMID:26210784

  13. Signal Peptide-Binding Drug as a Selective Inhibitor of Co-Translational Protein Translocation

    PubMed Central

    Vermeire, Kurt; Bell, Thomas W.; Van Puyenbroeck, Victor; Giraut, Anne; Noppen, Sam; Liekens, Sandra; Schols, Dominique; Hartmann, Enno

    2014-01-01

    In eukaryotic cells, surface expression of most type I transmembrane proteins requires translation and simultaneous insertion of the precursor protein into the endoplasmic reticulum (ER) membrane for subsequent routing to the cell surface. This co-translational translocation pathway is initiated when a hydrophobic N-terminal signal peptide (SP) on the nascent protein emerges from the ribosome, binds the cytosolic signal recognition particle (SRP), and targets the ribosome-nascent chain complex to the Sec61 translocon, a universally conserved protein-conducting channel in the ER-membrane. Despite their common function in Sec61 targeting and ER translocation, SPs have diverse but unique primary sequences. Thus, drugs that recognise SPs could be exploited to inhibit translocation of specific proteins into the ER. Here, through flow cytometric analysis the small-molecule macrocycle cyclotriazadisulfonamide (CADA) is identified as a highly selective human CD4 (hCD4) down-modulator. We show that CADA inhibits CD4 biogenesis and that this is due to its ability to inhibit co-translational translocation of CD4 into the lumen of the ER, both in cells as in a cell-free in vitro translation/translocation system. The activity of CADA maps to the cleavable N-terminal SP of hCD4. Moreover, through surface plasmon resonance analysis we were able to show direct binding of CADA to the SP of hCD4 and identify this SP as the target of our drug. Furthermore, CADA locks the SP in the translocon during a post-targeting step, possibly in a folded state, and prevents the translocation of the associated protein into the ER lumen. Instead, the precursor protein is routed to the cytosol for degradation. These findings demonstrate that a synthetic, cell-permeable small-molecule can be developed as a SP-binding drug to selectively inhibit protein translocation and to reversibly regulate the expression of specific target proteins. PMID:25460167

  14. Optimal Intermittence in Search Strategies under Speed-Selective Target Detection

    NASA Astrophysics Data System (ADS)

    Campos, Daniel; Méndez, Vicenç; Bartumeus, Frederic

    2012-01-01

    Random search theory has been previously explored for both continuous and intermittent scanning modes with full target detection capacity. Here we present a new class of random search problems in which a single searcher performs flights of random velocities, the detection probability when it passes over a target location being conditioned to the searcher speed. As a result, target detection involves an N-passage process for which the mean search time is here analytically obtained through a renewal approximation. We apply the idea of speed-selective detection to random animal foraging since a fast movement is known to significantly degrade perception abilities in many animals. We show that speed-selective detection naturally introduces an optimal level of behavioral intermittence in order to solve the compromise between fast relocations and target detection capability.

  15. Analysis and selection of optimal function implementations in massively parallel computer

    DOEpatents

    Archer, Charles Jens; Peters, Amanda; Ratterman, Joseph D.

    2011-05-31

    An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.

  16. Burnout and job performance: the moderating role of selection, optimization, and compensation strategies.

    PubMed

    Demerouti, Evangelia; Bakker, Arnold B; Leiter, Michael

    2014-01-01

    The present study aims to explain why research thus far has found only low to moderate associations between burnout and performance. We argue that employees use adaptive strategies that help them to maintain their performance (i.e., task performance, adaptivity to change) at acceptable levels despite experiencing burnout (i.e., exhaustion, disengagement). We focus on the strategies included in the selective optimization with compensation model. Using a sample of 294 employees and their supervisors, we found that compensation is the most successful strategy in buffering the negative associations of disengagement with supervisor-rated task performance and both disengagement and exhaustion with supervisor-rated adaptivity to change. In contrast, selection exacerbates the negative relationship of exhaustion with supervisor-rated adaptivity to change. In total, 42% of the hypothesized interactions proved to be significant. Our study uncovers successful and unsuccessful strategies that people use to deal with their burnout symptoms in order to achieve satisfactory job performance.

  17. Burnout and job performance: the moderating role of selection, optimization, and compensation strategies.

    PubMed

    Demerouti, Evangelia; Bakker, Arnold B; Leiter, Michael

    2014-01-01

    The present study aims to explain why research thus far has found only low to moderate associations between burnout and performance. We argue that employees use adaptive strategies that help them to maintain their performance (i.e., task performance, adaptivity to change) at acceptable levels despite experiencing burnout (i.e., exhaustion, disengagement). We focus on the strategies included in the selective optimization with compensation model. Using a sample of 294 employees and their supervisors, we found that compensation is the most successful strategy in buffering the negative associations of disengagement with supervisor-rated task performance and both disengagement and exhaustion with supervisor-rated adaptivity to change. In contrast, selection exacerbates the negative relationship of exhaustion with supervisor-rated adaptivity to change. In total, 42% of the hypothesized interactions proved to be significant. Our study uncovers successful and unsuccessful strategies that people use to deal with their burnout symptoms in order to achieve satisfactory job performance. PMID:24447224

  18. Structural and Functional Evidence Indicates Selective Oxygen Signaling in Caldanaerobacter subterraneus H-NOX.

    PubMed

    Hespen, Charles W; Bruegger, Joel J; Phillips-Piro, Christine M; Marletta, Michael A

    2016-08-19

    Acute and specific sensing of diatomic gas molecules is an essential facet of biological signaling. Heme nitric oxide/oxygen binding (H-NOX) proteins are a family of gas sensors found in diverse classes of bacteria and eukaryotes. The most commonly characterized bacterial H-NOX domains are from facultative anaerobes and are activated through a conformational change caused by formation of a 5-coordinate Fe(II)-NO complex. Members of this H-NOX subfamily do not bind O2 and therefore can selectively ligate NO even under aerobic conditions. In contrast, H-NOX domains encoded by obligate anaerobes do form stable 6-coordinate Fe(II)-O2 complexes by utilizing a conserved H-bonding network in the ligand-binding pocket. The biological function of O2-binding H-NOX domains has not been characterized. In this work, the crystal structures of an O2-binding H-NOX domain from the thermophilic obligate anaerobe Caldanaerobacter subterraneus (Cs H-NOX) in the Fe(II)-NO, Fe(II)-CO, and Fe(II)-unliganded states are reported. The Fe(II)-unliganded structure displays a conformational shift distinct from the NO-, CO-, and previously reported O2-coordinated structures. In orthogonal signaling assays using Cs H-NOX and the H-NOX signaling effector histidine kinase from Vibrio cholerae (Vc HnoK), Cs H-NOX regulates Vc HnoK in an O2-dependent manner and requires the H-bonding network to distinguish O2 from other ligands. The crystal structures of Fe(II) unliganded and NO- and CO-bound Cs H-NOX combined with functional assays herein provide the first evidence that H-NOX proteins from obligate anaerobes can serve as O2 sensors.

  19. Dynamic assembly of a membrane signaling complex enables selective activation of NFAT by Orai1.

    PubMed

    Kar, Pulak; Samanta, Krishna; Kramer, Holger; Morris, Otto; Bakowski, Daniel; Parekh, Anant B

    2014-06-16

    NFAT-dependent gene expression is essential for the development and function of the nervous, immune, and cardiovascular systems and kidney, bone, and skeletal muscle. Most NFAT protein resides in the cytoplasm because of extensive phosphorylation, which masks a nuclear localization sequence. Dephosphorylation by the Ca(2+)-calmodulin-activated protein phosphatase calcineurin triggers NFAT migration into the nucleus. In some cell types, NFAT can be activated by Ca(2+) nanodomains near open store-operated Orai1 and voltage-gated Ca(2+) channels in the plasma membrane. How local Ca(2+) near Orai1 is detected and whether other Orai channels utilize a similar mechanism remain unclear. Here, we report that the paralog Orai3 fails to activate NFAT. Orai1 is effective in activating gene expression via Ca(2+) nanodomains because it participates in a membrane-delimited signaling complex that forms after store depletion and brings calcineurin, via the scaffolding protein AKAP79, to calmodulin tethered to Orai1. By contrast, Orai3 interacts less well with AKAP79 after store depletion, rendering it ineffective in activating NFAT. A channel chimera of Orai3 with the N terminus of Orai1 was able to couple local Ca(2+) entry to NFAT activation, identifying the N-terminal domain of Orai1 as central to Ca(2+) nanodomain-transcription coupling. The formation of a store-dependent signaling complex at the plasma membrane provides for selective activation of a fundamental downstream response by Orai1.

  20. A Bootstrap Method for Statistical Power System Mode Estimation and Probing Signal Selection

    SciTech Connect

    Zhou, Ning; Pierre, John W; Trudnowski, Daniel

    2006-06-30

    Near real-time measurement-based electromechanical-mode estimation offers considerable potential for many future power-system operation and control strategies. Recent research has investigated the use of low-level Pseudo Random Noise (PRN) probing signals injected into power systems to estimate the low-frequency electromechanical modes. Because of the random na-ture of a power system, estimating the modes from a single prob-ing experiment is very difficult. Ideally, one would use a Monte-Carlo approach with multiple independent probing experiments resulting in a mode estimation distribution. Then, one could state that the mode is within a region of the complex plane. Unfortu-nately, conducting multiple probing experiments is prohibitive for most power-system applications. This paper presents a method-ology for estimating the mode distribution based on one probing test using a Bootstrap algorithm. The proposed method is applied to both simulation data and actual-system measurement data to illustrate its performance and application. It is demonstrated that the method can provide valuable information to PRN tests and guide future PRN probing signal design and selection.

  1. The Cdc42-selective GAP Rich regulates postsynaptic development and retrograde BMP transsynaptic signaling

    PubMed Central

    Nahm, Minyeop; Long, A. Ashleigh; Paik, Sang Kyoo; Kim, Sungdae; Bae, Yong Chul

    2010-01-01

    Retrograde bone morphogenetic protein signaling mediated by the Glass bottom boat (Gbb) ligand modulates structural and functional synaptogenesis at the Drosophila melanogaster neuromuscular junction. However, the molecular mechanisms regulating postsynaptic Gbb release are poorly understood. In this study, we show that Drosophila Rich (dRich), a conserved Cdc42-selective guanosine triphosphatase–activating protein (GAP), inhibits the Cdc42–Wsp pathway to stimulate postsynaptic Gbb release. Loss of dRich causes synaptic undergrowth and strongly impairs neurotransmitter release. These presynaptic defects are rescued by targeted postsynaptic expression of wild-type dRich but not a GAP-deficient mutant. dRich inhibits the postsynaptic localization of the Cdc42 effector Wsp (Drosophila orthologue of mammalian Wiskott-Aldrich syndrome protein, WASp), and manifestation of synaptogenesis defects in drich mutants requires Wsp signaling. In addition, dRich regulates postsynaptic organization independently of Cdc42. Importantly, dRich increases Gbb release and elevates presynaptic phosphorylated Mad levels. We propose that dRich coordinates the Gbb-dependent modulation of synaptic growth and function with postsynaptic development. PMID:21041451

  2. An Ant Colony Optimization Based Feature Selection for Web Page Classification

    PubMed Central

    2014-01-01

    The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods. PMID:25136678

  3. Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection

    PubMed Central

    Bogacz, Rafal; Martin Moraud, Eduardo; Abdi, Azzedine; Magill, Peter J.; Baufreton, Jérôme

    2016-01-01

    The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes’ equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called ‘prototypic’ and ‘arkypallidal’ neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions. PMID:27389780

  4. Small sample training and test selection method for optimized anomaly detection algorithms in hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Mindrup, Frank M.; Friend, Mark A.; Bauer, Kenneth W.

    2012-01-01

    There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques provide an avenue to select robust settings capable of operating consistently across a large variety of image scenes. Many researchers in this area are faced with a paucity of data. Unfortunately, there are no data splitting methods for model validation of datasets with small sample sizes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research has developed a framework for optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD; these characteristics include the Fisher's score, ratio of target pixels and number of clusters. We have developed method for selecting hyperspectral image training and test subsets that yields consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. The small sample training and test selection method is contrasted with randomly selected training sets as well as training sets chosen from the CADEX and DUPLEX algorithms for the well known Reed-Xiaoli anomaly detector.

  5. Selective mapping: a strategy for optimizing the construction of high-density linkage maps.

    PubMed Central

    Vision, T J; Brown, D G; Shmoys, D B; Durrett, R T; Tanksley, S D

    2000-01-01

    Historically, linkage mapping populations have consisted of large, randomly selected samples of progeny from a given pedigree or cell lines from a panel of radiation hybrids. We demonstrate that, to construct a map with high genome-wide marker density, it is neither necessary nor desirable to genotype all markers in every individual of a large mapping population. Instead, a reduced sample of individuals bearing complementary recombinational or radiation-induced breakpoints may be selected for genotyping subsequent markers from a large, but sparsely genotyped, mapping population. Choosing such a sample can be reduced to a discrete stochastic optimization problem for which the goal is a sample with breakpoints spaced evenly throughout the genome. We have developed several different methods for selecting such samples and have evaluated their performance on simulated and actual mapping populations, including the Lister and Dean Arabidopsis thaliana recombinant inbred population and the GeneBridge 4 human radiation hybrid panel. Our methods quickly and consistently find much-reduced samples with map resolution approaching that of the larger populations from which they are derived. This approach, which we have termed selective mapping, can facilitate the production of high-quality, high-density genome-wide linkage maps. PMID:10790413

  6. An ant colony optimization based feature selection for web page classification.

    PubMed

    Saraç, Esra; Özel, Selma Ayşe

    2014-01-01

    The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

  7. Optimization of the Dutch Matrix Test by Random Selection of Sentences From a Preselected Subset

    PubMed Central

    Dreschler, Wouter A.

    2015-01-01

    Matrix tests are available for speech recognition testing in many languages. For an accurate measurement, a steep psychometric function of the speech materials is required. For existing tests, it would be beneficial if it were possible to further optimize the available materials by increasing the function’s steepness. The objective is to show if the steepness of the psychometric function of an existing matrix test can be increased by selecting a homogeneous subset of recordings with the steepest sentence-based psychometric functions. We took data from a previous multicenter evaluation of the Dutch matrix test (45 normal-hearing listeners). Based on half of the data set, first the sentences (140 out of 311) with a similar speech reception threshold and with the steepest psychometric function (≥9.7%/dB) were selected. Subsequently, the steepness of the psychometric function for this selection was calculated from the remaining (unused) second half of the data set. The calculation showed that the slope increased from 10.2%/dB to 13.7%/dB. The resulting subset did not allow the construction of enough balanced test lists. Therefore, the measurement procedure was changed to randomly select the sentences during testing. Random selection may interfere with a representative occurrence of phonemes. However, in our material, the median phonemic occurrence remained close to that of the original test. This finding indicates that phonemic occurrence is not a critical factor. The work highlights the possibility that existing speech tests might be improved by selecting sentences with a steep psychometric function. PMID:25964195

  8. Optimization of the Dutch matrix test by random selection of sentences from a preselected subset.

    PubMed

    Houben, Rolph; Dreschler, Wouter A

    2015-05-11

    Matrix tests are available for speech recognition testing in many languages. For an accurate measurement, a steep psychometric function of the speech materials is required. For existing tests, it would be beneficial if it were possible to further optimize the available materials by increasing the function's steepness. The objective is to show if the steepness of the psychometric function of an existing matrix test can be increased by selecting a homogeneous subset of recordings with the steepest sentence-based psychometric functions. We took data from a previous multicenter evaluation of the Dutch matrix test (45 normal-hearing listeners). Based on half of the data set, first the sentences (140 out of 311) with a similar speech reception threshold and with the steepest psychometric function (≥9.7%/dB) were selected. Subsequently, the steepness of the psychometric function for this selection was calculated from the remaining (unused) second half of the data set. The calculation showed that the slope increased from 10.2%/dB to 13.7%/dB. The resulting subset did not allow the construction of enough balanced test lists. Therefore, the measurement procedure was changed to randomly select the sentences during testing. Random selection may interfere with a representative occurrence of phonemes. However, in our material, the median phonemic occurrence remained close to that of the original test. This finding indicates that phonemic occurrence is not a critical factor. The work highlights the possibility that existing speech tests might be improved by selecting sentences with a steep psychometric function.

  9. Discovery of a potent class I selective ketone histone deacetylase inhibitor with antitumor activity in vivo and optimized pharmacokinetic properties.

    PubMed

    Kinzel, Olaf; Llauger-Bufi, Laura; Pescatore, Giovanna; Rowley, Michael; Schultz-Fademrecht, Carsten; Monteagudo, Edith; Fonsi, Massimiliano; Gonzalez Paz, Odalys; Fiore, Fabrizio; Steinkühler, Christian; Jones, Philip

    2009-06-11

    The optimization of a potent, class I selective ketone HDAC inhibitor is shown. It possesses optimized pharmacokinetic properties in preclinical species, has a clean off-target profile, and is negative in a microbial mutagenicity (Ames) test. In a mouse xenograft model it shows efficacy comparable to that of vorinostat at a 10-fold reduced dose.

  10. Optimizing the StackSlide setup and data selection for continuous-gravitational-wave searches in realistic detector data

    NASA Astrophysics Data System (ADS)

    Shaltev, M.

    2016-02-01

    The search for continuous gravitational waves in a wide parameter space at a fixed computing cost is most efficiently done with semicoherent methods, e.g., StackSlide, due to the prohibitive computing cost of the fully coherent search strategies. Prix and Shaltev [Phys. Rev. D 85, 084010 (2012)] have developed a semianalytic method for finding optimal StackSlide parameters at a fixed computing cost under ideal data conditions, i.e., gapless data and a constant noise floor. In this work, we consider more realistic conditions by allowing for gaps in the data and changes in the noise level. We show how the sensitivity optimization can be decoupled from the data selection problem. To find optimal semicoherent search parameters, we apply a numerical optimization using as an example the semicoherent StackSlide search. We also describe three different data selection algorithms. Thus, the outcome of the numerical optimization consists of the optimal search parameters and the selected data set. We first test the numerical optimization procedure under ideal conditions and show that we can reproduce the results of the analytical method. Then we gradually relax the conditions on the data and find that a compact data selection algorithm yields higher sensitivity compared to a greedy data selection procedure.

  11. Agonistic aptamer to the insulin receptor leads to biased signaling and functional selectivity through allosteric modulation.

    PubMed

    Yunn, Na-Oh; Koh, Ara; Han, Seungmin; Lim, Jong Hun; Park, Sehoon; Lee, Jiyoun; Kim, Eui; Jang, Sung Key; Berggren, Per-Olof; Ryu, Sung Ho

    2015-09-18

    Due to their high affinity and specificity, aptamers have been widely used as effective inhibitors in clinical applications. However, the ability to activate protein function through aptamer-protein interaction has not been well-elucidated. To investigate their potential as target-specific agonists, we used SELEX to generate aptamers to the insulin receptor (IR) and identified an agonistic aptamer named IR-A48 that specifically binds to IR, but not to IGF-1 receptor. Despite its capacity to stimulate IR autophosphorylation, similar to insulin, we found that IR-A48 not only binds to an allosteric site distinct from the insulin binding site, but also preferentially induces Y1150 phosphorylation in the IR kinase domain. Moreover, Y1150-biased phosphorylation induced by IR-A48 selectively activates specific signaling pathways downstream of IR. In contrast to insulin-mediated activation of IR, IR-A48 binding has little effect on the MAPK pathway and proliferation of cancer cells. Instead, AKT S473 phosphorylation is highly stimulated by IR-A48, resulting in increased glucose uptake both in vitro and in vivo. Here, we present IR-A48 as a biased agonist able to selectively induce the metabolic activity of IR through allosteric binding. Furthermore, our study also suggests that aptamers can be a promising tool for developing artificial biased agonists to targeted receptors. PMID:26245346

  12. Agonistic aptamer to the insulin receptor leads to biased signaling and functional selectivity through allosteric modulation

    PubMed Central

    Yunn, Na-Oh; Koh, Ara; Han, Seungmin; Lim, Jong Hun; Park, Sehoon; Lee, Jiyoun; Kim, Eui; Jang, Sung Key; Berggren, Per-Olof; Ryu, Sung Ho

    2015-01-01

    Due to their high affinity and specificity, aptamers have been widely used as effective inhibitors in clinical applications. However, the ability to activate protein function through aptamer-protein interaction has not been well-elucidated. To investigate their potential as target-specific agonists, we used SELEX to generate aptamers to the insulin receptor (IR) and identified an agonistic aptamer named IR-A48 that specifically binds to IR, but not to IGF-1 receptor. Despite its capacity to stimulate IR autophosphorylation, similar to insulin, we found that IR-A48 not only binds to an allosteric site distinct from the insulin binding site, but also preferentially induces Y1150 phosphorylation in the IR kinase domain. Moreover, Y1150-biased phosphorylation induced by IR-A48 selectively activates specific signaling pathways downstream of IR. In contrast to insulin-mediated activation of IR, IR-A48 binding has little effect on the MAPK pathway and proliferation of cancer cells. Instead, AKT S473 phosphorylation is highly stimulated by IR-A48, resulting in increased glucose uptake both in vitro and in vivo. Here, we present IR-A48 as a biased agonist able to selectively induce the metabolic activity of IR through allosteric binding. Furthermore, our study also suggests that aptamers can be a promising tool for developing artificial biased agonists to targeted receptors. PMID:26245346

  13. Stochastic approach to reconstruction of dynamical systems: optimal model selection criterion

    NASA Astrophysics Data System (ADS)

    Gavrilov, A.; Mukhin, D.; Loskutov, E. M.; Feigin, A. M.

    2011-12-01

    Most of known observable systems are complex and high-dimensional that doesn't allow to make the exact long-term forecast of their behavior. The stochastic approach to reconstruction of such systems gives a hope to describe important qualitative features of their behavior in a low-dimensional way while all other dynamics is modelled as stochastic disturbance. This report is devoted to application of Bayesian evidence for optimal stochastic model selection when reconstructing the evolution operator of observable system. The idea of Bayesian evidence is to find compromise between the model predictiveness and quality of fitting the model into the data. We represent the evolution operator of investigated system in a form of random dynamic system including deterministic and stochastic parts, both parameterized by artificial neural network. Then we use Bayesian evidence criterion to estimate optimal complexity of the model, i.e. both number of parameters and dimension corresponding to most probable model given the data. We demonstrate on the number of model examples that the model with non-uniformly distributed stochastic part (which corresponds to non-Gaussian perturbations of evolution operator) is optimal in general case. Further, we show that simple stochastic model can be the most preferred for reconstruction of the evolution operator underlying complex observed dynamics even in a case of deterministic high-dimensional system. Workability of suggested approach for modeling and prognosis of real-measured geophysical dynamics is investigated.

  14. Design-Optimization and Material Selection for a Proximal Radius Fracture-Fixation Implant

    NASA Astrophysics Data System (ADS)

    Grujicic, M.; Xie, X.; Arakere, G.; Grujicic, A.; Wagner, D. W.; Vallejo, A.

    2010-11-01

    The problem of optimal size, shape, and placement of a proximal radius-fracture fixation-plate is addressed computationally using a combined finite-element/design-optimization procedure. To expand the set of physiological loading conditions experienced by the implant during normal everyday activities of the patient, beyond those typically covered by the pre-clinical implant-evaluation testing procedures, the case of a wheel-chair push exertion is considered. Toward that end, a musculoskeletal multi-body inverse-dynamics analysis is carried out of a human propelling a wheelchair. The results obtained are used as input to a finite-element structural analysis for evaluation of the maximum stress and fatigue life of the parametrically defined implant design. While optimizing the design of the radius-fracture fixation-plate, realistic functional requirements pertaining to the attainment of the required level of the devise safety factor and longevity/lifecycle were considered. It is argued that the type of analyses employed in the present work should be: (a) used to complement the standard experimental pre-clinical implant-evaluation tests (the tests which normally include a limited number of daily-living physiological loading conditions and which rely on single pass/fail outcomes/decisions with respect to a set of lower-bound implant-performance criteria) and (b) integrated early in the implant design and material/manufacturing-route selection process.

  15. Optimizing mass spectrometric detection for ion chromatographic analysis. I. Common anions and selected organic acids.

    PubMed

    Wang, Jinyuan; Schnute, William C

    2009-11-01

    We describe a systematic method of optimizing mass spectrometric (MS) detection for ion chromatographic (IC) analysis of common anions and three selected organic acids using response surface methodology (RSM). RSM was utilized in this study because it minimized the number of experiments required to achieve the optimum MS response and included the interactions between individual parameters for multivariable optimization. Five MS parameters, including probe temperature, nebulizer gas, assistant makeup flow, needle voltage and cone voltage, were screened and systematically optimized by two steps. Central composite design (CCD) was used to design the experiment points and a quadratic model was applied to fit the experimental data. Analysis of variance (ANOVA) was carried out to evaluate the validity of the statistical model and to determine the most significant parameters for MS response. The optimum MS conditions for each analyte were summarized and the method optimum condition was achieved by applying desirability function. Our observation showed good agreements between statistically predicted optimum response and the responses collected at the predicted optimum condition. Operable range of each parameter (with normalized MS response greater than 0.8 for each analyte) was provided for general anionic IC/MS applications.

  16. Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Asadzadeh, Masoud; Tolson, Bryan

    2013-12-01

    Pareto archived dynamically dimensioned search (PA-DDS) is a parsimonious multi-objective optimization algorithm with only one parameter to diminish the user's effort for fine-tuning algorithm parameters. This study demonstrates that hypervolume contribution (HVC) is a very effective selection metric for PA-DDS and Monte Carlo sampling-based HVC is very effective for higher dimensional problems (five objectives in this study). PA-DDS with HVC performs comparably to algorithms commonly applied to water resources problems (ɛ-NSGAII and AMALGAM under recommended parameter values). Comparisons on the CEC09 competition show that with sufficient computational budget, PA-DDS with HVC performs comparably to 13 benchmark algorithms and shows improved relative performance as the number of objectives increases. Lastly, it is empirically demonstrated that the total optimization runtime of PA-DDS with HVC is dominated (90% or higher) by solution evaluation runtime whenever evaluation exceeds 10 seconds/solution. Therefore, optimization algorithm runtime associated with the unbounded archive of PA-DDS is negligible in solving computationally intensive problems.

  17. Optimality and stability of symmetric evolutionary games with applications in genetic selection.

    PubMed

    Huang, Yuanyuan; Hao, Yiping; Wang, Min; Zhou, Wen; Wu, Zhijun

    2015-06-01

    Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.

  18. Role of the development scientist in compound lead selection and optimization.

    PubMed

    Venkatesh, S; Lipper, R A

    2000-02-01

    The R&D process for bringing drugs from discovery laboratories to the marketplace is undergoing rapid change, as enabled by new technologies and as demanded by the global pharmaceutical business environment. One consequence of the accelerated R&D paradigm is a blurring of the traditional discovery-development interface, which in turn impacts the traditional roles of discovery and development scientists. R&D organizations must find ways to screen out rapidly compounds that have relatively poor probability of successful registration. Quality of development candidates can be favorably influenced by early consideration of "developability" criteria along with receptor-based potency and specificity. Computational approaches and/or high-throughput experimental determinations will be used increasingly to profile compound characteristics which influence "developability." If such criteria are considered at the time of lead selection and optimization, the compound attrition rate during later development should be decreased from the historical norm. This article discusses the emerging role of development scientists during small-molecule lead selection and optimization. The changing role of development scientists also has implications for graduate curricula in the pharmaceutical sciences. PMID:10688744

  19. Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

    PubMed Central

    Deng, Ning

    2014-01-01

    In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

  20. Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.

    PubMed

    Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning

    2014-01-01

    In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

  1. Selective preparation of enantiomers by laser pulses: From optimal control to specific pump and dump transitions

    NASA Astrophysics Data System (ADS)

    González, L.; Hoki, K.; Kröner, D.; Leal, A. S.; Manz, J.; Ohtsuki, Y.

    2000-12-01

    Starting from optimal control, various series of infrared, ultrashort laser pulses with analytical shapes are designed in order to drive a preoriented molecule from its ground torsional state, which represents the coherent superposition of left and right atropisomers, towards a single enantiomer. Close analysis of the population dynamics, together with the underlying symmetry selection rules for the laser induced transitions, yields the mechanism. Namely, the molecule is driven from its ground vibrational state towards the coherent superposition of the lowest doublet of states via a doublet of excited torsional states with opposite symmetries. This pump-and-dump mechanism can be achieved by simpler series of analytical laser pulses. This decomposition of the optimal pulse into analytical subpulses allows us to design different scenarios for the selective preparation of left or right enantiomers. Exemplary this is demonstrated by quantum simulations of representative wave packets for the torsional motions of the model system, H2POSH, in the electronic ground state, based on the ab initio potential energy surface, and with ab initio dipole couplings.

  2. Selection on synonymous codons in mammalian rhodopsins: a possible role in optimizing translational processes

    PubMed Central

    2014-01-01

    . Our results suggest that codon bias in mammalian rhodopsin arises from selection to optimally balance high overall translational speed, accuracy, and proper protein folding, especially in structurally complicated regions. Selection at synonymous sites may also be contributing to mRNA stability and splicing efficiency at exonic-splicing-enhancer (ESE) regions. Our results highlight the importance of investigating highly expressed genes in a broader phylogenetic context in order to better understand the evolution of synonymous substitutions. PMID:24884412

  3. Optimized diffusion of buck semen for saving genetic variability in selected dairy goat populations

    PubMed Central

    2011-01-01

    Background Current research on quantitative genetics has provided efficient guidelines for the sustainable management of selected populations: genetic gain is maximized while the loss of genetic diversity is maintained at a reasonable rate. However, actual selection schemes are complex, especially for large domestic species, and they have to take into account many operational constraints. This paper deals with the actual selection of dairy goats where the challenge is to optimize diffusion of buck semen on the field. Three objectives are considered simultaneously: i) natural service buck replacement (NSR); ii) goat replacement (GR); iii) semen distribution of young bucks to be progeny-tested. An appropriate optimization method is developed, which involves five analytical steps. Solutions are obtained by simulated annealing and the corresponding algorithms are presented in detail. Results The whole procedure was tested on two French goat populations (Alpine and Saanen breeds) and the results presented in the abstract were based on the average of the two breeds. The procedure induced an immediate acceleration of genetic gain in comparison with the current annual genetic gain (0.15 genetic standard deviation unit), as shown by two facts. First, the genetic level of replacement natural service (NS) bucks was predicted, 1.5 years ahead at the moment of reproduction, to be equivalent to that of the progeny-tested bucks in service, born from the current breeding scheme. Second, the genetic level of replacement goats was much higher than that of their dams (0.86 unit), which represented 6 years of selection, although dams were only 3 years older than their replacement daughters. This improved genetic gain could be achieved while decreasing inbreeding coefficients substantially. Inbreeding coefficients (%) of NS bucks was lower than that of the progeny-tested bucks (-0.17). Goats were also less inbred than their dams (-0.67). Conclusions It was possible to account for

  4. Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection

    PubMed Central

    Bailey, Jacqueline; Timmis, Jon; Chtanova, Tatyana

    2016-01-01

    The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs) against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto fronts of optimal

  5. Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection.

    PubMed

    Read, Mark N; Bailey, Jacqueline; Timmis, Jon; Chtanova, Tatyana

    2016-09-01

    The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs) against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto fronts of optimal

  6. Application of differential evolution for optimization of least-square support vector machine classifier of signal-averaged electrocardiograms

    NASA Astrophysics Data System (ADS)

    Krys, Sebastian; Jankowski, Stanislaw; Piatkowska-Janko, Ewa

    2009-06-01

    This paper presents the application of differential evolution, an evolutionary algorithm of solving a single objective optimization problem - tuning the hiperparameters of least-square support vector machine classifier. The goal was to improve the classification of patients with sustained ventricular tachycardia after myocardial infarction based on a signal-averaged electrocardiography dataset received from the Medical University of Warsaw. The applied method attained a classification rate of 96% of the SVT+ group.

  7. Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces.

    PubMed

    Wei, Qingguo; Wei, Zhonghai

    2015-01-01

    A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.

  8. A TOTP-Based Enhanced Route Optimization Procedure for Mobile IPv6 to Reduce Handover Delay and Signalling Overhead

    PubMed Central

    Shah, Peer Azmat; Hasbullah, Halabi B.; Lawal, Ibrahim A.; Aminu Mu'azu, Abubakar; Tang Jung, Low

    2014-01-01

    Due to the proliferation of handheld mobile devices, multimedia applications like Voice over IP (VoIP), video conferencing, network music, and online gaming are gaining popularity in recent years. These applications are well known to be delay sensitive and resource demanding. The mobility of mobile devices, running these applications, across different networks causes delay and service disruption. Mobile IPv6 was proposed to provide mobility support to IPv6-based mobile nodes for continuous communication when they roam across different networks. However, the Route Optimization procedure in Mobile IPv6 involves the verification of mobile node's reachability at the home address and at the care-of address (home test and care-of test) that results in higher handover delays and signalling overhead. This paper presents an enhanced procedure, time-based one-time password Route Optimization (TOTP-RO), for Mobile IPv6 Route Optimization that uses the concepts of shared secret Token, time based one-time password (TOTP) along with verification of the mobile node via direct communication and maintaining the status of correspondent node's compatibility. The TOTP-RO was implemented in network simulator (NS-2) and an analytical analysis was also made. Analysis showed that TOTP-RO has lower handover delays, packet loss, and signalling overhead with an increased level of security as compared to the standard Mobile IPv6's Return-Routability-based Route Optimization (RR-RO). PMID:24688398

  9. A real-time characterization method to rapidly optimize molecular beacon signal for sensitive nucleic acids analysis.

    PubMed

    Hsieh, Albert Tsung-Hsi; Pan, Patrick J; Lee, Abraham P

    2014-05-01

    This research demonstrates an integrated microfluidic titration assay to characterize the cation concentrations in working buffer to rapidly optimize the signal-to-noise ratio (SNR) of molecular beacons (MBs). The "Microfluidic Droplet Array Titration Assay" (MiDATA) integrated the functions of sample dilution, sample loading, sample mixing, fluorescence analysis, and re-confirmation functions all together in a one-step process. It allows experimentalists to arbitrarily change sample concentration and acquire SNR measurements instantaneously. MiDATA greatly reduces sample dilution time, number of samples needed, sample consumption, and the total titration time. The maximum SNR of molecular beacons is achieved by optimizing the concentrations of the monovalent and divalent cation (i.e., Mg(2+) and K(+)) of the working buffer. MiDATA platform is able to reduce the total consumed reagents to less than 50 μL, and decrease the assay time to less than 30 min. The SNR of the designated MB is increased from 20 to 126 (i.e., enhanced the signal 630 %) using the optimal concentration of MgCl2 and KCl determined by MiDATA. This novel microfluidics-based titration method is not only useful for SNR optimization of molecular beacons but it also can be a general method for a wide range of fluorescence resonance energy transfer (FRET)-based molecular probes.

  10. A TOTP-based enhanced route optimization procedure for mobile IPv6 to reduce handover delay and signalling overhead.

    PubMed

    Shah, Peer Azmat; Hasbullah, Halabi B; Lawal, Ibrahim A; Aminu Mu'azu, Abubakar; Tang Jung, Low

    2014-01-01

    Due to the proliferation of handheld mobile devices, multimedia applications like Voice over IP (VoIP), video conferencing, network music, and online gaming are gaining popularity in recent years. These applications are well known to be delay sensitive and resource demanding. The mobility of mobile devices, running these applications, across different networks causes delay and service disruption. Mobile IPv6 was proposed to provide mobility support to IPv6-based mobile nodes for continuous communication when they roam across different networks. However, the Route Optimization procedure in Mobile IPv6 involves the verification of mobile node's reachability at the home address and at the care-of address (home test and care-of test) that results in higher handover delays and signalling overhead. This paper presents an enhanced procedure, time-based one-time password Route Optimization (TOTP-RO), for Mobile IPv6 Route Optimization that uses the concepts of shared secret Token, time based one-time password (TOTP) along with verification of the mobile node via direct communication and maintaining the status of correspondent node's compatibility. The TOTP-RO was implemented in network simulator (NS-2) and an analytical analysis was also made. Analysis showed that TOTP-RO has lower handover delays, packet loss, and signalling overhead with an increased level of security as compared to the standard Mobile IPv6's Return-Routability-based Route Optimization (RR-RO).

  11. A TOTP-based enhanced route optimization procedure for mobile IPv6 to reduce handover delay and signalling overhead.

    PubMed

    Shah, Peer Azmat; Hasbullah, Halabi B; Lawal, Ibrahim A; Aminu Mu'azu, Abubakar; Tang Jung, Low

    2014-01-01

    Due to the proliferation of handheld mobile devices, multimedia applications like Voice over IP (VoIP), video conferencing, network music, and online gaming are gaining popularity in recent years. These applications are well known to be delay sensitive and resource demanding. The mobility of mobile devices, running these applications, across different networks causes delay and service disruption. Mobile IPv6 was proposed to provide mobility support to IPv6-based mobile nodes for continuous communication when they roam across different networks. However, the Route Optimization procedure in Mobile IPv6 involves the verification of mobile node's reachability at the home address and at the care-of address (home test and care-of test) that results in higher handover delays and signalling overhead. This paper presents an enhanced procedure, time-based one-time password Route Optimization (TOTP-RO), for Mobile IPv6 Route Optimization that uses the concepts of shared secret Token, time based one-time password (TOTP) along with verification of the mobile node via direct communication and maintaining the status of correspondent node's compatibility. The TOTP-RO was implemented in network simulator (NS-2) and an analytical analysis was also made. Analysis showed that TOTP-RO has lower handover delays, packet loss, and signalling overhead with an increased level of security as compared to the standard Mobile IPv6's Return-Routability-based Route Optimization (RR-RO). PMID:24688398

  12. Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals.

    PubMed

    Yu, Lianchun; Liu, Liwei

    2014-03-01

    The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

  13. Selective olfactory attention of a specialised predator to intraspecific chemical signals of its prey

    NASA Astrophysics Data System (ADS)

    Cárdenas, Manuel; Jiroš, Pavel; Pekár, Stano

    2012-08-01

    Prey-specialised predators have evolved specific cognitive adaptations that increase their prey searching efficiency. In particular, when the prey is social, selection probably favours the use of prey intraspecific chemical signals by predatory arthropods. Using a specialised ant-eating zodariid spider, Zodarion rubidum, which is known to prey on several ant species and possesses capture and venom adaptations more effective on Formicinae ants, we tested its ability to recognise chemical cues produced by several ant species. Using an olfactometer, we tested the response of Z. rubidum towards air with chemical cues from six different ant species: Camponotus ligniperda, Lasius platythorax and Formica rufibarbis (all Formicinae); and Messor structor, Myrmica scabrinodis and Tetramorium caespitum (all Myrmicinae). Z. rubidum was attracted to air carrying chemical cues only from F. rufibarbis and L. platythorax. Then, we identified that the spiders were attracted to airborne cues coming from the F. rufibarbis gaster and Dufour's gland, in particular. Finally, we found that among several synthetic blends, the decyl acetate and undecane mixture produced significant attraction of spiders. These chemicals are produced only by three Formicine genera. Furthermore, we investigated the role of these chemical cues in the communication of F. rufibarbis and found that this blend reduces their movement. This study demonstrates the chemical cognitive capacity of Z. rubidum to locate its ant prey using chemical signals produced by the ants. The innate capacity of Z. rubidum to olfactory detect different ant species is narrow, as it includes only two ant genera, confirming trophic specialisation at lower than subfamily level. The olfactory cue detected by Zodarion spiders is probably a component of the recruitment or trail pheromone.

  14. Online stimulus optimization rapidly reveals multidimensional selectivity in auditory cortical neurons.

    PubMed

    Chambers, Anna R; Hancock, Kenneth E; Sen, Kamal; Polley, Daniel B

    2014-07-01

    Neurons in sensory brain regions shape our perception of the surrounding environment through two parallel operations: decomposition and integration. For example, auditory neurons decompose sounds by separately encoding their frequency, temporal modulation, intensity, and spatial location. Neurons also integrate across these various features to support a unified perceptual gestalt of an auditory object. At higher levels of a sensory pathway, neurons may select for a restricted region of feature space defined by the intersection of multiple, independent stimulus dimensions. To further characterize how auditory cortical neurons decompose and integrate multiple facets of an isolated sound, we developed an automated procedure that manipulated five fundamental acoustic properties in real time based on single-unit feedback in awake mice. Within several minutes, the online approach converged on regions of the multidimensional stimulus manifold that reliably drove neurons at significantly higher rates than predefined stimuli. Optimized stimuli were cross-validated against pure tone receptive fields and spectrotemporal receptive field estimates in the inferior colliculus and primary auditory cortex. We observed, from midbrain to cortex, increases in both level invariance and frequency selectivity, which may underlie equivalent sparseness of responses in the two areas. We found that onset and steady-state spike rates increased proportionately as the stimulus was tailored to the multidimensional receptive field. By separately evaluating the amount of leverage each sound feature exerted on the overall firing rate, these findings reveal interdependencies between stimulus features as well as hierarchical shifts in selectivity and invariance that may go unnoticed with traditional approaches. PMID:24990917

  15. Real-time 2D spatially selective MRI experiments: Comparative analysis of optimal control design methods.

    PubMed

    Maximov, Ivan I; Vinding, Mads S; Tse, Desmond H Y; Nielsen, Niels Chr; Shah, N Jon

    2015-05-01

    There is an increasing need for development of advanced radio-frequency (RF) pulse techniques in modern magnetic resonance imaging (MRI) systems driven by recent advancements in ultra-high magnetic field systems, new parallel transmit/receive coil designs, and accessible powerful computational facilities. 2D spatially selective RF pulses are an example of advanced pulses that have many applications of clinical relevance, e.g., reduced field of view imaging, and MR spectroscopy. The 2D spatially selective RF pulses are mostly generated and optimised with numerical methods that can handle vast controls and multiple constraints. With this study we aim at demonstrating that numerical, optimal control (OC) algorithms are efficient for the design of 2D spatially selective MRI experiments, when robustness towards e.g. field inhomogeneity is in focus. We have chosen three popular OC algorithms; two which are gradient-based, concurrent methods using first- and second-order derivatives, respectively; and a third that belongs to the sequential, monotonically convergent family. We used two experimental models: a water phantom, and an in vivo human head. Taking into consideration the challenging experimental setup, our analysis suggests the use of the sequential, monotonic approach and the second-order gradient-based approach as computational speed, experimental robustness, and image quality is key. All algorithms used in this work were implemented in the MATLAB environment and are freely available to the MRI community.

  16. A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.

    PubMed

    Yun, Yong-Huan; Wang, Wei-Ting; Tan, Min-Li; Liang, Yi-Zeng; Li, Hong-Dong; Cao, Dong-Sheng; Lu, Hong-Mei; Xu, Qing-Song

    2014-01-01

    Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list. PMID:24356218

  17. Real-time 2D spatially selective MRI experiments: Comparative analysis of optimal control design methods

    NASA Astrophysics Data System (ADS)

    Maximov, Ivan I.; Vinding, Mads S.; Tse, Desmond H. Y.; Nielsen, Niels Chr.; Shah, N. Jon

    2015-05-01

    There is an increasing need for development of advanced radio-frequency (RF) pulse techniques in modern magnetic resonance imaging (MRI) systems driven by recent advancements in ultra-high magnetic field systems, new parallel transmit/receive coil designs, and accessible powerful computational facilities. 2D spatially selective RF pulses are an example of advanced pulses that have many applications of clinical relevance, e.g., reduced field of view imaging, and MR spectroscopy. The 2D spatially selective RF pulses are mostly generated and optimised with numerical methods that can handle vast controls and multiple constraints. With this study we aim at demonstrating that numerical, optimal control (OC) algorithms are efficient for the design of 2D spatially selective MRI experiments, when robustness towards e.g. field inhomogeneity is in focus. We have chosen three popular OC algorithms; two which are gradient-based, concurrent methods using first- and second-order derivatives, respectively; and a third that belongs to the sequential, monotonically convergent family. We used two experimental models: a water phantom, and an in vivo human head. Taking into consideration the challenging experimental setup, our analysis suggests the use of the sequential, monotonic approach and the second-order gradient-based approach as computational speed, experimental robustness, and image quality is key. All algorithms used in this work were implemented in the MATLAB environment and are freely available to the MRI community.

  18. Optimization methods for selecting founder individuals for captive breeding or reintroduction of endangered species.

    PubMed

    Miller, Webb; Wright, Stephen J; Zhang, Yu; Schuster, Stephan C; Hayes, Vanessa M

    2010-01-01

    Methods from genetics and genomics can be employed to help save endangered species. One potential use is to provide a rational strategy for selecting a population of founders for a captive breeding program. The hope is to capture most of the available genetic diversity that remains in the wild population, to provide a safe haven where representatives of the species can be bred, and eventually to release the progeny back into the wild. However, the founders are often selected based on a random-sampling strategy whose validity is based on unrealistic assumptions. Here we outline an approach that starts by using cutting-edge genome sequencing and genotyping technologies to objectively assess the available genetic diversity. We show how combinatorial optimization methods can be applied to these data to guide the selection of the founder population. In particular, we develop a mixed-integer linear programming technique that identifies a set of animals whose genetic profile is as close as possible to specified abundances of alleles (i.e., genetic variants), subject to constraints on the number of founders and their genders and ages. PMID:19908356

  19. Real-time 2D spatially selective MRI experiments: Comparative analysis of optimal control design methods.

    PubMed

    Maximov, Ivan I; Vinding, Mads S; Tse, Desmond H Y; Nielsen, Niels Chr; Shah, N Jon

    2015-05-01

    There is an increasing need for development of advanced radio-frequency (RF) pulse techniques in modern magnetic resonance imaging (MRI) systems driven by recent advancements in ultra-high magnetic field systems, new parallel transmit/receive coil designs, and accessible powerful computational facilities. 2D spatially selective RF pulses are an example of advanced pulses that have many applications of clinical relevance, e.g., reduced field of view imaging, and MR spectroscopy. The 2D spatially selective RF pulses are mostly generated and optimised with numerical methods that can handle vast controls and multiple constraints. With this study we aim at demonstrating that numerical, optimal control (OC) algorithms are efficient for the design of 2D spatially selective MRI experiments, when robustness towards e.g. field inhomogeneity is in focus. We have chosen three popular OC algorithms; two which are gradient-based, concurrent methods using first- and second-order derivatives, respectively; and a third that belongs to the sequential, monotonically convergent family. We used two experimental models: a water phantom, and an in vivo human head. Taking into consideration the challenging experimental setup, our analysis suggests the use of the sequential, monotonic approach and the second-order gradient-based approach as computational speed, experimental robustness, and image quality is key. All algorithms used in this work were implemented in the MATLAB environment and are freely available to the MRI community. PMID:25863895

  20. A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.

    PubMed

    Yun, Yong-Huan; Wang, Wei-Ting; Tan, Min-Li; Liang, Yi-Zeng; Li, Hong-Dong; Cao, Dong-Sheng; Lu, Hong-Mei; Xu, Qing-Song

    2014-01-01

    Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.

  1. 3 CFR 13546 - Executive Order 13546 of July 2, 2010. Optimizing the Security of Biological Select Agents and...

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... the Security of Biological Select Agents and Toxins in the United States 13546 Order 13546 Presidential Documents Executive Orders Executive Order 13546 of July 2, 2010 EO 13546 Optimizing the Security... enterprise that utilizes biological select agents and toxins (BSAT) is essential to national security;...

  2. Evaluation of the selection methods used in the exIWO algorithm based on the optimization of multidimensional functions

    NASA Astrophysics Data System (ADS)

    Kostrzewa, Daniel; Josiński, Henryk

    2016-06-01

    The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version inspired by dynamic growth of weeds colony. The authors of the present paper have modified the exIWO algorithm introducing a set of both deterministic and non-deterministic strategies of individuals' selection. The goal of the project was to evaluate the modified exIWO by testing its usefulness for multidimensional numerical functions optimization. The optimized functions: Griewank, Rastrigin, and Rosenbrock are frequently used as benchmarks because of their characteristics.

  3. X-ray backscatter imaging for radiography by selective detection and snapshot: Evolution, development, and optimization

    NASA Astrophysics Data System (ADS)

    Shedlock, Daniel

    Compton backscatter imaging (CBI) is a single-sided imaging technique that uses the penetrating power of radiation and unique interaction properties of radiation with matter to image subsurface features. CBI has a variety of applications that include non-destructive interrogation, medical imaging, security and military applications. Radiography by selective detection (RSD), lateral migration radiography (LMR) and shadow aperture backscatter radiography (SABR) are different CBI techniques that are being optimized and developed. Radiography by selective detection (RSD) is a pencil beam Compton backscatter imaging technique that falls between highly collimated and uncollimated techniques. Radiography by selective detection uses a combination of single- and multiple-scatter photons from a projected area below a collimation plane to generate an image. As a result, the image has a combination of first- and multiple-scatter components. RSD techniques offer greater subsurface resolution than uncollimated techniques, at speeds at least an order of magnitude faster than highly collimated techniques. RSD scanning systems have evolved from a prototype into near market-ready scanning devices for use in a variety of single-sided imaging applications. The design has changed to incorporate state-of-the-art detectors and electronics optimized for backscatter imaging with an emphasis on versatility, efficiency and speed. The RSD system has become more stable, about 4 times faster, and 60% lighter while maintaining or improving image quality and contrast over the past 3 years. A new snapshot backscatter radiography (SBR) CBI technique, shadow aperture backscatter radiography (SABR), has been developed from concept and proof-of-principle to a functional laboratory prototype. SABR radiography uses digital detection media and shaded aperture configurations to generate near-surface Compton backscatter images without scanning, similar to how transmission radiographs are taken. Finally, a

  4. [Study on optimal selection of structure of vaneless centrifugal blood pump with constraints on blood perfusion and on blood damage indexes].

    PubMed

    Hu, Zhaoyan; Pan, Youlian; Chen, Zhenglong; Zhang, Tianyi; Lu, Lijun

    2012-12-01

    This paper is aimed to study the optimal selection of structure of vaneless centrifugal blood pump. The optimal objective is determined according to requirements of clinical use. Possible schemes are generally worked out based on structural feature of vaneless centrifugal blood pump. The optimal structure is selected from possible schemes with constraints on blood perfusion and blood damage indexes. Using an optimal selection method one can find the optimum structure scheme from possible schemes effectively. The results of numerical simulation of optimal blood pump showed that the method of constraints of blood perfusion and blood damage is competent for the requirements of selection of the optimal blood pumps.

  5. Synthesis and Assessment of DNA/Silver Nanoclusters Probes for Optimal and Selective Detection of Tristeza Virus Mild Strains.

    PubMed

    Shokri, Ehsan; Hosseini, Morteza; Faridbod, Farnoush; Rahaie, Mahdi

    2016-09-01

    Citrus Tristeza virus (CTV) is one of the most destructive pathogens worldwide that exist as a mixture of malicious (Sever) and tolerable (Mild) strains. Mild strains of CTV can be used to immunize healthy plants from more Severe strains damage. Recently, innovative methods based on the fluorescent properties of DNA/silver nanoclusters have been developed for molecular detection purposes. In this study, a simple procedure was followed to create more active DNA/AgNCs probe for accurate and selective detection of Tristeza Mild-RNA. To this end, four distinct DNA emitter scaffolds (C12, Red, Green, Yellow) were tethered to the Mild capture sequence and investigated in various buffers in order to find highly emissive combinations. Then, to achieve specific and reliable results, several chemical additives, including organic solvents, PEG and organo-soluble salts were used to enhance control fluorescence signals and optimize the hybridization solution. The data showed that, under adjusted conditions, the target sensitivity is enhanced by a factor of five and the high discrimination between Mild and Severe RNAs were obtained. The emission ratio of the DNA/AgNCs was dropped in the presence of target RNAs and I0/I intensity linearly ranged from 1.5 × 10(-8) M to 1.8 × 10(-6) M with the detection limit of 4.3 × 10(-9) M. PMID:27349801

  6. A Residue Quartet in the Extracellular Domain of the Prolactin Receptor Selectively Controls Mitogen-activated Protein Kinase Signaling.

    PubMed

    Zhang, Chi; Nygaard, Mads; Haxholm, Gitte W; Boutillon, Florence; Bernadet, Marie; Hoos, Sylviane; England, Patrick; Broutin, Isabelle; Kragelund, Birthe B; Goffin, Vincent

    2015-05-01

    Cytokine receptors elicit several signaling pathways, but it is poorly understood how they select and discriminate between them. We have scrutinized the prolactin receptor as an archetype model of homodimeric cytokine receptors to address the role of the extracellular membrane proximal domain in signal transfer and pathway selection. Structure-guided manipulation of residues involved in the receptor dimerization interface identified one residue (position 170) that in cell-based assays profoundly altered pathway selectivity and species-specific bio-characteristics. Subsequent in vitro spectroscopic and nuclear magnetic resonance analyses revealed that this residue was part of a residue quartet responsible for specific local structural changes underlying these effects. This included alteration of a novel aromatic T-stack within the membrane proximal domain, which promoted selective signaling affecting primarily the MAPK (ERK1/2) pathway. Importantly, activation of the MAPK pathway correlated with in vitro stabilities of ternary ligand·receptor complexes, suggesting a threshold mean lifetime of the complex necessary to achieve maximal activation. No such dependence was observed for STAT5 signaling. Thus, this study establishes a residue quartet in the extracellular membrane proximal domain of homodimeric cytokine receptors as a key regulator of intracellular signaling discrimination.

  7. Aripiprazole has functionally selective actions at dopamine D2 receptor-mediated signaling pathways.

    PubMed

    Urban, Jonathan D; Vargas, Gabriel A; von Zastrow, Mark; Mailman, Richard B

    2007-01-01

    Aripiprazole is a unique atypical antipsychotic drug with an excellent side-effect profile presumed, in part, to be due to lack of typical D(2) dopamine receptor antagonist properties. Whether aripiprazole is a typical D(2) partial agonist, or a functionally selective D(2) ligand, remains controversial (eg D(2)-mediated inhibition of adenylate cyclase is system dependent; aripiprazole antagonizes D(2) receptor-mediated G-protein-coupled inwardly rectifying potassium channels and guanosine triphosphate nucleotide (GTP)gammaS coupling). The current study examined the D(2L) receptor binding properties of aripiprazole, as well as the effects of the drug on three downstream D(2) receptor-mediated functional effectors: mitogen-activated protein kinase (MAPK) phosphorylation, potentiation of arachidonic acid (AA) release, and D(2) receptor internalization. Unlike quinpirole (a full D(2) agonist) or (-)3PPP (S(-)-3-(3-hydroxyphenyl)-N-propylpiperidine hydrochloride, a D(2) partial agonist), the apparent D(2) affinity of aripiprazole was not decreased significantly by GTP. Moreover, full or partial agonists are expected to have Hill slopes <1.0, yet that of aripiprazole was significantly >1.0. Whereas aripiprazole partially activated both the MAPK and AA pathways, its potency vs MAPK phosphorylation was much lower relative to potencies in assays either of AA release or inhibition of cyclic adenosine 3',5'-cyclic monophosphate accumulation. In addition, unlike typical agonists, neither aripiprazole nor (-)3PPP produced significant internalization of the D(2L) receptor. These data are clear evidence that aripiprazole affects D(2L)-mediated signaling pathways in a differential manner. The results are consistent with the hypothesis that aripiprazole is a functionally selective D(2) ligand rather than a simple partial agonist. Such data may be useful in understanding the novel clinical actions of this drug.

  8. Methodology and method and appartus for signaling with capacity optimized constellations

    NASA Technical Reports Server (NTRS)

    Barsoum, Maged F. (Inventor); Jones, Christopher R. (Inventor)

    2012-01-01

    Communication systems are described that use geometrically shaped constellations that have increased capacity compared to conventional constellations operating within a similar SNR band. In several embodiments, the geometrically shaped is optimized based upon a capacity measure such as parallel decoding capacity or joint capacity. In many embodiments, a capacity optimized geometrically shaped constellation can be used to replace a conventional constellation as part of a firmware upgrade to transmitters and receivers within a communication system. In a number of embodiments, the geometrically shaped constellation is optimized for an Additive White Gaussian Noise channel or a fading channel.

  9. Optimization Of Mean-Semivariance-Skewness Portfolio Selection Model In Fuzzy Random Environment

    NASA Astrophysics Data System (ADS)

    Chatterjee, Amitava; Bhattacharyya, Rupak; Mukherjee, Supratim; Kar, Samarjit

    2010-10-01

    The purpose of the paper is to construct a mean-semivariance-skewness portfolio selection model in fuzzy random environment. The objective is to maximize the skewness with predefined maximum risk tolerance and minimum expected return. Here the security returns in the objectives and constraints are assumed to be fuzzy random variables in nature and then the vagueness of the fuzzy random variables in the objectives and constraints are transformed into fuzzy variables which are similar to trapezoidal numbers. The newly formed fuzzy model is then converted into a deterministic optimization model. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). The exact parameters of fuzzy membership function and probability density function are obtained through fuzzy random simulating the past dates.

  10. Optimal space communications techniques. [discussion of video signals and delta modulation

    NASA Technical Reports Server (NTRS)

    Schilling, D. L.

    1974-01-01

    The encoding of video signals using the Song Adaptive Delta Modulator (Song ADM) is discussed. The video signals are characterized as a sequence of pulses having arbitrary height and width. Although the ADM is suited to tracking signals having fast rise times, it was found that the DM algorithm (which permits an exponential rise for estimating an input step) results in a large overshoot and an underdamped response to the step. An overshoot suppression algorithm which significantly reduces the ringing while not affecting the rise time is presented along with formuli for the rise time and the settling time. Channel errors and their effect on the DM encoded bit stream were investigated.

  11. Signal-to-Noise Enhancement Techniques for Quantum Cascade Absorption Spectrometers Employing Optimal Filtering and Other Approaches

    SciTech Connect

    Disselkamp, Robert S.; Kelly, James F.; Sams, Robert L.; Anderson, Gordon A.

    2002-09-01

    Optical feedback to the laser source in tunable diode laser spectroscopy (TDLS) is known to create intensity modulation noise due to elatoning and optical feedback (i.e., multiplicative technical noise) that usually limits spectral signal-to-noise (S/N). The large technical noise often limits absorption spectroscopy to noise floors 100-fold greater than the Poisson shot noise limit due to fluctuations in the laser intensity. The high output powers generated from quantum cascade (QC) lasers, along with their high gain, makes these injection laser systems especially susceptible to technical noise. In this article we discuss a method of using optimal filtering to reduce technical noise. We have observed S/N enhancements ranging from {approx}20% to a factor of {approx}50. The degree to which optimal filtering will enhance S/N depends on the similarity between the Fourier components of the technical noise and those of the signal, with lower S/N enhancements observed for more similar Fourier decompositions of the signal and technical noise. We also examine the linearity of optimal filtered spectra for both time and intensity. This was accomplished by creating a synthetic spectrum for the species being studied (CH4, N2O, CO2, H2O in ambient air) utilizing line-positions and line-widths with an assumed Voight-profile from a previous database (HITRAN). Agreement better than 0.036% in wavenumber, and 1.64% in intensity (up to a 260-fold intensity ratio employed), was observed. Our results suggest that rapid ex post facto digital optimal filtering can be used to enhance S/N for routine trace gas detection.

  12. Optimal site selection for a high-resolution ice core record in East Antarctica

    NASA Astrophysics Data System (ADS)

    Vance, Tessa R.; Roberts, Jason L.; Moy, Andrew D.; Curran, Mark A. J.; Tozer, Carly R.; Gallant, Ailie J. E.; Abram, Nerilie J.; van Ommen, Tas D.; Young, Duncan A.; Grima, Cyril; Blankenship, Don D.; Siegert, Martin J.

    2016-03-01

    Ice cores provide some of the best-dated and most comprehensive proxy records, as they yield a vast and growing array of proxy indicators. Selecting a site for ice core drilling is nonetheless challenging, as the assessment of potential new sites needs to consider a variety of factors. Here, we demonstrate a systematic approach to site selection for a new East Antarctic high-resolution ice core record. Specifically, seven criteria are considered: (1) 2000-year-old ice at 300 m depth; (2) above 1000 m elevation; (3) a minimum accumulation rate of 250 mm years-1 IE (ice equivalent); (4) minimal surface reworking to preserve the deposited climate signal; (5) a site with minimal displacement or elevation change in ice at 300 m depth; (6) a strong teleconnection to midlatitude climate; and (7) an appropriately complementary relationship to the existing Law Dome record (a high-resolution record in East Antarctica). Once assessment of these physical characteristics identified promising regions, logistical considerations (for site access and ice core retrieval) were briefly considered. We use Antarctic surface mass balance syntheses, along with ground-truthing of satellite data by airborne radar surveys to produce all-of-Antarctica maps of surface roughness, age at specified depth, elevation and displacement change, and surface air temperature correlations to pinpoint promising locations. We also use the European Centre for Medium-Range Weather Forecast ERA 20th Century reanalysis (ERA-20C) to ensure that a site complementary to the Law Dome record is selected. We find three promising sites in the Indian Ocean sector of East Antarctica in the coastal zone from Enderby Land to the Ingrid Christensen Coast (50-100° E). Although we focus on East Antarctica for a new ice core site, the methodology is more generally applicable, and we include key parameters for all of Antarctica which may be useful for ice core site selection elsewhere and/or for other purposes.

  13. Optimal site selection for a high resolution ice core record in East Antarctica

    NASA Astrophysics Data System (ADS)

    Vance, T.; Roberts, J.; Moy, A.; Curran, M.; Tozer, C.; Gallant, A.; Abram, N.; van Ommen, T.; Young, D.; Grima, C.; Blankenship, D.; Siegert, M.

    2015-11-01

    Ice cores provide some of the best dated and most comprehensive proxy records, as they yield a vast and growing array of proxy indicators. Selecting a site for ice core drilling is nonetheless challenging, as the assessment of potential new sites needs to consider a variety of factors. Here, we demonstrate a systematic approach to site selection for a new East Antarctic high resolution ice core record. Specifically, seven criteria are considered: (1) 2000 year old ice at 300 m depth, (2) above 1000 m elevation, (3) a minimum accumulation rate of 250 mm yr-1 IE, (4) minimal surface re-working to preserve the deposited climate signal, (5) a site with minimal displacement or elevation change of ice at 300 m depth, (6) a strong teleconnection to mid-latitude climate and (7) an appropriately complementary relationship to the existing Law Dome record (a high resolution record in East Antarctica). Once assessment of these physical characteristics identified promising regions, logistical considerations (for site access and ice core retrieval) were briefly considered. We use Antarctic surface mass balance syntheses, along with ground-truthing of satellite data by airborne radar surveys to produce all-of-Antarctica maps of surface roughness, age at specified depth, elevation and displacement change and surface air temperature correlations to pinpoint promising locations. We also use the European Centre for Medium-Range Weather Forecast ERA 20th Century reanalysis (ERA-20C) to ensure a site complementary to the Law Dome record is selected. We find three promising sites in the Indian Ocean sector of East Antarctica in the coastal zone from Enderby Land to the Ingrid Christensen Coast (50-100° E). Although we focus on East Antarctica for a new ice core site, the methodology is more generally applicable and we include key parameters for all of Antarctica which may be useful for ice core site selection elsewhere and/or for other purposes.

  14. Reduced 5-HT2A receptor signaling following selective bilateral amygdala damage

    PubMed Central

    Schlaepfer, Thomas E.; Matusch, Andreas; Reich, Harald; Shah, Nadim J.; Zilles, Karl; Maier, Wolfgang; Bauer, Andreas

    2009-01-01

    Neurobiological evidence implicates the amygdala as well as serotonergic (serotonin, 5-HT) signaling via postsynaptic 5-HT2A receptors as essential substrates of anxiety behaviors. Assuming a functional interdependence of these substrates, we hypothesized that a low-fear behavioral phenotype due to bilateral lesion of the amygdala would be associated with significant 5-HT2A receptor changes. Thus, we used [18F]altanserin positron emission tomography (PET) referenced to radioligand plasma levels and corrected for partial volume effects to quantify the spatial distribution of 5-HT2A receptor binding potential (BPP) in a rare patient with Urbach–Wiethe disease and selective bilateral amygdala calcification damage relative to 10 healthy control subjects. Consistent with our a priori hypothesis, we observed a 70% global decrease in 5-HT2A receptor BPP in the Urbach–Wiethe patient relative to controls. Thus, brain abnormalities in this patient are not restricted to the amygdala, but extend to overall 5-HT neurotransmission via 5-HT2A receptors. Our findings provide important insights into the molecular architecture of human anxiety behaviors and suggest the 5-HT2A receptor as a promising pharmacological target to control pathological anxiety. PMID:19015089

  15. Functional mechanics of beetle mandibles: Honest signaling in a sexually selected system.

    PubMed

    Mills, Maria R; Nemri, Rahmi S; Carlson, Emily A; Wilde, William; Gotoh, Hiroki; Lavine, Laura C; Swanson, Brook O

    2016-01-01

    Male stag beetles possess colossal mandibles, which they wield in combat to obtain access to females. As with many other sexually selected weapons, males with longer mandibles win more fights. However, variation in the functional morphology of these structures, used in male-male combat, is less well understood. In this study, mandible bite force, gape, structural strength, and potential tradeoffs are examined across a wide size range for one species of stag beetle, Cyclommatus metallifer. We found that not only does male mandible size demonstrate steep positive allometry, but the shape, relative bite force, relative gape, and safety factor of the mandibles also change with male size. Allometry in these functionally important mandibular traits suggests that larger males with larger mandibles should be better fighters, and that the mandibles can be considered an honest signal of male fighting ability. However, negative allometry in mandible structural safety factor, wing size, and flight muscle mass suggest significant costs and a possible limit on the size of the mandibles. J. Exp. Zool. 325A:3-12, 2016. © 2015 Wiley Periodicals, Inc.

  16. Desired Alteration of Protein Affinities: Competitive Selection of Protein Variants Using Yeast Signal Transduction Machinery

    PubMed Central

    Kaishima, Misato; Fukuda, Nobuo; Ishii, Jun; Kondo, Akihiko

    2014-01-01

    Molecules that can control protein-protein interactions (PPIs) have recently drawn attention as new drug pipeline compounds. Here, we report a technique to screen desirable affinity-altered (affinity-enhanced and affinity-attenuated) protein variants. We previously constructed a screening system based on a target protein fused to a mutated G-protein γ subunit (Gγcyto) lacking membrane localization ability. This ability, required for signal transmission, is restored by recruiting Gγcyto into the membrane only when the target protein interacts with an artificially membrane-anchored candidate protein, thereby allowing interacting partners (Gγ recruitment system) to be searched and identified. In the present study, the Gγ recruitment system was altered by integrating the cytosolic expression of a third protein as a competitor to set a desirable affinity threshold. This enabled the reliable selection of both affinity-enhanced and affinity-attenuated protein variants. The presented approach may facilitate the development of therapeutic proteins that allow the control of PPIs. PMID:25244640

  17. Fine-tuning somatostatin receptor signalling by agonist-selective phosphorylation and dephosphorylation: IUPHAR Review 5.

    PubMed

    Schulz, Stefan; Lehmann, Andreas; Kliewer, Andrea; Nagel, Falko

    2014-04-01

    The biological actions of somatostatin are mediated by a family of five GPCRs, named sst1 to sst5 . Somatostatin receptors exhibit equally high-binding affinities to their natural ligand somatostatin-14 and largely overlapping distributions. The overexpression of somatostatin receptors in human tumours is the molecular basis for diagnostic and therapeutic application of the stable somatostatin analogues octreotide, lanreotide and pasireotide. The efficiency of somatostatin receptor signalling is tightly regulated and ultimately limited by the coordinated phosphorylation and dephosphorylation of intracellular carboxyl-terminal serine and threonine residues. Here, we review and discuss recent progress in the generation and application of phosphosite-specific antibodies for human sst2 and sst5 receptors. These phosphosite-specific antibodies are unique tools to monitor the spatial and temporal dynamics of receptors phosphorylation and dephosphorylation. Using a combined approach of phosphosite-specific antibodies and siRNA knock-down screening, relevant kinases and phosphatases were identified. Emerging evidence suggests distinct mechanisms of agonist-selective fine-tuning for individual somatostatin receptors. The recently uncovered differences in phosphorylation and dephosphorylation of these receptors may hence be of physiological significance in mediating responses to acute, persistent or repeated stimuli in a variety of target tissues.

  18. A Linked Simulation-Optimization (LSO) Model for Conjunctive Irrigation Management using Clonal Selection Algorithm

    NASA Astrophysics Data System (ADS)

    Islam, Sirajul; Talukdar, Bipul

    2016-08-01

    A Linked Simulation-Optimization (LSO) model based on a Clonal Selection Algorithm (CSA) was formulated for application in conjunctive irrigation management. A series of measures were considered for reducing the computational burden associated with the LSO approach. Certain modifications were incurred to the formulated CSA, so as to decrease the number of function evaluations. In addition, a simple problem specific code for a two dimensional groundwater flow simulation model was developed. The flow model was further simplified by a novel approach of area reduction, in order to save computational time in simulation. The LSO model was applied in the irrigation command of the Pagladiya Dam Project in Assam, India. With a view to evaluate the performance of the CSA, a Genetic Algorithm (GA) was used as a comparison base. The results from the CSA compared well with those from the GA. In fact, the CSA was found to consume less computational time than the GA while converging to the optimal solution, due to the modifications incurred in it.

  19. Bone Mineral Density and Fracture Risk Assessment to Optimize Prosthesis Selection in Total Hip Replacement

    PubMed Central

    Pétursson, Þröstur; Edmunds, Kyle Joseph; Gíslason, Magnús Kjartan; Magnússon, Benedikt; Magnúsdóttir, Gígja; Halldórsson, Grétar; Jónsson, Halldór; Gargiulo, Paolo

    2015-01-01

    The variability in patient outcome and propensity for surgical complications in total hip replacement (THR) necessitates the development of a comprehensive, quantitative methodology for prescribing the optimal type of prosthetic stem: cemented or cementless. The objective of the research presented herein was to describe a novel approach to this problem as a first step towards creating a patient-specific, presurgical application for determining the optimal prosthesis procedure. Finite element analysis (FEA) and bone mineral density (BMD) calculations were performed with ten voluntary primary THR patients to estimate the status of their operative femurs before surgery. A compilation model of the press-fitting procedure was generated to define a fracture risk index (FRI) from incurred forces on the periprosthetic femoral head. Comparing these values to patient age, sex, and gender elicited a high degree of variability between patients grouped by implant procedure, reinforcing the notion that age and gender alone are poor indicators for prescribing prosthesis type. Additionally, correlating FRI and BMD measurements indicated that at least two of the ten patients may have received nonideal implants. This investigation highlights the utility of our model as a foundation for presurgical software applications to assist orthopedic surgeons with selecting THR prostheses. PMID:26417376

  20. Catalyst optimization strategy: selective oxidation of o-xylene to phthalic anhydride.

    PubMed

    Wöelk, Hans-Jörg; Mestl, Gerhard

    2012-02-01

    The oxidation of o-xylene and/or naphthalene to phthalic anhydride is one of the important industrial processes based on catalytic selective oxidation reactions. Vanadia--titania catalysts have been used in the industrial phthalic anyhdride process for the last 50 years. The operation parameters like the temperature range of operation, reactor inlet pressures, contact times, o-xylene loadings, etc. were constantly improved during this period of continuous process optimization so as to optimize catalyst performance and increase its life time. However, a fundamental understanding of the mutual interaction of the rather complex reaction network and the catalyst formulation is still missing. Recently, a detailed study of by-product formation as function of process conditions allowed us to develop a novel, improved reaction scheme for the catalytic oxidation of o-xylene. Based on this understanding, a detailed investigation was conducted for the first time of the by-product formation under varying operation conditions and as a function of the active mass variation exploiting high-throughput, as well as bench scales reactors. This high-throughput testing allowed us to relate reaction kinetics to novel catalyst formulations.

  1. A Linked Simulation-Optimization (LSO) Model for Conjunctive Irrigation Management using Clonal Selection Algorithm

    NASA Astrophysics Data System (ADS)

    Islam, Sirajul; Talukdar, Bipul

    2016-09-01

    A Linked Simulation-Optimization (LSO) model based on a Clonal Selection Algorithm (CSA) was formulated for application in conjunctive irrigation management. A series of measures were considered for reducing the computational burden associated with the LSO approach. Certain modifications were incurred to the formulated CSA, so as to decrease the number of function evaluations. In addition, a simple problem specific code for a two dimensional groundwater flow simulation model was developed. The flow model was further simplified by a novel approach of area reduction, in order to save computational time in simulation. The LSO model was applied in the irrigation command of the Pagladiya Dam Project in Assam, India. With a view to evaluate the performance of the CSA, a Genetic Algorithm (GA) was used as a comparison base. The results from the CSA compared well with those from the GA. In fact, the CSA was found to consume less computational time than the GA while converging to the optimal solution, due to the modifications incurred in it.

  2. Bone Mineral Density and Fracture Risk Assessment to Optimize Prosthesis Selection in Total Hip Replacement.

    PubMed

    Pétursson, Þröstur; Edmunds, Kyle Joseph; Gíslason, Magnús Kjartan; Magnússon, Benedikt; Magnúsdóttir, Gígja; Halldórsson, Grétar; Jónsson, Halldór; Gargiulo, Paolo

    2015-01-01

    The variability in patient outcome and propensity for surgical complications in total hip replacement (THR) necessitates the development of a comprehensive, quantitative methodology for prescribing the optimal type of prosthetic stem: cemented or cementless. The objective of the research presented herein was to describe a novel approach to this problem as a first step towards creating a patient-specific, presurgical application for determining the optimal prosthesis procedure. Finite element analysis (FEA) and bone mineral density (BMD) calculations were performed with ten voluntary primary THR patients to estimate the status of their operative femurs before surgery. A compilation model of the press-fitting procedure was generated to define a fracture risk index (FRI) from incurred forces on the periprosthetic femoral head. Comparing these values to patient age, sex, and gender elicited a high degree of variability between patients grouped by implant procedure, reinforcing the notion that age and gender alone are poor indicators for prescribing prosthesis type. Additionally, correlating FRI and BMD measurements indicated that at least two of the ten patients may have received nonideal implants. This investigation highlights the utility of our model as a foundation for presurgical software applications to assist orthopedic surgeons with selecting THR prostheses. PMID:26417376

  3. Closed-form solutions for linear regulator design of mechanical systems including optimal weighting matrix selection

    NASA Technical Reports Server (NTRS)

    Hanks, Brantley R.; Skelton, Robert E.

    1991-01-01

    Vibration in modern structural and mechanical systems can be reduced in amplitude by increasing stiffness, redistributing stiffness and mass, and/or adding damping if design techniques are available to do so. Linear Quadratic Regulator (LQR) theory in modern multivariable control design, attacks the general dissipative elastic system design problem in a global formulation. The optimal design, however, allows electronic connections and phase relations which are not physically practical or possible in passive structural-mechanical devices. The restriction of LQR solutions (to the Algebraic Riccati Equation) to design spaces which can be implemented as passive structural members and/or dampers is addressed. A general closed-form solution to the optimal free-decay control problem is presented which is tailored for structural-mechanical system. The solution includes, as subsets, special cases such as the Rayleigh Dissipation Function and total energy. Weighting matrix selection is a constrained choice among several parameters to obtain desired physical relationships. The closed-form solution is also applicable to active control design for systems where perfect, collocated actuator-sensor pairs exist.

  4. Selection of energy optimized pump concepts for multi core and multi mode erbium doped fiber amplifiers.

    PubMed

    Krummrich, Peter M; Akhtari, Simon

    2014-12-01

    The selection of an appropriate pump concept has a major impact on amplifier cost and power consumption. The energy efficiency of different pump concepts is compared for multi core and multi mode active fibers. In preamplifier stages, pump power density requirements derived from full C-band low noise WDM operation result in superior energy efficiency of direct pumping of individual cores in a multi core fiber with single mode pump lasers compared to cladding pumping with uncooled multi mode lasers. Even better energy efficiency is achieved by direct pumping of the core in multi mode active fibers. Complexity of pump signal combiners for direct pumping of multi core fibers can be reduced by deploying integrated components.

  5. Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

    NASA Astrophysics Data System (ADS)

    Sutrisno; Widowati; Solikhin

    2016-06-01

    In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

  6. Signal Processing Variables for Optimization of Flaw Detection in Composites Using Ultrasonic Guided Wave Scanning

    NASA Technical Reports Server (NTRS)

    Roth, Don J.; Cosgriff, Laura M.; Martin, Richard E.; Teemer, LeTarrie

    2004-01-01

    This study analyzes the effect of signal processing variables on the ability of the ultrasonic guided wave scan method at NASA Glenn Research Center to distinguish various flaw conditions in ceramic matrix composites samples. In the ultrasonic guided wave scan method, several time- and frequency-domain parameters are calculated from the ultrasonic guided wave signal at each scan location to form images. The parameters include power spectral density, centroid mean time, total energy (zeroth moment), centroid frequency, and ultrasonic decay rate. A number of signal processing variables are available to the user when calculating these parameters. These signal processing variables include 1) the time portion of the time-domain waveform processed, 2) integration type for the properties requiring integrations, 3) bounded versus unbounded integrations, 4) power spectral density window type, 5) and the number of time segments chosen if using the short-time fourier transform to calculate ultrasonic decay rate. Flaw conditions examined included delamination, cracking, and density variation.

  7. Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

    NASA Astrophysics Data System (ADS)

    Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

    2016-06-01

    With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

  8. A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

    PubMed Central

    Lin, Jingjing; Jing, Honglei

    2016-01-01

    Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662

  9. An Optimization Model for the Selection of Bus-Only Lanes in a City.

    PubMed

    Chen, Qun

    2015-01-01

    The planning of urban bus-only lane networks is an important measure to improve bus service and bus priority. To determine the effective arrangement of bus-only lanes, a bi-level programming model for urban bus lane layout is developed in this study that considers accessibility and budget constraints. The goal of the upper-level model is to minimize the total travel time, and the lower-level model is a capacity-constrained traffic assignment model that describes the passenger flow assignment on bus lines, in which the priority sequence of the transfer times is reflected in the passengers' route-choice behaviors. Using the proposed bi-level programming model, optimal bus lines are selected from a set of candidate bus lines; thus, the corresponding bus lane network on which the selected bus lines run is determined. The solution method using a genetic algorithm in the bi-level programming model is developed, and two numerical examples are investigated to demonstrate the efficacy of the proposed model.

  10. An Optimization Model for the Selection of Bus-Only Lanes in a City

    PubMed Central

    Chen, Qun

    2015-01-01

    The planning of urban bus-only lane networks is an important measure to improve bus service and bus priority. To determine the effective arrangement of bus-only lanes, a bi-level programming model for urban bus lane layout is developed in this study that considers accessibility and budget constraints. The goal of the upper-level model is to minimize the total travel time, and the lower-level model is a capacity-constrained traffic assignment model that describes the passenger flow assignment on bus lines, in which the priority sequence of the transfer times is reflected in the passengers’ route-choice behaviors. Using the proposed bi-level programming model, optimal bus lines are selected from a set of candidate bus lines; thus, the corresponding bus lane network on which the selected bus lines run is determined. The solution method using a genetic algorithm in the bi-level programming model is developed, and two numerical examples are investigated to demonstrate the efficacy of the proposed model. PMID:26214001

  11. A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

    PubMed Central

    Lin, Jingjing; Jing, Honglei

    2016-01-01

    Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive.

  12. ESCRT-II/Vps25 constrains digit number by endosome-mediated selective modulation of FGF-SHH signaling

    PubMed Central

    Handschuh, Karen; Feenstra, Jennifer; Koss, Matthew; Ferretti, Elisabetta; Risolino, Maurizio; Zewdu, Rediet; Sahai, Michelle A.; Bénazet, Jean-Denis; Peng, Xiao P.; Depew, Michael J.; Quintana, Laura; Sharpe, James; Wang, Baolin; Alcorn, Heather; Rivi, Roberta; Butcher, Stephen; Manak, J Robert; Vaccari, Thomas; Weinstein, Harel; Anderson, Kathryn V.; Lacy, Elizabeth; Selleri, Licia

    2014-01-01

    Summary Sorting and degradation of receptors and associated signaling molecules maintain homeostasis of conserved signaling pathways during cell specification and tissue development. Yet, whether machineries that sort signaling proteins act preferentially on different receptors and ligands in different contexts remains mysterious. Here we show that Vacuolar protein sorting 25, Vps25, a component of ESCRT-II (Endosomal Sorting Complex Required for Transport II), directs preferential endosome-mediated modulation of FGF signaling in limbs. By ENU-induced mutagenesis we isolated a polydactylous mouse line carrying a hypomorphic mutation of Vps25 (Vps25ENU). Unlike Vps25-null embryos we generated, Vps25ENU/ENU mutants survive until late gestation. Their limbs display FGF signaling enhancement and consequent hyper-activation of the FGF-SHH feedback loop causing polydactyly, whereas WNT and BMP signaling remain unperturbed. Notably, Vps25ENU/ENU Mouse Embryonic Fibroblasts exhibit aberrant FGFR trafficking and degradation; however SHH signaling is unperturbed. These studies establish that the ESCRT-II machinery selectively limits FGF signaling in vertebrate skeletal patterning. PMID:25373905

  13. Optimization and small-signal modeling of zero-bias InAs self-switching diode detectors

    NASA Astrophysics Data System (ADS)

    Westlund, A.; Sangaré, P.; Ducournau, G.; Iñiguez-de-la-Torre, I.; Nilsson, P.-Å.; Gaquière, C.; Desplanque, L.; Wallart, X.; Millithaler, J. F.; González, T.; Mateos, J.; Grahn, J.

    2015-02-01

    Design optimization of the InAs self-switching diode (SSD) intended for direct zero-bias THz detection is presented. The SSD, which consists of nanometer-sized channels in parallel, was described using an equivalent small-signal circuit. Expressions for voltage responsivity and noise equivalent power (NEP) were derived in terms of geometrical design parameters of the SSD, i.e. the channel length and the number of channels. Modeled design dependencies were confirmed by RF and DC measurements on InAs SSDs. In terms of NEP, an optimum number of channels were found with the detector driven by a 50 Ω source. With a matched source, the model predicted a responsivity of 1900 V/W and NEP of 7.7 pW/Hz½ for a single-channel InAs SSD with 35 nm channel width. Monte Carlo device simulations supported observed design dependencies. The proposed small-signal model can be used to optimize SSDs of any material system for low-noise and high-frequency operation as zero-bias detectors. In large signal measurements, the responsivity of the InAs SSDs exhibited a 1 dB deviation from linear responsivity at an input power of -3 dBm from a 50 Ω source.

  14. Optimization of Cu-Zn Massive Sulphide Flotation by Selective Reagents

    NASA Astrophysics Data System (ADS)

    Soltani, F.; Koleini, S. M. J.; Abdollahy, M.

    2014-10-01

    Selective floatation of base metal sulphide minerals can be achieved by using selective reagents. Sequential floatation of chalcopyrite-sphalerite from Taknar (Iran) massive sulphide ore with 3.5 % Zn and 1.26 % Cu was studied. D-optimal design of response surface methodology was used. Four mixed collector types (Aer238 + SIPX, Aero3477 + SIPX, TC1000 + SIPX and X231 + SIPX), two depressant systems (CuCN-ZnSO4 and dextrin-ZnSO4), pH and ZnSO4 dosage were considered as operational factors in the first stage of flotation. Different conditions of pH, CuSO4 dosage and SIPX dosage were studied for sphalerite flotation from first stage tailings. Aero238 + SIPX induced better selectivity for chalcopyrite against pyrite and sphalerite. Dextrin-ZnSO4 was as effective as CuCN-ZnSO4 in sphalerite-pyrite depression. Under optimum conditions, Cu recovery, Zn recovery and pyrite content in Cu concentrate were 88.99, 33.49 and 1.34 % by using Aero238 + SIPX as mixed collector, CuCN-ZnSO4 as depressant system, at ZnSO4 dosage of 200 g/t and pH 10.54. When CuCN was used at the first stage, CuSO4 consumption increased and Zn recovery decreased during the second stage. Maximum Zn recovery was 72.19 % by using 343.66 g/t of CuSO4, 22.22 g/t of SIPX and pH 9.99 at the second stage.

  15. Process optimization for lattice-selective wet etching of crystalline silicon structures

    NASA Astrophysics Data System (ADS)

    Dixson, Ronald G.; Guthrie, William F.; Allen, Richard A.; Orji, Ndubuisi G.; Cresswell, Michael W.; Murabito, Christine E.

    2016-01-01

    Lattice-selective etching of silicon is used in a number of applications, but it is particularly valuable in those for which the lattice-defined sidewall angle can be beneficial to the functional goals. A relatively small but important niche application is the fabrication of tip characterization standards for critical dimension atomic force microscopes (CD-AFMs). CD-AFMs are commonly used as reference tools for linewidth metrology in semiconductor manufacturing. Accurate linewidth metrology using CD-AFM, however, is critically dependent upon calibration of the tip width. Two national metrology institutes and at least two commercial vendors have explored the development of tip calibration standards using lattice-selective etching of crystalline silicon. The National Institute of Standards and Technology standard of this type is called the single crystal critical dimension reference material. These specimens, which are fabricated using a lattice-plane-selective etch on (110) silicon, exhibit near vertical sidewalls and high uniformity and can be used to calibrate CD-AFM tip width to a standard uncertainty of less than 1 nm. During the different generations of this project, we evaluated variations of the starting material and process conditions. Some of our starting materials required a large etch bias to achieve the desired linewidths. During the optimization experiment described in this paper, we found that for potassium hydroxide etching of the silicon features, it was possible to independently tune the target linewidth and minimize the linewidth nonuniformity. Consequently, this process is particularly well suited for small-batch fabrication of CD-AFM linewidth standards.

  16. Multi-objective selection and optimization of shaped materials and laminated composites

    NASA Astrophysics Data System (ADS)

    Singh, Jasveer

    Most of the current optimization techniques for the design of light-weight structures are unable to generate structural alternatives at the concept stage of design. This research tackles the challenge of developing methods for the early stage of design involving structures made up of conventional materials and composite laminates. For conventional materials, the recently introduced shape transformer approach is used. This work extends the method to deal with the case of torsional stiffness design, and generalizes it to single and multi-criteria selection of lightweight shafts subjected to a combination of bending, shear, and torsional load. The prominent feature of the work is the useful integration of shape and material to model and visualize multi-objective selection problems. The scheme is centered on concept selection in structural design, and hinges on measures that govern the shape properties of a cross-section regardless of its size. These measures, referred to as shape transformers, can classify shapes in a way similar to material classification. The procedure is demonstrated by considering torsional stiffness as a constraint. Performance charts are developed for both single and multi-criteria cases to let the reader visualize in a glance the whole range of cross-sectional shapes for each material. Each design chart is explained with a brief example. The above mentioned approach is also extended to incorporate orthotropic composite laminates. Design charts are obtained for the selection of five generic design variables: shape, size, material, layup, and number of plies. These charts also aid in comparing the performances of two commonly used laminates in bending and torsion - angle plies and cross plies. For a generic composite laminate, due to the number of variables involved, these kinds of design charts are very difficult. However, other tactics like using an analytical model for function evaluation can be used at conceptual stage of design. This is

  17. The influence of the pressure force control signal on selected parameters of the vehicle continuously variable transmission

    NASA Astrophysics Data System (ADS)

    Bieniek, A.; Graba, M.; Prażnowski, K.

    2016-09-01

    The paper presents results of research on the effect of frequency control signal on the course selected operating parameters of the continuously variable transmission CVT. The study used a gear Fuji Hyper M6 with electro-hydraulic control system and proprietary software for control and data acquisition developed in LabView environment.

  18. 49 CFR 236.303 - Control circuits for signals, selection through circuit controller operated by switch points or...

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ...-point frogs and derails shall be selected through circuit controller operated directly by switch points... switch, movable-point frog, and derail in the routes governed by such signal. Circuits shall be arranged... when each switch, movable-point frog, and derail in the route is in proper position....

  19. 49 CFR 236.303 - Control circuits for signals, selection through circuit controller operated by switch points or...

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ...-point frogs and derails shall be selected through circuit controller operated directly by switch points... switch, movable-point frog, and derail in the routes governed by such signal. Circuits shall be arranged... when each switch, movable-point frog, and derail in the route is in proper position....

  20. 49 CFR 236.303 - Control circuits for signals, selection through circuit controller operated by switch points or...

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ...-point frogs and derails shall be selected through circuit controller operated directly by switch points... switch, movable-point frog, and derail in the routes governed by such signal. Circuits shall be arranged... when each switch, movable-point frog, and derail in the route is in proper position....

  1. 49 CFR 236.303 - Control circuits for signals, selection through circuit controller operated by switch points or...

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ...-point frogs and derails shall be selected through circuit controller operated directly by switch points... switch, movable-point frog, and derail in the routes governed by such signal. Circuits shall be arranged... when each switch, movable-point frog, and derail in the route is in proper position....

  2. 49 CFR 236.303 - Control circuits for signals, selection through circuit controller operated by switch points or...

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ...-point frogs and derails shall be selected through circuit controller operated directly by switch points... switch, movable-point frog, and derail in the routes governed by such signal. Circuits shall be arranged... when each switch, movable-point frog, and derail in the route is in proper position....

  3. Sensor selection and chemo-sensory optimization: toward an adaptable chemo-sensory system.

    PubMed

    Vergara, Alexander; Llobet, Eduard

    2011-01-01

    Over the past two decades, despite the tremendous research on chemical sensors and machine olfaction to develop micro-sensory systems that will accomplish the growing existent needs in personal health (implantable sensors), environment monitoring (widely distributed sensor networks), and security/threat detection (chemo/bio warfare agents), simple, low-cost molecular sensing platforms capable of long-term autonomous operation remain beyond the current state-of-the-art of chemical sensing. A fundamental issue within this context is that most of the chemical sensors depend on interactions between the targeted species and the surfaces functionalized with receptors that bind the target species selectively, and that these binding events are coupled with transduction processes that begin to change when they are exposed to the messy world of real samples. With the advent of fundamental breakthroughs at the intersection of materials science, micro- and nano-technology, and signal processing, hybrid chemo-sensory systems have incorporated tunable, optimizable operating parameters, through which changes in the response characteristics can be modeled and compensated as the environmental conditions or application needs change. The objective of this article, in this context, is to bring together the key advances at the device, data processing, and system levels that enable chemo-sensory systems to "adapt" in response to their environments. Accordingly, in this review we will feature the research effort made by selected experts on chemical sensing and information theory, whose work has been devoted to develop strategies that provide tunability and adaptability to single sensor devices or sensory array systems. Particularly, we consider sensor-array selection, modulation of internal sensing parameters, and active sensing. The article ends with some conclusions drawn from the results presented and a visionary look toward the future in terms of how the field may evolve. PMID

  4. Methodology and Method and Apparatus for Signaling With Capacity Optimized Constellations

    NASA Technical Reports Server (NTRS)

    Barsoum, Maged F. (Inventor); Jones, Christopher R. (Inventor)

    2014-01-01

    Communication systems are described that use geometrically shaped constellations that have increased capacity compared to conventional constellations operating within a similar SNR band. In several embodiments, the geometrically shaped is optimized based upon a capacity measure such as parallel decoding capacity or joint capacity. In many embodiments, a capacity optimized geometrically shaped constellation can be used to replace a conventional constellation as part of a firmware upgrade to transmitters and receivers within a communication system. In a number of embodiments, the geometrically shaped constellation is optimized for an Additive White Gaussian Noise channel or a fading channel. In numerous embodiments, the communication uses adaptive rate encoding and the location of points within the geometrically shaped constellation changes as the code rate changes.

  5. Feedforward suppression of force ripple based on a simplex-optimized dither signal.

    PubMed

    Tan, K K; Chin, S J; Dou, H F

    2003-01-01

    This paper presents the design and realization of a feedforward dither signal to reduce the force ripple in an iron-core permanent magnet linear motor (PMLM). A composite control structure is used, consisting of three components: a simple feedforward component, a PID feedback component, and a ripple compensator (RC). The first two components are designed based on a dominant linear model of the motor. The dither signal is generated based on a signal model which is identified using a multidimensional simplex downhill method. In this way, a simple approach is available to eliminate or suppress the inherent force ripple, thus facilitating smooth precise motion while uncompromising on the maximum force achievable. Real-time experimental results verify the effectiveness of the proposed scheme for high precision motion trajectory tracking.

  6. An interpretive review of selective sweep studies in Bos taurus cattle populations: identification of unique and shared selection signals across breeds

    PubMed Central

    Gutiérrez-Gil, Beatriz; Arranz, Juan J.; Wiener, Pamela

    2015-01-01

    This review compiles the results of 21 genomic studies of European Bos taurus breeds and thus provides a general picture of the selection signatures in taurine cattle identified by genome-wide selection-mapping scans. By performing a comprehensive summary of the results reported in the literature, we compiled a list of 1049 selection sweeps described across 37 cattle breeds (17 beef breeds, 14 dairy breeds, and 6 dual-purpose breeds), and four different beef-vs.-dairy comparisons, which we subsequently grouped into core selective sweep (CSS) regions, defined as consecutive signals within 1 Mb of each other. We defined a total of 409 CSSs across the 29 bovine autosomes, 232 (57%) of which were associated with a single-breed (Single-breed CSSs), 134 CSSs (33%) were associated with a limited number of breeds (Two-to-Four-breed CSSs) and 39 CSSs (9%) were associated with five or more breeds (Multi-breed CSSs). For each CSS, we performed a candidate gene survey that identified 291 genes within the CSS intervals (from the total list of 5183 BioMart-extracted genes) linked to dairy and meat production, stature, and coat color traits. A complementary functional enrichment analysis of the CSS positional candidates highlighted other genes related to pathways underlying behavior, immune response, and reproductive traits. The Single-breed CSSs revealed an over-representation of genes related to dairy and beef production, this was further supported by over-representation of production-related pathway terms in these regions based on a functional enrichment analysis. Overall, this review provides a comparative map of the selection sweeps reported in European cattle breeds and presents for the first time a characterization of the selection sweeps that are found in individual breeds. Based on their uniqueness, these breed-specific signals could be considered as “divergence signals,” which may be useful in characterizing and protecting livestock genetic diversity. PMID:26029239

  7. Lymphotoxin signals from positively selected thymocytes regulate the terminal differentiation of medullary thymic epithelial cells.

    PubMed

    White, Andrea J; Nakamura, Kyoko; Jenkinson, William E; Saini, Manoj; Sinclair, Charles; Seddon, Benedict; Narendran, Parth; Pfeffer, Klaus; Nitta, Takeshi; Takahama, Yousuke; Caamano, Jorge H; Lane, Peter J L; Jenkinson, Eric J; Anderson, Graham

    2010-10-15

    The thymic medulla represents a key site for the induction of T cell tolerance. In particular, autoimmune regulator (Aire)-expressing medullary thymic epithelial cells (mTECs) provide a spectrum of tissue-restricted Ags that, through both direct presentation and cross-presentation by dendritic cells, purge the developing T cell repertoire of autoimmune specificities. Despite this role, the mechanisms of Aire(+) mTEC development remain unclear, particularly those stages that occur post-Aire expression and represent mTEC terminal differentiation. In this study, in mouse thymus, we analyze late-stage mTEC development in relation to the timing and requirements for Aire and involucrin expression, the latter a marker of terminally differentiated epithelium including Hassall's corpuscles. We show that Aire expression and terminal differentiation within the mTEC lineage are temporally separable events that are controlled by distinct mechanisms. We find that whereas mature thymocytes are not essential for Aire(+) mTEC development, use of an inducible ZAP70 transgenic mouse line--in which positive selection can be temporally controlled--demonstrates that the emergence of involucrin(+) mTECs critically depends upon the presence of mature single positive thymocytes. Finally, although initial formation of Aire(+) mTECs depends upon RANK signaling, continued mTEC development to the involucrin(+) stage maps to activation of the LTα-LTβR axis by mature thymocytes. Collectively, our results reveal further complexity in the mechanisms regulating thymus medulla development and highlight the role of distinct TNFRs in initial and terminal differentiation stages in mTECs.

  8. Central nervous system effects of prenatal selective serotonin reuptake inhibitors: sensing the signal through the noise

    PubMed Central

    Gur, Tamar L.; Kim, Deborah R.

    2013-01-01

    Rationale Women are increasingly prescribed selective serotonin reuptake inhibitors (SSRIs) during pregnancy, with potential implications for neurodevelopment. Whether prenatal SSRI exposure has an effect on neurodevelopment and behavior in the offspring is an important area of investigation. Objectives The aim of this paper was to review the existing preclinical and clinical literature of prenatal SSRI exposure on serotonin-related behaviors and markers in the offspring. The goal is to determine if there is a signal in the literature that could guide clinical care and/or inform research. Results Preclinical studies (n = 4) showed SSRI exposure during development enhanced depression-like behavior. Half of rodent studies examining anxiety-like behavior (n = 13) noted adverse effects with SSRI exposure. A majority of studies of social behavior (n = 4) noted a decrease in sociability in SSRI exposed offspring. Human studies (n = 4) examining anxiety in the offspring showed no adverse effects of prenatal SSRI exposure. The outcome of one study suggested that children with autism were more likely to have a mother who was prescribed an SSRI during pregnancy. Conclusions Preclinical findings in rodents exposed to SSRIs during development point to an increase in depression- and anxiety-like behavior and alteration in social behaviors in the offspring, though both the methods used and the findings were not uniform. These data are not robust enough to discourage use of SSRIs during human pregnancy, particularly given the known adverse effects of maternal mental illness on pregnancy outcomes and infant neurodevelopment. Future research should focus on consistent animal models and prospective human studies with larger samples. PMID:23681158

  9. Analysis of functional selectivity through G protein-dependent and -independent signaling pathways at the adrenergic α(2C) receptor.

    PubMed

    Kurko, Dalma; Kapui, Zoltán; Nagy, József; Lendvai, Balázs; Kolok, Sándor

    2014-08-01

    Although G protein-coupled receptors (GPCRs) are traditionally categorized as Gs-, Gq-, or Gi/o-coupled, their signaling is regulated by multiple mechanisms. GPCRs can couple to several effector pathways, having the capacity to interact not only with more than one G protein subtype but also with alternative signaling or effector proteins such as arrestins. Moreover, GPCR ligands can have different efficacies for activating these signaling pathways, a characteristic referred to as biased agonism or functional selectivity. In this work our aim was to detect differences in the ability of various agonists acting at the α2C type of adrenergic receptors (α2C-ARs) to modulate cAMP accumulation, cytoplasmic Ca(2+) release, β-arrestin recruitment and receptor internalization. A detailed comparative pharmacological characterization of G protein-dependent and -independent signaling pathways was carried out using adrenergic agonists (norepinephrine, phenylephrine, brimonidine, BHT-920, oxymetazoline, clonidine, moxonidine, guanabenz) and antagonists (MK912, yohimbine). As initial analysis of agonist Emax and EC50 values suggested possible functional selectivity, ligand bias was quantified by applying the relative activity scale and was compared to that of the endogenous agonist norepinephrine. Values significantly different from 0 between pathways indicated an agonist that promoted different level of activation of diverse effector pathways most likely due to the stabilization of a subtly different receptor conformation from that induced by norepinephrine. Our results showed that a series of agonists acting at the α2C-AR displayed different degree of functional selectivity (bias factors ranging from 1.6 to 36.7) through four signaling pathways. As signaling via these pathways seems to have distinct functional and physiological outcomes, studying all these stages of receptor activation could have further implications for the development of more selective therapeutics with

  10. Analysis of functional selectivity through G protein-dependent and -independent signaling pathways at the adrenergic α(2C) receptor.

    PubMed

    Kurko, Dalma; Kapui, Zoltán; Nagy, József; Lendvai, Balázs; Kolok, Sándor

    2014-08-01

    Although G protein-coupled receptors (GPCRs) are traditionally categorized as Gs-, Gq-, or Gi/o-coupled, their signaling is regulated by multiple mechanisms. GPCRs can couple to several effector pathways, having the capacity to interact not only with more than one G protein subtype but also with alternative signaling or effector proteins such as arrestins. Moreover, GPCR ligands can have different efficacies for activating these signaling pathways, a characteristic referred to as biased agonism or functional selectivity. In this work our aim was to detect differences in the ability of various agonists acting at the α2C type of adrenergic receptors (α2C-ARs) to modulate cAMP accumulation, cytoplasmic Ca(2+) release, β-arrestin recruitment and receptor internalization. A detailed comparative pharmacological characterization of G protein-dependent and -independent signaling pathways was carried out using adrenergic agonists (norepinephrine, phenylephrine, brimonidine, BHT-920, oxymetazoline, clonidine, moxonidine, guanabenz) and antagonists (MK912, yohimbine). As initial analysis of agonist Emax and EC50 values suggested possible functional selectivity, ligand bias was quantified by applying the relative activity scale and was compared to that of the endogenous agonist norepinephrine. Values significantly different from 0 between pathways indicated an agonist that promoted different level of activation of diverse effector pathways most likely due to the stabilization of a subtly different receptor conformation from that induced by norepinephrine. Our results showed that a series of agonists acting at the α2C-AR displayed different degree of functional selectivity (bias factors ranging from 1.6 to 36.7) through four signaling pathways. As signaling via these pathways seems to have distinct functional and physiological outcomes, studying all these stages of receptor activation could have further implications for the development of more selective therapeutics with

  11. Selective Regulation of TCR Signaling Pathways by the CD45 Protein Tyrosine Phosphatase During Thymocyte Development1

    PubMed Central

    Falahati, Rustom; Leitenberg, David

    2009-01-01

    In CD45 deficient animals there is a severe defect in thymocyte positive selection, resulting in an absence of mature T cells and the accumulation of thymocytes at the double positive stage of development. However, the signaling defect(s) responsible for the block in development of mature single positive T cells are not well characterized. Previous studies have found that early signal transduction events in CD45 deficient cell lines and thymocytes are markedly diminished following stimulation with anti-CD3. Nevertheless, there are also situations in which T cell activation and TCR signaling events can be induced in the absence of CD45. For example, CD45 independent TCR signaling can be recovered upon simultaneous antibody cross-linking of CD3 and CD4 compared to cross-linking of CD3 alone. These data suggest that CD45 may differentially regulate TCR signaling events depending on the nature of the signal and/or on the differentiation state of the cell. In the current study we have assessed the role of CD45 in regulating primary thymocyte activation following physiologic stimulation with peptide. Unlike CD3 mediated stimulation, peptide stimulation of CD45 deficient thymocytes induces diminished, but readily detectable TCR mediated signaling events such as phosphorylation of TCR-associated zeta, ZAP70, LAT, and Akt, and increased intracellular calcium concentration. In contrast, phosphorylation of ERK, which is essential for positive selection, is more severely affected in the absence of CD45. These data suggest that CD45 has a selective role in regulating different aspects of T cell activation. PMID:18941197

  12. Optimization of quadrature signal processing for laser interferometers for demanding applications

    NASA Astrophysics Data System (ADS)

    PodŻorny, Tomasz; Budzyń, Grzegorz; Tkaczyk, Jakub

    2016-06-01

    Presented paper performs an analysis of quadrature signal processing algorithms for high demanding laser interferometry applications. Careful signal processing is required to minimize nonlinearities which come from optical path and components' imperfections, and reduce overall instrumental error. Paper focuses on algebraic fits, because implementation for real time systems was a main requirement. The most demanding applications are stationary measurements where the position slightly fluctuates in the range below one fringe period. Therefore, analysis was performed for samples that were spread along a few milliradians of a full circle.

  13. Measurement of oxygen extraction fraction (OEF): An optimized BOLD signal model for use with hypercapnic and hyperoxic calibration.

    PubMed

    Merola, Alberto; Murphy, Kevin; Stone, Alan J; Germuska, Michael A; Griffeth, Valerie E M; Blockley, Nicholas P; Buxton, Richard B; Wise, Richard G

    2016-04-01

    Several techniques have been proposed to estimate relative changes in cerebral metabolic rate of oxygen consumption (CMRO2) by exploiting combined BOLD fMRI and cerebral blood flow data in conjunction with hypercapnic or hyperoxic respiratory challenges. More recently, methods based on respiratory challenges that include both hypercapnia and hyperoxia have been developed to assess absolute CMRO2, an important parameter for understanding brain energetics. In this paper, we empirically optimize a previously presented "original calibration model" relating BOLD and blood flow signals specifically for the estimation of oxygen extraction fraction (OEF) and absolute CMRO2. To do so, we have created a set of synthetic BOLD signals using a detailed BOLD signal model to reproduce experiments incorporating hypercapnic and hyperoxic respiratory challenges at 3T. A wide range of physiological conditions was simulated by varying input parameter values (baseline cerebral blood volume (CBV0), baseline cerebral blood flow (CBF0), baseline oxygen extraction fraction (OEF0) and hematocrit (Hct)). From the optimization of the calibration model for estimation of OEF and practical considerations of hypercapnic and hyperoxic respiratory challenges, a new "simplified calibration model" is established which reduces the complexity of the original calibration model by substituting the standard parameters α and β with a single parameter θ. The optimal value of θ is determined (θ=0.06) across a range of experimental respiratory challenges. The simplified calibration model gives estimates of OEF0 and absolute CMRO2 closer to the true values used to simulate the experimental data compared to those estimated using the original model incorporating literature values of α and β. Finally, an error propagation analysis demonstrates the susceptibility of the original and simplified calibration models to measurement errors and potential violations in the underlying assumptions of isometabolism

  14. Joint Channel Estimation and Signal Detection for the OFDM System Without Cyclic Prefix Over Doubly-Selective Channels

    NASA Astrophysics Data System (ADS)

    Song, Lijun; Lei, Xia; Jin, Maozhu; Lv, Zhihan

    2015-12-01

    In the high-speed railway wireless communication, a joint channel estimation and signal detection algorithm is proposed for the orthogonal frequency division multiplexing (OFDM) system without cyclic prefix in the doubly-selective fading channels. Our proposed method first combines the basis expansion model (BEM) and the inter symbol interference (ISI) cancellation to overcome the situation that exists with the fast time-varying channel and the normalized maximum multipath channel exceeding the length of the cyclic prefix (CP). At first, the channel estimation and signal detection can be approximated without considering the ISI. Then, the chann