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Sample records for optimal signal selection

  1. Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

    PubMed

    Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  2. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

    PubMed Central

    Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  3. An ECG signal compressor based on the selection of optimal threshold levels of discrete wavelet transform coefficients.

    PubMed

    Al-Ajlouni, A F; Abo-Zahhad, M; Ahmed, S M; Schilling, R J

    2008-01-01

    Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. Discrete wavelet transform (DWT) has recently emerged as a powerful technique for ECG signal compression due to its multi-resolution signal decomposition and locality properties. This paper presents an ECG compressor based on the selection of optimum threshold levels of DWT coefficients in different subbands that achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction. First, the ECG is wavelet transformed into m subbands and the wavelet coefficients of each subband are thresholded using an optimal threshold level. Thresholding removes excessively small features and replaces them with zeroes. The threshold levels are defined for each signal so that the bit rate is minimized for a target distortion or, alternatively, the distortion is minimized for a target compression ratio. After thresholding, the resulting significant wavelet coefficients are coded using multi embedded zero tree (MEZW) coding technique. In order to assess the performance of the proposed compressor, records from the MIT-BIH Arrhythmia Database were compressed at different distortion levels, measured by the percentage rms difference (PRD), and compression ratios (CR). The method achieves good CR values with excellent reconstruction quality that compares favourably with various classical and state-of-the-art ECG compressors. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing a target PRD and a target CR a priori, respectively. PMID:19005960

  4. 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

  5. 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. PMID:25799141

  6. 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.

  7. A signal invariant wavelet function selection algorithm.

    PubMed

    Garg, Girisha

    2016-04-01

    This paper addresses the problem of mother wavelet selection for wavelet signal processing in feature extraction and pattern recognition. The problem is formulated as an optimization criterion, where a wavelet library is defined using a set of parameters to find the best mother wavelet function. For estimating the fitness function, adopted to evaluate the performance of the wavelet function, analysis of variance is used. Genetic algorithm is exploited to optimize the determination of the best mother wavelet function. For experimental evaluation, solutions for best mother wavelet selection are evaluated on various biomedical signal classification problems, where the solutions of the proposed algorithm are assessed and compared with manual hit-and-trial methods. The results show that the solutions of automated mother wavelet selection algorithm are consistent with the manual selection of wavelet functions. The algorithm is found to be invariant to the type of signals used for classification. PMID:26253283

  8. 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

  9. 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

  10. 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

  11. 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

  12. Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients' speech signal with unilateral vocal fold paralysis.

    PubMed

    Behroozmand, Roozbeh; Almasganj, Farshad

    2007-04-01

    Unilateral vocal fold paralysis (UVFP) is one of the most severe types of neurogenic laryngeal disorder in which the patients, due to their vocal cords malfunction, are confronted by some serious problems. As the effect of such pathologies would be significantly evident in the reduced quality and feature variation of dysphonic voices, this study is designed to scrutinize the piecewise variation of some specific types of these features, known as energy and entropy, all over the frequency range of pathological speech signals. In order to do so, the wavelet-packet coefficients, in five consecutive levels of decomposition, are used to extract the energy and entropy measures at different spectral sub-bands. As the decomposition procedure leads to a set of high-dimensional feature vectors, genetic algorithm is invoked to search for a group of optimal sub-band indexes for which the extracted features result in the highest recognition rate for pathological and normal subjects' classification. The results of our simulations, using support vector machine classifier, show that the highest recognition rate, for both optimized energy and entropy measures, is achieved at the fifth level of wavelet-packet decomposition. It is also found that entropy feature, with the highest recognition rate of 100% vs. 93.62% for energy, is more prominent in discriminating patients with UVFP from normal subjects. Therefore, entropy feature, in comparison with energy, demonstrates a more efficient description of such pathological voices and provides us a valuable tool for clinical diagnosis of unilateral laryngeal paralysis. PMID:17034780

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

    SciTech Connect

    Doerr, Christian; /Karlsruhe U., EKP

    2006-06-01

    The work presented in this thesis is mainly focused on the application in a {Delta}m{sub s} measurement. Chapter 1 starts with a general theoretical introduction on the unitarity triangle with a focus on the impact of a {Delta}m{sub s} 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 {Delta}m{sub s} ({Delta}m{sub d}) 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 B{sup 0} {yields} D{pi}, D {yields} K{pi}{pi} and then applied to the kinematically very similar decay B{sub s} {yields} D{sub s}{pi}, D{sub s} {yields} {phi}{pi}, {phi} {yields} KK. Chapter 7 uses events selected by the neural network selection as input to an unbinned maximum likelihood fit and extracts the B{sup 0} lifetime and {Delta}m{sub d}. In addition, an amplitude scan and an unbinned maximum likelihood fit of {Delta}m{sub s} is performed, applying the neural network selection developed for the decay channel B{sub s} {yields} D{sub s}{pi}, D{sub s} {yields} {phi}{pi}, {phi} {yields} KK. Finally chapter 8 summarizes and gives an outlook.

  14. Optimizing calcium selective fluorimetric nanospheres.

    PubMed

    Kisiel, Anna; Kłucińska, Katarzyna; Gniadek, Marianna; Maksymiuk, Krzysztof; Michalska, Agata

    2015-11-01

    Recently it was shown that optical nanosensors based on alternating polymers e.g. poly(maleic anhydride-alt-1-octadecene) were characterized by a linear dependence of emission intensity on logarithm of concentration over a few of orders of magnitude range. In this work we focus on the material used to prepare calcium selective nanosensors. It is shown that alternating polymer nanosensors offer competitive performance in the absence of calcium ionophore, due to interaction of the nanospheres building blocks with analyte ions. The emission increase corresponds to increase of calcium ions contents in the sample within the range from 10(-4) to 10(-1) M. Further improvement in sensitivity (from 10(-6) to 10(-1) M) and selectivity can be achieved by incorporating calcium ionophore in the nanospheres. The optimal results were obtained for core-shell nanospheres, where the core was prepared from poly(styrene-co-maleic anhydride) and the outer layer from poly(maleic anhydride-alt-1-octadecene). Thus obtained chemosensors were showing linear dependence of emission on logarithm of calcium ions concentration within the range from 10(-7) to 10(-1) M. PMID:26452839

  15. Global and Local Optimization Algorithms for Optimal Signal Set Design

    PubMed Central

    Kearsley, Anthony J.

    2001-01-01

    The problem of choosing an optimal signal set for non-Gaussian detection was reduced to a smooth inequality constrained mini-max nonlinear programming problem by Gockenbach and Kearsley. Here we consider the application of several optimization algorithms, both global and local, to this problem. The most promising results are obtained when special-purpose sequential quadratic programming (SQP) algorithms are embedded into stochastic global algorithms.

  16. 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.

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

    NASA Astrophysics Data System (ADS)

    Guo, Wei; Tse, Peter W.

    2013-01-01

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

  18. Selected optimal shuttle entry computations

    NASA Technical Reports Server (NTRS)

    Sullivan, H. C.

    1974-01-01

    Parameterization and the Davidon-Fletcher-Powell method are used to study the characteristics of optimal shuttle entry trajectories. Two problems of thermal protective system weight minimization are considered: roll modulation and roll plus an angle-of-attack modulation. Both problems are targeted for the edges of the entry footprint. Results consistent with constraints on loads and control bounds are particularly well-behaved and strongly support 'energy' approximation results obtained for the case of symmetric flight by Kelley and Sullivan (1973). Furthermore, results indicate that optimal shuttle entry trajectories should be easy to duplicate and to analyze by using simple techniques.

  19. 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. PMID:22040263

  20. 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

  1. Optimizing secondary tailgate support selection

    SciTech Connect

    Harwood, C.; Karmis, M.; Haycocks, C.; Luo, J.

    1996-12-01

    A model was developed to facilitate secondary tailgate support selection based on analysis of over 100 case studies, compiled from two different surveys of operating longwall coal mines in the United States. The ALPS (Analysis of Longwall Pillar Stability) program was used to determine adequacy of pillar design for the successful longwall case histories. A relationship was developed between the secondary support density necessary to maintain a stable tailgate entry during mining and the CMRR (Coal Mine Roof Rating). This relationship defines the lower bound of secondary support density currently used in longwall mines. The model used only successful tailgate case history data, with adequate ALPS SF according to the CMRR for each case. This model facilitates mine design by predicting secondary support density required for a tailgate entry depending on the ALPS SF and CMRR, which can result in significant economic benefits.

  2. Self-extinction through optimizing selection

    PubMed Central

    Parvinen, Kalle; Dieckmann, Ulf

    2013-01-01

    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

  3. Feature Selection via Modified Gravitational Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Nabizadeh, Nooshin; John, Nigel

    2015-03-01

    Feature selection is the process of selecting a subset of relevant and most informative features, which efficiently represents the input data. We proposed a feature selection algorithm based on n-dimensional gravitational optimization algorithm (NGOA), which is based on the principle of gravitational fields. The objective function of optimization algorithm is a non-linear function of variables, which are called masses and defined based on extracted features. The forces between the masses as well as their new locations are calculated using the value of the objective function and the values of masses. We extracted variety of features applying different wavelet transforms and statistical methods on FLAIR and T1-weighted MR brain images. There are two classes of normal and abnormal tissues. Extracted features are divided into groups of five features. The best feature is selected in each group using N-dimensional gravitational optimization algorithm and support vector machine classifier. Then the selected features from each group make several groups of five features again and so on till desired number of features is selected. The advantage of NGOA algorithm is that the possibility of being drawn into a local optimal solution is very low. The experimental results show that our method outperforms some standard feature selection algorithms on both real-data and simulated brain tumor data.

  4. Optimizing Site Selection for HEDS

    NASA Astrophysics Data System (ADS)

    Marshall, J. R.

    1999-01-01

    MSP 2001 will be conducting environmental assessment for the Human exploration and Development of Space (HEDS) Program in order to safeguard future human exploration of the planet, in addition to geological studies being addressed by the APEX payload. In particular, the MECA experiment (see other abstracts, this volume), will address chemical toxicity of the soil, the presence of adhesive or abrasive soil dust components, and the geoelectrical-triboelectrical character of the surface environment. The attempt will be to quantify hazards to humans and machinery structures deriving from compounds that poison, corrode, abrade, invade (lungs or machinery), contaminate, or electrically interfere with the human presence. The DART experiment, will also address the size and electrical nature of airborne dust. Photo-imaging of the local scene with RAC and Pancam will be able to assess dust raising events such as local thermal vorticity-driven dust devils. The need to introduce discussion of HEDS landing site requirements stems from potential conflict, but also potential synergism with other '01 site requirements. In-situ Resource Utilization (ISRU) mission components desire as much solar radiation as possible, with some very limited amount of dust available; the planetary-astrobiology mission component desires sufficient rock abundance without inhibiting rover activities (and an interesting geological niche if available), the radiation component may again have special requirements, as will the engineers concerned with mission safety and mission longevity. The '01 mission affords an excellent opportunity to emphasize HEDS landing site requirements, given the constraint that both recent missions (Pathfinder, Mars '98) and future missions (MSP '03 & '05) have had or will have strong geological science drivers in the site selection process. What type of landing site best facilitates investigation of the physical, chemical, and behavioral properties of soil and dust? There are

  5. Overlay mark optimization using the KTD signal simulation system

    NASA Astrophysics Data System (ADS)

    Marchelli, Anat; Gutjahr, Karsten; Kubis, Michael; Sparka, Christian; Ghinovker, Mark; Navarra, Alessandra; Widmann, Amir

    2009-03-01

    As the overlay performance and accuracy requirements become tighter, the impact of process parameters on the target signal becomes more significant. Traditionally, in order to choose the optimum overlay target, several candidates are placed in the kerf area. The candidate targets are tested under different process conditions, before the target to be used in mass production is selected. The varieties of targets are left on the mass production mask and although they will not be used for overlay measurements they still consume kerf real estate. To improve the efficiency of the process we are proposing the KTD (KLA-Tencor Target Designer). It is an easy to use system that enables the user to select the optimum target based on advanced signal simulation. Implementing the KTD in production is expected to save 30% of kerf real estate due to more efficient target design process as well as reduced engineering time. In this work we demonstrate the capability of the KTD to simulate the Archer signal in the context of advanced DRAM processes. For several stacks we are comparing simulated target signals with the Archer100 signals. We demonstrate the robustness feature in the KTD application that enables the user to test the target sensitivity to process changes. The results indicate the benefit of using KTD in the target optimization process.

  6. 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

  7. 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

  8. 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

  9. Optimizing Clinical Research Participant Selection with Informatics

    PubMed Central

    Weng, Chunhua

    2015-01-01

    Clinical research participants are often not reflective of the real-world patients due to overly restrictive eligibility criteria. Meanwhile, unselected participants introduce confounding factors and reduce research efficiency. Biomedical Informatics, especially Big Data increasingly made available from electronic health records, offers promising aids to optimize research participant selection through data-driven transparency. PMID:26549161

  10. 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.

  11. 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.

  12. Optimization of Pulse Shape Discrimination of PROSPECT Liquid Scintillator Signals

    NASA Astrophysics Data System (ADS)

    Han, Ke; Prospect Collaboration

    2015-04-01

    PROSPECT, A Precision Oscillation and Spectrum Experiment, will use a segmented Li-6 doped liquid scintillator detector for precision measurement of the reactor anti-neutrino spectrum at the High Flux Isotope Reactor at Oak Ridge National Laboratory. PROSPECT also searches for very short baseline neutrino oscillation, an indication of the existence of eV-scale sterile neutrinos. Pulse shape analysis of the prompt anti-neutino signal and delayed neutron capture on Li-6 signal will greatly suppress background sources such as fast neutrons and accidental coincidence of gammas. In this talk, I will discuss different pulse shape parameters used in PROSPECT prototype detectors and multivariate optimization of event selection cuts based on those parameters.

  13. Optimal probe selection in diagnostic search

    NASA Technical Reports Server (NTRS)

    Bhandari, Inderpal S.; Simon, Herbert A.; Siewiorek, Daniel P.

    1990-01-01

    Probe selection (PS) in machine diagnosis is viewed as a collection of models that apply under specific conditions. This makes it possible for three polynomial-time optimal algorithms to be developed for simplified PS models that allow different probes to have different costs. The work is compared with the research of Simon and Kadane (1975), who developed a collection of models for optimal problem-solving search. The relationship between these models and the three newly developed algorithms for PS is explored. Two of the algorithms are unlike the ones discussed by Simon and Kadane. The third cannot be related to the problem-solving models.

  14. Optimal selection theory for superconcurrency. Technical document

    SciTech Connect

    Freund, R.F.

    1989-10-01

    This paper describes a mathematical programming approach to finding an optimal, heterogeneous suite of processors to solve supercomputing problems. This technique, called superconcurrency, works best when the computational requirements are diverse and significant portions of the code are not tightly-coupled. It is also dependent on new methods of benchmarking and code profiling, as well as eventual use of AI techniques for intelligent management of the selected superconcurrent suite.

  15. Optimal remediation policy selection under general conditions

    SciTech Connect

    Wang, M.; Zheng, C.

    1997-09-01

    A new simulation-optimization model has been developed for the optimal design of ground-water remediation systems under a variety of field conditions. The model couples genetic algorithm (GA), a global search technique inspired by biological evolution, with MODFLOW and MT3D, two commonly used ground-water flow and solute transport codes. The model allows for multiple management periods in which optimal pumping/injection rates vary with time to reflect the changes in the flow and transport conditions during the remediation process. The objective function of the model incorporates multiple cost terms including the drilling cost, the installation cost, and the costs to extract and treat the contaminated ground water. The simulation-optimization model is first applied to a typical two-dimensional pump-and-treat example with one and three management periods to demonstrate the effectiveness and robustness of the new model. The model is then applied to a large-scale three-dimensional field problem to determine the minimum pumping needed to contain an existing contaminant plume. The optimal solution as determined in this study is compared with a previous solution based on trial-and-error selection.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. Optimal signal recovery for pulsed balanced detection

    NASA Astrophysics Data System (ADS)

    de Icaza Astiz, Yannick A.; Lucivero, Vito Giovanni; León-Montiel, R. de J.; Mitchell, Morgan W.

    2014-09-01

    We demonstrate a tool for filtering technical and electronic noises from pulses of light, especially relevant for signal processing methods in quantum optics experiments as a means to achieve the shot-noise level and reduce strong technical noise by means of a pattern function. We provide the theory of this pattern-function filtering based on balance detection. Moreover, we implement an experimental demonstration where 10 dB of technical noise is filtered after balance detection. Such filter can readily be used for probing magnetic atomic ensembles in environments with strong technical noise.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. Optical clock signal distribution and packaging optimization

    NASA Astrophysics Data System (ADS)

    Wu, Linghui

    Polymer-based waveguides for optoelectronic interconnects and packagings were fabricated by a fabrication process that is compatible with the Si CMOS packaging process. An optoelectronic interconnection layer (OIL) for the high-speed massive clock signal distribution for the Cray T-90 supercomputer board employing optical multimode channel waveguides in conjunction with surface-normal waveguide grating couplers and a 1-to-2 3 dB splitter was constructed. Equalized optical paths were realized using an optical H-tree structure having 48 optical fanouts. This device could be increased to 64 without introducing any additional complications. A 1-to-48 fanout H-tree structure using Ultradel 9000D series polyimide was fabricated. The propagation loss and splitting loss have been measured as 0.21 dB/cm and 0.4 dB/splitter at 850 nm. The power budget was discussed, and the H-tree waveguide fully satisfies the power budget requirement. A tapered waveguide coupler was employed to match the mode profile between the single-mode fiber and the multimode channel waveguides of the OIL. A thermo-optical based multimode switch was designed, fabricated, and tested. The finite difference method was used to simulate the thermal distribution in the polymer waveguide. Both stable and transient conditions have been calculated. The thermo-optical switch was fabricated and tested. The switching speed of 1 ms was experimentally confirmed, fitting well with the simulation results. Thermo-optic switching for randomly polarized light at wavelengths of 850 nm was experimental confirmed, as was a stable attenuation of 25 dB. The details of tapered waveguide fabrication were investigated. Compression-molded 3-D tapered waveguides were demonstrated for the first time. Not only the vertical depth variation but also the linear dimensions of the molded waveguides were well beyond the limits of what any other conventional waveguide fabrication method is capable of providing. Molded waveguides with

  12. 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.

  13. 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

  14. Predicting resistance by selection of signaling pathways

    PubMed Central

    Rosell, Rafael; Molina, Miguel Angel; Viteri, Santiago

    2014-01-01

    Epidermal growth factor receptor (EGFR) mutations occur in 17% of non-small-cell lung cancer (NSCLC) patients with notable response to single agent therapy but with low complete remission rate and, eventually, disease progression. Priming BIM, a pro-apoptotic signaling BH3-only protein, induces sensitivity to erlotinib in EGFR-mutant cell lines. Synthetic lethal approaches and preemptive therapies based on the initial expression of BIM may significantly improve the treatment outcome. EGFR mutations result in transient pro-death imbalance of survival and apoptotic signaling in response to EGFR inhibition. SHP2 is essential to the balance between ERK and the phosphoinositide-3-kinase (PI3K)/AKT and signal transducer activator of transcription (STAT) activity, while mTOR can be an additional marker for patients with high BIM expression. Furthermore, stromal hepatocyte growth factor (HGF) confers EGFR tyrosine kinase inhibitor (TKI) resistance and induces interreceptor crosstalk with integrin-b4, Eph2, CUB domain-containing protein-1 (CDCP1), AXL and JAK1. Only by understanding better, and in more depth, complex cancer molecular biology will we have the information that will help us to design strategies to augment efficacy of EGFR TKIs and offer our patients the best, most correct therapeutic option. PMID:25806289

  15. On optimization of integration properties of biphase coded signals

    NASA Astrophysics Data System (ADS)

    Qiu, Wanzhi; Xiang, Jingcheng

    Within the context of the requirements for agile waveforms with a large compression ratio in biphase coded radars and on the basis of the characteristics of interpulse integration processing of radar signals, the study proposes two sequence optimization criteria which are suitable for radar processing patterns: interpulse waveform agility - pulse compression - FFT, and MTI - pulse compression - noncoherent integration. Applications of these criteria to optimizing sequences of length 127 are carried out. The output peak ratio of mainlobe to sidelobe (RMS) is improved considerably without a weighting network, while the autocorrelation and cross correlation profles of the sequences are very satisfactory. The RMS of coherent integration and noncoherent integration of eight sequences are 34.12 and 28.1 dB, respectively, when the return signals have zero Doppler shift. These values are about 12 and 6 dB higher than the RMS of single signals before integration.

  16. Optimal sampling and quantization of synthetic aperture radar signals

    NASA Technical Reports Server (NTRS)

    Wu, C.

    1978-01-01

    Some theoretical and experimental results on optimal sampling and quantization of synthetic aperture radar (SAR) signals are presented. It includes a description of a derived theoretical relationship between the pixel signal to noise ratio of processed SAR images and the number of quantization bits per sampled signal, assuming homogeneous extended targets. With this relationship known, a solution may be realized for the problem of optimal allocation of a fixed data bit-volume (for specified surface area and resolution criterion) between the number of samples and the number of bits per sample. The results indicate that to achieve the best possible image quality for a fixed bit rate and a given resolution criterion, one should quantize individual samples coarsely and thereby maximize the number of multiple looks. The theoretical results are then compared with simulation results obtained by processing aircraft SAR data.

  17. 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.

  18. 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.

  19. 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

  20. Optimized source selection for intracavitary low dose rate brachytherapy

    SciTech Connect

    Nurushev, T.; Kim, Jinkoo

    2005-05-01

    A procedure has been developed for automating optimal selection of sources from an available inventory for the low dose rate brachytherapy, as a replacement for the conventional trial-and-error approach. The method of optimized constrained ratios was applied for clinical source selection for intracavitary Cs-137 implants using Varian BRACHYVISION software as initial interface. However, this method can be easily extended to another system with isodose scaling and shaping capabilities. Our procedure provides optimal source selection results independent of the user experience and in a short amount of time. This method also generates statistics on frequently requested ideal source strengths aiding in ordering of clinically relevant sources.

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

    PubMed

    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

  2. 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

  3. Optimizing Site Selection in Urban Areas in Northern Switzerland

    NASA Astrophysics Data System (ADS)

    Plenkers, K.; Kraft, T.; Bethmann, F.; Husen, S.; Schnellmann, M.

    2012-04-01

    There is a need to observe weak seismic events (M<2) in areas close to potential nuclear-waste repositories or nuclear power plants, in order to analyze the underlying seismo-tectonic processes and estimate their seismic hazard. We are therefore densifying the existing Swiss Digital Seismic Network in northern Switzerland by additional 20 stations. The new network that will be in operation by the end of 2012, aims at observing seismicity in northern Switzerland with a completeness of M_c=1.0 and a location error < 0.5 km in epicenter and < 2 km in focal depth. Monitoring of weak seismic events in this region is challenging, because the area of interest is densely populated and geology is dominated by the Swiss molasse basin. A optimal network-design and a thoughtful choice for station-sites is, therefore, mandatory. To help with decision making we developed a step-wise approach to find the optimum network configuration. Our approach is based on standard network optimization techniques regarding the localization error. As a new feature, our approach uses an ambient noise model to compute expected signal-to-noise ratios for a given site. The ambient noise model uses information on land use and major infrastructures such as highways and train lines. We ran a series of network optimizations with increasing number of stations until the requirements regarding localization error and magnitude of completeness are reached. The resulting network geometry serves as input for the site selection. Site selection is done by using a newly developed multi-step assessment-scheme that takes into account local noise level, geology, infrastructure, and costs necessary to realize the station. The assessment scheme is weighting the different parameters and the most promising sites are identified. In a first step, all potential sites are classified based on information from topographic maps and site inspection. In a second step, local noise conditions are measured at selected sites. We

  4. 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.

  5. 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.

  6. Defect profile estimation from magnetic flux leakage signal via efficient managing particle swarm optimization.

    PubMed

    Han, Wenhua; Xu, Jun; Wang, Ping; Tian, Guiyun

    2014-01-01

    In this paper, efficient managing particle swarm optimization (EMPSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signal. In the proposed EMPSO, in order to strengthen exchange of information among particles, particle pair model was built. For more efficient searching when facing different landscapes of problems, velocity updating scheme including three velocity updating models was also proposed. In addition, for more chances to search optimum solution out, automatic particle selection for re-initialization was implemented. The optimization results of six benchmark functions show EMPSO performs well when optimizing 100-D problems. The defect simulation results demonstrate that the inversing technique based on EMPSO outperforms the one based on self-learning particle swarm optimizer (SLPSO), and the estimated profiles are still close to the desired profiles with the presence of low noise in MFL signal. The results estimated from real MFL signal by EMPSO-based inversing technique also indicate that the algorithm is capable of providing an accurate solution of the defect profile with real signal. Both the simulation results and experiment results show the computing time of the EMPSO-based inversing technique is reduced by 20%-30% than that of the SLPSO-based inversing technique. PMID:24926693

  7. 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.

  8. 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.

  9. Optimizing of selective laser sintering method

    SciTech Connect

    Guo Suiyan

    1996-12-31

    In a SLS process, a computer-controlled laser scanner moves laser beam spot on flat powder bed and the laser beam heat the powder to cause sintering in the specific area. A series of such flat planes is linked together to construct a 3D object. SLS is a complex process which involves many process parameters. The laser beam properties, such as laser beam profile, intensity, and wave length, as well as its scanning speed and scanning path, are very important parameters. Laser properties, powder properties and sintering environment work together in a SLS process to determine whether SLS is successful. The objective of SLS is to make a part which has the same size as the CAD data. The accuracy of the final part from SLS is affected by a lot of parameters as mentioned above. How to control these parameters is a key to produce an acceptable final part. Laser parameters, powder material properties and processing environment can all affect the quality of SLS part. A lot of effort has been made in parametric analysis, material properties and processing environment for SLS by other researchers. The focus of this paper is to optimize laser parameters and scanning path to improve quality of SLS part and the processing speed. A scanning method is discussed to improve the quality and speed together.

  10. 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.

  11. 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.

  12. Signal-to-noise-optimal scaling of heterogenous population codes.

    PubMed

    Leibold, Christian

    2013-01-01

    Similarity measures for neuronal population responses that are based on scalar products can be little informative if the neurons have different firing statistics. Based on signal-to-noise optimality, this paper derives positive weighting factors for the individual neurons' response rates in a heterogeneous neuronal population. The weights only depend on empirical statistics. If firing follows Poisson statistics, the weights can be interpreted as mutual information per spike. The scaling is shown to improve linear separability and clustering as compared to unscaled inputs. PMID:23984844

  13. Power optimization in wearable biomedical systems: a signal processing perspective

    NASA Astrophysics Data System (ADS)

    Ghasemzadeh, Hassan

    2012-10-01

    Wearable monitoring systems have caught considerable attention recently due to their potential in many domains including smart health and well-being. These new biomedical monitoring systems aim to provide continuous patient monitoring and proactive care options. Realization of this vision requires research that addresses a number of challenges, in particular, regarding limited resources that the wearable sensor networks offer. This paper presents an overview of different strategies for prolonging system lifetimes through power optimization in such systems. Particular emphasis is given to enhancing processing and communication architectures with respect to the signal processing requirements of the system.

  14. 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.

  15. 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

  16. Selecting optimal partitioning schemes for phylogenomic datasets

    PubMed Central

    2014-01-01

    Background Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics. Methods We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere. Results We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores. Conclusions These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets. PMID:24742000

  17. 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.

  18. 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

  19. Spatial and Functional Selectivity of Peripheral Nerve Signal Recording With the Transversal Intrafascicular Multichannel Electrode (TIME).

    PubMed

    Badia, Jordi; Raspopovic, Stanisa; Carpaneto, Jacopo; Micera, Silvestro; Navarro, Xavier

    2016-01-01

    The selection of suitable peripheral nerve electrodes for biomedical applications implies a trade-off between invasiveness and selectivity. The optimal design should provide the highest selectivity for targeting a large number of nerve fascicles with the least invasiveness and potential damage to the nerve. The transverse intrafascicular multichannel electrode (TIME), transversally inserted in the peripheral nerve, has been shown to be useful for the selective activation of subsets of axons, both at inter- and intra-fascicular levels, in the small sciatic nerve of the rat. In this study we assessed the capabilities of TIME for the selective recording of neural activity, considering the topographical selectivity and the distinction of neural signals corresponding to different sensory types. Topographical recording selectivity was proved by the differential recording of CNAPs from different subsets of nerve fibers, such as those innervating toes 2 and 4 of the hindpaw of the rat. Neural signals elicited by sensory stimuli applied to the rat paw were successfully recorded. Signal processing allowed distinguishing three different types of sensory stimuli such as tactile, proprioceptive and nociceptive ones with high performance. These findings further support the suitability of TIMEs for neuroprosthetic applications, by exploiting the transversal topographical structure of the peripheral nerves. PMID:26087496

  20. Optimal Selection of Parameters for Nonuniform Embedding of Chaotic Time Series Using Ant Colony Optimization.

    PubMed

    Shen, Meie; Chen, Wei-Neng; Zhang, Jun; Chung, Henry Shu-Hung; Kaynak, Okyay

    2013-04-01

    The optimal selection of parameters for time-delay embedding is crucial to the analysis and the forecasting of chaotic time series. Although various parameter selection techniques have been developed for conventional uniform embedding methods, the study of parameter selection for nonuniform embedding is progressed at a slow pace. In nonuniform embedding, which enables different dimensions to have different time delays, the selection of time delays for different dimensions presents a difficult optimization problem with combinatorial explosion. To solve this problem efficiently, this paper proposes an ant colony optimization (ACO) approach. Taking advantage of the characteristic of incremental solution construction of the ACO, the proposed ACO for nonuniform embedding (ACO-NE) divides the solution construction procedure into two phases, i.e., selection of embedding dimension and selection of time delays. In this way, both the embedding dimension and the time delays can be optimized, along with the search process of the algorithm. To accelerate search speed, we extract useful information from the original time series to define heuristics to guide the search direction of ants. Three geometry- or model-based criteria are used to test the performance of the algorithm. The optimal embeddings found by the algorithm are also applied in time-series forecasting. Experimental results show that the ACO-NE is able to yield good embedding solutions from both the viewpoints of optimization performance and prediction accuracy. PMID:23144038

  1. 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.

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

    PubMed

    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

  3. 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

  4. 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.

  5. 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...

  6. 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)

  7. 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…

  8. 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 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 determ...

  9. 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

  10. Collimator Width Optimization in X-Ray Luminescent Computed Tomography (XLCT) with Selective Excitation Scheme

    PubMed Central

    Mishra, S.; Kappiyoor, R.

    2015-01-01

    X-ray luminescent computed tomography (XLCT) is a promising new functional imaging modality based on computed tomography (CT). This imaging technique uses X-ray excitable nanophosphors to illuminate objects of interest in the visible spectrum. Though there are several validations of the underlying technology, none of them have addressed the issues of performance optimality for a given design of the imaging system. This study addresses the issue of obtaining best image quality through optimizing collimator width to balance the signal to noise ratio (SNR) and resolution. The results can be generalized as to any XLCT system employing a selective excitation scheme. PMID:25642356

  11. The importance of female choice, male-male competition, and signal transmission as causes of selection on male mating signals.

    PubMed

    Sullivan-Beckers, Laura; Cocroft, Reginald B

    2010-11-01

    Selection on advertisement signals arises from interacting sources including female choice, male-male competition, and the communication channel (i.e., the signaling environment). To identify the contribution of individual sources of selection, we used previously quantified relationships between signal traits and each putative source to predict relationships between signal variation and fitness in Enchenopa binotata treehoppers (Hemiptera: Membracidae). We then measured phenotypic selection on signals and compared predicted and realized relationships between signal traits and mating success. We recorded male signals, then measured lifetime mating success at two population densities in a realistic environment in which sources of selection could interact. We identified which sources best predicted the relationship between signal variation and mating success using a multiple regression approach. All signal traits were under selection in at least one of the two breeding seasons measured, and in some cases selection was variable between years. Female preference was the strongest source of selection shaping male signals. The E. binotata species complex is a model of ecological speciation initiated by host shifts. Signal and preference divergence contribute to behavioral isolation within the complex, and the finding that female mate preferences drive signal evolution suggests that speciation in this group results from both ecological divergence and sexual selection. PMID:20624180

  12. 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%). PMID:26819671

  13. 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

  14. 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

  15. 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.

  16. 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. PMID:26283263

  17. 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.

  18. 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.

  19. 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.

  20. 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. PMID:26575896

  1. PDZ Domain Binding Selectivity Is Optimized Across the Mouse Proteome

    PubMed Central

    Stiffler, Michael A.; Chen, Jiunn R.; Grantcharova, Viara P.; Lei, Ying; Fuchs, Daniel; Allen, John E.; Zaslavskaia, Lioudmila A.; MacBeath, Gavin

    2009-01-01

    PDZ domains have long been thought to cluster into discrete functional classes defined by their peptide-binding preferences. We used protein microarrays and quantitative fluorescence polarization to characterize the binding selectivity of 157 mouse PDZ domains with respect to 217 genome-encoded peptides. We then trained a multidomain selectivity model to predict PDZ domain–peptide interactions across the mouse proteome with an accuracy that exceeds many large-scale, experimental investigations of protein-protein interactions. Contrary to the current paradigm, PDZ domains do not fall into discrete classes; instead, they are evenly distributed throughout selectivity space, which suggests that they have been optimized across the proteome to minimize cross-reactivity. We predict that focusing on families of interaction domains, which facilitates the integration of experimentation and modeling, will play an increasingly important role in future investigations of protein function. PMID:17641200

  2. 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. PMID:25912446

  3. Optimal precursor ion selection for LC-MALDI MS/MS

    PubMed Central

    2013-01-01

    Background Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins. Results We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time. Conclusions We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well-defined protein standard and a complex human cell lysate, we demonstrate that our methods outperform standard approaches. Our algorithms are implemented as part of OpenMS and are available under http://www.openms.de. PMID:23418672

  4. 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.

  5. 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.

  6. Implementing stationary-phase optimized selectivity in supercritical fluid chromatography.

    PubMed

    Delahaye, Sander; Lynen, Frédéric

    2014-12-16

    The performance of stationary-phase optimized selectivity liquid chromatography (SOS-LC) for improved separation of complex mixtures has been demonstrated before. A dedicated kit containing column segments of different lengths and packed with different stationary phases is commercially available together with algorithms capable of predicting and ranking isocratic and gradient separations over vast amounts of possible column combinations. Implementation in chromatographic separations involving compressible fluids, as is the case in supercritical fluid chromatography, had thus far not been attempted. The challenge of this approach is the dependency of solute retention with the mobile-phase density, complicating linear extrapolation of retention over longer or shorter columns segments, as is the case in conventional SOS-LC. In this study, the possibilities of performing stationary-phase optimized selectivity supercritical fluid chromatography (SOS-SFC) are demonstrated with typical low density mobile phases (94% CO2). The procedure is optimized with the commercially available column kit and with the classical isocratic SOS-LC algorithm. SOS-SFC appears possible without any density correction, although optimal correspondence between prediction and experiment is obtained when isopycnic conditions are maintained. As also the influence of the segment order appears significantly less relevant than expected, the use of the approach in SFC appears as promising as is the case in HPLC. Next to the classical use of SOS for faster baseline separation of all solutes in a mixture, the benefits of the approach for predicting as wide as possible separation windows around to-be-purified solutes in semipreparative SFC are illustrated, leading to significant production rate improvements in (semi)preparative SFC. PMID:25393519

  7. 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.

  8. Membrane association of the CD3ε signaling domain is required for optimal T cell development and function1

    PubMed Central

    Bettini, Matthew L.; Guy, Clifford; Dash, Pradyot; Vignali, Kate M.; Hamm, David E.; Dobbins, Jessica; Gagnon, Etienne; Thomas, Paul G.; Wucherpfennig, Kai W.; Vignali, Dario A.A.

    2014-01-01

    The T cell receptor (TCR):CD3 complex transduces signals that are critical for optimal T cell development and adaptive immunity. In resting T cells, the CD3ε cytoplasmic tail associates with the plasma membrane via a proximal basic-rich stretch (BRS). Here we show that mice lacking a functional CD3ε-BRS exhibited substantial reductions in thymic cellularity and limited CD4−CD8− double negative-3 (DN3) to DN4 thymocyte transition, due to enhanced DN4 TCR signaling resulting in increased cell death and TCR downregulation in all subsequent populations. Furthermore, positive, but not negative, T cell selection was affected in mice lacking a functional CD3ε-BRS, which led to limited peripheral T cell function and substantially reduced responsiveness to influenza infection. Collectively, these results indicate membrane association of the CD3ε signaling domain is required for optimal thymocyte development and peripheral T cell function. PMID:24899501

  9. 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.

  10. 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'. PMID:26577486

  11. 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%.

  12. 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

  13. 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.

  14. Batch-Form Solutions to Optimal Input Signal Recovery in the Presence of Noises

    NASA Astrophysics Data System (ADS)

    Lin, Ping; Phan, Minh Q.; Ketcham, Stephen A.

    2013-12-01

    This paper studies the problem of optimally recovering the input signals to a linear time-invariant system in the presence of input and measurement noises. The emphasis is on batch-form solutions which are suitable for short-duration large-domain signal propagation applications. The system, the input and measurement noise covariances, the noise-corrupted output signals are assumed known, and we seek to recover the input signals that enter the system prior to being corrupted by input noise. The input signal recovery is optimal in the sense that the optimal Kalman filter residual is correctly recovered from the given information. Various solution techniques are considered and a weighted least-squares solution is found to be the simplest and most practical in short-duration signal recovery applications.

  15. 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.

  16. 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.

  17. Multiobjective optimization for model selection in kernel methods in regression.

    PubMed

    You, Di; Benitez-Quiroz, Carlos Fabian; Martinez, Aleix M

    2014-10-01

    Regression plays a major role in many scientific and engineering problems. The goal of regression is to learn the unknown underlying function from a set of sample vectors with known outcomes. In recent years, kernel methods in regression have facilitated the estimation of nonlinear functions. However, two major (interconnected) problems remain open. The first problem is given by the bias-versus-variance tradeoff. If the model used to estimate the underlying function is too flexible (i.e., high model complexity), the variance will be very large. If the model is fixed (i.e., low complexity), the bias will be large. The second problem is to define an approach for selecting the appropriate parameters of the kernel function. To address these two problems, this paper derives a new smoothing kernel criterion, which measures the roughness of the estimated function as a measure of model complexity. Then, we use multiobjective optimization to derive a criterion for selecting the parameters of that kernel. The goal of this criterion is to find a tradeoff between the bias and the variance of the learned function. That is, the goal is to increase the model fit while keeping the model complexity in check. We provide extensive experimental evaluations using a variety of problems in machine learning, pattern recognition, and computer vision. The results demonstrate that the proposed approach yields smaller estimation errors as compared with methods in the state of the art. PMID:25291740

  18. 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.

  19. Multiobjective Optimization for Model Selection in Kernel Methods in Regression

    PubMed Central

    You, Di; Benitez-Quiroz, C. Fabian; Martinez, Aleix M.

    2016-01-01

    Regression plays a major role in many scientific and engineering problems. The goal of regression is to learn the unknown underlying function from a set of sample vectors with known outcomes. In recent years, kernel methods in regression have facilitated the estimation of nonlinear functions. However, two major (interconnected) problems remain open. The first problem is given by the bias-vs-variance trade-off. If the model used to estimate the underlying function is too flexible (i.e., high model complexity), the variance will be very large. If the model is fixed (i.e., low complexity), the bias will be large. The second problem is to define an approach for selecting the appropriate parameters of the kernel function. To address these two problems, this paper derives a new smoothing kernel criterion, which measures the roughness of the estimated function as a measure of model complexity. Then, we use multiobjective optimization to derive a criterion for selecting the parameters of that kernel. The goal of this criterion is to find a trade-off between the bias and the variance of the learned function. That is, the goal is to increase the model fit while keeping the model complexity in check. We provide extensive experimental evaluations using a variety of problems in machine learning, pattern recognition and computer vision. The results demonstrate that the proposed approach yields smaller estimation errors as compared to methods in the state of the art. PMID:25291740

  20. 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…

  1. Induction motor fault diagnosis based on the k-NN and optimal feature selection

    NASA Astrophysics Data System (ADS)

    Nguyen, Ngoc-Tu; Lee, Hong-Hee

    2010-09-01

    The k-nearest neighbour (k-NN) rule is applied to diagnose the conditions of induction motors. The features are extracted from the time vibration signals while the optimal features are selected by a genetic algorithm based on a distance criterion. A weight value is assigned to each feature to help select the best quality features. To improve the classification performance of the k-NN rule, each of the k neighbours are evaluated by a weight factor based on the distance to the test pattern. The proposed k-NN is compared to the conventional k-NN and support vector machine classification to verify the performance of an induction motor fault diagnosis.

  2. 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

  3. New indicator for optimal preprocessing and wavelength selection of near-infrared spectra.

    PubMed

    Skibsted, E T S; Boelens, H F M; Westerhuis, J A; Witte, D T; Smilde, A K

    2004-03-01

    Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing techniques and selecting different wavelengths is to compare prediction statistics computed with an independent set of data not used to make the actual calibration model. When the errors of reference value are large, no such values are available at all, or only a limited number of samples are available, other methods exist to evaluate the preprocessing method and wavelength selection. In this work we present a new indicator (SE) that only requires blank sample spectra, i.e., spectra of samples that are mixtures of the interfering constituents (everything except the analyte), a pure analyte spectrum, or alternatively, a sample spectrum where the analyte is present. The indicator is based on computing the net analyte signal of the analyte and the total error, i.e., instrumental noise and bias. By comparing the indicator values when different preprocessing techniques and wavelength selections are applied to the spectra, the optimal preprocessing technique and the optimal wavelength selection can be determined without knowledge of reference values, i.e., it minimizes the non-related spectral variation. The SE indicator is compared to two other indicators that also use net analyte signal computations. To demonstrate the feasibility of the SE indicator, two near-infrared spectral data sets from the pharmaceutical industry were used, i.e., diffuse reflectance spectra of powder samples and transmission spectra of tablets. Especially in pharmaceutical spectroscopic applications, it is expected beforehand that the non-related spectral variation is rather large and it is important to remove it. The indicator gave excellent results with respect to wavelength selection and optimal

  4. Signal Scaling Improves the Signal-to-Noise Ratio of Measurements with Segmented 2D-Selective Radiofrequency Excitations

    PubMed Central

    Finsterbusch, Jürgen; Busch, Martin G.; Larson, Peder E. Z.

    2016-01-01

    Purpose Segmented 2D-selective radiofrequency excitations can be used to acquire irregularly shaped target regions, e.g., in single-voxel MR spectroscopy, without involving excessive radiofrequency pulse durations. However, segments covering only outer k-space regions nominally use reduced B1 amplitudes (i.e., smaller flip angles) and yield lower signal contributions, which decreases the efficiency of the measurement. The purpose of this study was to show that applying the full flip angle for all segments and scaling down the acquired signal appropriately (signal scaling) retains the desired signal amplitude but reduces the noise level accordingly and, thus, increases the signal-to-noise ratio. Methods The principles and improvements of signal scaling were demonstrated with MR imaging and spectroscopy experiments at 3 T for a single-line segmentation of a blipped-planar trajectory. Results The observed signal-to-noise ration gain depended on the 2D-selective radiofrequency excitation’s resolution, field-of-excitation, and its excitation profile and was between 40 and 500% for typical acquisition parameters. Conclusion Signal scaling can further improve the performance of measurements with segmented 2D-selective radiofrequency excitations, e.g., for MR spectroscopy of anatomically defined voxels. PMID:23440633

  5. Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS

    NASA Astrophysics Data System (ADS)

    Vinding, Mads S.; Laustsen, Christoffer; Maximov, Ivan I.; Søgaard, Lise Vejby; Ardenkjær-Larsen, Jan H.; Nielsen, Niels Chr.

    2013-02-01

    Aimed at 13C metabolic magnetic resonance imaging (MRI) and spectroscopy (MRS) applications, we demonstrate that dynamic nuclear polarization (DNP) may be combined with optimal control 2D spatial selection to simultaneously obtain high sensitivity and well-defined spatial restriction. This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-13C]pyruvate on a 4.7 T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region-of-interest (ROI) single metabolite signals available for higher image resolution or single-peak spectra. The 2D spatial-selective rf pulses were designed using a novel Krotov-based optimal control approach capable of iteratively fast providing successful pulse sequences in the absence of qualified initial guesses. The technique may be important for early detection of abnormal metabolism, monitoring disease progression, and drug research.

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. Making the optimal decision in selecting protective clothing

    SciTech Connect

    Price, J. Mark

    2007-07-01

    Protective Clothing plays a major role in the decommissioning and operation of nuclear facilities. Literally thousands of employee dress-outs occur over the life of a decommissioning project and during outages at operational plants. In order to make the optimal decision on which type of protective clothing is best suited for the decommissioning or maintenance and repair work on radioactive systems, a number of interrelating factors must be considered, including - Protection; - Personnel Contamination; - Cost; - Radwaste; - Comfort; - Convenience; - Logistics/Rad Material Considerations; - Reject Rate of Laundered Clothing; - Durability; - Security; - Personnel Safety including Heat Stress; - Disposition of Gloves and Booties. In addition, over the last several years there has been a trend of nuclear power plants either running trials or switching to Single Use Protective Clothing (SUPC) from traditional protective clothing. In some cases, after trial usage of SUPC, plants have chosen not to switch. In other cases after switching to SUPC for a period of time, some plants have chosen to switch back to laundering. Based on these observations, this paper reviews the 'real' drivers, issues, and interrelating factors regarding the selection and use of protective clothing throughout the nuclear industry. (authors)

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. 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. PMID:25006973

  18. 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.

  19. A quadratic weight selection algorithm. [for optimal flight control

    NASA Technical Reports Server (NTRS)

    Broussard, J. R.

    1981-01-01

    A new numerical algorithm is presented which determines a positive semi-definite state weighting matrix in the linear-quadratic optimal control design problem. The algorithm chooses the weighting matrix by placing closed-loop eigenvalues and eigenvectors near desired locations using optimal feedback gains. A simplified flight control design example is used to illustrate the algorithms capabilities.

  20. 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

  1. 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. PMID:26197438

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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...

  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. 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.

  9. Energetic optimization of ion conduction rate by the K+ selectivity filter

    NASA Astrophysics Data System (ADS)

    Morais-Cabral, João H.; Zhou, Yufeng; MacKinnon, Roderick

    2001-11-01

    The K+ selectivity filter catalyses the dehydration, transfer and rehydration of a K+ ion in about ten nanoseconds. This physical process is central to the production of electrical signals in biology. Here we show how nearly diffusion-limited rates are achieved, by analysing ion conduction and the corresponding crystallographic ion distribution in the selectivity filter of the KcsA K+ channel. Measurements with K+ and its slightly larger analogue, Rb+, lead us to conclude that the selectivity filter usually contains two K+ ions separated by one water molecule. The two ions move in a concerted fashion between two configurations, K+-water-K+-water (1,3 configuration) and water-K+-water-K+ (2,4 configuration), until a third ion enters, displacing the ion on the opposite side of the queue. For K+, the energy difference between the 1,3 and 2,4 configurations is close to zero, the condition of maximum conduction rate. The energetic balance between these configurations is a clear example of evolutionary optimization of protein function.

  10. 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.

  11. 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.

  12. 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…

  13. 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.

  14. 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. PMID:23556586

  15. 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.

  16. 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. PMID:26592625

  17. 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…

  18. 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…

  19. 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.

  20. 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

  1. 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.

  2. Signal Transduction and Molecular Targets of Selected Flavonoids

    PubMed Central

    Bode, Ann M.

    2013-01-01

    Abstract Significance: Diet exerts a major influence on the risk for developing cancer and heart disease. Food factors such as flavonoids are alleged to protect cells from premature aging and disease by shielding DNA, proteins, and lipids from oxidative damage. Recent Advances: Our work has focused on clarifying the effects of dietary components on cancer cell proliferation and tumor growth, discovering mechanisms to explain the effects, and identifying the specific molecular targets of these compounds. Our strategy for identifying specific molecular targets of phytochemicals involves the use of supercomputer technology combined with protein crystallography, molecular biology, and experimental laboratory verification. Critical Issues: One of the greatest challenges for scientists is to reduce the accumulation of distortion and half truths reported in the popular media regarding the health benefits of certain foods or food supplements. The use of these is not new, but interest has increased dramatically because of perceived health benefits that are presumably acquired without unpleasant side effects. Flavonoids are touted to exert many beneficial effects in vitro. However, whether they can produce these effects in vivo is disputed. Future Directions: The World Health Organization indicates that one third of all cancer deaths are preventable and that diet is closely linked to prevention. Based on this idea and epidemiological findings, attention has centered on dietary phytochemicals as an effective intervention in cancer development. However, an unequivocal link between diet and cancer has not been established. Thus, identifying cancer preventive dietary agents with specific molecular targets is essential to move forward toward successful cancer prevention. Antioxid. Redox Signal. 19, 163–180. PMID:23458437

  3. Transferability of optimally-selected climate models in the quantification of climate change impacts on hydrology

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe

    2016-02-01

    Given the ever increasing number of climate change simulations being carried out, it has become impractical to use all of them to cover the uncertainty of climate change impacts. Various methods have been proposed to optimally select subsets of a large ensemble of climate simulations for impact studies. However, the behaviour of optimally-selected subsets of climate simulations for climate change impacts is unknown, since the transfer process from climate projections to the impact study world is usually highly non-linear. Consequently, this study investigates the transferability of optimally-selected subsets of climate simulations in the case of hydrological impacts. Two different methods were used for the optimal selection of subsets of climate scenarios, and both were found to be capable of adequately representing the spread of selected climate model variables contained in the original large ensemble. However, in both cases, the optimal subsets had limited transferability to hydrological impacts. To capture a similar variability in the impact model world, many more simulations have to be used than those that are needed to simply cover variability from the climate model variables' perspective. Overall, both optimal subset selection methods were better than random selection when small subsets were selected from a large ensemble for impact studies. However, as the number of selected simulations increased, random selection often performed better than the two optimal methods. To ensure adequate uncertainty coverage, the results of this study imply that selecting as many climate change simulations as possible is the best avenue. Where this was not possible, the two optimal methods were found to perform adequately.

  4. Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN.

    PubMed

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

    2016-05-01

    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

  5. 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

  6. 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.

  7. 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…

  8. 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.

  9. 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

  10. 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.

  11. Optimal coherent control of coherent anti-Stokes Raman scattering: Signal enhancement and background elimination

    NASA Astrophysics Data System (ADS)

    Gao, Fang; Shuang, Feng; Shi, Junhui; Rabitz, Herschel; Wang, Haifeng; Cheng, Ji-Xin

    2012-04-01

    The ability to enhance resonant signals and eliminate the non-resonant background is analyzed for coherent anti-Stokes Raman scattering (CARS). The analysis is done at a specific frequency as well as for broadband excitation using femtosecond pulse-shaping techniques. An appropriate objective functional is employed to balance resonant signal enhancement against non-resonant background suppression. Optimal enhancement of the signal and minimization of the background can be achieved by shaping the probe pulse alone while keeping the pump and Stokes pulses unshaped. In some cases analytical forms for the probe pulse can be found, and numerical simulations are carried out for other circumstances. It is found that a good approximate optimal solution for resonant signal enhancement in two-pulse CARS is a superposition of linear and arctangent-type phases for the pump. The well-known probe delay method is shown to be a quasi-optimal scheme for broadband background suppression. The results should provide a basis to improve the performance of CARS spectroscopy and microscopy.

  12. Optimizing selection with several constraints in poultry breeding.

    PubMed

    Chapuis, H; Pincent, C; Colleau, J J

    2016-02-01

    Poultry breeding schemes permanently face the need to control the evolution of coancestry and some critical traits, while selecting for a main breeding objective. The main aims of this article are first to present an efficient selection algorithm adapted to this situation and then to measure how the severity of constraints impacted on the degree of loss for the main trait, compared to BLUP selection on the main trait, without any constraint. Broiler dam and sire line schemes were mimicked by simulation over 10 generations and selection was carried out on the main trait under constraints for coancestry and for another trait, antagonistic with the main trait. The selection algorithm was a special simulated annealing (adaptative simulated annealing (ASA)). It was found to be rapid and able to meet constraints very accurately. A constraint on the second trait was found to induce an impact similar to or even greater than the impact of the constraint on coancestry. The family structure of selected poultry populations made it easy to control the evolution of coancestry at a reasonable cost but was not as useful for reducing the cost of controlling evolution of the antagonistic traits. Multiple constraints impacted almost additively on the genetic gain for the main trait. Adding constraints for several traits would therefore be justified in real life breeding schemes, possibly after evaluating their impact through simulated annealing. PMID:26220593

  13. 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

  14. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 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...

  15. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 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...

  16. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 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...

  17. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 49 Transportation 4 2012-10-01 2012-10-01 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...

  18. 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...

  19. 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

  20. Adaptive Optimal Kernel Smooth-Windowed Wigner-Ville Distribution for Digital Communication Signal

    NASA Astrophysics Data System (ADS)

    Tan, Jo Lynn; Sha'ameri, Ahmad Zuribin

    2009-12-01

    Time-frequency distributions (TFDs) are powerful tools to represent the energy content of time-varying signal in both time and frequency domains simultaneously but they suffer from interference due to cross-terms. Various methods have been described to remove these cross-terms and they are typically signal-dependent. Thus, there is no single TFD with a fixed window or kernel that can produce accurate time-frequency representation (TFR) for all types of signals. In this paper, a globally adaptive optimal kernel smooth-windowed Wigner-Ville distribution (AOK-SWWVD) is designed for digital modulation signals such as ASK, FSK, and M-ary FSK, where its separable kernel is determined automatically from the input signal, without prior knowledge of the signal. This optimum kernel is capable of removing the cross-terms and maintaining accurate time-frequency representation at SNR as low as 0 dB. It is shown that this system is comparable to the system with prior knowledge of the signal.

  1. An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer

    PubMed Central

    Lorenzo, Enery; Camacho-Caceres, Katia; Ropelewski, Alexander J.; Rosas, Juan; Ortiz-Mojer, Michael; Perez-Marty, Lynn; Irizarry, Juan; Gonzalez, Valerie; Rodríguez, Jesús A.; Cabrera-Rios, Mauricio; Isaza, Clara

    2015-01-01

    Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path. PMID:26388997

  2. 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.

  3. Optimal search-based gene subset selection for gene array cancer classification.

    PubMed

    Li, Jiexun; Su, Hua; Chen, Hsinchun; Futscher, Bernard W

    2007-07-01

    High dimensionality has been a major problem for gene array-based cancer classification. It is critical to identify marker genes for cancer diagnoses. We developed a framework of gene selection methods based on previous studies. This paper focuses on optimal search-based subset selection methods because they evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this paper is the first to introduce tabu search (TS) to gene selection from high-dimensional gene array data. Our comparative study of gene selection methods demonstrated the effectiveness of optimal search-based gene subset selection to identify cancer marker genes. TS was shown to be a promising tool for gene subset selection. PMID:17674622

  4. Optimization of heavy chain and light chain signal peptides for high level expression of therapeutic antibodies in CHO cells.

    PubMed

    Haryadi, Ryan; Ho, Steven; Kok, Yee Jiun; Pu, Helen X; Zheng, Lu; Pereira, Natasha A; Li, Bin; Bi, Xuezhi; Goh, Lin-Tang; Yang, Yuansheng; Song, Zhiwei

    2015-01-01

    Translocation of a nascent protein from the cytosol into the ER mediated by its signal peptide is a critical step in protein secretion. The aim of this work was to develop a platform technology to optimize the signal peptides for high level production of therapeutic antibodies in CHO cells. A database of signal peptides from a large number of human immunoglobulin (Ig) heavy chain (HC) and kappa light chain (LC) was generated. Most of the HC signal peptides contain 19 amino acids which can be divided into three domains and the LC signal peptides contain 22 amino acids. The signal peptides were then clustered according to sequence similarity. Based on the clustering, 8 HC and 2 LC signal peptides were analyzed for their impacts on the production of 5-top selling antibody therapeutics, namely, Herceptin, Avastin, Remicade, Rituxan, and Humira. The best HC and LC signal peptides for producing these 5 antibodies were identified. The optimized signal peptides for Rituxan is 2-fold better compared to its native signal peptides which are available in the public database. Substitution of a single amino acid in the optimized HC signal peptide for Avastin reduced its production significantly. Mass spectrometry analyses revealed that all optimized signal peptides are accurately removed in the mature antibodies. The results presented in this report are particularly important for the production of these 5 antibodies as biosimilar drugs. They also have the potential to be the best signal peptides for the production of new antibodies in CHO cells. PMID:25706993

  5. Optimization of Heavy Chain and Light Chain Signal Peptides for High Level Expression of Therapeutic Antibodies in CHO Cells

    PubMed Central

    Haryadi, Ryan; Ho, Steven; Kok, Yee Jiun; Pu, Helen X.; Zheng, Lu; Pereira, Natasha A.; Li, Bin; Bi, Xuezhi; Goh, Lin-Tang; Yang, Yuansheng; Song, Zhiwei

    2015-01-01

    Translocation of a nascent protein from the cytosol into the ER mediated by its signal peptide is a critical step in protein secretion. The aim of this work was to develop a platform technology to optimize the signal peptides for high level production of therapeutic antibodies in CHO cells. A database of signal peptides from a large number of human immunoglobulin (Ig) heavy chain (HC) and kappa light chain (LC) was generated. Most of the HC signal peptides contain 19 amino acids which can be divided into three domains and the LC signal peptides contain 22 amino acids. The signal peptides were then clustered according to sequence similarity. Based on the clustering, 8 HC and 2 LC signal peptides were analyzed for their impacts on the production of 5-top selling antibody therapeutics, namely, Herceptin, Avastin, Remicade, Rituxan, and Humira. The best HC and LC signal peptides for producing these 5 antibodies were identified. The optimized signal peptides for Rituxan is 2-fold better compared to its native signal peptides which are available in the public database. Substitution of a single amino acid in the optimized HC signal peptide for Avastin reduced its production significantly. Mass spectrometry analyses revealed that all optimized signal peptides are accurately removed in the mature antibodies. The results presented in this report are particularly important for the production of these 5 antibodies as biosimilar drugs. They also have the potential to be the best signal peptides for the production of new antibodies in CHO cells. PMID:25706993

  6. 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

  7. 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. PMID:25741689

  8. 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

  9. 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. PMID:24927129

  10. Selecting radiotherapy dose distributions by means of constrained optimization problems.

    PubMed

    Alfonso, J C L; Buttazzo, G; García-Archilla, B; Herrero, M A; Núñez, L

    2014-05-01

    The main steps in planning radiotherapy consist in selecting for any patient diagnosed with a solid tumor (i) a prescribed radiation dose on the tumor, (ii) bounds on the radiation side effects on nearby organs at risk and (iii) a fractionation scheme specifying the number and frequency of therapeutic sessions during treatment. The goal of any radiotherapy treatment is to deliver on the tumor a radiation dose as close as possible to that selected in (i), while at the same time conforming to the constraints prescribed in (ii). To this day, considerable uncertainties remain concerning the best manner in which such issues should be addressed. In particular, the choice of a prescription radiation dose is mostly based on clinical experience accumulated on the particular type of tumor considered, without any direct reference to quantitative radiobiological assessment. Interestingly, mathematical models for the effect of radiation on biological matter have existed for quite some time, and are widely acknowledged by clinicians. However, the difficulty to obtain accurate in vivo measurements of the radiobiological parameters involved has severely restricted their direct application in current clinical practice.In this work, we first propose a mathematical model to select radiation dose distributions as solutions (minimizers) of suitable variational problems, under the assumption that key radiobiological parameters for tumors and organs at risk involved are known. Second, by analyzing the dependence of such solutions on the parameters involved, we then discuss the manner in which the use of those minimizers can improve current decision-making processes to select clinical dosimetries when (as is generally the case) only partial information on model radiosensitivity parameters is available. A comparison of the proposed radiation dose distributions with those actually delivered in a number of clinical cases strongly suggests that solutions of our mathematical model can be

  11. Improvement of the energy resolution via an optimized digital signal processing in GERDA Phase I

    NASA Astrophysics Data System (ADS)

    Agostini, M.; Allardt, M.; Bakalyarov, A. M.; Balata, M.; Barabanov, I.; Barros, N.; Baudis, L.; Bauer, C.; Becerici-Schmidt, N.; Bellotti, E.; Belogurov, S.; Belyaev, S. T.; Benato, G.; Bettini, A.; Bezrukov, L.; Bode, T.; Borowicz, D.; Brudanin, V.; Brugnera, R.; Budjáš, D.; Caldwell, A.; Cattadori, C.; Chernogorov, A.; D'Andrea, V.; Demidova, E. V.; Vacri, A. di; Domula, A.; Doroshkevich, E.; Egorov, V.; Falkenstein, R.; Fedorova, O.; Freund, K.; Frodyma, N.; Gangapshev, A.; Garfagnini, A.; Grabmayr, P.; Gurentsov, V.; Gusev, K.; Hegai, A.; Heisel, M.; Hemmer, S.; Heusser, G.; Hofmann, W.; Hult, M.; Inzhechik, L. V.; Janicskó Csáthy, J.; Jochum, J.; Junker, M.; Kazalov, V.; Kihm, T.; Kirpichnikov, I. V.; Kirsch, A.; Klimenko, A.; Knöpfle, K. T.; Kochetov, O.; Kornoukhov, V. N.; Kuzminov, V. V.; Laubenstein, ********************M.; Lazzaro, A.; Lebedev, V. I.; Lehnert, B.; Liao, H. Y.; Lindner, M.; Lippi, I.; Lubashevskiy, A.; Lubsandorzhiev, B.; Lutter, G.; Macolino, C.; Majorovits, B.; Maneschg, W.; Medinaceli, E.; Misiaszek, M.; Moseev, P.; Nemchenok, I.; Palioselitis, D.; Panas, K.; Pandola, L.; Pelczar, K.; Pullia, A.; Riboldi, S.; Rumyantseva, N.; Sada, C.; Salathe, M.; Schmitt, C.; Schneider, B.; Schönert, S.; Schreiner, J.; Schütz, A.-K.; Schulz, O.; Schwingenheuer, B.; Selivanenko, O.; Shirchenko, M.; Simgen, H.; Smolnikov, A.; Stanco, L.; Stepaniuk, M.; Ur, C. A.; Vanhoefer, L.; Vasenko, A. A.; Veresnikova, A.; von Sturm, K.; Wagner, V.; Walter, M.; Wegmann, A.; Wester, T.; Wilsenach, H.; Wojcik, M.; Yanovich, E.; Zavarise, P.; Zhitnikov, I.; Zhukov, S. V.; Zinatulina, D.; Zuber, K.; Zuzel, G.

    2015-06-01

    An optimized digital shaping filter has been developed for the Gerda experiment which searches for neutrinoless double beta decay in Ge. The Gerda Phase I energy calibration data have been reprocessed and an average improvement of 0.3 keV in energy resolution (FWHM) corresponding to 10 % at the value for decay in Ge is obtained. This is possible thanks to the enhanced low-frequency noise rejection of this Zero Area Cusp (ZAC) signal shaping filter.

  12. 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

  13. 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.

  14. Complex multivariate sexual selection on male acoustic signaling in a wild population of Teleogryllus commodus.

    PubMed

    Bentsen, Caroline L; Hunt, John; Jennions, Michael D; Brooks, Robert

    2006-04-01

    Mate choice may impose both linear (i.e., directional) and nonlinear (i.e., quadratic and correlational) sexual selection on advertisement traits. Traditionally, mate recognition and sensory tuning have been thought to impose stabilizing (i.e., negative quadratic) sexual selection, whereas adaptive mate choice effects directional selection. It has been suggested that adaptive choice may exert positive quadratic and/or correlational sexual selection. Earlier, we showed that five structural components of the advertisement call of male field crickets (Teleogryllus commodus) were under multivariate stabilizing selection under laboratory conditions. Here we experimentally estimate selection on these five traits plus a measure of calling activity (the number of repeats in a looped bout of calling) in the field. There was general support for multivariate stabilizing selection on call structure, and calling activity was under strong positive directional selection, as predicted for a signal of genetic quality. There was, however, also appreciable correlational selection, suggesting an interaction between male call structure and calling effort. Interestingly, selection for short interbout durations of silence favored longer intercall durations in the field, in contrast to results from continuous looped call playback in the laboratory. We discuss the general importance of nonlinear selection in the honest signaling of genetic quality. PMID:16670989

  15. 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.

  16. Optimization of gene sequences under constant mutational pressure and selection

    NASA Astrophysics Data System (ADS)

    Kowalczuk, M.; Gierlik, A.; Mackiewicz, P.; Cebrat, S.; Dudek, M. R.

    1999-12-01

    We have analyzed the influence of constant mutational pressure and selection on the nucleotide composition of DNA sequences of various size, which were represented by the genes of the Borrelia burgdorferi genome. With the help of MC simulations we have found that longer DNA sequences accumulate much less base substitutions per sequence length than short sequences. This leads us to the conclusion that the accuracy of replication may determine the size of genome.

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. 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

  2. An approach to selecting the optimal sensing coil configuration structure for switched reluctance motor rotor position measurement

    NASA Astrophysics Data System (ADS)

    Cai, Jun; Deng, Zhiquan

    2015-02-01

    Accurate rotor position signal is highly required for controlling the switched reluctance motor (SRM). The use of galvanic isolated sensing coils can provide independent circuit for position estimation without affecting the SRM actuation. However, the cross-coupling between main winding and sensing coil, and the mutual coupling between adjacent phase sensing coils may affect the position estimation performance seriously. In this paper, three sensing coil configurations in a 12/8 structure SRM are analyzed and compared for selecting an optimal configuration that can effectively minimize the bad effects of the cross-coupling factors. The finite element analysis and experimental results are provided for verification.

  3. An approach to selecting the optimal sensing coil configuration structure for switched reluctance motor rotor position measurement.

    PubMed

    Cai, Jun; Deng, Zhiquan

    2015-02-01

    Accurate rotor position signal is highly required for controlling the switched reluctance motor (SRM). The use of galvanic isolated sensing coils can provide independent circuit for position estimation without affecting the SRM actuation. However, the cross-coupling between main winding and sensing coil, and the mutual coupling between adjacent phase sensing coils may affect the position estimation performance seriously. In this paper, three sensing coil configurations in a 12/8 structure SRM are analyzed and compared for selecting an optimal configuration that can effectively minimize the bad effects of the cross-coupling factors. The finite element analysis and experimental results are provided for verification. PMID:25725876

  4. Confinement Sensing and Signal Optimization via Piezo1/PKA and Myosin II Pathways.

    PubMed

    Hung, Wei-Chien; Yang, Jessica R; Yankaskas, Christopher L; Wong, Bin Sheng; Wu, Pei-Hsun; Pardo-Pastor, Carlos; Serra, Selma A; Chiang, Meng-Jung; Gu, Zhizhan; Wirtz, Denis; Valverde, Miguel A; Yang, Joy T; Zhang, Jin; Konstantopoulos, Konstantinos

    2016-05-17

    Cells adopt distinct signaling pathways to optimize cell locomotion in different physical microenvironments. However, the underlying mechanism that enables cells to sense and respond to physical confinement is unknown. Using microfabricated devices and substrate-printing methods along with FRET-based biosensors, we report that, as cells transition from unconfined to confined spaces, intracellular Ca(2+) level is increased, leading to phosphodiesterase 1 (PDE1)-dependent suppression of PKA activity. This Ca(2+) elevation requires Piezo1, a stretch-activated cation channel. Moreover, differential regulation of PKA and cell stiffness in unconfined versus confined cells is abrogated by dual, but not individual, inhibition of Piezo1 and myosin II, indicating that these proteins can independently mediate confinement sensing. Signals activated by Piezo1 and myosin II in response to confinement both feed into a signaling circuit that optimizes cell motility. This study provides a mechanism by which confinement-induced signaling enables cells to sense and adapt to different physical microenvironments. PMID:27160899

  5. 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.

  6. 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

  7. 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.

  8. A method to optimize selection on multiple identified quantitative trait loci

    PubMed Central

    Chakraborty, Reena; Moreau, Laurence; Dekkers, Jack CM

    2002-01-01

    A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models. PMID:12081805

  9. 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

  10. 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.

  11. 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.

  12. Key signaling pathways in the muscle-invasive bladder carcinoma: Clinical markers for disease modeling and optimized treatment.

    PubMed

    Kiselyov, Alex; Bunimovich-Mendrazitsky, Svetlana; Startsev, Vladimir

    2016-06-01

    In this review, we evaluate key molecular pathways and markers of muscle-invasive bladder cancer (MIBC). Overexpression and activation of EGFR, p63, and EMT genes are suggestive of basal MIBC subtype generally responsive to chemotherapy. Alterations in PPARγ, ERBB2/3, and FGFR3 gene products and their signaling along with deregulated p53, cytokeratins KRT5/6/14 in combination with the cellular proliferation (Ki-67), and cell cycle markers (p16) indicate the need for more radical treatment protocols. Similarly, the "bell-shape" dynamics of Shh expression levels may suggest aggressive MIBC. A panel of diverse biological markers may be suitable for simulation studies of MIBC and development of an optimized treatment protocol. We conducted a critical evaluation of PubMed/Medline and SciFinder databases related to MIBC covering the period 2009-2015. The free-text search was extended by adding the following keywords and phrases: bladder cancer, metastatic, muscle-invasive, basal, luminal, epithelial-to-mesenchymal transition, cancer stem cell, mutations, immune response, signaling, biological markers, molecular markers, mathematical models, simulation, epigenetics, transmembrane, transcription factor, kinase, predictor, prognosis. The resulting selection of ca 500 abstracts was further analyzed in order to select the latest publications relevant to MIBC molecular markers of immediate clinical significance. PMID:26547270

  13. 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

  14. 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

  15. Selection for social signalling drives the evolution of chameleon colour change.

    PubMed

    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

  16. 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

  17. 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…

  18. SLOPE—ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION

    PubMed Central

    Bogdan, Małgorzata; van den Berg, Ewout; Sabatti, Chiara; Su, Weijie; Candès, Emmanuel J.

    2015-01-01

    We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ + z, where X has dimensions n × p with p possibly larger than n. SLOPE, short for Sorted L-One Penalized Estimation, is the solution to minb∈ℝp12‖y−Xb‖ℓ22+λ1|b|(1)+λ2|b|(2)+⋯+λp|b|(p),where λ1 ≥ λ2 ≥ … ≥ λp ≥ 0 and |b|(1)≥|b|(2)≥⋯≥|b|(p) are the decreasing absolute values of the entries of b. This is a convex program and we demonstrate a solution algorithm whose computational complexity is roughly comparable to that of classical ℓ1 procedures such as the Lasso. Here, the regularizer is a sorted ℓ1 norm, which penalizes the regression coefficients according to their rank: the higher the rank—that is, stronger the signal—the larger the penalty. This is similar to the Benjamini and Hochberg [J. Roy. Statist. Soc. Ser. B 57 (1995) 289–300] procedure (BH) which compares more significant p-values with more stringent thresholds. One notable choice of the sequence {λi} is given by the BH critical values λBH(i)=z(1−i⋅q/2p), where q ∈ (0, 1) and z(α) is the quantile of a standard normal distribution. SLOPE aims to provide finite sample guarantees on the selected model; of special interest is the false discovery rate (FDR), defined as the expected proportion of irrelevant regressors among all selected predictors. Under orthogonal designs, SLOPE with λBH provably controls FDR at level q. Moreover, it also appears to have appreciable inferential properties under more general designs X while having substantial power, as demonstrated in a series of experiments running on both simulated and real data. PMID:26709357

  19. 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

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

    PubMed

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

    2012-08-01

    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

  1. A novel EMD selecting thresholding method based on multiple iteration for denoising LIDAR signal

    NASA Astrophysics Data System (ADS)

    Li, Meng; Jiang, Li-hui; Xiong, Xing-long

    2015-06-01

    Empirical mode decomposition (EMD) approach has been believed to be potentially useful for processing the nonlinear and non-stationary LIDAR signals. To shed further light on its performance, we proposed the EMD selecting thresholding method based on multiple iteration, which essentially acts as a development of EMD interval thresholding (EMD-IT), and randomly alters the samples of noisy parts of all the corrupted intrinsic mode functions to generate a better effect of iteration. Simulations on both synthetic signals and LIDAR signals from real world support this method.

  2. 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

  3. Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies

    NASA Astrophysics Data System (ADS)

    Anis, Aamir; Gadde, Akshay; Ortega, Antonio

    2016-07-01

    We study the problem of selecting the best sampling set for bandlimited reconstruction of signals on graphs. A frequency domain representation for graph signals can be defined using the eigenvectors and eigenvalues of variation operators that take into account the underlying graph connectivity. Smoothly varying signals defined on the nodes are of particular interest in various applications, and tend to be approximately bandlimited in the frequency basis. Sampling theory for graph signals deals with the problem of choosing the best subset of nodes for reconstructing a bandlimited signal from its samples. Most approaches to this problem require a computation of the frequency basis (i.e., the eigenvectors of the variation operator), followed by a search procedure using the basis elements. This can be impractical, in terms of storage and time complexity, for real datasets involving very large graphs. We circumvent this issue in our formulation by introducing quantities called graph spectral proxies, defined using the powers of the variation operator, in order to approximate the spectral content of graph signals. This allows us to formulate a direct sampling set selection approach that does not require the computation and storage of the basis elements. We show that our approach also provides stable reconstruction when the samples are noisy or when the original signal is only approximately bandlimited. Furthermore, the proposed approach is valid for any choice of the variation operator, thereby covering a wide range of graphs and applications. We demonstrate its effectiveness through various numerical experiments.

  4. 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

  5. Dual signaling of hydrazine by selective deprotection of dichlorofluorescein and resorufin acetates.

    PubMed

    Choi, Myung Gil; Moon, Jung Ok; Bae, Jihee; Lee, Jung Woo; Chang, Suk-Kyu

    2013-05-14

    The highly selective chemosignaling behaviors for hydrazine by a reaction-based probe of dichlorofluorescein and resorufin acetates were investigated. Hydrazinolysis of latent dichlorofluorescein and resorufin acetate fluorochromes caused prominent chromogenic and fluorescent turn-on type signals. The probes selectively detected hydrazine in the presence of commonly encountered metal ions and anions as background. Dichlorofluorescein and resorufin acetates selectively detected hydrazine with detection limits of 9.0 × 10(-8) M and 8.2 × 10(-7) M, respectively. Furthermore, hydrazine was selectively detected over other closely related compounds, such as hydroxylamine, ethylenediamine, and ammonia. As a possible application of the acetate probes, hydrazine signaling in tap water was tested. PMID:23487180

  6. 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.

  7. 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

  8. Selection of optimal muscle set for 16-channel standing neuroprosthesis

    PubMed Central

    Gartman, Steven J.; Audu, Musa L.; Kirsch, Robert F.; Triolo, Ronald J.

    2009-01-01

    The Case Western Reserve University/Department of Veterans Affairs 8-channel lower-limb neuroprosthesis can restore standing to selected individuals with paraplegia by application of functional electrical stimulation. The second generation of this system will include 16 channels of stimulation and a closed-loop control scheme to provide automatic postural corrections. This study used a musculoskeletal model of the legs and trunk to determine which muscles to target with the new system in order to maximize the range of postures that can be statically maintained, which should increase the system’s ability to provide adequate support to maintain standing when the user’s posture moves away from a neutral stance, either by an external disturbance or a volitional change in posture by the user. The results show that the prime muscle targets should be the medial gastrocnemius, tibialis anterior, vastus lateralis, semimembranosus, gluteus maximus, gluteus medius, adductor magnus, and erector spinae. This set of 16 muscles supports 42 percent of the standing postures that are attainable by the nondisabled model. Coactivation of the lateral gastrocnemius and peroneus longus with the medial gastrocnemius and of the peroneus tertius with the tibialis anterior increased the percentage of feasible postures to 71 percent. PMID:16847793

  9. 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. PMID:25666474

  10. Model-free approach to optimal signal light timing for system-wide traffic control

    SciTech Connect

    Spall, J.C.; Chin, D.C.

    1994-12-31

    A long-standing problem in traffic engineering is to optimize the flow of vehicles through a given road network. Improving the timing of the traffic signals at intersections in the network is generally the most powerful and cost-effective means of achieving this goal. However, because of the many complex aspects of a traffic system-human behavioral considerations, vehicle flow interactions within the network, weather effects, traffic accidents, long-term (e.g., seasonal) variation, etc.-it has been notoriously difficult to determine the optimal signal light timing. This is especially the case on a system- wide (multiple intersection) basis. Much of this difficulty has stemmed from the need to build extremely complex open-loop models of the traffic dynamics as a component of the control strategy. This paper presents a fundamentally different approach for optimal light timing that eliminates the need for such an open-loop model. The approach is based on a neural network (or other function approximator) serving as the basis for the control law, with the weight estimation occurring in closed-loop mode via the simultaneous perturbation stochastic approximation (SPSA) algorithm. Since the SPSA algorithm requires only loss function measurements (no gradients of the loss function), there is no open-loop model required for the weight estimation. The approach is illustrated by simulation on a six-intersection network with moderate congestion and stochastic, nonlinear effects.

  11. 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

  12. Signalling thresholds and negative B-cell selection in acute lymphoblastic leukaemia.

    PubMed

    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

    2015-05-21

    B cells are selected for an intermediate level of B-cell antigen receptor (BCR) signalling strength: attenuation below minimum (for example, non-functional BCR) or hyperactivation above maximum (for example, self-reactive BCR) thresholds of signalling strength causes negative selection. In ∼25% of cases, acute lymphoblastic leukaemia (ALL) cells carry the oncogenic BCR-ABL1 tyrosine kinase (Philadelphia chromosome positive), which mimics constitutively active pre-BCR signalling. Current therapeutic approaches are largely focused on the development of more potent tyrosine kinase inhibitors to suppress oncogenic signalling below a minimum threshold for survival. 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. Here we find, by testing various components of proximal pre-BCR signalling in mouse BCR-ABL1 cells, 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 signalling strength through recruitment of the inhibitory phosphatases Ptpn6 (ref. 7) and Inpp5d (ref. 8). Using a novel small-molecule inhibitor of INPP5D (also known as SHIP1), 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

  13. 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

  14. 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.

  15. Controlling chaos with weak periodic signals optimized by a genetic algorithm.

    PubMed

    Soong, C Y; Huang, W T; Lin, F P; Tzeng, P Y

    2004-01-01

    In the present study we develop a relatively novel and effective chaos control approach with a multimode periodic disturbance applied as a control signal and perform an in-depth analysis on this nonfeedback chaos control strategy. Different from previous chaos control schemes, the present method is of two characteristic features: (1) the parameters of the controlling signal are optimized by a genetic algorithm (GA) with the largest Lyapunov exponent used as an index of the stability, and (2) the optimization is justified by a fitness function defined with the target Lyapunov exponent and the controlling power. This novel method is then tested on the noted Rössler and Lorenz systems with and without the presence of noise. The results disclosed that, compared to the existing chaos control methods, the present GA-based control needs only significantly reduced signal power and a shorter transient stage to achieve the preset control goal. The switching control ability and the robustness of the proposed method for cases with sudden change in a system parameter and/or with the presence of noise environment are also demonstrated. PMID:15324156

  16. 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

  17. Fault detection of roller-bearings using signal processing and optimization algorithms.

    PubMed

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

    2013-01-01

    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

  18. 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.

  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. Dose selection for optimal treatment results and avoidance of complications.

    PubMed

    Nagano, Hisato; Nakayama, Satoshi; Shuto, Takashi; Asada, Hiroyuki; Inomori, Shigeo

    2009-01-01

    What is the optimal treatment for metastatic brain tumors (MBTs)? We present our experience with gamma knife (GK) treatments for patients with five or more MBTs. Our new formula for predicting patient survival time (ST), which was derived by combining tumor control probability (TCP) calculated by Colombo's formula and normal tissue complication probability (NTCP) estimated by Flickinger's integrated logistic formula, was also evaluated. ST=a*[(C-NTCP)*TCP]+b; a, b, C: const. Forty-one patients (23 male, 18 female) with more than five MBTs were treated between March 1992 and February 2000. The tumors originated in the lung in 15 cases, in the breast in 8. Four patients had previously undergone whole brain irradiation (WBI). Ten patients were given concomitant WBI. Thirteen patients had additional extracranial metastatic lesions. TCP and NTCP were calculated using Excel add-in software. Cox's proportional hazards model was used to evaluate correlations between certain variables and ST. The independent variables evaluated were patient factors (age in years and performance status), tumor factors (total volume and number of tumors in each patient), treatment factors (TCP, NTCP and marginal dose) and the values of (C-NTCP)*TCP. Total tumor number was 403 (median 7, range 5-56). The median total tumor volume was 9.8 cm3 (range 0.8-111.8 cm3). The marginal dose ranged from 8 to 22 Gy (median 16.0Gy), TCP from 0.0% to 83% (median 15%) and NTCP from 0.0% to 31% (median 6.0%). (0.39-NTCP)*TCP ranged from 0.0 to 0.21 (median 0.055). Follow-up was 0.2 to 26.2 months, with a median of 5.4 months. Multiple-sample tests revealed no differences in STs among patients with MBTs of different origins (p=0.50). The 50% STs of patients with MBTs originating from the breast, lung and other sites were 5.9, 7.8 and 3.5 months, respectively. Only TCP and (0.39-NTCP)*TCP were statistically significant covariates (p=0.014, 0.001, respectively), and the latter was a more important predictor of

  1. 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

  2. Fluorescence signaling of Zr4+ by hydrogen peroxide assisted selective desulfurization of thioamide.

    PubMed

    Hwang, Jiyoung; Choi, Myung Gil; Eor, Suyoung; Chang, Suk-Kyu

    2012-02-01

    Thioamide derivative with a pyrene fluorophore was smoothly transformed to its corresponding amide by Zr(4+) ions in the presence of hydrogen peroxide. The transformation was evidenced by (1)H NMR spectroscopy and the signaling was completed within 10 min after sample preparation. Interference from Ag(+) and Hg(2+) ions in Zr(4+)-selective fluorescence signaling was readily suppressed with the use of Sn(2+) as a reducing additive. Discrimination of Zr(4+) from closely related hafnium, which is a frequent contaminant in commercial zirconium, was not possible. Prominent Zr(4+)-selective turn-on type fluorescence signaling was possible with a detection limit of 4.6 × 10(-6) M in an aqueous 99% ethanol solution. PMID:22260347

  3. Calibration optimization of laser-induced deflection signal for measuring absorptance of laser components.

    PubMed

    Zhang, Xiaorong; Li, Bincheng

    2015-03-10

    Different configurations of the laser-induced deflection (LID) technique have been developed recently to measure the absolute bulk and coating absorption of laser components directly. In order to obtain the absolute absorptance value of the surface or coating of a laser component, a reference sample with the same geometry and material as the test sample and with resist heating mounted on the surface of the reference sample was employed to calibrate LID signals. Due to the difference in the excitation approaches in measuring LID signals of the test and reference samples (laser beam irradiation versus surface resist heating), this calibration procedure may bring significant errors in the determination of the absorptance of the test sample. In this paper, theoretical models describing the temperature rise distributions within a test sample excited with flat-top beam irradiation and within a reference sample excited with surface resist heating are developed. Based on these temperature models and the finite-element analysis method, the LID signals used to determine the absorptance of the surface or coating of a laser component and the corresponding calibration error are analyzed. The computation results show that the calibration error depends largely on the probe beam position for normal or transverse LID signals and may be minimized by optimizing the probe beam geometry. PMID:25968359

  4. 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. PMID:19523710

  5. Estimation of Optimal Measurement Position of Human Forearm EMG Signal by Discriminant Analysis Based on Wilks' lambda

    NASA Astrophysics Data System (ADS)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    This paper describes the estimation of the optimal measurement position by discriminant analysis based on Wilks' lambda for myoelectric hand control. In previous studies, for motion discrimination, the myoelectric signals were measured at the same positions. However, the optimal measurement positions of the myoelectric signals for motion discrimination differ depending on the remaining muscles of amputees. Therefore, the purpose of this study is to estimate the optimal and fewer measurement positions for precise motion discrimination of a human forearm. This study proposes a method for estimating the optimal measurement positions by discriminant analysis based on Wilks' lambda, using the myoelectric signals measured at multiple positions. The results of some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  6. Modeling Network Intrusion Detection System Using Feature Selection and Parameters Optimization

    NASA Astrophysics Data System (ADS)

    Kim, Dong Seong; Park, Jong Sou

    Previous approaches for modeling Intrusion Detection System (IDS) have been on twofold: improving detection model(s) in terms of (i) feature selection of audit data through wrapper and filter methods and (ii) parameters optimization of detection model design, based on classification, clustering algorithms, etc. In this paper, we present three approaches to model IDS in the context of feature selection and parameters optimization: First, we present Fusion of Genetic Algorithm (GA) and Support Vector Machines (SVM) (FuGAS), which employs combinations of GA and SVM through genetic operation and it is capable of building an optimal detection model with only selected important features and optimal parameters value. Second, we present Correlation-based Hybrid Feature Selection (CoHyFS), which utilizes a filter method in conjunction of GA for feature selection in order to reduce long training time. Third, we present Simultaneous Intrinsic Model Identification (SIMI), which adopts Random Forest (RF) and shows better intrusion detection rates and feature selection results, along with no additional computational overheads. We show the experimental results and analysis of three approaches on KDD 1999 intrusion detection datasets.

  7. 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

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

    PubMed

    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

  9. Debris Selection and Optimal Path Planning for Debris Removal on the SSO: Impulsive-Thrust Option

    NASA Astrophysics Data System (ADS)

    Olympio, J. T.; Frouvelle, N.

    2013-08-01

    The current paper deals with the mission design of a generic active space debris removal spacecraft. Considered debris are all on a sun-synchronous orbit. A perturbed Lambert's problem, modelling the transfer between two 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 optimal debris sequence and mission strategy. Manoeuvres optimization is then performed to refine the selected trajectory scenarii.

  10. 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

  11. 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.

  12. [Strategy of probe selection for studying mRNAs that participate in receptor-mediated apoptosis signaling].

    PubMed

    Solntsev, L A; Starikova, V D; Sakharnov, N A; Knyazev, D I; Utkin, O V

    2015-01-01

    Death receptors (DRs) and the participants of DR-mediated signaling are characterized by a large number of mRNA isoforms generated by alternative splicing. Due to their high labor intensity and high cost, conventional methods (RT-PCR and RT-PCR in real time) are ineffective when the simultaneous detection of a plurality of mRNA isoforms is needed. In this regard, the use of DNA biochips is has prospective applications in analyzing the expression of many genes simultaneously. In this paper, we suggest an optimal strategy of probes selection aimed at detecting the maximum number of mRNA splice variants generated by major participants of DR-signaling. The objects of the study were 185 genes that form 1134 mRNA isoforms. As a result, a biochip design was developed that enables the detection of 499 mRNA isoforms (44% of total mRNA splice variants). The proposed strategy combines a high degree of modularity, the use of modern high-performance computers, and broad opportunities for setting up the selection criteria in accordance with the objectives of the study. PMID:26107906

  13. Diversity of actions of GnRHs mediated by ligand-induced selective signaling

    PubMed Central

    Millar, Robert P.; Pawson, Adam J.; Morgan, Kevin; Rissman, Emilie F.; Lu, Zhi-Liang

    2009-01-01

    Geoffrey Wingfield Harris’ demonstration of hypothalamic hormones regulating pituitary function led to their structural identification and therapeutic utilization in a wide spectrum of diseases. Amongst these, Gonadotropin Releasing Hormone (GnRH) and its analogs are widely employed in modulating gonadotropin and sex steroid secretion to treat infertility, precocious puberty and many hormone-dependent diseases including endometriosis, uterine fibroids and prostatic cancer. While these effects are all mediated via modulation of the pituitary gonadotrope GnRH receptor and the Gq signaling pathway, it has become increasingly apparent that GnRH regulates many extrapituitary cells in the nervous system and periphery. This review focuses on two such examples, namely GnRH analog effects on reproductive behaviors and GnRH analog effects on the inhibition of cancer cell growth. For both effects the relative activities of a range of GnRH analogs is distinctly different from their effects on the pituitary gonadotrope and different signaling pathways are utilized. As there is only a single functional GnRH receptor type in man we have proposed that the GnRH receptor can assume different conformations which have different selectivity for GnRH analogs and intracellular signaling proteins complexes. This ligand-induced selective-signaling recruits certain pathways while by-passing others and has implications in developing more selective GnRH analogs for highly specific therapeutic intervention. PMID:17976709

  14. 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.

  15. 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

  16. selectSNP – An R package for selecting SNPs optimal for genetic evaluation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    There has been a huge increase in the number of SNPs in the public repositories. This has made it a challenge to design low and medium density SNP panels, which requires careful selection of available SNPs considering many criteria, such as map position, allelic frequency, possible biological functi...

  17. Optimization of noise in non-integrated instrumentation amplifier for the amplification of very low electrophysiological [corrected] signals. Case of electro cardio graphic signals (ECG).

    PubMed

    Ngounou, Guy Merlin; Kom, Martin

    2014-12-01

    In this paper we present an instrumentation amplifier with discrete elements and optimized noise for the amplification of very low signals. In amplifying signals of very weak amplitude, the noise can completely absorb these signals if the used amplifier does not present the optimal guarantee to minimize the noise. Based on related research and re-viewing of recent patents Journal of Medical Systems, 30:205-209, 2006, we suggest an approach of noise reduction in amplification much more thoroughly than re-viewing of recent patents and we deduce from it the general criteria necessary and essential to achieve this optimization. The comparison of these criteria with the provisions adopted in practice leads to the inadequacy of conventional amplifiers for effective noise reduction. The amplifier we propose is an instrumentation amplifier with active negative feedback and optimized noise for the amplification of signals with very low amplitude. The application of this method in the case of electro cardio graphic signals (ECG) provides simulation results fully in line with forecasts. PMID:25381049

  18. Secretory signal peptide modification for optimized antibody-fragment expression-secretion in Leishmania tarentolae

    PubMed Central

    2012-01-01

    Background Secretory signal peptides (SPs) are well-known sequence motifs targeting proteins for translocation across the endoplasmic reticulum membrane. After passing through the secretory pathway, most proteins are secreted to the environment. Here, we describe the modification of an expression vector containing the SP from secreted acid phosphatase 1 (SAP1) of Leishmania mexicana for optimized protein expression-secretion in the eukaryotic parasite Leishmania tarentolae with regard to recombinant antibody fragments. For experimental design the online tool SignalP was used, which predicts the presence and location of SPs and their cleavage sites in polypeptides. To evaluate the signal peptide cleavage site as well as changes of expression, SPs were N-terminally linked to single-chain Fragment variables (scFv’s). The ability of L. tarentolae to express complex eukaryotic proteins with highly diverse post-translational modifications and its easy bacteria-like handling, makes the parasite a promising expression system for secretory proteins. Results We generated four vectors with different SP-sequence modifications based on in-silico analyses with SignalP in respect to cleavage probability and location, named pLTEX-2 to pLTEX-5. To evaluate their functionality, we cloned four individual scFv-fragments into the vectors and transfected all 16 constructs into L. tarentolae. Independently from the expressed scFv, pLTEX-5 derived constructs showed the highest expression rate, followed by pLTEX-4 and pLTEX-2, whereas only low amounts of protein could be obtained from pLTEX-3 clones, indicating dysfunction of the SP. Next, we analysed the SP cleavage sites by Edman degradation. For pLTEX-2, -4, and -5 derived scFv’s, the results corresponded to in-silico predictions, whereas pLTEX-3 derived scFv’s contained one additional amino-acid (AA). Conclusions The obtained results demonstrate the importance of SP-sequence optimization for efficient expression-secretion of sc

  19. 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

  20. 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°). PMID:26974636

  1. Optimized selective lactate excitation with a refocused multiple-quantum filter

    NASA Astrophysics Data System (ADS)

    Holbach, Mirjam; Lambert, Jörg; Johst, Sören; Ladd, Mark E.; Suter, Dieter

    2015-06-01

    Selective detection of lactate signals in in vivo MR spectroscopy with spectral editing techniques is necessary in situations where strong lipid or signals from other molecules overlap the desired lactate resonance in the spectrum. Several pulse sequences have been proposed for this task. The double-quantum filter SSel-MQC provides very good lipid and water signal suppression in a single scan. As a major drawback, it suffers from significant signal loss due to incomplete refocussing in situations where long evolution periods are required. Here we present a refocused version of the SSel-MQC technique that uses only one additional refocussing pulse and regains the full refocused lactate signal at the end of the sequence.

  2. 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.

  3. 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

  4. An optimal linear filter for the reduction of noise superimposed to the EEG signal.

    PubMed

    Bartoli, F; Cerutti, S

    1983-10-01

    In the present paper a procedure for the reduction of super-imposed noise on EEG tracings is described, which makes use of linear digital filtering and identification methods. In particular, an optimal filter (a Kalman filter) has been developed which is intended to capture the disturbances of the electromyographic noise on the basis of an a priori modelling which considers a series of impulses with a temporal occurrence according to a Poisson distribution as a noise generating mechanism. The experimental results refer to the EEG tracings recorded from 20 patients in normal resting conditions: the procedure consists of a preprocessing phase (which uses also a low-pass FIR digital filter), followed by the implementation of the identification and the Kalman filter. The performance of the filters is satisfactory also from the clinical standpoint, obtaining a marked reduction of noise without distorting the useful information contained in the signal. Furthermore, when using the introduced method, the EEG signal generating mechanism is accordingly parametrized as AR/ARMA models, thus obtaining an extremely sensitive feature extraction with interesting and not yet completely studied pathophysiological meanings. The above procedure may find a general application in the field of noise reduction and the better enhancement of information contained in the wide set of biological signals. PMID:6632838

  5. 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

  6. 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.

  7. 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.

  8. 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. PMID:21905841

  9. [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

  10. 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

  11. 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

  12. SRF regulates craniofacial development through selective recruitment of MRTF cofactors by PDGF signaling

    PubMed Central

    Vasudevan, Harish N.; Soriano, Philippe

    2014-01-01

    Summary Receptor tyrosine kinase signaling is critical for mammalian craniofacial development, but the key downstream transcriptional effectors remain unknown. We demonstrate that SRF is induced by both PDGF and FGF signaling in mouse embryonic palatal mesenchyme cells, and Srf neural crest conditional mutants exhibit facial clefting accompanied by proliferation and migration defects. Srf and Pdgfra mutants interact genetically in craniofacial development, but Srf and Fgfr1 mutants do not. This signal specificity is recapitulated at the level of cofactor activation: while both PDGF and FGF target gene promoters show enriched genome-wide overlap with SRF ChIP-seq peaks, PDGF selectively activates a network of MRTF-dependent cytoskeletal genes. Collectively, our results identify a novel role for SRF in proliferation and migration during craniofacial development and delineate a mechanism of receptor tyrosine kinase specificity mediated through differential cofactor usage, leading to a unique PDGF-responsive SRF-driven transcriptional program in the midface. PMID:25453829

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

    PubMed Central

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

    2014-01-01

    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 (R2 = 0.97), coronary sinus (R2 = 0.96), left ventricular (R2 = 0.96), and epicardial (R2 = 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% (R2 = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% (R2 = 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. PMID:24858847

  14. Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features

    NASA Astrophysics Data System (ADS)

    Weinmann, M.; Jutzi, B.; Mallet, C.

    2014-08-01

    3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper, we focus on the interleaved issue of extracting relevant, but not redundant features and increasing their distinctiveness by considering the respective optimal 3D neighborhood of each individual 3D point. We present a new, fully automatic and versatile framework consisting of four successive steps: (i) optimal neighborhood size selection, (ii) feature extraction, (iii) feature selection, and (iv) classification. In a detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2 different classifiers, we demonstrate that optimal neighborhoods for individual 3D points significantly improve the results of scene interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results.

  15. 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…

  16. 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…

  17. 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. PMID:18809328

  18. 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…

  19. Cellular scanning strategy for selective laser melting: Generating reliable, optimized scanning paths and processing parameters

    NASA Astrophysics Data System (ADS)

    Mohanty, Sankhya; Hattel, Jesper H.

    2015-03-01

    Selective laser melting is yet to become a standardized industrial manufacturing technique. The process continues to suffer from defects such as distortions, residual stresses, localized deformations and warpage caused primarily due to the localized heating, rapid cooling and high temperature gradients that occur during the process. While process monitoring and control of selective laser melting is an active area of research, establishing the reliability and robustness of the process still remains a challenge. In this paper, a methodology for generating reliable, optimized scanning paths and process parameters for selective laser melting of a standard sample is introduced. The processing of the sample is simulated by sequentially coupling a calibrated 3D pseudo-analytical thermal model with a 3D finite element mechanical model. The optimized processing parameters are subjected to a Monte Carlo method based uncertainty and reliability analysis. The reliability of the scanning paths are established using cumulative probability distribution functions for process output criteria such as sample density, thermal homogeneity, etc. A customized genetic algorithm is used along with the simulation model to generate optimized cellular scanning strategies and processing parameters, with an objective of reducing thermal asymmetries and mechanical deformations. The optimized scanning strategies are used for selective laser melting of the standard samples, and experimental and numerical results are compared.

  20. 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.

  1. 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.

  2. Potent and Selective CK2 Kinase Inhibitors with Effects on Wnt Pathway Signaling in Vivo.

    PubMed

    Dowling, James E; Alimzhanov, Marat; Bao, Larry; Chuaqui, Claudio; Denz, Christopher R; Jenkins, Emma; Larsen, Nicholas A; Lyne, Paul D; Pontz, Timothy; Ye, Qing; Holdgate, Geoff A; Snow, Lindsay; O'Connell, Nichole; Ferguson, Andrew D

    2016-03-10

    The Wnt pathway is an evolutionarily conserved and tightly regulated signaling network with important roles in embryonic development and adult tissue regeneration. Impaired Wnt pathway regulation, arising from mutations in Wnt signaling components, such as Axin, APC, and β-catenin, results in uncontrolled cell growth and triggers oncogenesis. To explore the reported link between CK2 kinase activity and Wnt pathway signaling, we sought to identify a potent, selective inhibitor of CK2 suitable for proof of concept studies in vivo. Starting from a pyrazolo[1,5-a]pyrimidine lead (2), we identified compound 7h, a potent CK2 inhibitor with picomolar affinity that is highly selectivity against other kinase family enzymes and inhibits Wnt pathway signaling (IC50 = 50 nM) in DLD-1 cells. In addition, compound 7h has physicochemical properties that are suitable for formulation as an intravenous solution, has demonstrated good pharmacokinetics in preclinical species, and exhibits a high level of activity as a monotherapy in HCT-116 and SW-620 xenografts. PMID:26985319

  3. Optimization of a Dibenzodiazepine Hit to a Potent and Selective Allosteric PAK1 Inhibitor

    PubMed Central

    2015-01-01

    The discovery of inhibitors targeting novel allosteric kinase sites is very challenging. Such compounds, however, once identified could offer exquisite levels of selectivity across the kinome. Herein we report our structure-based optimization strategy of a dibenzodiazepine hit 1, discovered in a fragment-based screen, yielding highly potent and selective inhibitors of PAK1 such as 2 and 3. Compound 2 was cocrystallized with PAK1 to confirm binding to an allosteric site and to reveal novel key interactions. Compound 3 modulated PAK1 at the cellular level and due to its selectivity enabled valuable research to interrogate biological functions of the PAK1 kinase. PMID:26191365

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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. PMID:22737004

  9. 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.

  10. 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

  11. The effect of genomic information on optimal contribution selection in livestock breeding programs

    PubMed Central

    2013-01-01

    Background Long-term benefits in animal breeding programs require that increases in genetic merit be balanced with the need to maintain diversity (lost due to inbreeding). This can be achieved by using optimal contribution selection. The availability of high-density DNA marker information enables the incorporation of genomic data into optimal contribution selection but this raises the question about how this information affects the balance between genetic merit and diversity. Methods The effect of using genomic information in optimal contribution selection was examined based on simulated and real data on dairy bulls. We compared the genetic merit of selected animals at various levels of co-ancestry restrictions when using estimated breeding values based on parent average, genomic or progeny test information. Furthermore, we estimated the proportion of variation in estimated breeding values that is due to within-family differences. Results Optimal selection on genomic estimated breeding values increased genetic gain. Genetic merit was further increased using genomic rather than pedigree-based measures of co-ancestry under an inbreeding restriction policy. Using genomic instead of pedigree relationships to restrict inbreeding had a significant effect only when the population consisted of many large full-sib families; with a half-sib family structure, no difference was observed. In real data from dairy bulls, optimal contribution selection based on genomic estimated breeding values allowed for additional improvements in genetic merit at low to moderate inbreeding levels. Genomic estimated breeding values were more accurate and showed more within-family variation than parent average breeding values; for genomic estimated breeding values, 30 to 40% of the variation was due to within-family differences. Finally, there was no difference between constraining inbreeding via pedigree or genomic relationships in the real data. Conclusions The use of genomic estimated breeding

  12. 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

  13. Salinomycin induces selective cytotoxicity to MCF-7 mammosphere cells through targeting the Hedgehog signaling pathway.

    PubMed

    Fu, Ying-Zi; Yan, Yuan-Yuan; He, Miao; Xiao, Qing-Huan; Yao, Wei-Fan; Zhao, Lin; Wu, Hui-Zhe; Yu, Zhao-Jin; Zhou, Ming-Yi; Lv, Mu-Tian; Zhang, Shan-Shan; Chen, Jian-Jun; Wei, Min-Jie

    2016-02-01

    Breast cancer stem cells (BCSCs) are believed to be responsible for tumor chemoresistance, recurrence, and metastasis formation. Salinomycin (SAL), a carboxylic polyether ionophore, has been reported to act as a selective breast CSC inhibitor. However, the molecular mechanisms underlying SAL-induced cytotoxicity on BCSCs remain unclear. The Hedgehog (Hh) signaling pathway plays an important role in CSC maintenance and carcinogenesis. Here, we investigated whether SAL induces cytotoxicity on BCSCs through targeting Hh pathway. In the present study, we cultured breast cancer MCF-7 cells in suspension in serum-free medium to obtain breast CSC-enriched MCF-7 mammospheres (MCF-7 MS). MCF-7 MS cells possessed typical BCSC properties, such as CD44+CD24-/low phenotype, high expression of OCT4 (a stem cell marker), increased colony-forming ability, strong migration and invasion capabilities, differentiation potential, and strong tumorigenicity in xenografted mice. SAL exhibited selective cytotoxicity to MCF-7 MS cells relative to MCF-7 cells. The Hh pathway was highly activated in BCSC-enriched MCF-7 MS cells and SAL inhibited Hh signaling activation by downregulating the expression of critical components of the Hh pathway such as PTCH, SMO, Gli1, and Gli2, and subsequently repressing the expression of their essential downstream targets including C-myc, Bcl-2, and Snail (but not cyclin D1). Conversely, Shh-induced Hh signaling activation could largely reverse SAL-mediated inhibitory effects. These findings suggest that SAL-induced selective cytotoxicity against MCF-7 MS cells is associated with the inhibition of Hh signaling activation and the expression of downstream targets and the Hh pathway is an important player and a possible drug target in the pathogenesis of BCSCs. PMID:26718029

  14. Removing spurious signals from glow curves using an optimal Wiener filter.

    PubMed

    van Dijk, J W E; Stadtmann, H; Grimbergen, T W M

    2011-03-01

    During readout, the signal of the TLD is occasionally polluted with spurious signals. These most often take the shape of a spike on the glow curve. Often these spikes are only a few milliseconds wide but can have a height that significantly influences the outcome of the dose evaluation. The detection of spikes relies generally on comparing the raw glow curve with a smoothed version of it. A spike is detected when the height of the glow curve exceeds that of the smoothed curve, using criteria based on the absolute and relative differences. The procedure proposed is based on smoothing by an optimal Wiener filter, which is, on its turn, based on Fourier analysis for which numerically very efficient methods are available. Apart from having easy to understand tuning parameters, an attractive bonus is that, with only little additional computational effort, estimates of the position of peak maxima are found from second and third derivatives: a useful feature for glow curve quality control. PMID:21450703

  15. 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

  16. 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/. PMID:25083512

  17. 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.

  18. 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. PMID:27018524

  19. Binding Selectivity of Abaloparatide for PTH-Type-1-Receptor Conformations and Effects on Downstream Signaling.

    PubMed

    Hattersley, Gary; Dean, Thomas; Corbin, Braden A; Bahar, Hila; Gardella, Thomas J

    2016-01-01

    The PTH receptor type 1 (PTHR1) mediates the actions of two endogenous polypeptide ligands, PTH and PTHrP, and thereby plays key roles in bone biology. Based on its capacity to stimulate bone formation, the peptide fragment PTH (1-34) is currently in use as therapy for osteoporosis. Abaloparatide (ABL) is a novel synthetic analog of human PTHrP (1-34) that holds promise as a new osteoporosis therapy, as studies in animals suggest that it can stimulate bone formation with less of the accompanying bone resorption and hypercalcemic effects that can occur with PTH (1-34). Recent studies in vitro suggest that certain PTH or PTHrP ligand analogs can distinguish between two high-affinity PTHR1 conformations, R(0) and RG, and that efficient binding to R(0) results in prolonged signaling responses in cells and prolonged calcemic responses in animals, whereas selective binding to RG results in more transient responses. As intermittent PTH ligand action is known to favor the bone-formation response, whereas continuous ligand action favors the net bone-resorption/calcemic response, we hypothesized that ABL binds more selectively to the RG vs the R(0) PTHR1 conformation than does PTH (1-34), and thus induces more transient signaling responses in cells. We show that ABL indeed binds with greater selectivity to the RG conformation than does PTH (1-34), and as a result of this RG bias, ABL mediates more transient cAMP responses in PTHR1-expressing cells. The findings provide a plausible mechanism (ie, transient signaling via RG-selective binding) that can help account for the favorable anabolic effects that ABL has on bone. PMID:26562265

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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

  5. Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes.

    PubMed

    Clune, Jeff; Misevic, Dusan; Ofria, Charles; Lenski, Richard E; Elena, Santiago F; Sanjuán, Rafael

    2008-01-01

    The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms. PMID:18818724

  6. Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes

    PubMed Central

    Clune, Jeff; Misevic, Dusan; Ofria, Charles; Lenski, Richard E.; Elena, Santiago F.; Sanjuán, Rafael

    2008-01-01

    The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms. PMID:18818724

  7. Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays.

    PubMed

    Van Dijck, Gert; Van Hulle, Marc M

    2014-08-01

    Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the "usefulness" of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing "good quality signals" or "signals of interest." This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on "signal quality." The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist's appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature "electronic depth control" in contrast to the mechanical repositioning of the electrode shafts in search of "good quality signals" or "signals of interest." PMID:24231119

  8. 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.

  9. 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.

  10. Lee filtered burst selecting in the photon correlation LDA signal processing

    NASA Astrophysics Data System (ADS)

    Vámos, Lénárd; Jani, Péter

    2008-04-01

    The photon correlation Laser Doppler Anemometers were developed to measure the flow velocity also in the nanometer particle range. An LDA signal processing method has been developed for dividing the raw data line of photon correlation LDA into shorter parts corresponding to single particle transit (burst). The commonly used Lee filter was applied with some modification and an intelligent burst finding algorithm was developed. By this way the LDA system was adapted for single particle counting. The complete simulation algorithm gives an opportunity for discussing the burst selecting and so the particle counting efficiency as a function of the SNR. Size estimation from the burst size was discussed and compared to the model-based signal processing technique. The minimum detectable particle size was estimated.

  11. Parallel medicinal chemistry approaches to selective HDAC1/HDAC2 inhibitor (SHI-1:2) optimization.

    PubMed

    Kattar, Solomon D; Surdi, Laura M; Zabierek, Anna; Methot, Joey L; Middleton, Richard E; Hughes, Bethany; Szewczak, Alexander A; Dahlberg, William K; Kral, Astrid M; Ozerova, Nicole; Fleming, Judith C; Wang, Hongmei; Secrist, Paul; Harsch, Andreas; Hamill, Julie E; Cruz, Jonathan C; Kenific, Candia M; Chenard, Melissa; Miller, Thomas A; Berk, Scott C; Tempest, Paul

    2009-02-15

    The successful application of both solid and solution phase library synthesis, combined with tight integration into the medicinal chemistry effort, resulted in the efficient optimization of a novel structural series of selective HDAC1/HDAC2 inhibitors by the MRL-Boston Parallel Medicinal Chemistry group. An initial lead from a small parallel library was found to be potent and selective in biochemical assays. Advanced compounds were the culmination of iterative library design and possess excellent biochemical and cellular potency, as well as acceptable PK and efficacy in animal models. PMID:19138845

  12. Discovery of GSK2656157: An Optimized PERK Inhibitor Selected for Preclinical Development.

    PubMed

    Axten, Jeffrey M; Romeril, Stuart P; Shu, Arthur; Ralph, Jeffrey; Medina, Jesús R; Feng, Yanhong; Li, William Hoi Hong; Grant, Seth W; Heerding, Dirk A; Minthorn, Elisabeth; Mencken, Thomas; Gaul, Nathan; Goetz, Aaron; Stanley, Thomas; Hassell, Annie M; Gampe, Robert T; Atkins, Charity; Kumar, Rakesh

    2013-10-10

    We recently reported the discovery of GSK2606414 (1), a selective first in class inhibitor of protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), which inhibited PERK activation in cells and demonstrated tumor growth inhibition in a human tumor xenograft in mice. In continuation of our drug discovery program, we applied a strategy to decrease inhibitor lipophilicity as a means to improve physical properties and pharmacokinetics. This report describes our medicinal chemistry optimization culminating in the discovery of the PERK inhibitor GSK2656157 (6), which was selected for advancement to preclinical development. PMID:24900593

  13. 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

  14. 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.

  15. A new approach to optimal selection of services in health care organizations.

    PubMed

    Adolphson, D L; Baird, M L; Lawrence, K D

    1991-01-01

    A new reimbursement policy adopted by Medicare in 1983 caused financial difficulties for many hospitals and health care organizations. Several organizations responded to these difficulties by developing systems to carefully measure their costs of providing services. The purpose of such systems was to provide relevant information about the profitability of hospital services. This paper presents a new method of making hospital service selection decisions: it is based on an optimization model that avoids arbitrary cost allocations as a basis for computing the costs of offering a given service. The new method provides more reliable information about which services are profitable or unprofitable, and it provides an accurate measure of the degree to which a service is profitable or unprofitable. The new method also provides useful information about the sensitivity of the optimal decision to changes in costs and revenues. Specialized algorithms for the optimization model lead to very efficient implementation of the method, even for the largest health care organizations. PMID:10111676

  16. 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

  17. Fusion of remote sensing images based on pyramid decomposition with Baldwinian Clonal Selection Optimization

    NASA Astrophysics Data System (ADS)

    Jin, Haiyan; Xing, Bei; Wang, Lei; Wang, Yanyan

    2015-11-01

    In this paper, we put forward a novel fusion method for remote sensing images based on the contrast pyramid (CP) using the Baldwinian Clonal Selection Algorithm (BCSA), referred to as CPBCSA. Compared with classical methods based on the transform domain, the method proposed in this paper adopts an improved heuristic evolutionary algorithm, wherein the clonal selection algorithm includes Baldwinian learning. In the process of image fusion, BCSA automatically adjusts the fusion coefficients of different sub-bands decomposed by CP according to the value of the fitness function. BCSA also adaptively controls the optimal search direction of the coefficients and accelerates the convergence rate of the algorithm. Finally, the fusion images are obtained via weighted integration of the optimal fusion coefficients and CP reconstruction. Our experiments show that the proposed method outperforms existing methods in terms of both visual effect and objective evaluation criteria, and the fused images are more suitable for human visual or machine perception.

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

    PubMed

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

    2015-01-01

    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. PMID:26151212

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

    PubMed Central

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

    2015-01-01

    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. PMID:26151212

  20. 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

  1. 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.

  2. 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.

  3. Optimal Needle Grasp Selection for Automatic Execution of Suturing Tasks in Robotic Minimally Invasive Surgery

    PubMed Central

    Liu, Taoming; Çavuşoğlu, M. Cenk

    2015-01-01

    This paper presents algorithms for optimal selection of needle grasp, for autonomous robotic execution of the minimally invasive surgical suturing task. In order to minimize the tissue trauma during the suturing motion, the best practices of needle path planning that are used by surgeons are applied for autonomous robotic surgical suturing tasks. Once an optimal needle trajectory in a well-defined suturing scenario is chosen, another critical issue for suturing is the choice of needle grasp for the robotic system. Inappropriate needle grasp increases operating time requiring multiple re-grasps to complete the desired task. The proposed methods use manipulability, dexterity and torque metrics for needle grasp selection. A simulation demonstrates the proposed methods and recommends a variety of grasps. Then a realistic demonstration compares the performances of the manipulator using different grasps. PMID:26413382

  4. Enhanced selectivity and search speed for method development using one-segment-per-component optimization strategies.

    PubMed

    Tyteca, Eva; Vanderlinden, Kim; Favier, Maxime; Clicq, David; Cabooter, Deirdre; Desmet, Gert

    2014-09-01

    Linear gradient programs are very frequently used in reversed phase liquid chromatography to enhance the selectivity compared to isocratic separations. Multi-linear gradient programs on the other hand are only scarcely used, despite their intrinsically larger separation power. Because the gradient-conformity of the latest generation of instruments has greatly improved, a renewed interest in more complex multi-segment gradient liquid chromatography can be expected in the future, raising the need for better performing gradient design algorithms. We explored the possibilities of a new type of multi-segment gradient optimization algorithm, the so-called "one-segment-per-group-of-components" optimization strategy. In this gradient design strategy, the slope is adjusted after the elution of each individual component of the sample, letting the retention properties of the different analytes auto-guide the course of the gradient profile. Applying this method experimentally to four randomly selected test samples, the separation time could on average be reduced with about 40% compared to the best single linear gradient. Moreover, the newly proposed approach performed equally well or better than the multi-segment optimization mode of a commercial software package. Carrying out an extensive in silico study, the experimentally observed advantage could also be generalized over a statistically significant amount of different 10 and 20 component samples. In addition, the newly proposed gradient optimization approach enables much faster searches than the traditional multi-step gradient design methods. PMID:25039066

  5. 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

  6. 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

  7. 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.

  8. Pipe degradation investigations for optimization of flow-accelerated corrosion inspection location selection

    SciTech Connect

    Chandra, S.; Habicht, P.; Chexal, B.; Mahini, R.; McBrine, W.; Esselman, T.; Horowitz, J.

    1995-12-01

    A large amount of piping in a typical nuclear power plant is susceptible to Flow-Accelerated Corrosion (FAC) wall thinning to varying degrees. A typical PAC monitoring program includes the wall thickness measurement of a select number of components in order to judge the structural integrity of entire systems. In order to appropriately allocate resources and maintain an adequate FAC program, it is necessary to optimize the selection of components for inspection by focusing on those components which provide the best indication of system susceptibility to FAC. A better understanding of system FAC predictability and the types of FAC damage encountered can provide some of the insight needed to better focus and optimize the inspection plan for an upcoming refueling outage. Laboratory examination of FAC damaged components removed from service at Northeast Utilities` (NU) nuclear power plants provides a better understanding of the damage mechanisms involved and contributing causes. Selected results of this ongoing study are presented with specific conclusions which will help NU to better focus inspections and thus optimize the ongoing FAC inspection program.

  9. 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).

  10. Signal Selection in High-Resolution NMR by Pulsed Field Gradients. I. Geometrical Analysis

    NASA Astrophysics Data System (ADS)

    Mitschang, Lorenz

    1999-03-01

    A geometrical description for the selection of coherence transfer pathways in high resolution NMR by the application of pulsed field gradients along three orthogonal directions in space is presented. The response of the spin system is one point of the three-dimensional Fourier transform of the sample volume affected by a sequence of field gradients. The property that a pathway is retained (or suppressed) when a sequence of field gradients is applied is expressed by the property of vectors, representing the pathway and the sequence, respectively, to be orthogonal (or not orthogonal). Ignoring imperfections of RF pulses, and with the exception of sensitivity enhanced experiments and experiments where the relevant coherence order is zero while field gradients are applied, it is shown that at most only half of the relevant pathways, as compared to a phase cycled experiment, are retained when field gradients are used for signal selection.

  11. 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

  12. Design optimization of superlattice type-II IR-detection modules with temporal signal coincidence in two spectral ranges

    NASA Astrophysics Data System (ADS)

    Breiter, R.; Lutz, H.; Scheibner, R.; Wendler, J.; Hofmann, K.; Ziegler, J.; Walther, M.; Rehm, R.

    2008-04-01

    3rd Generation IR detectors providing e.g. dual-color capability are of great benefit for applications like aircraft missile approach warning systems using this feature for achieving low false alarm rates by separating the hot CO2 missile plume from background and clutter. AIM and IAF selected antimonide based type II Superlattices (SL) for such kind of applications. The type II SL technology provides an accurate engineering of sensitive layers by MBE with very good homogeneity and yield. IAF and AIM already managed to realize a dual-color 384x288 IR-Module based on this technology. It combines spectral selective detection in the 3-4 μm wavelength range and 4-5 μm wavelength range in each pixel with coincident integration in a 384x288x2 format and 40 μm pitch. Excellent thermal resolution with NETD < 17 mK @ F/2, 2.8 ms for the longer wavelength range (red color) and NETD < 30 mK @ F/2, 2.8 ms for the shorter wavelength range (blue color) were already reported. In order to increase further the quantum efficiency and subsequently decrease further the spectral crosstalk between the two colors the layer thickness of the SL-layer was optimized. This paper is intended to present the current status and trends at AIM on antimonide type II Superlattices (SL) IR module developments for ground and airborne applications in the high performance range, where rapidly changing scenes - like e.g. in case of missile warning applications for airborne platforms - require temporal signal coincidence with integration times of typically 1ms.

  13. A Topography Analysis Incorporated Optimization Method for the Selection and Placement of Best Management Practices

    PubMed Central

    Shen, Zhenyao; Chen, Lei; Xu, Liang

    2013-01-01

    Best Management Practices (BMPs) are one of the most effective methods to control nonpoint source (NPS) pollution at a watershed scale. In this paper, the use of a topography analysis incorporated optimization method (TAIOM) was proposed, which integrates topography analysis with cost-effective optimization. The surface status, slope and the type of land use were evaluated as inputs for the optimization engine. A genetic algorithm program was coded to obtain the final optimization. The TAIOM was validated in conjunction with the Soil and Water Assessment Tool (SWAT) in the Yulin watershed in Southwestern China. The results showed that the TAIOM was more cost-effective than traditional optimization methods. The distribution of selected BMPs throughout landscapes comprising relatively flat plains and gentle slopes, suggests the need for a more operationally effective scheme, such as the TAIOM, to determine the practicability of BMPs before widespread adoption. The TAIOM developed in this study can easily be extended to other watersheds to help decision makers control NPS pollution. PMID:23349917

  14. Selection of optimal complexity for ENSO-EMR model by minimum description length principle

    NASA Astrophysics Data System (ADS)

    Loskutov, E. M.; Mukhin, D.; Mukhina, A.; Gavrilov, A.; Kondrashov, D. A.; Feigin, A. M.

    2012-12-01

    One of the main problems arising in modeling of data taken from natural system is finding a phase space suitable for construction of the evolution operator model. Since we usually deal with strongly high-dimensional behavior, we are forced to construct a model working in some projection of system phase space corresponding to time scales of interest. Selection of optimal projection is non-trivial problem since there are many ways to reconstruct phase variables from given time series, especially in the case of a spatio-temporal data field. Actually, finding optimal projection is significant part of model selection, because, on the one hand, the transformation of data to some phase variables vector can be considered as a required component of the model. On the other hand, such an optimization of a phase space makes sense only in relation to the parametrization of the model we use, i.e. representation of evolution operator, so we should find an optimal structure of the model together with phase variables vector. In this paper we propose to use principle of minimal description length (Molkov et al., 2009) for selection models of optimal complexity. The proposed method is applied to optimization of Empirical Model Reduction (EMR) of ENSO phenomenon (Kravtsov et al. 2005, Kondrashov et. al., 2005). This model operates within a subset of leading EOFs constructed from spatio-temporal field of SST in Equatorial Pacific, and has a form of multi-level stochastic differential equations (SDE) with polynomial parameterization of the right-hand side. Optimal values for both the number of EOF, the order of polynomial and number of levels are estimated from the Equatorial Pacific SST dataset. References: Ya. Molkov, D. Mukhin, E. Loskutov, G. Fidelin and A. Feigin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series, Phys. Rev. E, Vol. 80, P 046207, 2009 Kravtsov S, Kondrashov D, Ghil M, 2005: Multilevel regression

  15. Formularization and simulation of Bragg selectivity of readout signals in angular-multiplexing holographic data storage.

    PubMed

    Ide, Tatsuro

    2016-04-01

    Bragg selectivity of readout signals in angular-multiplexing holographic data storage was investigated. The effects of degrading factors, namely, volume change, refractive-index change, and positional change (tilt and rotation) of a hologram, and the effects of compensating variables, namely, wavelength shift and reference-beam-angle shift, on Bragg selectivity were evaluated. Deviation of wave vectors of recovered pixels of a hologram from the Bragg condition under degrading factors and compensating variables, namely, Bragg mismatch, Δσ, was mathematically derived. Approximating Δσ by using the first-order Maclaurin series with respect to degrading factors and compensating variables revealed their effects on Bragg selectivity. The extent to which wavelength and angle of reference beam should be shifted to compensate for the degrading factors were determined. Then, readout images were simulated under multiple degrading factors and compensating variables. These simulated images were found to agree well with the experimentally obtained ones, which reveals the validity of the formalization of Bragg selectivity. PMID:27139671

  16. 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.

  17. Optimal Selection of Predictor Variables in Statistical Downscaling Models of Precipitation

    NASA Astrophysics Data System (ADS)

    Goly, A.; Teegavarapu, R. S. V.

    2014-12-01

    Statistical downscaling models developed for precipitation rely heavily on predictors chosen and on accurate relationships between regional scale predictand and GCM-scale predictor for providing future precipitation projections at different spatial and temporal scales. This study provides two new screening methods for selection of predictor variables for use in downscaling methods based on predictand-predictors relationships. Methods to characterize predictand-predictors relationships via rigid and flexible functional relationships using mixed integer nonlinear programming (MINLP) model with binary variables and artificial neural network (ANN) models respectively are developed and evaluated in this study. In addition to these two methods, a stepwise regression (SWR) and two models that do not use any pre-screening of variables are also evaluated. A two-step process is used to downscale precipitation data with optimal selection of predictors and using them in a statistical downscaling model based on support vector machine (SVM) approach. Experiments with the proposed two new methods and three additional methods based on correlation between predictors and predictand and the other based on principal component analysis are evaluated in this study. Results suggest that optimal selection of variables using MINLP albeit with linear relationship and ANN method provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Of all the three screening methods tested in this study, SWR method selected the least number of variables and also ranked lowest based on several performance measures.

  18. 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.

  19. Temporal artifact minimization in sonoelastography through optimal selection of imaging parameters.

    PubMed

    Torres, Gabriela; Chau, Gustavo R; Parker, Kevin J; Castaneda, Benjamin; Lavarello, Roberto J

    2016-07-01

    Sonoelastography is an ultrasonic technique that uses Kasai's autocorrelation algorithms to generate qualitative images of tissue elasticity using external mechanical vibrations. In the absence of synchronization between the mechanical vibration device and the ultrasound system, the random initial phase and finite ensemble length of the data packets result in temporal artifacts in the sonoelastography frames and, consequently, in degraded image quality. In this work, the analytic derivation of an optimal selection of acquisition parameters (i.e., pulse repetition frequency, vibration frequency, and ensemble length) is developed in order to minimize these artifacts, thereby eliminating the need for complex device synchronization. The proposed rule was verified through experiments with heterogeneous phantoms, where the use of optimally selected parameters increased the average contrast-to-noise ratio (CNR) by more than 200% and reduced the CNR standard deviation by 400% when compared to the use of arbitrarily selected imaging parameters. Therefore, the results suggest that the rule for specific selection of acquisition parameters becomes an important tool for producing high quality sonoelastography images. PMID:27475192

  20. 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

  1. The Activity of Surface Electromyographic Signal of Selected Muscles during Classic Rehabilitation Exercise

    PubMed Central

    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

  2. Positive selection of B10 cells is determined by BCR specificity and signaling strength.

    PubMed

    Zhang, Jigang; Wan, Ming; Ren, Jing; Gao, Jixin; Fu, Meng; Wang, Gang; Liu, Yufeng; Li, Wei

    2016-01-01

    B10 cells, a regulatory B cell subset, negatively regulate immune responses in an IL-10-dependent manner. However, the mechanism of B10 cell development is unclear. We found that B10 cells mainly identified self-antigens. TgVH3B4 transgenic mice, whose VH was derived from an actin-reactive natural antibody, exhibit elevated numbers of actin-binding B10 cells. Immunization of TgVH3B4 mice with actin induced elevated B10 cell numbers in an antigen-specific manner, indicating positive selection of B10 cells by self-antigens. Furthermore, higher BCR signaling strength facilitated B10 cell development. We also observed that actin-reactive IgG levels were unchanged in TgVH3B4 mice after immunization with actin in contrast to the elevated OVA-reactive IgG level after immunization with OVA, indicating that B10 cells acted in an antigen-specific manner to inhibit the immune response. Our data demonstrate for the first time that B10 cells are positively selected by self-reactivity and that higher BCR signaling strength promotes B10 cell development. PMID:27132875

  3. 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

  4. 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. PMID:25605923

  5. Selective Persistence of Sensorimotor Mismatch Signals in Visual Cortex of Behaving Alzheimer's Disease Mice.

    PubMed

    Liebscher, Sabine; Keller, Georg B; Goltstein, Pieter M; Bonhoeffer, Tobias; Hübener, Mark

    2016-04-01

    Neurodegenerative processes in Alzheimer's disease (AD) affect the structure and function of neurons [1-4], resulting in altered neuronal activity patterns comprising neuronal hypo- and hyperactivity [5, 6] and causing the disruption of long-range projections [7, 8]. Impaired information processing between functionally connected brain areas is evident in defective visuomotor integration, an early sign of the disease [9-11]. The cellular and neuronal circuit mechanisms underlying this disruption of information processing in AD, however, remain elusive. Recent studies in mice suggest that visuomotor integration already occurs in primary visual cortex (V1), as it not only processes sensory input but also exhibits strong motor-related activity, likely driven by neuromodulatory or excitatory inputs [12-17]. Here, we probed the integration of visual-and motor-related-inputs in V1 of behaving APP/PS1 [18] mice, a well-characterized mouse model of AD, using two-photon calcium imaging. We find that sensorimotor signals in APP/PS1 mice are differentially affected: while visually driven and motor-related signals are strongly reduced, neuronal responses signaling a mismatch between expected and actual visual flow are selectively spared. We furthermore observe an increase in aberrant activity during quiescent states in APP/PS1 mice. Jointly, the reduction in running-correlated activity and the enhanced aberrant activity degrade the coding accuracy of the network, indicating that the impairment of visuomotor integration in AD is already taking place at early stages of visual processing. PMID:27020746

  6. 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

  7. Selective Regulation of MAPK Signaling Mediates RANKL-dependent Osteoclast Differentiation

    PubMed Central

    Lee, Kyunghee; Chung, Yeoun Ho; Ahn, Heejin; Kim, Hyunsoo; Rho, Jaerang; Jeong, Daewon

    2016-01-01

    Different stimuli often activate the same intracellular signaling molecules but trigger distinct cell responses. We explored whether or not MAPK signaling induced by macrophage colony-stimulating factor (M-CSF), which is responsible for osteoclast proliferation, differs from that induced by receptor activator of NF-κB ligand (RANKL), which is essential for inducing osteoclast differentiation. The activation of MAPKs by M-CSF or RANKL differed in terms of the extent and duration of ERK, p38, and JNK phosphorylation as well as the isoform specificity of JNK phosphorylation. In particular, RANKL induced a second wave of MAPK activation coincident with the onset of osteoclast differentiation, whereas M-CSF triggered only a monophasic response. M-CSF was also able to trigger a full MAPK response on restimulation of cells earlier than was RANKL, representing that MAPK resensitization by M-CSF differs from that by RANKL. Furthermore, the adapter protein TRAF6 recruitment to the cytoplasmic tail of RANK in a submembrane compartment is specifically required for RANKL-induced activation of p38 MAPK, expression of osteoclastogenic transcription factors, and osteoclast differentiation, indicating that the switch from proliferation to differentiation in osteoclast precursors is dependent on p38 activation via the RANKL-RANK-TRAF6 axis. Our results suggest that selective control of MAPK signaling induced by M-CSF and by RANKL mediates the proliferation versus differentiation decision in osteoclast precursors. PMID:26884720

  8. 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.

  9. 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. PMID:25607183

  10. 49 CFR 236.311 - Signal control circuits, selection through track relays or devices functioning as track relays...

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 49 Transportation 4 2013-10-01 2013-10-01 false Signal control circuits, selection through track relays or devices functioning as track relays and through signal mechanism contacts and time releases at automatic interlocking. 236.311 Section 236.311 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL...

  11. 49 CFR 236.311 - Signal control circuits, selection through track relays or devices functioning as track relays...

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Signal control circuits, selection through track relays or devices functioning as track relays and through signal mechanism contacts and time releases at automatic interlocking. 236.311 Section 236.311 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL...

  12. 49 CFR 236.311 - Signal control circuits, selection through track relays or devices functioning as track relays...

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Signal control circuits, selection through track relays or devices functioning as track relays and through signal mechanism contacts and time releases at automatic interlocking. 236.311 Section 236.311 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL...

  13. 49 CFR 236.311 - Signal control circuits, selection through track relays or devices functioning as track relays...

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 49 Transportation 4 2012-10-01 2012-10-01 false Signal control circuits, selection through track relays or devices functioning as track relays and through signal mechanism contacts and time releases at automatic interlocking. 236.311 Section 236.311 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL...

  14. 49 CFR 236.311 - Signal control circuits, selection through track relays or devices functioning as track relays...

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Signal control circuits, selection through track relays or devices functioning as track relays and through signal mechanism contacts and time releases at automatic interlocking. 236.311 Section 236.311 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL...

  15. 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. PMID:26374065

  16. Target Selection Signals Influence Perceptual Decisions by Modulating the Onset and Rate of Evidence Accumulation.

    PubMed

    Loughnane, Gerard M; Newman, Daniel P; Bellgrove, Mark A; Lalor, Edmund C; Kelly, Simon P; O'Connell, Redmond G

    2016-02-22

    Computational and neurophysiological research has highlighted neural processes that accumulate sensory evidence for perceptual decisions [1]. These processes have been studied in the context of highly simplified perceptual discrimination paradigms in which the physical evidence appears at times and locations that are either entirely predictable or exogenously cued (e.g., by the onset of the stimulus itself). Yet, we are rarely afforded such certainty in everyday life. For example, when driving along a busy motorway, we must continually monitor the movements of surrounding vehicles for events that call for a lane change. In such scenarios, it is unknown which of the continuously present information sources will become relevant or when. Although it is well established that evidence integration provides an effective mechanism for countering the impact of noise [2], the question of how this mechanism is implemented in the face of uncertain evidence onsets has yet to be answered. Here, we show that when monitoring two potential sources of information for evidence occurring unpredictably in both time and space, the human brain employs discrete, early target selection signals that significantly modulate the onset and rate of neural evidence accumulation, and thereby the timing and accuracy of perceptual reports. These selection signals share many of the key characteristics of the N2pc component highlighted in the literature on visual search [3, 4] yet are present even in the absence of distractors and under situations of low temporal and spatial uncertainty. These data provide novel insights into how target selection supports decision making in uncertain environments. PMID:26853360

  17. 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

  18. 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

  19. High diversity and no significant selection signal of human ADH1B gene in Tibet

    PubMed Central

    2012-01-01

    Background ADH1B is one of the most studied human genes with many polymorphic sites. One of the single nucleotide polymorphism (SNP), rs1229984, coding for the Arg48His substitution, have been associated with many serious diseases including alcoholism and cancers of the digestive system. The derived allele, ADH1B*48His, reaches high frequency only in East Asia and Southwest Asia, and is highly associated with agriculture. Micro-evolutionary study has defined seven haplogroups for ADH1B based on seven SNPs encompassing the gene. Three of those haplogroups, H5, H6, and H7, contain the ADH1B*48His allele. H5 occurs in Southwest Asia and the other two are found in East Asia. H7 is derived from H6 by the derived allele of rs3811801. The H7 haplotype has been shown to have undergone significant positive selection in Han Chinese, Hmong, Koreans, Japanese, Khazak, Mongols, and so on. Methods In the present study, we tested whether Tibetans also showed evidence for selection by typing 23 SNPs in the region covering the ADH1B gene in 1,175 individuals from 12 Tibetan populations representing all districts of the Tibet Autonomous Region. Multiple statistics were estimated to examine the gene diversities and positive selection signals among the Tibetans and other populations in East Asia. Results The larger Tibetan populations (Qamdo, Lhasa, Nagqu, Nyingchi, Shannan, and Shigatse) comprised mostly farmers, have around 12% of H7, and 2% of H6. The smaller populations, living on hunting or recently switched to farming, have lower H7 frequencies (Tingri 9%, Gongbo 8%, Monba and Sherpa 6%). Luoba (2%) and Deng (0%) have even lower frequencies. Long-range haplotype analyses revealed very weak signals of positive selection for H7 among Tibetans. Interestingly, the haplotype diversity of H7 is higher in Tibetans than in any other populations studied, indicating a longer diversification history for that haplogroup in Tibetans. Network analysis on the long-range haplotypes revealed

  20. 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.

  1. Complex Selection on Human Polyadenylation Signals Revealed by Polymorphism and Divergence Data.

    PubMed

    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. 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

  3. 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

  4. 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

  5. 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

  6. Tabu search and binary particle swarm optimization for feature selection using microarray data.

    PubMed

    Chuang, Li-Yeh; Yang, Cheng-Huei; Yang, Cheng-Hong

    2009-12-01

    Gene expression profiles have great potential as a medical diagnosis tool because they represent the state of a cell at the molecular level. In the classification of cancer type research, available training datasets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed to improve the predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this article, we propose to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection. BPSO acts as a local optimizer each time the TS has been run for a single generation. The K-nearest neighbor method with leave-one-out cross-validation and support vector machine with one-versus-rest serve as evaluators of the TS and BPSO. The proposed method is applied and compared to the 11 classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods. PMID:20047491

  7. 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

  8. Climate change signal over the Alpine region - sensitivity to GCM selection

    NASA Astrophysics Data System (ADS)

    Zubler, Elias M.; Fischer, Andreas M.; Liniger, Mark A.

    2015-04-01

    The use of multi-model ensembles has become a common and widely accepted practice to evaluate climate change signals and various aspects of the associated uncertainties. However, for regional analysis of climate change, it is not always feasible to use all of the available model simulations. Some models do not sufficiently represent processes that are important for a particular region, or they lack crucial topographic details to represent the corresponding climate in a realistic manner. When relying on regional climate model projections, a GCM selection is implicitly done, as not all of the available GCM simulations are being dynamically downscaled. Specifically, within EURO-CORDEX, more than 30 RCM simulations and more than 10 GCMs are provided for the strongest emission scenario RCP8.5 from the CMIP5 ensemble. Simulations with other emission scenarios are also provided. However, many RCMs in EURO-CORDEX are driven by one of only five of the available GCMs (CNRM-CM5, MPI-ESM, HadGEM, IPSL and EC-EARTH). It was shown previously that in particular RCM temperature responses tend to cluster according to their driving GCM. Therefore, it is important to better understand the relation among the GCMs. In multi-model ensembles as large as CMIP5, in which models tend to correlate due to their similar origin, model selection or weighting becomes an important issue. This study evaluates the distribution of climate change signals in the CMIP5 ensemble for temperature and precipitation over the Greater Alpine region and shows that different methods of model selection considerably influences the resulting temperature spread in the climate change signals at the end of the century relative to 1980-2009: excluding those GCMs with a poor representation of Alpine climate leads to a spread-difference of more than 1°C compared to a choice where all models are included and given the same weight. Furthermore, it is highlighted that the largest amount of spread can be retained with a

  9. An improved swarm optimization for parameter estimation and biological model selection.

    PubMed

    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

  10. 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

  11. 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

  12. A multi-fidelity analysis selection method using a constrained discrete optimization formulation

    NASA Astrophysics Data System (ADS)

    Stults, Ian C.

    The purpose of this research is to develop a method for selecting the fidelity of contributing analyses in computer simulations. Model uncertainty is a significant component of result validity, yet it is neglected in most conceptual design studies. When it is considered, it is done so in only a limited fashion, and therefore brings the validity of selections made based on these results into question. Neglecting model uncertainty can potentially cause costly redesigns of concepts later in the design process or can even cause program cancellation. Rather than neglecting it, if one were to instead not only realize the model uncertainty in tools being used but also use this information to select the tools for a contributing analysis, studies could be conducted more efficiently and trust in results could be quantified. Methods for performing this are generally not rigorous or traceable, and in many cases the improvement and additional time spent performing enhanced calculations are washed out by less accurate calculations performed downstream. The intent of this research is to resolve this issue by providing a method which will minimize the amount of time spent conducting computer simulations while meeting accuracy and concept resolution requirements for results. In many conceptual design programs, only limited data is available for quantifying model uncertainty. Because of this data sparsity, traditional probabilistic means for quantifying uncertainty should be reconsidered. This research proposes to instead quantify model uncertainty using an evidence theory formulation (also referred to as Dempster-Shafer theory) in lieu of the traditional probabilistic approach. Specific weaknesses in using evidence theory for quantifying model uncertainty are identified and addressed for the purposes of the Fidelity Selection Problem. A series of experiments was conducted to address these weaknesses using n-dimensional optimization test functions. These experiments found that model

  13. 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. PMID:25475755

  14. Worker Signals among New College Graduates: The Role of Selectivity and GPA. Upjohn Institute Working Paper No. 13-190

    ERIC Educational Resources Information Center

    Hershbein, Brad J.

    2013-01-01

    Recent studies have found a large earnings premium to attending a more selective college, but the mechanisms underlying this premium have received little attention and remain unclear. In order to shed light on this question, I develop a multidimensional signaling model relying on college grades and selectivity that rationalizes students' choices…

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. Warning signal brightness variation: sexual selection may work under the radar of natural selection in populations of a polytypic poison frog.

    PubMed

    Crothers, Laura R; Cummings, Molly E

    2013-05-01

    Though theory predicts consistency of warning signals in aposematic species to facilitate predator learning, variation in these signals often occurs in nature. The strawberry poison frog Dendrobates pumilio is an exceptionally polytypic (populations are phenotypically distinct) aposematic frog exhibiting variation in warning color and brightness. In the Solarte population, males and females both respond differentially to male brightness variation. Here, we demonstrate through spectrophotometry and visual modeling that aposematic brightness variation within this population is likely visible to two putative predators (crabs, snakes) and conspecifics but not to the presumed major predator (birds). This study thus suggests that signal brightness within D. pumilio populations can be shaped by sexual selection, with limited opportunity for natural selection to influence this trait due to predator sensory constraints. Because signal brightness changes can ultimately lead to changes in hue, our findings at the within-population level can provide insights into understanding this polytypism at across-population scales. PMID:23594556

  20. Improving the prediction of chemotherapeutic sensitivity of tumors in breast cancer via optimizing the selection of candidate genes.

    PubMed

    Jiang, Lina; Huang, Liqiu; Kuang, Qifan; Zhang, Juan; Li, Menglong; Wen, Zhining; He, Li

    2014-04-01

    Estrogen receptor status and the pathologic response to preoperative chemotherapy are two important indicators of chemotherapeutic sensitivity of tumors in breast cancer, which are used to guide the selection of specific regimens for patients. Microarray-based gene expression profiling, which is successfully applied to the discovery of tumor biomarkers and the prediction of drug response, was suggested to predict the cancer outcomes using the gene signatures differentially expressed between two clinical states. However, many false positive genes unrelated to the phenotypic differences will be involved in the lists of differentially expressed genes (DEGs) when only using the statistical methods for gene selection, e.g. Student's t test, and subsequently affect the performance of the predictive models. For the purpose of improving the prediction of clinical outcomes, we optimized the selection of DEGs by using a combined strategy, for which the DEGs were firstly identified by the statistical methods, and then filtered by a similarity profiling approach that used for candidate gene prioritization. In our study, we firstly verified the molecular functions of the DEGs identified by the combined strategy with the gene expression data generated in the microarray experiments of Si-Wu-Tang, which is a popular formula in traditional Chinese medicine. The results showed that, for Si-Wu-Tang experimental data set, the cancer-related signaling pathways were significantly enriched by gene set enrichment analysis when using the DEG lists generated by the combined strategy, confirming the potentially cancer-preventive effect of Si-Wu-Tang. To verify the performance of the predictive models in clinical application, we used the combined strategy to select the DEGs as features from the gene expression data of the clinical samples, which were collected from the breast cancer patients, and constructed models to predict the chemotherapeutic sensitivity of tumors in breast cancer. After

  1. Selection of a site for the DUMAND detector with optimal water transparency

    NASA Astrophysics Data System (ADS)

    Karabashev, G. S.; Kuleshov, A. F.

    1989-06-01

    With reference to selecting a site for the DUMAND detector with optimal water transparency, measurements were made of the spectral distribution of light attenuation coefficients in samples from diffearent bodies of water, including Lake Baikal, the Atlantic Ocean, and the Mediterranean Sea. Results on the detection efficiency of Cerenkov radiation by the DUMAND detector indicate that not only the abyssal waters of the open ocean but also depressions of land-locked seas have the optical properties suitable for the operation of the DUMAND detector.

  2. 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

  3. Hyperspectral band selection based on parallel particle swarm optimization and impurity function band prioritization schemes

    NASA Astrophysics Data System (ADS)

    Chang, Yang-Lang; Liu, Jin-Nan; Chen, Yen-Lin; Chang, Wen-Yen; Hsieh, Tung-Ju; Huang, Bormin

    2014-01-01

    In recent years, satellite imaging technologies have resulted in an increased number of bands acquired by hyperspectral sensors, greatly advancing the field of remote sensing. Accordingly, owing to the increasing number of bands, band selection in hyperspectral imagery for dimension reduction is important. This paper presents a framework for band selection in hyperspectral imagery that uses two techniques, referred to as particle swarm optimization (PSO) band selection and the impurity function band prioritization (IFBP) method. With the PSO band selection algorithm, highly correlated bands of hyperspectral imagery can first be grouped into modules to coarsely reduce high-dimensional datasets. Then, these highly correlated band modules are analyzed with the IFBP method to finely select the most important feature bands from the hyperspectral imagery dataset. However, PSO band selection is a time-consuming procedure when the number of hyperspectral bands is very large. Hence, this paper proposes a parallel computing version of PSO, namely parallel PSO (PPSO), using a modern graphics processing unit (GPU) architecture with NVIDIA's compute unified device architecture technology to improve the computational speed of PSO processes. The natural parallelism of the proposed PPSO lies in the fact that each particle can be regarded as an independent agent. Parallel computation benefits the algorithm by providing each agent with a parallel processor. The intrinsic parallel characteristics embedded in PPSO are, therefore, suitable for parallel computation. The effectiveness of the proposed PPSO is evaluated through the use of airborne visible/infrared imaging spectrometer hyperspectral images. The performance of PPSO is validated using the supervised K-nearest neighbor classifier. The experimental results demonstrate that the proposed PPSO/IFBP band selection method can not only improve computational speed, but also offer a satisfactory classification performance.

  4. HCV infection selectively impairs type I but not type III IFN signaling.

    PubMed

    Chandra, Partha K; Bao, Lili; Song, Kyoungsub; Aboulnasr, Fatma M; Baker, Darren P; Shores, Nathan; Wimley, William C; Liu, Shuanghu; Hagedorn, Curt H; Fuchs, Serge Y; Wu, Tong; Balart, Luis A; Dash, Srikanta

    2014-01-01

    A stable and persistent Hepatitis C virus (HCV) replication cell culture model was developed to examine clearance of viral replication during long-term treatment using interferon-α (IFN-α), IFN-λ, and ribavirin (RBV). Persistently HCV-infected cell culture exhibited an impaired antiviral response to IFN-α+RBV combination treatment, whereas IFN-λ treatment produced a strong and sustained antiviral response that cleared HCV replication. HCV replication in persistently infected cells induced chronic endoplasmic reticulum (ER) stress and an autophagy response that selectively down-regulated the functional IFN-α receptor-1 chain of type I, but not type II (IFN-γ) or type III (IFN-λ) IFN receptors. Down-regulation of IFN-α receptor-1 resulted in defective JAK-STAT signaling, impaired STAT phosphorylation, and impaired nuclear translocation of STAT. Furthermore, HCV replication impaired RBV uptake, because of reduced expression of the nucleoside transporters ENT1 and CNT1. Silencing ER stress and the autophagy response using chemical inhibitors or siRNA additively inhibited HCV replication and induced viral clearance by the IFN-α+RBV combination treatment. These results indicate that HCV induces ER stress and that the autophagy response selectively impairs type I (but not type III) IFN signaling, which explains why IFN-λ (but not IFN-α) produced a sustained antiviral response against HCV. The results also indicate that inhibition of ER stress and of the autophagy response overcomes IFN-α+RBV resistance mechanisms associated with HCV infection. PMID:24215913

  5. HCV Infection Selectively Impairs Type I but Not Type III IFN Signaling

    PubMed Central

    Chandra, Partha K.; Bao, Lili; Song, Kyoungsub; Aboulnasr, Fatma M.; Baker, Darren P.; Shores, Nathan; Wimley, William C.; Liu, Shuanghu; Hagedorn, Curt H.; Fuchs, Serge Y.; Wu, Tong; Balart, Luis A.; Dash, Srikanta

    2015-01-01

    A stable and persistent Hepatitis C virus (HCV) replication cell culture model was developed to examine clearance of viral replication during long-term treatment using interferon-α (IFN-α), IFN-λ, and ribavirin (RBV). Persistently HCV-infected cell culture exhibited an impaired antiviral response to IFN-α+RBV combination treatment, whereas IFN-λ treatment produced a strong and sustained antiviral response that cleared HCV replication. HCV replication in persistently infected cells induced chronic endoplasmic reticulum (ER) stress and an autophagy response that selectively down-regulated the functional IFN-α receptor-1 chain of type I, but not type II (IFN-γ) or type III (IFN-λ) IFN receptors. Down-regulation of IFN-α receptor-1 resulted in defective JAK–STAT signaling, impaired STAT phosphorylation, and impaired nuclear translocation of STAT. Furthermore, HCV replication impaired RBV uptake, because of reduced expression of the nucleoside transporters ENT1 and CNT1. Silencing ER stress and the autophagy response using chemical inhibitors or siRNA additively inhibited HCV replication and induced viral clearance by the IFN-α+RBV combination treatment. These results indicate that HCV induces ER stress and that the autophagy response selectively impairs type I (but not type III) IFN signaling, which explains why IFN-λ (but not IFN-α) produced a sustained antiviral response against HCV. The results also indicate that inhibition of ER stress and of the autophagy response overcomes IFN-α+RBV resistance mechanisms associated with HCV infection. PMID:24215913

  6. 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

  7. 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

  8. 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. PMID:27328180

  9. 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

  10. A Neurodynamic Optimization Method for Recovery of Compressive Sensed Signals With Globally Converged Solution Approximating to l0 Minimization.

    PubMed

    Guo, Chengan; Yang, Qingshan

    2015-07-01

    Finding the optimal solution to the constrained l0 -norm minimization problems in the recovery of compressive sensed signals is an NP-hard problem and it usually requires intractable combinatorial searching operations for getting the global optimal solution, unless using other objective functions (e.g., the l1 norm or lp norm) for approximate solutions or using greedy search methods for locally optimal solutions (e.g., the orthogonal matching pursuit type algorithms). In this paper, a neurodynamic optimization method is proposed to solve the l0 -norm minimization problems for obtaining the global optimum using a recurrent neural network (RNN) model. For the RNN model, a group of modified Gaussian functions are constructed and their sum is taken as the objective function for approximating the l0 norm and for optimization. The constructed objective function sets up a convexity condition under which the neurodynamic system is guaranteed to obtain the globally convergent optimal solution. An adaptive adjustment scheme is developed for improving the performance of the optimization algorithm further. Extensive experiments are conducted to test the proposed approach in this paper and the output results validate the effectiveness of the new method. PMID:25122603

  11. Optimization of the excitation light sheet in selective plane illumination microscopy.

    PubMed

    Gao, Liang

    2015-03-01

    Selective plane illumination microscopy (SPIM) allows rapid 3D live fluorescence imaging on biological specimens with high 3D spatial resolution, good optical sectioning capability and minimal photobleaching and phototoxic effect. SPIM gains its advantage by confining the excitation light near the detection focal plane, and its performance is determined by the ability to create a thin, large and uniform excitation light sheet. Several methods have been developed to create such an excitation light sheet for SPIM. However, each method has its own strengths and weaknesses, and tradeoffs must be made among different aspects in SPIM imaging. In this work, we present a strategy to select the excitation light sheet among the latest SPIM techniques, and to optimize its geometry based on spatial resolution, field of view, optical sectioning capability, and the sample to be imaged. Besides the light sheets discussed in this work, the proposed strategy is also applicable to estimate the SPIM performance using other excitation light sheets. PMID:25798312

  12. 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.

  13. Strategies to optimize shock wave lithotripsy outcome: Patient selection and treatment parameters

    PubMed Central

    Semins, Michelle Jo; Matlaga, Brian R

    2015-01-01

    Shock wave lithotripsy (SWL) was introduced in 1980, modernizing the treatment of upper urinary tract stones, and quickly became the most commonly utilized technique to treat kidney stones. Over the past 5-10 years, however, use of SWL has been declining because it is not as reliably effective as more modern technology. SWL success rates vary considerably and there is abundant literature predicting outcome based on patient- and stone-specific parameters. Herein we discuss the ways to optimize SWL outcomes by reviewing proper patient selection utilizing stone characteristics and patient features. Stone size, number, location, density, composition, and patient body habitus and renal anatomy are all discussed. We also review the technical parameters during SWL that can be controlled to improve results further, including type of anesthesia, coupling, shock wave rate, focal zones, pressures, and active monitoring. Following these basic principles and selection criteria will help maximize success rate. PMID:25949936

  14. Optimization of 1,2,5-Thiadiazole Carbamates as Potent and Selective ABHD6 Inhibitors #

    PubMed Central

    Patel, Jayendra Z.; Nevalainen, Tapio J.; Savinainen, Juha R.; Adams, Yahaya; Laitinen, Tuomo; Runyon, Robert S.; Vaara, Miia; Ahenkorah, Stephen; Kaczor, Agnieszka A.; Navia-Paldanius, Dina; Gynther, Mikko; Aaltonen, Niina; Joharapurkar, Amit A.; Jain, Mukul R.; Haka, Abigail S.; Maxfield, Frederick R.; Laitinen, Jarmo T.; Parkkari, Teija

    2015-01-01

    At present, inhibitors of α/β-hydrolase domain 6 (ABHD6) are viewed as a promising approach to treat inflammation and metabolic disorders. This article describes the optimization of 1,2,5-thiadiazole carbamates as ABHD6 inhibitors. Altogether, 34 compounds were synthesized and their inhibitory activity was tested using lysates of HEK293 cells transiently expressing human ABHD6 (hABHD6). Among the compound series, 4-morpholino-1,2,5-thiadiazol-3-yl cyclooctyl(methyl)carbamate (JZP-430, 55) potently and irreversibly inhibited hABHD6 (IC50 44 nM) and showed good selectivity (∼230 fold) over fatty acid amide hydrolase (FAAH) and lysosomal acid lipase (LAL), the main off-targets of related compounds. Additionally, activity-based protein profiling (ABPP) indicated that compound 55 (JZP-430) displayed good selectivity among the serine hydrolases of mouse brain membrane proteome. PMID:25504894

  15. Selectivity of seismic electric signal (SES) of the 2000 Izu earthquake swarm: a 3D FEM numerical simulation model.

    PubMed

    Huang, Qinghua; Lin, Yufeng

    2010-01-01

    Although seismic electric signal (SES) has been used for short-term prediction of earthquakes, selectivity of SES still remains as one of the mysterious features. As a case study, we made a numerical simulation based on a 3D finite element method (FEM) on the selectivity of SES observed in the case of the 2000 Izu earthquake swarm. Our numerical results indicated that the existence of conductive channel under Niijima island could explain the reported SES selectivity. PMID:20228625

  16. Selectivity of seismic electric signal (SES) of the 2000 Izu earthquake swarm: a 3D FEM numerical simulation model

    PubMed Central

    Huang, Qinghua; Lin, Yufeng

    2010-01-01

    Although seismic electric signal (SES) has been used for short-term prediction of earthquakes, selectivity of SES still remains as one of the mysterious features. As a case study, we made a numerical simulation based on a 3D finite element method (FEM) on the selectivity of SES observed in the case of the 2000 Izu earthquake swarm. Our numerical results indicated that the existence of conductive channel under Niijima island could explain the reported SES selectivity. PMID:20228625

  17. Contrast based band selection for optimized weathered oil detection in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Levaux, Florian; Bostater, Charles R., Jr.; Neyt, Xavier

    2012-09-01

    Hyperspectral imagery offers unique benefits for detection of land and water features due to the information contained in reflectance signatures such as the bi-directional reflectance distribution function or BRDF. The reflectance signature directly shows the relative absorption and backscattering features of targets. These features can be very useful in shoreline monitoring or surveillance applications, for example to detect weathered oil. In real-time detection applications, processing of hyperspectral data can be an important tool and Optimal band selection is thus important in real time applications in order to select the essential bands using the absorption and backscatter information. In the present paper, band selection is based upon the optimization of target detection using contrast algorithms. The common definition of the contrast (using only one band out of all possible combinations available within a hyperspectral image) is generalized in order to consider all the possible combinations of wavelength dependent contrasts using hyperspectral images. The inflection (defined here as an approximation of the second derivative) is also used in order to enhance the variations in the reflectance spectra as well as in the contrast spectrua in order to assist in optimal band selection. The results of the selection in term of target detection (false alarms and missed detection) are also compared with a previous method to perform feature detection, namely the matched filter. In this paper, imagery is acquired using a pushbroom hyperspectral sensor mounted at the bow of a small vessel. The sensor is mechanically rotated using an optical rotation stage. This opto-mechanical scanning system produces hyperspectral images with pixel sizes on the order of mm to cm scales, depending upon the distance between the sensor and the shoreline being monitored. The motion of the platform during the acquisition induces distortions in the collected HSI imagery. It is therefore

  18. Stochastic optimization algorithm selection in hydrological model calibration based on fitness landscape characterization

    NASA Astrophysics Data System (ADS)

    Arsenault, Richard; Brissette, François P.; Poulin, Annie; Côté, Pascal; Martel, Jean-Luc

    2014-05-01

    The process of hydrological model parameter calibration is routinely performed with the help of stochastic optimization algorithms. Many such algorithms have been created and they sometimes provide varying levels of performance (as measured by an efficiency metric such as Nash-Sutcliffe). This is because each algorithm is better suited for one type of optimization problem rather than another. This research project's aim was twofold. First, it was sought upon to find various features in the calibration problem fitness landscapes to map the encountered problem types to the best possible optimization algorithm. Second, the optimal number of model evaluations in order to minimize resources usage and maximize overall model quality was investigated. A total of five stochastic optimization algorithms (SCE-UA, CMAES, DDS, PSO and ASA) were used to calibrate four lumped hydrological models (GR4J, HSAMI, HMETS and MOHYSE) on 421 basins from the US MOPEX database. Each of these combinations was performed using three objective functions (Log(RMSE), NSE, and a metric combining NSE, RMSE and BIAS) to add sufficient diversity to the fitness landscapes. Each run was performed 30 times for statistical analysis. With every parameter set tested during the calibration process, the validation value was taken on a separate period. It was then possible to outline the calibration skill versus the validation skill for the different algorithms. Fitness landscapes were characterized by various metrics, such as the dispersion metric, the mean distance between random points and their respective local minima (found through simple hill-climbing algorithms) and the mean distance between the local minima and the best local optimum found. These metrics were then compared to the calibration score of the various optimization algorithms. Preliminary results tend to show that fitness landscapes presenting a globally convergent structure are more prevalent than other types of landscapes in this

  19. 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

  20. 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. PMID:25136678

  1. 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.

  2. Optimal sequence selection in proteins of known structure by simulated evolution.

    PubMed Central

    Hellinga, H W; Richards, F M

    1994-01-01

    Rational design of protein structure requires the identification of optimal sequences to carry out a particular function within a given backbone structure. A general solution to this problem requires that a potential function describing the energy of the system as a function of its atomic coordinates be minimized simultaneously over all available sequences and their three-dimensional atomic configurations. Here we present a method that explicitly minimizes a semiempirical potential function simultaneously in these two spaces, using a simulated annealing approach. The method takes the fixed three-dimensional coordinates of a protein backbone and stochastically generates possible sequences through the introduction of random mutations. The corresponding three-dimensional coordinates are constructed for each sequence by "redecorating" the backbone coordinates of the original structure with the corresponding side chains. These are then allowed to vary in their structure by random rotations around free torsional angles to generate a stochastic walk in configurational space. We have named this method protein simulated evolution, because, in loose analogy with natural selection, it randomly selects for allowed solutions in the sequence of a protein subject to the "selective pressure" of a potential function. Energies predicted by this method for sequences of a small group of residues in the hydrophobic core of the phage lambda cI repressor correlate well with experimentally determined biological activities. This "genetic selection by computer" approach has potential applications in protein engineering, rational protein design, and structure-based drug discovery. PMID:8016069

  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. Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection.

    PubMed

    Bogacz, Rafal; Martin Moraud, Eduardo; Abdi, Azzedine; Magill, Peter J; Baufreton, Jérôme

    2016-07-01

    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

  5. Sequential determination of norfloxaxin and levofloxacin in the presence of other fluorquinolones using synchronous scanning room-temperature phosphorimetry and Th (IV) as the selective signal inducer

    NASA Astrophysics Data System (ADS)

    Nava-Júnior, I. S.; Aucelio, R. Q.

    2009-03-01

    The selective determination of norfloxacin in mixtures with other fluorquinolones was achieved by synchronous scanning solid surface room-temperature phosphorimetry (SSRTP) and Th(NO 3) 4 as selective phosphorescence inducer. The method also allowed the determination of levofloxacin in a sequential way. The optimization of experimental conditions was made through an univariate approach, in order to find the best conditions for norfloxacin phosphorescence, followed by a 2 3 factorial design in order to verify interaction among relevant variables, to check robustness for each variable and to perform final adjustment of parameters. Absolute limit of detection (ALOD) for norfloxacin was 12 ng with a linear signal response extending up to 400 ng. Under the same experimental conditions set for norfloxacin, the ALOD for levofloxacin was 13 ng with linear signal response up to 450 ng. Accuracy of the method, using Th (IV) as selective phosphorescence inducer, was evaluated through the analysis of commercial and simulated pharmaceutical formulations with recoveries between 94.4 and 101% for norfloxacin and 95.9 and 103.8% for levofloxacin. The use of Cd (II), a traditional phosphorescence inducer for fluorquinolones, did not allow selective determination of norfloxacin. Further studies indicated the potential application of the method in urine samples.

  6. 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

  7. 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

  8. Effect of selected signals of interest on ultrasonic backscattering measurement in cancellous bones

    NASA Astrophysics Data System (ADS)

    Liu, ChengCheng; Han, HaiJie; Ta, DeAn; Wang, WeiQi

    2013-07-01

    This study examined how the signals of interest (SOI) effect on the backscattering measurement numerically based on 3-D finite-difference time-domain (FDTD) method. High resolution microstructure mappings of bovine cancellous bones provided by micro-CT were used as the input geometry for simulations. Backscatter coefficient (BSC), integrated backscatter coefficient (IBC) and apparent integrated backscatter (AIB) were calculated with changing the start ( L1) and duration ( L2) of the SOI. The results demonstrated that BSC and IBC decrease as L1 increases, and AIB decreases more rapidly as L1 increases. The backscattering parameters increase with fluctuations as a function of L2 when L2 is less than 6 mm. However, BSC and IBC change little as L2 continues to increase, while AIB slowly decreases as L2 continues to increase. The results showed how the selections of the SOI effect on the backscattering measurement. An explicit standard for SOI selection was proposed in this study and short L1 (about 1.5 mm) and appropriate L2 (6 mm-12 mm) were recommended for the calculations of backscattering parameters.

  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. PMID:19441846

  10. 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

  11. 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.

  12. 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.

  13. Optimization strategies for fast detection of positive selection on phylogenetic trees

    PubMed Central

    Valle, Mario; Schabauer, Hannes; Pacher, Christoph; Stockinger, Heinz; Stamatakis, Alexandros; Robinson-Rechavi, Marc; Salamin, Nicolas

    2014-01-01

    Motivation: The detection of positive selection is widely used to study gene and genome evolution, but its application remains limited by the high computational cost of existing implementations. We present a series of computational optimizations for more efficient estimation of the likelihood function on large-scale phylogenetic problems. We illustrate our approach using the branch-site model of codon evolution. Results: We introduce novel optimization techniques that substantially outperform both CodeML from the PAML package and our previously optimized sequential version SlimCodeML. These techniques can also be applied to other likelihood-based phylogeny software. Our implementation scales well for large numbers of codons and/or species. It can therefore analyse substantially larger datasets than CodeML. We evaluated FastCodeML on different platforms and measured average sequential speedups of FastCodeML (single-threaded) versus CodeML of up to 5.8, average speedups of FastCodeML (multi-threaded) versus CodeML on a single node (shared memory) of up to 36.9 for 12 CPU cores, and average speedups of the distributed FastCodeML versus CodeML of up to 170.9 on eight nodes (96 CPU cores in total). Availability and implementation: ftp://ftp.vital-it.ch/tools/FastCodeML/. Contact: selectome@unil.ch or nicolas.salamin@unil.ch PMID:24389654

  14. Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation

    PubMed Central

    Li, Xingyuan; He, Zhili; Zhou, Jizhong

    2005-01-01

    The oligonucleotide specificity for microarray hybridization can be predicted by its sequence identity to non-targets, continuous stretch to non-targets, and/or binding free energy to non-targets. Most currently available programs only use one or two of these criteria, which may choose ‘false’ specific oligonucleotides or miss ‘true’ optimal probes in a considerable proportion. We have developed a software tool, called CommOligo using new algorithms and all three criteria for selection of optimal oligonucleotide probes. A series of filters, including sequence identity, free energy, continuous stretch, GC content, self-annealing, distance to the 3′-untranslated region (3′-UTR) and melting temperature (Tm), are used to check each possible oligonucleotide. A sequence identity is calculated based on gapped global alignments. A traversal algorithm is used to generate alignments for free energy calculation. The optimal Tm interval is determined based on probe candidates that have passed all other filters. Final probes are picked using a combination of user-configurable piece-wise linear functions and an iterative process. The thresholds for identity, stretch and free energy filters are automatically determined from experimental data by an accessory software tool, CommOligo_PE (CommOligo Parameter Estimator). The program was used to design probes for both whole-genome and highly homologous sequence data. CommOligo and CommOligo_PE are freely available to academic users upon request. PMID:16246912

  15. 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.

  16. 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

  17. 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

  18. Optimal energy window selection of a CZT-based small-animal SPECT for quantitative accuracy

    NASA Astrophysics Data System (ADS)

    Park, Su-Jin; Yu, A. Ram; Choi, Yun Young; Kim, Kyeong Min; Kim, Hee-Joung

    2015-05-01

    Cadmium zinc telluride (CZT)-based small-animal single-photon emission computed tomography (SPECT) has desirable characteristics such as superior energy resolution, but data acquisition for SPECT imaging has been widely performed with a conventional energy window. The aim of this study was to determine the optimal energy window settings for technetium-99 m (99mTc) and thallium-201 (201Tl), the most commonly used isotopes in SPECT imaging, using CZT-based small-animal SPECT for quantitative accuracy. We experimentally investigated quantitative measurements with respect to primary count rate, contrast-to-noise ratio (CNR), and scatter fraction (SF) within various energy window settings using Triumph X-SPECT. The two ways of energy window settings were considered: an on-peak window and an off-peak window. In the on-peak window setting, energy centers were set on the photopeaks. In the off-peak window setting, the ratios of energy differences between the photopeak from the lower- and higher-threshold varied from 4:6 to 3:7. In addition, the energy-window width for 99mTc varied from 5% to 20%, and that for 201Tl varied from 10% to 30%. The results of this study enabled us to determine the optimal energy windows for each isotope in terms of primary count rate, CNR, and SF. We selected the optimal energy window that increases the primary count rate and CNR while decreasing SF. For 99mTc SPECT imaging, the energy window of 138-145 keV with a 5% width and off-peak ratio of 3:7 was determined to be the optimal energy window. For 201Tl SPECT imaging, the energy window of 64-85 keV with a 30% width and off-peak ratio of 3:7 was selected as the optimal energy window. Our results demonstrated that the proper energy window should be carefully chosen based on quantitative measurements in order to take advantage of desirable characteristics of CZT-based small-animal SPECT. These results provided valuable reference information for the establishment of new protocol for CZT

  19. 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

  20. Optimal selection of proton exchange membrane fuel cell condition monitoring thresholds

    NASA Astrophysics Data System (ADS)

    Boškoski, Pavle; Debenjak, Andrej

    2014-12-01

    When commissioning or restarting a system after a maintenance action there is a need to properly tune the decision thresholds of the diagnostic system. Too low or too high thresholds may implicate either missed alarms or false alarm rates. This paper suggests an efficient data-driven approach to optimal setting of decision thresholds for a PEM fuel cell system based solely on data acquired from the system in reference state of health (i.e. under fault free operation). The only design parameter is the desired false alarm rate. Technically, the problem reduces to analytically determining the probability distribution of the fuel cell's complex impedance and its particular components. Employing pseudo-random binary sequence perturbation signals, the distribution of the impedance is estimated through the complex wavelet coefficients of the fuel cell voltage and current. The approach is validated on a PEM fuel cell system subjected to various faults.

  1. 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.

  2. Algorithm for selection of optimized EPR distance restraints for de novo protein structure determination

    PubMed Central

    Kazmier, Kelli; Alexander, Nathan S.; Meiler, Jens; Mchaourab, Hassane S.

    2010-01-01

    A hybrid protein structure determination approach combining sparse Electron Paramagnetic Resonance (EPR) distance restraints and Rosetta de novo protein folding has been previously demonstrated to yield high quality models (Alexander et al., 2008). However, widespread application of this methodology to proteins of unknown structures is hindered by the lack of a general strategy to place spin label pairs in the primary sequence. In this work, we report the development of an algorithm that optimally selects spin labeling positions for the purpose of distance measurements by EPR. For the α-helical subdomain of T4 lysozyme (T4L), simulated restraints that maximize sequence separation between the two spin labels while simultaneously ensuring pairwise connectivity of secondary structure elements yielded vastly improved models by Rosetta folding. 50% of all these models have the correct fold compared to only 21% and 8% correctly folded models when randomly placed restraints or no restraints are used, respectively. Moreover, the improvements in model quality require a limited number of optimized restraints, the number of which is determined by the pairwise connectivities of T4L α-helices. The predicted improvement in Rosetta model quality was verified by experimental determination of distances between spin labels pairs selected by the algorithm. Overall, our results reinforce the rationale for the combined use of sparse EPR distance restraints and de novo folding. By alleviating the experimental bottleneck associated with restraint selection, this algorithm sets the stage for extending computational structure determination to larger, traditionally elusive protein topologies of critical structural and biochemical importance. PMID:21074624

  3. 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

  4. 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

  5. Photonic chip based transmitter optimization and receiver demultiplexing of a 1.28 Tbit/s OTDM signal.

    PubMed

    Vo, T D; Hu, H; Galili, M; Palushani, E; Xu, J; Oxenløwe, L K; Madden, S J; Choi, D-Y; Bulla, D A P; Pelusi, M D; Schröder, J; Luther-Davies, B; Eggleton, B J

    2010-08-01

    We demonstrate chip-based Tbaud optical signal processing for all-optical performance monitoring, switching and demultiplexing based on the instantaneous Kerr nonlinearity in a dispersion-engineered As(2)S(3) planar waveguide. At the Tbaud transmitter, we use a THz bandwidth radio-frequency spectrum analyzer to perform all-optical performance monitoring and to optimize the optical time division multiplexing stages as well as mitigate impairments, for example, dispersion. At the Tbaud receiver, we demonstrate error-free demultiplexing of a 1.28 Tbit/s single wavelength, return-to-zero signal to 10 Gbit/s via four-wave mixing with negligible system penalty (< 0.5 dB). Excellent performance, including high four-wave mixing conversion efficiency and no indication of an error-floor, was achieved. Our results establish the feasibility of Tbaud signal processing using compact nonlinear planar waveguides for Tbit/s Ethernet applications. PMID:20721113

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. Model selection based on FDR-thresholding optimizing the area under the ROC-curve.

    PubMed

    Graf, Alexandra C; Bauer, Peter

    2009-01-01

    We evaluate variable selection by multiple tests controlling the false discovery rate (FDR) to build a linear score for prediction of clinical outcome in high-dimensional data. Quality of prediction is assessed by the receiver operating characteristic curve (ROC) for prediction in independent patients. Thus we try to combine both goals: prediction and controlled structure estimation. We show that the FDR-threshold which provides the ROC-curve with the largest area under the curve (AUC) varies largely over the different parameter constellations not known in advance. Hence, we investigated a new cross validation procedure based on the maximum rank correlation estimator to determine the optimal selection threshold. This procedure (i) allows choosing an appropriate selection criterion, (ii) provides an estimate of the FDR close to the true FDR and (iii) is simple and computationally feasible for rather moderate to small sample sizes. Low estimates of the cross validated AUC (the estimates generally being positively biased) and large estimates of the cross validated FDR may indicate a lack of sufficiently prognostic variables and/or too small sample sizes. The method is applied to an oncology dataset. PMID:19572830

  11. Optimal feature selection for automated classification of FDG-PET in patients with suspected dementia

    NASA Astrophysics Data System (ADS)

    Serag, Ahmed; Wenzel, Fabian; Thiele, Frank; Buchert, Ralph; Young, Stewart

    2009-02-01

    FDG-PET is increasingly used for the evaluation of dementia patients, as major neurodegenerative disorders, such as Alzheimer's disease (AD), Lewy body dementia (LBD), and Frontotemporal dementia (FTD), have been shown to induce specific patterns of regional hypo-metabolism. However, the interpretation of FDG-PET images of patients with suspected dementia is not straightforward, since patients are imaged at different stages of progression of neurodegenerative disease, and the indications of reduced metabolism due to neurodegenerative disease appear slowly over time. Furthermore, different diseases can cause rather similar patterns of hypo-metabolism. Therefore, classification of FDG-PET images of patients with suspected dementia may lead to misdiagnosis. This work aims to find an optimal subset of features for automated classification, in order to improve classification accuracy of FDG-PET images in patients with suspected dementia. A novel feature selection method is proposed, and performance is compared to existing methods. The proposed approach adopts a combination of balanced class distributions and feature selection methods. This is demonstrated to provide high classification accuracy for classification of FDG-PET brain images of normal controls and dementia patients, comparable with alternative approaches, and provides a compact set of features selected.

  12. ACTIVITY-DEPENDENT, STRESS-RESPONSIVE BDNF SIGNALING AND THE QUEST FOR OPTIMAL BRAIN HEALTH AND RESILIENCE THROUGHOUT THE LIFESPAN

    PubMed Central

    Rothman, S. M.; Mattson, M. P.

    2013-01-01

    During development of the nervous system, the formation of connections (synapses) between neurons is dependent upon electrical activity in those neurons, and neurotrophic factors produced by target cells play a pivotal role in such activity-dependent sculpting of the neural networks. A similar interplay between neurotransmitter and neurotrophic factor signaling pathways mediates adaptive responses of neural networks to environmental demands in adult mammals, with the excitatory neurotransmitter glutamate and brain-derived neurotrophic factor (BDNF) being particularly prominent regulators of synaptic plasticity throughout the central nervous system. Optimal brain health throughout the lifespan is promoted by intermittent challenges such as exercise, cognitive stimulation and dietary energy restriction, that subject neurons to activity-related metabolic stress. At the molecular level, such challenges to neurons result in the production of proteins involved in neurogenesis, learning and memory and neuronal survival; examples include proteins that regulate mitochondrial biogenesis, protein quality control, and resistance of cells to oxidative, metabolic and proteotoxic stress. BDNF signaling mediates up-regulation of several such proteins including the protein chaperone GRP-78, antioxidant enzymes, the cell survival protein Bcl-2, and the DNA repair enzyme APE1. Insufficient exposure to such challenges, genetic factors may conspire to impair BDNF production and/or signaling resulting in the vulnerability of the brain to injury and neurodegenerative disorders including Alzheimer’s, Parkinson’s and Huntington’s diseases. Further, BDNF signaling is negatively regulated by glucocorticoids. Glucocorticoids impair synaptic plasticity in the brain by negatively regulating spine density, neurogenesis and long-term potentiation, effects that are potentially linked to glucocorticoid regulation of BDNF. Findings suggest that BDNF signaling in specific brain regions mediates

  13. 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.

  14. 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

  15. [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. PMID:23469557

  16. A computational strategy to select optimized protein targets for drug development toward the control of cancer diseases.

    PubMed

    Carels, Nicolas; Tilli, Tatiana; Tuszynski, Jack A

    2015-01-01

    In this report, we describe a strategy for the optimized selection of protein targets suitable for drug development against neoplastic diseases taking the particular case of breast cancer as an example. We combined human interactome and transcriptome data from malignant and control cell lines because highly connected proteins that are up-regulated in malignant cell lines are expected to be suitable protein targets for chemotherapy with a lower rate of undesirable side effects. We normalized transcriptome data and applied a statistic treatment to objectively extract the sub-networks of down- and up-regulated genes whose proteins effectively interact. We chose the most connected ones that act as protein hubs, most being in the signaling network. We show that the protein targets effectively identified by the combination of protein connectivity and differential expression are known as suitable targets for the successful chemotherapy of breast cancer. Interestingly, we found additional proteins, not generally targeted by drug treatments, which might justify the extension of existing formulation by addition of inhibitors designed against these proteins with the consequence of improving therapeutic outcomes. The molecular alterations observed in breast cancer cell lines represent either driver events and/or driver pathways that are necessary for breast cancer development or progression. However, it is clear that signaling mechanisms of the luminal A, B and triple negative subtypes are different. Furthermore, the up- and down-regulated networks predicted subtype-specific drug targets and possible compensation circuits between up- and down-regulated genes. We believe these results may have significant clinical implications in the personalized treatment of cancer patients allowing an objective approach to the recycling of the arsenal of available drugs to the specific case of each breast cancer given their distinct qualitative and quantitative molecular traits. PMID:25625699

  17. A Computational Strategy to Select Optimized Protein Targets for Drug Development toward the Control of Cancer Diseases

    PubMed Central

    Carels, Nicolas; Tilli, Tatiana; Tuszynski, Jack A.

    2015-01-01

    In this report, we describe a strategy for the optimized selection of protein targets suitable for drug development against neoplastic diseases taking the particular case of breast cancer as an example. We combined human interactome and transcriptome data from malignant and control cell lines because highly connected proteins that are up-regulated in malignant cell lines are expected to be suitable protein targets for chemotherapy with a lower rate of undesirable side effects. We normalized transcriptome data and applied a statistic treatment to objectively extract the sub-networks of down- and up-regulated genes whose proteins effectively interact. We chose the most connected ones that act as protein hubs, most being in the signaling network. We show that the protein targets effectively identified by the combination of protein connectivity and differential expression are known as suitable targets for the successful chemotherapy of breast cancer. Interestingly, we found additional proteins, not generally targeted by drug treatments, which might justify the extension of existing formulation by addition of inhibitors designed against these proteins with the consequence of improving therapeutic outcomes. The molecular alterations observed in breast cancer cell lines represent either driver events and/or driver pathways that are necessary for breast cancer development or progression. However, it is clear that signaling mechanisms of the luminal A, B and triple negative subtypes are different. Furthermore, the up- and down-regulated networks predicted subtype-specific drug targets and possible compensation circuits between up- and down-regulated genes. We believe these results may have significant clinical implications in the personalized treatment of cancer patients allowing an objective approach to the recycling of the arsenal of available drugs to the specific case of each breast cancer given their distinct qualitative and quantitative molecular traits. PMID:25625699

  18. A Residue Quartet in the Extracellular Domain of the Prolactin Receptor Selectively Controls Mitogen-activated Protein Kinase Signaling*

    PubMed Central

    Zhang, Chi; Nygaard, Mads; Haxholm, Gitte W.; Boutillon, Florence; Bernadet, Marie; Hoos, Sylviane; England, Patrick; Broutin, Isabelle; Kragelund, Birthe B.; Goffin, Vincent

    2015-01-01

    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. PMID:25784554

  19. 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

  20. 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.

  1. 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.

  2. Selective activation of a putative reinforcement signal conditions cued interval timing in primary visual cortex

    PubMed Central

    Liu, Cheng-Hang; Coleman, Jason E.; Davoudi, Heydar; Zhang, Kechen; Hussain Shuler, Marshall G.

    2015-01-01

    Summary As a consequence of conditioning visual cues with delayed reward, cue-evoked neural activity that predicts the time of expected future reward emerges in the primary visual cortex (V1). We hypothesized that this reward timing activity is engendered by a reinforcement signal conveying reward acquisition to V1. In lieu of behavioral conditioning, we assessed in vivo whether selective activation of either basal forebrain (BF) or cholinergic innervation is sufficient to condition cued interval timing activity. Substituting for actual reward, optogenetic activation of BF or cholinergic input within V1 at fixed delays following visual stimulation entrains neural responses that mimic behaviorally-conditioned reward timing activity. Optogenetically-conditioned neural responses express cue-evoked temporal intervals that correspond to the conditioning intervals, are bidirectionally modifiable, display experience-dependent refinement, and exhibit a scale invariance to the encoded delay. Our results demonstrate that the activation of BF or cholinergic input within V1is sufficient to encode cued interval timing activity, and indicate that V1 itself is a substrate for associative learning that may inform the timing of visually-cued behaviors. PMID:26004763

  3. 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

  4. Fine-tuning somatostatin receptor signalling by agonist-selective phosphorylation and dephosphorylation: IUPHAR Review 5

    PubMed Central

    Schulz, Stefan; Lehmann, Andreas; Kliewer, Andrea; Nagel, Falko

    2014-01-01

    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. PMID:24328848

  5. 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. PMID:26350941

  6. Field trials for corrosion inhibitor selection and optimization, using a new generation of electrical resistance probes

    SciTech Connect

    Ridd, B.; Blakset, T.J.; Queen, D.

    1998-12-31

    Even with today`s availability of corrosion resistant alloys, carbon steels protected by corrosion inhibitors still dominate the material selection for pipework in the oil and gas production. Even though laboratory screening tests of corrosion inhibitor performance provides valuable data, the real performance of the chemical can only be studied through field trials which provide the ultimate test to evaluate the effectiveness of an inhibitor under actual operating conditions. A new generation of electrical resistance probe has been developed, allowing highly sensitive and immediate response to changes in corrosion rates on the internal environment of production pipework. Because of the high sensitivity, the probe responds to small changes in the corrosion rate, and it provides the corrosion engineer with a highly effective method of optimizing the use of inhibitor chemicals resulting in confidence in corrosion control and minimizing detrimental environmental effects.

  7. 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.

  8. Testability requirement uncertainty analysis in the sensor selection and optimization model for PHM

    NASA Astrophysics Data System (ADS)

    Yang, S. M.; Qiu, J.; Liu, G. J.; Yang, P.; Zhang, Y.

    2012-05-01

    Prognostics and health management (PHM) has been an important part to guarantee the reliability and safety of complex systems. Design for testability (DFT) developed concurrently with system design is considered as a fundamental way to improve PHM performance, and sensor selection and optimization (SSO) is one of the important parts in DFT. To address the problem that testability requirement analysis in the existing SSO models does not take test uncertainty in actual scenario into account, fault detection uncertainty is analyzed from the view of fault attributes, sensor attributes and fault-sensor matching attributes qualitatively. And then, quantitative uncertainty analysis is given, which assigns a rational confidence level to fault size. A case is presented to demonstrate the proposed methodology for an electromechanical servo-controlled system, and application results show the proposed approach is reasonable and feasible.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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. PMID:25606957

  17. 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.

  18. 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.

  19. 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

  20. Discovery and optimization of sulfonyl acrylonitriles as selective, covalent inhibitors of protein phosphatase methylesterase-1.

    PubMed

    Bachovchin, Daniel A; Zuhl, Andrea M; Speers, Anna E; Wolfe, Monique R; Weerapana, Eranthie; Brown, Steven J; Rosen, Hugh; Cravatt, Benjamin F

    2011-07-28

    The serine hydrolase protein phosphatase methylesterase-1 (PME-1) regulates the methylesterification state of protein phosphatase 2A (PP2A) and has been implicated in cancer and Alzheimer's disease. We recently reported a fluorescence polarization-activity-based protein profiling (fluopol-ABPP) high-throughput screen for PME-1 that uncovered a remarkably potent and selective class of aza-β-lactam (ABL) PME-1 inhibitors. Here, we describe a distinct set of sulfonyl acrylonitrile inhibitors that also emerged from this screen. The optimized compound, 28 (AMZ30), selectively inactivates PME-1 and reduces the demethylated form of PP2A in living cells. Considering that 28 is structurally unrelated to ABL inhibitors of PME-1, these agents, together, provide a valuable set of pharmacological probes to study the role of methylation in regulating PP2A function. We furthermore observed that several serine hydrolases were sensitive to analogues of 28, suggesting that more extensive structural exploration of the sulfonyl acrylonitrile chemotype may result in useful inhibitors for other members of this large enzyme class. PMID:21639134

  1. 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

  2. Discovery and Optimization of Sulfonyl Acrylonitriles as Selective, Covalent Inhibitors of Protein Phosphatase Methylesterase-1

    PubMed Central

    Bachovchin, Daniel A.; Zuhl, Andrea M.; Speers, Anna E.; Wolfe, Monique R.; Weerapana, Eranthie; Brown, Steven J.; Rosen, Hugh; Cravatt, Benjamin F.

    2011-01-01

    The serine hydrolase protein phosphatase methylesterase-1 (PME-1) regulates the methylesterification state of protein phosphatase 2A (PP2A) and has been implicated in cancer and Alzheimer's disease. We recently reported a fluorescence polarization-activity-based protein profiling (fluopol-ABPP) high-throughput screen for PME-1 that uncovered a remarkably potent and selective class of aza-β-lactam (ABL) PME-1 inhibitors. Here, we describe a distinct set of sulfonyl acrylonitrile inhibitors that also emerged from this screen. The optimized compound, 28 (AMZ30), selectively inactivates PME-1 and reduces the demethylated form of PP2A in living cells. Considering that 28 is structurally unrelated to ABL inhibitors of PME-1, these agents, together, provide a valuable set of pharmacological probes to study the role of methylation in regulating PP2A function. We furthermore observed that several serine hydrolases were sensitive to analogs of 28, suggesting that more extensive structural exploration of the sulfonyl acrylonitrile chemotype may result in useful inhibitors for other members of this large enzyme class. PMID:21639134

  3. 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. PMID:26214001

  4. Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

    PubMed Central

    2014-01-01

    Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. PMID:24555567

  5. Noncovalent Mutant Selective Epidermal Growth Factor Receptor Inhibitors: A Lead Optimization Case Study.

    PubMed

    Heald, Robert; Bowman, Krista K; Bryan, Marian C; Burdick, Daniel; Chan, Bryan; Chan, Emily; Chen, Yuan; Clausen, Saundra; Dominguez-Fernandez, Belen; Eigenbrot, Charles; Elliott, Richard; Hanan, Emily J; Jackson, Philip; Knight, Jamie; La, Hank; Lainchbury, Michael; Malek, Shiva; Mann, Sam; Merchant, Mark; Mortara, Kyle; Purkey, Hans; Schaefer, Gabriele; Schmidt, Stephen; Seward, Eileen; Sideris, Steve; Shao, Lily; Wang, Shumei; Yeap, Kuen; Yen, Ivana; Yu, Christine; Heffron, Timothy P

    2015-11-25

    Because of their increased activity against activating mutants, first-generation epidermal growth factor receptor (EGFR) kinase inhibitors have had remarkable success in treating non-small-cell lung cancer (NSCLC) patients, but acquired resistance, through a secondary mutation of the gatekeeper residue, means that clinical responses only last for 8-14 months. Addressing this unmet medical need requires agents that can target both of the most common double mutants: T790M/L858R (TMLR) and T790M/del(746-750) (TMdel). Herein we describe how a noncovalent double mutant selective lead compound was optimized using a strategy focused on the structure-guided increase in potency without added lipophilicity or reduction of three-dimensional character. Following successive rounds of design and synthesis it was discovered that cis-fluoro substitution on 4-hydroxy- and 4-methoxypiperidinyl groups provided synergistic, substantial, and specific potency gain through direct interaction with the enzyme and/or effects on the proximal ligand oxygen atom. Further development of the fluorohydroxypiperidine series resulted in the identification of a pair of diastereomers that showed 50-fold enzyme and cell based selectivity for T790M mutants over wild-type EGFR (wtEGFR) in vitro and pathway knock-down in an in vivo xenograft model. PMID:26455919

  6. 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....

  7. 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....

  8. 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....

  9. 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....

  10. 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....

  11. 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.

  12. 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.

  13. Selectivity optimization of substituted 1,2,3-triazoles as α7 nicotinic acetylcholine receptor agonists.

    PubMed

    Arunrungvichian, Kuntarat; Fokin, Valery V; Vajragupta, Opa; Taylor, Palmer

    2015-08-19

    Three series of substituted anti-1,2,3-triazoles (IND, PPRD, and QND), synthesized by cycloaddition from azide and alkyne building blocks, were designed to enhance selectivity and potency profiles of a lead α7 nicotinic acetylcholine receptor (α7-nAChR) agonist, TTIn-1. Designed compounds were synthesized and screened for affinity by a radioligand binding assay. Their functional characterization as agonists and antagonists was performed by fluorescence resonance energy transfer assay using cell lines expressing transfected cDNAs, α7-nAChRs, α4β2-nAChRs, and 5HT3A receptors, and a fluorescence cell reporter. In the IND series, a tropane ring of TTIn-1, substituted at N1, was replaced by mono- and bicyclic amines to vary length and conformational flexibility of a carbon linker between nitrogen atom and N1 of the triazole. Compounds with a two-carbon atom linker optimized binding with Kd's at the submicromolar level. Further modification at the hydrophobic indole of TTIn-1 was made in PPRD and QND series by fixing the amine center with the highest affinity building blocks in the IND series. Compounds from IND and PPRD series are selective as agonists for the α7-nAChRs over α4β2-nAChRs and 5HT3A receptors. Lead compounds in the three series have EC50's between 28 and 260 nM. Based on the EC50, affinity, and selectivity determined from the binding and cellular responses, two of the leads have been advanced to behavioral studies described in the companion article (DOI: 10.1021/acschemneuro.5b00059). PMID:25932897

  14. 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

  15. Selective and Efficient Neural Coding of Communication Signals Depends on Early Acoustic and Social Environment

    PubMed Central

    Amin, Noopur; Gastpar, Michael; Theunissen, Frédéric E.

    2013-01-01

    Previous research has shown that postnatal exposure to simple, synthetic sounds can affect the sound representation in the auditory cortex as reflected by changes in the tonotopic map or other relatively simple tuning properties, such as AM tuning. However, their functional implications for neural processing in the generation of ethologically-based perception remain unexplored. Here we examined the effects of noise-rearing and social isolation on the neural processing of communication sounds such as species-specific song, in the primary auditory cortex analog of adult zebra finches. Our electrophysiological recordings reveal that neural tuning to simple frequency-based synthetic sounds is initially established in all the laminae independent of patterned acoustic experience; however, we provide the first evidence that early exposure to patterned sound statistics, such as those found in native sounds, is required for the subsequent emergence of neural selectivity for complex vocalizations and for shaping neural spiking precision in superficial and deep cortical laminae, and for creating efficient neural representations of song and a less redundant ensemble code in all the laminae. Our study also provides the first causal evidence for ‘sparse coding’, such that when the statistics of the stimuli were changed during rearing, as in noise-rearing, that the sparse or optimal representation for species-specific vocalizations disappeared. Taken together, these results imply that a layer-specific differential development of the auditory cortex requires patterned acoustic input, and a specialized and robust sensory representation of complex communication sounds in the auditory cortex requires a rich acoustic and social environment. PMID:23630587

  16. 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

  17. Sensor Selection and Chemo-Sensory Optimization: Toward an Adaptable Chemo-Sensory System

    PubMed Central

    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

  18. 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. PMID:12546465

  19. 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

  20. Quantitative Signaling and Structure-Activity Analyses Demonstrate Functional Selectivity at the Nociceptin/Orphanin FQ Opioid Receptor.

    PubMed

    Chang, Steven D; Mascarella, S Wayne; Spangler, Skylar M; Gurevich, Vsevolod V; Navarro, Hernan A; Carroll, F Ivy; Bruchas, Michael R

    2015-09-01

    Comprehensive studies that consolidate selective ligands, quantitative comparisons of G protein versus arrestin-2/3 coupling, together with structure-activity relationship models for G protein-coupled receptor (GPCR) systems are less commonly employed. Here we examine biased signaling at the nociceptin/orphanin FQ opioid receptor (NOPR), the most recently identified member of the opioid receptor family. Using real-time, live-cell assays, we identified the signaling profiles of several NOPR-selective ligands in upstream GPCR signaling (G protein and arrestin pathways) to determine their relative transduction coefficients and signaling bias. Complementing this analysis, we designed novel ligands on the basis of NOPR antagonist J-113,397 [(±)-1-[(3R*,4R*)-1-(cyclooctylmethyl)-3-(hydroxymethyl)-4-piperidinyl]-3-ethyl-1,3-dihydro-2H-benzimidazol-2-one] to explore structure-activity relationships. Our study shows that NOPR is capable of biased signaling, and further, the NOPR selective ligands MCOPPB [1-[1-(1-methylcyclooctyl)-4-piperidinyl]-2-(3R)-3-piperidinyl-1H-benzimidazole trihydrochloride] and NNC 63-0532 [8-(1-naphthalenylmethyl)-4-oxo-1-phenyl-1,3,8-triazaspiro[4.5]decane-3-acetic acid, methyl ester] are G protein-biased agonists. Additionally, minor structural modification of J-113,397 can dramatically shift signaling from antagonist to partial agonist activity. We explore these findings with in silico modeling of binding poses. This work is the first to demonstrate functional selectivity and identification of biased ligands at the nociceptin opioid receptor. PMID:26134494

  1. 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

  2. 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

  3. Slow Inhibition and Conformation Selective Properties of Extracellular Signal-Regulated Kinase 1 and 2 Inhibitors

    PubMed Central

    Rudolph, Johannes; Xiao, Yao; Pardi, Arthur; Ahn, Natalie G.

    2016-01-01

    The mitogen-activated protein (MAP) kinase pathway is a target for anticancer therapy, validated using inhibitors of B-Raf and MAP kinase kinase (MKK) 1 and 2. Clinical outcomes show a high frequency of acquired resistance in patient tumors, involving upregulation of activity of the MAP kinase, extracellular signal-regulated kinase (ERK) 1 and 2. Thus, inhibitors for ERK1/2 are potentially important for targeted therapeutics against cancer. The structures and potencies of different ERK inhibitors have been published, but their kinetic mechanisms have not been characterized. Here we perform enzyme kinetic studies on six representative ERK inhibitors, with potencies varying from 100 pM to 20 μM. Compounds with significant biological activity (IC50 < 100 nM) that inhibit in the subnanomolar range (Vertex-11e and SCH772984) display slow-onset inhibition and represent the first inhibitors of ERK2 known to demonstrate slow dissociation rate constants (values of 0.2 and 1.1 h−1, respectively). Furthermore, we demonstrate using kinetic competition assays that Vertex-11e binds with differing affinities to ERK2 in its inactive, unphosphorylated and active, phosphorylated forms. Finally, two-dimensional heteronuclear multiple-quantum correlation nuclear magnetic resonance experiments reveal that distinct conformational states are formed in complexes of Vertex-11e with inactive and active ERK2. Importantly, two conformers interconvert in equilibrium in the active ERK2 apoenzyme, but Vertex-11e strongly shifts the equilibrium completely to one conformer. Thus, a high-affinity, slow dissociation inhibitor stabilizes different enzyme conformations depending on the activity state of ERK2 and reveals properties of conformational selection toward the active kinase. PMID:25350931

  4. 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.

  5. 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

  6. Optimization of training sequence for DFT-spread DMT signal in optical access network with direct detection utilizing DML.

    PubMed

    Li, Fan; Li, Xinying; Yu, Jianjun; Chen, Lin

    2014-09-22

    We experimentally demonstrated the transmission of 79.86-Gb/s discrete-Fourier-transform spread 32 QAM discrete multi-tone (DFT-spread 32 QAM-DMT) signal over 20-km standard single-mode fiber (SSMF) utilizing directly modulated laser (DML). The experimental results show DFT-spread effectively reduces Peak-to-Average Power Ratio (PAPR) of DMT signal, and also well overcomes narrowband interference and high frequencies power attenuation. We compared different types of training sequence (TS) symbols and found that the optimized TS for channel estimation is the symbol with digital BPSK/QPSK modulation format due to its best performance against optical link noise during channel estimation. PMID:25321766

  7. Local improvement of the signal-to-noise ratio for diffractive optical elements designed by unidirectional optimization methods

    NASA Astrophysics Data System (ADS)

    Meister, Martin; Winfield, Richard J.

    2002-12-01

    We present a straightforward method to design multilevel phase-only diffractive optical elements with a locally improved signal-to-noise ratio in the reconstruction. The method is generally applicable to all unidirectional design schemes, such as direct search, simulated annealing, or genetic optimization. As the shape and the location of the desired low noise areas are supplied by a bit map file the method allows for the design of basically any two-dimensional low noise area. The improvement in the signal-to-noise ratio that may be achieved is considerable but also entails reduced diffraction efficiency. The suggested method is applied to different beam-splitter design examples. All examples are calculated with the scalar diffraction approximation in the far field.

  8. Lidar Application to Early Forest Fire Detection : Signal To Noise Ratio Optimization

    NASA Astrophysics Data System (ADS)

    Traïche, M.; Beggar, R.; Almabouada, F.

    2009-09-01

    In this communication we deal with a lidar system utilising an eye safe laser wavelength at 1.57 μm and an avalanche photodiode detector where it is question to optimize the SNR ratio so as to manage the detector performance when resolving the smoke plume presence. This is by considering supplement rangefinder and visibilimeter options in the considered Lidar system.

  9. 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 channel parameters and signal detection are updated through ISI cancellation and circular convolution reconstruction from the frequency domain. The simulations show the algorithm can improve the performance of channel estimation and signal detection.

  10. Parallel Grid approach to solve Feature Selection problem in volcanic infrasound signals classification

    NASA Astrophysics Data System (ADS)

    Reitano, Danilo; Pistagna, Fabrizio; Russo, Gaetano; Valenti, Vincenzo

    2010-05-01

    The continuous monitoring of an active volcano, such as Mt. Etna (Sicily, Italy), represents one of the main tasks for the Italian National Institute of Geophysics and Volcanology (INGV), Catania Branch. Around the volcano summit area, four infrasound sensors have been installed during the last recent years, which allow to acquire, real time waveforms that are subsequently stored on a server, located inside the INGV Control Room. A new method here introduced, based on Genetic Algorithms (GA), has been used to analyze the data coming from the remote infrasound sensors stations. In particular, the acquired signals have been processed by a custom modular software: the first module allows the visual manipulation, filtering and, in order to optimize performances, resampling the data to better elaborate them. The second module, using an alghorithm (G. Russo, 2009 ) based on two different thresholds (upper and lower) and the standard deviation, is able to recognize and collect infrasound events (IE) from the stored data. In the third step, the Green & Nueberg algorithm (2006) is used to correlate different families of IE and define the clusters nodes. Once a minimum number of families are characterized, they define the main features inside each cluster. Feature extraction process is a very complex algorithm due to the large number of requested correlations. In order to speed up the time needed to carry out so many simulations, the code has been deployed and executed on the Sicilian Grid infrastructure owned and managed by the Consorzio Cometa, a not-for-profit organisation including INGV among its members. The infrastructure, distributed across the Sicilian territory, is composed of 7 sites for a total of about 2000 CPU cores and more than 250 TB of storage. All the sites of the infrastructure are equipped with low latency Infiniband networks and are installed with MPI libraries. A complete workflow has been created from scratch to execute the various phases of the

  11. Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks.

    PubMed

    DeDora, Daniel J; Nedic, Sanja; Katti, Pratha; Arnab, Shafique; Wald, Lawrence L; Takahashi, Atsushi; Van Dijk, Koene R A; Strey, Helmut H; Mujica-Parodi, Lilianne R

    2016-01-01

    Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS-and not tSNR-is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network. PMID:27199643

  12. Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks

    PubMed Central

    DeDora, Daniel J.; Nedic, Sanja; Katti, Pratha; Arnab, Shafique; Wald, Lawrence L.; Takahashi, Atsushi; Van Dijk, Koene R. A.; Strey, Helmut H.; Mujica-Parodi, Lilianne R.

    2016-01-01

    Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS—and not tSNR—is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network. PMID:27199643

  13. Optimal landmarks selection and fiducial marker placement for minimal target registration error in image-guided neurosurgery

    NASA Astrophysics Data System (ADS)

    Shamir, Reuben R.; Joskowicz, Leo; Shoshan, Yigal

    2009-02-01

    We describe a new framework and method for the optimal selection of anatomical landmarks and optimal placement of fiducial markers in image-guided neurosurgery. The method allows the surgeon to optimally plan the markers locations on routine diagnostic images before preoperative imaging and to intraoperatively select the fiducial markers and the anatomical landmarks that minimize the Target Registration Error (TRE). The optimal fiducial marker configuration selection is performed by the surgeon on the diagnostic image following the target selection based on a visual Estimated TRE (E-TRE) map. The E-TRE map is automatically updated when the surgeon interactively adds and deletes candidate markers and targets. The method takes the guesswork out of the registration process, provides a reliable localization uncertainty error for navigation, and can reduce the localization error without additional imaging and hardware. Our clinical experiments on five patients who underwent brain surgery with a navigation system show that optimizing one marker location and the anatomical landmarks configuration reduces the average TRE from 4.7mm to 3.2mm, with a maximum improvement of 4mm. The reduction of the target registration error has the potential to support safer and more accurate minimally invasive neurosurgical procedures.

  14. Neural Network Cascade Optimizes MicroRNA Biomarker Selection for Nasopharyngeal Cancer Prognosis

    PubMed Central

    Zhu, Wenliang; Kan, Xuan

    2014-01-01

    MicroRNAs (miRNAs) have been shown to be promising biomarkers in predicting cancer prognosis. However, inappropriate or poorly optimized processing and modeling of miRNA expression data can negatively affect prediction performance. Here, we propose a holistic solution for miRNA biomarker selection and prediction model building. This work introduces the use of a neural network cascade, a cascaded constitution of small artificial neural network units, for evaluating miRNA expression and patient outcome. A miRNA microarray dataset of nasopharyngeal carcinoma was retrieved from Gene Expression Omnibus to illustrate the methodology. Results indicated a nonlinear relationship between miRNA expression and patient death risk, implying that direct comparison of expression values is inappropriate. However, this method performs transformation of miRNA expression values into a miRNA score, which linearly measures death risk. Spearman correlation was calculated between miRNA scores and survival status for each miRNA. Finally, a nine-miRNA signature was optimized to predict death risk after nasopharyngeal carcinoma by establishing a neural network cascade consisting of 13 artificial neural network units. Area under the ROC was 0.951 for the internal validation set and had a prediction accuracy of 83% for the external validation set. In particular, the established neural network cascade was found to have strong immunity against noise interference that disturbs miRNA expression values. This study provides an efficient and easy-to-use method that aims to maximize clinical application of miRNAs in prognostic risk assessment of patients with cancer. PMID:25310846

  15. Experiments for practical education in process parameter optimization for selective laser sintering to increase workpiece quality

    NASA Astrophysics Data System (ADS)

    Reutterer, Bernd; Traxler, Lukas; Bayer, Natascha; Drauschke, Andreas

    2016-04-01

    Selective Laser Sintering (SLS) is considered as one of the most important additive manufacturing processes due to component stability and its broad range of usable materials. However the influence of the different process parameters on mechanical workpiece properties is still poorly studied, leading to the fact that further optimization is necessary to increase workpiece quality. In order to investigate the impact of various process parameters, laboratory experiments are implemented to improve the understanding of the SLS limitations and advantages on an educational le