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Sample records for optimal signal selection

  1. Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection.

    PubMed

    Brechet, Laurent; Lucas, Marie-Françoise; Doncarli, Christian; Farina, Dario

    2007-12-01

    We propose a novel scheme for signal compression based on the discrete wavelet packet transform (DWPT) decompositon. The mother wavelet and the basis of wavelet packets were optimized and the wavelet coefficients were encoded with a modified version of the embedded zerotree algorithm. This signal dependant compression scheme was designed by a two-step process. The first (internal optimization) was the best basis selection that was performed for a given mother wavelet. For this purpose, three additive cost functions were applied and compared. The second (external optimization) was the selection of the mother wavelet based on the minimal distortion of the decoded signal given a fixed compression ratio. The mother wavelet was parameterized in the multiresolution analysis framework by the scaling filter, which is sufficient to define the entire decomposition in the orthogonal case. The method was tested on two sets of ten electromyographic (EMG) and ten electrocardiographic (ECG) signals that were compressed with compression ratios in the range of 50%-90%. For 90% compression ratio of EMG (ECG) signals, the percent residual difference after compression decreased from (mean +/- SD) 48.6 +/- 9.9% (21.5 +/- 8.4%) with discrete wavelet transform (DWT) using the wavelet leading to poorest performance to 28.4 +/- 3.0% (6.7 +/- 1.9%) with DWPT, with optimal basis selection and wavelet optimization. In conclusion, best basis selection and optimization of the mother wavelet through parameterization led to substantial improvement of performance in signal compression with respect to DWT and randon selection of the mother wavelet. The method provides an adaptive approach for optimal signal representation for compression and can thus be applied to any type of biomedical signal.

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

    PubMed Central

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

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

  4. 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.

  5. Optimal temporal patterns for dynamical cellular signaling

    NASA Astrophysics Data System (ADS)

    Hasegawa, Yoshihiko

    2016-11-01

    Cells use temporal dynamical patterns to transmit information via signaling pathways. As optimality with respect to the environment plays a fundamental role in biological systems, organisms have evolved optimal ways to transmit information. Here, we use optimal control theory to obtain the dynamical signal patterns for the optimal transmission of information, in terms of efficiency (low energy) and reliability (low uncertainty). Adopting an activation-deactivation decoding network, we reproduce several dynamical patterns found in actual signals, such as steep, gradual, and overshooting dynamics. Notably, when minimizing the energy of the input signal, the optimal signals exhibit overshooting, which is a biphasic pattern with transient and steady phases; this pattern is prevalent in actual dynamical patterns. We also identify conditions in which these three patterns (steep, gradual, and overshooting) confer advantages. Our study shows that cellular signal transduction is governed by the principle of minimizing free energy dissipation and uncertainty; these constraints serve as selective pressures when designing dynamical signaling patterns.

  6. A Theoretical and Empirical Integrated Method to Select the Optimal Combined Signals for Geometry-Free and Geometry-Based Three-Carrier Ambiguity Resolution

    PubMed Central

    Zhao, Dongsheng; Roberts, Gethin Wyn; Lau, Lawrence; Hancock, Craig M.; Bai, Ruibin

    2016-01-01

    Twelve GPS Block IIF satellites, out of the current constellation, can transmit on three-frequency signals (L1, L2, L5). Taking advantages of these signals, Three-Carrier Ambiguity Resolution (TCAR) is expected to bring much benefit for ambiguity resolution. One of the research areas is to find the optimal combined signals for a better ambiguity resolution in geometry-free (GF) and geometry-based (GB) mode. However, the existing researches select the signals through either pure theoretical analysis or testing with simulated data, which might be biased as the real observation condition could be different from theoretical prediction or simulation. In this paper, we propose a theoretical and empirical integrated method, which first selects the possible optimal combined signals in theory and then refines these signals with real triple-frequency GPS data, observed at eleven baselines of different lengths. An interpolation technique is also adopted in order to show changes of the AR performance with the increase in baseline length. The results show that the AR success rate can be improved by 3% in GF mode and 8% in GB mode at certain intervals of the baseline length. Therefore, the TCAR can perform better by adopting the combined signals proposed in this paper when the baseline meets the length condition. PMID:27854324

  7. A Theoretical and Empirical Integrated Method to Select the Optimal Combined Signals for Geometry-Free and Geometry-Based Three-Carrier Ambiguity Resolution.

    PubMed

    Zhao, Dongsheng; Roberts, Gethin Wyn; Lau, Lawrence; Hancock, Craig M; Bai, Ruibin

    2016-11-16

    Twelve GPS Block IIF satellites, out of the current constellation, can transmit on three-frequency signals (L1, L2, L5). Taking advantages of these signals, Three-Carrier Ambiguity Resolution (TCAR) is expected to bring much benefit for ambiguity resolution. One of the research areas is to find the optimal combined signals for a better ambiguity resolution in geometry-free (GF) and geometry-based (GB) mode. However, the existing researches select the signals through either pure theoretical analysis or testing with simulated data, which might be biased as the real observation condition could be different from theoretical prediction or simulation. In this paper, we propose a theoretical and empirical integrated method, which first selects the possible optimal combined signals in theory and then refines these signals with real triple-frequency GPS data, observed at eleven baselines of different lengths. An interpolation technique is also adopted in order to show changes of the AR performance with the increase in baseline length. The results show that the AR success rate can be improved by 3% in GF mode and 8% in GB mode at certain intervals of the baseline length. Therefore, the TCAR can perform better by adopting the combined signals proposed in this paper when the baseline meets the length condition.

  8. 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.

  9. Selective disruption of the AKAP signaling complexes.

    PubMed

    Kennedy, Eileen J; Scott, John D

    2015-01-01

    Synthesis of the second messenger cAMP activates a variety of signaling pathways critical for all facets of intracellular regulation. Protein kinase A (PKA) is the major cAMP-responsive effector. Where and when this enzyme is activated has profound implications on the cellular role of PKA. A-Kinase Anchoring Proteins (AKAPs) play a critical role in this process by orchestrating spatial and temporal aspects of PKA action. A popular means of evaluating the impact of these anchored signaling events is to biochemically interfere with the PKA-AKAP interface. Hence, peptide disruptors of PKA anchoring are valuable tools in the investigation of local PKA action. This article outlines the development of PKA isoform-selective disruptor peptides, documents the optimization of cell-soluble peptide derivatives, and introduces alternative cell-based approaches that interrogate other aspects of the PKA-AKAP interface.

  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.

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

    SciTech Connect

    Doerr, Christian

    2006-06-23

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

  12. 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.

  13. 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

  14. 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

  15. Selective signalling by cuttlefish to predators.

    PubMed

    Langridge, Keri V; Broom, Mark; Osorio, Daniel

    2007-12-18

    Rather than simply escaping, prey animals often attempt to deter an attack by signalling to an approaching predator, but this is a risky strategy if it allows time for the predator to draw closer (especially when the signal is a bluff). Because prey are vulnerable to multiple predators, the hunting techniques of which vary widely, it could well be beneficial for a prey animal to discriminate predators and to signal only to those that are likely to be deterred. Higher vertebrates make alarm calls that can identify the type of predator to the signaller's conspecifics, and a recent study shows that squirrels direct an infrared deterrent signal specifically at infrared-sensitive pit-vipers and not at other snakes. We show here that naïve juvenile cuttlefish (Sepia officinalis L.) use a visual signal selectively during encounters with different predatory species. We analysed sequences of defensive behaviours produced by cuttlefish, to control for effects of relative threat level (or 'response urgency'). This showed that a high contrast 'eyespot' signal, known as the deimatic display, was used before flight against visually oriented teleost fish, but not crabs and dogfish, which are chemosensory predators.

  16. Optimal Hamiltonian Simulation by Quantum Signal Processing

    NASA Astrophysics Data System (ADS)

    Low, Guang Hao; Chuang, Isaac L.

    2017-01-01

    The physics of quantum mechanics is the inspiration for, and underlies, quantum computation. As such, one expects physical intuition to be highly influential in the understanding and design of many quantum algorithms, particularly simulation of physical systems. Surprisingly, this has been challenging, with current Hamiltonian simulation algorithms remaining abstract and often the result of sophisticated but unintuitive constructions. We contend that physical intuition can lead to optimal simulation methods by showing that a focus on simple single-qubit rotations elegantly furnishes an optimal algorithm for Hamiltonian simulation, a universal problem that encapsulates all the power of quantum computation. Specifically, we show that the query complexity of implementing time evolution by a d -sparse Hamiltonian H ^ for time-interval t with error ɛ is O [t d ∥H ^ ∥max+log (1 /ɛ ) /log log (1 /ɛ ) ] , which matches lower bounds in all parameters. This connection is made through general three-step "quantum signal processing" methodology, comprised of (i) transducing eigenvalues of H ^ into a single ancilla qubit, (ii) transforming these eigenvalues through an optimal-length sequence of single-qubit rotations, and (iii) projecting this ancilla with near unity success probability.

  17. 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

  18. Optimizing Clinical Research Participant Selection with Informatics.

    PubMed

    Weng, Chunhua

    2015-11-01

    Clinical research participants are often not reflective of 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.

  19. 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.

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

    NASA Technical Reports Server (NTRS)

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

    1995-01-01

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

  1. 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.

  2. Active Learning With Optimal Instance Subset Selection.

    PubMed

    Fu, Yifan; Zhu, Xingquan; Elmagarmid, A K

    2013-04-01

    Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assess individual instances and label the ones with the maximum values for training. In this paper, we argue that such approaches cannot produce good labeling subsets mainly because instances are evaluated independently without considering their interactions, and individuals with maximal ability do not necessarily form an optimal instance subset for learning. Alternatively, we propose to achieve AL with optimal subset selection (ALOSS), where the key is to find an instance subset with a maximum utility value. To achieve the goal, ALOSS simultaneously considers the following: 1) the importance of individual instances and 2) the disparity between instances, to build an instance-correlation matrix. As a result, AL is transformed to a semidefinite programming problem to select a k-instance subset with a maximum utility value. Experimental results demonstrate that ALOSS outperforms state-of-the-art approaches for AL.

  3. Optimal signal processing for continuous qubit readout

    NASA Astrophysics Data System (ADS)

    Ng, Shilin; Tsang, Mankei

    2014-08-01

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

  4. 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.

  5. Optimal Sensor Selection for Health Monitoring Systems

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  6. Selected Isotopes for Optimized Fuel Assembly Tags

    SciTech Connect

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

    2008-10-01

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

  7. 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

  8. A nonparametric stochastic optimizer for TDMA-based neuronal signaling.

    PubMed

    Suzuki, Junichi; Phan, Dũng H; Budiman, Harry

    2014-09-01

    This paper considers neurons as a physical communication medium for intrabody networks of nano/micro-scale machines and formulates a noisy multiobjective optimization problem for a Time Division Multiple Access (TDMA) communication protocol atop the physical layer. The problem is to find the Pareto-optimal TDMA configurations that maximize communication performance (e.g., latency) by multiplexing a given neuronal network to parallelize signal transmissions while maximizing communication robustness (i.e., unlikeliness of signal interference) against noise in neuronal signaling. Using a nonparametric significance test, the proposed stochastic optimizer is designed to statistically determine the superior-inferior relationship between given two solution candidates and seek the optimal trade-offs among communication performance and robustness objectives. Simulation results show that the proposed optimizer efficiently obtains quality TDMA configurations in noisy environments and outperforms existing noise-aware stochastic optimizers.

  9. 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.

  10. 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.

  11. Optimal test selection for prediction uncertainty reduction

    DOE PAGES

    Mullins, Joshua; Mahadevan, Sankaran; Urbina, Angel

    2016-12-02

    Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecisemore » data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.« less

  12. Optimal test selection for prediction uncertainty reduction

    SciTech Connect

    Mullins, Joshua; Mahadevan, Sankaran; Urbina, Angel

    2016-12-02

    Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecise data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.

  13. Selectivity in neurotrophin signaling: theme and variations.

    PubMed

    Segal, Rosalind A

    2003-01-01

    Neurotrophins are a family of growth factors critical for the development and functioning of the nervous system. Although originally identified as neuronal survival factors, neurotrophins elicit many biological effects, ranging from proliferation to synaptic modulation to axonal pathfinding. Recent data indicate that the nature of the signaling cascades activated by neurotrophins, and the biological responses that ensue, are specified not only by the ligand itself but also by the temporal pattern and spatial location of stimulation. Studies on neurotrophin signaling have revealed variations in the Ras/MAP kinase, PI3 kinase, and phospholipase C pathways, which transmit spatial and temporal information. The anatomy of neurons makes them particularly appropriate for studying how the location and tempo of stimulation determine the signal cascades that are activated by receptor tyrosine kinases such as the Trk receptors. These signaling variations may represent a general mechanism eliciting specificity in growth factor responses.

  14. Optimal selection of biochars for remediating metals ...

    EPA Pesticide Factsheets

    Approximately 500,000 abandoned mines across the U.S. pose a considerable, pervasive risk to human health and the environment due to possible exposure to the residuals of heavy metal extraction. Historically, a variety of chemical and biological methods have been used to reduce the bioavailability of the metals at mine sites. Biochar with its potential to complex and immobilize heavy metals, is an emerging alternative for reducing bioavailability. Furthermore, biochar has been reported to improve soil conditions for plant growth and can be used for promoting the establishment of a soil-stabilizing native plant community to reduce offsite movement of metal-laden waste materials. Because biochar properties depend upon feedstock selection, pyrolysis production conditions, and activation procedures used, they can be designed to meet specific remediation needs. As a result biochar with specific properties can be produced to correspond to specific soil remediation situations. However, techniques are needed to optimally match biochar characteristics with metals contaminated soils to effectively reduce metal bioavailability. Here we present experimental results used to develop a generalized method for evaluating the ability of biochar to reduce metals in mine spoil soil from an abandoned Cu and Zn mine. Thirty-eight biochars were produced from approximately 20 different feedstocks and produced via slow pyrolysis or gasification, and were allowed to react with a f

  15. 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.

  16. 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.

  17. 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.

  18. MIMO-OFDM signal optimization for SAR imaging radar

    NASA Astrophysics Data System (ADS)

    Baudais, J.-Y.; Méric, S.; Riché, V.; Pottier, É.

    2016-12-01

    This paper investigates the optimization of the coded orthogonal frequency division multiplexing (OFDM) transmitted signal in a synthetic aperture radar (SAR) context. We propose to design OFDM signals to achieve range ambiguity mitigation. Indeed, range ambiguities are well known to be a limitation for SAR systems which operates with pulsed transmitted signal. The ambiguous reflected signal corresponding to one pulse is then detected when the radar has already transmitted the next pulse. In this paper, we demonstrate that the range ambiguity mitigation is possible by using orthogonal transmitted wave as OFDM pulses. The coded OFDM signal is optimized through genetic optimization procedures based on radar image quality parameters. Moreover, we propose to design a multiple-input multiple-output (MIMO) configuration to enhance the noise robustness of a radar system and this configuration is mainly efficient in the case of using orthogonal waves as OFDM pulses. The results we obtain show that OFDM signals outperform conventional radar chirps for range ambiguity suppression and for robustness enhancement in 2 ×2 MIMO configuration.

  19. Optimal wavelength selection for noncontact reflection photoplethysmography

    NASA Astrophysics Data System (ADS)

    Corral Martinez, Luis F.; Paez, Gonzalo; Strojnik, Marija

    2011-08-01

    In this work, we obtain backscattered signals from human forehead for wavelengths from 380 to 980 nm. The results reveal bands with strong pulsatile signals that carry useful information. We describe those bands as the most suitable wavelengths in the visible and NIR regions from which heart and respiratory rate parameters can be derived using long distance non-contact reflection photoplethysmography analysis. The latter results show the feasibility of a novel technique for remotely detection of vital signs in humans. This technique, which may include morphological analysis or maps of tissue oxygenation, is a further step to real non-invasive remote monitoring of patients.

  20. 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

  1. Optimization of a crossing system using mate selection

    PubMed Central

    Li, Yongjun; Werf, Julius HJ van der; Kinghorn, Brian P

    2006-01-01

    A simple model based on one single identified quantitative trait locus (QTL) in a two-way crossing system was used to demonstrate the power of mate selection algorithms as a natural means of opportunistic line development for optimization of crossbreeding programs over multiple generations. Mate selection automatically invokes divergent selection in two parental lines for an over-dominant QTL and increased frequency of the favorable allele toward fixation in the sire-line for a fully-dominant QTL. It was concluded that an optimal strategy of line development could be found by mate selection algorithms for a given set of parameters such as genetic model of QTL, breeding objective and initial frequency of the favorable allele in the base populations, etc. The same framework could be used in other scenarios, such as programs involving crossing to exploit breed effects and heterosis. In contrast to classical index selection, this approach to mate selection can optimize long-term responses. PMID:16492372

  2. Optimization of a crossing system using mate selection.

    PubMed

    Li, Yongjun; van der Werf, Julius H J; Kinghorn, Brian P

    2006-01-01

    A simple model based on one single identified quantitative trait locus (QTL) in a two-way crossing system was used to demonstrate the power of mate selection algorithms as a natural means of opportunistic line development for optimization of crossbreeding programs over multiple generations. Mate selection automatically invokes divergent selection in two parental lines for an over-dominant QTL and increased frequency of the favorable allele toward fixation in the sire-line for a fully-dominant QTL. It was concluded that an optimal strategy of line development could be found by mate selection algorithms for a given set of parameters such as genetic model of QTL, breeding objective and initial frequency of the favorable allele in the base populations, etc. The same framework could be used in other scenarios, such as programs involving crossing to exploit breed effects and heterosis. In contrast to classical index selection, this approach to mate selection can optimize long-term responses.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. A Tightly Regulated Genetic Selection System with Signaling-Active Alleles of Phytochrome B.

    PubMed

    Hu, Wei; Lagarias, J Clark

    2017-01-01

    Selectable markers derived from plant genes circumvent the potential risk of antibiotic/herbicide-resistance gene transfer into neighboring plant species, endophytic bacteria, and mycorrhizal fungi. Toward this goal, we have engineered and validated signaling-active alleles of phytochrome B (eYHB) as plant-derived selection marker genes in the model plant Arabidopsis (Arabidopsis thaliana). By probing the relationship of construct size and induction conditions to optimal phenotypic selection, we show that eYHB-based alleles are robust substitutes for antibiotic/herbicide-dependent marker genes as well as surprisingly sensitive reporters of off-target transgene expression.

  9. A Tightly Regulated Genetic Selection System with Signaling-Active Alleles of Phytochrome B1[OPEN

    PubMed Central

    2017-01-01

    Selectable markers derived from plant genes circumvent the potential risk of antibiotic/herbicide-resistance gene transfer into neighboring plant species, endophytic bacteria, and mycorrhizal fungi. Toward this goal, we have engineered and validated signaling-active alleles of phytochrome B (eYHB) as plant-derived selection marker genes in the model plant Arabidopsis (Arabidopsis thaliana). By probing the relationship of construct size and induction conditions to optimal phenotypic selection, we show that eYHB-based alleles are robust substitutes for antibiotic/herbicide-dependent marker genes as well as surprisingly sensitive reporters of off-target transgene expression. PMID:27881727

  10. 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.

  11. Approaches to Assess Functional Selectivity in GPCRs: Evaluating G Protein Signaling in an Endogenous Environment

    PubMed Central

    Bohn, Laura M.; Zhou, Lei; Ho, Jo-Hao

    2016-01-01

    Ligand-directed signaling, biased agonism, and functional selectivity are terms that describe the propensity of a ligand to drive signaling toward one GPCR pathway over another. Most of the early examples demonstrated to date examine the divergence between GPCR signaling to G protein coupling and βarrestin2 recruitment. As biased agonists begin to become available based on cell-based screening criteria, a need arises to determine if G protein signaling biases will be maintained in the endogenous setting, wherein receptors are functioning to control relevant biological responses. This report presents our method and offers tips for evaluating G protein signaling in endogenous tissues. Predominately, brain tissues are discussed here; optimization points that can be applied to any tissues are highlighted. PMID:26260601

  12. Selection of Structures with Grid Optimization, in Multiagent Data Warehouse

    NASA Astrophysics Data System (ADS)

    Gorawski, Marcin; Bańkowski, Sławomir; Gorawski, Michał

    The query optimization problem in data base and data warehouse management systems is quite similar. Changes to Joins sequences, projections and selections, usage of indexes, and aggregations are all decided during the analysis of an execution schedule. The main goal of these changes is to decrease the query response time. The optimization operation is often dedicated to a single node. This paper proposes optimization to grid or cluster data warehouses / databases. Tests were conducted in a multi-agent environment, and the optimization focused not only on a single node but on the whole system as well. A new idea is proposed here with multi-criteria optimization that is based on user-given parameters. Depending on query time, result admissible errors, and the level of system usage, task results were obtained along with grid optimization.

  13. Optimal ROS Signaling Is Critical for Nuclear Reprogramming.

    PubMed

    Zhou, Gang; Meng, Shu; Li, Yanhui; Ghebre, Yohannes T; Cooke, John P

    2016-05-03

    Efficient nuclear reprogramming of somatic cells to pluripotency requires activation of innate immunity. Because innate immune activation triggers reactive oxygen species (ROS) signaling, we sought to determine whether there was a role of ROS signaling in nuclear reprogramming. We examined ROS production during the reprogramming of doxycycline (dox)-inducible mouse embryonic fibroblasts (MEFs) carrying the Yamanaka factors (Oct4, Sox2, Klf4, and c-Myc [OSKM]) into induced pluripotent stem cells (iPSCs). ROS generation was substantially increased with the onset of reprogramming. Depletion of ROS via antioxidants or Nox inhibitors substantially decreased reprogramming efficiency. Similarly, both knockdown and knockout of p22(phox)-a critical subunit of the Nox (1-4) complex-decreased reprogramming efficiency. However, excessive ROS generation using genetic and pharmacological approaches also impaired reprogramming. Overall, our data indicate that ROS signaling is activated early with nuclear reprogramming, and optimal levels of ROS signaling are essential to induce pluripotency.

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

    PubMed

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

    2015-08-01

    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.

  15. 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

  16. Fast Simulation and Optimization Tool to Explore Selective Neural Stimulation.

    PubMed

    Dali, Mélissa; Rossel, Olivier; Guiraud, David

    2016-06-13

    In functional electrical stimulation, selective stimulation of axons is desirable to activate a specific target, in particular muscular function. This implies to simulate a fascicule without activating neighboring ones i.e. to be spatially selective. Spatial selectivity is achieved by the use of multicontact cuff electrodes over which the stimulation current is distributed. Because of the large number of parameters involved, numerical simulations provide a way to find and optimize electrode configuration. The present work offers a computation effective scheme and associated tool chain capable of simulating electrode-nerve interface and find the best spread of current to achieve spatial selectivity.

  17. Fast Simulation and Optimization Tool to Explore Selective Neural Stimulation

    PubMed Central

    Dali, Mélissa; Rossel, Olivier; Guiraud, David

    2016-01-01

    In functional electrical stimulation, selective stimulation of axons is desirable to activate a specific target, in particular muscular function. This implies to simulate a fascicule without activating neighboring ones i.e. to be spatially selective. Spatial selectivity is achieved by the use of multicontact cuff electrodes over which the stimulation current is distributed. Because of the large number of parameters involved, numerical simulations provide a way to find and optimize electrode configuration. The present work offers a computation effective scheme and associated tool chain capable of simulating electrode-nerve interface and find the best spread of current to achieve spatial selectivity. PMID:27990231

  18. Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

    PubMed

    Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

    2015-04-01

    Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained.

  19. Low-power slice selective imaging of broad signals

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

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

  20. Low-power slice selective imaging of broad signals.

    PubMed

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

    2016-11-01

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

  1. Natural selection on social signals: signal efficacy and the evolution of chameleon display coloration.

    PubMed

    Stuart-Fox, Devi; Moussalli, Adnan; Whiting, Martin J

    2007-12-01

    Whether general patterns of signal evolution can be explained by selection for signal efficacy (detectability) has yet to be established. To establish the importance of signal efficacy requires evidence that both signals and their detectability to receivers have evolved in response to habitat shifts in a predictable fashion. Here, we test whether habitat structure has predictable effects on the evolution of male and female display coloration in 21 lineages of African dwarf chameleon (Bradypodion), based on a phylogenetic comparative analysis. We used quantitative measures of display coloration and estimated signal detectability as the contrast of those colors among body regions or against the background vegetation as perceived by the chameleon visual system. Both male and female display colors varied predictably with different aspects of habitat structure. In several (but not all) instances, habitat-associated shifts in display coloration resulted in habitat-associated variation in detectability. While males exhibit a remarkable variety of colors and patterns, female display coloration is highly conserved, consisting in all populations of contrasting dark and light elements. This color pattern may maximize detectability across all habitat types, potentially explaining female conservatism. Overall, our results support the view that selection for signal efficacy plays an important role in the evolution of animal signals.

  2. 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

  3. 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.

  4. Simple signaling games of sexual selection (Grafen's revisited).

    PubMed

    Bernhard, Pierre; Hamelin, Frédéric M

    2014-12-01

    We investigate several versions of a simple game of sexual selection, to explore the role of secondary sexual characters (the "handicap paradox") with the tools of signaling theory. Our models admit closed form solutions. They are very much inspired by Grafen's (J Theor Biol 144:517-546, 1990a; J Theor Biol 144:473-516, 1990b) seminal companion papers. By merging and simplifying his two approaches, we identify a not so minor artifact in the seminal study. We propose an alternative model to start with Grafen's sexual selection theory, with several similarities with Getty (Anim Behav 56:127-130, 1998).

  5. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

    PubMed

    Subasi, Abdulhamit

    2013-06-01

    Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders.

  6. 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…

  7. 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...

  8. 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)

  9. Optimal selection of nodes to propagate influence on networks

    NASA Astrophysics Data System (ADS)

    Sun, Yifan

    2016-11-01

    How to optimize the spreading process on networks has been a hot issue in complex networks, marketing, epidemiology, finance, etc. In this paper, we investigate a problem of optimizing locally the spreading: identifying a fixed number of nodes as seeds which would maximize the propagation of influence to their direct neighbors. All the nodes except the selected seeds are assumed not to spread their influence to their neighbors. This problem can be mapped onto a spin glass model with a fixed magnetization. We provide a message-passing algorithm based on replica symmetrical mean-field theory in statistical physics, which can find the nearly optimal set of seeds. Extensive numerical results on computer-generated random networks and real-world networks demonstrate that this algorithm has a better performance than several other optimization algorithms.

  10. Efficient and scalable Pareto optimization by evolutionary local selection algorithms.

    PubMed

    Menczer, F; Degeratu, M; Street, W N

    2000-01-01

    Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm.

  11. 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

  12. Optimizing SNR for indoor visible light communication via selecting communicating LEDs

    NASA Astrophysics Data System (ADS)

    Wang, Lang; Wang, Chunyue; Chi, Xuefen; Zhao, Linlin; Dong, Xiaoli

    2017-03-01

    In this paper, we investigate the layout of LED to optimize SNR by selecting communicating LEDs (C-LEDs) in indoor visible light communication (VLC) system. Due to the inter-symbol interference (ISI) caused by the different arrival time of different optical rays, the SNR for any user is not optimal if a simple layout is adopted. It is interesting to investigate the LEDs layout for achieving optimal SNR in indoor VLC system. For a single user, LED signal is divided into the positive and negative components, they provide the power of desired signal and the power of ISI respectively. We introduce the concept of valid ratio (VR) which refers to the value of positive component over the negative component. Then we propose a VR threshold-based LED selection scheme which chooses C-LEDs by their VRs. For downlink broadcast VLC with multiple users, the SNRs of all users are different in a layout of C-LEDs. It is difficult to find a proper layout of C-LEDs to guarantee the BER of all users. To solve this problem, we propose an evolutionary algorithm (EA)-based scheme to optimize the SNR. The simulation results show that it is an effective method to improve SNR by selecting C-LEDs.

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

    PubMed Central

    Christensen-Dalsgaard, Jakob; Kelley, Darcy B.

    2011-01-01

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

  14. Constrained nonlinear optimization approaches to color-signal separation.

    PubMed

    Chang, P R; Hsieh, T H

    1995-01-01

    Separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This can be carried out by minimizing the error in the least squares (LS) fit of the product of the illumination and the surface spectral reflectance to the actual color signal. When taking in account the physical realizability constraints on the surface reflectance and illumination, the feasible solutions to the nonlinear LS problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear LS fitting error. The first approach, which is based on Ritter's superlinear convergent method (Luengerger, 1980), provides a computationally superior algorithm to find the minimum solution to the nonlinear LS error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is slow. To tackle this, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra (1973) is adopted to reduce the nonlinear LS problem to be a small-scale nonlinear LS problem. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution.

  15. Optimal Signal Processing in Small Stochastic Biochemical Networks

    PubMed Central

    Ziv, Etay; Nemenman, Ilya; Wiggins, Chris H.

    2007-01-01

    We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the “cross-talk” phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive. PMID:17957259

  16. 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%).

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

    PubMed Central

    2015-01-01

    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

  18. Optimized LOWESS normalization parameter selection for DNA microarray data

    PubMed Central

    Berger, John A; Hautaniemi, Sampsa; Järvinen, Anna-Kaarina; Edgren, Henrik; Mitra, Sanjit K; Astola, Jaakko

    2004-01-01

    Background Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail. Results and discussion In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that represents the mean-squared difference between the LOWESS estimates and the normalization reference level. We demonstrate the utility of the systematic parameter selection using two publicly available data sets. The first data set consists of three self versus self hybridizations, which allow for a quantitative study of the optimization method. The second data set contains a collection of DNA microarray data from a breast cancer study utilizing four breast cancer cell lines. Our results show that different parameter choices for the bandwidth window yield dramatically different calibration results in both studies. Conclusions Results derived from the self versus self experiment indicate that the proposed optimization approach is a plausible solution for estimating the LOWESS parameters, while results from the breast cancer experiment show that the optimization procedure is readily applicable to real-life microarray data normalization. In summary, the systematic approach to obtain critical

  19. Dopamine signaling tunes spatial pattern selectivity in C. elegans

    PubMed Central

    Han, Bicheng; Dong, Yongming; Zhang, Lin; Liu, Yan; Rabinowitch, Ithai; Bai, Jihong

    2017-01-01

    Animals with complex brains can discriminate the spatial arrangement of physical features in the environment. It is unknown whether such sensitivity to spatial patterns can be accomplished in simpler nervous systems that lack long-range sensory modalities such as vision and hearing. Here we show that the nematode Caenorhabditis elegans can discriminate spatial patterns in its surroundings, despite having a nervous system of only 302 neurons. This spatial pattern selectivity requires touch-dependent dopamine signaling, including the mechanosensory TRP-4 channel in dopaminergic neurons and the D2-like dopamine receptor DOP-3. We find that spatial pattern selectivity varies significantly among C. elegans wild isolates. Electrophysiological recordings show that natural variations in TRP-4 reduce the mechanosensitivity of dopaminergic neurons. Polymorphic substitutions in either TRP-4 or DOP-3 alter the selectivity of spatial patterns. Together, these results demonstrate an ancestral role for dopamine signaling in tuning spatial pattern preferences in a simple nervous system. DOI: http://dx.doi.org/10.7554/eLife.22896.001 PMID:28349862

  20. 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

  1. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

    PubMed

    Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

    2017-01-01

    Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

  2. Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

    PubMed Central

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

    2015-01-01

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

  3. Optimization of the selective frequency damping parameters using model reduction

    NASA Astrophysics Data System (ADS)

    Cunha, Guilherme; Passaggia, Pierre-Yves; Lazareff, Marc

    2015-09-01

    In the present work, an optimization methodology to compute the best control parameters, χ and Δ, for the selective frequency damping method is presented. The optimization does not suppose any a priori knowledge of the flow physics, neither of the underlying numerical methods, and is especially suited for simulations requiring large quantity of grid elements and processors. It allows for obtaining an optimal convergence rate to a steady state of the damped Navier-Stokes system. This is achieved using the Dynamic Mode Decomposition, which is a snapshot-based method, to estimate the eigenvalues associated with global unstable dynamics. Validations test cases are presented for the numerical configurations of a laminar flow past a 2D cylinder, a separated boundary-layer over a shallow bump, and a 3D turbulent stratified-Poiseuille flow.

  4. Bayesian analysis. II. Signal detection and model selection

    NASA Astrophysics Data System (ADS)

    Bretthorst, G. Larry

    In the preceding. paper, Bayesian analysis was applied to the parameter estimation problem, given quadrature NMR data. Here Bayesian analysis is extended to the problem of selecting the model which is most probable in view of the data and all the prior information. In addition to the analytic calculation, two examples are given. The first example demonstrates how to use Bayesian probability theory to detect small signals in noise. The second example uses Bayesian probability theory to compute the probability of the number of decaying exponentials in simulated T1 data. The Bayesian answer to this question is essentially a microcosm of the scientific method and a quantitative statement of Ockham's razor: theorize about possible models, compare these to experiment, and select the simplest model that "best" fits the data.

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

    SciTech Connect

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

    1998-11-01

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

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

    PubMed

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

    2015-09-08

    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.

  7. [Structural Life Science towards the Regulation of Selective GPCR Signaling].

    PubMed

    Kobayashi, Takuya

    2016-01-01

    G protein-coupled receptors (GPCRs) are the largest family of receptors in the human genome. They are involved in many diseases and also the target of approximately 30% of all modern medicinal drugs. GPCRs respond to a broad spectrum of chemical entities, ranging from photons, protons, and calcium ions to small organic molecules (including odorants and neurotransmitters), peptides, and glycoproteins. Many GPCRs are members of closely related subfamilies that respond to the same hormone or neurotransmitter. However, they have different physiologic functions based on the cells in which they are expressed and the different signaling pathways that they exploit (e.g., coupling through heterotrimeric G-proteins such as Gs, Gi, and Gq, as well as β-arrestins). Antibody fragments including Fab and Fv can effectively stabilize and crystallize membrane proteins. However, using the mouse hybridoma technology it has been difficult to develop monoclonal antibodies that can recognize conformational epitopes of native GPCRs. We have recently succeeded in developing antibodies against native GPCRs using this technology in combination with our improved immunization and screening methods. In this symposium review, I present a successful example of prostaglandin E2 receptor (one of the GPCRs) crystallization using antibody fragments. To avoid several adverse effects of current therapeutics, it is essential to understand the molecular mechanism of GPCR signaling in a monomeric, dimeric, or oligomeric state. Also, we are interested in selectively regulating GPCR signaling via functional antibodies developed using our methods and/or the designed small organic molecules depending on the GPCR structure.

  8. Optimization of topical gels with betamethasone dipropionate: selection of gel forming and optimal cosolvent system.

    PubMed

    Băiţan, Mariana; Lionte, Mihaela; Moisuc, Lăcrămioara; Gafiţanu, Eliza

    2011-01-01

    The purpose of these studies was to develop a 0.05% betamethasone gel characterized by physical-chemical stability and good release properties. The preliminary studies were designed to select the gel-forming agents and the excipients compatible with betamethasone dipropionate. In order to formulate a clear gel without particles of drug substances in suspension, a solvent system for the drug substance was selected. The content of drug substance released, the rheological and in vitro release tests were the tools used for the optimal formulation selection. A stable carbomer gel was obtained by solubilization of betamethasone dipropionate in a vehicle composed by 40% PEG 400, 10% ethanol and 5% Transcutol.

  9. An R-D optimized transcoding resilient motion vector selection

    NASA Astrophysics Data System (ADS)

    Aminlou, Alireza; Semsarzadeh, Mehdi; Fatemi, Omid

    2014-12-01

    Selection of motion vector (MV) has a significant impact on the quality of an encoded, and particularly a transcoded video, in terms of rate-distortion (R-D) performance. The conventional motion estimation process, in most existing video encoders, ignores the rate of residuals by utilizing rate and distortion of motion compensation step. This approach implies that the selected MV depends on the quantization parameter. Hence, the same MV that has been selected for high bit rate compression may not be suitable for low bit rate ones when transcoding the video with motion information reuse technique, resulting in R-D performance degradation. In this paper, we propose an R-D optimized motion selection criterion that takes into account the effect of residual rate in MV selection process. Based on the proposed criterion, a new two-piece Lagrange multiplier selection is introduced for motion estimation process. Analytical evaluations indicate that our proposed scheme results in MVs that are less sensitive to changes in bit rate or quantization parameter. As a result, MVs in the encoded bitstream may be used even after the encoded sequence has been transcoded to a lower bit rate one using re-quantization. Simulation results indicate that the proposed technique improves the quality performance of coding and transcoding without any computational overhead.

  10. 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.

  11. 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.

  12. 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.

  13. Brachytherapy for clinically localized prostate cancer: optimal patient selection.

    PubMed

    Kollmeier, Marisa A; Zelefsky, Michael J

    2011-10-01

    The objective of this review is to present an overview of each modality and delineate how to best select patients who are optimal candidates for these treatment approaches. Prostate brachytherapy as a curative modality for clinically localized prostate cancer has become increasingly utilized over the past decade; 25% of all early cancers are now treated this way in the United States (1). The popularity of this treatment strategy lies in the highly conformal nature of radiation dose, low morbidity, patient convenience, and high efficacy rates. Prostate brachytherapy can be delivered by either a permanent interstitial radioactive seed implantation (low dose rate [LDR]) or a temporary interstitial insertion of iridium-192 (Ir192) afterloading catheters. The objective of both of these techniques is to deliver a high dose of radiation to the prostate gland while exposing normal surrounding tissues to minimal radiation dose. Brachytherapy techniques are ideal to achieve this goal given the close proximity of the radiation source to tumor and sharp fall off of the radiation dose cloud proximate to the source. Brachytherapy provides a powerful means of delivering dose escalation above and beyond that achievable with intensity-modulated external beam radiotherapy alone. Careful selection of appropriate patients for these therapies, however, is critical for optimizing both disease-related outcomes and treatment-related toxicity.

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

    DOE PAGES

    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

  15. 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.

  16. 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%.

  17. 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'.

  18. Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.

    PubMed

    Bollinger, James G; Stergachis, Andrew B; Johnson, Richard S; Egertson, Jarrett D; MacCoss, Michael J

    2016-01-01

    In targeted proteomics, the development of robust methodologies is dependent upon the selection of a set of optimal peptides for each protein-of-interest. Unfortunately, predicting which peptides and respective product ion transitions provide the greatest signal-to-noise ratio in a particular assay matrix is complicated. Using in vitro synthesized proteins as analytical standards, we report here an empirically driven method for the selection of said peptides in a human plasma assay matrix.

  19. Ant colony optimization with selective evaluation for feature selection in character recognition

    NASA Astrophysics Data System (ADS)

    Oh, Il-Seok; Lee, Jin-Seon

    2010-01-01

    This paper analyzes the size characteristics of character recognition domain with the aim of developing a feature selection algorithm adequate for the domain. Based on the results, we further analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. We propose a novel scheme called selective evaluation to improve convergence of ACO. The scheme cut down the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.

  20. Optimal sinusoidal modelling of gear mesh vibration signals for gear diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Man, Zhihong; Wang, Wenyi; Khoo, Suiyang; Yin, Juliang

    2012-11-01

    In this paper, the synchronous signal average of gear mesh vibration signals is modelled with the multiple modulated sinusoidal representations. The signal model parameters are optimised against the measured signal averages by using the batch learning of the least squares technique. With the optimal signal model, all components of a gear mesh vibration signal, including the amplitude modulations, the phase modulations and the impulse vibration component induced by gear tooth cracking, are identified and analysed with insight of the gear tooth crack development and propagation. In particular, the energy distribution of the impulse vibration signal, extracted from the optimal signal model, provides sufficient information for monitoring and diagnosing the evolution of the tooth cracking process, leading to the prognosis of gear tooth cracking. The new methodologies for gear mesh signal modelling and the diagnosis of the gear tooth fault development and propagation are validated with a set of rig test data, which has shown excellent performance.

  1. Discovery, Optimization, and Characterization of Novel D2 Dopamine Receptor Selective Antagonists

    PubMed Central

    2015-01-01

    The D2 dopamine receptor (D2 DAR) is one of the most validated drug targets for neuropsychiatric and endocrine disorders. However, clinically approved drugs targeting D2 DAR display poor selectivity between the D2 and other receptors, especially the D3 DAR. This lack of selectivity may lead to undesirable side effects. Here we describe the chemical and pharmacological characterization of a novel D2 DAR antagonist series with excellent D2 versus D1, D3, D4, and D5 receptor selectivity. The final probe 65 was obtained through a quantitative high-throughput screening campaign, followed by medicinal chemistry optimization, to yield a selective molecule with good in vitro physical properties, metabolic stability, and in vivo pharmacokinetics. The optimized molecule may be a useful in vivo probe for studying D2 DAR signal modulation and could also serve as a lead compound for the development of D2 DAR-selective druglike molecules for the treatment of multiple neuropsychiatric and endocrine disorders. PMID:24666157

  2. Real-Time Traffic Signal Control for Optimization of Traffic Jam Probability

    NASA Astrophysics Data System (ADS)

    Cui, Cheng-You; Shin, Ji-Sun; Miyazaki, Michio; Lee, Hee-Hyol

    Real-time traffic signal control is an integral part of urban traffic control system. It can control traffic signals online according to variation of traffic flow. In this paper, we propose a new method for the real-time traffic signal control system. The system uses a Cellular Automaton model and a Bayesian Network model to predict probabilistic distributions of standing vehicles, and uses a Particle Swarm Optimization method to calculate optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed real-time traffic signal control system CAPSOBN using a micro traffic simulator.

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

    SciTech Connect

    Kudaka, Shoju; Matsumoto, Shuichi

    2007-07-15

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

  4. Optimal experiment design for model selection in biochemical networks

    PubMed Central

    2014-01-01

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

  5. 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.

  6. Optimal band selection for dimensionality reduction of hyperspectral imagery

    NASA Technical Reports Server (NTRS)

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

    1993-01-01

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

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

    PubMed

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

    2016-03-18

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

  8. Spatial filter and feature selection optimization based on EA for multi-channel EEG.

    PubMed

    Wang, Yubo; Mohanarangam, Krithikaa; Mallipeddi, Rammohan; Veluvolu, K C

    2015-01-01

    The EEG signals employed for BCI systems are generally band-limited. The band-limited multiple Fourier linear combiner (BMFLC) with Kalman filter was developed to obtain amplitude estimates of the EEG signal in a pre-fixed frequency band in real-time. However, the high-dimensionality of the feature vector caused by the application of BMFLC to multi-channel EEG based BCI deteriorates the performance of the classifier. In this work, we apply evolutionary algorithm (EA) to tackle this problem. The real-valued EA encodes both the spatial filter and the feature selection into its solution and optimizes it with respect to the classification error. Three BMFLC based BCI configurations are proposed. Our results show that the BMFLC-KF with covariance matrix adaptation evolution strategy (CMAES) has the best overall performance.

  9. Adaptive Signal Detection for the Optimal Communications Receiver,

    DTIC Science & Technology

    1983-06-01

    atmospheric noise are considered. Since the liklihood ratio test nn which thp thpnrv ic: h~czaa i - DD ,*A 3 1473 EDITION OF INOV GS IS OBSOLESTE...transmitter and receiver at opposite ends of an additive noise channel can be improved (1) by increasing the ratio of signal power to noise power , (2...by changing the form of the signal while holding power constant, or (3) by designing better noise immunity into the receiver. This publication

  10. Chromogenic and fluorogenic signaling of sulfite by selective deprotection of resorufin levulinate.

    PubMed

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

    2010-12-17

    A new sulfite-selective probe system based on resorufin was investigated. Levulinate of resorufin exhibited a prominent chromogenic and turn-on type fluorogenic signaling toward sulfite ions in aqueous media based on the selective deprotection of the levulinate group. The sulfite-selective signaling was possible in the presence of commonly encountered anions.

  11. Optimization of killer assays for yeast selection protocols.

    PubMed

    Lopes, C A; Sangorrín, M P

    2010-01-01

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

  12. Optimized bioregenerative space diet selection with crew choice.

    PubMed

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

    2003-01-01

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

  13. 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.

  14. Optimal Methods for Classification of Digitally Modulated Signals

    DTIC Science & Technology

    2013-03-01

    was developed for classifying MPSK and QAM . Future work will refine these two proposed concepts. Finally, this research explored MIMO codes design...24 3.4.3 QAM Case...The focus will be mainly in types such as QAM , MPSK and BPSK Spread Spectrum. The algorithms must have medium complexity and provide some optimal

  15. 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.

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

    PubMed

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

    2011-05-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

    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

  19. 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…

  20. 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

    ... 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 the... and productive scientific enterprise that utilizes biological select agents and toxins (BSAT)...

  1. Genetic Algorithms Evolve Optimized Transforms for Signal Processing Applications

    DTIC Science & Technology

    2005-04-01

    Systems and Filter Banks , Prentice-Hall. Venkatachalam, V. and J. Aravena 1998. Detecting Periodic Behavior in Nonstationary Signals, IEEE-SP...Square Adaptive Filters , John Wiley. Holland, J. 1975. Adaptation in Natural and Artificial Systems , University of Michigan Press. Jones, E., P...Nonlinear Sciences, ISBN 0-620-23629-9. Odegard, J., R. Gopinath, and C. Burrus 1994. Design of Linear Phase Cosine Modulated Filter Banks for

  2. Coupling Condition In A Hololens - Optical Fiber System : Output Signal Optimization

    NASA Astrophysics Data System (ADS)

    Calvo, M. L.; De Pedraza, L.

    1988-04-01

    Based upon the scalar diffraction theory we have derived a very simple condition to control the optimization in the coupling phenomenon in a holocoupler - optical fiber system. A systematic numerical procedure allows a scanning simulation at the output plane of the system. The influence of the physical optimization parameters can be easily obtained giving an interesting criterium for the optimization of the output signal in a suitable experimental set up.

  3. Uncovering noisy social signals: Using optimization methods from experimental physics to study social phenomena

    PubMed Central

    2017-01-01

    Due to the ubiquitous presence of treatment heterogeneity, measurement error, and contextual confounders, numerous social phenomena are hard to study. Precise control of treatment variables and possible confounders is often key to the success of studies in the social sciences, yet often proves out of the realm of control of the experimenter. To amend this situation we propose a novel approach coined “lock-in feedback” which is based on a method that is routinely used in high-precision physics experiments to extract small signals out of a noisy environment. Here, we adapt the method to noisy social signals in multiple dimensions and evaluate it by studying an inherently noisy topic: the perception of (subjective) beauty. We show that the lock-in feedback approach allows one to select optimal treatment levels despite the presence of considerable noise. Furthermore, through the introduction of an external contextual shock we demonstrate that we can find relationships between noisy variables that were hitherto unknown. We therefore argue that lock-in methods may provide a valuable addition to the social scientist’s experimental toolbox and we explicitly discuss a number of future applications. PMID:28306728

  4. Uncovering noisy social signals: Using optimization methods from experimental physics to study social phenomena.

    PubMed

    Kaptein, Maurits; van Emden, Robin; Iannuzzi, Davide

    2017-01-01

    Due to the ubiquitous presence of treatment heterogeneity, measurement error, and contextual confounders, numerous social phenomena are hard to study. Precise control of treatment variables and possible confounders is often key to the success of studies in the social sciences, yet often proves out of the realm of control of the experimenter. To amend this situation we propose a novel approach coined "lock-in feedback" which is based on a method that is routinely used in high-precision physics experiments to extract small signals out of a noisy environment. Here, we adapt the method to noisy social signals in multiple dimensions and evaluate it by studying an inherently noisy topic: the perception of (subjective) beauty. We show that the lock-in feedback approach allows one to select optimal treatment levels despite the presence of considerable noise. Furthermore, through the introduction of an external contextual shock we demonstrate that we can find relationships between noisy variables that were hitherto unknown. We therefore argue that lock-in methods may provide a valuable addition to the social scientist's experimental toolbox and we explicitly discuss a number of future applications.

  5. [Optimized protocols for interphase FISH analysis of imprints and sections using split signal probes].

    PubMed

    Pelluard-Nehme, F; Dupont, T; Turmo, M; Merlio, J-P; Belaud-Rotureau, M-A

    2007-03-01

    Fluorescent in situ hybridization (FISH) analysis is a molecular technique allowing the detection of recurrent translocations in cancer. Several hybridization protocols were assayed in order to evaluate their performances for interphase FISH analysis of histological sections and imprints using split probes. Adult and foetal lymphoid tissues were selected. Touch imprints of fresh (EF) or frozen (EC) tissues, sections (CF) and isolated nuclei (NI) of formol-fixed paraffin-embedded tissues were performed. The cut-off values of the IGH, IGlambda, BCL-2, BCL-6, CCND1 and MYC DNA FISH split signal probes were calculated for adult reactive lymph nodes on the different histological preparations (EC, CF, CC, NI) and on several tissues for the IGH and BCL-6 probes. In reactive lymph nodes, the cut-off values of the probes were between 3 and 13% and found independent of the preparation type. Conversely, slight but significant variations of the cut-off level were observed when different foetal control tissues were assayed with the same probe set. Finally, this study provided optimized-protocols for FISH analysis of either fresh/frozen imprints or formalin-fixed paraffin-embedded sections using split signal DNA probes.

  6. 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.

  7. 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)

  8. Influence of multi-valued diagnostic signals on optimal sensor placement

    NASA Astrophysics Data System (ADS)

    Rostek, Kornel

    2017-01-01

    In this paper the comparison of results of optimal sensor placement with the use of binary and multi-valued diagnostic signals is given. The exoneration assumption was introduced and its effects were discussed. The influence of multi-valued diagnostic signals on several fault isolability metrics were discussed. The optimal sensor placement problem under budgetary constraints is formulated. A branch-and-bound algorithm solving this problem is described. It is used in context of three tanks system with 25 possible diagnostic signals.

  9. 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.

  10. 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.

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

    PubMed

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

    2015-02-01

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

  12. 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

  13. 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.

  14. Depth resolution improvement of streak tube imaging lidar using optimal signal width

    NASA Astrophysics Data System (ADS)

    Ye, Guangchao; Fan, Rongwei; Lu, Wei; Dong, Zhiwei; Li, Xudong; He, Ping; Chen, Deying

    2016-10-01

    Streak tube imaging lidar (STIL) is an active imaging system that has a high depth resolution with the use of a pulsed laser transmitter and streak tube receiver to produce three-dimensional (3-D) range images. This work investigates the optimal signal width of the lidar system, which is helpful to improve the depth resolution based on the centroid algorithm. Theoretical analysis indicates that the signal width has a significant effect on the depth resolution and the optimal signal width can be determined for a given STIL system, which is verified by both the simulation and experimental results. An indoor experiment with a planar target was carried out to validate the relation that the range error decreases first and then increases with the signal width, resulting in an optimal signal width of 8.6 pixels. Finer 3-D range images of a cartoon model were acquired by using the optimal signal width and a minimum range error of 5.5 mm was achieved in a daylight environment.

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

    PubMed

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

    2016-10-01

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

  16. Discovery and optimization of indazoles as potent and selective interleukin-2 inducible T cell kinase (ITK) inhibitors.

    PubMed

    Pastor, Richard M; Burch, Jason D; Magnuson, Steven; Ortwine, Daniel F; Chen, Yuan; De La Torre, Kelly; Ding, Xiao; Eigenbrot, Charles; Johnson, Adam; Liimatta, Marya; Liu, Yichin; Shia, Steven; Wang, Xiaolu; Wu, Lawren C; Pei, Zhonghua

    2014-06-01

    There is evidence that small molecule inhibitors of the non-receptor tyrosine kinase ITK, a component of the T-cell receptor signaling cascade, could represent a novel asthma therapeutic class. Moreover, given the expected chronic dosing regimen of any asthma treatment, highly selective as well as potent inhibitors would be strongly preferred in any potential therapeutic. Here we report hit-to-lead optimization of a series of indazoles that demonstrate sub-nanomolar inhibitory potency against ITK with strong cellular activity and good kinase selectivity. We also elucidate the binding mode of these inhibitors by solving the X-ray crystal structures of the complexes.

  17. FPGA based hardware optimized implementation of signal processing system for LFM pulsed radar

    NASA Astrophysics Data System (ADS)

    Azim, Noor ul; Jun, Wang

    2016-11-01

    Signal processing is one of the main parts of any radar system. Different signal processing algorithms are used to extract information about different parameters like range, speed, direction etc, of a target in the field of radar communication. This paper presents LFM (Linear Frequency Modulation) pulsed radar signal processing algorithms which are used to improve target detection, range resolution and to estimate the speed of a target. Firstly, these algorithms are simulated in MATLAB to verify the concept and theory. After the conceptual verification in MATLAB, the simulation is converted into implementation on hardware using Xilinx FPGA. Chosen FPGA is Xilinx Virtex-6 (XC6LVX75T). For hardware implementation pipeline optimization is adopted and also other factors are considered for resources optimization in the process of implementation. Focusing algorithms in this work for improving target detection, range resolution and speed estimation are hardware optimized fast convolution processing based pulse compression and pulse Doppler processing.

  18. 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…

  19. 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.

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

    PubMed

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

    2013-10-01

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

  1. Signal enhancement and suppression during visual-spatial selective attention.

    PubMed

    Couperus, J W; Mangun, G R

    2010-11-04

    Selective attention involves the relative enhancement of relevant versus irrelevant stimuli. However, whether this relative enhancement involves primarily enhancement of attended stimuli, or suppression of irrelevant stimuli, remains controversial. Moreover, if both enhancement and suppression are involved, whether they result from a single mechanism or separate mechanisms during attentional control or selection is not known. In two experiments using a spatial cuing paradigm with task-relevant targets and irrelevant distractors, target, and distractor processing was examined as a function of distractor expectancy. Additionally, in the second study the interaction of perceptual load and distractor expectancy was explored. In both experiments, distractors were either validly cued (70%) or invalidly cued (30%) in order to examine the effects of distractor expectancy on attentional control as well as target and distractor processing. The effects of distractor expectancy were assessed using event-related potentials recorded during the cue-to-target period (preparatory attention) and in response to the task-relevant target stimuli (selective stimulus processing). Analyses of distractor-present displays (anticipated versus unanticipated), showed modulations in brain activity during both the preparatory period and during target processing. The pattern of brain responses suggest both facilitation of attended targets and suppression of unattended distractors. These findings provide evidence for a two-process model of visual-spatial selective attention, where one mechanism (facilitation) influences relevant stimuli and another (suppression) acts to filter distracting stimuli.

  2. An artificial system for selecting the optimal surgical team.

    PubMed

    Saberi, Nahid; Mahvash, Mohsen; Zenati, Marco

    2015-01-01

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

  3. 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…

  4. Using Fisher Information Criteria for Chemical Sensor Selection via Convex Optimization Methods

    DTIC Science & Technology

    2016-11-16

    best sensors after an optimization procedure. Due to the positive definite nature of the Fisher information matrix, convex optimization may be used to...parametrized to select the best sensors after an optimization procedure. Due to the positive definite nature of the Fisher information matrix, convex op...Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/6180--16-9711 Using Fisher Information Criteria for Chemical Sensor Selection via Convex

  5. 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.

  6. Learning to sense sparse signals: simultaneous sensing matrix and sparsifying dictionary optimization.

    PubMed

    Duarte-Carvajalino, Julio Martin; Sapiro, Guillermo

    2009-07-01

    Sparse signal representation, analysis, and sensing have received a lot of attention in recent years from the signal processing, optimization, and learning communities. On one hand, learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. On the other hand, the framework of compressed sensing (CS) has shown that sparse signals can be recovered from far less samples than those required by the classical Shannon-Nyquist Theorem. The samples used in CS correspond to linear projections obtained by a sensing projection matrix. It has been shown that, for example, a nonadaptive random sampling matrix satisfies the fundamental theoretical requirements of CS, enjoying the additional benefit of universality. On the other hand, a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy or further reduce the necessary number of samples. In this paper, we introduce a framework for the joint design and optimization, from a set of training images, of the nonparametric dictionary and the sensing matrix. We show that this joint optimization outperforms both the use of random sensing matrices and those matrices that are optimized independently of the learning of the dictionary. Particular cases of the proposed framework include the optimization of the sensing matrix for a given dictionary as well as the optimization of the dictionary for a predefined sensing environment. The presentation of the framework and its efficient numerical optimization is complemented with numerous examples on classical image datasets.

  7. Neyman-Pearson Optimal and Suboptimal Detection for Signals in General Clutter Mixture Distributions

    DTIC Science & Technology

    2011-03-01

    UNCLASSIFIED Neyman -Pearson Optimal and Suboptimal Detection for Signals in General Clutter Mixture Distributions Graham V. Weinberg Electronic...construction of the Neyman - Pearson optimal detector for a single general target model embedded within complex clutter whose amplitude distribution forms a...Commonwealth of Australia 2011 AR No. AR-014-933 March 2011 APPROVED FOR PUBLIC RELEASE ii UNCLASSIFIED UNCLASSIFIED DSTO–RR–0363 Neyman -Pearson

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. Optimization of processing parameters for the analysis and detection of embolic signals.

    PubMed

    Aydin, N; Markus, H S

    2000-09-01

    The fast Fourier transform (FFT), which is employed by all commercially available ultrasonic systems, provides a time-frequency representation of Doppler ultrasonic signals obtained from blood flow. The FFT assumes that the signal is stationary within the analysis window. However, the presence of short duration embolic signals invalidates this assumption. For optimal detection of embolic signals if FFT is used for signal processing, it is important that the FFT parameters such as window size, window type, and required overlap ratio should be optimized. The effect of varying window type, window size and window overlap ratio were investigated for both simulated embolic signals, and recorded from patients with carotid artery stenosis. An optimal compromise is the use of a Hamming or Hanning window with a FFT size of 64 (8.9 ms) or 128 (17.9 ms). A high overlap ratio should also be employed in order not to miss embolic events occurring at the edges of analysis windows. The degree of overlap required will depend on the FFT size. The minimum overlap should be 65% for a 64-point window and 80% for a 128-point window.

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

    SciTech Connect

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

    1995-12-31

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

  14. 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…

  15. 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.

  16. 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…

  17. A Regression Design Approach to Optimal and Robust Spacing Selection.

    DTIC Science & Technology

    1981-07-01

    release and sale; its distribution is unlimited Acceso For NTIS GRA&I DEPARTMENT OF STATISTICS DTIC TAB Unannounced Southern Methodist University F...such as the Cauchy where A is a constant multiple of the identity. In fact, for the Cauchy distribution asymptotically optimal spacing sequences for

  18. 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.

  19. Optimal effector functions in human natural killer cells rely upon autocrine bone morphogenetic protein signaling

    PubMed Central

    Mc Alpine, Tristan; Wei, Heng; Martínez, Víctor G.; Entrena, Ana; Melen, Gustavo J; MacDonald, Andrew S.; Phythian-Adams, Alexander; Sacedón, Rosa; Maraskovsky, Eugene; Cebon, Jonathan; Ramírez, Manuel

    2014-01-01

    Natural killer (NK) cells are critical for innate tumor immunity due to their specialized ability to recognize and kill neoplastically transformed cells. However, NK cells require a specific set of cytokine-mediated signals to achieve optimal effector function. Th1-associated cytokines promote effector functions which are inhibited by the prototypic Th-2 cytokine IL-4 and the TGF-β superfamily members TGF-β1 and activin-A. Interestingly, the largest subgroup of the TGF-β superfamily are the bone morphogenetic proteins (BMP), but the effects of BMP signaling to NK cell effector functions have not been evaluated. Here we demonstrate that blood-circulating NK cells express type I and II BMP receptors, BMP-2 and BMP-6 ligands, and phosphorylated isoforms of Smad-1/-5/-8 which mediate BMP family member signaling. In opposition to the inhibitory effects of TGF-β1 or activin-A, autocrine BMP signaling was supportive to NK cell function. Mechanistic investigations in cytokine and TLR-L activated NK cells revealed that BMP signaling optimized IFN-γ and global cytokine and chemokine production; phenotypic activation and proliferation; autologous DC activation and target cytotoxicity. Collectively, our findings identify a novel auto-activatory pathway that is essential for optimal NK cell effector function, one which might be therapeutically manipulated to help eradicate tumors. PMID:25038228

  20. Optimal effector functions in human natural killer cells rely upon autocrine bone morphogenetic protein signaling.

    PubMed

    Robson, Neil C; Hidalgo, Laura; McAlpine, Tristan; Wei, Heng; Martínez, Víctor G; Entrena, Ana; Melen, Gustavo J; MacDonald, Andrew S; Phythian-Adams, Alexander; Sacedón, Rosa; Maraskovsky, Eugene; Cebon, Jonathan; Ramírez, Manuel; Vicente, Angeles; Varas, Alberto

    2014-09-15

    Natural killer (NK) cells are critical for innate tumor immunity due to their specialized ability to recognize and kill neoplastically transformed cells. However, NK cells require a specific set of cytokine-mediated signals to achieve optimal effector function. Th1-associated cytokines promote effector functions that are inhibited by the prototypic Th2 cytokine IL4 and the TGFβ superfamily members TGFβ1 and activin-A. Interestingly, the largest subgroup of the TGFβ superfamily are the bone morphogenetic proteins (BMP), but the effects of BMP signaling on NK cell effector functions have not been evaluated. Here, we demonstrate that blood-circulating NK cells express type I and II BMP receptors, BMP-2 and BMP-6 ligands, and phosphorylated isoforms of Smad-1/-5/-8, which mediate BMP family member signaling. In opposition to the inhibitory effects of TGFβ1 or activin-A, autocrine BMP signaling was supportive to NK cell function. Mechanistic investigations in cytokine and TLR-L-activated NK cells revealed that BMP signaling optimized IFNγ and global cytokine and chemokine production, phenotypic activation and proliferation, and autologous dendritic cell activation and target cytotoxicity. Collectively, our findings identify a novel auto-activatory pathway that is essential for optimal NK cell effector function, one that might be therapeutically manipulated to help eradicate tumors. Cancer Res; 74(18); 5019-31. ©2014 AACR.

  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. Optimizing drilling performance using a selected drilling fluid

    DOEpatents

    Judzis, Arnis [Salt Lake City, UT; Black, Alan D [Coral Springs, FL; Green, Sidney J [Salt Lake City, UT; Robertson, Homer A [West Jordan, UT; Bland, Ronald G [Houston, TX; Curry, David Alexander [The Woodlands, TX; 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.

  3. 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

  4. Phase modulation signals optimization automatically for suppression of stimulated Brillouin scattering

    NASA Astrophysics Data System (ADS)

    Jiang, Min; Ran, Yang; Su, Rongtao; Zhou, Pu

    2016-11-01

    Phase modulation of the signal laser into multiple laser-lines is one of the common methods to suppress the stimulated Brillouin scattering (SBS) effect in high power narrow linewidth fiber amplifiers. In order to achieve optimal effect, the multiple laser-lines should have equal amplitudes. In this paper, the phase modulation signal we employed is the sum of a finite number of sinusoidal signals with different initial phases and different weights. The stochastic parallel gradient descent (SPGD) algorithm is used to search for the optimal initial phases and weights. Through numerical simulation, we obtain homogeneous symmetrical spectra with 11, 19 and 29 lines whose mean square errors of power density are less than 3%.

  5. 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.

  6. 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.

  7. 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.

  8. Selection of magnetorheological brake types via optimal design considering maximum torque and constrained volume

    NASA Astrophysics Data System (ADS)

    Nguyen, Q. H.; Choi, S. B.

    2012-01-01

    This research focuses on optimal design of different types of magnetorheological brakes (MRBs), from which an optimal selection of MRB types is identified. In the optimization, common types of MRB such as disc-type, drum-type, hybrid-types, and T-shaped type are considered. The optimization problem is to find the optimal value of significant geometric dimensions of the MRB that can produce a maximum braking torque. The MRB is constrained in a cylindrical volume of a specific radius and length. After a brief description of the configuration of MRB types, the braking torques of the MRBs are derived based on the Herschel-Bulkley model of the MR fluid. The optimal design of MRBs constrained in a specific cylindrical volume is then analysed. The objective of the optimization is to maximize the braking torque while the torque ratio (the ratio of maximum braking torque and the zero-field friction torque) is constrained to be greater than a certain value. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions of the MRBs. Optimal solutions of MRBs constrained in different volumes are obtained based on the proposed optimization procedure. From the results, discussions on the optimal selection of MRB types depending on constrained volumes are given.

  9. Optimizing the yield and selectivity of high purity nanoparticle clusters

    NASA Astrophysics Data System (ADS)

    Pease, Leonard F.

    2011-05-01

    Here we investigate the parameters that govern the yield and selectivity of small clusters composed of nanoparticles using a Monte Carlo simulation that accounts for spatial and dimensional distributions in droplet and nanoparticle density and size. Clustering nanoparticles presents a powerful paradigm with which to access properties not otherwise available using individual molecules, individual nanoparticles or bulk materials. However, the governing parameters that precisely tune the yield and selectivity of clusters fabricated via an electrospray droplet evaporation method followed by purification with differential mobility analysis (DMA) remain poorly understood. We find that the product of the electrospray droplet mean diameter to the third power and nanoparticle concentration governs the yield of individual clusters, while the ratio of the nanoparticle standard deviation to the mean diameter governs the selectivity. The resulting, easily accessible correlations may be used to minimize undesirable clustering, such as protein aggregation in the biopharmaceutical industry, and maximize the yield of a particular type of cluster for nanotechnology and energy applications.

  10. 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

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. A Cathodic "Signal-off" Photoelectrochemical Aptasensor for Ultrasensitive and Selective Detection of Oxytetracycline.

    PubMed

    Yan, Kai; Liu, Yong; Yang, Yaohua; Zhang, Jingdong

    2015-12-15

    A novel cathodic "signal-off" strategy was proposed for photoelectrochemical (PEC) aptasensing of oxytetracycline (OTC). The PEC sensor was constructed by employing a p-type semiconductor BiOI doped with graphene (G) as photoactive species and OTC-binding aptamer as a recognition element. The morphological structure and crystalline phases of obtained BiOI-G nanocomposites were characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The UV-visible absorption spectroscopic analysis indicated that doping of BiOI with graphene improved the absorption of materials in the visible light region. Moreover, graphene could facilitate the electron transfer of BiOI modified electrode. As a result, the cathodic photocurrent response of BiOI under visible light irradiation was significantly promoted when a suitable amount of graphene was doped. When amine-functionalized OTC-binding aptamer was immobilized on the BiOI-G modified electrode, a cathodic PEC aptasensor was fabricated, which exhibited a declined photocurrent response to OTC. Under the optimized conditions, the photocurrent response of aptamer/BiOI-G/FTO was linearly proportional to the concentration of OTC ranging from 4.0 to 150 nM, with a detection limit (3S/N) of 0.9 nM. This novel PEC sensing strategy demonstrated an ultrasensitive method for OTC detection with high selectivity and good stability.

  17. 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.

  18. 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.

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

    NASA Astrophysics Data System (ADS)

    Mao, Lei; Jackson, Lisa

    2016-10-01

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

  20. 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

  1. Optimization of the Design of Pre-Signal System Using Improved Cellular Automaton

    PubMed Central

    Li, Yan; Li, Ke; Tao, Siran; Chen, Kuanmin

    2014-01-01

    The pre-signal system can improve the efficiency of intersection approach under rational design. One of the main obstacles in optimizing the design of pre-signal system is that driving behaviors in the sorting area cannot be well evaluated. The NaSch model was modified by considering slow probability, turning-deceleration rules, and lane changing rules. It was calibrated with field observed data to explore the interactions among design parameters. The simulation results of the proposed model indicate that the length of sorting area, traffic demand, signal timing, and lane allocation are the most important influence factors. The recommendations of these design parameters are demonstrated. The findings of this paper can be foundations for the design of pre-signal system and show promising improvement in traffic mobility. PMID:25435871

  2. Optimization of the design of pre-signal system using improved cellular automaton.

    PubMed

    Li, Yan; Li, Ke; Tao, Siran; Wan, Xia; Chen, Kuanmin

    2014-01-01

    The pre-signal system can improve the efficiency of intersection approach under rational design. One of the main obstacles in optimizing the design of pre-signal system is that driving behaviors in the sorting area cannot be well evaluated. The NaSch model was modified by considering slow probability, turning-deceleration rules, and lane changing rules. It was calibrated with field observed data to explore the interactions among design parameters. The simulation results of the proposed model indicate that the length of sorting area, traffic demand, signal timing, and lane allocation are the most important influence factors. The recommendations of these design parameters are demonstrated. The findings of this paper can be foundations for the design of pre-signal system and show promising improvement in traffic mobility.

  3. Dynamic optimization of open-loop input signals for ramp-up current profiles in tokamak plasmas

    NASA Astrophysics Data System (ADS)

    Ren, Zhigang; Xu, Chao; Lin, Qun; Loxton, Ryan; Teo, Kok Lay

    2016-03-01

    Establishing a good current spatial profile in tokamak fusion reactors is crucial to effective steady-state operation. The evolution of the current spatial profile is related to the evolution of the poloidal magnetic flux, which can be modeled in the normalized cylindrical coordinates using a parabolic partial differential equation (PDE) called the magnetic diffusion equation. In this paper, we consider the dynamic optimization problem of attaining the best possible current spatial profile during the ramp-up phase of the tokamak. We first use the Galerkin method to obtain a finite-dimensional ordinary differential equation (ODE) model based on the original magnetic diffusion PDE. Then, we combine the control parameterization method with a novel time-scaling transformation to obtain an approximate optimal parameter selection problem, which can be solved using gradient-based optimization techniques such as sequential quadratic programming (SQP). This control parameterization approach involves approximating the tokamak input signals by piecewise-linear functions whose slopes and break-points are decision variables to be optimized. We show that the gradient of the objective function with respect to the decision variables can be computed by solving an auxiliary dynamic system governing the state sensitivity matrix. Finally, we conclude the paper with simulation results for an example problem based on experimental data from the DIII-D tokamak in San Diego, California.

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

    NASA Astrophysics Data System (ADS)

    Kawamoto, Shigeru; Takamoto, Masanori; Kobayashi, Yasuhiro

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

  5. 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.

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

    NASA Technical Reports Server (NTRS)

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

    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.

  7. 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.

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

    PubMed Central

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

    2015-01-01

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

  9. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    NASA Astrophysics Data System (ADS)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  10. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    PubMed Central

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias. PMID:28139677

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

    NASA Technical Reports Server (NTRS)

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

    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

  12. 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

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. Optimizing drug exposure to minimize selection of antibiotic resistance.

    PubMed

    Olofsson, Sara K; Cars, Otto

    2007-09-01

    The worldwide increase in antibiotic resistance is a concern for public health. The fact that the choice of dose and treatment duration can affect the selection of antibiotic-resistant mutants is becoming more evident, and an increased number of studies have used pharmacodynamic models to describe the drug exposure and pharmacodynamic breakpoints needed to minimize and predict the development of resistance. However, there remains a lack of sufficient data, and future work is needed to fully characterize these target drug concentrations. More knowledge is also needed of drug pharmacodynamics versus bacteria with different resistance mutations and susceptibility levels. The dosing regimens should exhibit high efficacy not only against susceptible wild-type bacteria but, preferably, also against mutated bacteria that may exist in low numbers in "susceptible" populations. Thus, to prolong the life span of existing and new antibiotics, it is important that dosing regimens be carefully selected on the basis of pharmacokinetic and pharmacodynamic properties that prevent emergence of preexisting and newly formed mutants.

  18. Stationary phase optimized selectivity liquid chromatography: Basic possibilities of serially connected columns using the "PRISMA" principle.

    PubMed

    Nyiredy, Sz; Szucs, Zoltán; Szepesy, L

    2007-07-20

    A new procedure (stationary phase optimized selectivity liquid chromatography: SOS-LC) is described for the optimization of the HPLC stationary phase, using serially connected columns and the principle of the "PRISMA" model. The retention factors (k) of the analytes were determined on three different stationary phases. By use of these data the k values were predicted applying theoretically combined stationary phases. These predictions resulted in numerous intermediate theoretical separations from among which only the optimal one was assembled and tested. The overall selectivity of this separation was better than that of any individual base stationary phase. SOS-LC is independent of the mechanism and the scale of separation.

  19. 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

  20. Particle swarm optimizer for weighting factor selection in intensity-modulated radiation therapy optimization algorithms.

    PubMed

    Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo

    2017-01-01

    In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human

  1. 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…

  2. 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

  3. Optimal filtering of gear signals for early damage detection based on the spectral kurtosis

    NASA Astrophysics Data System (ADS)

    Combet, F.; Gelman, L.

    2009-04-01

    In this paper, we propose a methodology for the enhancement of small transients in gear vibration signals in order to detect local tooth faults, such as pitting, at an early stage of damage. We propose to apply the optimal denoising (Wiener) filter based on the spectral kurtosis (SK). The originality is to estimate and apply this filter to the gear residual signal, as classically obtained after removing the mesh harmonics from the time synchronous average (TSA). This presents several advantages over the direct estimation from the raw vibration signal: improved signal/noise ratio, reduced interferences from other stages of the gearbox and easier detection of excited structural resonance(s) within the range of the mesh harmonic components. From the SK-based filtered residual signal, called SK-residual, we define the local power as the smoothed squared envelope, which reflects both the energy and the degree of non-stationarity of the fault-induced transients. The methodology is then applied to an industrial case and shows the possibility of detection of relatively small tooth surface pitting (less than 10%) in a two-stage helical reduction gearbox. The adjustment of the resolution for the SK estimation appears to be optimal when the length of the analysis window is approximately matched with the mesh period of the gear. The proposed approach is also compared to an inverse filtering (blind deconvolution) approach. However, the latter turns out to be more unstable and sensitive to noise and shows a lower degree of separation, quantified by the Fisher criterion, between the estimated diagnostic features in the pitted and unpitted cases. Thus, the proposed optimal filtering methodology based on the SK appears to be well adapted for the early detection of local tooth damage in gears.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. Automatic parameter optimization in epsilon-filter for acoustical signal processing utilizing correlation coefficient.

    PubMed

    Abe, Tomomi; Hashimoto, Shuji; Matsumoto, Mitsuharu

    2010-02-01

    epsilon-filter can reduce most kinds of noise from a single-channel noisy signal while preserving signals that vary drastically such as speech signals. It can reduce not only stationary noise but also nonstationary noise. However, it has some parameters whose values are set empirically. So far, there have been few studies to evaluate the appropriateness of the parameter settings for epsilon-filter. This paper employs the correlation coefficient of the filter output and the difference between the filter input and output as the evaluation function of the parameter setting. This paper also describes the algorithm to set the optimal parameter value of epsilon-filter automatically. To evaluate the adequateness of the obtained parameter, the mean absolute error is calculated. The experimental results show that the adequate parameter in epsilon-filter can be obtained automatically by using the proposed method.

  10. Optimal design of optical reference signals by use of a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Saez-Landete, José; Salcedo-Sanz, Sancho; Rosa-Zurera, Manuel; Alonso, José; Bernabeu, Eusebio

    2005-10-01

    A new technique for the generation of optical reference signals with optimal properties is presented. In grating measurement systems a reference signal is needed to achieve an absolute measurement of the position. The optical signal is the autocorrelation of two codes with binary transmittance. For a long time, the design of this type of code has required great computational effort, which limits the size of the code to ˜30 elements. Recently, the application of the dividing rectangles (DIRECT) algorithm has allowed the automatic design of codes up to 100 elements. Because of the binary nature of the problem and the parallel processing of the genetic algorithms, these algorithms are efficient tools for obtaining codes with particular autocorrelation properties. We design optimum zero reference codes with arbitrary length by means of a genetic algorithm enhanced with a restricted search operator.

  11. EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials

    PubMed Central

    Hokari, Haruhide

    2014-01-01

    Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy. PMID:24982944

  12. On the complexity of discrete feature selection for optimal classification.

    PubMed

    Peña, Jose M; Nilsson, Roland

    2010-08-01

    Consider a classification problem involving only discrete features that are represented as random variables with some prescribed discrete sample space. In this paper, we study the complexity of two feature selection problems. The first problem consists in finding a feature subset of a given size k that has minimal Bayes risk. We show that for any increasing ordering of the Bayes risks of the feature subsets (consistent with an obvious monotonicity constraint), there exists a probability distribution that exhibits that ordering. This implies that solving the first problem requires an exhaustive search over the feature subsets of size k. The second problem consists of finding the minimal feature subset that has minimal Bayes risk. In the light of the complexity of the first problem, one may think that solving the second problem requires an exhaustive search over all of the feature subsets. We show that, under mild assumptions, this is not true. We also study the practical implications of our solutions to the second problem.

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

    PubMed

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

    2014-06-01

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

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

    PubMed

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

    2015-05-01

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

  15. Applying optimal model selection in principal stratification for causal inference.

    PubMed

    Odondi, Lang'o; McNamee, Roseanne

    2013-05-20

    Noncompliance to treatment allocation is a key source of complication for causal inference. Efficacy estimation is likely to be compounded by the presence of noncompliance in both treatment arms of clinical trials where the intention-to-treat estimate provides a biased estimator for the true causal estimate even under homogeneous treatment effects assumption. Principal stratification method has been developed to address such posttreatment complications. The present work extends a principal stratification method that adjusts for noncompliance in two-treatment arms trials by developing model selection for covariates predicting compliance to treatment in each arm. We apply the method to analyse data from the Esprit study, which was conducted to ascertain whether unopposed oestrogen (hormone replacement therapy) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We adjust for noncompliance in both treatment arms under a Bayesian framework to produce causal risk ratio estimates for each principal stratum. For mild values of a sensitivity parameter and using separate predictors of compliance in each arm, principal stratification results suggested that compliance with hormone replacement therapy only would reduce the risk for death and myocardial reinfarction by about 47% and 25%, respectively, whereas compliance with either treatment would reduce the risk for death by 13% and reinfarction by 60% among the most compliant. However, the results were sensitive to the user-defined sensitivity parameter.

  16. 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

  17. Mode-selective wavelength conversion of OFDM-QPSK signals in a multimode silicon waveguide.

    PubMed

    Qiu, Ying; Li, Xiang; Luo, Ming; Chen, Daigao; Wang, Jiamin; Xu, Jing; Yang, Qi; Yu, Shaohua

    2017-02-20

    We experimentally demonstrate on-chip mode-selective wavelength conversions based on the degenerate four-wave mixing (FWM) nonlinear effect in a few-mode silicon waveguide. A multimode waveguide with tapered directional coupler based mode (de)multiplexers is designed and fabricated. Using signals with advanced modulation formats all-optical wavelength conversions of 102.6-Gb/s OFDM-QPSK signals are verified. Experimental results show that only small optical signal-to-noise ratio (OSNR) penalties are observed after wavelength conversion of both modes, which are less than 2 dB for OFDM-QPSK at 7% forward error correction (FEC) threshold.

  18. 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

  19. 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.

  20. 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.

  1. 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.

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

    PubMed Central

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

    2017-01-01

    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. PMID:27008349

  3. Visualization of multi-property landscapes for compound selection and optimization

    NASA Astrophysics Data System (ADS)

    de la Vega de León, Antonio; Kayastha, Shilva; Dimova, Dilyana; Schultz, Thomas; Bajorath, Jürgen

    2015-08-01

    Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.

  4. Visualization of multi-property landscapes for compound selection and optimization.

    PubMed

    de la Vega de León, Antonio; Kayastha, Shilva; Dimova, Dilyana; Schultz, Thomas; Bajorath, Jürgen

    2015-08-01

    Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.

  5. Regulation of cardiomyocyte signaling by RGS proteins: differential selectivity towards G proteins and susceptibility to regulation.

    PubMed

    Hao, Jianming; Michalek, Christina; Zhang, Wei; Zhu, Ming; Xu, Xiaomei; Mende, Ulrike

    2006-07-01

    Many signals that regulate cardiomyocyte growth, differentiation and function are mediated via heterotrimeric G proteins, which are under the control of RGS proteins (Regulators of G protein Signaling). Several RGS proteins are expressed in the heart, but so far little is known about their function and regulation. Using adenoviral gene transfer, we conducted the first comprehensive analysis of the capacity and selectivity of the major cardiac RGS proteins (RGS2-RGS5) to regulate central G protein-mediated signaling pathways in adult ventricular myocytes (AVM). All four RGS proteins potently inhibited Gq/11-mediated phospholipase C beta stimulation and cell growth (assessed in neonatal myocytes). Importantly, RGS2 selectively inhibited Gq/11 signaling, whereas RGS3, RGS4 and RGS5 had the capacity to regulate both Gq/11 and Gi/o signaling (carbachol-induced cAMP inhibition). Gs signaling was unaffected, and, contrary to reports in other cell lines, RGS2-RGS5 did not appear to regulate adenylate cyclase directly in AVM. Since RGS proteins can be highly regulated in their expression by many different stimuli, we also tested the hypothesis that RGS expression is subject to G protein-mediated regulation in AVM and determined the specificity with which enhanced G protein signaling alters endogenous RGS expression in AVM. RGS2 mRNA and protein were markedly but transiently up-regulated by enhanced Gq/11 signaling (alpha1-adrenergic stimulation or Galphaq* overexpression), possibly by a negative feedback mechanism. In contrast, the other negative regulators of Gq/11 signaling (RGS3-RGS5) were unchanged. Endogenous RGS2 (but not RGS3-RGS5) expression was also up-regulated in cells with enhanced AC signaling (beta-adrenergic or forskolin stimulation). Taken together, these findings suggest diverse roles of RGS proteins in regulating myocyte signaling. RGS2 emerged as the only selective and highly regulated inhibitor of Gq/11 signaling that could potentially become a promising

  6. 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.

  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.

  8. A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning.

    PubMed

    Li, Yongjie; Yao, Dezhong; Yao, Jonathan; Chen, Wufan

    2005-08-07

    Automatic beam angle selection is an important but challenging problem for intensity-modulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the beam angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new population-based evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the beam angles are optimized using PSO by treating each beam configuration as a particle (individual), and the beam intensity maps for each beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal beam angles and two clinical cases (a prostate case and a head-and-neck case) show that PSO is valid and efficient and can speed up the beam angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSO-based algorithm seems to outperform, or at least compete with, the GA-based algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the beam angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated.

  9. Coupling between protein level selection and codon usage optimization in the evolution of bacteria and archaea.

    PubMed

    Ran, Wenqi; Kristensen, David M; Koonin, Eugene V

    2014-03-25

    The relationship between the selection affecting codon usage and selection on protein sequences of orthologous genes in diverse groups of bacteria and archaea was examined by using the Alignable Tight Genome Clusters database of prokaryote genomes. The codon usage bias is generally low, with 57.5% of the gene-specific optimal codon frequencies (Fopt) being below 0.55. This apparent weak selection on codon usage contrasts with the strong purifying selection on amino acid sequences, with 65.8% of the gene-specific dN/dS ratios being below 0.1. For most of the genomes compared, a limited but statistically significant negative correlation between Fopt and dN/dS was observed, which is indicative of a link between selection on protein sequence and selection on codon usage. The strength of the coupling between the protein level selection and codon usage bias showed a strong positive correlation with the genomic GC content. Combined with previous observations on the selection for GC-rich codons in bacteria and archaea with GC-rich genomes, these findings suggest that selection for translational fine-tuning could be an important factor in microbial evolution that drives the evolution of genome GC content away from mutational equilibrium. This type of selection is particularly pronounced in slowly evolving, "high-status" genes. A significantly stronger link between the two aspects of selection is observed in free-living bacteria than in parasitic bacteria and in genes encoding metabolic enzymes and transporters than in informational genes. These differences might reflect the special importance of translational fine-tuning for the adaptability of gene expression to environmental changes. The results of this work establish the coupling between protein level selection and selection for translational optimization as a distinct and potentially important factor in microbial evolution. IMPORTANCE Selection affects the evolution of microbial genomes at many levels, including both

  10. 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

  11. 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.

  12. Rational optimization of tolC as a powerful dual selectable marker for genome engineering

    PubMed Central

    Gregg, Christopher J.; Lajoie, Marc J.; Napolitano, Michael G.; Mosberg, Joshua A.; Goodman, Daniel B.; Aach, John; Isaacs, Farren J.; Church, George M.

    2014-01-01

    Selection has been invaluable for genetic manipulation, although counter-selection has historically exhibited limited robustness and convenience. TolC, an outer membrane pore involved in transmembrane transport in E. coli, has been implemented as a selectable/counter-selectable marker, but counter-selection escape frequency using colicin E1 precludes using tolC for inefficient genetic manipulations and/or with large libraries. Here, we leveraged unbiased deep sequencing of 96 independent lineages exhibiting counter-selection escape to identify loss-of-function mutations, which offered mechanistic insight and guided strain engineering to reduce counter-selection escape frequency by ∼40-fold. We fundamentally improved the tolC counter-selection by supplementing a second agent, vancomycin, which reduces counter-selection escape by 425-fold, compared colicin E1 alone. Combining these improvements in a mismatch repair proficient strain reduced counter-selection escape frequency by 1.3E6-fold in total, making tolC counter-selection as effective as most selectable markers, and adding a valuable tool to the genome editing toolbox. These improvements permitted us to perform stable and continuous rounds of selection/counter-selection using tolC, enabling replacement of 10 alleles without requiring genotypic screening for the first time. Finally, we combined these advances to create an optimized E. coli strain for genome engineering that is ∼10-fold more efficient at achieving allelic diversity than previous best practices. PMID:24452804

  13. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis.

    PubMed

    Li, Qiang; Chen, Huiling; Huang, Hui; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

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

    PubMed

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

    2012-02-06

    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.

  15. Genome-wide association study reveals sex-specific selection signals against autosomal nucleotide variants.

    PubMed

    Ryu, Dongchan; Ryu, Jihye; Lee, Chaeyoung

    2016-05-01

    A genome-wide association study (GWAS) was conducted to examine genetic associations of common autosomal nucleotide variants with sex in a Korean population with 4183 males and 4659 females. Nine genetic association signals were identified in four intragenic and five intergenic regions (P<5 × 10(-8)). Further analysis with an independent data set confirmed two intragenic association signals in the genes encoding protein phosphatase 1, regulatory subunit 12B (PPP1R12B, intron 12, rs1819043) and dynein, axonemal, heavy chain 11 (DNAH11, intron 61, rs10255013), which are directly involved in the reproductive system. This study revealed autosomal genetic variants associated with sex ratio by GWAS for the first time. This implies that genetic variants in proximity to the association signals may influence sex-specific selection and contribute to sex ratio variation. Further studies are required to reveal the mechanisms underlying sex-specific selection.

  16. Enhanced Refocusing of Fat Signals using Optimized Multi-pulse Echo Sequences

    PubMed Central

    Stokes, Ashley M.; Feng, Yesu; Mitropoulos, Tanya; Warren, Warren S.

    2012-01-01

    Endogenous magnetic resonance contrast based on the localized composition of fat in vivo can provide functional information. We found that the unequal pulse timings of the Uhrig’s Dynamical Decoupling (UDD) multipulse echo sequences significantly alter the signal intensity compared to conventional, equal-spaced Carr-Purcell-Meiboom-Gill (CPMG) sequences. The signal increases and decreases depending on the tissue and sequence parameters, as well as on the interpulse spacings; particularly strong differences were observed in fatty tissues, which have a highly structured morphology and a wide range of chemical shifts and J-couplings. We found that the predominant mechanism for fat refocusing under multipulse echo sequences is the chemical structure, with stimulated echoes playing a pivotal role. As a result, specialized pulse sequences can be designed to optimize refocusing of the fat chemical shifts and J-couplings, where the degree of refocusing can be tailored to specific types of fats. To determine the optimal time delays, we simulated various UDD and CPMG pulse sequence timings, and these results are compared to experimental results obtained on excised and in vivo fatty tissue. Applications to intermolecular multiple-quantum coherence (iMQC) imaging, where the improved echo refocusing translates directly into signal enhancements, are presented as well. PMID:22627966

  17. 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.

  18. Optimizing seismic monitoring for landslides by using on-site artificially generated signals

    NASA Astrophysics Data System (ADS)

    Yfantis, Georgios; Martinez Carvajal, Hernan Eduardo; Pytharouli, Stella; Lunn, Rebecca

    2013-04-01

    We develop a methodology for the optimization of seismic arrays used for landslide monitoring. We design an experimental field set-up that generates signals caused by soil friction, such as those during a landslide. The set-up allows for controlling the normal stress, moisture content and the size of the slippage interface area. The optimization is based on the frequency and energy content analysis of the emitted artificial landslide signals and how local geology affects them. This can significantly improve the detection threshold of the monitoring system, the design of which, to-date, is mainly based on speculations for the site conditions or expensive borehole logs and its optimization on time-consuming trial and error procedures. We use a concrete cylinder, 0.5m high and 0.65m wide filled with high porous tropical clay excavated from the experimental site. The cylinder is placed on a 4m long, 2m wide clay strip, free from any surficial vegetation. As the cylinder is moved horizontally along the corridor, soil friction generates signals. By varying the load applied by the material within the cylinder we simulate slippage at different depths. Five different normal stress levels between 11.9kPa and 22.5kPa, corresponding to depths of 0.7 and 1.4m respectively, are simulated. The load applied on the slippage surface is the only variable, thus allowing the investigation of the normal stress effect on the emitted signals. The experiment is completed within 3 hours, under the same weather conditions. Therefore, no changes in the clay, i.e. moisture content, take place. We minimize the ambient noise by performing the experiment during the night. For the monitoring of the generated seismic signals we use 12 short period 3-component seismometers with natural frequency of 2Hz. Eight sensors are deployed as a linear array (spacing between them is 1 to 2m with the first and last sensors being 2m and 15m away from the strip, respectively) perpendicular to the direction of

  19. Optimization of meander line radiators for frequency selective surfaces by using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Bucuci, Stefania C.; Dumitrascu, Ana; Danisor, Alin; Berescu, Serban; Tamas, Razvan D.

    2015-02-01

    In this paper we propose the use of frequency selective surfaces based on meander line radiators, as targets for monitoring slow displacements with synthetic aperture radars. The optimization of the radiators is performed by using genetic algorithms on only two parameters i.e., gain and size. As an example, we have optimized a single meander antenna, resonating in the X-band, at 9.65 GHz.

  20. The emotion recognition system based on autoregressive model and sequential forward feature selection of electroencephalogram signals.

    PubMed

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

    2014-07-01

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

  1. Signals of selection in outlier loci in a widely dispersing species across an environmental mosaic.

    PubMed

    Pespeni, Melissa H; Palumbi, Stephen R

    2013-07-01

    Local adaptation reflects a balance between natural selection and gene flow and is classically thought to require the retention of locally adapted alleles. However, organisms with high dispersal potential across a spatially or temporally heterogeneous landscape pose an interesting challenge to this view requiring local selection every generation or when environmental conditions change to generate adaptation in adults. Here, we test for geographical and sequence-based signals of selection in five putatively adaptive and two putatively neutral genes identified in a previous genome scan of the highly dispersing purple sea urchin, Strongylocentrotus purpuratus. Comparing six populations spanning the species' wide latitudinal range from Canada to Baja California, Mexico, we find positive tests for selection in the putative adaptive genes and not in the putative neutral genes. Specifically, we find an excess of low-frequency and nonsynonymous polymorphisms in two transcription factors and a transporter protein, and an excess of common amino acid polymorphisms in the two transcription factors, suggestive of spatially balancing selection. We test for a genetic correlation with temperature, a dominant environmental variable in this coastal ecosystem. We find mild clines and a stronger association of genetic variation with temperature than latitude in four of the five putative adaptive loci and a signal of local adaptation in the Southern California Bight. Overall, patterns of genetic variation match predictions based on spatially or temporally balancing selection in a heterogeneous landscape and illustrate the value of geographical and coalescent tests on candidate loci identified in a genome-wide scan for selection.

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

    PubMed

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

    2012-01-01

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

  3. Photorefractive two-beam coupling optimal thresholding filter for additive signal-dependent noise reduction

    NASA Astrophysics Data System (ADS)

    Fu, Jack; Khoury, Jehad; Cronin-Golomb, Mark; Woods, Charles L.

    1995-01-01

    Computer simulations of photorefractive thresholding filters for the reduction of artifact or dust noise demonstrate an increase in signal-to-noise ratio (SNR) of 70% to 95%, respectively, of that provided by the Wiener filter for inputs with a SNR of approximately 3. These simple, nearly optimal filters use a spectral thresholding profile that is proportional to the envelope of the noise spectrum. Alternative nonlinear filters with either 1/ nu or constant thresholding profiles increase the SNR almost as much as the noise-envelope thresholding filter.

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

    PubMed

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

    2014-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  6. Selective Optimization

    DTIC Science & Technology

    2015-07-06

    thanks Dave Goldsman and Alexander Shapiro for valuable discussions. References [1] Shabbir Ahmed and Alper Atamtürk. Maximizing a class of...Progressive hedging-based metaheuristics for stochastic network design. Networks, 58(2):114–124, 2011. [7] Edgar Gabriel, Graham E. Fagg, George...J. Daniel, Richard L. Graham , and Timothy S. Woodall. Open MPI: Goals, concept, and design of a next generation MPI implementation. In Proceedings

  7. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

  9. Targeted loss of SHP1 in murine thymocytes dampens TCR signaling late in selection.

    PubMed

    Martinez, Ryan J; Morris, Anna B; Neeld, Dennis K; Evavold, Brian D

    2016-09-01

    SHP1 is a tyrosine phosphatase critical to proximal regulation of TCR signaling. Here, analysis of CD4-Cre SHP1(fl/fl) conditional knockout thymocytes using CD53, TCRβ, CD69, CD4, and CD8α expression demonstrates the importance of SHP1 in the survival of post selection (CD53(+) ), single-positive thymocytes. Using Ca(2+) flux to assess the intensity of TCR signaling demonstrated that SHP1 dampens the signal strength of these same mature, postselection thymocytes. Consistent with its dampening effect, TCR signal strength was also probed functionally using peptides that can mediate selection of the OT-I TCR, to reveal increased negative selection mediated by lower-affinity ligand in the absence of SHP1. Our data show that SHP1 is required for the survival of mature thymocytes and the generation of the functional T-cell repertoire, as its absence leads to a reduction in the numbers of CD4(+) and CD8(+) naïve T cells in the peripheral lymphoid compartments.

  10. 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.

  11. 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

  12. EUROmediCAT signal detection: an evaluation of selected congenital anomaly‐medication associations

    PubMed Central

    Given, Joanne E.; Loane, Maria; Luteijn, Johannes M.; Morris, Joan K.; de Jong van den Berg, Lolkje T.W.; Garne, Ester; Addor, Marie‐Claude; Barisic, Ingeborg; de Walle, Hermien; Gatt, Miriam; Klungsoyr, Kari; Khoshnood, Babak; Latos‐Bielenska, Anna; Nelen, Vera; Neville, Amanda J.; O'Mahony, Mary; Pierini, Anna; Tucker, David; Wiesel, Awi

    2016-01-01

    Aims To evaluate congenital anomaly (CA)‐medication exposure associations produced by the new EUROmediCAT signal detection system and determine which require further investigation. Methods Data from 15 EUROCAT registries (1995–2011) with medication exposures at the chemical substance (5th level of Anatomic Therapeutic Chemical classification) and chemical subgroup (4th level) were analysed using a 50% false detection rate. After excluding antiepileptics, antidiabetics, antiasthmatics and SSRIs/psycholeptics already under investigation, 27 associations were evaluated. If evidence for a signal persisted after data validation, a literature review was conducted for prior evidence of human teratogenicity. Results Thirteen out of 27 CA‐medication exposure signals, based on 389 exposed cases, passed data validation. There was some prior evidence in the literature to support six signals (gastroschisis and levonorgestrel/ethinylestradiol (OR 4.10, 95% CI 1.70–8.53; congenital heart disease/pulmonary valve stenosis and nucleoside/tide reverse transcriptase inhibitors (OR 5.01, 95% CI 1.99–14.20/OR 28.20, 95% CI 4.63–122.24); complete absence of a limb and pregnen (4) derivatives (OR 6.60, 95% CI 1.70–22.93); hypospadias and pregnadien derivatives (OR 1.40, 95% CI 1.10–1.76); hypospadias and synthetic ovulation stimulants (OR 1.89, 95% CI 1.28–2.70). Antipropulsives produced a signal for syndactyly while the literature revealed a signal for hypospadias. There was no prior evidence to support the remaining six signals involving the ordinary salt combinations, propulsives, bulk‐forming laxatives, hydrazinophthalazine derivatives, gonadotropin releasing hormone analogues and selective serotonin agonists. Conclusion Signals which strengthened prior evidence should be prioritized for further investigation, and independent evidence sought to confirm the remaining signals. Some chance associations are expected and confounding by indication is possible. PMID

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

    PubMed

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

    2015-05-01

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

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

    PubMed

    Li, Susan S Y; McNally, Gavan P

    2015-06-01

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

  15. A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions

    NASA Astrophysics Data System (ADS)

    Feng, Ke; Wang, KeSheng; Zhang, Mian; Ni, Qing; Zuo, Ming J.

    2017-03-01

    The planetary gearbox, due to its unique mechanical structures, is an important rotating machine for transmission systems. Its engineering applications are often in non-stationary operational conditions, such as helicopters, wind energy systems, etc. The unique physical structures and working conditions make the vibrations measured from planetary gearboxes exhibit a complex time-varying modulation and therefore yield complicated spectral structures. As a result, traditional signal processing methods, such as Fourier analysis, and the selection of characteristic fault frequencies for diagnosis face serious challenges. To overcome this drawback, this paper proposes a signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques. The basic procedures for the proposed scheme are as follows. (1) Computed order tracking is applied to reveal the order contents and identify the order(s) of interest. (2) Vold-Kalman filter order tracking is used to extract the order(s) of interest—these filtered order(s) constitute the so-called selected vibrations. (3) Time domain statistic indicators are applied to the selected vibrations for faulty information-emphasized diagnostics. The proposed scheme is explained and demonstrated in a signal simulation model and experimental studies and the method proves to be effective for planetary gearbox fault diagnosis.

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

    PubMed Central

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

    2015-01-01

    Selective 2′ Hydroxyl Acylation analyzed by Primer Extension (SHAPE) is an accurate method for probing of RNA secondary structure. In existing SHAPE methods, the SHAPE probing signal is normalized to a no-reagent control to correct for the background caused by premature termination of the reverse transcriptase. Here, we introduce a SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling between the SHAPES reagent and a biotin molecule. We demonstrate that SHAPES-based selection of cDNA–RNA hybrids on streptavidin beads effectively removes the large majority of background signal present in SHAPE probing data and that sequencing-based SHAPES data contain the same amount of RNA structure data as regular sequencing-based SHAPE data obtained through normalization to a no-reagent control. Moreover, the selection efficiently enriches for probed RNAs, suggesting that the SHAPES strategy will be useful for applications with high-background and low-probing signal such as in vivo RNA structure probing. PMID:25805860

  17. 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.

  18. 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.

  19. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

    PubMed Central

    Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

    2017-01-01

    Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

  20. 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.

  1. 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.

  2. Adaptive feature selection using v-shaped binary particle swarm optimization

    PubMed Central

    Dong, Hongbin; Zhou, Xiurong

    2017-01-01

    Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers. PMID:28358850

  3. 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

  4. 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.

  5. 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…

  6. Auto-OBSD: Automatic parameter selection for reliable Oscillatory Behavior-based Signal Decomposition with an application to bearing fault signature extraction

    NASA Astrophysics Data System (ADS)

    Huang, Huan; Baddour, Natalie; Liang, Ming

    2017-03-01

    Bearing signals are often contaminated by in-band interferences and random noise. Oscillatory Behavior-based Signal Decomposition (OBSD) is a new technique which decomposes a signal according to its oscillatory behavior, rather than frequency or scale. Due to the low oscillatory transients of bearing fault-induced signals, the OBSD can be used to effectively extract bearing fault signatures from a blurred signal. However, the quality of the result highly relies on the selection of method-related parameters. Such parameters are often subjectively selected and a systematic approach has not been reported in the literature. As such, this paper proposes a systematic approach to automatic selection of OBSD parameters for reliable extraction of bearing fault signatures. The OBSD utilizes the idea of Morphological Component Analysis (MCA) that optimally projects the original signal to low oscillatory wavelets and high oscillatory wavelets established via the Tunable Q-factor Wavelet Transform (TQWT). In this paper, the effects of the selection of each parameter on the performance of the OBSD for bearing fault signature extraction are investigated. It is found that some method-related parameters can be fixed at certain values due to the nature of bearing fault-induced impulses. To adaptively tune the remaining parameters, index-guided parameter selection algorithms are proposed. A Convergence Index (CI) is proposed and a CI-guided self-tuning algorithm is developed to tune the convergence-related parameters, namely, penalty factor and number of iterations. Furthermore, a Smoothness Index (SI) is employed to measure the effectiveness of the extracted low oscillatory component (i.e. bearing fault signature). It is shown that a minimum SI implies an optimal result with respect to the adjustment of relevant parameters. Thus, two SI-guided automatic parameter selection algorithms are also developed to specify two other parameters, i.e., Q-factor of high-oscillatory wavelets and

  7. Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes

    PubMed Central

    Liu, Sheng; Jin, Haiqiang; Mao, Xiaojun; Zhai, Binbin; Zhan, Ye; Feng, Xiaofei

    2013-01-01

    This paper proposes a segmentation-based global optimization method for depth estimation. Firstly, for obtaining accurate matching cost, the original local stereo matching approach based on self-adapting matching window is integrated with two matching cost optimization strategies aiming at handling both borders and occlusion regions. Secondly, we employ a comprehensive smooth term to satisfy diverse smoothness request in real scene. Thirdly, a selective segmentation term is used for enforcing the plane trend constraints selectively on the corresponding segments to further improve the accuracy of depth results from object level. Experiments on the Middlebury image pairs show that the proposed global optimization approach is considerably competitive with other state-of-the-art matching approaches. PMID:23766717

  8. 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.

  9. 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

  10. 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°).

  11. Noradrenergic control of associative synaptic plasticity by selective modulation of instructive signals

    PubMed Central

    Carey, Megan R.; Regehr, Wade G.

    2010-01-01

    Synapses throughout the brain are modified through associative mechanisms in which one input provides an instructive signal for changes in the strength of a second co-activated input. In cerebellar Purkinje cells, climbing fiber synapses provide an instructive signal for plasticity at parallel fiber synapses. Here we show that noradrenaline activates α2-adrenergic receptors to control short-term and long-term associative plasticity of parallel fiber synapses. This regulation of plasticity does not reflect a conventional direct modulation of the postsynaptic Purkinje cell or presynaptic parallel fibers. Instead, noradrenaline reduces associative plasticity by selectively decreasing the probability of release at the climbing fiber synapse, which in turn decreases climbing fiber-evoked dendritic calcium signals. These findings raise the possibility that targeted presynaptic modulation of instructive synapses could provide a general mechanism for dynamic context-dependent modulation of associative plasticity. PMID:19376071

  12. German criteria for selection of hearing protectors in the interest of good signal audibility.

    PubMed

    Liedtke, Martin

    2009-01-01

    The German transport and personal protective equipment (PPE) technical committees of the German Social Accident Insurance have laid down criteria, which have since become established, for hearing protectors to be used in railway systems and road traffic in Germany: only hearing protectors which do not significantly impair the audibility of auditory warning signals may be used. In addition, the Institute for Occupational Safety and Health of the German Social Accident Insurance (BGIA) has proposed a simple criterion for the selection of hearing protectors for workplaces outside railway systems and road traffic which perform well with regard to signal audibility (general), speech intelligibility, and perception of informative operating sound (AIP). This criterion is based upon the research carried out in the field of signal audibility in railway systems and road traffic and upon an additional study. It has been established by the German PPE technical committee and is presented here.

  13. 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.

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

    PubMed

    Lin, Ciyun; Gong, Bowen; Qu, Xin

    2015-01-01

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

  15. 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

  16. 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.

  17. Application of RGS box proteins to evaluate G-protein selectivity in receptor-promoted signaling.

    PubMed

    Hains, Melinda D; Siderovski, David P; Harden, T Kendall

    2004-01-01

    Regulator of G-protein signaling (RGS) domains bind directly to GTP-bound Galpha subunits and accelerate their intrinsic GTPase activity by up to several thousandfold. The selectivity of RGS proteins for individual Galpha subunits has been illustrated. Thus, the expression of RGS proteins can be used to inhibit signaling pathways activated by specific G protein-coupled receptors (GPCRs). This article describes the use of specific RGS domain constructs to discriminate among G(i/o), Gq-and G(12/13)-mediated activation of phospholipase C (PLC) isozymes in COS-7 cells. Overexpression of the N terminus of GRK2 (amino acids 45-178) or p115 RhoGEF (amino acids 1-240) elicited selective inhibition of Galphaq- or Galpha(12/13)-mediated signaling to PLC activation, respectively. In contrast, RGS2 overexpression was found to inhibit PLC activation by both G(i/o)- and Gq-coupled GPCRs. RGS4 exhibited dramatic receptor selectivity in its inhibitory actions; of the G(i/o)- and Gq-coupled GPCRs tested (LPA1, LPA2, P2Y1, S1P3), only the Gq-coupled lysophosphatidic acid-activated LPA2 receptor was found to be inhibited by RGS4 overexpression.

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

    NASA Astrophysics Data System (ADS)

    Shirangi, Mehrdad G.; Durlofsky, Louis J.

    2016-11-01

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

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

    PubMed Central

    Tian, Shulin; Yang, Chenglin; Liu, Cheng

    2016-01-01

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

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

    PubMed

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

    2014-08-01

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

  1. 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

  2. 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

  3. 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

    2014-10-07

    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/.

  4. Optimization of adiponectin-derived peptides for inhibition of cancer cell growth and signaling.

    PubMed

    Otvos, Laszlo; Kovalszky, Ilona; Olah, Julia; Coroniti, Roberta; Knappe, Daniel; Nollmann, Friederike I; Hoffmann, Ralf; Wade, John D; Lovas, Sandor; Surmacz, Eva

    2015-05-01

    Adiponectin, an adipose tissue-excreted adipokine plays protective roles in metabolic and cardiovascular diseases and exerts anti-cancer activities, partially by interfering with leptin-induced signaling. Previously we identified the active site in the adiponectin protein, and generated both a nanomolar monomeric agonist of the adiponectin receptor (10-mer ADP355) and an antagonist (8-mer ADP400) to modulate various adiponectin receptor-mediated cellular functions. As physiologically circulating adiponectin forms multimeric complexes, we also generated an agonist dimer with improved biodistribution and in vitro efficacy. In the current report, we attempted to optimize the monomeric agonist structure. Neither extension of the peptide up to 14-mer analogs nor reinstallation of native residues in permissible positions enhanced significantly the activity profile. The only substitutions that resulted in 5-10-fold improved agonistic activity were the replacement of turn-forming Gly4 and Tyr7 residues with Pro and Hyp, respectively, yielding the more active native β-sheet structure. All peptides retained good stability in human serum exhibiting half-lives >2 h. The cellular efficacy and stability rankings among the peptides followed expected structure-activity relationship trends. To investigate whether simultaneous activation of adiponectin pathways and inhibition of leptin-induced signals can result in cytostatic and anti-oncogenic signal transduction processes, we developed a chimera of the leptin receptor antagonist peptide Allo-aca (placed to the N-terminus) and ADP355 (at the C-terminus). The in vitro anti-tumor activity and intracellular signaling of the chimera were dominated by the more active Allo-aca component. The ADP355 part, however, reversed unfavorable in vivo metabolic effects of the leptin receptor antagonist.

  5. 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.

  6. Isotope selective photoionization of NaK by optimal control: theory and experiment.

    PubMed

    Schäfer-Bung, Boris; Bonacić-Koutecký, Vlasta; Sauer, Franziska; Weber, Stefan M; Wöste, Ludger; Lindinger, Albrecht

    2006-12-07

    We present a joint theoretical and experimental study of the maximization of the isotopomer ratio (23)Na(39)K(23)Na(41)K using tailored phase-only as well as amplitude and phase modulated femtosecond laser fields obtained in the framework of optimal control theory and closed loop learning (CLL) technique. A good agreement between theoretically and experimentally optimized pulse shapes is achieved which allows to assign the optimized processes directly to the pulse shapes obtained by the experimental isotopomer selective CLL approach. By analyzing the dynamics induced by the optimized pulses we show that the mechanism involving the dephasing of the wave packets between the isotopomers (23)Na (39)K and (23)Na (41)K on the first excited state is responsible for high isotope selective ionization. Amplitude and phase modulated pulses, moreover, allow to establish the connection between the spectral components of the pulse and corresponding occupied vibronic states. It will be also shown that the leading features of the theoretically shaped pulses are independent from the initial conditions. Since the underlying processes can be assigned to the individual features of the shaped pulses, we show that optimal control can be used as a tool for analysis.

  7. 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

  8. 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.

  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. 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.

  11. Optimization of high-resolution continuous flow analysis for transient climate signals in ice cores.

    PubMed

    Bigler, Matthias; Svensson, Anders; Kettner, Ernesto; Vallelonga, Paul; Nielsen, Maibritt E; Steffensen, Jørgen Peder

    2011-05-15

    Over the past two decades, continuous flow analysis (CFA) systems have been refined and widely used to measure aerosol constituents in polar and alpine ice cores in very high-depth resolution. Here we present a newly designed system consisting of sodium, ammonium, dust particles, and electrolytic meltwater conductivity detection modules. The system is optimized for high-resolution determination of transient signals in thin layers of deep polar ice cores. Based on standard measurements and by comparing sections of early Holocene and glacial ice from Greenland, we find that the new system features a depth resolution in the ice of a few millimeters which is considerably better than other CFA systems. Thus, the new system can resolve ice strata down to 10 mm thickness and has the potential of identifying annual layers in both Greenland and Antarctic ice cores throughout the last glacial cycle.

  12. 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.

  13. MAXBAND Version 3.1: Heuristic and optimal approach for setting the left turn phase sequences in signalized networks

    SciTech Connect

    Pillai, R.S.; Rathi, A.K.

    1995-02-01

    The main objective of synchronized signal timing is to keep traffic moving along arterials in platoons throughout the signal system by proper setting of left turn phase sequence at signals along the arterials/networks. The synchronization of traffic signals located along the urban/suburban arterials in metropolitan areas is perhaps one of the most cost-effective methods for improving traffic flow along these streets. MAXBAND Version 2.1 (formerly known as MAXBAND-86), a progression-based optimization model, is used for generating signal timing plan for urban networks. This model formulates the problem as a mixed integer linear program and uses Land and Powell branch and bound search to arrive at the optimal solution. The computation time of MAXBAND Version 2.1 tends to be excessive for realistic multiarterial network problems due to the exhaustive nature of the branch and bound search technique. Furthermore, the Land and Powell branch and bound code is known to be numerically unstable, which results in suboptimal solutions for network problems with a range on the cycle time variable. This report presents the development of a new version of MAXBAND called MAXBAND Version 3.1. This new version has a fast heuristic algorithm and a fast optimal algorithm for generating signal timing plan for arterials and networks. MAXBAND 3.1 can generate optimal/near-optimal solutions in fraction of the time needed to compute the optimal solution by Version 2.1. The heuristic algorithm in the new model is based on restricted search using branch and bound technique. The algorithm for generating the optimal solution is faster and more efficient than version 2.1 algorithm. Furthermore, the new version is numerically stable. The efficiency of the new model is demonstrated by numerical results for a set of test problems.

  14. 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.

  15. 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.

  16. 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

  17. An Approach to Feature Selection Based on Ant Colony Optimization and Rough Set

    NASA Astrophysics Data System (ADS)

    Wu, Junyun; Qiu, Taorong; Wang, Lu; Huang, Haiquan

    Feature selection plays an important role in many fields. This paper proposes a method for feature selection which combined the rough set method and ant colony optimization algorithm. The algorithm used the attribute dependence and the attribute importance as the inspiration factor which applied to the transfer rules. For further, the quality of classification based on rough set method and the length of the feature subset were used to build the pheromone update strategy. Through the test of data set, results show that the proposed method is feasible.

  18. 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.

  19. Optimization of signal extraction and front-end design in a fast,multigap ionization chamber

    SciTech Connect

    Datte, P.S.; Manfredi, P.F.; Millaud, J.E.; Placidi, M.; Ratti,L.; Speziali, V.; Traversi, G.; Turner, W.C.

    2001-11-05

    This paper discusses the criteria that have been adopted tooptimize the signal processing in a shower detector to be employed as LHCbeam luminosity monitor. The original aspect ofthis instrument is itsablility to operate on a bunch-by-bunch basis. This means that it mustperform accurate charge measurements at a repetition rate of 40 MHz. Thedetector must withstand an integrated dose of 100 Grad, that is, two tothree orders of magnitude beyond those expected in the experiments. Tomeet the above requirements, an ionization chamber consisting of severalgaps of thickness 0.5 mm, filled with a gas that is expected to beradiation resistant, has been designed. Crucial in the development of thesystem is the signal processing, as the electronic noise may set thedominant limitation to the accuracy of the measurement. This is relatedto two aspects. One is the short time available for the chargemeasurement. The second one is the presence of a few meter cable betweenthe detector and the preamplifier, as this must be located out of theregion of highest radiation field. Therefore the optimization of thesignal-to-noise ratio requires that the best configuration of the chambergaps be determined under the constraint of the presence of a cable ofnon-negligible length between detector and preamplifier. The remoteplacement of the amplifying electronics will require that the front-endelectronics be radiation hard although to a lesser extent than thedetector.

  20. 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."

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. 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

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

    PubMed

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

    2014-05-01

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

  9. Salinomycin inhibits Wnt signaling and selectively induces apoptosis in chronic lymphocytic leukemia cells.

    PubMed

    Lu, Desheng; Choi, Michael Y; Yu, Jian; Castro, Januario E; Kipps, Thomas J; Carson, Dennis A

    2011-08-09

    Salinomycin, an antibiotic potassium ionophore, has been reported recently to act as a selective breast cancer stem cell inhibitor, but the biochemical basis for its anticancer effects is not clear. The Wnt/β-catenin signal transduction pathway plays a central role in stem cell development, and its aberrant activation can cause cancer. In this study, we identified salinomycin as a potent inhibitor of the Wnt signaling cascade. In Wnt-transfected HEK293 cells, salinomycin blocked the phosphorylation of the Wnt coreceptor lipoprotein receptor related protein 6 (LRP6) and induced its degradation. Nigericin, another potassium ionophore with activity against cancer stem cells, exerted similar effects. In otherwise unmanipulated chronic lymphocytic leukemia cells with constitutive Wnt activation nanomolar concentrations of salinomycin down-regulated the expression of Wnt target genes such as LEF1, cyclin D1, and fibronectin, depressed LRP6 levels, and limited cell survival. Normal human peripheral blood lymphocytes resisted salinomycin toxicity. These results indicate that ionic changes induced by salinomycin and related drugs inhibit proximal Wnt signaling by interfering with LPR6 phosphorylation, and thus impair the survival of cells that depend on Wnt signaling at the plasma membrane.

  10. Bz-423 superoxide signals apoptosis via selective activation of JNK, Bak, and Bax.

    PubMed

    Blatt, Neal B; Boitano, Anthony E; Lyssiotis, Costas A; Opipari, Anthony W; Glick, Gary D

    2008-11-01

    Bz-423 is a proapoptotic 1,4-benzodiazepine with potent therapeutic properties in murine models of lupus and psoriasis. Bz-423 modulates the F(1)F(0)-ATPase, inducing the formation of superoxide within the mitochondrial respiratory chain, which then functions as a second messenger initiating apoptosis. Herein, we report the signaling pathway activated by Bz-423 in mouse embryonic fibroblasts containing knockouts of key apoptotic proteins. Bz-423-induced superoxide activates cytosolic ASK1 and its release from thioredoxin. A mitogen-activated protein kinase cascade follows, leading to the specific phosphorylation of JNK. JNK signals activation of Bax and Bak which then induces mitochondrial outer membrane permeabilization to cause the release of cytochrome c and a commitment to apoptosis. The response of these cells to Bz-423 is critically dependent on both superoxide and JNK activation as antioxidants and the JNK inhibitor SP600125 prevents Bax translocation, cytochrome c release, and cell death. These results demonstrate that superoxide generated from the mitochondrial respiratory chain as a consequence of a respiratory transition can signal a sequential and specific apoptotic response. Collectively, these data suggest that the selectivity of Bz-423 observed in vivo results from cell-type specific differences in redox balance and signaling by ASK1 and Bcl-2 proteins.

  11. Selection, optimization, and compensation as strategies of life management: correction to Freund and Baltes (1998)

    PubMed

    Freund; Baltes

    1999-12-01

    Because of a scoring error, the data reported in Freund and Baltes (1998) were reanalyzed. Except for finding a lower positive manifold involving the 3 components of selection, optimization, and compensation (SOC), the outcome of this reanalysis supports the major findings previously reported: Old and very old participants of the Berlin Aging Study reporting SOC-related behaviors also reported higher levels of well-being and aging well. Corrected versions of Tables 3, 6, and 7 are presented.

  12. Estimating the Ages of Selection Signals from Different Epochs in Human History

    PubMed Central

    Nakagome, Shigeki; Alkorta-Aranburu, Gorka; Amato, Roberto; Howie, Bryan; Peter, Benjamin M.; Hudson, Richard R.; Di Rienzo, Anna

    2016-01-01

    Genetic variation harbors signatures of natural selection driven by selective pressures that are often unknown. Estimating the ages of selection signals may allow reconstructing the history of environmental changes that shaped human phenotypes and diseases. We have developed an approximate Bayesian computation (ABC) approach to estimate allele ages under a model of selection on new mutations and under demographic models appropriate for human populations. We have applied it to two resequencing data sets: An ultra-high depth data set from a relatively small sample of unrelated individuals and a lower depth data set in a larger sample with transmission information. In addition to evaluating the accuracy of our method based on simulations, for each SNP, we assessed the consistency between the posterior probabilities estimated by the ABC approach and the ancient DNA record, finding good agreement between the two types of data and methods. Applying this ABC approach to data for eight single nucleotide polymorphisms (SNPs), we were able to rule out an onset of selection prior to the dispersal out-of-Africa for three of them and more recent than the spread of agriculture for an additional three SNPs. PMID:26545921

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

    PubMed

    Jona, J B; Nagaveni, N

    2014-01-15

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

  14. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

    PubMed Central

    Li, Qiang; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts. PMID:28246543

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

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

  16. Optimal landing site selection based on safety index during planetary descent

    NASA Astrophysics Data System (ADS)

    Cui, Pingyuan; Ge, Dantong; Gao, Ai

    2017-03-01

    Landing safety is the prior concern in planetary exploration missions. With the development of precise landing technology, future missions require vehicles to land on places of great scientific interest which are usually surrounded by rocks and craters. In order to perform a safe landing, the vehicle should be capable of detecting hazards, estimating its fuel consumption as well as touchdown performance, and locating a safe spot to land. The landing site selection process can be treated as an optimization problem which, however, cannot be efficiently solved through traditional optimization methods due to its complexity. Hence, the paper proposes a synthetic landing area assessment criterion, safety index, as a solution of the problem, which selects the best landing site by assessing terrain safety, fuel consumption and touchdown performance during descent. The computation effort is cut down after reducing the selection scope and the optimal landing site is found through a quick one-dimensional search. A typical example based on the Mars Science Laboratory mission is simulated to demonstrate the capability of the method. It is proved that the proposed strategy manages to pick out a safe landing site for the mission effectively. The safety index can be applied in various planetary descent phases and provides reference for future mission designs.

  17. Successful aging at work: an applied study of selection, optimization, and compensation through impression management.

    PubMed

    Abraham, J D; Hansson, R O

    1995-03-01

    Although many abilities basic to human performance appear to decrease with age, research has shown that job performance does not generally show comparable declines. Baltes and Baltes (1990) have proposed a model of successful aging involving Selection, Optimization, and Compensation (SOC), that may help explain how individuals maintain important competencies despite age-related losses. In the present study, involving a total of 224 working adults ranging in age from 40 to 69 years, occupational measures of Selection, Optimization, and Compensation through impression management (Compensation-IM) were developed. The three measures were factorially distinct and reliable (Cronbach's alpha > .80). Moderated regression analyses indicated that: (1) the relationship between Selection and self-reported ability/performance maintenance increased with age (p < or = .05); and (2) the relationship between both Optimization and Compensation-IM and goal attainment (i.e., importance-weighted ability/performance maintenance) increased with age (p < or = .05). Results suggest that the SOC model of successful aging may be useful in explaining how older workers can maintain important job competencies. Correlational evidence also suggests, however, that characteristics of the job, workplace, and individual may mediate the initiation and effectiveness of SOC behaviors.

  18. Selective loss of fine tuning of Gq/11 signaling by RGS2 protein exacerbates cardiomyocyte hypertrophy.

    PubMed

    Zhang, Wei; Anger, Thomas; Su, Jialin; Hao, Jianming; Xu, Xiaomei; Zhu, Ming; Gach, Agnieszka; Cui, Lei; Liao, Ronglih; Mende, Ulrike

    2006-03-03

    Alterations in cardiac G protein-mediated signaling, most prominently G(q/11) signaling, are centrally involved in hypertrophy and heart failure development. Several RGS proteins that can act as negative regulators of G protein signaling are expressed in the heart, but their functional roles are still poorly understood. RGS expression changes have been described in hypertrophic and failing hearts. In this study, we report a marked decrease in RGS2 (but not other major cardiac RGS proteins (RGS3-RGS5)) that occurs prior to hypertrophy development in different models with enhanced G(q/11) signaling (transgenic expression of activated Galpha(q)(*) and pressure overload due to aortic constriction). To assess functional consequences of selective down-regulation of endogenous RGS2, we identified targeting sequences for effective RGS2 RNA interference and used lipid-based transfection to achieve uptake of fluorescently labeled RGS2 small interfering RNA in >90% of neonatal and adult ventricular myocytes. Endogenous RGS2 expression was dose-dependently suppressed (up to 90%) with no major change in RGS3-RGS5. RGS2 knockdown increased phenylephrine- and endothelin-1-induced phospholipase Cbeta stimulation in both cell types and exacerbated the hypertrophic effect (increase in cell size and radiolabeled protein) in neonatal myocytes, with no major change in G(q/11)-mediated ERK1/2, p38, or JNK activation. Taken together, this study demonstrates that endogenous RGS2 exerts functionally important inhibitory restraint on G(q/11)-mediated phospholipase Cbeta activation and hypertrophy in ventricular myocytes. Our findings point toward a potential pathophysiological role of loss of fine tuning due to selective RGS2 down-regulation in G(q/11)-mediated remodeling. Furthermore, this study shows the feasibility of effective RNA interference in cardiomyocytes using lipid-based small interfering RNA transfection.

  19. Parameter-adjusted stochastic resonance system for the aperiodic echo chirp signal in optimal FrFT domain

    NASA Astrophysics Data System (ADS)

    Lin, Li-feng; Yu, Lei; Wang, Huiqi; Zhong, Suchuan

    2017-02-01

    In order to improve the system performance for moving target detection and localization, this paper presents a new aperiodic chirp signal and additive noise driving stochastic dynamical system, in which the internal frequency has the linear variation matching with the driving frequency. By using the fractional Fourier transform (FrFT) operator with the optimal order, the proposed time-domain dynamical system is transformed into the equivalent FrFT-domain system driven by the periodic signal and noise. Therefore, system performance is conveniently analyzed from the view of output signal-to-noise ratio (SNR) in optimal FrFT domain. Simulation results demonstrate that the output SNR, as a function of system parameter, shows the different generalized SR behaviors in the case of various internal parameters of driving chirp signal and external parameters of the moving target.

  20. Empirical Performance Model-Driven Data Layout Optimization and Library Call Selection for Tensor Contraction Expressions

    SciTech Connect

    Lu, Qingda; Gao, Xiaoyang; Krishnamoorthy, Sriram; Baumgartner, Gerald; Ramanujam, J.; Sadayappan, Ponnuswamy

    2012-03-01

    Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empirically measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to perform data layout optimization together with the selection of library calls and layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry.

  1. A topography analysis incorporated optimization method for the selection and placement of best management practices.

    PubMed

    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.

  2. A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

    NASA Astrophysics Data System (ADS)

    Maringanti, C.; Chaubey, I.; Arabi, M.; Engel, B.

    2008-07-01

    Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs), such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solution that satisfies the given watershed management objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP selection and placement problem. Most optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC) watersheds. However, most previous works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. In this study, the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II). The limitation with the dynamic linkage with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine concentration reduction

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

    PubMed

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

    2013-03-01

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

  4. [Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

    PubMed

    Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

    2013-03-01

    Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

  5. 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.

  6. 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.

  7. Structural Modifications of (Z)-3-(2-aminoethyl)-5-(4-ethoxybenzylidene)thiazolidine-2,4-dione that Improve Selectivity for the Inhibition of Melanoma Cells Containing Active ERK Signaling

    PubMed Central

    Jung, Kwan-Young; Samadani, Ramin; Chauhan, Jay; Nevels, Kerrick; Yap, Jeremy L.; Zhang, Jun; Worlikar, Shilpa; Lanning, Maryanna E.; Chen, Lijia; Ensey, Mary; Shukla, Sagar; Salmo, Rosene; Heinzl, Geoffrey; Gordon, Caryn; Dukes, Troy; MacKerell, Alexander D.; Shapiro, Paul; Fletcher, Steven

    2013-01-01

    Towards the development of potent and selective inhibitors of melanoma cells containing active ERK signaling, we herein report on the pharmacophore determination and optimization of the ERK docking domain inhibitor (Z)-3-(2-aminoethyl)-5-(4-ethoxybenzylidene)thiazolidine-2,4-dione. PMID:23624850

  8. On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning

    PubMed Central

    Song, Rui; Kosorok, Michael; Zeng, Donglin; Zhao, Yingqi; Laber, Eric; Yuan, Ming

    2015-01-01

    As a new strategy for treatment which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules (ITRs) for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data. PMID:25883393

  9. 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

    2016-08-24

    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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

    Dutta, Anindita; Bahar, Ivet

    2010-01-01

    Many proteins require bound metals to achieve their function. We take advantage of increasing structural data on metal-binding proteins to elucidate three properties: the involvement of metal-binding sites in the global dynamics of the protein, predicted by elastic network models, their exposure/burial to solvent, and their signal-processing properties indicated by Markovian stochastics analysis. Systematic analysis of a dataset of 145 structures reveals that the residues that coordinate metal ions enjoy remarkably efficient and precise signal transduction properties. These properties are rationalized in terms of their physical properties: participation in hinge sites that control the softest modes collectively accessible to the protein and occupancy of central positions minimally exposed to solvent. Our observations suggest that metal-binding sites may have been evolutionary selected to achieve optimum allosteric communication. They also provide insights into basic principles for designing metal-binding sites, which are verified to be met by recently designed de novo metal-binding proteins. PMID:20826340

  12. Selection for individual recognition and the evolution of polymorphic identity signals in Polistes paper wasps.

    PubMed

    Sheehan, M J; Tibbetts, E A

    2010-03-01

    Individual recognition (IR) requires individuals to uniquely identify their social partners based on phenotypic variation. Because IR is so specific, distinctive phenotypes that stand out from the crowd facilitate efficient recognition. Over time, the benefits of unique appearances are predicted to produce a correlation between IR and phenotypic variation. Here, we test whether there is an association between elevated phenotypic polymorphism and IR in paper wasps. Previous work has shown that Polistes fuscatus use variable colour patterns for IR. We test whether two less variable wasp species, Polistes dominulus and Polistes metricus, are capable of IR. As predicted, neither species is capable of IR, suggesting that highly variable colour patterns are confined to Polistes species with IR. This association suggests that elevated phenotypic variation in taxa with IR may be the result of selection for identity signals rather than neutral processes. Given that IR is widespread among social taxa, selection for identity signalling may be an underappreciated mechanism for the origin and maintenance of polymorphism.

  13. An electrostatic selection mechanism controls sequential kinase signaling downstream of the T cell receptor

    PubMed Central

    Shah, Neel H; Wang, Qi; Yan, Qingrong; Karandur, Deepti; Kadlecek, Theresa A; Fallahee, Ian R; Russ, William P; Ranganathan, Rama; Weiss, Arthur; Kuriyan, John

    2016-01-01

    The sequence of events that initiates T cell signaling is dictated by the specificities and order of activation of the tyrosine kinases that signal downstream of the T cell receptor. Using a platform that combines exhaustive point-mutagenesis of peptide substrates, bacterial surface-display, cell sorting, and deep sequencing, we have defined the specificities of the first two kinases in this pathway, Lck and ZAP-70, for the T cell receptor ζ chain and the scaffold proteins LAT and SLP-76. We find that ZAP-70 selects its substrates by utilizing an electrostatic mechanism that excludes substrates with positively-charged residues and favors LAT and SLP-76 phosphosites that are surrounded by negatively-charged residues. This mechanism prevents ZAP-70 from phosphorylating its own activation loop, thereby enforcing its strict dependence on Lck for activation. The sequence features in ZAP-70, LAT, and SLP-76 that underlie electrostatic selectivity likely contribute to the specific response of T cells to foreign antigens. DOI: http://dx.doi.org/10.7554/eLife.20105.001 PMID:27700984

  14. 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

  15. 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.

  16. Spatial Division Multiplexed Microwave Signal processing by selective grating inscription in homogeneous multicore fibers

    PubMed Central

    Gasulla, Ivana; Barrera, David; Hervás, Javier; Sales, Salvador

    2017-01-01

    The use of Spatial Division Multiplexing for Microwave Photonics signal processing is proposed and experimentally demonstrated, for the first time to our knowledge, based on the selective inscription of Bragg gratings in homogeneous multicore fibers. The fabricated devices behave as sampled true time delay elements for radiofrequency signals offering a wide range of operation possibilities within the same optical fiber. The key to processing flexibility comes from the implementation of novel multi-cavity configurations by inscribing a variety of different fiber Bragg gratings along the different cores of a 7-core fiber. This entails the development of the first fabrication method to inscribe high-quality gratings characterized by arbitrary frequency spectra and located in arbitrary longitudinal positions along the individual cores of a multicore fiber. Our work opens the way towards the development of unique compact fiber-based solutions that enable the implementation of a wide variety of 2D (spatial and wavelength diversity) signal processing functionalities that will be key in future fiber-wireless communications scenarios. We envisage that Microwave Photonics systems and networks will benefit from this technology in terms of compactness, operation versatility and performance stability. PMID:28134304

  17. Selection of Antibodies Interfering with Cell Surface Receptor Signaling Using Embryonic Stem Cell Differentiation.

    PubMed

    Melidoni, Anna N; Dyson, Michael R; McCafferty, John

    2016-01-01

    Antibodies able to bind and modify the function of cell surface signaling components in vivo are increasingly being used as therapeutic drugs. The identification of such "functional" antibodies from within large antibody pools is, therefore, the subject of intense research. Here we describe a novel cell-based expression and reporting system for the identification of functional antibodies from antigen-binding populations preselected with phage display. The system involves inducible expression of the antibody gene population from the Rosa-26 locus of embryonic stem (ES) cells, followed by secretion of the antibodies during ES cell differentiation. Target antigens are cell-surface signaling components (receptors or ligands) with a known effect on the direction of cell differentiation (FGFR1 mediating ES cell exit from self renewal in this particular protocol). Therefore, inhibition or activation of these components by functional antibodies in a few elite clones causes a shift in the differentiation outcomes of these clones, leading to their phenotypic selection. Functional antibody genes are then recovered from positive clones and used to produce the purified antibodies, which can be tested for their ability to affect cell fates exogenously. Identified functional antibody genes can be further introduced in different stem cell types. Inducible expression of functional antibodies has a temporally controlled protein-knockdown capability, which can be used to study the unknown role of the signaling pathway in different developmental contexts. Moreover, it provides a means for control of stem cell differentiation with potential in vivo applications.

  18. Sequential Markov chain Monte Carlo filter with simultaneous model selection for electrocardiogram signal modeling.

    PubMed

    Edla, Shwetha; Kovvali, Narayan; Papandreou-Suppappola, Antonia

    2012-01-01

    Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.

  19. 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.

  20. Spatial Division Multiplexed Microwave Signal processing by selective grating inscription in homogeneous multicore fibers.

    PubMed

    Gasulla, Ivana; Barrera, David; Hervás, Javier; Sales, Salvador

    2017-01-30

    The use of Spatial Division Multiplexing for Microwave Photonics signal processing is proposed and experimentally demonstrated, for the first time to our knowledge, based on the selective inscription of Bragg gratings in homogeneous multicore fibers. The fabricated devices behave as sampled true time delay elements for radiofrequency signals offering a wide range of operation possibilities within the same optical fiber. The key to processing flexibility comes from the implementation of novel multi-cavity configurations by inscribing a variety of different fiber Bragg gratings along the different cores of a 7-core fiber. This entails the development of the first fabrication method to inscribe high-quality gratings characterized by arbitrary frequency spectra and located in arbitrary longitudinal positions along the individual cores of a multicore fiber. Our work opens the way towards the development of unique compact fiber-based solutions that enable the implementation of a wide variety of 2D (spatial and wavelength diversity) signal processing functionalities that will be key in future fiber-wireless communications scenarios. We envisage that Microwave Photonics systems and networks will benefit from this technology in terms of compactness, operation versatility and performance stability.

  1. 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.

  2. Spatial Division Multiplexed Microwave Signal processing by selective grating inscription in homogeneous multicore fibers

    NASA Astrophysics Data System (ADS)

    Gasulla, Ivana; Barrera, David; Hervás, Javier; Sales, Salvador

    2017-01-01

    The use of Spatial Division Multiplexing for Microwave Photonics signal processing is proposed and experimentally demonstrated, for the first time to our knowledge, based on the selective inscription of Bragg gratings in homogeneous multicore fibers. The fabricated devices behave as sampled true time delay elements for radiofrequency signals offering a wide range of operation possibilities within the same optical fiber. The key to processing flexibility comes from the implementation of novel multi-cavity configurations by inscribing a variety of different fiber Bragg gratings along the different cores of a 7-core fiber. This entails the development of the first fabrication method to inscribe high-quality gratings characterized by arbitrary frequency spectra and located in arbitrary longitudinal positions along the individual cores of a multicore fiber. Our work opens the way towards the development of unique compact fiber-based solutions that enable the implementation of a wide variety of 2D (spatial and wavelength diversity) signal processing functionalities that will be key in future fiber-wireless communications scenarios. We envisage that Microwave Photonics systems and networks will benefit from this technology in terms of compactness, operation versatility and performance stability.

  3. 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).

  4. Potential accuracy of measuring the angular coordinates of signal sources and accuracy of measuring them using optimal spatial filtration

    NASA Astrophysics Data System (ADS)

    Kalenov, E. N.

    2015-03-01

    The paper investigates the potential accuracy of measuring the angular coordinates of a signal source in the presence of interference sources, as well as the accuracy of measuring these coordinates via the formation of a signal's spatial spectrum using optimal spatial filtration. For a linear equidistant array, analytical solutions are obtained that determine the dependence of the accuracies in measuring the angular coordinates on the array parameters, the angular distance to the noise source, and spectral power densities of a signal, noise, and an interference source.

  5. RasG signaling is important for optimal folate chemotaxis in Dictyostelium

    PubMed Central

    2014-01-01

    Background Signaling pathways linking receptor activation to actin reorganization and pseudopod dynamics during chemotaxis are arranged in complex networks. Dictyostelium discoideum has proven to be an excellent model system for studying these networks and a body of evidence has indicated that RasG and RasC, members of the Ras GTPase subfamily function as key chemotaxis regulators. However, recent evidence has been presented indicating that Ras signaling is not important for Dictyostelium chemotaxis. In this study, we have reexamined the role of Ras proteins in folate chemotaxis and then, having re-established the importance of Ras for this process, identified the parts of the RasG protein molecule that are involved. Results A direct comparison of folate chemotaxis methodologies revealed that rasG-C- cells grown in association with a bacterial food source were capable of positive chemotaxis, only when their initial position was comparatively close to the folate source. In contrast, cells grown in axenic medium orientate randomly regardless of their distance to the micropipette. Folate chemotaxis is restored in rasG-C- cells by exogenous expression of protein chimeras containing either N- or C- terminal halves of the RasG protein. Conclusions Conflicting data regarding the importance of Ras to Dictyostelium chemotaxis were the result of differing experimental methodologies. Both axenic and bacterially grown cells require RasG for optimal folate chemotaxis, particularly in weak gradients. In strong gradients, the requirement for RasG is relaxed, but only in bacterially grown cells. Both N- and C- terminal portions of the RasG protein are important for folate chemotaxis, suggesting that there are functionally important amino acids outside the well established switch I and switch II interaction surfaces. PMID:24742374

  6. Method for selection of optimal road safety composite index with examples from DEA and TOPSIS method.

    PubMed

    Rosić, Miroslav; Pešić, Dalibor; Kukić, Dragoslav; Antić, Boris; Božović, Milan

    2017-01-01

    Concept of composite road safety index is a popular and relatively new concept among road safety experts around the world. As there is a constant need for comparison among different units (countries, municipalities, roads, etc.) there is need to choose an adequate method which will make comparison fair to all compared units. Usually comparisons using one specific indicator (parameter which describes safety or unsafety) can end up with totally different ranking of compared units which is quite complicated for decision maker to determine "real best performers". Need for composite road safety index is becoming dominant since road safety presents a complex system where more and more indicators are constantly being developed to describe it. Among wide variety of models and developed composite indexes, a decision maker can come to even bigger dilemma than choosing one adequate risk measure. As DEA and TOPSIS are well-known mathematical models and have recently been increasingly used for risk evaluation in road safety, we used efficiencies (composite indexes) obtained by different models, based on DEA and TOPSIS, to present PROMETHEE-RS model for selection of optimal method for composite index. Method for selection of optimal composite index is based on three parameters (average correlation, average rank variation and average cluster variation) inserted into a PROMETHEE MCDM method in order to choose the optimal one. The model is tested by comparing 27 police departments in Serbia.

  7. Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

    PubMed Central

    Huang, Xiaoqiang; Han, Kehang; Zhu, Yushan

    2013-01-01

    A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph-theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions. PMID:23649589

  8. [Selection of back-ground electrolyte in capillary zone electrophoresis by triangle and tetrahedron optimization methods].

    PubMed

    Sun, Guoxiang; Song, Wenjing; Lin, Ting

    2008-03-01

    The triangle and tetrahedron optimization methods were developed for the selection of back-ground electrolyte (BGE) in capillary zone electrophoresis (CZE). Chromatographic fingerprint index F and chromatographic fingerprint relative index F(r) were used as the objective functions for the evaluation, and the extract of Saussurea involucrate by water was used as the sample. The BGE was composed of borax, boric acid, dibasic sodium phosphate and sodium dihydrogen phosphate solution with different concentrations using triangle and tetrahedron optimization methods. Re-optimization was carried out by adding organic modifier to the BGE and adjusting the pH value. In triangle method, when 50 mmol/L borax-150 mmol/L sodium dihydrogen phosphate (containing 3% acetonitrile) (1 : 1, v/v) was used as BGE, the isolation was considered to be satisfactory. In tetrahedron method, the best BGE was 50 mmol/L borax-150 mmol/L sodium dihydrogen phosphate-200 mmol/L boric acid (1 : 1 : 2, v/v/v; adjusting the pH value to 8.55 by 0.1 mol/L sodium hydroxide). There were 28 peaks and 25 peaks under the different conditions respectively. The results showed that the methods could be applied to the selection of BGE in CZE of the extract of traditional Chinese medicine by water or ethanol.

  9. Memory control beliefs and everyday forgetfulness in adulthood: the effects of selection, optimization, and compensation strategies.

    PubMed

    Scheibner, Gunnar Benjamin; Leathem, Janet

    2012-01-01

    Controlling for age, gender, education, and self-rated health, the present study used regression analyses to examine the relationships between memory control beliefs and self-reported forgetfulness in the context of the meta-theory of Selective Optimization with Compensation (SOC). Findings from this online survey (N = 409) indicate that, among adult New Zealanders, a higher sense of memory control accounts for a 22.7% reduction in self-reported forgetfulness. Similarly, optimization was found to account for a 5% reduction in forgetfulness while the strategies of selection and compensation were not related to self-reports of forgetfulness. Optimization partially mediated the beneficial effects that some memory beliefs (e.g., believing that memory decline is inevitable and believing in the potential for memory improvement) have on forgetfulness. It was concluded that memory control beliefs are important predictors of self-reported forgetfulness while the support for the SOC model in the context of memory controllability and everyday forgetfulness is limited.

  10. Systematic optimization model and algorithm for binding sequence selection in computational enzyme design.

    PubMed

    Huang, Xiaoqiang; Han, Kehang; Zhu, Yushan

    2013-07-01

    A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph-theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions.

  11. 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

    ... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... at restricted speed” shall be selected through track relays, or through devices that function...

  12. 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

    ... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... at restricted speed” shall be selected through track relays, or through devices that function...

  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

    ... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... at restricted speed” shall be selected through track relays, or through devices that function...

  14. 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

    ... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... at restricted speed” shall be selected through track relays, or through devices that function...

  15. 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

    ... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... relays or devices functioning as track relays and through signal mechanism contacts and time releases at... at restricted speed” shall be selected through track relays, or through devices that function...

  16. Particle swarm optimization for feature selection in classification: a multi-objective approach.

    PubMed

    Xue, Bing; Zhang, Mengjie; Browne, Will N

    2013-12-01

    Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. However, most existing feature selection algorithms treat the task as a single objective problem. This paper presents the first study on multi-objective particle swarm optimization (PSO) for feature selection. The task is to generate a Pareto front of nondominated solutions (feature subsets). We investigate two PSO-based multi-objective feature selection algorithms. The first algorithm introduces the idea of nondominated sorting into PSO to address feature selection problems. The second algorithm applies the ideas of crowding, mutation, and dominance to PSO to search for the Pareto front solutions. The two multi-objective algorithms are compared with two conventional feature selection methods, a single objective feature selection method, a two-stage feature selection algorithm, and three well-known evolutionary multi-objective algorithms on 12 benchmark data sets. The experimental results show that the two PSO-based multi-objective algorithms can automatically evolve a set of nondominated solutions. The first algorithm outperforms the two conventional methods, the single objective method, and the two-stage algorithm. It achieves comparable results with the existing three well-known multi-objective algorithms in most cases. The second algorithm achieves better results than the first algorithm and all other methods mentioned previously.

  17. 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.

  18. Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

    PubMed

    Engemann, Denis A; Gramfort, Alexandre

    2015-03-01

    Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.

  19. 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.

  20. 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

  1. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

    PubMed

    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.

  2. Selecting a proper design period for heliostat field layout optimization using Campo code

    NASA Astrophysics Data System (ADS)

    Saghafifar, Mohammad; Gadalla, Mohamed

    2016-09-01

    In this paper, different approaches are considered to calculate the cosine factor which is utilized in Campo code to expand the heliostat field layout and maximize its annual thermal output. Furthermore, three heliostat fields containing different number of mirrors are taken into consideration. Cosine factor is determined by considering instantaneous and time-average approaches. For instantaneous method, different design days and design hours are selected. For the time average method, daily time average, monthly time average, seasonally time average, and yearly time averaged cosine factor determinations are considered. Results indicate that instantaneous methods are more appropriate for small scale heliostat field optimization. Consequently, it is proposed to consider the design period as the second design variable to ensure the best outcome. For medium and large scale heliostat fields, selecting an appropriate design period is more important. Therefore, it is more reliable to select one of the recommended time average methods to optimize the field layout. Optimum annual weighted efficiency for heliostat fields (small, medium, and large) containing 350, 1460, and 3450 mirrors are 66.14%, 60.87%, and 54.04%, respectively.

  3. Optimal feature selection in the classification of synchronous fluorescence of petroleum oils

    NASA Astrophysics Data System (ADS)

    Siddiqui, Khalid J.; Eastwood, DeLyle

    1996-03-01

    Pattern classification of UV-visible synchronous fluorescence of petroleum oils is performed using a composite system developed by the authors. The system consists of three phases, namely, feature extraction, feature selection and pattern classification. Each of these phases are briefly reviewed, focusing particularly on the feature selection method. Without assuming any particular classification algorithm the method extracts as much information (features) from spectra as conveniently possible and then applies the proposed successive feature elimination process to remove the redundant features. From the remaining features a significantly smaller, yet optimal, feature subset is selected that enhances the recognition performance of the classifier. The successive feature elimination process and optimal feature selection method are formally described. These methods are successfully applied for the classification of UV-visible synchronous fluorescence spectra. The features selected by the algorithm are used to classify twenty different sets of petroleum oils (the design set). A proximity index classifier using the Mahalanobis distance as the proximity criterion is developed using the smaller feature subset. The system was trained on the design set. The recognition performance on the design set was 100%. The recognition performance on the testing set was over 93% by successfully identifying 28 out of 30 samples in six classes. This performance is very encouraging. In addition, the method is computationally inexpensive and is equally useful for large data set problems as it always partitions the problem into a set of two class problems. The method further reduces the need for a careful feature determination problem which a system designer usually encounters during the initial design phase of a pattern classifier.

  4. Optimization of Contrast-to-Tissue Ratio by Adaptation of Transmitted Ternary Signal in Ultrasound Pulse Inversion Imaging

    PubMed Central

    Girault, Jean-Marc

    2013-01-01

    Ultrasound contrast imaging has provided more accurate medical diagnoses thanks to the development of innovating modalities like the pulse inversion imaging. However, this latter modality that improves the contrast-to-tissue ratio (CTR) is not optimal, since the frequency is manually chosen jointly with the probe. However, an optimal choice of this command is possible, but it requires precise information about the transducer and the medium which can be experimentally difficult to obtain, even inaccessible. It turns out that the optimization can become more complex by taking into account the kind of generators, since the generators of electrical signals in a conventional ultrasound scanner can be unipolar, bipolar, or tripolar. Our aim was to seek the ternary command which maximized the CTR. By combining a genetic algorithm and a closed loop, the system automatically proposed the optimal ternary command. In simulation, the gain compared with the usual ternary signal could reach about 3.9 dB. Another interesting finding was that, in contrast to what is generally accepted, the optimal command was not a fixed-frequency signal but had harmonic components. PMID:23573167

  5. Optimization of electron transfer dissociation via informed selection of reagents and operating parameters.

    PubMed

    Compton, Philip D; Strukl, Joseph V; Bai, Dina L; Shabanowitz, Jeffrey; Hunt, Donald F

    2012-02-07

    Electron transfer dissociation (ETD) has improved the mass spectrometric analysis of proteins and peptides with labile post-translational modifications and larger intact masses. Here, the parameters governing the reaction rate of ETD are examined experimentally. Currently, due to reagent injection and isolation events as well as longer reaction times, ETD spectra require significantly more time to acquire than collision-induced dissociation (CID) spectra (>100 ms), resulting in a trade-off in the dynamic range of tandem MS analyses when ETD-based methods are compared to CID-based methods. Through fine adjustment of reaction parameters and the selection of reagents with optimal characteristics, we demonstrate a drastic reduction in the time taken per ETD event. In fact, ETD can be performed with optimal efficiency in nearly the same time as CID at low precursor charge state (z = +3) and becomes faster at higher charge state (z > +3).

  6. Optimization of selection of chain amine scrubbers for CO2 capture.

    PubMed

    Al-Marri, Mohammed J; Khader, Mahmoud M; Giannelis, Emmanuel P; Shibl, Mohamed F

    2014-12-01

    In order to optimize the selection of a suitable amine molecule for CO2 scrubbers, a series of ab initio calculations were performed at the B3LYP/6-31+G(d,p) level of theory. Diethylenetriamine was used as a simple chain amine. Methyl and hydroxyl groups served as examples of electron donors, and electron withdrawing groups like trifluoromethyl and nitro substituents were also evaluated. Interaction distances and binding energies were employed as comparison operators. Moreover, natural bond orbital (NBO) analysis, namely the second order perturbation approach, was applied to determine whether the amine-CO2 interaction is chemical or physical. Different sizes of substituents affect the capture ability of diethylenetriamine. For instance, trifluoromethyl shields the nitrogen atom to which it attaches from the interaction with CO2. The results presented here provide a means of optimizing the choice of amine molecules for developing new amine scrubbers.

  7. Optimization of stress response through the nuclear receptor-mediated cortisol signalling network

    PubMed Central

    Kolodkin, Alexey; Sahin, Nilgun; Phillips, Anna; Hood, Steve R.; Bruggeman, Frank J.; Westerhoff, Hans V.; Plant, Nick

    2013-01-01

    It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model based upon realistic kinetic parameters we are able to reproduce the interaction of the stress hormone cortisol with its two nuclear receptors, the high-affinity glucocorticoid receptor and the low-affinity pregnane X-receptor. We demonstrate that regulatory signals between these two nuclear receptors are necessary to optimize the body’s response to stress episodes, attenuating both the magnitude and duration of the biological response. In addition, we predict that the activation of pregnane X-receptor by multiple, low-affinity endobiotic ligands is necessary for the significant pregnane X-receptor-mediated transcriptional response observed following stress episodes. This integration allows responses mediated through both the high and low-affinity nuclear receptors, which we predict is an important strategy to minimize the risk of disease from chronic stress. PMID:23653204

  8. 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

  9. Pretreatment of wastewater: optimal coagulant selection using Partial Order Scaling Analysis (POSA).

    PubMed

    Tzfati, Eran; Sein, Maya; Rubinov, Angelika; Raveh, Adi; Bick, Amos

    2011-06-15

    Jar-test is a well-known tool for chemical selection for physical-chemical wastewater treatment. Jar test results show the treatment efficiency in terms of suspended matter and organic matter removal. However, in spite of having all these results, coagulant selection is not an easy task because one coagulant can remove efficiently the suspended solids but at the same time increase the conductivity. This makes the final selection of coagulants very dependent on the relative importance assigned to each measured parameter. In this paper, the use of Partial Order Scaling Analysis (POSA) and multi-criteria decision analysis is proposed to help the selection of the coagulant and its concentration in a sequencing batch reactor (SBR). Therefore, starting from the parameters fixed by the jar-test results, these techniques will allow to weight these parameters, according to the judgments of wastewater experts, and to establish priorities among coagulants. An evaluation of two commonly used coagulation/flocculation aids (Alum and Ferric Chloride) was conducted and based on jar tests and POSA model, Ferric Chloride (100 ppm) was the best choice. The results obtained show that POSA and multi-criteria techniques are useful tools to select the optimal chemicals for the physical-technical treatment.

  10. 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

  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. 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.

  14. 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.

  15. 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.

  16. Heuristic Optimization Approach to Selecting a Transport Connection in City Public Transport

    NASA Astrophysics Data System (ADS)

    Kul'ka, Jozef; Mantič, Martin; Kopas, Melichar; Faltinová, Eva; Kachman, Daniel

    2017-02-01

    The article presents a heuristic optimization approach to select a suitable transport connection in the framework of a city public transport. This methodology was applied on a part of the public transport in Košice, because it is the second largest city in the Slovak Republic and its network of the public transport creates a complex transport system, which consists of three different transport modes, namely from the bus transport, tram transport and trolley-bus transport. This solution focused on examining the individual transport services and their interconnection in relevant interchange points.

  17. Burnout and job performance: the moderating role of selection, optimization, and compensation strategies.

    PubMed

    Demerouti, Evangelia; Bakker, Arnold B; Leiter, Michael

    2014-01-01

    The present study aims to explain why research thus far has found only low to moderate associations between burnout and performance. We argue that employees use adaptive strategies that help them to maintain their performance (i.e., task performance, adaptivity to change) at acceptable levels despite experiencing burnout (i.e., exhaustion, disengagement). We focus on the strategies included in the selective optimization with compensation model. Using a sample of 294 employees and their supervisors, we found that compensation is the most successful strategy in buffering the negative associations of disengagement with supervisor-rated task performance and both disengagement and exhaustion with supervisor-rated adaptivity to change. In contrast, selection exacerbates the negative relationship of exhaustion with supervisor-rated adaptivity to change. In total, 42% of the hypothesized interactions proved to be significant. Our study uncovers successful and unsuccessful strategies that people use to deal with their burnout symptoms in order to achieve satisfactory job performance.

  18. 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.

  19. Gas ultrasonic flow rate measurement through genetic-ant colony optimization based on the ultrasonic pulse received signal model

    NASA Astrophysics Data System (ADS)

    Hou, Huirang; Zheng, Dandan; Nie, Laixiao

    2015-04-01

    For gas ultrasonic flowmeters, the signals received by ultrasonic sensors are susceptible to noise interference. If signals are mingled with noise, a large error in flow measurement can be caused by triggering mistakenly using the traditional double-threshold method. To solve this problem, genetic-ant colony optimization (GACO) based on the ultrasonic pulse received signal model is proposed. Furthermore, in consideration of the real-time performance of the flow measurement system, the improvement of processing only the first three cycles of the received signals rather than the whole signal is proposed. Simulation results show that the GACO algorithm has the best estimation accuracy and ant-noise ability compared with the genetic algorithm, ant colony optimization, double-threshold and enveloped zero-crossing. Local convergence doesn’t appear with the GACO algorithm until -10 dB. For the GACO algorithm, the converging accuracy and converging speed and the amount of computation are further improved when using the first three cycles (called GACO-3cycles). Experimental results involving actual received signals show that the accuracy of single-gas ultrasonic flow rate measurement can reach 0.5% with GACO-3 cycles, which is better than with the double-threshold method.

  20. 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

  1. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.

    PubMed

    Garrett, Deon; Peterson, David A; Anderson, Charles W; Thaut, Michael H

    2003-06-01

    The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.

  2. 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

  3. Chronic Deprivation of TrkB Signaling Leads to Selective Late-onset Nigrostriatal Dopaminergic Degeneration

    PubMed Central

    Baydyuk, Maryna; Nguyen, Madeline T.; Xu, Baoji

    2011-01-01

    The pathological hallmark of Parkinson's disease (PD) is a selective and progressive loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc). In the vast majority of cases the appearance of PD is sporadic, and its etiology remains unknown. Several postmortem studies demonstrate reduced levels of brain-derived neurotrophic factor (BDNF) in the SNc of PD patients. Application of BDNF promotes the survival of DA neurons in PD animal models. Here we show that BDNF signaling via its TrkB receptor tyrosine kinase is important for survival of nigrostriatal DA neurons in aging brains. Immunohistochemistry revealed that the TrkB receptor was expressed in DA neurons located in the SNc and ventral tegmental area (VTA). However, a significant loss of DA neurons occurred at 12-24 months of age only in the SNc but not in the VTA of TrkB hypomorphic mice in which the TrkB receptor was expressed at a quarter to a third of the normal amount. The neuronal loss was accompanied by a decrease in dopaminergic axonal terminals in the striatum and by gliosis in both the SNc and striatum. Furthermore, nigrostriatal DA neurons in the TrkB mutant mice were hypersensitive to the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), a mitochondrial complex I inhibitor that selectively kills DA neurons. These results suggest that BDNF-to-TrkB signaling plays an important role in the long-term maintenance of the nigrostriatal system and that its deficiency may contribute to the progression of PD. PMID:21192928

  4. ERK Signal Suppression and Sensitivity to CH5183284/Debio 1347, a Selective FGFR Inhibitor.

    PubMed

    Nakanishi, Yoshito; Mizuno, Hideaki; Sase, Hitoshi; Fujii, Toshihiko; Sakata, Kiyoaki; Akiyama, Nukinori; Aoki, Yuko; Aoki, Masahiro; Ishii, Nobuya

    2015-12-01

    Drugs that target specific gene alterations have proven beneficial in the treatment of cancer. Because cancer cells have multiple resistance mechanisms, it is important to understand the downstream pathways of the target genes and monitor the pharmacodynamic markers associated with therapeutic efficacy. We performed a transcriptome analysis to characterize the response of various cancer cell lines to a selective fibroblast growth factor receptor (FGFR) inhibitor (CH5183284/Debio 1347), a mitogen-activated protein kinase kinase (MEK) inhibitor, or a phosphoinositide 3-kinase (PI3K) inhibitor. FGFR and MEK inhibition produced similar expression patterns, and the extracellular signal-regulated kinase (ERK) gene signature was altered in several FGFR inhibitor-sensitive cell lines. Consistent with these findings, CH5183284/Debio 1347 suppressed phospho-ERK in every tested FGFR inhibitor-sensitive cell line. Because the mitogen-activated protein kinase (MAPK) pathway functions downstream of FGFR, we searched for a pharmacodynamic marker of FGFR inhibitor efficacy in a collection of cell lines with the ERK signature and identified dual-specificity phosphatase 6 (DUSP6) as a candidate marker. Although a MEK inhibitor suppressed the MAPK pathway, most FGFR inhibitor-sensitive cell lines are insensitive to MEK inhibitors and we found potent feedback activation of several pathways via FGFR. We therefore suggest that FGFR inhibitors exert their effect by suppressing ERK signaling without feedback activation. In addition, DUSP6 may be a pharmacodynamic marker of FGFR inhibitor efficacy in FGFR-addicted cancers.

  5. Selective mapping: a strategy for optimizing the construction of high-density linkage maps.

    PubMed Central

    Vision, T J; Brown, D G; Shmoys, D B; Durrett, R T; Tanksley, S D

    2000-01-01

    Historically, linkage mapping populations have consisted of large, randomly selected samples of progeny from a given pedigree or cell lines from a panel of radiation hybrids. We demonstrate that, to construct a map with high genome-wide marker density, it is neither necessary nor desirable to genotype all markers in every individual of a large mapping population. Instead, a reduced sample of individuals bearing complementary recombinational or radiation-induced breakpoints may be selected for genotyping subsequent markers from a large, but sparsely genotyped, mapping population. Choosing such a sample can be reduced to a discrete stochastic optimization problem for which the goal is a sample with breakpoints spaced evenly throughout the genome. We have developed several different methods for selecting such samples and have evaluated their performance on simulated and actual mapping populations, including the Lister and Dean Arabidopsis thaliana recombinant inbred population and the GeneBridge 4 human radiation hybrid panel. Our methods quickly and consistently find much-reduced samples with map resolution approaching that of the larger populations from which they are derived. This approach, which we have termed selective mapping, can facilitate the production of high-quality, high-density genome-wide linkage maps. PMID:10790413

  6. An ant colony optimization based feature selection for web page classification.

    PubMed

    Saraç, Esra; Özel, Selma Ayşe

    2014-01-01

    The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

  7. 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

  8. 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

  9. 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.

  10. Wide-angle narrow-bandpass optical detection system optimally designed to have a large signal-to-noise ratio

    NASA Astrophysics Data System (ADS)

    Schweitzer, Naftali; Arieli, Yoel

    2000-02-01

    A method for achieving optimal design of a wide-angle narrow-bandpass optical detection system composed of a spherical interference filter and a circular photodetector is introduced. It was found that there is an optimal photodetector diameter that maximizes the signal-to-noise ratio (SNR) for a given filter configuration. We show how to optimize optical detection systems based on spherical interference filters for all the important parameters simultaneously. The SNR values of these systems are compared with the SNR values of spherical-step-filter-based detection systems. When large silicon photodetectors are used, the two systems have equal SNR values so that the more economical step-filter systems are preferable. The results given here in the near-infrared region can be used for the optimization of any configuration of a detection system based on a spherical interference filter and a silicon photodetector working at the same wavelength range, without further calculations.

  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. Optimality and stability of symmetric evolutionary games with applications in genetic selection.

    PubMed

    Huang, Yuanyuan; Hao, Yiping; Wang, Min; Zhou, Wen; Wu, Zhijun

    2015-06-01

    Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.

  13. 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

  14. 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.

  15. Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm.

    PubMed

    Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

    2016-01-01

    Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm.

  16. Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Asadzadeh, Masoud; Tolson, Bryan

    2013-12-01

    Pareto archived dynamically dimensioned search (PA-DDS) is a parsimonious multi-objective optimization algorithm with only one parameter to diminish the user's effort for fine-tuning algorithm parameters. This study demonstrates that hypervolume contribution (HVC) is a very effective selection metric for PA-DDS and Monte Carlo sampling-based HVC is very effective for higher dimensional problems (five objectives in this study). PA-DDS with HVC performs comparably to algorithms commonly applied to water resources problems (ɛ-NSGAII and AMALGAM under recommended parameter values). Comparisons on the CEC09 competition show that with sufficient computational budget, PA-DDS with HVC performs comparably to 13 benchmark algorithms and shows improved relative performance as the number of objectives increases. Lastly, it is empirically demonstrated that the total optimization runtime of PA-DDS with HVC is dominated (90% or higher) by solution evaluation runtime whenever evaluation exceeds 10 seconds/solution. Therefore, optimization algorithm runtime associated with the unbounded archive of PA-DDS is negligible in solving computationally intensive problems.

  17. 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.

  18. Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

    PubMed

    Maity, Arnab; Hocht, Leonhard; Heise, Christian; Holzapfel, Florian

    2016-11-28

    A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

  19. Exemplar-Based Policy with Selectable Strategies and its Optimization Using GA

    NASA Astrophysics Data System (ADS)

    Ikeda, Kokolo; Kobayashi, Shigenobu; Kita, Hajime

    As an approach for dynamic control problems and decision making problems, usually formulated as Markov Decision Processes (MDPs), we focus direct policy search (DPS), where a policy is represented by a model with parameters, and the parameters are optimized so as to maximize the evaluation function by applying the parameterized policy to the problem. In this paper, a novel framework for DPS, an exemplar-based policy optimization using genetic algorithm (EBP-GA) is presented and analyzed. In this approach, the policy is composed of a set of virtual exemplars and a case-based action selector, and the set of exemplars are selected and evolved by a genetic algorithm. Here, an exemplar is a real or virtual, free-styled and suggestive information such as ``take the action A at the state S'' or ``the state S1 is better to attain than S2''. One advantage of EBP-GA is the generalization and localization ability for policy expression, based on case-based reasoning methods. Another advantage is that both the introduction of prior knowledge and the extraction of knowledge after optimization are relatively straightforward. These advantages are confirmed through the proposal of two new policy expressions, experiments on two different problems and their analysis.

  20. 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

  1. 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.

  2. 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.

  3. Optimized diffusion of buck semen for saving genetic variability in selected dairy goat populations

    PubMed Central

    2011-01-01

    Background Current research on quantitative genetics has provided efficient guidelines for the sustainable management of selected populations: genetic gain is maximized while the loss of genetic diversity is maintained at a reasonable rate. However, actual selection schemes are complex, especially for large domestic species, and they have to take into account many operational constraints. This paper deals with the actual selection of dairy goats where the challenge is to optimize diffusion of buck semen on the field. Three objectives are considered simultaneously: i) natural service buck replacement (NSR); ii) goat replacement (GR); iii) semen distribution of young bucks to be progeny-tested. An appropriate optimization method is developed, which involves five analytical steps. Solutions are obtained by simulated annealing and the corresponding algorithms are presented in detail. Results The whole procedure was tested on two French goat populations (Alpine and Saanen breeds) and the results presented in the abstract were based on the average of the two breeds. The procedure induced an immediate acceleration of genetic gain in comparison with the current annual genetic gain (0.15 genetic standard deviation unit), as shown by two facts. First, the genetic level of replacement natural service (NS) bucks was predicted, 1.5 years ahead at the moment of reproduction, to be equivalent to that of the progeny-tested bucks in service, born from the current breeding scheme. Second, the genetic level of replacement goats was much higher than that of their dams (0.86 unit), which represented 6 years of selection, although dams were only 3 years older than their replacement daughters. This improved genetic gain could be achieved while decreasing inbreeding coefficients substantially. Inbreeding coefficients (%) of NS bucks was lower than that of the progeny-tested bucks (-0.17). Goats were also less inbred than their dams (-0.67). Conclusions It was possible to account for

  4. Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction

    SciTech Connect

    Stassi, D.; Ma, H.; Schmidt, T. G.; Dutta, S.; Soderman, A.; Pazzani, D.; Gros, E.; Okerlund, D.

    2016-01-15

    Purpose: Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, making it suited for prospectively gated studies where only a subset of phases are available. Methods: An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three

  5. Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection

    PubMed Central

    Bailey, Jacqueline; Timmis, Jon; Chtanova, Tatyana

    2016-01-01

    The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs) against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto fronts of optimal

  6. 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

  7. The RNA-binding protein SERBP1 interacts selectively with the signaling protein RACK1.

    PubMed

    Bolger, Graeme B

    2017-03-04

    The RACK1 protein interacts with numerous proteins involved in signal transduction, the cytoskeleton, and mRNA splicing and translation. We used the 2-hybrid system to identify additional proteins interacting with RACK1 and isolated the RNA-binding protein SERBP1. SERPB1 shares amino acid sequence homology with HABP4 (also known as Ki-1/57), a component of the RNA spicing machinery that has been shown previously to interact with RACK1. Several different isoforms of SERBP1, generated by alternative mRNA splicing, interacted with RACK1 with indistinguishable interaction strength, as determined by a 2-hybrid beta-galactosidase assay. Analysis of deletion constructs of SERBP1 showed that the C-terminal third of the SERBP1 protein, which contains one of its two substrate sites for protein arginine N-methyltransferase 1 (PRMT1), is necessary and sufficient for it to interact with RACK1. Analysis of single amino acid substitutions in RACK1, identified in a reverse 2-hybrid screen, showed very substantial overlap with those implicated in the interaction of RACK1 with the cAMP-selective phosphodiesterase PDE4D5. These data are consistent with SERBP1 interacting selectively with RACK1, mediated by an extensive interaction surface on both proteins.

  8. Selecting Power-Efficient Signal Features for a Low-Power Fall Detector.

    PubMed

    Wang, Changhong; Redmond, Stephen; Lu, Wei; Stevens, Michael; Lord, Stephen; Lovell, Nigel

    2017-02-15

    Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects three features from a set of ten commonly-used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.

  9. 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

  10. Benzimidazole inhibitors from the Niclosamide chemotype inhibit Wnt/β-catenin signaling with selectivity over effects on ATP homeostasis.

    PubMed

    Mook, Robert A; Ren, Xiu-Rong; Wang, Jiangbo; Piao, Hailan; Barak, Larry S; Kim Lyerly, H; Chen, Wei

    2017-03-15

    The Wnt signaling pathway plays a key role in organ and tissue homeostasis, and when dysregulated, can become a major underlying mechanism of disease, particularly cancer. We reported previously that the anthelmintic drug Niclosamide inhibits Wnt/β-catenin signaling and suppresses colon cancer cell growth in vitro and in vivo. To define Niclosamide's mechanism of Wnt/β-catenin inhibition, and to improve its selectivity and pharmacokinetic properties as an anticancer treatment, we designed a novel class of benzimidazole inhibitors of Wnt/β-catenin signaling based on SAR studies of the Niclosamide salicylanilide chemotype. Niclosamide has multiple biological activities. To address selectivity in our design, we interrogated a protonophore SAR model and used the principle of conformational restriction to identify novel Wnt/β-catenin inhibitors with less effect on ATP cellular homeostasis. These studies led to the identification of 4-chloro-2-(5-(trifluoromethyl)-1H-benzo[d]imidazol-2-yl) phenol (4) and related derivatives with greater selectivity for Wnt/β-catenin signaling inhibition vs. differential effects on cellular ATP homeostasis. This is the first report that the Wnt signaling inhibitory activity of Niclosamide can be translated into a new chemical class and to show that its effects on ATP homeostasis can be separated from its inhibitory effects on Wnt signaling. These compounds could be useful tools to elucidate the mechanism of Niclosamide's inhibition of Wnt signaling, and aid the discovery of inhibitors with improved pharmacologic properties to treat cancer and diseases in which Niclosamide has important biological activity.

  11. An optimal method to segment piecewise poisson distributed signals with application to sequencing data.

    PubMed

    Duan, Junbo; Soussen, Charles; Brie, David; Idier, Jerome; Wang, Yu-Ping; Wan, Mingxi

    2015-01-01

    To analyze the next generation sequencing data, the so-called read depth signal is often segmented with standard segmentation tools. However, these tools usually assume the signal to be a piecewise constant signal and contaminated with zero mean Gaussian noise, and therefore modeling error occurs. This paper models the read depth signal with piecewise Poisson distribution, which is more appropriate to the next generation sequencing mechanism. Based on the proposed model, an opti- mal dynamic programming algorithm with parallel computing is proposed to segment the piecewise signal, and furthermore detect the copy number variation.

  12. Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces.

    PubMed

    Wei, Qingguo; Wei, Zhonghai

    2015-01-01

    A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.

  13. Identification and Optimization of the First Highly Selective GLUT1 Inhibitor BAY‐876

    PubMed Central

    Siebeneicher, Holger; Cleve, Arwed; Rehwinkel, Hartmut; Neuhaus, Roland; Heisler, Iring; Müller, Thomas; Bauser, Marcus

    2016-01-01

    Abstract Despite the long‐known fact that the facilitative glucose transporter GLUT1 is one of the key players safeguarding the increase in glucose consumption of many tumor entities even under conditions of normal oxygen supply (known as the Warburg effect), only few endeavors have been undertaken to find a GLUT1‐selective small‐molecule inhibitor. Because other transporters of the GLUT1 family are involved in crucial processes, these transporters should not be addressed by such an inhibitor. A high‐throughput screen against a library of ∼3 million compounds was performed to find a small molecule with this challenging potency and selectivity profile. The N‐(1H‐pyrazol‐4‐yl)quinoline‐4‐carboxamides were identified as an excellent starting point for further compound optimization. After extensive structure–activity relationship explorations, single‐digit nanomolar inhibitors with a selectivity factor of >100 against GLUT2, GLUT3, and GLUT4 were obtained. The most promising compound, BAY‐876 [N 4‐[1‐(4‐cyanobenzyl)‐5‐methyl‐3‐(trifluoromethyl)‐1H‐pyrazol‐4‐yl]‐7‐fluoroquinoline‐2,4‐dicarboxamide], showed good metabolic stability in vitro and high oral bioavailability in vivo. PMID:27552707

  14. Online stimulus optimization rapidly reveals multidimensional selectivity in auditory cortical neurons.

    PubMed

    Chambers, Anna R; Hancock, Kenneth E; Sen, Kamal; Polley, Daniel B

    2014-07-02

    Neurons in sensory brain regions shape our perception of the surrounding environment through two parallel operations: decomposition and integration. For example, auditory neurons decompose sounds by separately encoding their frequency, temporal modulation, intensity, and spatial location. Neurons also integrate across these various features to support a unified perceptual gestalt of an auditory object. At higher levels of a sensory pathway, neurons may select for a restricted region of feature space defined by the intersection of multiple, independent stimulus dimensions. To further characterize how auditory cortical neurons decompose and integrate multiple facets of an isolated sound, we developed an automated procedure that manipulated five fundamental acoustic properties in real time based on single-unit feedback in awake mice. Within several minutes, the online approach converged on regions of the multidimensional stimulus manifold that reliably drove neurons at significantly higher rates than predefined stimuli. Optimized stimuli were cross-validated against pure tone receptive fields and spectrotemporal receptive field estimates in the inferior colliculus and primary auditory cortex. We observed, from midbrain to cortex, increases in both level invariance and frequency selectivity, which may underlie equivalent sparseness of responses in the two areas. We found that onset and steady-state spike rates increased proportionately as the stimulus was tailored to the multidimensional receptive field. By separately evaluating the amount of leverage each sound feature exerted on the overall firing rate, these findings reveal interdependencies between stimulus features as well as hierarchical shifts in selectivity and invariance that may go unnoticed with traditional approaches.

  15. 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.

  16. Online Stimulus Optimization Rapidly Reveals Multidimensional Selectivity in Auditory Cortical Neurons

    PubMed Central

    Hancock, Kenneth E.; Sen, Kamal

    2014-01-01

    Neurons in sensory brain regions shape our perception of the surrounding environment through two parallel operations: decomposition and integration. For example, auditory neurons decompose sounds by separately encoding their frequency, temporal modulation, intensity, and spatial location. Neurons also integrate across these various features to support a unified perceptual gestalt of an auditory object. At higher levels of a sensory pathway, neurons may select for a restricted region of feature space defined by the intersection of multiple, independent stimulus dimensions. To further characterize how auditory cortical neurons decompose and integrate multiple facets of an isolated sound, we developed an automated procedure that manipulated five fundamental acoustic properties in real time based on single-unit feedback in awake mice. Within several minutes, the online approach converged on regions of the multidimensional stimulus manifold that reliably drove neurons at significantly higher rates than predefined stimuli. Optimized stimuli were cross-validated against pure tone receptive fields and spectrotemporal receptive field estimates in the inferior colliculus and primary auditory cortex. We observed, from midbrain to cortex, increases in both level invariance and frequency selectivity, which may underlie equivalent sparseness of responses in the two areas. We found that onset and steady-state spike rates increased proportionately as the stimulus was tailored to the multidimensional receptive field. By separately evaluating the amount of leverage each sound feature exerted on the overall firing rate, these findings reveal interdependencies between stimulus features as well as hierarchical shifts in selectivity and invariance that may go unnoticed with traditional approaches. PMID:24990917

  17. A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.

    PubMed

    Yun, Yong-Huan; Wang, Wei-Ting; Tan, Min-Li; Liang, Yi-Zeng; Li, Hong-Dong; Cao, Dong-Sheng; Lu, Hong-Mei; Xu, Qing-Song

    2014-01-07

    Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. [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.

  3. 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.

  4. 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.

  5. 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.

  6. Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition

    DTIC Science & Technology

    2014-10-01

    IMFs ), and each of the IMFs has better behaved instan- taneous frequency analysis. This paper presents an alternative approach for EMD. The main idea is...Therefore, an IMF can be produced by simply subtracting the average from the signal without iteration. Our numerical examples illustrate that the...stationary signals. It aims at decomposing a signal, via an iterative sifting procedure into several intrinsic mode functions ( IMFs ), and each of the

  7. Selection, optimization, and compensation as strategies of life management: correlations with subjective indicators of successful aging.

    PubMed

    Freund, A M; Baltes, P B

    1998-12-01

    The usefulness of self-reported processes of selection, optimization, and compensation (SOC) for predicting on a correlational level the subjective indicators of successful aging was examined. The sample of Berlin residents was a subset of the participants of the Berlin Aging Study. Three domains (marked by 6 variables) served as outcome measures of successful aging: subjective well-being, positive emotions, and absence of feelings of loneliness. Results confirm the central hypothesis of the SOC model: People who reported using SOC-related life-management behaviors (which were unrelated in content to the outcome measures) had higher scores on the 3 indicators of successful aging. The relationships obtained were robust even after controlling for other measures of successful mastery such as personal life investment, neuroticism, extraversion, openness, control beliefs, intelligence, subjective health, or age.

  8. Adapting to aging losses: do resources facilitate strategies of selection, compensation, and optimization in everyday functioning?

    PubMed

    Lang, Frieder R; Rieckmann, Nina; Baltes, Margret M

    2002-11-01

    Previous cross-sectional research has shown that older people who are rich in sensorimotor-cognitive and social-personality resources are better functioning in everyday life and exhibit fewer negative age differences than resource-poor adults. Longitudinal data from the Berlin Aging Study was used to examine these findings across a 4-year time interval and to compare cross-sectional indicators of adaptive everyday functioning among survivors and nonsurvivors. Apart from their higher survival rate, resource-rich older people (a) invest more social time with their family members, (b) reduce the diversity of activities within the most salient leisure domain, (c) sleep more often and longer during daytime, and (d) increase the variability of time investments across activities after 4 years. Overall, findings suggest a greater use of selection, compensation, and optimization strategies in everyday functioning among resource-rich older adults as compared with resource-poor older adults.

  9. PROBEmer: a web-based software tool for selecting optimal DNA oligos

    PubMed Central

    Emrich, Scott J.; Lowe, Mary; Delcher, Arthur L.

    2003-01-01

    PROBEmer (http://probemer.cs.loyola.edu) is a web-based software tool that enables a researcher to select optimal oligos for PCR applications and multiplex detection platforms including oligonucleotide microarrays and bead-based arrays. Given two groups of nucleic-acid sequences, a target group and a non-target group, the software identifies oligo sequences that occur in members of the target group, but not in the non-target group. To help predict potential cross hybridization, PROBEmer computes all near neighbors in the non-target group and displays their alignments. The software has been used to obtain genus-specific prokaryotic probes based on the 16S rRNA gene, gene-specific probes for expression analyses and PCR primers. In this paper, we describe how to use PROBEmer, the computational methods it employs, and experimental results for oligos identified by this software tool. PMID:12824409

  10. 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.

  11. Optimal pig donor selection in islet xenotransplantation: current status and future perspectives.

    PubMed

    Zhu, Hai-tao; Yu, Liang; Lyu, Yi; Wang, Bo

    2014-08-01

    Islet transplantation is an attractive treatment of type 1 diabetes mellitus. Xenotransplantation, using the pig as a donor, offers the possibility of an unlimited supply of islet grafts. Published studies demonstrated that pig islets could function in diabetic primates for a long time (>6 months). However, pig-islet xenotransplantation must overcome the selection of an optimal pig donor to obtain an adequate supply of islets with high-quality, to reduce xeno-antigenicity of islet and prolong xenograft survival, and to translate experimental findings into clinical application. This review discusses the suitable pig donor for islet xenotransplantation in terms of pig age, strain, structure/function of islet, and genetically modified pig.

  12. Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings.

    PubMed

    Luo, Meng; Li, Chaoshun; Zhang, Xiaoyuan; Li, Ruhai; An, Xueli

    2016-11-01

    This paper proposes a hybrid system named as HGSA-ELM for fault diagnosis of rolling element bearings, in which real-valued gravitational search algorithm (RGSA) is employed to optimize the input weights and bias of ELM, and the binary-valued of GSA (BGSA) is used to select important features from a compound feature set. Three types fault features, namely time and frequency features, energy features and singular value features, are extracted to compose the compound feature set by applying ensemble empirical mode decomposition (EEMD). For fault diagnosis of a typical rolling element bearing system with 56 working condition, comparative experiments were designed to evaluate the proposed method. And results show that HGSA-ELM achieves significant high classification accuracy compared with its original version and methods in literatures.

  13. Research on the optimal selection method of image complexity assessment model index parameter

    NASA Astrophysics Data System (ADS)

    Zhu, Yong; Duan, Jin; Qian, Xiaofei; Xiao, Bo

    2015-10-01

    Target recognition is widely used in national economy, space technology and national defense and other fields. There is great difference between the difficulty of the target recognition and target extraction. The image complexity is evaluating the difficulty level of extracting the target from background. It can be used as a prior evaluation index of the target recognition algorithm's effectiveness. The paper, from the perspective of the target and background characteristics measurement, describe image complexity metrics parameters using quantitative, accurate mathematical relationship. For the collinear problems between each measurement parameters, image complexity metrics parameters are clustered with gray correlation method. It can realize the metrics parameters of extraction and selection, improve the reliability and validity of image complexity description and representation, and optimize the image the complexity assessment calculation model. Experiment results demonstrate that when gray system theory is applied to the image complexity analysis, target characteristics image complexity can be measured more accurately and effectively.

  14. 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.

  15. Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing.

    PubMed

    Wang, Yuhao; Li, Xin; Xu, Kai; Ren, Fengbo; Yu, Hao

    2017-04-01

    Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6× and the silicon area by 1.9× over the data-driven real-valued embedding.

  16. 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.

  17. 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.

  18. Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

    PubMed Central

    Ahmed, Gulnaz; Zou, Jianhua; Zhao, Xi; Sadiq Fareed, Mian Muhammad

    2017-01-01

    The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput. PMID:28241492

  19. Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks.

    PubMed

    Ahmed, Gulnaz; Zou, Jianhua; Zhao, Xi; Sadiq Fareed, Mian Muhammad

    2017-02-23

    The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

  20. 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

  1. 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).

  2. Charge optimization increases the potency and selectivity of a chorismate mutase inhibitor.

    PubMed

    Mandal, Ajay; Hilvert, Donald

    2003-05-14

    The highest affinity inhibitor for chorismate mutases, a conformationally constrained oxabicyclic dicarboxylate transition state analogue, was modified as suggested by computational charge optimization methods. As predicted, replacement of the C10 carboxylate in this molecule with a nitro group yields an even more potent inhibitor of a chorismate mutase from Bacillus subtilis (BsCM), but the magnitude of the improvement (roughly 3-fold, corresponding to a DeltaDeltaG of -0.7 kcal/mol) is substantially lower than the gain of 2-3 kcal/mol binding free energy anticipated for the reduced desolvation penalty upon binding. Experiments with a truncated version of the enzyme show that the flexible C terminus, which was only partially resolved in the crystal structure and hence omitted from the calculations, provides favorable interactions with the C10 group that partially compensate for its desolvation. Although truncation diminishes the affinity of the enzyme for both inhibitors, the nitro derivative binds 1.7 kcal/mol more tightly than the dicarboxylate, in reasonable agreement with the calculations. Significantly, substitution of the C10 carboxylate with a nitro group also enhances the selectivity of inhibition of BsCM relative to a chorismate mutase from Escherichia coli (EcCM), which has a completely different fold and binding pocket, by 10-fold. These results experimentally verify the utility of charge optimization methods for improving interactions between proteins and low-molecular weight ligands.

  3. A Linked Simulation-Optimization (LSO) Model for Conjunctive Irrigation Management using Clonal Selection Algorithm

    NASA Astrophysics Data System (ADS)

    Islam, Sirajul; Talukdar, Bipul

    2016-09-01

    A Linked Simulation-Optimization (LSO) model based on a Clonal Selection Algorithm (CSA) was formulated for application in conjunctive irrigation management. A series of measures were considered for reducing the computational burden associated with the LSO approach. Certain modifications were incurred to the formulated CSA, so as to decrease the number of function evaluations. In addition, a simple problem specific code for a two dimensional groundwater flow simulation model was developed. The flow model was further simplified by a novel approach of area reduction, in order to save computational time in simulation. The LSO model was applied in the irrigation command of the Pagladiya Dam Project in Assam, India. With a view to evaluate the performance of the CSA, a Genetic Algorithm (GA) was used as a comparison base. The results from the CSA compared well with those from the GA. In fact, the CSA was found to consume less computational time than the GA while converging to the optimal solution, due to the modifications incurred in it.

  4. 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.

  5. Dynamic optimization approach for integrated supplier selection and tracking control of single product inventory system with product discount

    NASA Astrophysics Data System (ADS)

    Sutrisno; Widowati; Heru Tjahjana, R.

    2017-01-01

    In this paper, we propose a mathematical model in the form of dynamic/multi-stage optimization to solve an integrated supplier selection problem and tracking control problem of single product inventory system with product discount. The product discount will be stated as a piece-wise linear function. We use dynamic programming to solve this proposed optimization to determine the optimal supplier and the optimal product volume that will be purchased from the optimal supplier for each time period so that the inventory level tracks a reference trajectory given by decision maker with minimal total cost. We give a numerical experiment to evaluate the proposed model. From the result, the optimal supplier was determined for each time period and the inventory level follows the given reference well.

  6. 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.

  7. Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

    PubMed

    Erguzel, Turker Tekin; Ozekes, Serhat; Tan, Oguz; Gultekin, Selahattin

    2015-10-01

    Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

  8. 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.

  9. Model-based multirate Kalman filtering approach for optimal two-dimensional signal reconstruction from noisy subband systems

    NASA Astrophysics Data System (ADS)

    Ni, Jiang Q.; Ho, Ka L.; Tse, Kai W.

    1998-08-01

    Conventional synthesis filters in subband systems lose their optimality when additive noise (due, for example, to signal quantization) disturbs the subband components. The multichannel representation of subband signals is combined with the statistical model of input signal to derive the multirate state-space model for the filter bank system with additive subband noises. Thus the signal reconstruction problem in subband systems can be formulated as the process of optimal state estimation in the equivalent multirate state-space model. Incorporated with the vector dynamic model, a 2D multirate state-space model suitable for 2D Kalman filtering is developed. The performance of the proposed 2D multirate Kalman filter can be further improved through adaptive segmentation of the object plane. The object plane is partitioned into disjoint regions based on their spatial activity, and different vector dynamical models are used to characterize the nonstationary object- plane distributions. Finally, computer simulations with the proposed 2D multirate Kalman filter give favorable results.

  10. Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals.

    PubMed

    Yu, Lianchun; Liu, Liwei

    2014-03-01

    The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

  11. 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.

  12. Autocrine signaling based selection of combinatorial antibodies that transdifferentiate human stem cells.

    PubMed

    Xie, Jia; Zhang, Hongkai; Yea, Kyungmoo; Lerner, Richard A

    2013-05-14

    We report here the generation of antibody agonists from intracellular combinatorial libraries that transdifferentiate human stem cells. Antibodies that are agonists for the granulocyte colony stimulating factor receptor were selected from intracellular libraries on the basis of their ability to activate signaling pathways in reporter cells. We used a specialized "near neighbor" approach in which the entire antibody library and its target receptor are cointegrated into the plasma membranes of a population of reporter cells. This format favors unusual interactions between receptors and their protein ligands and ensures that the antibody acts in an autocrine manner on the cells that produce it. Unlike the natural granulocyte-colony stimulating factor that activates cells to differentiate along a predetermined pathway, the isolated agonist antibodies transdifferentiated human myeloid lineage CD34+ bone marrow cells into neural progenitors. This transdifferentiation by agonist antibodies is different from more commonly used methods because initiation is agenetic. Antibodies that act at the plasma membrane may have therapeutic potential as agents that transdifferentiate autologous cells.

  13. Neuroligins Are Selectively Essential for NMDAR Signaling in Cerebellar Stellate Interneurons

    PubMed Central

    Südhof, Thomas C.

    2016-01-01

    autism. However, the contributions of neuroligins to interneuron functions remain largely unknown. Here, we analyzed the role of neuroligins in cerebellar stellate interneurons. We deleted neuroligin-1, neuroligin-2, and neuroligin-3, the major cerebellar neuroligin isoforms, from stellate cells in triple NL123 conditional knock-out mice and analyzed synaptic responses by acute slice electrophysiology. We find that neuroligins are selectively essential for extrasynaptic NMDAR-mediated signaling, but dispensable for both AMPAR-mediated and inhibitory synaptic transmission. Our results reveal a critical and selective role for neuroligins in the regulation of NMDAR responses in cerebellar stellate interneurons. PMID:27581450

  14. 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.

  15. A bi-objective optimization approach for exclusive bus lane selection and scheduling design

    NASA Astrophysics Data System (ADS)

    Khoo, Hooi Ling; Eng Teoh, Lay; Meng, Qiang

    2014-07-01

    This study proposes a methodology to solve the integrated problems of selection and scheduling of the exclusive bus lane. The selection problem intends to determine which roads (links) should have a lane reserved for buses while the scheduling problem intends to find the time period of the application. It is formulated as a bi-objective optimization model that aims to minimize the total travel time of non-bus traffic and buses simultaneously. The proposed model formulation is solved by the hybrid non-dominated sorting genetic algorithm with Paramics. The results show that the proposed methodology is workable. Sets of Pareto solutions are obtained indicating that a trade-off between buses and non-bus traffic for the improvement of the bus transit system is necessary when the exclusive bus lane is applied. This allows the engineer to choose the best solutions that could balance the performance of both modes in a multimode transport system environment to achieve a sustainable transport system.

  16. On the incomplete architecture of human ontogeny. Selection, optimization, and compensation as foundation of developmental theory.

    PubMed

    Baltes, P B

    1997-04-01

    Drawing on both evolutionary and ontogenetic perspectives, the basic biological-genetic and social-cultural architecture of human development is outlined. Three principles are involved. First, evolutionary selection pressure predicts a negative age correlation, and therefore, genome-based plasticity and biological potential decrease with age. Second, for growth aspects of human development to extend further into the life span, culture-based resources are required at ever-increasing levels. Third, because of age-related losses in biological plasticity, the efficiency of culture is reduced as life span development unfolds. Joint application of these principles suggests that the life span architecture becomes more and more incomplete with age. Degree of completeness can be defined as the ratio between gains and losses in functioning. Two examples illustrate the implications of the life span architecture proposed. The first is a general theory of development involving the orchestration of 3 component processes: selection, optimization, and compensation. The second considers the task of completing the life course in the sense of achieving a positive balance between gains and losses for all age levels. This goal is increasingly more difficult to attain as human development is extended into advanced old age.

  17. A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

    PubMed Central

    Lin, Jingjing; Jing, Honglei

    2016-01-01

    Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662

  18. 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.

  19. Structure based lead optimization approach in discovery of selective DPP4 inhibitors.

    PubMed

    Ghate, Manjunath; Jain, Shailesh V

    2013-05-01

    Diabetes mellitus is a chronic progressive metabolic disorder that has profound consequences for individuals, families, and society. To date, main available oral antidiabetic medications target either insulin resistance (metformin, glitazones), or insulin deficiency (sulfonylureas, glinides), but leading to shortfalls in medication. Advancement in modern oral hypoglycemic agents may be encouraged with or in place of traditional therapies. The lower risk for hypoglycemic events as compared with other insulinotropic or insulin-sensitizing agents make DPP-4 inhibitors very promising candidates for a more physiological treatment of type-2 diabetes. Only some DPP-4 inhibitors are currently used for the treatment of type 2 diabetes (T2DM) and various inhibitors currently undergoing animal and human testing. A number of catalytically active DPPs distinct from DPP-4 (DPP II, FAP, DPP-8, and DPP-9) have been described that is associated with side-effect and toxicity. To discover potent and selective and safer drugs in a shorter time frame and with reduced cost it requires using an innovative approach for designing novel inhibitors. This review article focuses on the status of advanced lead candidates of DPP group and their binding affinity with the active site residue of target structure which help in discovery of potent and selective DPP-4 inhibitors by lead optimization approach.

  20. Combining heterogeneous features for face detection using multiscale feature selection with binary particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Pan, Hong; Xia, Si-Yu; Jin, Li-Zuo; Xia, Liang-Zheng

    2011-12-01

    We propose a fast multiscale face detector that boosts a set of SVM-based hierarchy classifiers constructed with two heterogeneous features, i.e. Multi-block Local Binary Patterns (MB-LBP) and Speeded Up Robust Features (SURF), at different image resolutions. In this hierarchical architecture, simple and fast classifiers using efficient MB-LBP descriptors remove large parts of the background in low and intermediate scale layers, thus only a small percentage of background patches look similar to faces and require a more accurate but slower classifier that uses distinctive SURF descriptor to avoid false classifications in the finest scale. By propagating only those patterns that are not classified as background, we can quickly decrease the amount of data need to be processed. To lessen the training burden of the hierarchy classifier, in each scale layer, a feature selection scheme using Binary Particle Swarm Optimization (BPSO) searches the entire feature space and filters out the minimum number of discriminative features that give the highest classification rate on a validation set, then these selected distinctive features are fed into the SVM classifier. We compared detection performance of the proposed face detector with other state-of-the-art methods on the CMU+MIT face dataset. Our detector achieves the best overall detection performance. The training time of our algorithm is 60 times faster than the standard Adaboost algorithm. It takes about 70 ms for our face detector to process a 320×240 image, which is comparable to Viola and Jones' detector.

  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. End-to-End Rate-Distortion Optimized MD Mode Selection for Multiple Description Video Coding

    NASA Astrophysics Data System (ADS)

    Heng, Brian A.; Apostolopoulos, John G.; Lim, Jae S.

    2006-12-01

    Multiple description (MD) video coding can be used to reduce the detrimental effects caused by transmission over lossy packet networks. A number of approaches have been proposed for MD coding, where each provides a different tradeoff between compression efficiency and error resilience. How effectively each method achieves this tradeoff depends on the network conditions as well as on the characteristics of the video itself. This paper proposes an adaptive MD coding approach which adapts to these conditions through the use of adaptive MD mode selection. The encoder in this system is able to accurately estimate the expected end-to-end distortion, accounting for both compression and packet loss-induced distortions, as well as for the bursty nature of channel losses and the effective use of multiple transmission paths. With this model of the expected end-to-end distortion, the encoder selects between MD coding modes in a rate-distortion (R-D) optimized manner to most effectively tradeoff compression efficiency for error resilience. We show how this approach adapts to both the local characteristics of the video and network conditions and demonstrates the resulting gains in performance using an H.264-based adaptive MD video coder.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

    PubMed

    Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

    2017-01-01

    This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

  9. An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

    PubMed Central

    Lou, Xin Yuan; Sun, Lin Fu

    2017-01-01

    This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

  10. The influence of the pressure force control signal on selected parameters of the vehicle continuously variable transmission

    NASA Astrophysics Data System (ADS)

    Bieniek, A.; Graba, M.; Prażnowski, K.

    2016-09-01

    The paper presents results of research on the effect of frequency control signal on the course selected operating parameters of the continuously variable transmission CVT. The study used a gear Fuji Hyper M6 with electro-hydraulic control system and proprietary software for control and data acquisition developed in LabView environment.

  11. 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

    ... circuit controller operated by switch points or by switch locking mechanism. 236.303 Section 236.303... § 236.303 Control circuits for signals, selection through circuit controller operated by switch points or by switch locking mechanism. The control circuit for each aspect with indication more...

  12. 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

    ... circuit controller operated by switch points or by switch locking mechanism. 236.303 Section 236.303... § 236.303 Control circuits for signals, selection through circuit controller operated by switch points or by switch locking mechanism. The control circuit for each aspect with indication more...

  13. 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....

  14. 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....

  15. 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....

  16. 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.

  17. Evaluating Varied Label Designs for Use with Medical Devices: Optimized Labels Outperform Existing Labels in the Correct Selection of Devices and Time to Select

    PubMed Central

    Seo, Do Chan; Ladoni, Moslem; Brunk, Eric; Becker, Mark W.

    2016-01-01

    Purpose Effective standardization of medical device labels requires objective study of varied designs. Insufficient empirical evidence exists regarding how practitioners utilize and view labeling. Objective Measure the effect of graphic elements (boxing information, grouping information, symbol use and color-coding) to optimize a label for comparison with those typical of commercial medical devices. Design Participants viewed 54 trials on a computer screen. Trials were comprised of two labels that were identical with regard to graphics, but differed in one aspect of information (e.g., one had latex, the other did not). Participants were instructed to select the label along a given criteria (e.g., latex containing) as quickly as possible. Dependent variables were binary (correct selection) and continuous (time to correct selection). Participants Eighty-nine healthcare professionals were recruited at Association of Surgical Technologists (AST) conferences, and using a targeted e-mail of AST members. Results Symbol presence, color coding and grouping critical pieces of information all significantly improved selection rates and sped time to correct selection (α = 0.05). Conversely, when critical information was graphically boxed, probability of correct selection and time to selection were impaired (α = 0.05). Subsequently, responses from trials containing optimal treatments (color coded, critical information grouped with symbols) were compared to two labels created based on a review of those commercially available. Optimal labels yielded a significant positive benefit regarding the probability of correct choice ((P<0.0001) LSM; UCL, LCL: 97.3%; 98.4%, 95.5%)), as compared to the two labels we created based on commercial designs (92.0%; 94.7%, 87.9% and 89.8%; 93.0%, 85.3%) and time to selection. Conclusions Our study provides data regarding design factors, namely: color coding, symbol use and grouping of critical information that can be used to significantly enhance

  18. 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

  19. Optimal FPGA implementation of CL multiwavelets architecture for signal denoising application

    NASA Astrophysics Data System (ADS)

    Mohan Kumar, B.; Vidhya Lavanya, R.; Sumesh, E. P.

    2013-03-01

    Wavelet transform is considered one of the efficient transforms of this decade for real time signal processing. Due to implementation constraints scalar wavelets do not possess the properties such as compact support, regularity, orthogonality and symmetry, which are desirable qualities to provide a good signal to noise ratio (SNR) in case of signal denoising. This leads to the evolution of the new dimension of wavelet called 'multiwavelets', which possess more than one scaling and wavelet filters. The architecture implementation of multiwavelets is an emerging area of research. In real time, the signals are in scalar form, which demands the processing architecture to be scalar. But the conventional Donovan Geronimo Hardin Massopust (DGHM) and Chui-Lian (CL) multiwavelets are vectored and are also unbalanced. In this article, the vectored multiwavelet transforms are converted into a scalar form and its architecture is implemented in FPGA (Field Programmable Gate Array) for signal denoising application. The architecture is compared with DGHM multiwavelets architecture in terms of several objective and performance measures. The CL multiwavelets architecture is further optimised for best performance by using DSP48Es. The results show that CL multiwavelet architecture is suited better for the signal denoising application.

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

    PubMed Central

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

    2015-01-01

    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. Aldehyde dehydrogenase activity selects for lung adenocarcinoma stem cells dependent on notch signaling.

    PubMed

    Sullivan, James P; Spinola, Monica; Dodge, Michael; Raso, Maria G; Behrens, Carmen; Gao, Boning; Schuster, Katja; Shao, Chunli; Larsen, Jill E; Sullivan, Laura A; Honorio, Sofia; Xie, Yang; Scaglioni, Pier P; DiMaio, J Michael; Gazdar, Adi F; Shay, Jerry W; Wistuba, Ignacio I; Minna, John D

    2010-12-01

    Aldehyde dehydrogenase (ALDH) is a candidate marker for lung cancer cells with stem cell-like properties. Immunohistochemical staining of a large panel of primary non-small cell lung cancer (NSCLC) samples for ALDH1A1, ALDH3A1, and CD133 revealed a significant correlation between ALDH1A1 (but not ALDH3A1 or CD133) expression and poor prognosis in patients including those with stage I and N0 disease. Flow cytometric analysis of a panel of lung cancer cell lines and patient tumors revealed that most NSCLCs contain a subpopulation of cells with elevated ALDH activity, and that this activity is associated with ALDH1A1 expression. Isolated ALDH(+) lung cancer cells were observed to be highly tumorigenic and clonogenic as well as capable of self-renewal compared with their ALDH(-) counterparts. Expression analysis of sorted cells revealed elevated Notch pathway transcript expression in ALDH(+) cells. Suppression of the Notch pathway by treatment with either a γ-secretase inhibitor or stable expression of shRNA against NOTCH3 resulted in a significant decrease in ALDH(+) lung cancer cells, commensurate with a reduction in tumor cell proliferation and clonogenicity. Taken together, these findings indicate that ALDH selects for a subpopulation of self-renewing NSCLC stem-like cells with increased tumorigenic potential, that NSCLCs harboring tumor cells with ALDH1A1 expression have inferior prognosis, and that ALDH1A1 and CD133 identify different tumor subpopulations. Therapeutic targeting of the Notch pathway reduces this ALDH(+) component, implicating Notch signaling in lung cancer stem cell maintenance.

  2. Selective effects of potassium elevations on glutamate signaling and action potential conduction in hippocampus.

    PubMed

    Meeks, Julian P; Mennerick, Steven

    2004-01-07

    High-frequency synaptic transmission is depressed by moderate rises in the extracellular potassium concentration ([K+]o). Previous reports have indicated that depression of action potential signaling may underlie the synaptic depression. Here, we investigated the specific contribution of K+-induced action potential changes to synaptic depression. We found that glutamatergic transmission in the hippocampal area CA1 was significantly depressed by 8-10 mM [K+]o, but that GABAergic transmission remained intact. Riluzole, a drug that slows recovery from inactivation of voltage-gated sodium channels (NaChs), interacts with subthreshold [K+]o to depress afferent volleys and EPSCs strongly. Thus, elevated [K+]o likely depresses synapses by slowing NaCh recovery from inactivation. It is unclear from previous studies whether [K+]o-induced action potential depression is caused by changes in initiation, reliability, or waveform. We investigated these possibilities explicitly. [K+]o-induced afferent volley depression was independent of stimulus strength, suggesting that changes in action potential initiation do not explain [K+]o-induced depression. Measurements of action potentials from single axons revealed that 8 mM [K+]o increased conduction failures in a subpopulation of fibers and depressed action potential amplitude in all fibers. Together, these changes quantitatively account for the afferent volley depression. We estimate that conduction failure explains more than half of the synaptic depression observed at 8 mM [K+]o, with the remaining depression likely explained by waveform changes. These mechanisms of selective sensitivity of glutamate release to [K+]o accumulation represent a unique neuromodulatory mechanism and a brake on runaway excitation.

  3. Acute paraquat exposure impairs colonic motility by selectively attenuating nitrergic signalling in the mouse.

    PubMed

    Diss, Lucy; Dyball, Sarah; Ghela, Tina; Golding, Jonathan; Morris, Rachel; Robinson, Stephen; Tucker, Rosemary; Walter, Talia; Young, Paul; Allen, Marcus; Fidalgo, Sara; Gard, Paul; Mabley, Jon; Patel, Bhavik; Chatterjee, Prabal; Yeoman, Mark

    2016-02-01

    Paraquat, a common herbicide, is responsible for large numbers of deaths worldwide through both deliberate and accidental ingestion. Previous studies have eluded that the bioavailability of paraquat increases substantially with increasing dose and that these changes may in part be due to the effects that these high concentrations have on the gastrointestinal tract (GI tract). To date, the actions of acute, high concentrations (20mM for 60 min) of paraquat on the GI tract, particularly the colon which is a major site of paraquat absorption, are unknown. This study examined the effects of acute paraquat administration on colonic motility in the C57BL/6 mouse. Acute paraquat exposure decreased colonic motility and the amplitude of colonic migrating motor complexes (CMMCs), which are major motor patterns involved in faecal pellet propulsion. In isolated segments of distal colon, paraquat increased resting tension and markedly attenuated electrical field stimulation-evoked relaxations. Pharmacological dissection of paraquat's mechanism of action on both the CMMCs and field stimulated tissue using the nitric oxide synthase inhibitor NG-nitro-L-arginine and direct measurement of NO release from the myenteric plexus, demonstrated that paraquat selectively attenuates nitrergic signalling pathways. These changes did not appear to be due to alterations in colonic oxidative stress, inflammation or complex 1 activity, but were most likely caused by paraquat's ability to act as a redox couple. In summary, these data demonstrate that acute paraquat exposure attenuates colonic transit. These changes may facilitate the absorption of paraquat into the circulation and so facilitate its toxicity.

  4. Neural network cascade optimizes microRNA biomarker selection for nasopharyngeal cancer prognosis.

    PubMed

    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.

  5. 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 level. Experiments are based on two different workstations, used to teach students the fundamentals of SLS. First of all a 50 W CO2 laser workstation is used to investigate the interaction of the laser beam with the used material in accordance with varied process parameters to analyze a single-layered test piece. Second of all the FORMIGA P110 laser sintering system from EOS is used to print different 3D test pieces in dependence on various process parameters. Finally quality attributes are tested including warpage, dimension accuracy or tensile strength. For dimension measurements and evaluation of the surface structure a telecentric lens in combination with a camera is used. A tensile test machine allows testing of the tensile strength and the interpreting of stress-strain curves. The developed laboratory experiments are suitable to teach students the influence of processing parameters. In this context they will be able to optimize the input parameters depending on the component which has to be manufactured and to increase the overall quality of the final workpiece.

  6. Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

    NASA Astrophysics Data System (ADS)

    Ozkat, Erkan Caner; Franciosa, Pasquale; Ceglarek, Dariusz

    2017-08-01

    Remote laser welding technology offers opportunities for high production throughput at a competitive cost. However, the remote laser welding process of zinc-coated sheet metal parts in lap joint configuration poses a challenge due to the difference between the melting temperature of the steel (∼1500 °C) and the vapourizing temperature of the zinc (∼907 °C). In fact, the zinc layer at the faying surface is vapourized and the vapour might be trapped within the melting pool leading to weld defects. Various solutions have been proposed to overcome this problem over the years. Among them, laser dimpling has been adopted by manufacturers because of its flexibility and effectiveness along with its cost advantages. In essence, the dimple works as a spacer between the two sheets in lap joint and allows the zinc vapour escape during welding process, thereby preventing weld defects. However, there is a lack of comprehensive characterization of dimpling process for effective implementation in real manufacturing system taking into consideration inherent changes in variability of process parameters. This paper introduces a methodology to develop (i) surrogate model for dimpling process characterization considering multiple-inputs (i.e. key control characteristics) and multiple-outputs (i.e. key performance indicators) system by conducting physical experimentation and using multivariate adaptive regression splines; (ii) process capability space (Cp-Space) based on the developed surrogate model that allows the estimation of a desired process fallout rate in the case of violation of process requirements in the presence of stochastic variation; and, (iii) selection and optimization of the process parameters based on the process capability space. The proposed methodology provides a unique capability to: (i) simulate the effect of process variation as generated by manufacturing process; (ii) model quality requirements with multiple and coupled quality requirements; and (iii

  7. Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms.

    PubMed

    Le Thi, Hoai An; Vo, Xuan Thanh; Pham Dinh, Tao

    2014-11-01

    In this paper, we consider the problem of feature selection for linear SVMs on uncertain data that is inherently prevalent in almost all datasets. Using principles of Robust Optimization, we propose robust schemes to handle data with ellipsoidal model and box model of uncertainty. The difficulty in treating ℓ0-norm in feature selection problem is overcome by using appropriate approximations and Difference of Convex functions (DC) programming and DC Algorithms (DCA). The computational results show that the proposed robust optimization approaches are superior than a traditional approach in immunizing perturbation of the data.

  8. Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Garg, Sanjay

    2011-01-01

    An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined 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 that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation

  9. Enhanced NMR with Optical Pumping Yields (75)As Signals Selectively from a Buried GaAs Interface.

    PubMed

    Willmering, Matthew M; Ma, Zayd L; Jenkins, Melanie A; Conley, John F; Hayes, Sophia E

    2017-03-22

    We have measured the (75)As signals arising from the interface region of single-crystal semi-insulating GaAs that has been coated and passivated with an aluminum oxide film deposited by atomic layer deposition (ALD) with optically pumped NMR (OPNMR). Using wavelength-selective optical pumping, the laser restricts the volume from which OPNMR signals are collected. Here, OPNMR signals were obtained from the interface region and distinguished from signals arising from the bulk. The interface region is highlighted by interactions that disrupt the cubic symmetry of the GaAs lattice, resulting in quadrupolar satellites for nuclear [Formula: see text] isotopes, whereas NMR of the "bulk" lattice is nominally unsplit. Quadrupolar splitting at the interface arises from strain based on lattice mismatch between the GaAs and ALD-deposited aluminum oxide due to their different coefficients of thermal expansion. Such spectroscopic evidence of strain can be useful for measuring lattice distortions at heterojunction boundaries and interfaces.

  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. Protein purification using chromatography: selection of type, modelling and optimization of operating conditions.

    PubMed

    Asenjo, J A; Andrews, B A

    2009-01-01

    To achieve a high level of purity in the purification of recombinant proteins for therapeutic or analytical application, it is necessary to use several chromatographic steps. There is a range of techniques available including anion and cation exchange, which can be carried out at different pHs, hydrophobic interaction chromatography, gel filtration and affinity chromatography. In the case of a complex mixture of partially unknown proteins or a clarified cell extract, there are many different routes one can take in order to choose the minimum and most efficient number of purification steps to achieve a desired level of purity (e.g. 98%, 99.5% or 99.9%). This review shows how an initial 'proteomic' characterization of the complex mixture of target protein and protein contaminants can be used to select the most efficient chromatographic separation steps in order to achieve a specific level of purity with a minimum number of steps. The chosen methodology was implemented in a computer- based Expert System. Two algorithms were developed, the first algorithm was used to select the most efficient purification method to separate a protein from its contaminants based on the physicochemical properties of the protein product and the protein contaminants and the second algorithm was used to predict the number and concentration of contaminants after each separation as well as protein product purity. The application of the Expert System approach was experimentally tested and validated with a mixture of four proteins and the experimental validation was also carried out with a supernatant of Bacillus subtilis producing a recombinant beta-1,3-glucanase. Once the type of chromatography is chosen, optimization of the operating conditions is essential. Chromatographic elution curves for a three-protein mixture (alpha-lactoalbumin, ovalbumin and beta-lactoglobulin), carried out under different flow rates and ionic strength conditions, were simulated using two different mathematical

  12. Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

    NASA Astrophysics Data System (ADS)

    Zhu, Zhe; Gallant, Alisa L.; Woodcock, Curtis E.; Pengra, Bruce; Olofsson, Pontus; Loveland, Thomas R.; Jin, Suming; Dahal, Devendra; Yang, Limin; Auch, Roger F.

    2016-12-01

    The U.S. Geological Survey's Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4-8. Training data were selected from map products of the U.S. Geological Survey's Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening "Function of mask" (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow

  13. 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.

  14. Mass cytometry panel optimization through the designed distribution of signal interference.

    PubMed

    Takahashi, Chikara; Au-Yeung, Amelia; Fuh, Franklin; Ramirez-Montagut, Teresa; Bolen, Chris; Mathews, William; O'Gorman, William E

    2017-01-01

    Mass cytometry is capable of measuring more than 40 distinct proteins on individual cells making it a promising technology for innovating biomarker discovery. However, in order for this potential to be fully realized, best practices in panel design need to be further defined in order to achieve consistency and reproducibility in data analysis. Of particular importance are controls that reveal, and panel design principles that mitigate the effects of signal interference or overlap. We observed a disparity between the staining profiles of two noncompeting anti- integrin β7 mAbs and hypothesized that signal interference was responsible. A mass-minus-one (MMO) control was applied and demonstrated that signal overlap caused the perceived interclonal discrepancy in β7 expression. Panel redesign in consideration of mass-cytometry specific interference dynamics dramatically improved concordance between both mAbs by redistributing background signals caused by overlap. These studies visualize how signal overlap can complicate mass cytometry data interpretation and demonstrate how the rational distribution of interference can greatly improve panel design and data quality. © 2016 International Society for Advancement of Cytometry.

  15. Analysis of boutique arrays: a universal method for the selection of the optimal data normalization procedure.

    PubMed

    Uszczyńska, Barbara; Zyprych-Walczak, Joanna; Handschuh, Luiza; Szabelska, Alicja; Kaźmierczak, Maciej; Woronowicz, Wiesława; Kozłowski, Piotr; Sikorski, Michał M; Komarnicki, Mieczysław; Siatkowski, Idzi; Figlerowicz, Marek

    2013-09-01

    DNA microarrays, which are among the most popular genomic tools, are widely applied in biology and medicine. Boutique arrays, which are small, spotted, dedicated microarrays, constitute an inexpensive alternative to whole-genome screening methods. The data extracted from each microarray-based experiment must be transformed and processed prior to further analysis to eliminate any technical bias. The normalization of the data is the most crucial step of microarray data pre-processing and this process must be carefully considered as it has a profound effect on the results of the analysis. Several normalization algorithms have been developed and implemented in data analysis software packages. However, most of these methods were designed for whole-genome analysis. In this study, we tested 13 normalization strategies (ten for double-channel data and three for single-channel data) available on R Bioconductor and compared their effectiveness in the normalization of four boutique array datasets. The results revealed that boutique arrays can be successfully normalized using standard methods, but not every method is suitable for each dataset. We also suggest a universal seven-step workflow that can be applied for the selection of the optimal normalization procedure for any boutique array dataset. The described workflow enables the evaluation of the investigated normalization methods based on the bias and variance values for the control probes, a differential expression analysis and a receiver operating characteristic curve analysis. The analysis of each component results in a separate ranking of the normalization methods. A combination of the ranks obtained from all the normalization procedures facilitates the selection of the most appropriate normalization method for the studied dataset and determines which methods can be used interchangeably.

  16. A data driven model for optimal orthosis selection in children with cerebral palsy.

    PubMed

    Ries, Andrew J; Novacheck, Tom F; Schwartz, Michael H

    2014-09-01

    A statistical orthosis selection model was developed using the Random Forest Algorithm (RFA). The model's performance and potential clinical benefit was evaluated. The model predicts which of five orthosis designs - solid (SAFO), posterior leaf spring (PLS), hinged (HAFO), supra-malleolar (SMO), or foot orthosis (FO) - will provide the best gait outcome for individuals with diplegic cerebral palsy (CP). Gait outcome was defined as the change in Gait Deviation Index (GDI) between walking while wearing an orthosis compared to barefoot (ΔGDI=GDIOrthosis-GDIBarefoot). Model development was carried out using retrospective data from 476 individuals who wore one of the five orthosis designs bilaterally. Clinical benefit was estimated by predicting the optimal orthosis and ΔGDI for 1016 individuals (age: 12.6 (6.7) years), 540 of whom did not have an existing orthosis prescription. Among limbs with an orthosis, the model agreed with the prescription only 14% of the time. For 56% of limbs without an orthosis, the model agreed that no orthosis was expected to provide benefit. Using the current standard of care orthosis (i.e. existing orthosis prescriptions), ΔGDI is only +0.4 points on average. Using the orthosis prediction model, average ΔGDI for orthosis users was estimated to improve to +5.6 points. The results of this study suggest that an orthosis selection model derived from the RFA can significantly improve outcomes from orthosis use for the diplegic CP population. Further validation of the model is warranted using data from other centers and a prospective study.

  17. Stochastic Responses May Allow Genetically Diverse Cell Populations to Optimize Performance with Simpler Signaling Networks

    PubMed Central

    Govern, Christopher C.; Chakraborty, Arup K.

    2013-01-01

    Two theories have emerged for the role that stochasticity plays in biological responses: first, that it degrades biological responses, so the performance of biological signaling machinery could be improved by increasing molecular copy numbers of key proteins; second, that it enhances biological performance, by enabling diversification of population-level responses. Using T cell biology as an example, we demonstrate that these roles for stochastic responses are not sufficient to understand experimental observations of stochastic response in complex biological systems that utilize environmental and genetic diversity to make cooperative responses. We propose a new role for stochastic responses in biology: they enable populations to make complex responses with simpler biochemical signaling machinery than would be required in the absence of stochasticity. Thus, the evolution of stochastic responses may be linked to the evolvability of different signaling machineries. PMID:23950860

  18. 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

  19. 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.

  20. Genome-wide comparison of African-ancestry populations from CARe and other cohorts reveals signals of natural selection.

    PubMed

    Bhatia, Gaurav; Patterson, Nick; Pasaniuc, Bogdan; Zaitlen, Noah; Genovese, Giulio; Pollack, Samuela; Mallick, Swapan; Myers, Simon; Tandon, Arti; Spencer, Chris; Palmer, Cameron D; Adeyemo, Adebowale A; Akylbekova, Ermeg L; Cupples, L Adrienne; Divers, Jasmin; Fornage, Myriam; Kao, W H Linda; Lange, Leslie; Li, Mingyao; Musani, Solomon; Mychaleckyj, Josyf C; Ogunniyi, Adesola; Papanicolaou, George; Rotimi, Charles N; Rotter, Jerome I; Ruczinski, Ingo; Salako, Babatunde; Siscovick, David S; Tayo, Bamidele O; Yang, Qiong; McCarroll, Steve; Sabeti, Pardis; Lettre, Guillaume; De Jager, Phil; Hirschhorn, Joel; Zhu, Xiaofeng; Cooper, Richard; Reich, David; Wilson, James G; Price, Alkes L

    2011-09-09

    The study of recent natural selection in human populations has important applications to human history and medicine. Positive natural selection drives the increase in beneficial alleles and plays a role in explaining diversity across human populations. By discovering traits subject to positive selection, we can better understand the population level response to environmental pressures including infectious disease. Our study examines unusual population differentiation between three large data sets to detect natural selection. The populations examined, African Americans, Nigerians, and Gambians, are genetically close to one another (F(ST) < 0.01 for all pairs), allowing us to detect selection even with moderate changes in allele frequency. We also develop a tree-based method to pinpoint the population in which selection occurred, incorporating information across populations. Our genome-wide significant results corroborate loci previously reported to be under selection in Africans including HBB and CD36. At the HLA locus on chromosome 6, results suggest the existence of multiple, independent targets of population-specific selective pressure. In addition, we report a genome-wide significant (p = 1.36 × 10(-11)) signal of selection in the prostate stem cell antigen (PSCA) gene. The most significantly differentiated marker in our analysis, rs2920283, is highly differentiated in both Africa and East Asia and has prior genome-wide significant associations to bladder and gastric cancers.