Kac determinant and singular vector of the level N representation of Ding-Iohara-Miki algebra

NASA Astrophysics Data System (ADS)

Ohkubo, Yusuke

2018-05-01

In this paper, we obtain the formula for the Kac determinant of the algebra arising from the level N representation of the Ding-Iohara-Miki algebra. It is also discovered that its singular vectors correspond to generalized Macdonald functions (the q-deformed version of the AFLT basis).

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

Black light - How sensors filter spectral variation of the illuminant

NASA Technical Reports Server (NTRS)

Brainard, David H.; Wandell, Brian A.; Cowan, William B.

1989-01-01

Visual sensor responses may be used to classify objects on the basis of their surface reflectance functions. In a color image, the image data are represented as a vector of sensor responses at each point in the image. This vector depends both on the surface reflectance functions and on the spectral power distribution of the ambient illumination. Algorithms designed to classify objects on the basis of their surface reflectance functions typically attempt to overcome the dependence of the sensor responses on the illuminant by integrating sensor data collected from multiple surfaces. In machine vision applications, it is shown that it is often possible to design the sensor spectral responsivities so that the vector direction of the sensor responses does not depend upon the illuminant. The conditions under which this is possible are given and an illustrative calculation is performed. In biological systems, where the sensor responsivities are fixed, it is shown that some changes in the illumination cause no change in the sensor responses. Such changes in illuminant are called black illuminants. It is possible to express any illuminant as the sum of two unique components. One component is a black illuminant. The second component is called the visible component. The visible component of an illuminant completely characterizes the effect of the illuminant on the vector of sensor responses.

Paired Pulse Basis Functions for the Method of Moments EFIE Solution of Electromagnetic Problems Involving Arbitrarily-shaped, Three-dimensional Dielectric Scatterers

NASA Technical Reports Server (NTRS)

MacKenzie, Anne I.; Rao, Sadasiva M.; Baginski, Michael E.

2007-01-01

A pair of basis functions is presented for the surface integral, method of moment solution of scattering by arbitrarily-shaped, three-dimensional dielectric bodies. Equivalent surface currents are represented by orthogonal unit pulse vectors in conjunction with triangular patch modeling. The electric field integral equation is employed with closed geometries for dielectric bodies; the method may also be applied to conductors. Radar cross section results are shown for dielectric bodies having canonical spherical, cylindrical, and cubic shapes. Pulse basis function results are compared to results by other methods.

Coherent states for the relativistic harmonic oscillator

NASA Technical Reports Server (NTRS)

Aldaya, Victor; Guerrero, J.

1995-01-01

Recently we have obtained, on the basis of a group approach to quantization, a Bargmann-Fock-like realization of the Relativistic Harmonic Oscillator as well as a generalized Bargmann transform relating fock wave functions and a set of relativistic Hermite polynomials. Nevertheless, the relativistic creation and annihilation operators satisfy typical relativistic commutation relations of the Lie product (vector-z, vector-z(sup dagger)) approximately equals Energy (an SL(2,R) algebra). Here we find higher-order polarization operators on the SL(2,R) group, providing canonical creation and annihilation operators satisfying the Lie product (vector-a, vector-a(sup dagger)) = identity vector 1, the eigenstates of which are 'true' coherent states.

Signal detection using support vector machines in the presence of ultrasonic speckle

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine L.; Pitas, Ioannis

2002-04-01

Support Vector Machines are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

Reduced Order Model Basis Vector Generation: Generates Basis Vectors fro ROMs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arrighi, Bill

2016-03-03

libROM is a library that implements order reduction via singular value decomposition (SVD) of sampled state vectors. It implements 2 parallel, incremental SVD algorithms and one serial, non-incremental algorithm. It also provides a mechanism for adaptive sampling of basis vectors.

Mixed kernel function support vector regression for global sensitivity analysis

NASA Astrophysics Data System (ADS)

Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

2017-11-01

Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

Stable computations with flat radial basis functions using vector-valued rational approximations

NASA Astrophysics Data System (ADS)

Wright, Grady B.; Fornberg, Bengt

2017-02-01

One commonly finds in applications of smooth radial basis functions (RBFs) that scaling the kernels so they are 'flat' leads to smaller discretization errors. However, the direct numerical approach for computing with flat RBFs (RBF-Direct) is severely ill-conditioned. We present an algorithm for bypassing this ill-conditioning that is based on a new method for rational approximation (RA) of vector-valued analytic functions with the property that all components of the vector share the same singularities. This new algorithm (RBF-RA) is more accurate, robust, and easier to implement than the Contour-Padé method, which is similarly based on vector-valued rational approximation. In contrast to the stable RBF-QR and RBF-GA algorithms, which are based on finding a better conditioned base in the same RBF-space, the new algorithm can be used with any type of smooth radial kernel, and it is also applicable to a wider range of tasks (including calculating Hermite type implicit RBF-FD stencils). We present a series of numerical experiments demonstrating the effectiveness of this new method for computing RBF interpolants in the flat regime. We also demonstrate the flexibility of the method by using it to compute implicit RBF-FD formulas in the flat regime and then using these for solving Poisson's equation in a 3-D spherical shell.

Hyperspectral recognition of processing tomato early blight based on GA and SVM

NASA Astrophysics Data System (ADS)

Yin, Xiaojun; Zhao, SiFeng

2013-03-01

Processing tomato early blight seriously affect the yield and quality of its.Determine the leaves spectrum of different disease severity level of processing tomato early blight.We take the sensitive bands of processing tomato early blight as support vector machine input vector.Through the genetic algorithm(GA) to optimize the parameters of SVM, We could recognize different disease severity level of processing tomato early blight.The result show:the sensitive bands of different disease severity levels of processing tomato early blight is 628-643nm and 689-692nm.The sensitive bands are as the GA and SVM input vector.We get the best penalty parameters is 0.129 and kernel function parameters is 3.479.We make classification training and testing by polynomial nuclear,radial basis function nuclear,Sigmoid nuclear.The best classification model is the radial basis function nuclear of SVM. Training accuracy is 84.615%,Testing accuracy is 80.681%.It is combined GA and SVM to achieve multi-classification of processing tomato early blight.It is provided the technical support of prediction processing tomato early blight occurrence, development and diffusion rule in large areas.

Recognition and Classification of Road Condition on the Basis of Friction Force by Using a Mobile Robot

NASA Astrophysics Data System (ADS)

Watanabe, Tatsuhito; Katsura, Seiichiro

A person operating a mobile robot in a remote environment receives realistic visual feedback about the condition of the road on which the robot is moving. The categorization of the road condition is necessary to evaluate the conditions for safe and comfortable driving. For this purpose, the mobile robot should be capable of recognizing and classifying the condition of the road surfaces. This paper proposes a method for recognizing the type of road surfaces on the basis of the friction between the mobile robot and the road surfaces. This friction is estimated by a disturbance observer, and a support vector machine is used to classify the surfaces. The support vector machine identifies the type of the road surface using feature vector, which is determined using the arithmetic average and variance derived from the torque values. Further, these feature vectors are mapped onto a higher dimensional space by using a kernel function. The validity of the proposed method is confirmed by experimental results.

Quantum and electromagnetic propagation with the conjugate symmetric Lanczos method.

PubMed

Acevedo, Ramiro; Lombardini, Richard; Turner, Matthew A; Kinsey, James L; Johnson, Bruce R

2008-02-14

The conjugate symmetric Lanczos (CSL) method is introduced for the solution of the time-dependent Schrodinger equation. This remarkably simple and efficient time-domain algorithm is a low-order polynomial expansion of the quantum propagator for time-independent Hamiltonians and derives from the time-reversal symmetry of the Schrodinger equation. The CSL algorithm gives forward solutions by simply complex conjugating backward polynomial expansion coefficients. Interestingly, the expansion coefficients are the same for each uniform time step, a fact that is only spoiled by basis incompleteness and finite precision. This is true for the Krylov basis and, with further investigation, is also found to be true for the Lanczos basis, important for efficient orthogonal projection-based algorithms. The CSL method errors roughly track those of the short iterative Lanczos method while requiring fewer matrix-vector products than the Chebyshev method. With the CSL method, only a few vectors need to be stored at a time, there is no need to estimate the Hamiltonian spectral range, and only matrix-vector and vector-vector products are required. Applications using localized wavelet bases are made to harmonic oscillator and anharmonic Morse oscillator systems as well as electrodynamic pulse propagation using the Hamiltonian form of Maxwell's equations. For gold with a Drude dielectric function, the latter is non-Hermitian, requiring consideration of corrections to the CSL algorithm.

Balancing aggregation and smoothing errors in inverse models

DOE PAGES

Turner, A. J.; Jacob, D. J.

2015-06-30

Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function ofmore » state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.« less

Balancing aggregation and smoothing errors in inverse models

NASA Astrophysics Data System (ADS)

Turner, A. J.; Jacob, D. J.

2015-01-01

Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function of state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.

Balancing aggregation and smoothing errors in inverse models

NASA Astrophysics Data System (ADS)

Turner, A. J.; Jacob, D. J.

2015-06-01

Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function of state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.

Using an expanding nondirect product harmonic basis with an iterative eigensolver to compute vibrational energy levels with as many as seven atoms.

PubMed

Brown, James; Carrington, Tucker

2016-10-14

We demonstrate that it is possible to use a variational method to compute 50 vibrational levels of ethylene oxide (a seven-atom molecule) with convergence errors less than 0.01 cm -1 . This is done by beginning with a small basis and expanding it to include product basis functions that are deemed to be important. For ethylene oxide a basis with fewer than 3 × 10 6 functions is large enough. Because the resulting basis has no exploitable structure we use a mapping to evaluate the matrix-vector products required to use an iterative eigensolver. The expanded basis is compared to bases obtained from pre-determined pruning condition. Similar calculations are presented for molecules with 3, 4, 5, and 6 atoms. For the 6-atom molecule, CH 3 CH, the required expanded basis has about 106 000 functions and is about an order of magnitude smaller than bases made with a pre-determined pruning condition.

Numerically Exact Calculation of Rovibrational Levels of Cl^-H_2O

NASA Astrophysics Data System (ADS)

Wang, Xiao-Gang; Carrington, Tucker

2014-06-01

Large amplitude vibrations of Van der Waals clusters are important because they reveal large regions of a potential energy surface (PES). To calculate spectra of Van der Waals clusters it is common to use an adiabatic approximation. When coupling between intra- and inter-molecular coordinates is important non-adiabatic coupling cannot be neglected and it is therefore critical to develop and test theoretical methods that couple both types of coordinates. We have developed new product basis and contracted basis Lanczos methods for Van der Waals complexes and tested them by computing rovibrational energy levels of Cl^-H_2O. The new product basis is made of functions of the inter-monomer distance, Wigner functions that depend on Euler angles specifying the orientation of H_2O with respect to a frame attached to the inter-monomer Jacobi vector, basis functions for H_2O vibration, and Wigner functions that depend on Euler angles specifying the orientation of the inter-monomer Jacobi vector with respect to a space-fixed frame. An advantage of this product basis is that it can be used to make an efficient contracted basis by replacing the vibrational basis functions for the monomer with monomer vibrational wavefunctions. Due to weak coupling between intra- and inter-molecular coordinates, only a few tens of monomer vibrational wavefunctions are necessary. The validity of the two new methods is established by comparing energy levels with benchmark rovibrational levels obtained with polyspherical coordinates and spherical harmonic type basis functions. For all bases, product structure is exploited to calculate eigenvalues with the Lanczos algorithm. For Cl^-H_2O, we are able, for the first time, to compute accurate splittings due to tunnelling between the two equivalent C_s minima. We use the PES of Rheinecker and Bowman (RB). Our results are in good agreement with experiment for the five fundamental bands observed. J. Rheinecker and J. M. Bowman, J. Chem. Phys. 124 131102 (2006) J. Rheinecker and J. M. Bowman, J. Chem. Phys. 125 133206 (2006)} S. Horvath, A. B. McCoy, B. M. Elliott, G. H. Weddle, J. R. Roscioli, and M. A. Johnson J. Phys. Chem. A 114 1556 (2010)

On vector-valued Poincaré series of weight 2

NASA Astrophysics Data System (ADS)

Meneses, Claudio

2017-10-01

Given a pair (Γ , ρ) of a Fuchsian group of the first kind, and a unitary representation ρ of Γ of arbitrary rank, the problem of construction of vector-valued Poincaré series of weight 2 is considered. Implications in the theory of parabolic bundles are discussed. When the genus of the group is zero, it is shown how an explicit basis for the space of these functions can be constructed.

Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

NASA Astrophysics Data System (ADS)

Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

2015-06-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

Biomedical Mathematics, Unit I: Measurement, Linear Functions and Dimensional Algebra. Student Text. Revised Version, 1975.

ERIC Educational Resources Information Center

Biomedical Interdisciplinary Curriculum Project, Berkeley, CA.

This text presents lessons relating specific mathematical concepts to the ideas, skills, and tasks pertinent to the health care field. Among other concepts covered are linear functions, vectors, trigonometry, and statistics. Many of the lessons use data acquired during science experiments as the basis for exercises in mathematics. Lessons present…

Reducing the cost of using collocation to compute vibrational energy levels: Results for CH2NH.

PubMed

Avila, Gustavo; Carrington, Tucker

2017-08-14

In this paper, we improve the collocation method for computing vibrational spectra that was presented in the work of Avila and Carrington, Jr. [J. Chem. Phys. 143, 214108 (2015)]. Known quadrature and collocation methods using a Smolyak grid require storing intermediate vectors with more elements than points on the Smolyak grid. This is due to the fact that grid labels are constrained among themselves and basis labels are constrained among themselves. We show that by using the so-called hierarchical basis functions, one can significantly reduce the memory required. In this paper, the intermediate vectors have only as many elements as the Smolyak grid. The ideas are tested by computing energy levels of CH 2 NH.

Image classification at low light levels

NASA Astrophysics Data System (ADS)

Wernick, Miles N.; Morris, G. Michael

1986-12-01

An imaging photon-counting detector is used to achieve automatic sorting of two image classes. The classification decision is formed on the basis of the cross correlation between a photon-limited input image and a reference function stored in computer memory. Expressions for the statistical parameters of the low-light-level correlation signal are given and are verified experimentally. To obtain a correlation-based system for two-class sorting, it is necessary to construct a reference function that produces useful information for class discrimination. An expression for such a reference function is derived using maximum-likelihood decision theory. Theoretically predicted results are used to compare on the basis of performance the maximum-likelihood reference function with Fukunaga-Koontz basis vectors and average filters. For each method, good class discrimination is found to result in milliseconds from a sparse sampling of the input image.

Equivalent magnetic vector potential model for low-frequency magnetic exposure assessment

NASA Astrophysics Data System (ADS)

Diao, Y. L.; Sun, W. N.; He, Y. Q.; Leung, S. W.; Siu, Y. M.

2017-10-01

In this paper, a novel source model based on a magnetic vector potential for the assessment of induced electric field strength in a human body exposed to the low-frequency (LF) magnetic field of an electrical appliance is presented. The construction of the vector potential model requires only a single-component magnetic field to be measured close to the appliance under test, hence relieving considerable practical measurement effort—the radial basis functions (RBFs) are adopted for the interpolation of discrete measurements; the magnetic vector potential model can then be directly constructed by summing a set of simple algebraic functions of RBF parameters. The vector potentials are then incorporated into numerical calculations as the equivalent source for evaluations of the induced electric field in the human body model. The accuracy and effectiveness of the proposed model are demonstrated by comparing the induced electric field in a human model to that of the full-wave simulation. This study presents a simple and effective approach for modelling the LF magnetic source. The result of this study could simplify the compliance test procedure for assessing an electrical appliance regarding LF magnetic exposure.

Equivalent magnetic vector potential model for low-frequency magnetic exposure assessment.

PubMed

Diao, Y L; Sun, W N; He, Y Q; Leung, S W; Siu, Y M

2017-09-21

In this paper, a novel source model based on a magnetic vector potential for the assessment of induced electric field strength in a human body exposed to the low-frequency (LF) magnetic field of an electrical appliance is presented. The construction of the vector potential model requires only a single-component magnetic field to be measured close to the appliance under test, hence relieving considerable practical measurement effort-the radial basis functions (RBFs) are adopted for the interpolation of discrete measurements; the magnetic vector potential model can then be directly constructed by summing a set of simple algebraic functions of RBF parameters. The vector potentials are then incorporated into numerical calculations as the equivalent source for evaluations of the induced electric field in the human body model. The accuracy and effectiveness of the proposed model are demonstrated by comparing the induced electric field in a human model to that of the full-wave simulation. This study presents a simple and effective approach for modelling the LF magnetic source. The result of this study could simplify the compliance test procedure for assessing an electrical appliance regarding LF magnetic exposure.

Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators.

PubMed

Karayiannis, N B

2000-01-01

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.

New method for solving inductive electric fields in the non-uniformly conducting ionosphere

NASA Astrophysics Data System (ADS)

Vanhamäki, H.; Amm, O.; Viljanen, A.

2006-10-01

We present a new calculation method for solving inductive electric fields in the ionosphere. The time series of the potential part of the ionospheric electric field, together with the Hall and Pedersen conductances serves as the input to this method. The output is the time series of the induced rotational part of the ionospheric electric field. The calculation method works in the time-domain and can be used with non-uniform, time-dependent conductances. In addition, no particular symmetry requirements are imposed on the input potential electric field. The presented method makes use of special non-local vector basis functions called the Cartesian Elementary Current Systems (CECS). This vector basis offers a convenient way of representing curl-free and divergence-free parts of 2-dimensional vector fields and makes it possible to solve the induction problem using simple linear algebra. The new calculation method is validated by comparing it with previously published results for Alfvén wave reflection from a uniformly conducting ionosphere.

A simple procedure for construction of the orthonormal basis vectors of irreducible representations of O(5) in the OT (3) ⊗ON (2) basis

NASA Astrophysics Data System (ADS)

Pan, Feng; Ding, Xiaoxue; Launey, Kristina D.; Draayer, J. P.

2018-06-01

A simple and effective algebraic isospin projection procedure for constructing orthonormal basis vectors of irreducible representations of O (5) ⊃OT (3) ⊗ON (2) from those in the canonical O (5) ⊃ SUΛ (2) ⊗ SUI (2) basis is outlined. The expansion coefficients are components of null space vectors of the projection matrix with four nonzero elements in each row in general. Explicit formulae for evaluating OT (3)-reduced matrix elements of O (5) generators are derived.

Influence of molecular weight upon mannosylated bio-synthetic hybrids for targeted antigen presenting cell gene delivery

PubMed Central

Jones, Charles H.; Gollakota, Akhila; Chen, Mingfu; Chung, Tai-Chun; Ravikrishnan, Anitha; Zhang, Guojian; Pfeifer, Blaine A.

2015-01-01

Given the rise of antibiotic resistant microbes, genetic vaccination is a promising prophylactic strategy that enables rapid design and manufacture. Facilitating this process is the choice of vector, which is often situationally-specific and limited in engineering capacity. Furthermore, these shortcomings are usually tied to an incomplete understanding of the structure-function relationships driving vector-mediated gene delivery. Building upon our initial report of a hybrid bacterial-biomaterial gene delivery vector, a comprehensive structure-function assessment was completed using a class of mannosylated poly(beta-amino esters). Through a top-down screening methodology, an ideal polymer was selected on the basis of gene delivery efficacy and then used for the synthesis of a stratified molecular weight polymer library. By eliminating contributions of polymer chemical background, we were able to complete an in-depth assessment of gene delivery as a function of (1) polymer molecular weight, (2) relative mannose content, (3) polymer-membrane biophysical properties, (4) APC uptake specificity, and (5) serum inhibition. In summary, the flexibility and potential of the hybrid design featured in this work highlights the ability to systematically probe vector-associated properties for the development of translational gene delivery candidates. PMID:25941787

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

NASA Astrophysics Data System (ADS)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations

PubMed Central

2016-01-01

Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT) of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping), thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT) ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB). As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3) classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The KLT and LPT present new possibilities for human-expert diagnostics, and for automated ischaemia detection. PMID:26863140

Bayesian anomaly detection in monitoring data applying relevance vector machine

NASA Astrophysics Data System (ADS)

Saito, Tomoo

2011-04-01

A method for automatically classifying the monitoring data into two categories, normal and anomaly, is developed in order to remove anomalous data included in the enormous amount of monitoring data, applying the relevance vector machine (RVM) to a probabilistic discriminative model with basis functions and their weight parameters whose posterior PDF (probabilistic density function) conditional on the learning data set is given by Bayes' theorem. The proposed framework is applied to actual monitoring data sets containing some anomalous data collected at two buildings in Tokyo, Japan, which shows that the trained models discriminate anomalous data from normal data very clearly, giving high probabilities of being normal to normal data and low probabilities of being normal to anomalous data.

Reduced basis technique for evaluating the sensitivity coefficients of the nonlinear tire response

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.; Tanner, John A.; Peters, Jeanne M.

1992-01-01

An efficient reduced-basis technique is proposed for calculating the sensitivity of nonlinear tire response to variations in the design variables. The tire is modeled using a 2-D, moderate rotation, laminated anisotropic shell theory, including the effects of variation in material and geometric parameters. The vector of structural response and its first-order and second-order sensitivity coefficients are each expressed as a linear combination of a small number of basis vectors. The effectiveness of the basis vectors used in approximating the sensitivity coefficients is demonstrated by a numerical example involving the Space Shuttle nose-gear tire, which is subjected to uniform inflation pressure.

Positivity of the universal pairing in 3 dimensions

NASA Astrophysics Data System (ADS)

Calegari, Danny; Freedman, Michael H.; Walker, Kevin

2010-01-01

Associated to a closed, oriented surface S is the complex vector space with basis the set of all compact, oriented 3 -manifolds which it bounds. Gluing along S defines a Hermitian pairing on this space with values in the complex vector space with basis all closed, oriented 3 -manifolds. The main result in this paper is that this pairing is positive, i.e. that the result of pairing a nonzero vector with itself is nonzero. This has bearing on the question of what kinds of topological information can be extracted in principle from unitary (2+1) -dimensional TQFTs. The proof involves the construction of a suitable complexity function c on all closed 3 -manifolds, satisfying a gluing axiom which we call the topological Cauchy-Schwarz inequality, namely that c(AB) le max(c(AA),c(BB)) for all A,B which bound S , with equality if and only if A=B . The complexity function c involves input from many aspects of 3 -manifold topology, and in the process of establishing its key properties we obtain a number of results of independent interest. For example, we show that when two finite-volume hyperbolic 3 -manifolds are glued along an incompressible acylindrical surface, the resulting hyperbolic 3 -manifold has minimal volume only when the gluing can be done along a totally geodesic surface; this generalizes a similar theorem for closed hyperbolic 3 -manifolds due to Agol-Storm-Thurston.

Critical Analysis of the Mathematical Formalism of Theoretical Physics. II. Foundations of Vector Calculus

NASA Astrophysics Data System (ADS)

Kalanov, Temur Z.

2014-03-01

A critical analysis of the foundations of standard vector calculus is proposed. The methodological basis of the analysis is the unity of formal logic and of rational dialectics. It is proved that the vector calculus is incorrect theory because: (a) it is not based on a correct methodological basis - the unity of formal logic and of rational dialectics; (b) it does not contain the correct definitions of ``movement,'' ``direction'' and ``vector'' (c) it does not take into consideration the dimensions of physical quantities (i.e., number names, denominate numbers, concrete numbers), characterizing the concept of ''physical vector,'' and, therefore, it has no natural-scientific meaning; (d) operations on ``physical vectors'' and the vector calculus propositions relating to the ''physical vectors'' are contrary to formal logic.

Influence of molecular weight upon mannosylated bio-synthetic hybrids for targeted antigen presenting cell gene delivery.

PubMed

Jones, Charles H; Gollakota, Akhila; Chen, Mingfu; Chung, Tai-Chun; Ravikrishnan, Anitha; Zhang, Guojian; Pfeifer, Blaine A

2015-07-01

Given the rise of antibiotic resistant microbes, genetic vaccination is a promising prophylactic strategy that enables rapid design and manufacture. Facilitating this process is the choice of vector, which is often situationally-specific and limited in engineering capacity. Furthermore, these shortcomings are usually tied to an incomplete understanding of the structure-function relationships driving vector-mediated gene delivery. Building upon our initial report of a hybrid bacterial-biomaterial gene delivery vector, a comprehensive structure-function assessment was completed using a class of mannosylated poly(beta-amino esters). Through a top-down screening methodology, an ideal polymer was selected on the basis of gene delivery efficacy and then used for the synthesis of a stratified molecular weight polymer library. By eliminating contributions of polymer chemical background, we were able to complete an in-depth assessment of gene delivery as a function of (1) polymer molecular weight, (2) relative mannose content, (3) polymer-membrane biophysical properties, (4) APC uptake specificity, and (5) serum inhibition. In summary, the flexibility and potential of the hybrid design featured in this work highlights the ability to systematically probe vector-associated properties for the development of translational gene delivery candidates. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mass Spectrometry Based Proteomic Analysis of Salivary Glands of Urban Malaria Vector Anopheles stephensi

PubMed Central

Vijay, Sonam

2014-01-01

Salivary gland proteins of Anopheles mosquitoes offer attractive targets to understand interactions with sporozoites, blood feeding behavior, homeostasis, and immunological evaluation of malaria vectors and parasite interactions. To date limited studies have been carried out to elucidate salivary proteins of An. stephensi salivary glands. The aim of the present study was to provide detailed analytical attributives of functional salivary gland proteins of urban malaria vector An. stephensi. A proteomic approach combining one-dimensional electrophoresis (1DE), ion trap liquid chromatography mass spectrometry (LC/MS/MS), and computational bioinformatic analysis was adopted to provide the first direct insight into identification and functional characterization of known salivary proteins and novel salivary proteins of An. stephensi. Computational studies by online servers, namely, MASCOT and OMSSA algorithms, identified a total of 36 known salivary proteins and 123 novel proteins analysed by LC/MS/MS. This first report describes a baseline proteomic catalogue of 159 salivary proteins belonging to various categories of signal transduction, regulation of blood coagulation cascade, and various immune and energy pathways of An. stephensi sialotranscriptome by mass spectrometry. Our results may serve as basis to provide a putative functional role of proteins in concept of blood feeding, biting behavior, and other aspects of vector-parasite host interactions for parasite development in anopheline mosquitoes. PMID:25126571

Mass spectrometry based proteomic analysis of salivary glands of urban malaria vector Anopheles stephensi.

PubMed

Vijay, Sonam; Rawat, Manmeet; Sharma, Arun

2014-01-01

Salivary gland proteins of Anopheles mosquitoes offer attractive targets to understand interactions with sporozoites, blood feeding behavior, homeostasis, and immunological evaluation of malaria vectors and parasite interactions. To date limited studies have been carried out to elucidate salivary proteins of An. stephensi salivary glands. The aim of the present study was to provide detailed analytical attributives of functional salivary gland proteins of urban malaria vector An. stephensi. A proteomic approach combining one-dimensional electrophoresis (1DE), ion trap liquid chromatography mass spectrometry (LC/MS/MS), and computational bioinformatic analysis was adopted to provide the first direct insight into identification and functional characterization of known salivary proteins and novel salivary proteins of An. stephensi. Computational studies by online servers, namely, MASCOT and OMSSA algorithms, identified a total of 36 known salivary proteins and 123 novel proteins analysed by LC/MS/MS. This first report describes a baseline proteomic catalogue of 159 salivary proteins belonging to various categories of signal transduction, regulation of blood coagulation cascade, and various immune and energy pathways of An. stephensi sialotranscriptome by mass spectrometry. Our results may serve as basis to provide a putative functional role of proteins in concept of blood feeding, biting behavior, and other aspects of vector-parasite host interactions for parasite development in anopheline mosquitoes.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

Diagnostic Classification of Schizophrenia Patients on the Basis of Regional Reward-Related fMRI Signal Patterns

PubMed Central

Koch, Stefan P.; Hägele, Claudia; Haynes, John-Dylan; Heinz, Andreas; Schlagenhauf, Florian; Sterzer, Philipp

2015-01-01

Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way. PMID:25799236

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

PubMed

Chen, S; Samingan, A K; Hanzo, L

2001-01-01

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

Attenuated Shigella as a DNA Delivery Vehicle for DNA-Mediated Immunization

NASA Astrophysics Data System (ADS)

Sizemore, Donata R.; Branstrom, Arthur A.; Sadoff, Jerald C.

1995-10-01

Direct inoculation of DNA, in the form of purified bacterial plasmids that are unable to replicate in mammalian cells but are able to direct cell synthesis of foreign proteins, is being explored as an approach to vaccine development. Here, a highly attenuated Shigella vector invaded mammalian cells and delivered such plasmids into the cytoplasm of cells, and subsequent production of functional foreign protein was measured. Because this Shigella vector was designed to deliver DNA to colonic mucosa, the method is a potential basis for oral and other mucosal DNA immunization and gene therapy strategies.

On the Runge-Lenz-Pauli vector operator as an aid to the calculation of atomic processes in laboratory and astrophysical plasmas

NASA Astrophysics Data System (ADS)

Hey, J. D.

2015-09-01

On the basis of the original definition and analysis of the vector operator by Pauli (1926 Z. Phys. 36 336-63), and further developments by Flamand (1966 J. Math. Phys. 7 1924-31), and by Becker and Bleuler (1976 Z. Naturforsch. 31a 517-23), we consider the action of the operator on both spherical polar and parabolic basis state wave functions, both with and without direct use of Pauli’s identity (Valent 2003 Am. J. Phys. 71 171-75). Comparison of the results, with the aid of two earlier papers (Hey 2006 J. Phys. B: At. Mol. Opt. Phys. 39 2641-64, Hey 2007 J. Phys. B: At. Mol. Opt. Phys. 40 4077-96), yields a convenient ladder technique in the form of a recurrence relation for calculating the transformation coefficients between the two sets of basis states, without explicit use of generalized hypergeometric functions. This result is therefore very useful for application to Stark effect and impact broadening calculations applied to high-n radio recombination lines from tenuous space plasmas. We also demonstrate the versatility of the Runge-Lenz-Pauli vector operator as a means of obtaining recurrence relations between expectation values of successive powers of quantum mechanical operators, by using it to provide, as an example, a derivation of the Kramers-Pasternack relation. It is suggested that this operator, whose potential use in Stark- and Zeeman-effect calculations for magnetically confined fusion edge plasmas (Rosato, Marandet and Stamm 2014 J. Phys. B: At. Mol. Opt. Phys. 47 105702) and tenuous space plasmas ( H II regions) has not been fully explored and exploited, may yet be found to yield a number of valuable results for applications to plasma diagnostic techniques based upon rate calculations of atomic processes.

Information-Efficient Spectral Imaging Sensor With Tdi

DOEpatents

Rienstra, Jeffrey L.; Gentry, Stephen M.; Sweatt, William C.

2004-01-13

A programmable optical filter for use in multispectral and hyperspectral imaging employing variable gain time delay and integrate arrays. A telescope focuses an image of a scene onto at least one TDI array that is covered by a multispectral filter that passes separate bandwidths of light onto the rows in the TDI array. The variable gain feature of the TDI array allows individual rows of pixels to be attenuated individually. The attenuations are functions of the magnitudes of the positive and negative components of a spectral basis vector. The spectral basis vector is constructed so that its positive elements emphasize the presence of a target and its negative elements emphasize the presence of the constituents of the background of the imaged scene. This system provides for a very efficient determination of the presence of the target, as opposed to the very data intensive data manipulations that are required in conventional hyperspectral imaging systems.

Combining Relevance Vector Machines and exponential regression for bearing residual life estimation

NASA Astrophysics Data System (ADS)

Di Maio, Francesco; Tsui, Kwok Leung; Zio, Enrico

2012-08-01

In this paper we present a new procedure for estimating the bearing Residual Useful Life (RUL) by combining data-driven and model-based techniques. Respectively, we resort to (i) Relevance Vector Machines (RVMs) for selecting a low number of significant basis functions, called Relevant Vectors (RVs), and (ii) exponential regression to compute and continuously update residual life estimations. The combination of these techniques is developed with reference to partially degraded thrust ball bearings and tested on real world vibration-based degradation data. On the case study considered, the proposed procedure outperforms other model-based methods, with the added value of an adequate representation of the uncertainty associated to the estimates of the quantification of the credibility of the results by the Prognostic Horizon (PH) metric.

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

SU-E-T-422: Fast Analytical Beamlet Optimization for Volumetric Intensity-Modulated Arc Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chan, Kenny S K; Lee, Louis K Y; Xing, L

2015-06-15

Purpose: To implement a fast optimization algorithm on CPU/GPU heterogeneous computing platform and to obtain an optimal fluence for a given target dose distribution from the pre-calculated beamlets in an analytical approach. Methods: The 2D target dose distribution was modeled as an n-dimensional vector and estimated by a linear combination of independent basis vectors. The basis set was composed of the pre-calculated beamlet dose distributions at every 6 degrees of gantry angle and the cost function was set as the magnitude square of the vector difference between the target and the estimated dose distribution. The optimal weighting of the basis,more » which corresponds to the optimal fluence, was obtained analytically by the least square method. Those basis vectors with a positive weighting were selected for entering into the next level of optimization. Totally, 7 levels of optimization were implemented in the study.Ten head-and-neck and ten prostate carcinoma cases were selected for the study and mapped to a round water phantom with a diameter of 20cm. The Matlab computation was performed in a heterogeneous programming environment with Intel i7 CPU and NVIDIA Geforce 840M GPU. Results: In all selected cases, the estimated dose distribution was in a good agreement with the given target dose distribution and their correlation coefficients were found to be in the range of 0.9992 to 0.9997. Their root-mean-square error was monotonically decreasing and converging after 7 cycles of optimization. The computation took only about 10 seconds and the optimal fluence maps at each gantry angle throughout an arc were quickly obtained. Conclusion: An analytical approach is derived for finding the optimal fluence for a given target dose distribution and a fast optimization algorithm implemented on the CPU/GPU heterogeneous computing environment greatly reduces the optimization time.« less

Development and comparison of advanced reduced-basis methods for the transient structural analysis of unconstrained structures

NASA Technical Reports Server (NTRS)

Mcgowan, David M.; Bostic, Susan W.; Camarda, Charles J.

1993-01-01

The development of two advanced reduced-basis methods, the force derivative method and the Lanczos method, and two widely used modal methods, the mode displacement method and the mode acceleration method, for transient structural analysis of unconstrained structures is presented. Two example structural problems are studied: an undamped, unconstrained beam subject to a uniformly distributed load which varies as a sinusoidal function of time and an undamped high-speed civil transport aircraft subject to a normal wing tip load which varies as a sinusoidal function of time. These example problems are used to verify the methods and to compare the relative effectiveness of each of the four reduced-basis methods for performing transient structural analyses on unconstrained structures. The methods are verified with a solution obtained by integrating directly the full system of equations of motion, and they are compared using the number of basis vectors required to obtain a desired level of accuracy and the associated computational times as comparison criteria.

Patch-based image reconstruction for PET using prior-image derived dictionaries

NASA Astrophysics Data System (ADS)

Tahaei, Marzieh S.; Reader, Andrew J.

2016-09-01

In PET image reconstruction, regularization is often needed to reduce the noise in the resulting images. Patch-based image processing techniques have recently been successfully used for regularization in medical image reconstruction through a penalized likelihood framework. Re-parameterization within reconstruction is another powerful regularization technique in which the object in the scanner is re-parameterized using coefficients for spatially-extensive basis vectors. In this work, a method for extracting patch-based basis vectors from the subject’s MR image is proposed. The coefficients for these basis vectors are then estimated using the conventional MLEM algorithm. Furthermore, using the alternating direction method of multipliers, an algorithm for optimizing the Poisson log-likelihood while imposing sparsity on the parameters is also proposed. This novel method is then utilized to find sparse coefficients for the patch-based basis vectors extracted from the MR image. The results indicate the superiority of the proposed methods to patch-based regularization using the penalized likelihood framework.

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

PubMed Central

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M.

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks.

PubMed

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.

Spherical space Bessel-Legendre-Fourier localized modes solver for electromagnetic waves.

PubMed

Alzahrani, Mohammed A; Gauthier, Robert C

2015-10-05

Maxwell's vector wave equations are solved for dielectric configurations that match the symmetry of a spherical computational domain. The electric or magnetic field components and the inverse of the dielectric profile are series expansion defined using basis functions composed of the lowest order spherical Bessel function, polar angle single index dependant Legendre polynomials and azimuthal complex exponential (BLF). The series expressions and non-traditional form of the basis functions result in an eigenvalue matrix formulation of Maxwell's equations that are relatively compact and accurately solvable on a desktop PC. The BLF matrix returns the frequencies and field profiles for steady states modes. The key steps leading to the matrix populating expressions are provided. The validity of the numerical technique is confirmed by comparing the results of computations to those published using complementary techniques.

Flanking sequence determination and specific PCR identification of transgenic wheat B102-1-2.

PubMed

Cao, Jijuan; Xu, Junyi; Zhao, Tongtong; Cao, Dongmei; Huang, Xin; Zhang, Piqiao; Luan, Fengxia

2014-01-01

The exogenous fragment sequence and flanking sequence between the exogenous fragment and recombinant chromosome of transgenic wheat B102-1-2 were successfully acquired using genome walking technology. The newly acquired exogenous fragment encoded the full-length sequence of transformed genes with transformed plasmid and corresponding functional genes including ubi, vector pBANF-bar, vector pUbiGUSPlus, vector HSP, reporter vector pUbiGUSPlus, promoter ubiquitin, and coli DH1. A specific polymerase chain reaction (PCR) identification method for transgenic wheat B102-1-2 was established on the basis of designed primers according to flanking sequence. This established specific PCR strategy was validated by using transgenic wheat, transgenic corn, transgenic soybean, transgenic rice, and non-transgenic wheat. A specifically amplified target band was observed only in transgenic wheat B102-1-2. Therefore, this method is characterized by high specificity, high reproducibility, rapid identification, and excellent accuracy for the identification of transgenic wheat B102-1-2.

Separation of spatial-temporal patterns ('climatic modes') by combined analysis of really measured and generated numerically vector time series

NASA Astrophysics Data System (ADS)

Feigin, A. M.; Mukhin, D.; Volodin, E. M.; Gavrilov, A.; Loskutov, E. M.

2013-12-01

The new method of decomposition of the Earth's climate system into well separated spatial-temporal patterns ('climatic modes') is discussed. The method is based on: (i) generalization of the MSSA (Multichannel Singular Spectral Analysis) [1] for expanding vector (space-distributed) time series in basis of spatial-temporal empirical orthogonal functions (STEOF), which makes allowance delayed correlations of the processes recorded in spatially separated points; (ii) expanding both real SST data, and longer by several times SST data generated numerically, in STEOF basis; (iii) use of the numerically produced STEOF basis for exclusion of 'too slow' (and thus not represented correctly) processes from real data. The application of the method allows by means of vector time series generated numerically by the INM RAS Coupled Climate Model [2] to separate from real SST anomalies data [3] two climatic modes possessing by noticeably different time scales: 3-5 and 9-11 years. Relations of separated modes to ENSO and PDO are investigated. Possible applications of spatial-temporal climatic patterns concept to prognosis of climate system evolution is discussed. 1. Ghil, M., R. M. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, et al. (2002) "Advanced spectral methods for climatic time series", Rev. Geophys. 40(1), 3.1-3.41. 2. http://83.149.207.89/GCM_DATA_PLOTTING/GCM_INM_DATA_XY_en.htm 3. http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Knowledge Space: A Conceptual Basis for the Organization of Knowledge

ERIC Educational Resources Information Center

Meincke, Peter P. M.; Atherton, Pauline

1976-01-01

Proposes a new conceptual basis for visualizing the organization of information, or knowledge, which differentiates between the concept "vectors" for a field of knowledge represented in a multidimensional space, and the state "vectors" for a person based on his understanding of these concepts, and the representational…

Evaluation of modulation transfer function of optical lens system by support vector regression methodologies - A comparative study

NASA Astrophysics Data System (ADS)

Petković, Dalibor; Shamshirband, Shahaboddin; Saboohi, Hadi; Ang, Tan Fong; Anuar, Nor Badrul; Rahman, Zulkanain Abdul; Pavlović, Nenad T.

2014-07-01

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict estimate MTF value of the actual optical system according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR_rbf approach in compare to SVR_poly soft computing methodology.

Eigenanalysis and Graph Theory Combined to Determine the Seasonal and Solar-Cycle Variations of Polar Magnetic Fields

NASA Astrophysics Data System (ADS)

Shore, R. M.; Freeman, M. P.; Gjerloev, J. W.

2017-12-01

We apply the meteorological analysis method of Empirical Orthogonal Functions (EOF) to ground magnetometer measurements, and subsequently use graph theory to classify the results. The EOF method is used to characterise and separate contributions to the variability of the Earth's external magnetic field (EMF) in the northern polar region. EOFs decompose the noisy EMF data into a small number of independent spatio-temporal basis functions, which collectively describe the majority of the magnetic field variance. We use these basis functions (computed monthly) to infill where data are missing, providing a self-consistent description of the EMF at 5-minute resolution spanning 1997-2009 (solar cycle 23). The EOF basis functions are calculated independently for each of the 144 months (i.e. 1997-2009) analysed. Since (by definition) the basis vectors are ranked by their contribution to the total variance, their rank will change from month to month. We use graph theory to find clusters of quantifiably-similar spatial basis functions, and thereby track similar patterns throughout the span of 144 months. We find that the discovered clusters can be associated with well-known individual Disturbance Polar (DP)-type equivalent current systems (e.g. DP2, DP1, DPY, NBZ), or with the motion of these systems. Via this method, we thus describe the varying behaviour of these current systems over solar cycle 23. We present their seasonal and solar cycle variations and examine the response of each current system to solar wind driving.

The relationship between wind vector and normalized radar cross section used to derive Seasat-A Satellite Scatterometer winds

NASA Technical Reports Server (NTRS)

Schroeder, L. C.; Jones, W. L.; Boggs, D. H.; Halberstam, I. M.; Dome, G.; Pierson, W. J.; Wentz, F. J.

1982-01-01

The Seasat-A Satellite Scatterometer (SASS) ocean normalized radar cross section (NRCS) dependence on the 19.5-m neutral stability wind vector may be specified as a function of radar incidence angle, the angle between wind direction and radar azimuth, and the neutral stability wind speed expressed in m/sec at a height of 19.5 m. An account is given of the development of models both expressing this relationship and providing the basis of inversion of NRCS to SASS winds, from initially aircraft scatterometer measurement-based forms to three Seasat field-validation experiments which furnish model NRCS versus surface windspeed data for comparison with SASS data.

Developmental neurogenetics of sexual dimorphism in Aedes aegypti

PubMed Central

Duman-Scheel, Molly; Syed, Zainulabeuddin

2015-01-01

Sexual dimorphism, a poorly understood but crucial aspect of vector mosquito biology, encompasses sex-specific physical, physiological, and behavioral traits related to mosquito reproduction. The study of mosquito sexual dimorphism has largely focused on analysis of the differences between adult female and male mosquitoes, particularly with respect to sex-specific behaviors related to disease transmission. However, sexually dimorphic behaviors are the products of differential gene expression that initiates during development and therefore must also be studied during development. Recent technical advancements are facilitating functional genetic studies in the dengue vector Aedes aegypti, an emerging model for mosquito development. These methodologies, many of which could be extended to other non-model insect species, are facilitating analysis of the development of sexual dimorphism in neural tissues, particularly the olfactory system. These studies are providing insight into the neurodevelopmental genetic basis for sexual dimorphism in vector mosquitoes. PMID:26949699

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Geographical traceability of Marsdenia tenacissima by Fourier transform infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Li, Chao; Yang, Sheng-Chao; Guo, Qiao-Sheng; Zheng, Kai-Yan; Wang, Ping-Li; Meng, Zhen-Gui

2016-01-01

A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine-genetic algorithms, support vector machine-particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine-grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way.

General MoM Solutions for Large Arrays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fasenfest, B; Capolino, F; Wilton, D R

2003-07-22

This paper focuses on a numerical procedure that addresses the difficulties of dealing with large, finite arrays while preserving the generality and robustness of full-wave methods. We present a fast method based on approximating interactions between sufficiently separated array elements via a relatively coarse interpolation of the Green's function on a uniform grid commensurate with the array's periodicity. The interaction between the basis and testing functions is reduced to a three-stage process. The first stage is a projection of standard (e.g., RWG) subdomain bases onto a set of interpolation functions that interpolate the Green's function on the array face. Thismore » projection, which is used in a matrix/vector product for each array cell in an iterative solution process, need only be carried out once for a single cell and results in a low-rank matrix. An intermediate stage matrix/vector product computation involving the uniformly sampled Green's function is of convolutional form in the lateral (transverse) directions so that a 2D FFT may be used. The final stage is a third matrix/vector product computation involving a matrix resulting from projecting testing functions onto the Green's function interpolation functions; the low-rank matrix is either identical to (using Galerkin's method) or similar to that for the bases projection. An effective MoM solution scheme is developed for large arrays using a modification of the AIM (Adaptive Integral Method) method. The method permits the analysis of arrays with arbitrary contours and nonplanar elements. Both fill and solve times within the MoM method are improved with respect to more standard MoM solvers.« less

Horizontal vectorization of electron repulsion integrals.

PubMed

Pritchard, Benjamin P; Chow, Edmond

2016-10-30

We present an efficient implementation of the Obara-Saika algorithm for the computation of electron repulsion integrals that utilizes vector intrinsics to calculate several primitive integrals concurrently in a SIMD vector. Initial benchmarks display a 2-4 times speedup with AVX instructions over comparable scalar code, depending on the basis set. Speedup over scalar code is found to be sensitive to the level of contraction of the basis set, and is best for (lAlB|lClD) quartets when lD  = 0 or lB=lD=0, which makes such a vectorization scheme particularly suitable for density fitting. The basic Obara-Saika algorithm, how it is vectorized, and the performance bottlenecks are analyzed and discussed. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

An implementation of the QMR method based on coupled two-term recurrences

NASA Technical Reports Server (NTRS)

Freund, Roland W.; Nachtigal, Noeel M.

1992-01-01

The authors have proposed a new Krylov subspace iteration, the quasi-minimal residual algorithm (QMR), for solving non-Hermitian linear systems. In the original implementation of the QMR method, the Lanczos process with look-ahead is used to generate basis vectors for the underlying Krylov subspaces. In the Lanczos algorithm, these basis vectors are computed by means of three-term recurrences. It has been observed that, in finite precision arithmetic, vector iterations based on three-term recursions are usually less robust than mathematically equivalent coupled two-term vector recurrences. This paper presents a look-ahead algorithm that constructs the Lanczos basis vectors by means of coupled two-term recursions. Implementation details are given, and the look-ahead strategy is described. A new implementation of the QMR method, based on this coupled two-term algorithm, is described. A simplified version of the QMR algorithm without look-ahead is also presented, and the special case of QMR for complex symmetric linear systems is considered. Results of numerical experiments comparing the original and the new implementations of the QMR method are reported.

Higher Order Bases in a 2D Hybrid BEM/FEM Formulation

NASA Technical Reports Server (NTRS)

Fink, Patrick W.; Wilton, Donald R.

2002-01-01

The advantages of using higher order, interpolatory basis functions are examined in the analysis of transverse electric (TE) plane wave scattering by homogeneous, dielectric cylinders. A boundary-element/finite-element (BEM/FEM) hybrid formulation is employed in which the interior dielectric region is modeled with the vector Helmholtz equation, and a radiation boundary condition is supplied by an Electric Field Integral Equation (EFIE). An efficient method of handling the singular self-term arising in the EFIE is presented. The iterative solution of the partially dense system of equations is obtained using the Quasi-Minimal Residual (QMR) algorithm with an Incomplete LU Threshold (ILUT) preconditioner. Numerical results are shown for the case of an incident wave impinging upon a square dielectric cylinder. The convergence of the solution is shown versus the number of unknowns as a function of the completeness order of the basis functions.

Classification of Phylogenetic Profiles for Protein Function Prediction: An SVM Approach

NASA Astrophysics Data System (ADS)

Kotaru, Appala Raju; Joshi, Ramesh C.

Predicting the function of an uncharacterized protein is a major challenge in post-genomic era due to problems complexity and scale. Having knowledge of protein function is a crucial link in the development of new drugs, better crops, and even the development of biochemicals such as biofuels. Recently numerous high-throughput experimental procedures have been invented to investigate the mechanisms leading to the accomplishment of a protein’s function and Phylogenetic profile is one of them. Phylogenetic profile is a way of representing a protein which encodes evolutionary history of proteins. In this paper we proposed a method for classification of phylogenetic profiles using supervised machine learning method, support vector machine classification along with radial basis function as kernel for identifying functionally linked proteins. We experimentally evaluated the performance of the classifier with the linear kernel, polynomial kernel and compared the results with the existing tree kernel. In our study we have used proteins of the budding yeast saccharomyces cerevisiae genome. We generated the phylogenetic profiles of 2465 yeast genes and for our study we used the functional annotations that are available in the MIPS database. Our experiments show that the performance of the radial basis kernel is similar to polynomial kernel is some functional classes together are better than linear, tree kernel and over all radial basis kernel outperformed the polynomial kernel, linear kernel and tree kernel. In analyzing these results we show that it will be feasible to make use of SVM classifier with radial basis function as kernel to predict the gene functionality using phylogenetic profiles.

Thermodynamic integration of the free energy along a reaction coordinate in Cartesian coordinates

NASA Astrophysics Data System (ADS)

den Otter, W. K.

2000-05-01

A generalized formulation of the thermodynamic integration (TI) method for calculating the free energy along a reaction coordinate is derived. Molecular dynamics simulations with a constrained reaction coordinate are used to sample conformations. These are then projected onto conformations with a higher value of the reaction coordinate by means of a vector field. The accompanying change in potential energy plus the divergence of the vector field constitute the derivative of the free energy. Any vector field meeting some simple requirements can be used as the basis of this TI expression. Two classes of vector fields are of particular interest here. The first recovers the conventional TI expression, with its cumbersome dependence on a full set of generalized coordinates. As the free energy is a function of the reaction coordinate only, it should in principle be possible to derive an expression depending exclusively on the definition of the reaction coordinate. This objective is met by the second class of vector fields to be discussed. The potential of mean constraint force (PMCF) method, after averaging over the unconstrained momenta, falls in this second class. The new method is illustrated by calculations on the isomerization of n-butane, and is compared with existing methods.

Human pose tracking from monocular video by traversing an image motion mapped body pose manifold

NASA Astrophysics Data System (ADS)

Basu, Saurav; Poulin, Joshua; Acton, Scott T.

2010-01-01

Tracking human pose from monocular video sequences is a challenging problem due to the large number of independent parameters affecting image appearance and nonlinear relationships between generating parameters and the resultant images. Unlike the current practice of fitting interpolation functions to point correspondences between underlying pose parameters and image appearance, we exploit the relationship between pose parameters and image motion flow vectors in a physically meaningful way. Change in image appearance due to pose change is realized as navigating a low dimensional submanifold of the infinite dimensional Lie group of diffeomorphisms of the two dimensional sphere S2. For small changes in pose, image motion flow vectors lie on the tangent space of the submanifold. Any observed image motion flow vector field is decomposed into the basis motion vector flow fields on the tangent space and combination weights are used to update corresponding pose changes in the different dimensions of the pose parameter space. Image motion flow vectors are largely invariant to style changes in experiments with synthetic and real data where the subjects exhibit variation in appearance and clothing. The experiments demonstrate the robustness of our method (within +/-4° of ground truth) to style variance.

a Gsa-Svm Hybrid System for Classification of Binary Problems

NASA Astrophysics Data System (ADS)

Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan

2011-06-01

This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.

Dynamics and certain mechanisms in the changes of the skeletal-muscular system of man under bedrest conditions

NASA Technical Reports Server (NTRS)

Oganov, V. G.

1977-01-01

Bed rest conditions evaluated varied in the longitudinal axis of the body, perpendicular to the vector gravitational forces, and the cranial portion of the body inclined from the horizontal. The duration of bed rest fluctuated in various experiments from 30 to 182 days. The state of muscle and neuromuscular system was judged on the basis of the recording of various functional indices, as well as by certain results of morphological and biochemical studies and data from the study of motor functions.

Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis.

PubMed

Faradji, Farhad; Ward, Rabab K; Birch, Gary E

2009-06-15

The feasibility of having a self-paced brain-computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.

Preconditioned MoM Solutions for Complex Planar Arrays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fasenfest, B J; Jackson, D; Champagne, N

2004-01-23

The numerical analysis of large arrays is a complex problem. There are several techniques currently under development in this area. One such technique is the FAIM (Faster Adaptive Integral Method). This method uses a modification of the standard AIM approach which takes into account the reusability properties of matrices that arise from identical array elements. If the array consists of planar conducting bodies, the array elements are meshed using standard subdomain basis functions, such as the RWG basis. These bases are then projected onto a regular grid of interpolating polynomials. This grid can then be used in a 2D ormore » 3D FFT to accelerate the matrix-vector product used in an iterative solver. The method has been proven to greatly reduce solve time by speeding the matrix-vector product computation. The FAIM approach also reduces fill time and memory requirements, since only the near element interactions need to be calculated exactly. The present work extends FAIM by modifying it to allow for layered material Green's Functions and dielectrics. In addition, a preconditioner is implemented to greatly reduce the number of iterations required for a solution. The general scheme of the FAIM method is reported in; this contribution is limited to presenting new results.« less

Proper Analytic Point Spread Function for Lateral Modulation

NASA Astrophysics Data System (ADS)

Chikayoshi Sumi,; Kunio Shimizu,; Norihiko Matsui,

2010-07-01

For ultrasonic lateral modulation for the imaging and measurement of tissue motion, better envelope shapes of the point spread function (PSF) than of a parabolic function are searched for within analytic functions or windows on the basis of the knowledge of the ideal shape of PSF previously obtained, i.e., having a large full width at half maximum and short feet. Through simulation of displacement vector measurement, better shapes are determined. As a better shape, a new window is obtained from a Turkey window by changing Hanning windows by power functions with an order larger than the second order. The order of measurement accuracies obtained is as follows, the new window > rectangular window > power function with a higher order > parabolic function > Akaike window.

Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

NASA Astrophysics Data System (ADS)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

Using a pruned, nondirect product basis in conjunction with the multi-configuration time-dependent Hartree (MCTDH) method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wodraszka, Robert, E-mail: Robert.Wodraszka@chem.queensu.ca; Carrington, Tucker, E-mail: Tucker.Carrington@queensu.ca

In this paper, we propose a pruned, nondirect product multi-configuration time dependent Hartree (MCTDH) method for solving the Schrödinger equation. MCTDH uses optimized 1D basis functions, called single particle functions, but the size of the standard direct product MCTDH basis scales exponentially with D, the number of coordinates. We compare the pruned approach to standard MCTDH calculations for basis sizes small enough that the latter are possible and demonstrate that pruning the basis reduces the CPU cost of computing vibrational energy levels of acetonitrile (D = 12) by more than two orders of magnitude. Using the pruned method, it ismore » possible to do calculations with larger bases, for which the cost of standard MCTDH calculations is prohibitive. Pruning the basis complicates the evaluation of matrix-vector products. In this paper, they are done term by term for a sum-of-products Hamiltonian. When no attempt is made to exploit the fact that matrices representing some of the factors of a term are identity matrices, one needs only to carefully constrain indices. In this paper, we develop new ideas that make it possible to further reduce the CPU time by exploiting identity matrices.« less

Visualizing vector field topology in fluid flows

NASA Technical Reports Server (NTRS)

Helman, James L.; Hesselink, Lambertus

1991-01-01

Methods of automating the analysis and display of vector field topology in general and flow topology in particular are discussed. Two-dimensional vector field topology is reviewed as the basis for the examination of topology in three-dimensional separated flows. The use of tangent surfaces and clipping in visualizing vector field topology in fluid flows is addressed.

Characterizing Atomistic Geometries and Potential Functions Using Strain Functionals

NASA Astrophysics Data System (ADS)

Kober, Edward; Mathew, Nithin; Rudin, Sven

2017-06-01

We demonstrate the use of strain tensor functionals for characterizing arbitrarily ordered atomistic structures. This approach defines a Gaussian-weighted neighborhood around each atom and characterizes that local geometry in terms of n-th order strain tensors, which are equivalent to the n-th order moments/derivatives of the neighborhood. Fourth order expansions can distinguish the cubic structures (and deformations thereof), but sixth order expansions are required to fully characterize hexagonal structures. These functions are continuous and smooth and much less sensitive to thermal fluctuations than other descriptors based on discrete neighborhoods. Reducing these metrics to rotational invariant descriptors allows a large number of defect structures to be readily identified and forms the basis of a classification scheme that allows molecular dynamics simulations to be readily analyzed. Applications to the analysis of shock waves impinging on samples of Cu, Ta and Ti will be presented. The method has been extended to vector fields as well, enabling the local stress to be cast in terms of rotationally invariant functions as well. The stress-strain correlations can then be used as the basis for developing and analyzing potential functions.

[Construction of rAAV2-GPIIb/IIIa vector and test of its expression and function in vitro].

PubMed

Wang, Kai; Peng, Jian-Qiang; Chen, Fang-Ping; Wu, Xiao-Bin

2006-04-01

This study was aimed to explore the possibility of rAAV2 vector-mediating gene therapy for Glanzmann' s thrombasthenia. The rAAV2-GPIIb/IIIa vector was constructed. The GPIIb/IIIa gene expression in mammal cell were examined by different methods, such as: detection of mRNA expression in BHK-21 cells after 24 hours of infection (MOI = 1 x 10(5) v.g/cell) was performed by RT-PCR; the relation between MOI and quantity of GPII6/IIIa gene expression was detected by FACS after 48 hours of infection; GPIIb/IIIa protein expression in BHK-21 cells after 48 hours of infection (MOI = 10(5) v x g/cell) was assayed by Western blot, GPIIb/IIIa protein expression on cell surface was detected by immunofluorescence, and the biological function of expressing product was determined by PAC-1 conjunct experiments. The results showed that GPIIb/IIIa gene expression in mRNA level could be detected in BHK-21 cells after 24 hours of infection at MOI = 1 x 10(5) v x g/cell and the GPIIb/IIIa gene expression in protein level could be detected in BHK-21 cells after 48 hours of infection at MOI = 1 x 10(5) v x g/cell. In certain range, quantity of GPIIb/IIIa gene expression increased with MOI, but overdose of MOI decreased quantity of GPIIb/IIIa gene expression. Activated product of GPIIb/IIIa gene expression could combined with PAC-I, and possesed normal biological function. In conclusion, rAAV2 vactor can effectively mediate GPIIb and GPIIIa gene expressing in mammal cells, and the products of these genes exhibit biological function. This result may provide a basis for application of rAAV2 vector in Glanzmann's thrombasthenia gene therapy in furture.

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM.

PubMed

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

2016-10-14

A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine. The chaotic ions motion algorithm is introduced to find the best hyper-parameters of the Least Squares Support Vector Machine. The temperature data from a calibration experiment is conducted to validate the proposed method. With attention on algorithm robustness and engineering applications, the compensation result shows the proposed scheme outperforms other compared methods on several performance measures as maximum absolute relative error, minimum absolute relative error mean and variance of the averaged value on fifty runs. Furthermore, the proposed temperature compensation approach lays a foundation for more extensive research.

Blending Velocities In Task Space In Computing Robot Motions

NASA Technical Reports Server (NTRS)

Volpe, Richard A.

1995-01-01

Blending of linear and angular velocities between sequential specified points in task space constitutes theoretical basis of improved method of computing trajectories followed by robotic manipulators. In method, generalized velocity-vector-blending technique provides relatively simple, common conceptual framework for blending linear, angular, and other parametric velocities. Velocity vectors originate from straight-line segments connecting specified task-space points, called "via frames" and represent specified robot poses. Linear-velocity-blending functions chosen from among first-order, third-order-polynomial, and cycloidal options. Angular velocities blended by use of first-order approximation of previous orientation-matrix-blending formulation. Angular-velocity approximation yields small residual error, quantified and corrected. Method offers both relative simplicity and speed needed for generation of robot-manipulator trajectories in real time.

Modeling global vector fields of chaotic systems from noisy time series with the aid of structure-selection techniques.

PubMed

Xu, Daolin; Lu, Fangfang

2006-12-01

We address the problem of reconstructing a set of nonlinear differential equations from chaotic time series. A method that combines the implicit Adams integration and the structure-selection technique of an error reduction ratio is proposed for system identification and corresponding parameter estimation of the model. The structure-selection technique identifies the significant terms from a pool of candidates of functional basis and determines the optimal model through orthogonal characteristics on data. The technique with the Adams integration algorithm makes the reconstruction available to data sampled with large time intervals. Numerical experiment on Lorenz and Rossler systems shows that the proposed strategy is effective in global vector field reconstruction from noisy time series.

A new implementation of the CMRH method for solving dense linear systems

NASA Astrophysics Data System (ADS)

Heyouni, M.; Sadok, H.

2008-04-01

The CMRH method [H. Sadok, Methodes de projections pour les systemes lineaires et non lineaires, Habilitation thesis, University of Lille1, Lille, France, 1994; H. Sadok, CMRH: A new method for solving nonsymmetric linear systems based on the Hessenberg reduction algorithm, Numer. Algorithms 20 (1999) 303-321] is an algorithm for solving nonsymmetric linear systems in which the Arnoldi component of GMRES is replaced by the Hessenberg process, which generates Krylov basis vectors which are orthogonal to standard unit basis vectors rather than mutually orthogonal. The iterate is formed from these vectors by solving a small least squares problem involving a Hessenberg matrix. Like GMRES, this method requires one matrix-vector product per iteration. However, it can be implemented to require half as much arithmetic work and less storage. Moreover, numerical experiments show that this method performs accurately and reduces the residual about as fast as GMRES. With this new implementation, we show that the CMRH method is the only method with long-term recurrence which requires not storing at the same time the entire Krylov vectors basis and the original matrix as in the GMRES algorithmE A comparison with Gaussian elimination is provided.

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

PubMed Central

He, Peilin; Jia, Pengfei; Qiao, Siqi; Duan, Shukai

2017-01-01

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol); however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy. PMID:28991154

User's Manual for FEMOM3DS. Version 1.0

NASA Technical Reports Server (NTRS)

Reddy, C.J.; Deshpande, M. D.

1997-01-01

FEMOM3DS is a computer code written in FORTRAN 77 to compute electromagnetic(EM) scattering characteristics of a three dimensional object with complex materials using combined Finite Element Method (FEM)/Method of Moments (MoM) technique. This code uses the tetrahedral elements, with vector edge basis functions for FEM in the volume of the cavity and the triangular elements with the basis functions similar to that described for MoM at the outer boundary. By virtue of FEM, this code can handle any arbitrarily shaped three-dimensional cavities filled with inhomogeneous lossy materials. The User's Manual is written to make the user acquainted with the operation of the code. The user is assumed to be familiar with the FORTRAN 77 language and the operating environment of the computers on which the code is intended to run.

Using monomer vibrational wavefunctions as contracted basis functions to compute rovibrational levels of an H2O-atom complex in full dimensionality.

PubMed

Wang, Xiao-Gang; Carrington, Tucker

2017-03-14

In this paper, we present new ideas for computing rovibrational energy levels of molecules composed of two components and apply them to H 2 O-Cl - . When both components are themselves molecules, Euler angles that specify their orientation with respect to an axis system attached to the inter-monomer vector are used as vibrational coordinates. For H 2 O-Cl - , there is only one set of Euler angles. Using Euler angles as intermolecular vibrational coordinates is advantageous because in many cases coupling between them and coordinates that describe the shape of the monomers is unimportant. The monomers are not assumed to be rigid. In the most efficient calculation, vibrational wavefunctions of the monomers are used as contracted basis functions. Energy levels are calculated using the Lanczos algorithm.

Using monomer vibrational wavefunctions as contracted basis functions to compute rovibrational levels of an H2O-atom complex in full dimensionality

NASA Astrophysics Data System (ADS)

Wang, Xiao-Gang; Carrington, Tucker

2017-03-01

In this paper, we present new ideas for computing rovibrational energy levels of molecules composed of two components and apply them to H2O-Cl-. When both components are themselves molecules, Euler angles that specify their orientation with respect to an axis system attached to the inter-monomer vector are used as vibrational coordinates. For H2O-Cl-, there is only one set of Euler angles. Using Euler angles as intermolecular vibrational coordinates is advantageous because in many cases coupling between them and coordinates that describe the shape of the monomers is unimportant. The monomers are not assumed to be rigid. In the most efficient calculation, vibrational wavefunctions of the monomers are used as contracted basis functions. Energy levels are calculated using the Lanczos algorithm.

Tool Wear Feature Extraction Based on Hilbert Marginal Spectrum

NASA Astrophysics Data System (ADS)

Guan, Shan; Song, Weijie; Pang, Hongyang

2017-09-01

In the metal cutting process, the signal contains a wealth of tool wear state information. A tool wear signal’s analysis and feature extraction method based on Hilbert marginal spectrum is proposed. Firstly, the tool wear signal was decomposed by empirical mode decomposition algorithm and the intrinsic mode functions including the main information were screened out by the correlation coefficient and the variance contribution rate. Secondly, Hilbert transform was performed on the main intrinsic mode functions. Hilbert time-frequency spectrum and Hilbert marginal spectrum were obtained by Hilbert transform. Finally, Amplitude domain indexes were extracted on the basis of the Hilbert marginal spectrum and they structured recognition feature vector of tool wear state. The research results show that the extracted features can effectively characterize the different wear state of the tool, which provides a basis for monitoring tool wear condition.

Characterization of a Theta-Type Plasmid from Lactobacillus sakei: a Potential Basis for Low-Copy-Number Vectors in Lactobacilli

PubMed Central

Alpert, Carl-Alfred; Crutz-Le Coq, Anne-Marie; Malleret, Christine; Zagorec, Monique

2003-01-01

The complete nucleotide sequence of the 13-kb plasmid pRV500, isolated from Lactobacillus sakei RV332, was determined. Sequence analysis enabled the identification of genes coding for a putative type I restriction-modification system, two genes coding for putative recombinases of the integrase family, and a region likely involved in replication. The structural features of this region, comprising a putative ori segment containing 11- and 22-bp repeats and a repA gene coding for a putative initiator protein, indicated that pRV500 belongs to the pUCL287 subfamily of theta-type replicons. A 3.7-kb fragment encompassing this region was fused to an Escherichia coli replicon to produce the shuttle vector pRV566 and was observed to be functional in L. sakei for plasmid replication. The L. sakei replicon alone could not support replication in E. coli. Plasmid pRV500 and its derivative pRV566 were determined to be at very low copy numbers in L. sakei. pRV566 was maintained at a reasonable rate over 20 generations in several lactobacilli, such as Lactobacillus curvatus, Lactobacillus casei, and Lactobacillus plantarum, in addition to L. sakei, making it an interesting basis for developing vectors. Sequence relationships with other plasmids are described and discussed. PMID:12957947

New Method for Solving Inductive Electric Fields in the Ionosphere

NASA Astrophysics Data System (ADS)

Vanhamäki, H.

2005-12-01

We present a new method for calculating inductive electric fields in the ionosphere. It is well established that on large scales the ionospheric electric field is a potential field. This is understandable, since the temporal variations of large scale current systems are generally quite slow, in the timescales of several minutes, so inductive effects should be small. However, studies of Alfven wave reflection have indicated that in some situations inductive phenomena could well play a significant role in the reflection process, and thus modify the nature of ionosphere-magnetosphere coupling. The input to our calculation method are the time series of the potential part of the ionospheric electric field together with the Hall and Pedersen conductances. The output is the time series of the induced rotational part of the ionospheric electric field. The calculation method works in the time-domain and can be used with non-uniform, time-dependent conductances. In addition no particular symmetry requirements are imposed on the input potential electric field. The presented method makes use of special non-local vector basis functions called Cartesian Elementary Current Systems (CECS). This vector basis offers a convenient way of representing curl-free and divergence-free parts of 2-dimensional vector fields and makes it possible to solve the induction problem using simple linear algebra. The new calculation method is validated by comparing it with previously published results for Alfven wave reflection from uniformly conducting ionosphere.

Vector control of wind turbine on the basis of the fuzzy selective neural net*

NASA Astrophysics Data System (ADS)

Engel, E. A.; Kovalev, I. V.; Engel, N. E.

2016-04-01

An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

Temperature-based estimation of global solar radiation using soft computing methodologies

NASA Astrophysics Data System (ADS)

Mohammadi, Kasra; Shamshirband, Shahaboddin; Danesh, Amir Seyed; Abdullah, Mohd Shahidan; Zamani, Mazdak

2016-07-01

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures ( T max, T min, and T avg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of T max, T min, and T avg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using T max- T min and T max as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.

Identification of the CAD gene from Eucalyptus urophylla GLU4 and its functional analysis in transgenic tobacco.

PubMed

Chen, B W; Xiao, Y F; Li, J J; Liu, H L; Qin, Z H; Gai, Y; Jiang, X N

2016-12-02

Cinnamyl alcohol dehydrogenase (CAD) catalyzes the final step in lignin biosynthesis. The genus Eucalyptus belongs to the family Myrtaceae, which is the main cultivated species in China. Eucalyptus urophylla GLU4 (GLU4) is widely grown in Guangxi. It is preferred for pulping because of its excellent cellulose content and fiber length. Based on GLU4 and CAD gene expression, a Eucalyptus variety low in lignin content should be obtained using transgenic technology, which could reduce the cost of pulp and improve the pulping rate, and have favorable prospects for application. However, the role and function of CAD in GLU4 is still unclear. In the present study, EuCAD was cloned from GLU4 and identified using bioinformatic tools. Subsequently, in order to evaluate its impact on lignin synthesis, a full-length EuCAD RNAi vector was constructed, and transgenic tobacco was obtained via Agrobacterium-mediated transformation. A significant decrease in CAD expression and lignin content in transgenic tobacco demonstrated a key role for EuCAD in lignin biosynthesis and established a regulatory role for RNAi. In our study, the direct molecular basis of EuCAD expression was determined, and the potential regulatory effects of this RNAi vector on lignin biosynthesis in E. urophylla GLU4 were demonstrated. Our results provide a theoretical basis for the study of lignin biosynthesis in Eucalyptus.

Application of support vector machine for the separation of mineralised zones in the Takht-e-Gonbad porphyry deposit, SE Iran

NASA Astrophysics Data System (ADS)

Mahvash Mohammadi, Neda; Hezarkhani, Ardeshir

2018-07-01

Classification of mineralised zones is an important factor for the analysis of economic deposits. In this paper, the support vector machine (SVM), a supervised learning algorithm, based on subsurface data is proposed for classification of mineralised zones in the Takht-e-Gonbad porphyry Cu-deposit (SE Iran). The effects of the input features are evaluated via calculating the accuracy rates on the SVM performance. Ultimately, the SVM model, is developed based on input features namely lithology, alteration, mineralisation, the level and, radial basis function (RBF) as a kernel function. Moreover, the optimal amount of parameters λ and C, using n-fold cross-validation method, are calculated at level 0.001 and 0.01 respectively. The accuracy of this model is 0.931 for classification of mineralised zones in the Takht-e-Gonbad porphyry deposit. The results of the study confirm the efficiency of SVM method for classification the mineralised zones.

Intraventricular Flow Velocity Vector Visualization Based on the Continuity Equation and Measurements of Vorticity and Wall Shear Stress

NASA Astrophysics Data System (ADS)

Itatani, Keiichi; Okada, Takashi; Uejima, Tokuhisa; Tanaka, Tomohiko; Ono, Minoru; Miyaji, Kagami; Takenaka, Katsu

2013-07-01

We have developed a system to estimate velocity vector fields inside the cardiac ventricle by echocardiography and to evaluate several flow dynamical parameters to assess the pathophysiology of cardiovascular diseases. A two-dimensional continuity equation was applied to color Doppler data using speckle tracking data as boundary conditions, and the velocity component perpendicular to the echo beam line was obtained. We determined the optimal smoothing method of the color Doppler data, and the 8-pixel standard deviation of the Gaussian filter provided vorticity without nonphysiological stripe shape noise. We also determined the weight function at the bilateral boundaries given by the speckle tracking data of the ventricle or vascular wall motion, and the weight function linear to the distance from the boundary provided accurate flow velocities not only inside the vortex flow but also around near-wall regions on the basis of the results of the validation of a digital phantom of a pipe flow model.

User's manual for CBS3DS, version 1.0

NASA Astrophysics Data System (ADS)

Reddy, C. J.; Deshpande, M. D.

1995-10-01

CBS3DS is a computer code written in FORTRAN 77 to compute the backscattering radar cross section of cavity backed apertures in infinite ground plane and slots in thick infinite ground plane. CBS3DS implements the hybrid Finite Element Method (FEM) and Method of Moments (MoM) techniques. This code uses the tetrahedral elements, with vector edge basis functions for FEM in the volume of the cavity/slot and the triangular elements with the basis functions for MoM at the apertures. By virtue of FEM, this code can handle any arbitrarily shaped three-dimensional cavities filled with inhomogeneous lossy materials; due to MoM, the apertures can be of any arbitrary shape. The User's Manual is written to make the user acquainted with the operation of the code. The user is assumed to be familiar with the FORTRAN 77 language and the operating environment of the computer the code is intended to run.

User's Manual for FEMOM3DR. Version 1.0

NASA Technical Reports Server (NTRS)

Reddy, C. J.

1998-01-01

FEMoM3DR is a computer code written in FORTRAN 77 to compute radiation characteristics of antennas on 3D body using combined Finite Element Method (FEM)/Method of Moments (MoM) technique. The code is written to handle different feeding structures like coaxial line, rectangular waveguide, and circular waveguide. This code uses the tetrahedral elements, with vector edge basis functions for FEM and triangular elements with roof-top basis functions for MoM. By virtue of FEM, this code can handle any arbitrary shaped three dimensional bodies with inhomogeneous lossy materials; and due to MoM the computational domain can be terminated in any arbitrary shape. The User's Manual is written to make the user acquainted with the operation of the code. The user is assumed to be familiar with the FORTRAN 77 language and the operating environment of the computers on which the code is intended to run.

Quantized kernel least mean square algorithm.

PubMed

Chen, Badong; Zhao, Songlin; Zhu, Pingping; Príncipe, José C

2012-01-01

In this paper, we propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel adaptive filtering. The basic idea behind this method is to quantize and hence compress the input (or feature) space. Different from sparsification, the new approach uses the "redundant" data to update the coefficient of the closest center. In particular, a quantized kernel least mean square (QKLMS) algorithm is developed, which is based on a simple online vector quantization method. The analytical study of the mean square convergence has been carried out. The energy conservation relation for QKLMS is established, and on this basis we arrive at a sufficient condition for mean square convergence, and a lower and upper bound on the theoretical value of the steady-state excess mean square error. Static function estimation and short-term chaotic time-series prediction examples are presented to demonstrate the excellent performance.

Natural infection of Culex theileri (Diptera: Culicidae) with Dirofilaria immitis (Nematoda: Filarioidea) on Madeira Island, Portugal.

PubMed

Santa-Ana, Marta; Khadem, Manhaz; Capela, Ruben

2006-01-01

Field and laboratory studies were performed to verify whether Culex theileri Theobald functions as a natural vector of Dirofilaria immitis (Leidy) on Madeira Island, Portugal. CO2-baited light traps (EVS traps) were use to sample mosquitoes monthly basis between February 2002 and February 2003 in the area of Quebradas (Funchal). Three mosquito species were captured, including 58 Culex pipiens L., 790 Cx. theileri, and three Culiseta longiareolata (Macquart). Only C. theileri tested positive for D. immitis. The presence of this filarial worm was detected by direct observation, infectivity assay dissection technique, and polymerase chain reaction methods. Infected mosquitoes were recovered in October and December 2002 and January 2003. These data provide evidence that Cx. theileri could be the main vector of D. immitis in Funchal, Madeira.

Solution of free-boundary problems using finite-element/Newton methods and locally refined grids - Application to analysis of solidification microstructure

NASA Technical Reports Server (NTRS)

Tsiveriotis, K.; Brown, R. A.

1993-01-01

A new method is presented for the solution of free-boundary problems using Lagrangian finite element approximations defined on locally refined grids. The formulation allows for direct transition from coarse to fine grids without introducing non-conforming basis functions. The calculation of elemental stiffness matrices and residual vectors are unaffected by changes in the refinement level, which are accounted for in the loading of elemental data to the global stiffness matrix and residual vector. This technique for local mesh refinement is combined with recently developed mapping methods and Newton's method to form an efficient algorithm for the solution of free-boundary problems, as demonstrated here by sample calculations of cellular interfacial microstructure during directional solidification of a binary alloy.

Reduced conservatism in stability robustness bounds by state transformation

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.; Liang, Z.

1986-01-01

This note addresses the issue of 'conservatism' in the time domain stability robustness bounds obtained by the Liapunov approach. A state transformation is employed to improve the upper bounds on the linear time-varying perturbation of an asymptotically stable linear time-invariant system for robust stability. This improvement is due to the variance of the conservatism of the Liapunov approach with respect to the basis of the vector space in which the Liapunov function is constructed. Improved bounds are obtained, using a transformation, on elemental and vector norms of perturbations (i.e., structured perturbations) as well as on a matrix norm of perturbations (i.e., unstructured perturbations). For the case of a diagonal transformation, an algorithm is proposed to find the 'optimal' transformation. Several examples are presented to illustrate the proposed analysis.

A Galerkin Approach to Define Measured Terrain Surfaces with Analytic Basis Vectors to Produce a Compact Representation

DTIC Science & Technology

2010-11-01

defined herein as terrain whose surface deformation due to a single vehicle traversing the surface is negligible, such as paved roads (both asphalt ...ground vehicle reliability predictions. Current application of this work is limited to the analysis of U.S. Highways, comprised of both asphalt and...Highways that are consistent between asphalt and concrete roads b. The principle terrain characteristics are defined with analytic basis vectors

Approximate techniques of structural reanalysis

NASA Technical Reports Server (NTRS)

Noor, A. K.; Lowder, H. E.

1974-01-01

A study is made of two approximate techniques for structural reanalysis. These include Taylor series expansions for response variables in terms of design variables and the reduced-basis method. In addition, modifications to these techniques are proposed to overcome some of their major drawbacks. The modifications include a rational approach to the selection of the reduced-basis vectors and the use of Taylor series approximation in an iterative process. For the reduced basis a normalized set of vectors is chosen which consists of the original analyzed design and the first-order sensitivity analysis vectors. The use of the Taylor series approximation as a first (initial) estimate in an iterative process, can lead to significant improvements in accuracy, even with one iteration cycle. Therefore, the range of applicability of the reanalysis technique can be extended. Numerical examples are presented which demonstrate the gain in accuracy obtained by using the proposed modification techniques, for a wide range of variations in the design variables.

Anomalous current from the covariant Wigner function

NASA Astrophysics Data System (ADS)

Prokhorov, George; Teryaev, Oleg

2018-04-01

We consider accelerated and rotating media of weakly interacting fermions in local thermodynamic equilibrium on the basis of kinetic approach. Kinetic properties of such media can be described by covariant Wigner function incorporating the relativistic distribution functions of particles with spin. We obtain the formulae for axial current by summation of the terms of all orders of thermal vorticity tensor, chemical potential, both for massive and massless particles. In the massless limit all the terms of fourth and higher orders of vorticity and third order of chemical potential and temperature equal zero. It is shown, that axial current gets a topological component along the 4-acceleration vector. The similarity between different approaches to baryon polarization is established.

Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding

NASA Astrophysics Data System (ADS)

Kang, Shouqiang; Ma, Danyang; Wang, Yujing; Lan, Chaofeng; Chen, Qingguo; Mikulovich, V. I.

2017-03-01

To effectively assess different fault locations and different degrees of performance degradation of a rolling bearing with a unified assessment index, a novel state assessment method based on the relative compensation distance of multiple-domain features and locally linear embedding is proposed. First, for a single-sample signal, time-domain and frequency-domain indexes can be calculated for the original vibration signal and each sensitive intrinsic mode function obtained by improved ensemble empirical mode decomposition, and the singular values of the sensitive intrinsic mode function matrix can be extracted by singular value decomposition to construct a high-dimensional hybrid-domain feature vector. Second, a feature matrix can be constructed by arranging each feature vector of multiple samples, the dimensions of each row vector of the feature matrix can be reduced by the locally linear embedding algorithm, and the compensation distance of each fault state of the rolling bearing can be calculated using the support vector machine. Finally, the relative distance between different fault locations and different degrees of performance degradation and the normal-state optimal classification surface can be compensated, and on the basis of the proposed relative compensation distance, the assessment model can be constructed and an assessment curve drawn. Experimental results show that the proposed method can effectively assess different fault locations and different degrees of performance degradation of the rolling bearing under certain conditions.

Electric transition dipole moment in pre-Born-Oppenheimer molecular structure theory.

PubMed

Simmen, Benjamin; Mátyus, Edit; Reiher, Markus

2014-10-21

This paper presents the calculation of the electric transition dipole moment in a pre-Born-Oppenheimer framework. Electrons and nuclei are treated equally in terms of the parametrization of the non-relativistic total wave function, which is written as a linear combination of basis functions constructed from explicitly correlated Gaussian functions and the global vector representation. The integrals of the electric transition dipole moment are derived corresponding to these basis functions in both the length and the velocity representation. The calculations are performed in laboratory-fixed Cartesian coordinates without relying on coordinates which separate the center of mass from the translationally invariant degrees of freedom. The effect of the overall motion is eliminated through translationally invariant integral expressions. The electric transition dipole moment is calculated between two rovibronic levels of the H2 molecule assignable to the lowest rovibrational states of the X (1)Σ(g)(+) and B (1)Σ(u)(+) electronic states in the clamped-nuclei framework. This is the first evaluation of this quantity in a full quantum mechanical treatment without relying on the Born-Oppenheimer approximation.

Autonomous learning derived from experimental modeling of physical laws.

PubMed

Grabec, Igor

2013-05-01

This article deals with experimental description of physical laws by probability density function of measured data. The Gaussian mixture model specified by representative data and related probabilities is utilized for this purpose. The information cost function of the model is described in terms of information entropy by the sum of the estimation error and redundancy. A new method is proposed for searching the minimum of the cost function. The number of the resulting prototype data depends on the accuracy of measurement. Their adaptation resembles a self-organized, highly non-linear cooperation between neurons in an artificial NN. A prototype datum corresponds to the memorized content, while the related probability corresponds to the excitability of the neuron. The method does not include any free parameters except objectively determined accuracy of the measurement system and is therefore convenient for autonomous execution. Since representative data are generally less numerous than the measured ones, the method is applicable for a rather general and objective compression of overwhelming experimental data in automatic data-acquisition systems. Such compression is demonstrated on analytically determined random noise and measured traffic flow data. The flow over a day is described by a vector of 24 components. The set of 365 vectors measured over one year is compressed by autonomous learning to just 4 representative vectors and related probabilities. These vectors represent the flow in normal working days and weekends or holidays, while the related probabilities correspond to relative frequencies of these days. This example reveals that autonomous learning yields a new basis for interpretation of representative data and the optimal model structure. Copyright © 2012 Elsevier Ltd. All rights reserved.

Vector-based navigation using grid-like representations in artificial agents.

PubMed

Banino, Andrea; Barry, Caswell; Uria, Benigno; Blundell, Charles; Lillicrap, Timothy; Mirowski, Piotr; Pritzel, Alexander; Chadwick, Martin J; Degris, Thomas; Modayil, Joseph; Wayne, Greg; Soyer, Hubert; Viola, Fabio; Zhang, Brian; Goroshin, Ross; Rabinowitz, Neil; Pascanu, Razvan; Beattie, Charlie; Petersen, Stig; Sadik, Amir; Gaffney, Stephen; King, Helen; Kavukcuoglu, Koray; Hassabis, Demis; Hadsell, Raia; Kumaran, Dharshan

2018-05-01

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go 1,2 . Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning 3-5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex 6 . Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space 7,8 and is critical for integrating self-motion (path integration) 6,7,9 and planning direct trajectories to goals (vector-based navigation) 7,10,11 . Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types 12 . We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments-optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation 7,10,11 , demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments.

Principle component analysis to separate deformation signals from multiple sources during a 2015 intrusive sequence at Kīlauea Volcano

NASA Astrophysics Data System (ADS)

Johanson, I. A.; Miklius, A.; Poland, M. P.

2016-12-01

A sequence of magmatic events in April-May 2015 at Kīlauea Volcano produced a complex deformation pattern that can be described by multiple deforming sources, active simultaneously. The 2015 intrusive sequence began with inflation in the volcano's summit caldera near Halema`uma`u (HMM) Crater, which continued over a few weeks, followed by rapid deflation of the HMM source and inflation of a source in the south caldera region during the next few days. In Kīlauea Volcano's summit area, multiple deformation centers are active at varying times, and all contribute to the overall pattern observed with GPS, tiltmeters, and InSAR. Isolating the contribution of different signals related to each source is a challenge and complicates the determination of optimal source geometry for the underlying magma bodies. We used principle component analysis of continuous GPS time series from the 2015 intrusion sequence to determine three basis vectors which together account for 83% of the variance in the data set. The three basis vectors are non-orthogonal and not strictly the principle components of the data set. In addition to separating deformation sources in the continuous GPS data, the basis vectors provide a means to scale the contribution of each source in a given interferogram. This provides an additional constraint in a joint model of GPS and InSAR data (COSMO-SkyMed and Sentinel-1A) to determine source geometry. The first basis vector corresponds with inflation in the south caldera region, an area long recognized as the location of a long-term storage reservoir. The second vector represents deformation of the HMM source, which is in the same location as a previously modeled shallow reservoir, however InSAR data suggest a more complicated source. Preliminary modeling of the deformation attributed to the third basis vector shows that it is consistent with inflation of a steeply dipping ellipsoid centered below Keanakāko`i crater, southeast of HMM. Keanakāko`i crater is the locus of a known, intermittently active deformation source, which was not previously recognized to have been active during the 2015 event.

The Current State and TRL Assessment of People Tracking Technology for Video Surveillance Applications

DTIC Science & Technology

2014-09-01

the feature-space used to represent the target. Sometimes we trade off keeping information about one domain of the target in exchange for robustness... Kullback - Leibler distance), can be used as a similarity function between a candidate target and a template. This approach is invariant to changes in scale...basis vectors to adapt to appearance change and learns the visual information that the set of targets have in common, which is used to reduce the

Internal and external potential-field estimation from regional vector data at varying satellite altitude

NASA Astrophysics Data System (ADS)

Plattner, Alain; Simons, Frederik J.

2017-10-01

When modelling satellite data to recover a global planetary magnetic or gravitational potential field, the method of choice remains their analysis in terms of spherical harmonics. When only regional data are available, or when data quality varies strongly with geographic location, the inversion problem becomes severely ill-posed. In those cases, adopting explicitly local methods is to be preferred over adapting global ones (e.g. by regularization). Here, we develop the theory behind a procedure to invert for planetary potential fields from vector observations collected within a spatially bounded region at varying satellite altitude. Our method relies on the construction of spatiospectrally localized bases of functions that mitigate the noise amplification caused by downward continuation (from the satellite altitude to the source) while balancing the conflicting demands for spatial concentration and spectral limitation. The `altitude-cognizant' gradient vector Slepian functions (AC-GVSF) enjoy a noise tolerance under downward continuation that is much improved relative to the `classical' gradient vector Slepian functions (CL-GVSF), which do not factor satellite altitude into their construction. Furthermore, venturing beyond the realm of their first application, published in a preceding paper, in the present article we extend the theory to being able to handle both internal and external potential-field estimation. Solving simultaneously for internal and external fields under the limitation of regional data availability reduces internal-field artefacts introduced by downward-continuing unmodelled external fields, as we show with numerical examples. We explain our solution strategies on the basis of analytic expressions for the behaviour of the estimation bias and variance of models for which signal and noise are uncorrelated, (essentially) space- and band-limited, and spectrally (almost) white. The AC-GVSF are optimal linear combinations of vector spherical harmonics. Their construction is not altogether very computationally demanding when the concentration domains (the regions of spatial concentration) have circular symmetry, for example, on spherical caps or rings—even when the spherical-harmonic bandwidth is large. Data inversion proceeds by solving for the expansion coefficients of truncated function sequences, by least-squares analysis in a reduced-dimensional space. Hence, our method brings high-resolution regional potential-field modelling from incomplete and noisy vector-valued satellite data within reach of contemporary desktop machines.

Automated image segmentation using support vector machines

NASA Astrophysics Data System (ADS)

Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

2007-03-01

Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

Optimizing structure of complex technical system by heterogeneous vector criterion in interval form

NASA Astrophysics Data System (ADS)

Lysenko, A. V.; Kochegarov, I. I.; Yurkov, N. K.; Grishko, A. K.

2018-05-01

The article examines the methods of development and multi-criteria choice of the preferred structural variant of the complex technical system at the early stages of its life cycle in the absence of sufficient knowledge of parameters and variables for optimizing this structure. The suggested methods takes into consideration the various fuzzy input data connected with the heterogeneous quality criteria of the designed system and the parameters set by their variation range. The suggested approach is based on the complex use of methods of interval analysis, fuzzy sets theory, and the decision-making theory. As a result, the method for normalizing heterogeneous quality criteria has been developed on the basis of establishing preference relations in the interval form. The method of building preferential relations in the interval form on the basis of the vector of heterogeneous quality criteria suggest the use of membership functions instead of the coefficients considering the criteria value. The former show the degree of proximity of the realization of the designed system to the efficient or Pareto optimal variants. The study analyzes the example of choosing the optimal variant for the complex system using heterogeneous quality criteria.

A force vector and surface orientation sensor for intelligent grasping

NASA Technical Reports Server (NTRS)

Mcglasson, W. D.; Lorenz, R. D.; Duffie, N. A.; Gale, K. L.

1991-01-01

The paper discusses a force vector and surface orientation sensor suitable for intelligent grasping. The use of a novel four degree-of-freedom force vector robotic fingertip sensor allows efficient, real time intelligent grasping operations. The basis of sensing for intelligent grasping operations is presented and experimental results demonstrate the accuracy and ease of implementation of this approach.

Dynamics and Synchronization of Nonlinear Oscillators with Time Delays: A Study with Fiber Lasers

DTIC Science & Technology

2007-07-19

or coupling lines PC Polarization Controller PD Photodetector VA Variable Attenuator WDM Wavelength Division Multiplexer x Chapter 1 Introduction 1.1...lasers and detectors. Injection locking of lasers is a common practice that can be used to lock the frequency and phase of a laser to an injected signal...finding a basis vector that maximizes the mean squared projection of the data. Succeeding basis vectors are found that max- imize the projection with the

A Prototype SSVEP Based Real Time BCI Gaming System

PubMed Central

Martišius, Ignas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel. PMID:27051414

A Prototype SSVEP Based Real Time BCI Gaming System.

PubMed

Martišius, Ignas; Damaševičius, Robertas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

Geminiviruses for biotechnology: the art of parasite taming.

PubMed

Lozano-Durán, Rosa

2016-04-01

Viruses are intracellular pathogens that have evolved efficient strategies for replication and expression of their proteins in the host cells. Geminiviruses - plant viruses with small circular single-stranded DNA genomes - effectively manipulate plant cell processes for viral functions, entailing great potential for biotechnological applications. This potentiality has been realized in the form of protein expression and gene-silencing vectors, and, more recently, vectors for genome editing - a technology that these viruses seem particularly well-suited to facilitate. This insight offers an overview of the biological properties of geminiviruses, with emphasis on those leveraging development of geminivirus-based replicons. It illustrates the basis for engineering geminivirus-based replicons and their applications. Furthermore, it discusses the reported use and future perspectives of geminivirus-based replicons for genome editing. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Structuring Stokes correlation functions using vector-vortex beam

NASA Astrophysics Data System (ADS)

Kumar, Vijay; Anwar, Ali; Singh, R. P.

2018-01-01

Higher order statistical correlations of the optical vector speckle field, formed due to scattering of a vector-vortex beam, are explored. Here, we report on the experimental construction of the Stokes parameters covariance matrix, consisting of all possible spatial Stokes parameters correlation functions. We also propose and experimentally realize a new Stokes correlation functions called Stokes field auto correlation functions. It is observed that the Stokes correlation functions of the vector-vortex beam will be reflected in the respective Stokes correlation functions of the corresponding vector speckle field. The major advantage of proposing Stokes correlation functions is that the Stokes correlation function can be easily tuned by manipulating the polarization of vector-vortex beam used to generate vector speckle field and to get the phase information directly from the intensity measurements. Moreover, this approach leads to a complete experimental Stokes characterization of a broad range of random fields.

GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China

NASA Astrophysics Data System (ADS)

Xu, Chong; Dai, Fuchu; Xu, Xiwei; Lee, Yuan Hsi

2012-04-01

Support vector machine (SVM) modeling is based on statistical learning theory. It involves a training phase with associated input and target output values. In recent years, the method has become increasingly popular. The main purpose of this study is to evaluate the mapping power of SVM modeling in earthquake triggered landslide-susceptibility mapping for a section of the Jianjiang River watershed using a Geographic Information System (GIS) software. The river was affected by the Wenchuan earthquake of May 12, 2008. Visual interpretation of colored aerial photographs of 1-m resolution and extensive field surveys provided a detailed landslide inventory map containing 3147 landslides related to the 2008 Wenchuan earthquake. Elevation, slope angle, slope aspect, distance from seismogenic faults, distance from drainages, and lithology were used as the controlling parameters. For modeling, three groups of positive and negative training samples were used in concert with four different kernel functions. Positive training samples include the centroids of 500 large landslides, those of all 3147 landslides, and 5000 randomly selected points in landslide polygons. Negative training samples include 500, 3147, and 5000 randomly selected points on slopes that remained stable during the Wenchuan earthquake. The four kernel functions are linear, polynomial, radial basis, and sigmoid. In total, 12 cases of landslide susceptibility were mapped. Comparative analyses of landslide-susceptibility probability and area relation curves show that both the polynomial and radial basis functions suitably classified the input data as either landslide positive or negative though the radial basis function was more successful. The 12 generated landslide-susceptibility maps were compared with known landslide centroid locations and landslide polygons to verify the success rate and predictive accuracy of each model. The 12 results were further validated using area-under-curve analysis. Group 3 with 5000 randomly selected points on the landslide polygons, and 5000 randomly selected points along stable slopes gave the best results with a success rate of 79.20% and predictive accuracy of 79.13% under the radial basis function. Of all the results, the sigmoid kernel function was the least skillful when used in concert with the centroid data of all 3147 landslides as positive training samples, and the negative training samples of 3147 randomly selected points in regions of stable slope (success rate = 54.95%; predictive accuracy = 61.85%). This paper also provides suggestions and reference data for selecting appropriate training samples and kernel function types for earthquake triggered landslide-susceptibility mapping using SVM modeling. Predictive landslide-susceptibility maps could be useful in hazard mitigation by helping planners understand the probability of landslides in different regions.

Pure state consciousness and its local reduction to neuronal space

NASA Astrophysics Data System (ADS)

Duggins, A. J.

2013-01-01

The single neuronal state can be represented as a vector in a complex space, spanned by an orthonormal basis of integer spike counts. In this model a scalar element of experience is associated with the instantaneous firing rate of a single sensory neuron over repeated stimulus presentations. Here the model is extended to composite neural systems that are tensor products of single neuronal vector spaces. Depiction of the mental state as a vector on this tensor product space is intended to capture the unity of consciousness. The density operator is introduced as its local reduction to the single neuron level, from which the firing rate can again be derived as the objective correlate of a subjective element. However, the relational structure of perceptual experience only emerges when the non-local mental state is considered. A metric of phenomenal proximity between neuronal elements of experience is proposed, based on the cross-correlation function of neurophysiology, but constrained by the association of theoretical extremes of correlation/anticorrelation in inseparable 2-neuron states with identical and opponent elements respectively.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Encoding of head acceleration in vestibular neurons. I. Spatiotemporal response properties to linear acceleration

NASA Technical Reports Server (NTRS)

Bush, G. A.; Perachio, A. A.; Angelaki, D. E.

1993-01-01

1. Extracellular recordings were made in and around the medial vestibular nuclei in decerebrated rats. Neurons were functionally identified according to their semicircular canal input on the basis of their responses to angular head rotations around the yaw, pitch, and roll head axes. Those cells responding to angular acceleration were classified as either horizontal semicircular canal-related (HC) or vertical semicircular canal-related (VC) neurons. The HC neurons were further characterized as either type I or type II, depending on the direction of rotation producing excitation. Cells that lacked a response to angular head acceleration, but exhibited sensitivity to a change in head position, were classified as purely otolith organ-related (OTO) neurons. All vestibular neurons were then tested for their response to sinusoidal linear translation in the horizontal head plane. 2. Convergence of macular and canal inputs onto central vestibular nuclei neurons occurred in 73% of the type I HC, 79% of the type II HC, and 86% of the VC neurons. Out of the 223 neurons identified as receiving macular input, 94 neurons were further studied, and their spatiotemporal response properties to sinusoidal stimulation with pure linear acceleration were quantified. Data were obtained from 33 type I HC, 22 type II HC, 22 VC, and 17 OTO neurons. 3. For each neuron the angle of the translational stimulus vector was varied by 15, 30, or 45 degrees increments in the horizontal head plane. In all tested neurons, a direction of maximum sensitivity was identified. An interesting difference among neurons was their response to translation along the direction perpendicular to that that produced the maximum response ("null" direction). For the majority of neurons tested, it was possible to evoke a nonzero response during stimulation along the null direction always had response phases that varied as a function of stimulus direction. 4. These spatiotemporal response properties were quantified in two independent ways. First, the data were evaluated on the basis of the traditional one-dimensional principle governed by the "cosine gain rule" and constant response phase at different stimulus orientations. Second, the response gain and phase values that were empirically determined for each orientation of the applied linear stimulus vector were fitted on the basis of a newly developed formalism that treats neuronal responses as exhibiting two-dimensional spatial sensitivity. Thus two response vectors were determined for each neuron on the basis of its response gain and phase at different stimulus directions in the horizontal head plane.(ABSTRACT TRUNCATED AT 400 WORDS).

How should spin-weighted spherical functions be defined?

NASA Astrophysics Data System (ADS)

Boyle, Michael

2016-09-01

Spin-weighted spherical functions provide a useful tool for analyzing tensor-valued functions on the sphere. A tensor field can be decomposed into complex-valued functions by taking contractions with tangent vectors on the sphere and the normal to the sphere. These component functions are usually presented as functions on the sphere itself, but this requires an implicit choice of distinguished tangent vectors with which to contract. Thus, we may more accurately say that spin-weighted spherical functions are functions of both a point on the sphere and a choice of frame in the tangent space at that point. The distinction becomes extremely important when transforming the coordinates in which these functions are expressed, because the implicit choice of frame will also transform. Here, it is proposed that spin-weighted spherical functions should be treated as functions on the spin or rotation groups, which simultaneously tracks the point on the sphere and the choice of tangent frame by rotating elements of an orthonormal basis. In practice, the functions simply take a quaternion argument and produce a complex value. This approach more cleanly reflects the geometry involved, and allows for a more elegant description of the behavior of spin-weighted functions. In this form, the spin-weighted spherical harmonics have simple expressions as elements of the Wigner 𝔇 representations, and transformations under rotation are simple. Two variants of the angular-momentum operator are defined directly in terms of the spin group; one is the standard angular-momentum operator L, while the other is shown to be related to the spin-raising operator ð.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Nonparametric methods for drought severity estimation at ungauged sites

NASA Astrophysics Data System (ADS)

Sadri, S.; Burn, D. H.

2012-12-01

The objective in frequency analysis is, given extreme events such as drought severity or duration, to estimate the relationship between that event and the associated return periods at a catchment. Neural networks and other artificial intelligence approaches in function estimation and regression analysis are relatively new techniques in engineering, providing an attractive alternative to traditional statistical models. There are, however, few applications of neural networks and support vector machines in the area of severity quantile estimation for drought frequency analysis. In this paper, we compare three methods for this task: multiple linear regression, radial basis function neural networks, and least squares support vector regression (LS-SVR). The area selected for this study includes 32 catchments in the Canadian Prairies. From each catchment drought severities are extracted and fitted to a Pearson type III distribution, which act as observed values. For each method-duration pair, we use a jackknife algorithm to produce estimated values at each site. The results from these three approaches are compared and analyzed, and it is found that LS-SVR provides the best quantile estimates and extrapolating capacity.

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

NASA Astrophysics Data System (ADS)

Çiftçi, B. B.; Kuter, S.; Akyürek, Z.; Weber, G.-W.

2017-11-01

Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine

NASA Astrophysics Data System (ADS)

Li, Shaoxin; Zhang, Yanjiao; Xu, Junfa; Li, Linfang; Zeng, Qiuyao; Lin, Lin; Guo, Zhouyi; Liu, Zhiming; Xiong, Honglian; Liu, Songhao

2014-09-01

This study aims to present a noninvasive prostate cancer screening methods using serum surface-enhanced Raman scattering (SERS) and support vector machine (SVM) techniques through peripheral blood sample. SERS measurements are performed using serum samples from 93 prostate cancer patients and 68 healthy volunteers by silver nanoparticles. Three types of kernel functions including linear, polynomial, and Gaussian radial basis function (RBF) are employed to build SVM diagnostic models for classifying measured SERS spectra. For comparably evaluating the performance of SVM classification models, the standard multivariate statistic analysis method of principal component analysis (PCA) is also applied to classify the same datasets. The study results show that for the RBF kernel SVM diagnostic model, the diagnostic accuracy of 98.1% is acquired, which is superior to the results of 91.3% obtained from PCA methods. The receiver operating characteristic curve of diagnostic models further confirm above research results. This study demonstrates that label-free serum SERS analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive prostate cancer screening.

A novel dual vector coexpressing PhiX174 lysis E gene and staphylococcal nuclease A gene on the basis of lambda promoter pR and pL, respectively.

PubMed

Fu, Lixia; Lu, Chengping

2013-06-01

Bacterial ghost is a novel vaccine platform, and its safe and efficient production depends largely upon a suitable and functional vector. In this study, a series of temperature-inducible plasmids, carrying Phix174 lysis gene E and/or staphylococcal nuclease A (SNA) gene, were constructed and evaluated in Escherichia coli. The results showed that the direct product of SNA (pBV220-SNA) could degrade the plasmid and genomic DNA of E. coli while the fusion product of gene E and partial Cro gene (pKF396M-2) lost the ability to lyse the host strain. The insertion of enhancer T7g10 elements and Shine-Dalgarno box (ESD) between them (pKF396M-3) could resume the function of gene E. Using plasmid pKF396M-4 with gene E and SNA, respectively, under the immediate control of promoter pR and pL, the remnant plasmids and genomic DNA of E. coli were eliminated, and the rates of inactivation increased by two orders of magnitude over that obtained with the exclusive use of E-mediated lysis plasmid. By substituting these two genes with customized multiple cloning sites sequences, the plasmid could be modified to a dual expression vector (pKF396M-5).

Anopheles atroparvus density modeling using MODIS NDVI in a former malarious area in Portugal.

PubMed

Lourenço, Pedro M; Sousa, Carla A; Seixas, Júlia; Lopes, Pedro; Novo, Maria T; Almeida, A Paulo G

2011-12-01

Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the model's ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities. © 2011 The Society for Vector Ecology.

Teaching Vectors Through an Interactive Game Based Laboratory

NASA Astrophysics Data System (ADS)

O'Brien, James; Sirokman, Gergely

2014-03-01

In recent years, science and particularly physics education has been furthered by the use of project based interactive learning [1]. There is a tremendous amount of evidence [2] that use of these techniques in a college learning environment leads to a deeper appreciation and understanding of fundamental concepts. Since vectors are the basis for any advancement in physics and engineering courses the cornerstone of any physics regimen is a concrete and comprehensive introduction to vectors. Here, we introduce a new turn based vector game that we have developed to help supplement traditional vector learning practices, which allows students to be creative, work together as a team, and accomplish a goal through the understanding of basic vector concepts.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

PubMed

Hong, Xia

2006-07-01

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Nonstationary Dynamics Data Analysis with Wavelet-SVD Filtering

NASA Technical Reports Server (NTRS)

Brenner, Marty; Groutage, Dale; Bessette, Denis (Technical Monitor)

2001-01-01

Nonstationary time-frequency analysis is used for identification and classification of aeroelastic and aeroservoelastic dynamics. Time-frequency multiscale wavelet processing generates discrete energy density distributions. The distributions are processed using the singular value decomposition (SVD). Discrete density functions derived from the SVD generate moments that detect the principal features in the data. The SVD standard basis vectors are applied and then compared with a transformed-SVD, or TSVD, which reduces the number of features into more compact energy density concentrations. Finally, from the feature extraction, wavelet-based modal parameter estimation is applied.

Micrometeoroid/space debris effects on materials

NASA Technical Reports Server (NTRS)

Zwiener, James M.; Finckenor, Miria M.

1993-01-01

The Long Duration Exposure Facility (LDEF) micrometeoroid/space debris impact data has been reduced in terms that are convenient for evaluating the overall quantitative effect on material properties. Impact crater flux has been evaluated as a function of angle from velocity vector and as a function of crater size. This data is combined with spall data from flight and ground testing to calculate effective solar absorption and emittance values versus time. Results indicate that the surface damage from micrometeoroid/space debris does not significantly affect the overall surface optical thermal physical properties. Of course the local damage around impact craters radically alter optical properties. Damage to composites and solar cells on an overall basis was minimal.

Equilibrium Spline Interface (ESI) for magnetic confinement codes

NASA Astrophysics Data System (ADS)

Li, Xujing; Zakharov, Leonid E.

2017-12-01

A compact and comprehensive interface between magneto-hydrodynamic (MHD) equilibrium codes and gyro-kinetic, particle orbit, MHD stability, and transport codes is presented. Its irreducible set of equilibrium data consists of three (in the 2-D case with occasionally one extra in the 3-D case) functions of coordinates and four 1-D radial profiles together with their first and mixed derivatives. The C reconstruction routines, accessible also from FORTRAN, allow the calculation of basis functions and their first derivatives at any position inside the plasma and in its vicinity. After this all vector fields and geometric coefficients, required for the above mentioned types of codes, can be calculated using only algebraic operations with no further interpolation or differentiation.

Green’s Functions for a Theoretical Model of an Aperture Fed Stacked-Patch Microstrip Antenna

DTIC Science & Technology

1989-12-01

44 4 - 1 Normalized values of D bk3b on the real axis for (a) f = 4 GHz, bib = 1.6 mm, b2b = 4.8 mm, Flb = 5 o’ 2b = 2.5 Eo’ 3b = Co, P’lb = 2b...dielectric la. bIb Thickness of dielectric lb. b2b Total thickness of dielectrics lb and 2b. Cli Observer cell on the aperture, i is an index variable...interface 3b (patch 2). Sfj Source current cell on the feedline. tb Thickness of dielectric layer 2b ( b2b - bib). T lj Vector rooftop basis function

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Cao, Jin; Jiang, Zhibin; Wang, Kangzhou

2017-07-01

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

Third International Kharkov Symposium "Physics and Engineering of Millimeter and Submillimeter Waves" MSMW󈨦 Symposium Proceedings, Volume 1,

DTIC Science & Technology

1998-09-01

potential of the surface wave electromagnetic field; ea is the unit of the polarization vectors : ex = ela. = e2x= (qx/\\q\\)\\/L\\q\\/(ei + e0), ely... polarization basis of the incident wave: EB°=eB^(/kr), (1) where e„ is the cyclic unit vector , n = ±1, k is the wave vector . The equation describing...rectangular grid. From the direction determined by wave vector k0, the plane electromagnetic wave of linear polarization incidents onto the array. It

Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study.

PubMed

Lima, Clodoaldo A M; Coelho, André L V

2011-10-01

We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely, Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). Copyright © 2011 Elsevier B.V. All rights reserved.

[Cloning of human CD45 gene and its expression in Hela cells].

PubMed

Li, Jie; Xu, Tianyu; Wu, Lulin; Zhang, Liyun; Lu, Xiao; Zuo, Daming; Chen, Zhengliang

2015-11-01

To clone human CD45 gene PTPRC and establish Hela cells overexpressing recombinant human CD45 protein. The intact cDNA encoding human CD45 amplified using RT-PCR from the total RNA extracted from peripheral blood mononuclear cells (PBMCs) of a healthy donor was cloned into pMD-18T vector. The CD45 cDNA fragment amplified from the pMD-18T-CD45 by PCR was inserted to the coding region of the PcDNA3.1-3xflag vector, and the resultant recombinant expression vector PcDNA3.1-3xflag-CD45 was transfected into Hela cells. The expression of CD45 in Hela cells was detected by flow cytometry and Western blotting, and the phosphastase activity of CD45 was quantified using an alkaline phosphatase assay kit. The cDNA fragment of about 3 900 bp was amplified from human PBMCs and cloned into pMD-18T vector. The recombinant expression vector PcDNA3.1-3xflag-CD45 was constructed, whose restriction maps and sequence were consistent with those expected. The expression of CD45 in transfected Hela cells was detected by flow cytometry and Western blotting, and the expressed recombinant CD45 protein in Hela cells showed a phosphastase activity. The cDNA of human CD45 was successfully cloned and effectively expressed in Hela cells, which provides a basis for further exploration of the functions of CD45.

Elements of the quality management in the materials' industry

NASA Astrophysics Data System (ADS)

Ioana, Adrian; Semenescu, Augustin; Costoiu, Mihnea; Marcu, Dragoş

2017-12-01

The criteria function concept consists of transforming the criteria function (CF) in a quality-economical matrix math MQE. The levels of prescribing the criteria function was obtained by using a composition algorithm for three vectors: T¯ vector - technical parameters' vector (ti); Ē vector - economical parameters' vector (ej) and P¯ vector - weight vector (p1). For each product or service, the area of the circle represents the value of its sales. The BCG Matrix thus offers a very useful map of the organization's service strengths and weaknesses, at least in terms of current profitability, as well as the likely cash flows.

Deep neural mapping support vector machines.

PubMed

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Heavy and Heavy-Light Mesons in the Covariant Spectator Theory

NASA Astrophysics Data System (ADS)

Stadler, Alfred; Leitão, Sofia; Peña, M. T.; Biernat, Elmar P.

2018-05-01

The masses and vertex functions of heavy and heavy-light mesons, described as quark-antiquark bound states, are calculated with the Covariant Spectator Theory (CST). We use a kernel with an adjustable mixture of Lorentz scalar, pseudoscalar, and vector linear confining interaction, together with a one-gluon-exchange kernel. A series of fits to the heavy and heavy-light meson spectrum were calculated, and we discuss what conclusions can be drawn from it, especially about the Lorentz structure of the kernel. We also apply the Brodsky-Huang-Lepage prescription to express the CST wave functions for heavy quarkonia in terms of light-front variables. They agree remarkably well with light-front wave functions obtained in the Hamiltonian basis light-front quantization approach, even in excited states.

BASiNET-BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification.

PubMed

Ito, Eric Augusto; Katahira, Isaque; Vicente, Fábio Fernandes da Rocha; Pereira, Luiz Filipe Protasio; Lopes, Fabrício Martins

2018-06-05

With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET.

Representation of the five- and six-dimensional harmonic oscillators in a u(5) ⊃ so(5) ⊃ so(3) basis

NASA Astrophysics Data System (ADS)

Rowe, D. J.

1994-06-01

The duality that exists between the two subgroups SU(1,1) and O(5) of Sp(5,R) to construct basis states for the five-dimensional harmonic oscillator which simultaneously reduce the Sp(5,R)⊇U(5)⊇O(5)⊇SO(3) and Sp(5,R)⊇ SU(1,1)⊇U(1) subgroup chains is used. It is shown that the vector-coherent-state wave functions of the fundamental five-dimensional SO(5) irrep [1,0] realize the traceless bosons introduced by Lohe and Hurst to classify the irreps of the orthogonal groups and employed in Chacon, Moshinsky, and Sharp's construction of a basis for the five-dimensional harmonic oscillator. Moreover, it is shown that VCS theory provides a simple mechanism for constructing matrix elements of the traceless boson operators. These matrix elements are used to extend the VCS representations of SO(5) in an SO(3) basis, given in a previous paper, to irreps of U(5) in an SO(5)⊇ SO(3) basis. The extension to U(6)⊇U(5)⊇SO(5)⊇SO(3) is also given.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements.

PubMed

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K; Cai, Chang; Nagarajan, Srikantan S

2018-06-01

Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements

NASA Astrophysics Data System (ADS)

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K.; Cai, Chang; Nagarajan, Srikantan S.

2018-06-01

Objective. Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. Approach. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Main results. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. Significance. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Geometric and computer-aided spline hob modeling

NASA Astrophysics Data System (ADS)

Brailov, I. G.; Myasoedova, T. M.; Panchuk, K. L.; Krysova, I. V.; Rogoza, YU A.

2018-03-01

The paper considers acquiring the spline hob geometric model. The objective of the research is the development of a mathematical model of spline hob for spline shaft machining. The structure of the spline hob is described taking into consideration the motion in parameters of the machine tool system of cutting edge positioning and orientation. Computer-aided study is performed with the use of CAD and on the basis of 3D modeling methods. Vector representation of cutting edge geometry is accepted as the principal method of spline hob mathematical model development. The paper defines the correlations described by parametric vector functions representing helical cutting edges designed for spline shaft machining with consideration for helical movement in two dimensions. An application for acquiring the 3D model of spline hob is developed on the basis of AutoLISP for AutoCAD environment. The application presents the opportunity for the use of the acquired model for milling process imitation. An example of evaluation, analytical representation and computer modeling of the proposed geometrical model is reviewed. In the mentioned example, a calculation of key spline hob parameters assuring the capability of hobbing a spline shaft of standard design is performed. The polygonal and solid spline hob 3D models are acquired by the use of imitational computer modeling.

Adaptive h -refinement for reduced-order models: ADAPTIVE h -refinement for reduced-order models

DOE PAGES

Carlberg, Kevin T.

2014-11-05

Our work presents a method to adaptively refine reduced-order models a posteriori without requiring additional full-order-model solves. The technique is analogous to mesh-adaptive h-refinement: it enriches the reduced-basis space online by ‘splitting’ a given basis vector into several vectors with disjoint support. The splitting scheme is defined by a tree structure constructed offline via recursive k-means clustering of the state variables using snapshot data. This method identifies the vectors to split online using a dual-weighted-residual approach that aims to reduce error in an output quantity of interest. The resulting method generates a hierarchy of subspaces online without requiring large-scale operationsmore » or full-order-model solves. Furthermore, it enables the reduced-order model to satisfy any prescribed error tolerance regardless of its original fidelity, as a completely refined reduced-order model is mathematically equivalent to the original full-order model. Experiments on a parameterized inviscid Burgers equation highlight the ability of the method to capture phenomena (e.g., moving shocks) not contained in the span of the original reduced basis.« less

Current Advances and Future Challenges in Adenoviral Vector Biology and Targeting

PubMed Central

Campos, Samuel K.; Barry, Michael A.

2008-01-01

Gene delivery vectors based on Adenoviral (Ad) vectors have enormous potential for the treatment of both hereditary and acquired disease. Detailed structural analysis of the Ad virion, combined with functional studies has broadened our knowledge of the structure/function relationships between Ad vectors and host cells/tissues and substantial achievement has been made towards a thorough understanding of the biology of Ad vectors. The widespread use of Ad vectors for clinical gene therapy is compromised by their inherent immunogenicity. The generation of safer and more effective Ad vectors, targeted to the site of disease, has therefore become a great ambition in the field of Ad vector development. This review provides a synopsis of the structure/function relationships between Ad vectors and host systems and summarizes the many innovative approaches towards achieving Ad vector targeting. PMID:17584037

The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models

PubMed Central

Noid, W. G.; Liu, Pu; Wang, Yanting; Chu, Jhih-Wei; Ayton, Gary S.; Izvekov, Sergei; Andersen, Hans C.; Voth, Gregory A.

2008-01-01

The multiscale coarse-graining (MS-CG) method [S. Izvekov and G. A. Voth, J. Phys. Chem. B 109, 2469 (2005);J. Chem. Phys. 123, 134105 (2005)] employs a variational principle to determine an interaction potential for a CG model from simulations of an atomically detailed model of the same system. The companion paper proved that, if no restrictions regarding the form of the CG interaction potential are introduced and if the equilibrium distribution of the atomistic model has been adequately sampled, then the MS-CG variational principle determines the exact many-body potential of mean force (PMF) governing the equilibrium distribution of CG sites generated by the atomistic model. In practice, though, CG force fields are not completely flexible, but only include particular types of interactions between CG sites, e.g., nonbonded forces between pairs of sites. If the CG force field depends linearly on the force field parameters, then the vector valued functions that relate the CG forces to these parameters determine a set of basis vectors that span a vector subspace of CG force fields. The companion paper introduced a distance metric for the vector space of CG force fields and proved that the MS-CG variational principle determines the CG force force field that is within that vector subspace and that is closest to the force field determined by the many-body PMF. The present paper applies the MS-CG variational principle for parametrizing molecular CG force fields and derives a linear least squares problem for the parameter set determining the optimal approximation to this many-body PMF. Linear systems of equations for these CG force field parameters are derived and analyzed in terms of equilibrium structural correlation functions. Numerical calculations for a one-site CG model of methanol and a molecular CG model of the EMIM+∕NO3− ionic liquid are provided to illustrate the method. PMID:18601325

Quantum mechanics of a photon

NASA Astrophysics Data System (ADS)

Babaei, Hassan; Mostafazadeh, Ali

2017-08-01

A first-quantized free photon is a complex massless vector field A =(Aμ ) whose field strength satisfies Maxwell's equations in vacuum. We construct the Hilbert space H of the photon by endowing the vector space of the fields A in the temporal-Coulomb gauge with a positive-definite and relativistically invariant inner product. We give an explicit expression for this inner product, identify the Hamiltonian for the photon with the generator of time translations in H , determine the operators representing the momentum and the helicity of the photon, and introduce a chirality operator whose eigenfunctions correspond to fields having a definite sign of energy. We also construct a position operator for the photon whose components commute with each other and with the chirality and helicity operators. This allows for the construction of the localized states of the photon with a definite sign of energy and helicity. We derive an explicit formula for the latter and compute the corresponding electric and magnetic fields. These turn out to diverge not just at the point where the photon is localized but on a plane containing this point. We identify the axis normal to this plane with an associated symmetry axis and show that each choice of this axis specifies a particular position operator, a corresponding position basis, and a position representation of the quantum mechanics of a photon. In particular, we examine the position wave functions determined by such a position basis, elucidate their relationship with the Riemann-Silberstein and Landau-Peierls wave functions, and give an explicit formula for the probability density of the spatial localization of the photon.

Genetic modification of hematopoietic cells using retroviral and lentiviral vectors: safety considerations for vector design and delivery into target cells.

PubMed

Dropulic, Boro

2005-07-01

The recent development of leukemia in three patients following retroviral vector gene transfer in hematopoietic stem cells, resulting in the death of one patient, has raised safety concerns for the use of integrating gene transfer vectors for human gene therapy. This review discusses these serious adverse events from the perspective of whether restrictions on vector design and vector-modified target cells are warranted at this time. A case is made against presently establishing specific restrictions for vector design and transduced cells; rather, their safety should be ascertained by empiric evaluation in appropriate preclinical models on a case-by-case basis. Such preclinical data, coupled with proper informed patient consent and a risk-benefit ratio analysis, provide the best available prospective evaluation of gene transfer vectors prior to their translation into the clinic.

Genome-Wide Transcription and Functional Analyses Reveal Heterogeneous Molecular Mechanisms Driving Pyrethroids Resistance in the Major Malaria Vector Anopheles funestus Across Africa

PubMed Central

Riveron, Jacob M.; Ibrahim, Sulaiman S.; Mulamba, Charles; Djouaka, Rousseau; Irving, Helen; Wondji, Murielle J.; Ishak, Intan H.; Wondji, Charles S.

2017-01-01

Pyrethroid resistance in malaria vector, An. funestus is increasingly reported across Africa, threatening the sustainability of pyrethroid-based control interventions, including long lasting insecticidal nets (LLINs). Managing this problem requires understanding of the molecular basis of the resistance from different regions of the continent, to establish whether it is being driven by a single or independent selective events. Here, using a genome-wide transcription profiling of pyrethroid resistant populations from southern (Malawi), East (Uganda), and West Africa (Benin), we investigated the molecular basis of resistance, revealing strong differences between the different African regions. The duplicated cytochrome P450 genes (CYP6P9a and CYP6P9b) which were highly overexpressed in southern Africa are not the most upregulated in other regions, where other genes are more overexpressed, including GSTe2 in West (Benin) and CYP9K1 in East (Uganda). The lack of directional selection on both CYP6P9a and CYP6P9b in Uganda in contrast to southern Africa further supports the limited role of these genes outside southern Africa. However, other genes such as the P450 CYP9J11 are commonly overexpressed in all countries across Africa. Here, CYP9J11 is functionally characterized and shown to confer resistance to pyrethroids and moderate cross-resistance to carbamates (bendiocarb). The consistent overexpression of GSTe2 in Benin is coupled with a role of allelic variation at this gene as GAL4-UAS transgenic expression in Drosophila flies showed that the resistant 119F allele is highly efficient in conferring both DDT and permethrin resistance than the L119. The heterogeneity in the molecular basis of resistance and cross-resistance to insecticides in An. funestus populations throughout sub-Saharan African should be taken into account in designing resistance management strategies. PMID:28428243

Genome-Wide Transcription and Functional Analyses Reveal Heterogeneous Molecular Mechanisms Driving Pyrethroids Resistance in the Major Malaria Vector Anopheles funestus Across Africa.

PubMed

Riveron, Jacob M; Ibrahim, Sulaiman S; Mulamba, Charles; Djouaka, Rousseau; Irving, Helen; Wondji, Murielle J; Ishak, Intan H; Wondji, Charles S

2017-06-07

Pyrethroid resistance in malaria vector, An. funestus is increasingly reported across Africa, threatening the sustainability of pyrethroid-based control interventions, including long lasting insecticidal nets (LLINs). Managing this problem requires understanding of the molecular basis of the resistance from different regions of the continent, to establish whether it is being driven by a single or independent selective events. Here, using a genome-wide transcription profiling of pyrethroid resistant populations from southern (Malawi), East (Uganda), and West Africa (Benin), we investigated the molecular basis of resistance, revealing strong differences between the different African regions. The duplicated cytochrome P450 genes ( CYP6P9a and CYP6P9b ) which were highly overexpressed in southern Africa are not the most upregulated in other regions, where other genes are more overexpressed, including GSTe2 in West (Benin) and CYP9K1 in East (Uganda). The lack of directional selection on both CYP6P9a and CYP6P9b in Uganda in contrast to southern Africa further supports the limited role of these genes outside southern Africa. However, other genes such as the P450 CYP9J11 are commonly overexpressed in all countries across Africa. Here, CYP9J11 is functionally characterized and shown to confer resistance to pyrethroids and moderate cross-resistance to carbamates (bendiocarb). The consistent overexpression of GSTe2 in Benin is coupled with a role of allelic variation at this gene as GAL4-UAS transgenic expression in Drosophila flies showed that the resistant 119F allele is highly efficient in conferring both DDT and permethrin resistance than the L119. The heterogeneity in the molecular basis of resistance and cross-resistance to insecticides in An. funestus populations throughout sub-Saharan African should be taken into account in designing resistance management strategies. Copyright © 2017 Riveron et al.

Uniqueness of thermodynamic projector and kinetic basis of molecular individualism

NASA Astrophysics Data System (ADS)

Gorban, Alexander N.; Karlin, Iliya V.

2004-05-01

Three results are presented: First, we solve the problem of persistence of dissipation for reduction of kinetic models. Kinetic equations with thermodynamic Lyapunov functions are studied. Uniqueness of the thermodynamic projector is proven: There exists only one projector which transforms any vector field equipped with the given Lyapunov function into a vector field with the same Lyapunov function for a given anzatz manifold which is not tangent to the Lyapunov function levels. Second, we use the thermodynamic projector for developing the short memory approximation and coarse-graining for general nonlinear dynamic systems. We prove that in this approximation the entropy production increases. ( The theorem about entropy overproduction.) In example, we apply the thermodynamic projector to derive the equations of reduced kinetics for the Fokker-Planck equation. A new class of closures is developed, the kinetic multipeak polyhedra. Distributions of this type are expected in kinetic models with multidimensional instability as universally as the Gaussian distribution appears for stable systems. The number of possible relatively stable states of a nonequilibrium system grows as 2 m, and the number of macroscopic parameters is in order mn, where n is the dimension of configuration space, and m is the number of independent unstable directions in this space. The elaborated class of closures and equations pretends to describe the effects of “molecular individualism”. This is the third result.

Construction of promoter-probe shuttle vectors for Escherichia coli and corynebacteria on the basis of promoterless alpha-amylase gene.

PubMed

Ugorcáková, J; Bukovská, G; Timko, J

2000-01-01

We constructed new promoter-probe vectors for E. coli and corynebacteria based on the promoterless alpha-amylase gene originating from Bacillus subtilis. Vectors pJUPAE1 and pJUPAE2 are suitable for isolation of transcriptionally active fragments from plasmids, phages or genomic DNA. alpha-Amylase activity can be easily visually detected on agar plates containing a chromogenic substrate, or by direct measurement of alpha-amylase activity.

Manifolds for pose tracking from monocular video

NASA Astrophysics Data System (ADS)

Basu, Saurav; Poulin, Joshua; Acton, Scott T.

2015-03-01

We formulate a simple human-pose tracking theory from monocular video based on the fundamental relationship between changes in pose and image motion vectors. We investigate the natural embedding of the low-dimensional body pose space into a high-dimensional space of body configurations that behaves locally in a linear manner. The embedded manifold facilitates the decomposition of the image motion vectors into basis motion vector fields of the tangent space to the manifold. This approach benefits from the style invariance of image motion flow vectors, and experiments to validate the fundamental theory show reasonable accuracy (within 4.9 deg of the ground truth).

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

NASA Astrophysics Data System (ADS)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

Bethe vectors for XXX-spin chain

NASA Astrophysics Data System (ADS)

Burdík, Čestmír; Fuksa, Jan; Isaev, Alexei

2014-11-01

The paper deals with algebraic Bethe ansatz for XXX-spin chain. Generators of Yang-Baxter algebra are expressed in basis of free fermions and used to calculate explicit form of Bethe vectors. Their relation to N-component models is used to prove conjecture about their form in general. Some remarks on inhomogeneous XXX-spin chain are included.

Accelerating molecular property calculations with nonorthonormal Krylov space methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Furche, Filipp; Krull, Brandon T.; Nguyen, Brian D.

Here, we formulate Krylov space methods for large eigenvalue problems and linear equation systems that take advantage of decreasing residual norms to reduce the cost of matrix-vector multiplication. The residuals are used as subspace basis without prior orthonormalization, which leads to generalized eigenvalue problems or linear equation systems on the Krylov space. These nonorthonormal Krylov space (nKs) algorithms are favorable for large matrices with irregular sparsity patterns whose elements are computed on the fly, because fewer operations are necessary as the residual norm decreases as compared to the conventional method, while errors in the desired eigenpairs and solution vectors remainmore » small. We consider real symmetric and symplectic eigenvalue problems as well as linear equation systems and Sylvester equations as they appear in configuration interaction and response theory. The nKs method can be implemented in existing electronic structure codes with minor modifications and yields speed-ups of 1.2-1.8 in typical time-dependent Hartree-Fock and density functional applications without accuracy loss. The algorithm can compute entire linear subspaces simultaneously which benefits electronic spectra and force constant calculations requiring many eigenpairs or solution vectors. The nKs approach is related to difference density methods in electronic ground state calculations, and particularly efficient for integral direct computations of exchange-type contractions. By combination with resolution-of-the-identity methods for Coulomb contractions, three- to fivefold speed-ups of hybrid time-dependent density functional excited state and response calculations are achieved.« less

Accelerating molecular property calculations with nonorthonormal Krylov space methods

DOE PAGES

Furche, Filipp; Krull, Brandon T.; Nguyen, Brian D.; ...

2016-05-03

Here, we formulate Krylov space methods for large eigenvalue problems and linear equation systems that take advantage of decreasing residual norms to reduce the cost of matrix-vector multiplication. The residuals are used as subspace basis without prior orthonormalization, which leads to generalized eigenvalue problems or linear equation systems on the Krylov space. These nonorthonormal Krylov space (nKs) algorithms are favorable for large matrices with irregular sparsity patterns whose elements are computed on the fly, because fewer operations are necessary as the residual norm decreases as compared to the conventional method, while errors in the desired eigenpairs and solution vectors remainmore » small. We consider real symmetric and symplectic eigenvalue problems as well as linear equation systems and Sylvester equations as they appear in configuration interaction and response theory. The nKs method can be implemented in existing electronic structure codes with minor modifications and yields speed-ups of 1.2-1.8 in typical time-dependent Hartree-Fock and density functional applications without accuracy loss. The algorithm can compute entire linear subspaces simultaneously which benefits electronic spectra and force constant calculations requiring many eigenpairs or solution vectors. The nKs approach is related to difference density methods in electronic ground state calculations, and particularly efficient for integral direct computations of exchange-type contractions. By combination with resolution-of-the-identity methods for Coulomb contractions, three- to fivefold speed-ups of hybrid time-dependent density functional excited state and response calculations are achieved.« less

Decision support system for diabetic retinopathy using discrete wavelet transform.

PubMed

Noronha, K; Acharya, U R; Nayak, K P; Kamath, S; Bhandary, S V

2013-03-01

Prolonged duration of the diabetes may affect the tiny blood vessels of the retina causing diabetic retinopathy. Routine eye screening of patients with diabetes helps to detect diabetic retinopathy at the early stage. It is very laborious and time-consuming for the doctors to go through many fundus images continuously. Therefore, decision support system for diabetic retinopathy detection can reduce the burden of the ophthalmologists. In this work, we have used discrete wavelet transform and support vector machine classifier for automated detection of normal and diabetic retinopathy classes. The wavelet-based decomposition was performed up to the second level, and eight energy features were extracted. Two energy features from the approximation coefficients of two levels and six energy values from the details in three orientations (horizontal, vertical and diagonal) were evaluated. These features were fed to the support vector machine classifier with various kernel functions (linear, radial basis function, polynomial of orders 2 and 3) to evaluate the highest classification accuracy. We obtained the highest average classification accuracy, sensitivity and specificity of more than 99% with support vector machine classifier (polynomial kernel of order 3) using three discrete wavelet transform features. We have also proposed an integrated index called Diabetic Retinopathy Risk Index using clinically significant wavelet energy features to identify normal and diabetic retinopathy classes using just one number. We believe that this (Diabetic Retinopathy Risk Index) can be used as an adjunct tool by the doctors during the eye screening to cross-check their diagnosis.

Imaging synthetic aperture radar

DOEpatents

Burns, Bryan L.; Cordaro, J. Thomas

1997-01-01

A linear-FM SAR imaging radar method and apparatus to produce a real-time image by first arranging the returned signals into a plurality of subaperture arrays, the columns of each subaperture array having samples of dechirped baseband pulses, and further including a processing of each subaperture array to obtain coarse-resolution in azimuth, then fine-resolution in range, and lastly, to combine the processed subapertures to obtain the final fine-resolution in azimuth. Greater efficiency is achieved because both the transmitted signal and a local oscillator signal mixed with the returned signal can be varied on a pulse-to-pulse basis as a function of radar motion. Moreover, a novel circuit can adjust the sampling location and the A/D sample rate of the combined dechirped baseband signal which greatly reduces processing time and hardware. The processing steps include implementing a window function, stabilizing either a central reference point and/or all other points of a subaperture with respect to doppler frequency and/or range as a function of radar motion, sorting and compressing the signals using a standard fourier transforms. The stabilization of each processing part is accomplished with vector multiplication using waveforms generated as a function of radar motion wherein these waveforms may be synthesized in integrated circuits. Stabilization of range migration as a function of doppler frequency by simple vector multiplication is a particularly useful feature of the invention; as is stabilization of azimuth migration by correcting for spatially varying phase errors prior to the application of an autofocus process.

Soft Computing Application in Fault Detection of Induction Motor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr.

2010-10-26

The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. SVM is also found to be a very good fault classifier which is highly competitive with RBF.

New QCD sum rules based on canonical commutation relations

NASA Astrophysics Data System (ADS)

Hayata, Tomoya

2012-04-01

New derivation of QCD sum rules by canonical commutators is developed. It is the simple and straightforward generalization of Thomas-Reiche-Kuhn sum rule on the basis of Kugo-Ojima operator formalism of a non-abelian gauge theory and a suitable subtraction of UV divergences. By applying the method to the vector and axial vector current in QCD, the exact Weinberg’s sum rules are examined. Vector current sum rules and new fractional power sum rules are also discussed.

Expanding the genetic toolbox for Leptospira species by generation of fluorescent bacteria.

PubMed

Aviat, Florence; Slamti, Leyla; Cerqueira, Gustavo M; Lourdault, Kristel; Picardeau, Mathieu

2010-12-01

Our knowledge of the genetics and molecular basis of the pathogenesis associated with Leptospira, in comparison to those of other bacterial species, is very limited. An improved understanding of pathogenic mechanisms requires reliable genetic tools for functional genetic analysis. Here, we report the expression of gfp and mRFP1 genes under the control of constitutive spirochetal promoters in both saprophytic and pathogenic Leptospira strains. We were able to reliably measure the fluorescence of Leptospira by fluorescence microscopy and a fluorometric microplate reader-based assay. We showed that the expression of the gfp gene had no significant effects on growth in vivo and pathogenicity in L. interrogans. We constructed an expression vector for L. biflexa that contains the lacI repressor, an inducible lac promoter, and gfp as the reporter, demonstrating that the lac system is functional in Leptospira. Green fluorescent protein (GFP) expression was induced by the addition of isopropyl-β-d-thiogalactopyranoside (IPTG) in L. biflexa transformants harboring the expression vector. Finally, we showed that GFP can be used as a reporter to assess promoter activity in different environmental conditions. These results may facilitate further advances for studying the genetics of Leptospira spp.

Variant Ionotropic Receptors in the Malaria Vector Mosquito Anopheles gambiae Tuned to Amines and Carboxylic Acids

PubMed Central

Pitts, R. Jason; Derryberry, Stephen L.; Zhang, Zhiwei; Zwiebel, Laurence J.

2017-01-01

The principal Afrotropical human malaria vector mosquito, Anopheles gambiae, remains a significant threat to global health. A critical component in the transmission of malaria is the ability of An. gambiae females to detect and respond to human-derived chemical kairomones in their search for blood meal hosts. The basis for host odor responses resides in olfactory receptor neurons (ORNs) that express chemoreceptors encoded by large gene families, including the odorant receptors (ORs) and the variant ionotropic receptors (IRs). While ORs have been the focus of extensive investigation, functional IR complexes and the chemical compounds that activate them have not been identified in An. gambiae. Here we report the transcriptional profiles and functional characterization of three An. gambiae IR (AgIr) complexes that specifically respond to amines or carboxylic acids - two classes of semiochemicals that have been implicated in mediating host-seeking by adult females but are not known to activate An. gambiae ORs (AgOrs). Our results suggest that AgIrs play critical roles in the detection and behavioral responses to important classes of host odors that are underrepresented in the AgOr chemical space. PMID:28067294

Collective charge excitations of the two-dimensional electride Ca2N

NASA Astrophysics Data System (ADS)

Cudazzo, Pierluigi; Gatti, Matteo

2017-09-01

Ca2N is a layered material that has been recently identified as a two-dimensional (2D) electride, an unusual ionic compound in which electrons serve as anions. The electronic properties of 2D electrides attract considerable interest as the anionic electrons, which form a 2D layer sandwiched between atomic planes, are highly mobile as they are not attached to any ion. Here, on the basis of first-principles time-dependent density-functional theory calculations, we investigate the collective excitations of the electrons—i.e., the plasmons—in Ca2N as a function of wave vector q . Our calculations reveal an intrinsic negative in-plane dispersion of the anionic plasmon, in striking contrast with the homogeneous electron gas. Moreover, for wave vectors q normal to the planes, we find a long-lived plasmon that continues to exist well beyond the first Brillouin zone. This is a mark of the electronic inhomogeneities in the charge response that Ca2N shares with other layered materials like transition-metal dichalcogenides and MgB2. Finally, we compare the plasmon properties of Ca2N in its bulk and monolayer forms, which shows the effect of the different electronic structures and dimensionalities.

Chiropractic biophysics technique: a linear algebra approach to posture in chiropractic.

PubMed

Harrison, D D; Janik, T J; Harrison, G R; Troyanovich, S; Harrison, D E; Harrison, S O

1996-10-01

This paper discusses linear algebra as applied to human posture in chiropractic, specifically chiropractic biophysics technique (CBP). Rotations, reflections and translations are geometric functions studied in vector spaces in linear algebra. These mathematical functions are termed rigid body transformations and are applied to segmental spinal movement in the literature. Review of the literature indicates that these linear algebra concepts have been used to describe vertebral motion. However, these rigid body movers are presented here as applying to the global postural movements of the head, thoracic cage and pelvis. The unique inverse functions of rotations, reflections and translations provide a theoretical basis for making postural corrections in neutral static resting posture. Chiropractic biophysics technique (CBP) uses these concepts in examination procedures, manual spinal manipulation, instrument assisted spinal manipulation, postural exercises, extension traction and clinical outcome measures.

Recent Selected Papers of Northwestern Polytechnical University in Two Parts, Part II.

DTIC Science & Technology

1981-08-28

OF CONTENTS Page Dual Properties of Elastic Structures 1 Matrix Analysis of Wings 76 On a Method for the Determination of Plane Stress Fracture...I= Ea]{(x, v,z) j l~i l’m mini The equation above means that the cisplacement function vector determines the strain function vector. (Assumption II...means that the distributed load function vector is determined by the stress function vector. In Section 1, there was an analysis of a three

[Cloning and gene expression in lactic acid bacteria].

PubMed

Bondarenko, V M; Beliavskaia, V A

2000-01-01

The possibility of using the genera Lactobacillus and Lactococcus as vector representatives is widely discussed at present. The prospects of the construction of recombinant bacteria are closely connected with the solution of a number of problems: the level of the transcription of cloned genes, the effectiveness of the translation of heterologous mRNA, the stability of protein with respect to bacterial intracellular proteases, the method by protein molecules leave the cell (by secretion or as the result of lysis). To prevent segregation instability, the construction of vector molecules on the basis of stable cryptic plasmids found in wild strains of lactic acid bacteria was proposed. High copying plasmids with low molecular weight were detected in L. plantarum and L. pentosus strains. Several plasmids with molecular weights of 1.7, 1.8 and 2.3 kb were isolated from bacterial cells to be used as the basis for the construction of vector molecules. Genes of chloramphenicol- and erythromycin-resistance from Staphylococcus aureus plasmids were used as marker genes ensuring cell transformation. The vector plasmids thus constructed exhibited high transformation activity in the electroporation of different strains, including L. casei, L. plantarum, L. acidophilus, L. fermentum and L. brevis which could be classified with the replicons of a wide circle of hosts. But the use of these plasmids was limited due to the risk of the uncontrolled dissemination of recombinant plasmids. L. acidophilus were also found to have strictly specific plasmids with good prospects of being used as the basis for the creation of vectors, incapable of dissemination. In addition to the search of strain-specific plasmids, incapable of uncontrolled gene transmission, the use of chromosome-integrated heterologous genes is recommended in cloning to ensure the maximum safety.

Geometry of generalized depolarizing channels

NASA Astrophysics Data System (ADS)

Burrell, Christian K.

2009-10-01

A generalized depolarizing channel acts on an N -dimensional quantum system to compress the “Bloch ball” in N2-1 directions; it has a corresponding compression vector. We investigate the geometry of these compression vectors and prove a conjecture of Dixit and Sudarshan [Phys. Rev. A 78, 032308 (2008)], namely, that when N=2d (i.e., the system consists of d qubits), and we work in the Pauli basis then the set of all compression vectors forms a simplex. We extend this result by investigating the geometry in other bases; in particular we find precisely when the set of all compression vectors forms a simplex.

Geometry of generalized depolarizing channels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burrell, Christian K.

2009-10-15

A generalized depolarizing channel acts on an N-dimensional quantum system to compress the 'Bloch ball' in N{sup 2}-1 directions; it has a corresponding compression vector. We investigate the geometry of these compression vectors and prove a conjecture of Dixit and Sudarshan [Phys. Rev. A 78, 032308 (2008)], namely, that when N=2{sup d} (i.e., the system consists of d qubits), and we work in the Pauli basis then the set of all compression vectors forms a simplex. We extend this result by investigating the geometry in other bases; in particular we find precisely when the set of all compression vectors formsmore » a simplex.« less

The Role of Innate Immunity in Conditioning Mosquito Susceptibility to West Nile Virus

PubMed Central

Prasad, Abhishek N.; Brackney, Doug. E.; Ebel, Gregory D.

2013-01-01

Arthropod-borne viruses (arboviruses) represent an emerging threat to human and livestock health globally. In particular, those transmitted by mosquitoes present the greatest challenges to disease control efforts. An understanding of the molecular basis for mosquito innate immunity to arbovirus infection is therefore critical to investigations regarding arbovirus evolution, virus-vector ecology, and mosquito vector competence. In this review, we discuss the current state of understanding regarding mosquito innate immunity to West Nile virus. We draw from the literature with respect to other virus-vector pairings to attempt to draw inferences to gaps in our knowledge about West Nile virus and relevant vectors. PMID:24351797

Vector Potential, Electromagnetic Induction and "Physical Meaning"

ERIC Educational Resources Information Center

Giuliani, G.

2010-01-01

A forgotten experiment by Andre Blondel (1914) proves, as held on the basis of theoretical arguments in a previous paper, that the time variation of the magnetic flux is not the cause of the induced emf; the physical agent is instead the vector potential through the term [equation omitted] (when the induced circuit is at rest). The "good…

A Demonstration of Optimal Apodization Determination for Proper Lateral Modulation

NASA Astrophysics Data System (ADS)

Sumi, Chikayoshi; Komiya, Yuichi; Uga, Shinya

2009-07-01

We have realized effective ultrasound (US) beamformings by the steering of plural beams and apodizations for B-mode imaging with a high lateral resolution and accurate measurement of tissue or blood displacement vector and/or strain tensor using the multidimensional cross-spectrum phase gradient method (MCSPGM), or multidimensional autocorrelation or Doppler methods (MAM and MDM) using multidimensional analytic signals. For instance, the coherent superposition of the steered beams performed in the lateral cosine modulation method (LCM) has a higher potential for realizing a more accurate measurement of a displacement vector than the synthesis of the displacement vector using the accurately measured axial displacements obtained by the multidimensional synthetic aperture method (MDSAM), multidirectional transmission method (MTM) or the use of plural US transducers. Originally, the apodization function to be used for realizing a designed point spread function (PSF) was obtained by the Fraunhofer approximation (FA). However, to obtain the best approximated, designed PSF in the least-squares sense, we proposed a linear optimization (LO) method. Furthermore, on the basis of the knowledge about the losts of US energy during the propagation, we have recently developed a nonlinear optimization (NLO) method, in which the feet of the main lobes in apodization function are properly truncated. Thus, NLO also allows the decrease in the number of channels or the confinement of the effective aperture. In this study, to gain insight into the ideal shape of the PSF, the accuracies of the two-dimensional (2D) displacement vector measurements were compared for typical PSFs with distinct lateral envelope shapes, particularly, in terms of full width at half maximum (FWHM) and the length of the feet, i.e., the Gaussian function, Hanning window and parabolic function. It was confirmed that a PSF having a wide FWHM and short feet was ideal. Such a PSF yielded an echo with a high signal-to-noise ratio (SNR), a large bandwidth and a large maximum spectrum of the center frequency. Moreover, for the three PSFs used, by calculating the PSFs using a typical transducer model and the apodization functions obtained by the respective LO and NLO methods and FA, we compare the approximation accuracies of the realized PSFs. NLO was effective for realizing such an ideal PSF. In addition, NLO allowed the significant decrease in the number of channels or the confinement of the effective aperture. Thus, in the comparisons of the three distinct PSFs, we obtain an appropriate apodization function. This study will assist the realization of the best lateral modulation.

Nonlinear temperature compensation of fluxgate magnetometers with a least-squares support vector machine

NASA Astrophysics Data System (ADS)

Pang, Hongfeng; Chen, Dixiang; Pan, Mengchun; Luo, Shitu; Zhang, Qi; Luo, Feilu

2012-02-01

Fluxgate magnetometers are widely used for magnetic field measurement. However, their accuracy is influenced by temperature. In this paper, a new method was proposed to compensate the temperature drift of fluxgate magnetometers, in which a least-squares support vector machine (LSSVM) is utilized. The compensation performance was analyzed by simulation, which shows that the LSSVM has better performance and less training time than backpropagation and radical basis function neural networks. The temperature characteristics of a DM fluxgate magnetometer were measured with a temperature experiment box. Forty-five measured data under different magnetic fields and temperatures were obtained and divided into 36 training data and nine test data. The training data were used to obtain the parameters of the LSSVM model, and the compensation performance of the LSSVM model was verified by the test data. Experimental results show that the temperature drift of magnetometer is reduced from 109.3 to 3.3 nT after compensation, which suggests that this compensation method is effective for the accuracy improvement of fluxgate magnetometers.

Plant Seeds as Model Vectors for the Transfer of Life Through Space

NASA Astrophysics Data System (ADS)

Tepfer, David; Leach, Sydney

2006-12-01

We consider plant seeds as terrestrial models for a vectored life form that could protect biological information in space. Seeds consist of maternal tissue surrounding and protecting an embryo. Some seeds resist deleterious conditions found in space: ultra low vacuum, extreme temperatures and radiation, including intense UV light. In a receptive environment, seeds could liberate a viable embryo, viable higher cells or a viable free-living organism (an endosymbiont or endophyte). Even if viability is lost, seeds still contain functional macro and small molecules (DNA, RNA, proteins, amino acids, lipids, etc.) that could provide the chemical basis for starting or modifying life. The possible release of endophytes or endosymbionts from a seed-like space traveler suggests that multiple domains of life, defined in DNA sequence phylogenies, could be disseminated simultaneously from Earth. We consider the possibility of exospermia, the outward transfer of life, as well as introspermia, the inward transfer of life-both as a contemporary and ancient events.

Breast Cancer Detection with Reduced Feature Set.

PubMed

Mert, Ahmet; Kılıç, Niyazi; Bilgili, Erdem; Akan, Aydin

2015-01-01

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

"Triplet" polycistronic vectors encoding Gata4, Mef2c, and Tbx5 enhances postinfarct ventricular functional improvement compared with singlet vectors.

PubMed

Mathison, Megumi; Singh, Vivek P; Gersch, Robert P; Ramirez, Maricela O; Cooney, Austin; Kaminsky, Stephen M; Chiuchiolo, Maria J; Nasser, Ahmed; Yang, Jianchang; Crystal, Ronald G; Rosengart, Todd K

2014-10-01

The in situ reprogramming of cardiac fibroblasts into induced cardiomyocytes by the administration of gene transfer vectors encoding Gata4 (G), Mef2c (M), and Tbx5 (T) has been shown to improve ventricular function in myocardial infarction models. The efficacy of this strategy could, however, be limited by the need for fibroblast targets to be infected 3 times--once by each of the 3 transgene vectors. We hypothesized that a polycistronic "triplet" vector encoding all 3 transgenes would enhance postinfarct ventricular function compared with use of "singlet" vectors. After validation of the polycistronic vector expression in vitro, adult male Fischer 344 rats (n=6) underwent coronary ligation with or without intramyocardial administration of an adenovirus encoding all 3 major vascular endothelial growth factor (VEGF) isoforms (AdVEGF-All6A positive), followed 3 weeks later by the administration to AdVEGF-All6A-positive treated rats of singlet lentivirus encoding G, M, or T (1×10(5) transducing units each) or the same total dose of a GMT "triplet" lentivirus vector. Western blots demonstrated that triplet and singlet vectors yielded equivalent GMT transgene expression, and fluorescence activated cell sorting demonstrated that triplet vectors were nearly twice as potent as singlet vectors in generating induced cardiomyocytes from cardiac fibroblasts. Echocardiography demonstrated that GMT triplet vectors were more effective than the 3 combined singlet vectors in enhancing ventricular function from postinfarct baselines (triplet, 37%±10%; singlet, 13%±7%; negative control, 9%±5%; P<.05). These data have confirmed that the in situ administration of G, M, and T induces postinfarct ventricular functional improvement and that GMT polycistronic vectors enhance the efficacy of this strategy. Copyright © 2014 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Transposons As Tools for Functional Genomics in Vertebrate Models.

PubMed

Kawakami, Koichi; Largaespada, David A; Ivics, Zoltán

2017-11-01

Genetic tools and mutagenesis strategies based on transposable elements are currently under development with a vision to link primary DNA sequence information to gene functions in vertebrate models. By virtue of their inherent capacity to insert into DNA, transposons can be developed into powerful tools for chromosomal manipulations. Transposon-based forward mutagenesis screens have numerous advantages including high throughput, easy identification of mutated alleles, and providing insight into genetic networks and pathways based on phenotypes. For example, the Sleeping Beauty transposon has become highly instrumental to induce tumors in experimental animals in a tissue-specific manner with the aim of uncovering the genetic basis of diverse cancers. Here, we describe a battery of mutagenic cassettes that can be applied in conjunction with transposon vectors to mutagenize genes, and highlight versatile experimental strategies for the generation of engineered chromosomes for loss-of-function as well as gain-of-function mutagenesis for functional gene annotation in vertebrate models, including zebrafish, mice, and rats. Copyright © 2017 Elsevier Ltd. All rights reserved.

Strain Variation in the Transcriptome of the Dengue Fever Vector, Aedes aegypti.

PubMed

Bonizzoni, Mariangela; Dunn, W Augustine; Campbell, Corey L; Olson, Ken E; Marinotti, Osvaldo; James, Anthony A

2012-01-01

Studies of transcriptome dynamics provide a basis for understanding functional elements of the genome and the complexity of gene regulation. The dengue vector mosquito, Aedes aegypti, exhibits great adaptability to diverse ecological conditions, is phenotypically polymorphic, and shows variation in vectorial capacity to arboviruses. Previous genome sequencing showed richness in repetitive DNA and transposable elements that can contribute to genome plasticity. Population genetic studies revealed a varying degree of worldwide genetic polymorphism. However, the extent of functional genetic polymorphism across strains is unknown. The transcriptomes of three Ae. aegypti strains, Chetumal (CTM), Rexville D-Puerto Rico (Rex-D) and Liverpool (LVP), were compared. CTM is more susceptible than Rex- D to infection by dengue virus serotype 2. A total of 4188 transcripts exhibit either no or small variation (<2-fold) among sugar-fed samples of the three strains and between sugar- and blood-fed samples within each strain, corresponding most likely to genes encoding products necessary for vital functions. Transcripts enriched in blood-fed mosquitoes encode proteins associated with catalytic activities, molecular transport, metabolism of lipids, carbohydrates and amino acids, and functions related to blood digestion and the progression of the gonotropic cycle. Significant qualitative and quantitative differences were found in individual transcripts among strains including differential representation of paralogous gene products. The majority of immunity-associated transcripts decreased in accumulation after a bloodmeal and the results are discussed in relation to the different susceptibility of CTM and Rex-D mosquitoes to DENV2 infection.

Quasiclassical description of a superconductor with a spin density wave

NASA Astrophysics Data System (ADS)

Moor, A.; Volkov, A. F.; Efetov, K. B.

2011-04-01

We derive equations for the quasiclassical Green’s functions ǧ within a simple model of a two-band superconductor with a spin density wave (SDW). The elements of the matrix ǧ are the retarded, advanced, and Keldysh functions, each of which is an 8×8 matrix in the Gor’kov-Nambu, the spin, and the band space. In equilibrium, these equations are a generalization of the Eilenberger equation. On the basis of the derived equations, we analyze the Knight shift, the proximity, and the dc Josephson effects in the superconductors under consideration. The Knight shift is shown to depend on the orientation of the external magnetic field with respect to the direction of the vector of the magnetization of the SDW. The proximity effect is analyzed for an interface between a superconductor with the SDW and a normal metal. The function describing both superconducting and magnetic correlations is shown to penetrate the normal metal or a metal with the SDW due to the proximity effect. The dc Josephson current in an SSDW/N/SSDW junction is also calculated as a function of the phase difference φ. It is shown that in our model, the Josephson current does not depend on the mutual orientation of the magnetic moments in the superconductors SSDW and is proportional to sinφ. The dissipationless spin current jsp depends on the angle α between the magnetization vectors in the same way (jsp~sinα) and is not zero above the superconducting transition temperature.

[Construction and functional identification of eukaryotic expression vector carrying Sprague-Dawley rat MSX-2 gene].

PubMed

Yang, Xian-Xian; Zhang, Mei; Yan, Zhao-Wen; Zhang, Ru-Hong; Mu, Xiong-Zheng

2008-01-01

To construct a high effective eukaryotic expressing plasmid PcDNA 3.1-MSX-2 encoding Sprague-Dawley rat MSX-2 gene for the further study of MSX-2 gene function. The full length SD rat MSX-2 gene was amplified by PCR, and the full length DNA was inserted in the PMD1 8-T vector. It was isolated by restriction enzyme digest with BamHI and Xhol, then ligated into the cloning site of the PcDNA3.1 expression plasmid. The positive recombinant was identified by PCR analysis, restriction endonudease analysis and sequence analysis. Expression of RNA and protein was detected by RT-PCR and Western blot analysis in PcDNA3.1-MSX-2 transfected HEK293 cells. Sequence analysis and restriction endonudease analysis of PcDNA3.1-MSX-2 demonstrated that the position and size of MSX-2 cDNA insertion were consistent with the design. RT-PCR and Western blot analysis showed specific expression of mRNA and protein of MSX-2 in the transfected HEK293 cells. The high effective eukaryotic expression plasmid PcDNA3.1-MSX-2 encoding Sprague-Dawley Rat MSX-2 gene which is related to craniofacial development can be successfully reconstructed. It may serve as the basis for the further study of MSX-2 gene function.

The Coordinate Orthogonality Check (corthog)

NASA Astrophysics Data System (ADS)

Avitabile, P.; Pechinsky, F.

1998-05-01

A new technique referred to as the coordinate orthogonality check (CORTHOG) helps to identify how each physical degree of freedom contributes to the overall orthogonality relationship between analytical and experimental modal vectors on a mass-weighted basis. Using the CORTHOG technique together with the pseudo-orthogonality check (POC) clarifies where potential discrepancies exist between the analytical and experimental modal vectors. CORTHOG improves the understanding of the correlation (or lack of correlation) that exists between modal vectors. The CORTHOG theory is presented along with the evaluation of several cases to show the use of the technique.

Expansion of Tabulated Scattering Matrices in Generalized Spherical Functions

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Geogdzhayev, Igor V.; Yang, Ping

2016-01-01

An efficient way to solve the vector radiative transfer equation for plane-parallel turbid media is to Fourier-decompose it in azimuth. This methodology is typically based on the analytical computation of the Fourier components of the phase matrix and is predicated on the knowledge of the coefficients appearing in the expansion of the normalized scattering matrix in generalized spherical functions. Quite often the expansion coefficients have to be determined from tabulated values of the scattering matrix obtained from measurements or calculated by solving the Maxwell equations. In such cases one needs an efficient and accurate computer procedure converting a tabulated scattering matrix into the corresponding set of expansion coefficients. This short communication summarizes the theoretical basis of this procedure and serves as the user guide to a simple public-domain FORTRAN program.

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

PubMed

Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

2015-07-01

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.

On the Prognostic Efficiency of Topological Descriptors for Magnetograms of Active Regions

NASA Astrophysics Data System (ADS)

Knyazeva, I. S.; Urtiev, F. A.; Makarenko, N. G.

2017-12-01

Solar flare prediction remains an important practical task of space weather. An increase in the amount and quality of observational data and the development of machine-learning methods has led to an improvement in prediction techniques. Additional information has been retrieved from the vector magnetograms; these have been recently supplemented by traditional line-of-sight (LOS) magnetograms. In this work, the problem of the comparative prognostic efficiency of features obtained on the basis of vector data and LOS magnetograms is discussed. Invariants obtained from a topological analysis of LOS magnetograms are used as complexity characteristics of magnetic patterns. Alternatively, the so-called SHARP parameters were used; they were calculated by the data analysis group of the Stanford University Laboratory on the basis of HMI/SDO vector magnetograms and are available online at the website (http://jsoc.stanford.edu/) with the solar dynamics observatory (SDO) database for the entire history of SDO observations. It has been found that the efficiency of large-flare prediction based on topological descriptors of LOS magnetograms in epignosis mode is at least s no worse than the results of prognostic schemes based on vector features. The advantages of the use of topological invariants based on LOS data are discussed.

SH c realization of minimal model CFT: triality, poset and Burge condition

NASA Astrophysics Data System (ADS)

Fukuda, M.; Nakamura, S.; Matsuo, Y.; Zhu, R.-D.

2015-11-01

Recently an orthogonal basis of {{W}}_N -algebra (AFLT basis) labeled by N-tuple Young diagrams was found in the context of 4D/2D duality. Recursion relations among the basis are summarized in the form of an algebra SH c which is universal for any N. We show that it has an {{S}}_3 automorphism which is referred to as triality. We study the level-rank duality between minimal models, which is a special example of the automorphism. It is shown that the nonvanishing states in both systems are described by N or M Young diagrams with the rows of boxes appropriately shuffled. The reshuffling of rows implies there exists partial ordering of the set which labels them. For the simplest example, one can compute the partition functions for the partially ordered set (poset) explicitly, which reproduces the Rogers-Ramanujan identities. We also study the description of minimal models by SH c . Simple analysis reproduces some known properties of minimal models, the structure of singular vectors and the N-Burge condition in the Hilbert space.

[Rapid identification of hogwash oil by using synchronous fluorescence spectroscopy].

PubMed

Sun, Yan-Hui; An, Hai-Yang; Jia, Xiao-Li; Wang, Juan

2012-10-01

To identify hogwash oil quickly, the characteristic delta lambda of hogwash oil was analyzed by three dimensional fluorescence spectroscopy with parallel factor analysis, and the model was built up by using synchronous fluorescence spectroscopy with support vector machines (SVM). The results showed that the characteristic delta lambda of hogwash oil was 60 nm. Collecting original spectrum of different samples under the condition of characteristic delta lambda 60 nm, the best model was established while 5 principal components were selected from original spectrum and the radial basis function (RBF) was used as the kernel function, and the optimal penalty factor C and kernel function g were 512 and 0.5 respectively obtained by the grid searching and 6-fold cross validation. The discrimination rate of the model was 100% for both training sets and prediction sets. Thus, it is quick and accurate to apply synchronous fluorescence spectroscopy to identification of hogwash oil.

A spectral mimetic least-squares method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bochev, Pavel; Gerritsma, Marc

We present a spectral mimetic least-squares method for a model diffusion–reaction problem, which preserves key conservation properties of the continuum problem. Casting the model problem into a first-order system for two scalar and two vector variables shifts material properties from the differential equations to a pair of constitutive relations. We also use this system to motivate a new least-squares functional involving all four fields and show that its minimizer satisfies the differential equations exactly. Discretization of the four-field least-squares functional by spectral spaces compatible with the differential operators leads to a least-squares method in which the differential equations are alsomore » satisfied exactly. Additionally, the latter are reduced to purely topological relationships for the degrees of freedom that can be satisfied without reference to basis functions. Furthermore, numerical experiments confirm the spectral accuracy of the method and its local conservation.« less

A spectral mimetic least-squares method

DOE PAGES

Bochev, Pavel; Gerritsma, Marc

2014-09-01

We present a spectral mimetic least-squares method for a model diffusion–reaction problem, which preserves key conservation properties of the continuum problem. Casting the model problem into a first-order system for two scalar and two vector variables shifts material properties from the differential equations to a pair of constitutive relations. We also use this system to motivate a new least-squares functional involving all four fields and show that its minimizer satisfies the differential equations exactly. Discretization of the four-field least-squares functional by spectral spaces compatible with the differential operators leads to a least-squares method in which the differential equations are alsomore » satisfied exactly. Additionally, the latter are reduced to purely topological relationships for the degrees of freedom that can be satisfied without reference to basis functions. Furthermore, numerical experiments confirm the spectral accuracy of the method and its local conservation.« less

Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

NASA Astrophysics Data System (ADS)

Imani, Moslem; Kao, Huan-Chin; Lan, Wen-Hau; Kuo, Chung-Yen

2018-02-01

The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

Computerized method to compensate for breathing body motion in dynamic chest radiographs

NASA Astrophysics Data System (ADS)

Matsuda, H.; Tanaka, R.; Sanada, S.

2017-03-01

Dynamic chest radiography combined with computer analysis allows quantitative analyses on pulmonary function and rib motion. The accuracy of kinematic analysis is directly linked to diagnostic accuracy, and thus body motion compensation is a major concern. Our purpose in this study was to develop a computerized method to reduce a breathing body motion in dynamic chest radiographs. Dynamic chest radiographs of 56 patients were obtained using a dynamic flat-panel detector. The images were divided into a 1 cm-square and the squares on body counter were used to detect the body motion. Velocity vector was measured using cross-correlation method on the body counter and the body motion was then determined on the basis of the summation of motion vector. The body motion was then compensated by shifting the images based on the measured vector. By using our method, the body motion was accurately detected by the order of a few pixels in clinical cases, mean 82.5% in right and left directions. In addition, our method detected slight body motion which was not able to be identified by human observations. We confirmed our method effectively worked in kinetic analysis of rib motion. The present method would be useful for the reduction of a breathing body motion in dynamic chest radiography.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Challenges Posed by Tick-Borne Rickettsiae: Eco-Epidemiology and Public Health Implications

PubMed Central

Eremeeva, Marina E.; Dasch, Gregory A.

2015-01-01

Rickettsiae are obligately intracellular bacteria that are transmitted to vertebrates by a variety of arthropod vectors, primarily by fleas and ticks. Once transmitted or experimentally inoculated into susceptible mammals, some rickettsiae may cause febrile illness of different morbidity and mortality, and which can manifest with different types of exhanthems in humans. However, most rickettsiae circulate in diverse sylvatic or peridomestic reservoirs without having obvious impacts on their vertebrate hosts or affecting humans. We have analyzed the key features of tick-borne maintenance of rickettsiae, which may provide a deeper basis for understanding those complex invertebrate interactions and strategies that have permitted survival and circulation of divergent rickettsiae in nature. Rickettsiae are found in association with a wide range of hard and soft ticks, which feed on very different species of large and small animals. Maintenance of rickettsiae in these vector systems is driven by both vertical and horizontal transmission strategies, but some species of Rickettsia are also known to cause detrimental effects on their arthropod vectors. Contrary to common belief, the role of vertebrate animal hosts in maintenance of rickettsiae is very incompletely understood. Some clearly play only the essential role of providing a blood meal to the tick while other hosts may supply crucial supplemental functions for effective agent transmission by the vectors. This review summarizes the importance of some recent findings with known and new vectors that afford an improved understanding of the eco-epidemiology of rickettsiae; the public health implications of that information for rickettsial diseases are also described. Special attention is paid to the co-circulation of different species and genotypes of rickettsiae within the same endemic areas and how these observations may influence, correctly or incorrectly, trends, and conclusions drawn from the surveillance of rickettsial diseases in humans. PMID:25954738

Challenges posed by tick-borne rickettsiae: eco-epidemiology and public health implications.

PubMed

Eremeeva, Marina E; Dasch, Gregory A

2015-01-01

Rickettsiae are obligately intracellular bacteria that are transmitted to vertebrates by a variety of arthropod vectors, primarily by fleas and ticks. Once transmitted or experimentally inoculated into susceptible mammals, some rickettsiae may cause febrile illness of different morbidity and mortality, and which can manifest with different types of exhanthems in humans. However, most rickettsiae circulate in diverse sylvatic or peridomestic reservoirs without having obvious impacts on their vertebrate hosts or affecting humans. We have analyzed the key features of tick-borne maintenance of rickettsiae, which may provide a deeper basis for understanding those complex invertebrate interactions and strategies that have permitted survival and circulation of divergent rickettsiae in nature. Rickettsiae are found in association with a wide range of hard and soft ticks, which feed on very different species of large and small animals. Maintenance of rickettsiae in these vector systems is driven by both vertical and horizontal transmission strategies, but some species of Rickettsia are also known to cause detrimental effects on their arthropod vectors. Contrary to common belief, the role of vertebrate animal hosts in maintenance of rickettsiae is very incompletely understood. Some clearly play only the essential role of providing a blood meal to the tick while other hosts may supply crucial supplemental functions for effective agent transmission by the vectors. This review summarizes the importance of some recent findings with known and new vectors that afford an improved understanding of the eco-epidemiology of rickettsiae; the public health implications of that information for rickettsial diseases are also described. Special attention is paid to the co-circulation of different species and genotypes of rickettsiae within the same endemic areas and how these observations may influence, correctly or incorrectly, trends, and conclusions drawn from the surveillance of rickettsial diseases in humans.

Study on diagnosis of micro-biomechanical structure using optical coherence tomography

NASA Astrophysics Data System (ADS)

Saeki, Souichi; Hashimoto, Youhei; Saito, Takashi; Hiro, Takafumi; Matsuzaki, Masunori

2007-02-01

Acute coronary syndromes, e.g. myocardial infarctions, are caused by the rupture of unstable plaques on coronary arteries. The stability of plaque, which depends on biomechanical properties of fibrous cap, should be diagnosed crucially. Recently, Optical Coherence Tomography (OCT) has been developed as a cross-sectional imaging method of microstructural biological tissue with high resolution 1~10 μm. Multi-functional OCT system has been promising, e.g. an estimator of biomechanical characteristics. It has been, however, difficult to estimate biomechanical characteristics, because OCT images have just speckle patterns by back-scattering light from tissue. In this study, presented is Optical Coherence Straingraphy (OCS) on the basis of OCT system, which can diagnose tissue strain distribution. This is basically composed of Recursive Cross-correlation technique (RC), which can provide a displacement vector distribution with high resolution. Furthermore, Adjacent Cross-correlation Multiplication (ACM) is introduced as a speckle noise reduction method. Multiplying adjacent correlation maps can eliminate anomalies from speckle noise, and then can enhance S/N in the determination of maximum correlation coefficient. Error propagation also can be further prevented by introducing to the recursive algorithm (RC). In addition, the spatial vector interpolation by local least square method is introduced to remove erroneous vectors and smooth the vector distribution. This was numerically applied to compressed elastic heterogeneous tissue samples to carry out the accuracy verifications. Consequently, it was quantitatively confirmed that its accuracy of displacement vectors and strain matrix components could be enhanced, comparing with the conventional method. Therefore, the proposed method was validated by the identification of different elastic objects with having nearly high resolution for that defined by optical system.

Nonnormal operators in physics, a singular-vectors approach: illustration in polarization optics.

PubMed

Tudor, Tiberiu

2016-04-20

The singular-vectors analysis of a general nonnormal operator defined on a finite-dimensional complex vector space is given in the frame of a pure operatorial ("nonmatrix," "coordinate-free") approach, performed in a Dirac language. The general results are applied in the field of polarization optics, where the nonnormal operators are widespread as operators of various polarization devices. Two nonnormal polarization devices representative for the class of nonnormal and even pathological operators-the standard two-layer elliptical ideal polarizer (singular operator) and the three-layer ambidextrous ideal polarizer (singular and defective operator)-are analyzed in detail. It is pointed out that the unitary polar component of the operator exists and preserves, in such pathological case too, its role of converting the input singular basis of the operator in its output singular basis. It is shown that for any nonnormal ideal polarizer a complementary one exists, so that the tandem of their operators uniquely determines their (common) unitary polar component.

Calibration Errors in Interferometric Radio Polarimetry

NASA Astrophysics Data System (ADS)

Hales, Christopher A.

2017-08-01

Residual calibration errors are difficult to predict in interferometric radio polarimetry because they depend on the observational calibration strategy employed, encompassing the Stokes vector of the calibrator and parallactic angle coverage. This work presents analytic derivations and simulations that enable examination of residual on-axis instrumental leakage and position-angle errors for a suite of calibration strategies. The focus is on arrays comprising alt-azimuth antennas with common feeds over which parallactic angle is approximately uniform. The results indicate that calibration schemes requiring parallactic angle coverage in the linear feed basis (e.g., the Atacama Large Millimeter/submillimeter Array) need only observe over 30°, beyond which no significant improvements in calibration accuracy are obtained. In the circular feed basis (e.g., the Very Large Array above 1 GHz), 30° is also appropriate when the Stokes vector of the leakage calibrator is known a priori, but this rises to 90° when the Stokes vector is unknown. These findings illustrate and quantify concepts that were previously obscure rules of thumb.

Ensemble of classifiers for ontology enrichment

NASA Astrophysics Data System (ADS)

Semenova, A. V.; Kureichik, V. M.

2018-05-01

A classifier is a basis of ontology learning systems. Classification of text documents is used in many applications, such as information retrieval, information extraction, definition of spam. A new ensemble of classifiers based on SVM (a method of support vectors), LSTM (neural network) and word embedding are suggested. An experiment was conducted on open data, which allows us to conclude that the proposed classification method is promising. The implementation of the proposed classifier is performed in the Matlab using the functions of the Text Analytics Toolbox. The principal difference between the proposed ensembles of classifiers is the high quality of classification of data at acceptable time costs.

Theoretical nuclear physics

NASA Astrophysics Data System (ADS)

Rost, E.; Shephard, J. R.

1992-08-01

This report discusses the following topics: Exact 1-loop vacuum polarization effects in 1 + 1 dimensional QHD; exact 1-fermion loop contributions in 1 + 1 dimensional solitons; exact scalar 1-loop contributions in 1 + 3 dimensions; exact vacuum calculations in a hyper-spherical basis; relativistic nuclear matter with self-consistent correlation energy; consistent RHA-RPA for finite nuclei; transverse response functions in the (triangle)-resonance region; hadronic matter in a nontopological soliton model; scalar and vector contributions to (bar p)p yields (bar lambda)lambda reaction; 0+ and 2+ strengths in pion double-charge exchange to double giant-dipole resonances; and nucleons in a hybrid sigma model including a quantized pion field.

Genotoxicity of retroviral hematopoietic stem cell gene therapy

PubMed Central

Trobridge, Grant D

2012-01-01

Introduction Retroviral vectors have been developed for hematopoietic stem cell (HSC) gene therapy and have successfully cured X-linked severe combined immunodeficiency (SCID-X1), adenosine deaminase deficiency (ADA-SCID), adrenoleukodystrophy, and Wiskott-Aldrich syndrome. However, in HSC gene therapy clinical trials, genotoxicity mediated by integrated vector proviruses has led to clonal expansion, and in some cases frank leukemia. Numerous studies have been performed to understand the molecular basis of vector-mediated genotoxicity with the aim of developing safer vectors and safer gene therapy protocols. These genotoxicity studies are critical to advancing HSC gene therapy. Areas covered This review provides an introduction to the mechanisms of retroviral vector genotoxicity. It also covers advances over the last 20 years in designing safer gene therapy vectors, and in integration site analysis in clinical trials and large animal models. Mechanisms of retroviral-mediated genotoxicity, and the risk factors that contribute to clonal expansion and leukemia in HSC gene therapy are introduced. Expert opinion Continued research on virus–host interactions and next-generation vectors should further improve the safety of future HSC gene therapy vectors and protocols. PMID:21375467

Hydrologic Process Regularization for Improved Geoelectrical Monitoring of a Lab-Scale Saline Tracer Experiment

NASA Astrophysics Data System (ADS)

Oware, E. K.; Moysey, S. M.

2016-12-01

Regularization stabilizes the geophysical imaging problem resulting from sparse and noisy measurements that render solutions unstable and non-unique. Conventional regularization constraints are, however, independent of the physics of the underlying process and often produce smoothed-out tomograms with mass underestimation. Cascaded time-lapse (CTL) is a widely used reconstruction technique for monitoring wherein a tomogram obtained from the background dataset is employed as starting model for the inversion of subsequent time-lapse datasets. In contrast, a proper orthogonal decomposition (POD)-constrained inversion framework enforces physics-based regularization based upon prior understanding of the expected evolution of state variables. The physics-based constraints are represented in the form of POD basis vectors. The basis vectors are constructed from numerically generated training images (TIs) that mimic the desired process. The target can be reconstructed from a small number of selected basis vectors, hence, there is a reduction in the number of inversion parameters compared to the full dimensional space. The inversion involves finding the optimal combination of the selected basis vectors conditioned on the geophysical measurements. We apply the algorithm to 2-D lab-scale saline transport experiments with electrical resistivity (ER) monitoring. We consider two transport scenarios with one and two mass injection points evolving into unimodal and bimodal plume morphologies, respectively. The unimodal plume is consistent with the assumptions underlying the generation of the TIs, whereas bimodality in plume morphology was not conceptualized. We compare difference tomograms retrieved from POD with those obtained from CTL. Qualitative comparisons of the difference tomograms with images of their corresponding dye plumes suggest that POD recovered more compact plumes in contrast to those of CTL. While mass recovery generally deteriorated with increasing number of time-steps, POD outperformed CTL in terms of mass recovery accuracy rates. POD is computationally superior requiring only 2.5 mins to complete each inversion compared to 3 hours for CTL to do the same.

Graph-theoretic quantum system modelling for neuronal microtubules as hierarchical clustered quantum Hopfield networks

NASA Astrophysics Data System (ADS)

Srivastava, D. P.; Sahni, V.; Satsangi, P. S.

2014-08-01

Graph-theoretic quantum system modelling (GTQSM) is facilitated by considering the fundamental unit of quantum computation and information, viz. a quantum bit or qubit as a basic building block. Unit directional vectors "ket 0" and "ket 1" constitute two distinct fundamental quantum across variable orthonormal basis vectors, for the Hilbert space, specifying the direction of propagation of information, or computation data, while complementary fundamental quantum through, or flow rate, variables specify probability parameters, or amplitudes, as surrogates for scalar quantum information measure (von Neumann entropy). This paper applies GTQSM in continuum of protein heterodimer tubulin molecules of self-assembling polymers, viz. microtubules in the brain as a holistic system of interacting components representing hierarchical clustered quantum Hopfield network, hQHN, of networks. The quantum input/output ports of the constituent elemental interaction components, or processes, of tunnelling interactions and Coulombic bidirectional interactions are in cascade and parallel interconnections with each other, while the classical output ports of all elemental components are interconnected in parallel to accumulate micro-energy functions generated in the system as Hamiltonian, or Lyapunov, energy function. The paper presents an insight, otherwise difficult to gain, for the complex system of systems represented by clustered quantum Hopfield network, hQHN, through the application of GTQSM construct.

Prediction of pH of cola beverage using Vis/NIR spectroscopy and least squares-support vector machine

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong

2008-02-01

Visible and near infrared (Vis/NIR) transmission spectroscopy and chemometric methods were utilized to predict the pH values of cola beverages. Five varieties of cola were prepared and 225 samples (45 samples for each variety) were selected for the calibration set, while 75 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay and standard normal variate (SNV) followed by first-derivative were used as the pre-processing methods. Partial least squares (PLS) analysis was employed to extract the principal components (PCs) which were used as the inputs of least squares-support vector machine (LS-SVM) model according to their accumulative reliabilities. Then LS-SVM with radial basis function (RBF) kernel function and a two-step grid search technique were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias were 0.961, 0.040 and 0.012 for PLS, while 0.975, 0.031 and 4.697x10 -3 for LS-SVM, respectively. Both methods obtained a satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be applied as an alternative way for the prediction of pH of cola beverages.

[Cloning, expression and identification of functional fragment rC3B of human complement C3 in E. Coli].

PubMed

Gan, Hui; Zhou, Yong; Sun, Ping; Zhu, Xiao-Xia; Wang, Quan-Li; Zhan, Lin-Sheng

2007-08-01

This study was purposed to verify the binding part of human complement C3 to complement receptor III (CRIII) in monocytes, the peptide rC3B, including the binding-site, was expressed, purified and identified. rC3B, the binding part of human complement C3 to CRIII, was selected by computer-aided modeling and summarizing researches published. Then, rC3B gene fragment was amplified by PCR, and cloned into prokaryotic vector pQE30a. The fusion protein rC3B was expressed in E.coli M15 and purified by Ni(2+)-chelating affinity chromatography. The activity of rC3B was identified by Western blot and adherence assay with monocytes. The results showed that rC3B fragment was obtained, and a prokaryotic expression vector pQE30-rC3B was constructed. rC3B was efficiently expressed and purified. In Western blot, the target protein showed the activity of binding with C3 antibody, while the purified protein showed the activity of adherence with monocytes. It is concluded that the recombinant C3B was obtained and identified, and this study lay the basis for the further functional analysis of C3.

Parasite killing in malaria non-vector mosquito Anopheles culicifacies species B: implication of nitric oxide synthase upregulation.

PubMed

Vijay, Sonam; Rawat, Manmeet; Adak, Tridibes; Dixit, Rajnikant; Nanda, Nutan; Srivastava, Harish; Sharma, Joginder K; Prasad, Godavarthi B K S; Sharma, Arun

2011-04-04

Anopheles culicifacies, the main vector of human malaria in rural India, is a complex of five sibling species. Despite being phylogenetically related, a naturally selected subgroup species B of this sibling species complex is found to be a poor vector of malaria. We have attempted to understand the differences between vector and non-vector Anopheles culicifacies mosquitoes in terms of transcriptionally activated nitric oxide synthase (AcNOS) physiologies to elucidate the mechanism of refractoriness. Identification of the differences between genes and gene products that may impart refractory phenotype can facilitate development of novel malaria transmission blocking strategies. We conducted a study on phylogenetically related susceptible (species A) and refractory (species B) sibling species of An. culicifacies mosquitoes to characterize biochemical and molecular differences in AcNOS gene and gene elements and their ability to inhibit oocyst growth. We demonstrate that in species B, AcNOS specific activity and nitrite/nitrates in mid-guts and haemolymph were higher as compared to species A after invasion of the mid-gut by P. vivax at the beginning and during the course of blood feeding. Semiquantitative RT-PCR and real time PCR data of AcNOS concluded that this gene is more abundantly expressed in midgut of species B than in species A and is transcriptionally upregulated post blood meals. Dietary feeding of L-NAME along with blood meals significantly inhibited midgut AcNOS activity leading to an increase in oocyst production in An. culicifacies species B. We hypothesize that upregulation of mosquito innate cytotoxicity due to NOS in refractory strain to Plasmodium vivax infection may contribute to natural refractoriness in An. culicifacies mosquito population. This innate capacity of refractory mosquitoes could represent the ancestral function of the mosquito immune system against the parasite and could be utilized to understand the molecular basis of refractoriness in planning effective vector control strategies.

Parasite Killing in Malaria Non-Vector Mosquito Anopheles culicifacies Species B: Implication of Nitric Oxide Synthase Upregulation

PubMed Central

Vijay, Sonam; Rawat, Manmeet; Adak, Tridibes; Dixit, Rajnikant; Nanda, Nutan; Srivastava, Harish; Sharma, Joginder K.; Prasad, Godavarthi B. K. S.; Sharma, Arun

2011-01-01

Background Anopheles culicifacies, the main vector of human malaria in rural India, is a complex of five sibling species. Despite being phylogenetically related, a naturally selected subgroup species B of this sibling species complex is found to be a poor vector of malaria. We have attempted to understand the differences between vector and non-vector Anopheles culicifacies mosquitoes in terms of transcriptionally activated nitric oxide synthase (AcNOS) physiologies to elucidate the mechanism of refractoriness. Identification of the differences between genes and gene products that may impart refractory phenotype can facilitate development of novel malaria transmission blocking strategies. Methodology/Principal Findings We conducted a study on phylogenetically related susceptible (species A) and refractory (species B) sibling species of An. culicifacies mosquitoes to characterize biochemical and molecular differences in AcNOS gene and gene elements and their ability to inhibit oocyst growth. We demonstrate that in species B, AcNOS specific activity and nitrite/nitrates in mid-guts and haemolymph were higher as compared to species A after invasion of the mid-gut by P. vivax at the beginning and during the course of blood feeding. Semiquantitative RT-PCR and real time PCR data of AcNOS concluded that this gene is more abundantly expressed in midgut of species B than in species A and is transcriptionally upregulated post blood meals. Dietary feeding of L-NAME along with blood meals significantly inhibited midgut AcNOS activity leading to an increase in oocyst production in An. culicifacies species B. Conclusions/Significance We hypothesize that upregulation of mosquito innate cytotoxicity due to NOS in refractory strain to Plasmodium vivax infection may contribute to natural refractoriness in An. culicifacies mosquito population. This innate capacity of refractory mosquitoes could represent the ancestral function of the mosquito immune system against the parasite and could be utilized to understand the molecular basis of refractoriness in planning effective vector control strategies. PMID:21483693

Ex Vivo Adenoviral Vector Gene Delivery Results in Decreased Vector-associated Inflammation Pre- and Post–lung Transplantation in the Pig

PubMed Central

Yeung, Jonathan C; Wagnetz, Dirk; Cypel, Marcelo; Rubacha, Matthew; Koike, Terumoto; Chun, Yi-Min; Hu, Jim; Waddell, Thomas K; Hwang, David M; Liu, Mingyao; Keshavjee, Shaf

2012-01-01

Acellular normothermic ex vivo lung perfusion (EVLP) is a novel method of donor lung preservation for transplantation. As cellular metabolism is preserved during perfusion, it represents a potential platform for effective gene transduction in donor lungs. We hypothesized that vector-associated inflammation would be reduced during ex vivo delivery due to isolation from the host immune system response. We compared ex vivo with in vivo intratracheal delivery of an E1-, E3-deleted adenoviral vector encoding either green fluorescent protein (GFP) or interleukin-10 (IL-10) to porcine lungs. Twelve hours after delivery, the lung was transplanted and the post-transplant function assessed. We identified significant transgene expression by 12 hours in both in vivo and ex vivo delivered groups. Lung function remained excellent in all ex vivo groups after viral vector delivery; however, as expected, lung function decreased in the in vivo delivered adenovirus vector encoding GFP (AdGFP) group with corresponding increases in IL-1β levels. Transplanted lung function was excellent in the ex vivo transduced lungs and inferior lung function was seen in the in vivo group after transplantation. In summary, ex vivo delivery of adenoviral gene therapy to the donor lung is superior to in vivo delivery in that it leads to less vector-associated inflammation and provides superior post-transplant lung function. PMID:22453765

Hydrologic Process Parameterization of Electrical Resistivity Imaging of Solute Plumes Using POD McMC

NASA Astrophysics Data System (ADS)

Awatey, M. T.; Irving, J.; Oware, E. K.

2016-12-01

Markov chain Monte Carlo (McMC) inversion frameworks are becoming increasingly popular in geophysics due to their ability to recover multiple equally plausible geologic features that honor the limited noisy measurements. Standard McMC methods, however, become computationally intractable with increasing dimensionality of the problem, for example, when working with spatially distributed geophysical parameter fields. We present a McMC approach based on a sparse proper orthogonal decomposition (POD) model parameterization that implicitly incorporates the physics of the underlying process. First, we generate training images (TIs) via Monte Carlo simulations of the target process constrained to a conceptual model. We then apply POD to construct basis vectors from the TIs. A small number of basis vectors can represent most of the variability in the TIs, leading to dimensionality reduction. A projection of the starting model into the reduced basis space generates the starting POD coefficients. At each iteration, only coefficients within a specified sampling window are resimulated assuming a Gaussian prior. The sampling window grows at a specified rate as the number of iteration progresses starting from the coefficients corresponding to the highest ranked basis to those of the least informative basis. We found this gradual increment in the sampling window to be more stable compared to resampling all the coefficients right from the first iteration. We demonstrate the performance of the algorithm with both synthetic and lab-scale electrical resistivity imaging of saline tracer experiments, employing the same set of basis vectors for all inversions. We consider two scenarios of unimodal and bimodal plumes. The unimodal plume is consistent with the hypothesis underlying the generation of the TIs whereas bimodality in plume morphology was not theorized. We show that uncertainty quantification using McMC can proceed in the reduced dimensionality space while accounting for the physics of the underlying process.

Generation of vector beams using a double-wedge depolarizer: Non-quantum entanglement

NASA Astrophysics Data System (ADS)

Samlan, C. T.; Viswanathan, Nirmal K.

2016-07-01

Propagation of horizontally polarized Gaussian beam through a double-wedge depolarizer generates vector beams with spatially varying state of polarization. Jones calculus is used to show that such beams are maximally nonseparable on the basis of even (Gaussian)-odd (Hermite-Gaussian) mode parity and horizontal-vertical polarization state. The maximum nonseparability in the two degrees of freedom of the vector beam at the double wedge depolarizer output is verified experimentally using a modified Sagnac interferometer and linear analyser projected interferograms to measure the concurrence 0.94±0.002 and violation of Clauser-Horne-Shimony-Holt form of Bell-like inequality 2.704±0.024. The investigation is carried out in the context of the use of vector beams for metrological applications.

On orthogonal expansions of the space of vector functions which are square-summable over a given domain and the vector analysis operators

NASA Technical Reports Server (NTRS)

Bykhovskiy, E. B.; Smirnov, N. V.

1983-01-01

The Hilbert space L2(omega) of vector functions is studied. A breakdown of L2(omega) into orthogonal subspaces is discussed and the properties of the operators for projection onto these subspaces are investigated from the standpoint of preserving the differential properties of the vectors being projected. Finally, the properties of the operators are examined.

Simplified model of statistically stationary spacecraft rotation and associated induced gravity environments

NASA Technical Reports Server (NTRS)

Fichtl, G. H.; Holland, R. L.

1978-01-01

A stochastic model of spacecraft motion was developed based on the assumption that the net torque vector due to crew activity and rocket thruster firings is a statistically stationary Gaussian vector process. The process had zero ensemble mean value, and the components of the torque vector were mutually stochastically independent. The linearized rigid-body equations of motion were used to derive the autospectral density functions of the components of the spacecraft rotation vector. The cross-spectral density functions of the components of the rotation vector vanish for all frequencies so that the components of rotation were mutually stochastically independent. The autospectral and cross-spectral density functions of the induced gravity environment imparted to scientific apparatus rigidly attached to the spacecraft were calculated from the rotation rate spectral density functions via linearized inertial frame to body-fixed principal axis frame transformation formulae. The induced gravity process was a Gaussian one with zero mean value. Transformation formulae were used to rotate the principal axis body-fixed frame to which the rotation rate and induced gravity vector were referred to a body-fixed frame in which the components of the induced gravity vector were stochastically independent. Rice's theory of exceedances was used to calculate expected exceedance rates of the components of the rotation and induced gravity vector processes.

Vector species richness increases haemorrhagic disease prevalence through functional diversity modulating the duration of seasonal transmission.

PubMed

Park, Andrew W; Cleveland, Christopher A; Dallas, Tad A; Corn, Joseph L

2016-06-01

Although many parasites are transmitted between hosts by a suite of arthropod vectors, the impact of vector biodiversity on parasite transmission is poorly understood. Positive relationships between host infection prevalence and vector species richness (SR) may operate through multiple mechanisms, including (i) increased vector abundance, (ii) a sampling effect in which species of high vectorial capacity are more likely to occur in species-rich communities, and (iii) functional diversity whereby communities comprised species with distinct phenologies may extend the duration of seasonal transmission. Teasing such mechanisms apart is impeded by a lack of appropriate data, yet could highlight a neglected role for functional diversity in parasite transmission. We used statistical modelling of extensive host, vector and microparasite data to test the hypothesis that functional diversity leading to longer seasonal transmission explained variable levels of disease in a wildlife population. We additionally developed a simple transmission model to guide our expectation of how an increased transmission season translates to infection prevalence. Our study demonstrates that vector SR is associated with increased levels of disease reporting, but not via increases in vector abundance or via a sampling effect. Rather, the relationship operates by extending the length of seasonal transmission, in line with theoretical predictions.

Objectivity in Quantum Measurement

NASA Astrophysics Data System (ADS)

Li, Sheng-Wen; Cai, C. Y.; Liu, X. F.; Sun, C. P.

2018-06-01

The objectivity is a basic requirement for the measurements in the classical world, namely, different observers must reach a consensus on their measurement results, so that they believe that the object exists "objectively" since whoever measures it obtains the same result. We find that this simple requirement of objectivity indeed imposes an important constraint upon quantum measurements, i.e., if two or more observers could reach a consensus on their quantum measurement results, their measurement basis must be orthogonal vector sets. This naturally explains why quantum measurements are based on orthogonal vector basis, which is proposed as one of the axioms in textbooks of quantum mechanics. The role of the macroscopicality of the observers in an objective measurement is discussed, which supports the belief that macroscopicality is a characteristic of classicality.

Objectivity in Quantum Measurement

NASA Astrophysics Data System (ADS)

Li, Sheng-Wen; Cai, C. Y.; Liu, X. F.; Sun, C. P.

2018-05-01

The objectivity is a basic requirement for the measurements in the classical world, namely, different observers must reach a consensus on their measurement results, so that they believe that the object exists "objectively" since whoever measures it obtains the same result. We find that this simple requirement of objectivity indeed imposes an important constraint upon quantum measurements, i.e., if two or more observers could reach a consensus on their quantum measurement results, their measurement basis must be orthogonal vector sets. This naturally explains why quantum measurements are based on orthogonal vector basis, which is proposed as one of the axioms in textbooks of quantum mechanics. The role of the macroscopicality of the observers in an objective measurement is discussed, which supports the belief that macroscopicality is a characteristic of classicality.

Nontraditional method for determining unperturbed orbits of unknown space objects using incomplete optical observational data

NASA Astrophysics Data System (ADS)

Perov, N. I.

1985-02-01

A physical-geometrical method for computing the orbits of earth satellites on the basis of an inadequate number of angular observations (N3) was developed. Specifically, a new method has been developed for calculating the elements of Keplerian orbits of unidentified artificial satellites using two angular observations (alpha sub k, S sub k, k = 1). The first section gives procedures for determining the topocentric distance to AES on the basis of one optical observation. This is followed by description of a very simple method for determining unperturbed orbits using two satellite position vectors and a time interval which is applicable even in the case of antiparallel AED position vectors, a method designated the R sub 2 iterations method.

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

A complementation assay for in vivo protein structure/function analysis in Physcomitrella patens (Funariaceae)

DOE PAGES

Scavuzzo-Duggan, Tess R.; Chaves, Arielle M.; Roberts, Alison W.

2015-07-14

Here, a method for rapid in vivo functional analysis of engineered proteins was developed using Physcomitrella patens. A complementation assay was designed for testing structure/function relationships in cellulose synthase (CESA) proteins. The components of the assay include (1) construction of test vectors that drive expression of epitope-tagged PpCESA5 carrying engineered mutations, (2) transformation of a ppcesa5 knockout line that fails to produce gametophores with test and control vectors, (3) scoring the stable transformants for gametophore production, (4) statistical analysis comparing complementation rates for test vectors to positive and negative control vectors, and (5) analysis of transgenic protein expression by Westernmore » blotting. The assay distinguished mutations that generate fully functional, nonfunctional, and partially functional proteins. In conclusion, compared with existing methods for in vivo testing of protein function, this complementation assay provides a rapid method for investigating protein structure/function relationships in plants.« less

Two-spinor description of massive particles and relativistic spin projection operators

NASA Astrophysics Data System (ADS)

Isaev, A. P.; Podoinitsyn, M. A.

2018-04-01

On the basis of the Wigner unitary representations of the covering group ISL (2 , C) of the Poincaré group, we obtain spin-tensor wave functions of free massive particles with arbitrary spin. The wave functions automatically satisfy the Dirac-Pauli-Fierz equations. In the framework of the two-spinor formalism we construct spin-vectors of polarizations and obtain conditions that fix the corresponding relativistic spin projection operators (Behrends-Fronsdal projection operators). With the help of these conditions we find explicit expressions for relativistic spin projection operators for integer spins (Behrends-Fronsdal projection operators) and then find relativistic spin projection operators for half integer spins. These projection operators determine the numerators in the propagators of fields of relativistic particles. We deduce generalizations of the Behrends-Fronsdal projection operators for arbitrary space-time dimensions D > 2.

One-loop corrections to light cone wave functions: The dipole picture DIS cross section

NASA Astrophysics Data System (ADS)

Hänninen, H.; Lappi, T.; Paatelainen, R.

2018-06-01

We develop methods to perform loop calculations in light cone perturbation theory using a helicity basis, refining the method introduced in our earlier work. In particular this includes implementing a consistent way to contract the four-dimensional tensor structures from the helicity vectors with d-dimensional tensors arising from loop integrals, in a way that can be fully automatized. We demonstrate this explicitly by calculating the one-loop correction to the virtual photon to quark-antiquark dipole light cone wave function. This allows us to calculate the deep inelastic scattering cross section in the dipole formalism to next-to-leading order accuracy. Our results, obtained using the four dimensional helicity scheme, agree with the recent calculation by Beuf using conventional dimensional regularization, confirming the regularization scheme independence of this cross section.

Machine learning study for the prediction of transdermal peptide

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Choi, Seung-Hoon; Lee, Nam Kyung; Kang, Sang-Kee; Choi, Yun-Jaie; Shin, Jae-Min; Choi, Kihang; Jung, Dong Hyun

2011-04-01

In order to develop a computational method to rapidly evaluate transdermal peptides, we report approaches for predicting the transdermal activity of peptides on the basis of peptide sequence information using Artificial Neural Network (ANN), Partial Least Squares (PLS) and Support Vector Machine (SVM). We identified 269 transdermal peptides by the phage display technique and use them as the positive controls to develop and test machine learning models. Combinations of three descriptors with neural network architectures, the number of latent variables and the kernel functions are tried in training to make appropriate predictions. The capacity of models is evaluated by means of statistical indicators including sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC score). In the ROC score-based comparison, three methods proved capable of providing a reasonable prediction of transdermal peptide. The best result is obtained by SVM model with a radial basis function and VHSE descriptors. The results indicate that it is possible to discriminate between transdermal peptides and random sequences using our models. We anticipate that our models will be applicable to prediction of transdermal peptide for large peptide database for facilitating efficient transdermal drug delivery through intact skin.

A Mathematical Basis for the Safety Analysis of Conflict Prevention Algorithms

NASA Technical Reports Server (NTRS)

Maddalon, Jeffrey M.; Butler, Ricky W.; Munoz, Cesar A.; Dowek, Gilles

2009-01-01

In air traffic management systems, a conflict prevention system examines the traffic and provides ranges of guidance maneuvers that avoid conflicts. This guidance takes the form of ranges of track angles, vertical speeds, or ground speeds. These ranges may be assembled into prevention bands: maneuvers that should not be taken. Unlike conflict resolution systems, which presume that the aircraft already has a conflict, conflict prevention systems show conflicts for all maneuvers. Without conflict prevention information, a pilot might perform a maneuver that causes a near-term conflict. Because near-term conflicts can lead to safety concerns, strong verification of correct operation is required. This paper presents a mathematical framework to analyze the correctness of algorithms that produce conflict prevention information. This paper examines multiple mathematical approaches: iterative, vector algebraic, and trigonometric. The correctness theories are structured first to analyze conflict prevention information for all aircraft. Next, these theories are augmented to consider aircraft which will create a conflict within a given lookahead time. Certain key functions for a candidate algorithm, which satisfy this mathematical basis are presented; however, the proof that a full algorithm using these functions completely satisfies the definition of safety is not provided.

Mathematical Methods for Optical Physics and Engineering

NASA Astrophysics Data System (ADS)

Gbur, Gregory J.

2011-01-01

1. Vector algebra; 2. Vector calculus; 3. Vector calculus in curvilinear coordinate systems; 4. Matrices and linear algebra; 5. Advanced matrix techniques and tensors; 6. Distributions; 7. Infinite series; 8. Fourier series; 9. Complex analysis; 10. Advanced complex analysis; 11. Fourier transforms; 12. Other integral transforms; 13. Discrete transforms; 14. Ordinary differential equations; 15. Partial differential equations; 16. Bessel functions; 17. Legendre functions and spherical harmonics; 18. Orthogonal functions; 19. Green's functions; 20. The calculus of variations; 21. Asymptotic techniques; Appendices; References; Index.

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2004-10-01

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

Blazed vector grating liquid crystal cells with photocrosslinkable polymeric alignment films fabricated by one-step polarizer rotation method

NASA Astrophysics Data System (ADS)

Kawai, Kotaro; Kuzuwata, Mitsuru; Sasaki, Tomoyuki; Noda, Kohei; Kawatsuki, Nobuhiro; Ono, Hiroshi

2014-12-01

Blazed vector grating liquid crystal (LC) cells, in which the directors of low-molar-mass LCs are antisymmetrically distributed, were fabricated by one-step exposure of an empty glass cell inner-coated with a photocrosslinkable polymer LC (PCLC) to UV light. By adopting a LC cell structure, twisted nematic (TN) and homogeneous (HOMO) alignments were obtained in the blazed vector grating LC cells. Moreover, the diffraction efficiency of the blazed vector grating LC cells was greatly improved by increasing the thickness of the device in comparison with that of a blazed vector grating with a thin film structure obtained in our previous study. In addition, the diffraction efficiency and polarization states of ±1st-order diffracted beams from the resultant blazed vector grating LC cells were controlled by designing a blazed pattern in the alignment films, and these diffraction properties were well explained on the basis of Jones calculus and the elastic continuum theory of nematic LCs.

A Fast MoM Solver (GIFFT) for Large Arrays of Microstrip and Cavity-Backed Antennas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fasenfest, B J; Capolino, F; Wilton, D

2005-02-02

A straightforward numerical analysis of large arrays of arbitrary contour (and possibly missing elements) requires large memory storage and long computation times. Several techniques are currently under development to reduce this cost. One such technique is the GIFFT (Green's function interpolation and FFT) method discussed here that belongs to the class of fast solvers for large structures. This method uses a modification of the standard AIM approach [1] that takes into account the reusability properties of matrices that arise from identical array elements. If the array consists of planar conducting bodies, the array elements are meshed using standard subdomain basismore » functions, such as the RWG basis. The Green's function is then projected onto a sparse regular grid of separable interpolating polynomials. This grid can then be used in a 2D or 3D FFT to accelerate the matrix-vector product used in an iterative solver [2]. The method has been proven to greatly reduce solve time by speeding up the matrix-vector product computation. The GIFFT approach also reduces fill time and memory requirements, since only the near element interactions need to be calculated exactly. The present work extends GIFFT to layered material Green's functions and multiregion interactions via slots in ground planes. In addition, a preconditioner is implemented to greatly reduce the number of iterations required for a solution. The general scheme of the GIFFT method is reported in [2]; this contribution is limited to presenting new results for array antennas made of slot-excited patches and cavity-backed patch antennas.« less

Larvicidal activity of few select indigenous plants of North East India against disease vector mosquitoes (Diptera: Culicidae).

PubMed

Dohutia, C; Bhattacharyya, D R; Sharma, S K; Mohapatra, P K; Bhattacharjee, K; Gogoi, K; Gogoi, P; Mahanta, J; Prakash, A

2015-03-01

Mosquitoes are the vectors of several life threatening diseases like dengue, malaria, Japanese encephalitis and lymphatic filariasis, which are widely present in the north-eastern states of India. Investigations on five local plants of north-east India, selected on the basis of their use by indigenous communities as fish poison, were carried out to study their mosquito larvicidal potential against Anopheles stephensi (malaria vector), Stegomyia aegypti (dengue vector) and Culex quinquefasciatus (lymphatic filariasis vector) mosquitoes. Crude Petroleum ether extracts of the roots of three plants viz. Derris elliptica, Linostoma decandrum and Croton tiglium were found to have remarkable larvicidal activity; D. elliptica extract was the most effective and with LC50 value of 0.307 μg/ml its activity was superior to propoxur, the standard synthetic larvicide. Half-life of larvicidal activity of D. elliptica and L. decandrum extracts ranged from 2-4 days.

[What makes an insect a vector?].

PubMed

Kampen, Helge

2009-01-01

Blood-feeding insects transmit numerous viruses, bacteria, protozoans and helminths to vertebrates. The developmental cycles of the microorganisms in their vectors and the mechanisms of transmission are generally extremely complex and the result of a long-lasting coevolution of vector and vectored pathogen based on mutual adaptation. The conditions necessary for an insect to become a vector are multiple but require an innate vector competence as a genetic basis. Next to the vector competence plenty of entomological, ecological and pathogen-related factors are decisive, given the availability of infection sources. The various modes of pathogen transmission by vectors are connected to the developmental routes of the microorganisms in their vectors. In particular, pathogens transmitted by saliva encounter a lot of cellular and acellular barriers during their migration from the insect's midgut through the hemocele into the salivary fluid, including components of the insect's immune system. With regard to intracellular development, receptor-mediated invasion mechanisms are of relevance. As an environmental factor, the temperature has a paramount impact on the vectorial roles of hematophagous insects. Not only has it a considerable influence on the duration of a pathogen's development in its vector (extrinsic incubation period) but it can render putatively vector-incompetent insects to vectors ("leaky gut" phenomenon). Equally crucial are behavioural aspects of both the insect and the pathogen such as blood host preferences, seasonal appearance and circadian biting activity on the vector's side and diurnal/nocturnal periodicity on the pathogen's side which facilitate a contact in the first place.

Modification and identification of a vector for making a large phage antibody library.

PubMed

Zhang, Guo-min; Chen, Yü-ping; Guan, Yuan-zhi; Wang, Yan; An, Yun-qing

2007-11-20

The large phage antibody library is used to obtain high-affinity human antibody, and the Loxp/cre site-specific recombination system is a potential method for constructing a large phage antibody library. In the present study, a phage antibody library vector pDF was reconstructed to construct diabody more quickly and conveniently without injury to homologous recombination and the expression function of the vector and thus to integrate construction of the large phage antibody library with the preparation of diabodies. scFv was obtained by overlap polymerase chain reaction (PCR) amplification with the newly designed VL and VH extension primers. loxp511 was flanked by VL and VH and the endonuclease ACC III encoding sequences were introduced on both sides of loxp511. scFv was cloned into the vector pDF to obtain the vector pDscFv. The vector expression function was identified and the feasibility of diabody preparation was evaluated. A large phage antibody library was constructed in pDscFv. Several antigens were used to screen the antibody library and the quality of the antibody library was evaluated. The phage antibody library expression vector pDscFv was successfully constructed and confirmed to express functional scFv. The large phage antibody library constructed using this vector was of high diversity. Screening of the library on 6 antigens confirmed the generation of specific antibodies to these antigens. Two antibodies were subjected to enzymatic digestion and were prepared into diabody with functional expression. The reconstructed vector pDscFv retains its recombination capability and expression function and can be used to construct large phage antibody libraries. It can be used as a convenient and quick method for preparing diabodies after simple enzymatic digestion, which facilitates clinical trials and application of antibody therapy.

Functional characterization of adenoviral/retroviral chimeric vectors and their use for efficient screening of retroviral producer cell lines.

PubMed

Duisit, G; Salvetti, A; Moullier, P; Cosset, F L

1999-01-20

We have generated three different E1-deleted replication-defective adenoviral vectors expressing either Moloney murine leukemia virus (Mo-MuLV) Gag-Pol core particle proteins, gibbon ape leukemia virus (GALV) envelope glycoproteins, or an MuLV-derived retroviral vector genome encoding mCD2 antigen, a murine cell surface marker easily detectable by flow cytometry. Each of the three vectors was first characterized individually by infection of cells providing the complementary retroviral function(s) and able to induce the production of retroviral vectors with an efficiency similar to or higher than that of FLY stable retroviral packaging cells [Cosset, F.-L., Takeuchi, Y., Battini, J.-L., Weiss, R.A., and Collins, M.K.L., (1995). J. Virol. 69, 7430-7436]. In small-scale pilot experiments, TE671 cells simultaneously coinfected with the three adenoviral vectors efficiently released helper-free retroviral vectors in their supernatant, with titers greater than 10(6) infectious particles per milliliter by end-point titrations. Our results also indicated that in contrast to retroviral vector-packageable RNAs, the adenovirus-mediated overexpression of both Gag-Pol and Env packaging functions had limited impact on retroviral titers. The primary mechanism suspected is the premature intracellular cleavage of the Pr65gag precursor that we found in gag-pol-expressing cells, which in turn may impair the normal incorporation of high loads of functional Env. Last, the characterization of the adenoviral/retroviral chimeric vectors allowed the screening of various primate cells for retroviral production and we found that three hepatocyte-derived cell lines were highly efficient in the assembly and release of infectious retroviral particles.

Estimating top-of-atmosphere thermal infrared radiance using MERRA-2 atmospheric data

NASA Astrophysics Data System (ADS)

Kleynhans, Tania; Montanaro, Matthew; Gerace, Aaron; Kanan, Christopher

2017-05-01

Thermal infrared satellite images have been widely used in environmental studies. However, satellites have limited temporal resolution, e.g., 16 day Landsat or 1 to 2 day Terra MODIS. This paper investigates the use of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product, produced by NASA's Global Modeling and Assimilation Office (GMAO) to predict global topof-atmosphere (TOA) thermal infrared radiance. The high temporal resolution of the MERRA-2 data product presents opportunities for novel research and applications. Various methods were applied to estimate TOA radiance from MERRA-2 variables namely (1) a parameterized physics based method, (2) Linear regression models and (3) non-linear Support Vector Regression. Model prediction accuracy was evaluated using temporally and spatially coincident Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data as reference data. This research found that Support Vector Regression with a radial basis function kernel produced the lowest error rates. Sources of errors are discussed and defined. Further research is currently being conducted to train deep learning models to predict TOA thermal radiance

A neural network detection model of spilled oil based on the texture analysis of SAR image

NASA Astrophysics Data System (ADS)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

Detection of Splice Sites Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Zeng, Qiu-Yao; Li, Lin-Fang; Zhang, Yan-Jiao; Wan, Ming-Ming; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Liu, Song-Hao

2013-02-01

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

Robust Spatial Approximation of Laser Scanner Point Clouds by Means of Free-form Curve Approaches in Deformation Analysis

NASA Astrophysics Data System (ADS)

Bureick, Johannes; Alkhatib, Hamza; Neumann, Ingo

2016-03-01

In many geodetic engineering applications it is necessary to solve the problem of describing a measured data point cloud, measured, e. g. by laser scanner, by means of free-form curves or surfaces, e. g., with B-Splines as basis functions. The state of the art approaches to determine B-Splines yields results which are seriously manipulated by the occurrence of data gaps and outliers. Optimal and robust B-Spline fitting depend, however, on optimal selection of the knot vector. Hence we combine in our approach Monte-Carlo methods and the location and curvature of the measured data in order to determine the knot vector of the B-Spline in such a way that no oscillating effects at the edges of data gaps occur. We introduce an optimized approach based on computed weights by means of resampling techniques. In order to minimize the effect of outliers, we apply robust M-estimators for the estimation of control points. The above mentioned approach will be applied to a multi-sensor system based on kinematic terrestrial laserscanning in the field of rail track inspection.

Computation of optimal output-feedback compensators for linear time-invariant systems

NASA Technical Reports Server (NTRS)

Platzman, L. K.

1972-01-01

The control of linear time-invariant systems with respect to a quadratic performance criterion was considered, subject to the constraint that the control vector be a constant linear transformation of the output vector. The optimal feedback matrix, f*, was selected to optimize the expected performance, given the covariance of the initial state. It is first shown that the expected performance criterion can be expressed as the ratio of two multinomials in the element of f. This expression provides the basis for a feasible method of determining f* in the case of single-input single-output systems. A number of iterative algorithms are then proposed for the calculation of f* for multiple input-output systems. For two of these, monotone convergence is proved, but they involve the solution of nonlinear matrix equations at each iteration. Another is proposed involving the solution of Lyapunov equations at each iteration, and the gradual increase of the magnitude of a penalty function. Experience with this algorithm will be needed to determine whether or not it does, indeed, possess desirable convergence properties, and whether it can be used to determine the globally optimal f*.

TMV-Gate vectors: Gateway compatible tobacco mosaic virus based expression vectors for functional analysis of proteins

PubMed Central

Kagale, Sateesh; Uzuhashi, Shihomi; Wigness, Merek; Bender, Tricia; Yang, Wen; Borhan, M. Hossein; Rozwadowski, Kevin

2012-01-01

Plant viral expression vectors are advantageous for high-throughput functional characterization studies of genes due to their capability for rapid, high-level transient expression of proteins. We have constructed a series of tobacco mosaic virus (TMV) based vectors that are compatible with Gateway technology to enable rapid assembly of expression constructs and exploitation of ORFeome collections. In addition to the potential of producing recombinant protein at grams per kilogram FW of leaf tissue, these vectors facilitate either N- or C-terminal fusions to a broad series of epitope tag(s) and fluorescent proteins. We demonstrate the utility of these vectors in affinity purification, immunodetection and subcellular localisation studies. We also apply the vectors to characterize protein-protein interactions and demonstrate their utility in screening plant pathogen effectors. Given its broad utility in defining protein properties, this vector series will serve as a useful resource to expedite gene characterization efforts. PMID:23166857

Intraspecific Competition and Population Dynamics of Aedes aegypti

NASA Astrophysics Data System (ADS)

Paixão, C. A.; Charret, I. C.; Lima, R. R.

2012-04-01

We report computational simulations for the evolution of the population of the dengue vector, Aedes aegypti mosquitoes. The results suggest that controlling the mosquito population, on the basis of intraspecific competition at the larval stage, can be an efficient mechanism for controlling the spread of the epidemic. The results also show the presence of a kind of genetic evolution in vector population, which results mainly in increasing the average lifespan of individuals in adulthood.

Stable orthogonal local discriminant embedding for linear dimensionality reduction.

PubMed

Gao, Quanxue; Ma, Jingjie; Zhang, Hailin; Gao, Xinbo; Liu, Yamin

2013-07-01

Manifold learning is widely used in machine learning and pattern recognition. However, manifold learning only considers the similarity of samples belonging to the same class and ignores the within-class variation of data, which will impair the generalization and stableness of the algorithms. For this purpose, we construct an adjacency graph to model the intraclass variation that characterizes the most important properties, such as diversity of patterns, and then incorporate the diversity into the discriminant objective function for linear dimensionality reduction. Finally, we introduce the orthogonal constraint for the basis vectors and propose an orthogonal algorithm called stable orthogonal local discriminate embedding. Experimental results on several standard image databases demonstrate the effectiveness of the proposed dimensionality reduction approach.

Objective scatterometer wind ambiguity removal using smoothness and dynamical constraints

NASA Technical Reports Server (NTRS)

Hoffman, R. N.

1984-01-01

In the present investigation, a variational analysis method (VAM) is used to remove the ambiguity of the Seasat-A Satellite Scatterometer (SASS) winds. At each SASS data point, two, three, or four wind vectors (termed ambiguities) are retrieved. It is pointed out that the VAM is basically a least squares method for fitting data. The problem may be nonlinear. The best fit to the data and constraints is obtained on the basis of a minimization of the objective function. The VAM was tested and tuned at 12 h GMT Sept. 10, 1978. Attention is given to a case study involving an intense cyclone centered south of Japan at 138 deg E.

Complex modulation of the Aedes aegypti transcriptome in response to dengue virus infection.

PubMed

Bonizzoni, Mariangela; Dunn, W Augustine; Campbell, Corey L; Olson, Ken E; Marinotti, Osvaldo; James, Anthony A

2012-01-01

Dengue fever is the most important arboviral disease world-wide, with Aedes aegypti being the major vector. Interactions between the mosquito host and dengue viruses (DENV) are complex and vector competence varies among geographically-distinct Ae. aegypti populations. Additionally, dengue is caused by four antigenically-distinct viral serotypes (DENV1-4), each with multiple genotypes. Each virus genotype interacts differently with vertebrate and invertebrate hosts. Analyses of alterations in mosquito transcriptional profiles during DENV infection are expected to provide the basis for identifying networks of genes involved in responses to viruses and contribute to the molecular-genetic understanding of vector competence. In addition, this knowledge is anticipated to support the development of novel disease-control strategies. RNA-seq technology was used to assess genome-wide changes in transcript abundance at 1, 4 and 14 days following DENV2 infection in carcasses, midguts and salivary glands of the Ae. aegypti Chetumal strain. DENV2 affected the expression of 397 Ae. aegypti genes, most of which were down-regulated by viral infection. Differential accumulation of transcripts was mainly tissue- and time-specific. Comparisons of our data with other published reports reveal conservation of functional classes, but limited concordance of specific mosquito genes responsive to DENV2 infection. These results indicate the necessity of additional studies of mosquito-DENV interactions, specifically those focused on recently-derived mosquito strains with multiple dengue virus serotypes and genotypes.

Numerical assessment of low-frequency dosimetry from sampled magnetic fields

NASA Astrophysics Data System (ADS)

Freschi, Fabio; Giaccone, Luca; Cirimele, Vincenzo; Canova, Aldo

2018-01-01

Low-frequency dosimetry is commonly assessed by evaluating the electric field in the human body using the scalar potential finite difference method. This method is effective only when the sources of the magnetic field are completely known and the magnetic vector potential can be analytically computed. The aim of the paper is to present a rigorous method to characterize the source term when only the magnetic flux density is available at discrete points, e.g. in case of field measurements. The method is based on the solution of the discrete magnetic curl equation. The system is restricted to the independent set of magnetic fluxes and circulations of magnetic vector potential using the topological information of the computational mesh. The solenoidality of the magnetic flux density is preserved using a divergence-free interpolator based on vector radial basis functions. The analysis of a benchmark problem shows that the complexity of the proposed algorithm is linearly dependent on the number of elements with a controllable accuracy. The method proposed in this paper also proves to be useful and effective when applied to a real world scenario, where the magnetic flux density is measured in proximity of a power transformer. A 8 million voxel body model is then used for the numerical dosimetric analysis. The complete assessment is completed in less than 5 min, that is more than acceptable for these problems.

Comprehensive gene expression profiling following DNA vaccination of rainbow trout against infectious hematopoietic necrosis virus

USGS Publications Warehouse

Purcell, Maureen K.; Nichols, Krista M.; Winton, James R.; Kurath, Gael; Thorgaard, Gary H.; Wheeler, Paul; Hansen, John D.; Herwig, Russell P.; Park, Linda K.

2006-01-01

The DNA vaccine based on the glycoprotein gene of Infectious hematopoietic necrosis virus induces a non-specific anti-viral immune response and long-term specific immunity against IHNV. This study characterized gene expression responses associated with the early anti-viral response. Homozygous rainbow trout were injected intra-muscularly (I.M.) with vector DNA or the IHNV DNA vaccine. Gene expression in muscle tissue (I.M. site) was evaluated using a 16,008 feature salmon cDNA microarray. Eighty different genes were significantly modulated in the vector DNA group while 910 genes were modulated in the IHNV DNA vaccinate group relative to control group. Quantitative reverse-transcriptase PCR was used to examine expression of selected immune genes at the I.M. site and in other secondary tissues. In the localized response (I.M. site), the magnitudes of gene expression changes were much greater in the vaccinate group relative to the vector DNA group for the majority of genes analyzed. At secondary systemic sites (e.g. gill, kidney and spleen), type I IFN-related genes were up-regulated in only the IHNV DNA vaccinated group. The results presented here suggest that the IHNV DNA vaccine induces up-regulation of the type I IFN system across multiple tissues, which is the functional basis of early anti-viral immunity.

Numerical assessment of low-frequency dosimetry from sampled magnetic fields.

PubMed

Freschi, Fabio; Giaccone, Luca; Cirimele, Vincenzo; Canova, Aldo

2017-12-29

Low-frequency dosimetry is commonly assessed by evaluating the electric field in the human body using the scalar potential finite difference method. This method is effective only when the sources of the magnetic field are completely known and the magnetic vector potential can be analytically computed. The aim of the paper is to present a rigorous method to characterize the source term when only the magnetic flux density is available at discrete points, e.g. in case of field measurements. The method is based on the solution of the discrete magnetic curl equation. The system is restricted to the independent set of magnetic fluxes and circulations of magnetic vector potential using the topological information of the computational mesh. The solenoidality of the magnetic flux density is preserved using a divergence-free interpolator based on vector radial basis functions. The analysis of a benchmark problem shows that the complexity of the proposed algorithm is linearly dependent on the number of elements with a controllable accuracy. The method proposed in this paper also proves to be useful and effective when applied to a real world scenario, where the magnetic flux density is measured in proximity of a power transformer. A 8 million voxel body model is then used for the numerical dosimetric analysis. The complete assessment is completed in less than 5 min, that is more than acceptable for these problems.

Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

NASA Astrophysics Data System (ADS)

Muduli, Pradyut; Das, Sarat

2014-06-01

This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

Comparison of Flux-Surface Aligned Curvilinear Coordinate Systems and Neoclassical Magnetic Field Predictions

NASA Astrophysics Data System (ADS)

Collart, T. G.; Stacey, W. M.

2015-11-01

Several methods are presented for extending the traditional analytic ``circular'' representation of flux-surface aligned curvilinear coordinate systems to more accurately describe equilibrium plasma geometry and magnetic fields in DIII-D. The formalism originally presented by Miller is extended to include different poloidal variations in the upper and lower hemispheres. A coordinate system based on separate Fourier expansions of major radius and vertical position greatly improves accuracy in edge plasma structure representation. Scale factors and basis vectors for a system formed by expanding the circular model minor radius can be represented using linear combinations of Fourier basis functions. A general method for coordinate system orthogonalization is presented and applied to all curvilinear models. A formalism for the magnetic field structure in these curvilinear models is presented, and the resulting magnetic field predictions are compared against calculations performed in a Cartesian system using an experimentally based EFIT prediction for the Grad-Shafranov equilibrium. Supported by: US DOE under DE-FG02-00ER54538.

Recognizing Activities via Bag of Words for Attribute Dynamics (Open Access)

DTIC Science & Technology

2013-10-03

56.4% 73.4% clean-jerk 83.2% 84.1% 78.2% 79.4% 85.1% 78.2% 85.4% javelin throw 61.1% 74.6% 79.5% 62.1% 87.5% 56.6% 76.7% ham. throw 65.1% 77.5% 70.5...1 ∈ Rτ is the vector of all ones. Each column of C is a basis vector of a latent subspace and the t-th column of X contains the coordinates of the yt...binary PCA is first applied to all attribute score vectors in P ′. The parame- ters of the hidden Gauss-Markov process are then learned by solving a least

Information-efficient spectral imaging sensor

DOEpatents

Sweatt, William C.; Gentry, Stephen M.; Boye, Clinton A.; Grotbeck, Carter L.; Stallard, Brian R.; Descour, Michael R.

2003-01-01

A programmable optical filter for use in multispectral and hyperspectral imaging. The filter splits the light collected by an optical telescope into two channels for each of the pixels in a row in a scanned image, one channel to handle the positive elements of a spectral basis filter and one for the negative elements of the spectral basis filter. Each channel for each pixel disperses its light into n spectral bins, with the light in each bin being attenuated in accordance with the value of the associated positive or negative element of the spectral basis vector. The spectral basis vector is constructed so that its positive elements emphasize the presence of a target and its negative elements emphasize the presence of the constituents of the background of the imaged scene. The attenuated light in the channels is re-imaged onto separate detectors for each pixel and then the signals from the detectors are combined to give an indication of the presence or not of the target in each pixel of the scanned scene. This system provides for a very efficient optical determination of the presence of the target, as opposed to the very data intensive data manipulations that are required in conventional hyperspectral imaging systems.

Singular vectors for the WN algebras

NASA Astrophysics Data System (ADS)

Ridout, David; Siu, Steve; Wood, Simon

2018-03-01

In this paper, we use free field realisations of the A-type principal, or Casimir, WN algebras to derive explicit formulae for singular vectors in Fock modules. These singular vectors are constructed by applying screening operators to Fock module highest weight vectors. The action of the screening operators is then explicitly evaluated in terms of Jack symmetric functions and their skew analogues. The resulting formulae depend on sequences of pairs of integers that completely determine the Fock module as well as the Jack symmetric functions.

Stable Local Volatility Calibration Using Kernel Splines

NASA Astrophysics Data System (ADS)

Coleman, Thomas F.; Li, Yuying; Wang, Cheng

2010-09-01

We propose an optimization formulation using L1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances the calibration accuracy with the model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a kernel function generating splines and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of the support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.

Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder.

PubMed

Wang, Jiaojian; Wei, Qiang; Bai, Tongjian; Zhou, Xiaoqin; Sun, Hui; Becker, Benjamin; Tian, Yanghua; Wang, Kai; Kendrick, Keith

2017-12-01

Electroconvulsive therapy (ECT) has been widely used to treat the major depressive disorder (MDD), especially for treatment-resistant depression. However, the neuroanatomical basis of ECT remains an open problem. In our study, we combined the voxel-based morphology (VBM), resting-state functional connectivity (RSFC) and granger causality analysis (GCA) to identify the longitudinal changes of structure and function in 23 MDD patients before and after ECT. In addition, multivariate pattern analysis using linear support vector machine (SVM) was applied to classify 23 depressed patients from 25 gender, age and education matched healthy controls. VBM analysis revealed the increased gray matter volume of left superficial amygdala after ECT. The following RSFC and GCA analyses further identified the enhanced functional connectivity between left amygdala and left fusiform face area (FFA) and effective connectivity from FFA to amygdala after ECT, respectively. Moreover, SVM-based classification achieved an accuracy of 83.33%, a sensitivity of 82.61% and a specificity of 84% by leave-one-out cross-validation. Our findings indicated that ECT may facilitate the neurogenesis of amygdala and selectively enhance the feedforward cortical-subcortical connectivity from FFA to amygdala. This study may shed new light on the pathological mechanism of MDD and may provide the neuroanatomical basis for ECT. © The Author (2017). Published by Oxford University Press.

[Discrimination of varieties of borneol using terahertz spectra based on principal component analysis and support vector machine].

PubMed

Li, Wu; Hu, Bing; Wang, Ming-wei

2014-12-01

In the present paper, the terahertz time-domain spectroscopy (THz-TDS) identification model of borneol based on principal component analysis (PCA) and support vector machine (SVM) was established. As one Chinese common agent, borneol needs a rapid, simple and accurate detection and identification method for its different source and being easily confused in the pharmaceutical and trade links. In order to assure the quality of borneol product and guard the consumer's right, quickly, efficiently and correctly identifying borneol has significant meaning to the production and transaction of borneol. Terahertz time-domain spectroscopy is a new spectroscopy approach to characterize material using terahertz pulse. The absorption terahertz spectra of blumea camphor, borneol camphor and synthetic borneol were measured in the range of 0.2 to 2 THz with the transmission THz-TDS. The PCA scores of 2D plots (PC1 X PC2) and 3D plots (PC1 X PC2 X PC3) of three kinds of borneol samples were obtained through PCA analysis, and both of them have good clustering effect on the 3 different kinds of borneol. The value matrix of the first 10 principal components (PCs) was used to replace the original spectrum data, and the 60 samples of the three kinds of borneol were trained and then the unknown 60 samples were identified. Four kinds of support vector machine model of different kernel functions were set up in this way. Results show that the accuracy of identification and classification of SVM RBF kernel function for three kinds of borneol is 100%, and we selected the SVM with the radial basis kernel function to establish the borneol identification model, in addition, in the noisy case, the classification accuracy rates of four SVM kernel function are above 85%, and this indicates that SVM has strong generalization ability. This study shows that PCA with SVM method of borneol terahertz spectroscopy has good classification and identification effects, and provides a new method for species identification of borneol in Chinese medicine.

The Researches on Damage Detection Method for Truss Structures

NASA Astrophysics Data System (ADS)

Wang, Meng Hong; Cao, Xiao Nan

2018-06-01

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

Pilot longitudinal mosquito surveillance study in the Danube Delta Biosphere Reserve and the first reports of Anopheles algeriensis Theobald, 1903 and Aedes hungaricus Mihályi, 1955 for Romania.

PubMed

Török, Edina; Tomazatos, Alexandru; Cadar, Daniel; Horváth, Cintia; Keresztes, Lujza; Jansen, Stephanie; Becker, Norbert; Kaiser, Achim; Popescu, Octavian; Schmidt-Chanasit, Jonas; Jöst, Hanna; Lühken, Renke

2016-04-11

Mosquito-borne viruses (moboviruses) are of growing importance in many countries of Europe. In Romania and especially in the Danube Delta Biosphere Reserve (DDBR), mosquito and mobovirus surveillance are not performed on a regular basis. However, this type of study is crucially needed to evaluate the risk of pathogen transmission, to understand the ecology of emerging moboviruses, or to plan vector control programmes. We initiated a longitudinal mosquito surveillance study with carbon dioxide-baited Heavy Duty Encephalitis Vector Survey traps at four sampling sites to analyse the spatio-temporal pattern of the (i) mosquito species composition and diversity, (ii) functional groups of mosquitoes (oviposition sites, overwintering stage, and number of generations), and (iii) the occurrence of potential West Nile virus (WNV) vectors. During 2014, a total of 240,546 female mosquitoes were collected. All species were identified using morphological characteristics and further confirmed by mitochondrial cytochrome c oxidase subunit I (COI) gene analysis of selected specimens. The two most common taxa were Coquilettidia richiardii (40.9 %) and Anopheles hyrcanus (34.1 %), followed by Culex pipiens (sensu lato) (s.l.)/Cx. torrentium (7.7 %), Aedes caspius (5.7 %), Cx. modestus (4.0 %), An. maculipennis (s.l.) (3.9 %), and Ae. vexans (3.0 %). A further seven species were less common in the area studied, including two new records for Romania: An. algeriensis and Ae. hungaricus. Phylogenetic analysis of COI gene demonstrated the evolutionary relatedness of most species with specimens of the same species collected in other European regions, except Ae. detritus and An. algeriensis, which exhibited high genetic diversity. Due to the dominance of Cq. richiardii and An. hyrcanus (75 % of all collected specimens), the overall phenology and temporal pattern of functional groups basically followed the phenology of both species. A huge proportion of the mosquito population in the course of the entire sampling period can be classified as potential WNV vectors. With 40 % of all collected specimens, the most frequent species Cq. richiardii is probably the most important vector of WNV in the DDBR. This is the first DNA-barcoding supported analysis of the mosquito fauna in the DDBR. The detection of two new species highlights the lack of knowledge about the mosquito fauna in Romania and in the DDBR in particular. The results provide detailed insights into the spatial-temporal mosquito species composition, which might lead to a better understanding of mobovirus activity in Romania and thus, can be used for the development of vector control programs.

Voltage-gated sodium channels as targets for pyrethroid insecticides.

PubMed

Field, Linda M; Emyr Davies, T G; O'Reilly, Andrias O; Williamson, Martin S; Wallace, B A

2017-10-01

The pyrethroid insecticides are a very successful group of compounds that have been used extensively for the control of arthropod pests of agricultural crops and vectors of animal and human disease. Unfortunately, this has led to the development of resistance to the compounds in many species. The mode of action of pyrethroids is known to be via interactions with the voltage-gated sodium channel. Understanding how binding to the channel is affected by amino acid substitutions that give rise to resistance has helped to elucidate the mode of action of the compounds and the molecular basis of their selectivity for insects vs mammals and between insects and other arthropods. Modelling of the channel/pyrethroid interactions, coupled with the ability to express mutant channels in oocytes and study function, has led to knowledge of both how the channels function and potentially how to design novel insecticides with greater species selectivity.

On the effect of addition of carbon nanotubes on the electric conductivity of alkali-activated slag mortars

NASA Astrophysics Data System (ADS)

Kusak, I.; Lunak, M.

2017-09-01

This paper presents basic electric properties of laboratory prepared alkali-activated composite materials on the basis of finely ground granular high furnace slag to which various quantities of carbon nanotubes (CNT) have been added. Impedance spectroscopy in the frequency range from 40 Hz to 1 MHz was used to measure the specimens. Electric resistivity ρ versus frequency and electric resistivity ρ versus CNT content relationships were examined on our specimens R&S ZNC vector analyser with DAK-12 coaxial probe (made by Speag) was used to carry out the measurements at higher frequencies (from 100 MHz to 3 GHz). Electric conductivity σ as a function of the frequency and as a function of the specimen CNT content was studied in this frequency range. Up-to-date instruments and a unique approach have evidently been employed to carry out non-destructive measurement of mortar materials.

Drift-wave turbulence and zonal flow generation.

PubMed

Balescu, R

2003-10-01

Drift-wave turbulence in a plasma is analyzed on the basis of the wave Liouville equation, describing the evolution of the distribution function of wave packets (quasiparticles) characterized by position x and wave vector k. A closed kinetic equation is derived for the ensemble-averaged part of this function by the methods of nonequilibrium statistical mechanics. It has the form of a non-Markovian advection-diffusion equation describing coupled diffusion processes in x and k spaces. General forms of the diffusion coefficients are obtained in terms of Lagrangian velocity correlations. The latter are calculated in the decorrelation trajectory approximation, a method recently developed for an accurate measure of the important trapping phenomena of particles in the rugged electrostatic potential. The analysis of individual decorrelation trajectories provides an illustration of the fragmentation of drift-wave structures in the radial direction and the generation of long-wavelength structures in the poloidal direction that are identified as zonal flows.

The explicit computation of integration algorithms and first integrals for ordinary differential equations with polynomials coefficients using trees

NASA Technical Reports Server (NTRS)

Crouch, P. E.; Grossman, Robert

1992-01-01

This note is concerned with the explicit symbolic computation of expressions involving differential operators and their actions on functions. The derivation of specialized numerical algorithms, the explicit symbolic computation of integrals of motion, and the explicit computation of normal forms for nonlinear systems all require such computations. More precisely, if R = k(x(sub 1),...,x(sub N)), where k = R or C, F denotes a differential operator with coefficients from R, and g member of R, we describe data structures and algorithms for efficiently computing g. The basic idea is to impose a multiplicative structure on the vector space with basis the set of finite rooted trees and whose nodes are labeled with the coefficients of the differential operators. Cancellations of two trees with r + 1 nodes translates into cancellation of O(N(exp r)) expressions involving the coefficient functions and their derivatives.

Low-memory iterative density fitting.

PubMed

Grajciar, Lukáš

2015-07-30

A new low-memory modification of the density fitting approximation based on a combination of a continuous fast multipole method (CFMM) and a preconditioned conjugate gradient solver is presented. Iterative conjugate gradient solver uses preconditioners formed from blocks of the Coulomb metric matrix that decrease the number of iterations needed for convergence by up to one order of magnitude. The matrix-vector products needed within the iterative algorithm are calculated using CFMM, which evaluates them with the linear scaling memory requirements only. Compared with the standard density fitting implementation, up to 15-fold reduction of the memory requirements is achieved for the most efficient preconditioner at a cost of only 25% increase in computational time. The potential of the method is demonstrated by performing density functional theory calculations for zeolite fragment with 2592 atoms and 121,248 auxiliary basis functions on a single 12-core CPU workstation. © 2015 Wiley Periodicals, Inc.

Vector critical points and generalized quasi-efficient solutions in nonsmooth multi-objective programming.

PubMed

Wang, Zhen; Li, Ru; Yu, Guolin

2017-01-01

In this work, several extended approximately invex vector-valued functions of higher order involving a generalized Jacobian are introduced, and some examples are presented to illustrate their existences. The notions of higher-order (weak) quasi-efficiency with respect to a function are proposed for a multi-objective programming. Under the introduced generalization of higher-order approximate invexities assumptions, we prove that the solutions of generalized vector variational-like inequalities in terms of the generalized Jacobian are the generalized quasi-efficient solutions of nonsmooth multi-objective programming problems. Moreover, the equivalent conditions are presented, namely, a vector critical point is a weakly quasi-efficient solution of higher order with respect to a function.

Novel adeno-associated viral vector delivering the utrophin gene regulator jazz counteracts dystrophic pathology in mdx mice.

PubMed

Strimpakos, Georgios; Corbi, Nicoletta; Pisani, Cinzia; Di Certo, Maria Grazia; Onori, Annalisa; Luvisetto, Siro; Severini, Cinzia; Gabanella, Francesca; Monaco, Lucia; Mattei, Elisabetta; Passananti, Claudio

2014-09-01

Over-expression of the dystrophin-related gene utrophin represents a promising therapeutic strategy for Duchenne muscular dystrophy (DMD). The strategy is based on the ability of utrophin to functionally replace defective dystrophin. We developed the artificial zinc finger transcription factor "Jazz" that up-regulates both the human and mouse utrophin promoter. We observed a significant recovery of muscle strength in dystrophic Jazz-transgenic mdx mice. Here we demonstrate the efficacy of an experimental gene therapy based on the systemic delivery of Jazz gene in mdx mice by adeno-associated virus (AAV). AAV serotype 8 was chosen on the basis of its high affinity for skeletal muscle. Muscle-specific expression of the therapeutic Jazz gene was enhanced by adding the muscle α-actin promoter to the AAV vector (mAAV). Injection of mAAV8-Jazz viral preparations into mdx mice resulted in muscle-specific Jazz expression coupled with up-regulation of the utrophin gene. We show a significant recovery from the dystrophic phenotype in mAAV8-Jazz-treated mdx mice. Histological and physiological analysis revealed a reduction of fiber necrosis and inflammatory cell infiltration associated with functional recovery in muscle contractile force. The combination of ZF-ATF technology with the AAV delivery can open a new avenue to obtain a therapeutic strategy for treatment of DMD. © 2014 Wiley Periodicals, Inc.

Analysis of regional crustal magnetization in Vector Cartesian Harmonics

NASA Astrophysics Data System (ADS)

Gubbins, D.; Ivers, D. J.; Williams, S.

2017-12-01

We introduce a set of basis functions for analysing magnetization in a plane layer, called Vector Cartesian Harmonics, that separate the part of the magnetization responsible for generating the external potential field from the part that generates no observable field. They are counterparts of similar functions defined on a sphere, Vector Spherical Harmonics, which we introduced earlier for magnetization in a spherical shell. We expand four example magnetizations in these functions and determine which parts are responsible for the observed magnetic field above the layer. For a point dipole, the component of magnetization responsible for the external potential field is the sum of a point dipole of half strength and a distributed magnetization that gives the same field. The dipping prism has no magnetic field if magnetized along strike; otherwise it, like the point dipole, has the correct dipping structure but of half the correct intensity accompanied by a distributed magnetization producing the same magnetic field. Interestingly, the distributed magnetization has singularities at the edges of the dipping slab. The buried cube is done numerically and again only a fraction of the true magnetization appears along with distributed magnetizations, strongest at the edges of the cube, making up the rest of the field. The Bishop model, a model of magnetization often used to test analysis methods, behaves similarly. In cases where the magnetization is induced by a known, non-horizontal field it is always possible to recover the vertically averaged susceptibility except for its horizontal average. Simple damped inversion of magnetic data will return only the harmonics responsible for the external field, so the analysis gives a clear indication of how any combination of induced and remanent magnetization would be returned. In practice, most interpretations of magnetic surveys are done in combination with other geological data and insights. We propose using this prior information to construct a quantitative magnetization that can be expanded in Vector Cartesian Harmonics to determine the part that generates the observed magnetic anomalies; this part can be refined to fit the data while the remaining part can only be improved using different information. The separation is simple and fast to implement using standard software because it involves only elementary manipulations of 2-dimensional Fourier transforms.

Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

PubMed

Kashyap, Kanchan L; Bajpai, Manish K; Khanna, Pritee; Giakos, George

2018-01-01

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function. Copyright © 2017 John Wiley & Sons, Ltd.

A group matrix representation relevant to scales of measurement of clinical disease states via stratified vectors.

PubMed

Sawamura, Jitsuki; Morishita, Shigeru; Ishigooka, Jun

2016-02-09

Previously, we applied basic group theory and related concepts to scales of measurement of clinical disease states and clinical findings (including laboratory data). To gain a more concrete comprehension, we here apply the concept of matrix representation, which was not explicitly exploited in our previous work. Starting with a set of orthonormal vectors, called the basis, an operator Rj (an N-tuple patient disease state at the j-th session) was expressed as a set of stratified vectors representing plural operations on individual components, so as to satisfy the group matrix representation. The stratified vectors containing individual unit operations were combined into one-dimensional square matrices [Rj]s. The [Rj]s meet the matrix representation of a group (ring) as a K-algebra. Using the same-sized matrix of stratified vectors, we can also express changes in the plural set of [Rj]s. The method is demonstrated on simple examples. Despite the incompleteness of our model, the group matrix representation of stratified vectors offers a formal mathematical approach to clinical medicine, aligning it with other branches of natural science.

A Turn-Projected State-Based Conflict Resolution Algorithm

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Lewis, Timothy A.

2013-01-01

State-based conflict detection and resolution (CD&R) algorithms detect conflicts and resolve them on the basis on current state information without the use of additional intent information from aircraft flight plans. Therefore, the prediction of the trajectory of aircraft is based solely upon the position and velocity vectors of the traffic aircraft. Most CD&R algorithms project the traffic state using only the current state vectors. However, the past state vectors can be used to make a better prediction of the future trajectory of the traffic aircraft. This paper explores the idea of using past state vectors to detect traffic turns and resolve conflicts caused by these turns using a non-linear projection of the traffic state. A new algorithm based on this idea is presented and validated using a fast-time simulator developed for this study.

Vector curvaton with varying kinetic function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimopoulos, Konstantinos; Karciauskas, Mindaugas; Wagstaff, Jacques M.

2010-01-15

A new model realization of the vector curvaton paradigm is presented and analyzed. The model consists of a single massive Abelian vector field, with a Maxwell-type kinetic term. By assuming that the kinetic function and the mass of the vector field are appropriately varying during inflation, it is shown that a scale-invariant spectrum of superhorizon perturbations can be generated. These perturbations can contribute to the curvature perturbation of the Universe. If the vector field remains light at the end of inflation it is found that it can generate substantial statistical anisotropy in the spectrum and bispectrum of the curvature perturbation.more » In this case the non-Gaussianity in the curvature perturbation is predominantly anisotropic, which will be a testable prediction in the near future. If, on the other hand, the vector field is heavy at the end of inflation then it is demonstrated that particle production is approximately isotropic and the vector field alone can give rise to the curvature perturbation, without directly involving any fundamental scalar field. The parameter space for both possibilities is shown to be substantial. Finally, toy models are presented which show that the desired variation of the mass and kinetic function of the vector field can be realistically obtained, without unnatural tunings, in the context of supergravity or superstrings.« less

Direct model-based predictive control scheme without cost function for voltage source inverters with reduced common-mode voltage

NASA Astrophysics Data System (ADS)

Kim, Jae-Chang; Moon, Sung-Ki; Kwak, Sangshin

2018-04-01

This paper presents a direct model-based predictive control scheme for voltage source inverters (VSIs) with reduced common-mode voltages (CMVs). The developed method directly finds optimal vectors without using repetitive calculation of a cost function. To adjust output currents with the CMVs in the range of -Vdc/6 to +Vdc/6, the developed method uses voltage vectors, as finite control resources, excluding zero voltage vectors which produce the CMVs in the VSI within ±Vdc/2. In a model-based predictive control (MPC), not using zero voltage vectors increases the output current ripples and the current errors. To alleviate these problems, the developed method uses two non-zero voltage vectors in one sampling step. In addition, the voltage vectors scheduled to be used are directly selected at every sampling step once the developed method calculates the future reference voltage vector, saving the efforts of repeatedly calculating the cost function. And the two non-zero voltage vectors are optimally allocated to make the output current approach the reference current as close as possible. Thus, low CMV, rapid current-following capability and sufficient output current ripple performance are attained by the developed method. The results of a simulation and an experiment verify the effectiveness of the developed method.

Models for discrete-time self-similar vector processes with application to network traffic

NASA Astrophysics Data System (ADS)

Lee, Seungsin; Rao, Raghuveer M.; Narasimha, Rajesh

2003-07-01

The paper defines self-similarity for vector processes by employing the discrete-time continuous-dilation operation which has successfully been used previously by the authors to define 1-D discrete-time stochastic self-similar processes. To define self-similarity of vector processes, it is required to consider the cross-correlation functions between different 1-D processes as well as the autocorrelation function of each constituent 1-D process in it. System models to synthesize self-similar vector processes are constructed based on the definition. With these systems, it is possible to generate self-similar vector processes from white noise inputs. An important aspect of the proposed models is that they can be used to synthesize various types of self-similar vector processes by choosing proper parameters. Additionally, the paper presents evidence of vector self-similarity in two-channel wireless LAN data and applies the aforementioned systems to simulate the corresponding network traffic traces.

A Note on a Sampling Theorem for Functions over GF(q)n Domain

NASA Astrophysics Data System (ADS)

Ukita, Yoshifumi; Saito, Tomohiko; Matsushima, Toshiyasu; Hirasawa, Shigeichi

In digital signal processing, the sampling theorem states that any real valued function ƒ can be reconstructed from a sequence of values of ƒ that are discretely sampled with a frequency at least twice as high as the maximum frequency of the spectrum of ƒ. This theorem can also be applied to functions over finite domain. Then, the range of frequencies of ƒ can be expressed in more detail by using a bounded set instead of the maximum frequency. A function whose range of frequencies is confined to a bounded set is referred to as bandlimited function. And a sampling theorem for bandlimited functions over Boolean domain has been obtained. Here, it is important to obtain a sampling theorem for bandlimited functions not only over Boolean domain (GF(q)n domain) but also over GF(q)n domain, where q is a prime power and GF(q) is Galois field of order q. For example, in experimental designs, although the model can be expressed as a linear combination of the Fourier basis functions and the levels of each factor can be represented by GF(q)n, the number of levels often take a value greater than two. However, the sampling theorem for bandlimited functions over GF(q)n domain has not been obtained. On the other hand, the sampling points are closely related to the codewords of a linear code. However, the relation between the parity check matrix of a linear code and any distinct error vectors has not been obtained, although it is necessary for understanding the meaning of the sampling theorem for bandlimited functions. In this paper, we generalize the sampling theorem for bandlimited functions over Boolean domain to a sampling theorem for bandlimited functions over GF(q)n domain. We also present a theorem for the relation between the parity check matrix of a linear code and any distinct error vectors. Lastly, we clarify the relation between the sampling theorem for functions over GF(q)n domain and linear codes.

Alterations to the orientation of the ground reaction force vector affect sprint acceleration performance in team sports athletes.

PubMed

Bezodis, Neil E; North, Jamie S; Razavet, Jane L

2017-09-01

A more horizontally oriented ground reaction force vector is related to higher levels of sprint acceleration performance across a range of athletes. However, the effects of acute experimental alterations to the force vector orientation within athletes are unknown. Fifteen male team sports athletes completed maximal effort 10-m accelerations in three conditions following different verbal instructions intended to manipulate the force vector orientation. Ground reaction forces (GRFs) were collected from the step nearest 5-m and stance leg kinematics at touchdown were also analysed to understand specific kinematic features of touchdown technique which may influence the consequent force vector orientation. Magnitude-based inferences were used to compare findings between conditions. There was a likely more horizontally oriented ground reaction force vector and a likely lower peak vertical force in the control condition compared with the experimental conditions. 10-m sprint time was very likely quickest in the control condition which confirmed the importance of force vector orientation for acceleration performance on a within-athlete basis. The stance leg kinematics revealed that a more horizontally oriented force vector during stance was preceded at touchdown by a likely more dorsiflexed ankle, a likely more flexed knee, and a possibly or likely greater hip extension velocity.

Decomposition-aggregation stability analysis. [for large scale dynamic systems with application to spinning Skylab control system

NASA Technical Reports Server (NTRS)

Siljak, D. D.; Weissenberger, S.; Cuk, S. M.

1973-01-01

This report presents the development and description of the decomposition aggregation approach to stability investigations of high dimension mathematical models of dynamic systems. The high dimension vector differential equation describing a large dynamic system is decomposed into a number of lower dimension vector differential equations which represent interconnected subsystems. Then a method is described by which the stability properties of each subsystem are aggregated into a single vector Liapunov function, representing the aggregate system model, consisting of subsystem Liapunov functions as components. A linear vector differential inequality is then formed in terms of the vector Liapunov function. The matrix of the model, which reflects the stability properties of the subsystems and the nature of their interconnections, is analyzed to conclude over-all system stability characteristics. The technique is applied in detail to investigate the stability characteristics of a dynamic model of a hypothetical spinning Skylab.

Regulation of human heme oxygenase in endothelial cells by using sense and antisense retroviral constructs

PubMed Central

Quan, Shuo; Yang, Liming; Abraham, Nader G.; Kappas, Attallah

2001-01-01

Our objective was to determine whether overexpression and underexpression of human heme oxygenase (HHO)-1 could be controlled on a long-term basis by introduction of the HO-1 gene in sense (S) and antisense (AS) orientation with an appropriate vector into endothelial cells. Retroviral vector (LXSN) containing viral long terminal repeat promoter-driven human HO-1 S (LSN-HHO-1) and LXSN vectors containing HHO-1 promoter (HOP)-controlled HHO-1 S and AS (LSN-HOP-HHO-1 and LSN-HOP-HHO-1-AS) sequences were constructed and used to transfect rat lung microvessel endothelial cells (RLMV cells) and human dermal microvessel endothelial cells (HMEC-1 cells). RLMV cells transduced with HHO-1 S expressed human HO-1 mRNA and HO-1 protein associated with elevation in total HO activity compared with nontransduced cells. Vector-mediated expression of HHO-1 S or AS under control of HOP resulted in effective production of HO-1 or blocked induction of endogenous human HO-1 in HMEC-1 cells, respectively. Overexpression of HO-1 AS was associated with a long-term decrease (45%) of endogenous HO-1 protein and an increase (167%) in unmetabolized exogenous heme in HMEC-1 cells. Carbon monoxide (CO) production in HO-1 S- or AS-transduced HMEC-1 cells after heme treatment was increased (159%) or decreased (50%), respectively, compared with nontransduced cells. HO-2 protein levels did not change. These findings demonstrate that HHO-1 S and AS retroviral constructs are functional in enhancing and reducing HO activity, respectively, and thus can be used to regulate cellular heme levels, the activity of heme-dependent enzymes, and the rate of heme catabolism to CO and bilirubin. PMID:11593038

Regulation of human heme oxygenase in endothelial cells by using sense and antisense retroviral constructs.

PubMed

Quan, S; Yang, L; Abraham, N G; Kappas, A

2001-10-09

Our objective was to determine whether overexpression and underexpression of human heme oxygenase (HHO)-1 could be controlled on a long-term basis by introduction of the HO-1 gene in sense (S) and antisense (AS) orientation with an appropriate vector into endothelial cells. Retroviral vector (LXSN) containing viral long terminal repeat promoter-driven human HO-1 S (LSN-HHO-1) and LXSN vectors containing HHO-1 promoter (HOP)-controlled HHO-1 S and AS (LSN-HOP-HHO-1 and LSN-HOP-HHO-1-AS) sequences were constructed and used to transfect rat lung microvessel endothelial cells (RLMV cells) and human dermal microvessel endothelial cells (HMEC-1 cells). RLMV cells transduced with HHO-1 S expressed human HO-1 mRNA and HO-1 protein associated with elevation in total HO activity compared with nontransduced cells. Vector-mediated expression of HHO-1 S or AS under control of HOP resulted in effective production of HO-1 or blocked induction of endogenous human HO-1 in HMEC-1 cells, respectively. Overexpression of HO-1 AS was associated with a long-term decrease (45%) of endogenous HO-1 protein and an increase (167%) in unmetabolized exogenous heme in HMEC-1 cells. Carbon monoxide (CO) production in HO-1 S- or AS-transduced HMEC-1 cells after heme treatment was increased (159%) or decreased (50%), respectively, compared with nontransduced cells. HO-2 protein levels did not change. These findings demonstrate that HHO-1 S and AS retroviral constructs are functional in enhancing and reducing HO activity, respectively, and thus can be used to regulate cellular heme levels, the activity of heme-dependent enzymes, and the rate of heme catabolism to CO and bilirubin.

[Construction of the eukaryotic recombinant vector and expression of the outer membrane protein LipL32 gene from Leptospira serovar Lai].

PubMed

Huang, Bi; Bao, Lang; Zhong, Qi; Shang, Zheng-ling; Zhang, Hui-dong; Zhang, Ying

2008-02-01

To construct the eukaryotic experssion vector of LipL32 gene from Leptospira serovar Lai and express the recombinant plasmid in COS-7 cell. The LipL32 gene was amplified from Leptospira strain 017 genomic DNA by PCR and cloned into pcDNA3.1, through restriction nuclease enzyme digestion. Then the recombinant plasmid was transformed into E.coli DH5alpha. After identified by nuclease digestion, PCR and sequencing analysis, the recombinant vector was transfected into COS-7 cell with lipsome. The expression of the target gene was detected by RT-PCR and Western blot. The eukaryotic experssion vector pcDNA3.1-LipL32 was successfully constructed and stably expressed in COS-7 cell. The eukaryotic recombinant vector of outer membrane protein LipL32 gene from Leptospira serovar Lai can be expressed in mammalian cell, which provides an experimental basis for the application of the Leptospira DNA vaccine.

Assessment of attenuated Salmonella vaccine strains in controlling experimental Salmonella Typhimurium infection in chickens

PubMed Central

Pei, Yanlong; Parreira, Valeria R.; Roland, Kenneth L.; Curtiss, Roy; Prescott, John F.

2014-01-01

Salmonella hold considerable promise as vaccine delivery vectors for heterologous antigens in chickens. Such vaccines have the potential additional benefit of also controlling Salmonella infection in immunized birds. As a way of selecting attenuated strains with optimal immunogenic potential as antigen delivery vectors, this study screened 20 novel Salmonella Typhimurium vaccine strains, differing in mutations associated with delayed antigen synthesis and delayed attenuation, for their efficacy in controlling colonization by virulent Salmonella Typhimurium, as well as for their persistence in the intestine and the spleen. Marked differences were observed between strains in these characteristics, which provide the basis for selection for further study as vaccine vectors. PMID:24396177

Assessment of attenuated Salmonella vaccine strains in controlling experimental Salmonella Typhimurium infection in chickens.

PubMed

Pei, Yanlong; Parreira, Valeria R; Roland, Kenneth L; Curtiss, Roy; Prescott, John F

2014-01-01

Salmonella hold considerable promise as vaccine delivery vectors for heterologous antigens in chickens. Such vaccines have the potential additional benefit of also controlling Salmonella infection in immunized birds. As a way of selecting attenuated strains with optimal immunogenic potential as antigen delivery vectors, this study screened 20 novel Salmonella Typhimurium vaccine strains, differing in mutations associated with delayed antigen synthesis and delayed attenuation, for their efficacy in controlling colonization by virulent Salmonella Typhimurium, as well as for their persistence in the intestine and the spleen. Marked differences were observed between strains in these characteristics, which provide the basis for selection for further study as vaccine vectors.

Chinese Text Summarization Algorithm Based on Word2vec

NASA Astrophysics Data System (ADS)

Chengzhang, Xu; Dan, Liu

2018-02-01

In order to extract some sentences that can cover the topic of a Chinese article, a Chinese text summarization algorithm based on Word2vec is used in this paper. Words in an article are represented as vectors trained by Word2vec, the weight of each word, the sentence vector and the weight of each sentence are calculated by combining word-sentence relationship with graph-based ranking model. Finally the summary is generated on the basis of the final sentence vector and the final weight of the sentence. The experimental results on real datasets show that the proposed algorithm has a better summarization quality compared with TF-IDF and TextRank.

Steganalysis of recorded speech

NASA Astrophysics Data System (ADS)

Johnson, Micah K.; Lyu, Siwei; Farid, Hany

2005-03-01

Digital audio provides a suitable cover for high-throughput steganography. At 16 bits per sample and sampled at a rate of 44,100 Hz, digital audio has the bit-rate to support large messages. In addition, audio is often transient and unpredictable, facilitating the hiding of messages. Using an approach similar to our universal image steganalysis, we show that hidden messages alter the underlying statistics of audio signals. Our statistical model begins by building a linear basis that captures certain statistical properties of audio signals. A low-dimensional statistical feature vector is extracted from this basis representation and used by a non-linear support vector machine for classification. We show the efficacy of this approach on LSB embedding and Hide4PGP. While no explicit assumptions about the content of the audio are made, our technique has been developed and tested on high-quality recorded speech.

Insertional engineering of chromosomes with Sleeping Beauty transposition: an overview.

PubMed

Grabundzija, Ivana; Izsvák, Zsuzsanna; Ivics, Zoltán

2011-01-01

Novel genetic tools and mutagenesis strategies based on the Sleeping Beauty (SB) transposable element are currently under development with a vision to link primary DNA sequence information to gene functions in vertebrate models. By virtue of its inherent capacity to insert into DNA, the SB transposon can be developed into powerful tools for chromosomal manipulations. Mutagenesis screens based on SB have numerous advantages including high throughput and easy identification of mutated alleles. Forward genetic approaches based on insertional mutagenesis by engineered SB transposons have the advantage of providing insight into genetic networks and pathways based on phenotype. Indeed, the SB transposon has become a highly instrumental tool to induce tumors in experimental animals in a tissue-specific -manner with the aim of uncovering the genetic basis of diverse cancers. Here, we describe a battery of mutagenic cassettes that can be applied in conjunction with SB transposon vectors to mutagenize genes, and highlight versatile experimental strategies for the generation of engineered chromosomes for loss-of-function as well as gain-of-function mutagenesis for functional gene annotation in vertebrate models.

Relativistic distribution function for particles with spin at local thermodynamical equilibrium

DOE Office of Scientific and Technical Information (OSTI.GOV)

Becattini, F., E-mail: becattini@fi.infn.it; INFN Sezione di Firenze, Florence; Universität Frankfurt, Frankfurt am Main

2013-11-15

We present an extension of relativistic single-particle distribution function for weakly interacting particles at local thermodynamical equilibrium including spin degrees of freedom, for massive spin 1/2 particles. We infer, on the basis of the global equilibrium case, that at local thermodynamical equilibrium particles acquire a net polarization proportional to the vorticity of the inverse temperature four-vector field. The obtained formula for polarization also implies that a steady gradient of temperature entails a polarization orthogonal to particle momentum. The single-particle distribution function in momentum space extends the so-called Cooper–Frye formula to particles with spin 1/2 and allows us to predict theirmore » polarization in relativistic heavy ion collisions at the freeze-out. -- Highlights: •Single-particle distribution function in local thermodynamical equilibrium with spin. •Polarization of spin 1/2 particles in a fluid at local thermodynamical equilibrium. •Prediction of a new effect: a steady gradient of temperature induces a polarization. •Application to the calculation of polarization in relativistic heavy ion collisions.« less

Circularly-symmetric complex normal ratio distribution for scalar transmissibility functions. Part I: Fundamentals

NASA Astrophysics Data System (ADS)

Yan, Wang-Ji; Ren, Wei-Xin

2016-12-01

Recent advances in signal processing and structural dynamics have spurred the adoption of transmissibility functions in academia and industry alike. Due to the inherent randomness of measurement and variability of environmental conditions, uncertainty impacts its applications. This study is focused on statistical inference for raw scalar transmissibility functions modeled as complex ratio random variables. The goal is achieved through companion papers. This paper (Part I) is dedicated to dealing with a formal mathematical proof. New theorems on multivariate circularly-symmetric complex normal ratio distribution are proved on the basis of principle of probabilistic transformation of continuous random vectors. The closed-form distributional formulas for multivariate ratios of correlated circularly-symmetric complex normal random variables are analytically derived. Afterwards, several properties are deduced as corollaries and lemmas to the new theorems. Monte Carlo simulation (MCS) is utilized to verify the accuracy of some representative cases. This work lays the mathematical groundwork to find probabilistic models for raw scalar transmissibility functions, which are to be expounded in detail in Part II of this study.

A Real-Time Phase Vector Display for EEG Monitoring

NASA Technical Reports Server (NTRS)

Finger, Herbert J.; Anliker, James E.; Rimmer, Tamara

1973-01-01

A real-time, computer-based, phase vector display system has been developed which will output a vector whose phase is equal to the delay between a trigger and the peak of a function which is quasi-coherent with respect to the trigger. The system also contains a sliding averager which enables the operator to average successive trials before calculating the phase vector. Data collection, averaging and display generation are performed on a LINC-8 computer. Output displays appear on several X-Y CRT display units and on a kymograph camera/oscilloscope unit which is used to generate photographs of time-varying phase vectors or contourograms of time-varying averages of input functions.

Comparative Analysis of the Magnitude, Quality, Phenotype and Protective Capacity of SIV Gag-Specific CD8+ T Cells Following Human-, Simian- and Chimpanzee-Derived Recombinant Adenoviral Vector Immunisation

PubMed Central

Quinn, Kylie M.; Costa, Andreia Da; Yamamoto, Ayako; Berry, Dana; Lindsay, Ross W.B.; Darrah, Patricia A.; Wang, Lingshu; Cheng, Cheng; Kong, Wing-Pui; Gall, Jason G.D.; Nicosia, Alfredo; Folgori, Antonella; Colloca, Stefano; Cortese, Riccardo; Gostick, Emma; Price, David A.; Gomez, Carmen E.; Esteban, Mariano; Wyatt, Linda S.; Moss, Bernard; Morgan, Cecilia; Roederer, Mario; Bailer, Robert T.; Nabel, Gary J.; Koup, Richard A.; Seder, Robert A.

2013-01-01

Recombinant adenoviral vectors (rAds) are the most potent recombinant vaccines for eliciting CD8+ T cell-mediated immunity in humans; however, prior exposure from natural adenoviral infection can decrease such responses. Here we show low seroreactivity in humans against simian- (sAd11, sAd16), or chimpanzee-derived (chAd3, chAd63) compared to human-derived (rAd5, rAd28, rAd35) vectors across multiple geographic regions. We then compared the magnitude, quality, phenotype and protective capacity of CD8+ T cell responses in mice vaccinated with rAds encoding SIV Gag. Using a dose range (1 × 107 to 109 PU), we defined a hierarchy among rAd vectors based on the magnitude and protective capacity of CD8+ T cell responses, from most to least as: rAd5 and chAd3, rAd28 and sAd11, chAd63, sAd16, and rAd35. Selection of rAd vector or dose could modulate the proportion and/or frequency of IFNγ+TNFα+IL-2+ and KLRG1+CD127- CD8+ T cells, but strikingly ~30–80% of memory CD8+ T cells co-expressed CD127 and KLRG1. To further optimise CD8+ T cell responses, we assessed rAds as part of prime-boost regimens. Mice primed with rAds and boosted with NYVAC generated Gag-specific responses that approached ~60% of total CD8+ T cells at peak. Alternatively, priming with DNA or rAd28 and boosting with rAd5 or chAd3 induced robust and equivalent CD8+ T cell responses compared to prime or boost alone. Collectively, these data provide the immunologic basis for using specific rAd vectors alone or as part of prime-boost regimens to induce CD8+ T cells for rapid effector function or robust long-term memory, respectively. PMID:23390298

Development of Cell Models as a Basis for Bioreactor Design for Genetically Modified Bacteria

DTIC Science & Technology

1986-10-30

of future behavior based on specifying the current state vector . Generally a total population greater than 10,000 is sufficient to allow treatment of...specifying the current state vector (essentially values for all variables in the model). Deterministic models become increasingly valid as the number of...host I A) and therein PARASItIS converts the host’s biomaterial or activities into its own + A and B are in physical contact. SYMBIOSIS (or perhaps Oi

Implementation details of the coupled QMR algorithm

NASA Technical Reports Server (NTRS)

Freund, Roland W.; Nachtigal, Noel M.

1992-01-01

The original quasi-minimal residual method (QMR) relies on the three-term look-ahead Lanczos process, to generate basis vectors for the underlying Krylov subspaces. However, empirical observations indicate that, in finite precision arithmetic, three-term vector recurrences are less robust than mathematically equivalent coupled two-term recurrences. Therefore, we recently proposed a new implementation of the QMR method based on a coupled two-term look-ahead Lanczos procedure. In this paper, we describe implementation details of this coupled QMR algorithm, and we present results of numerical experiments.

Using trees to compute approximate solutions to ordinary differential equations exactly

NASA Technical Reports Server (NTRS)

Grossman, Robert

1991-01-01

Some recent work is reviewed which relates families of trees to symbolic algorithms for the exact computation of series which approximate solutions of ordinary differential equations. It turns out that the vector space whose basis is the set of finite, rooted trees carries a natural multiplication related to the composition of differential operators, making the space of trees an algebra. This algebraic structure can be exploited to yield a variety of algorithms for manipulating vector fields and the series and algebras they generate.

Local Gram-Schmidt and covariant Lyapunov vectors and exponents for three harmonic oscillator problems

NASA Astrophysics Data System (ADS)

Hoover, Wm. G.; Hoover, Carol G.

2012-02-01

We compare the Gram-Schmidt and covariant phase-space-basis-vector descriptions for three time-reversible harmonic oscillator problems, in two, three, and four phase-space dimensions respectively. The two-dimensional problem can be solved analytically. The three-dimensional and four-dimensional problems studied here are simultaneously chaotic, time-reversible, and dissipative. Our treatment is intended to be pedagogical, for use in an updated version of our book on Time Reversibility, Computer Simulation, and Chaos. Comments are very welcome.

Algorithm for detection the QRS complexes based on support vector machine

NASA Astrophysics Data System (ADS)

Van, G. V.; Podmasteryev, K. V.

2017-11-01

The efficiency of computer ECG analysis depends on the accurate detection of QRS-complexes. This paper presents an algorithm for QRS complex detection based of support vector machine (SVM). The proposed algorithm is evaluated on annotated standard databases such as MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity Se = 98.32% and specificity Sp = 95.46% for MIT-BIH Arrhythmia database. This algorithm can be used as the basis for the software to diagnose electrical activity of the heart.

Shape functions for velocity interpolation in general hexahedral cells

USGS Publications Warehouse

Naff, R.L.; Russell, T.F.; Wilson, J.D.

2002-01-01

Numerical methods for grids with irregular cells require discrete shape functions to approximate the distribution of quantities across cells. For control-volume mixed finite-element (CVMFE) methods, vector shape functions approximate velocities and vector test functions enforce a discrete form of Darcy's law. In this paper, a new vector shape function is developed for use with irregular, hexahedral cells (trilinear images of cubes). It interpolates velocities and fluxes quadratically, because as shown here, the usual Piola-transformed shape functions, which interpolate linearly, cannot match uniform flow on general hexahedral cells. Truncation-error estimates for the shape function are demonstrated. CVMFE simulations of uniform and non-uniform flow with irregular meshes show first- and second-order convergence of fluxes in the L2 norm in the presence and absence of singularities, respectively.

Identifying Neural Patterns of Functional Dyspepsia Using Multivariate Pattern Analysis: A Resting-State fMRI Study

PubMed Central

Liu, Peng; Qin, Wei; Wang, Jingjing; Zeng, Fang; Zhou, Guangyu; Wen, Haixia; von Deneen, Karen M.; Liang, Fanrong; Gong, Qiyong; Tian, Jie

2013-01-01

Background Previous imaging studies on functional dyspepsia (FD) have focused on abnormal brain functions during special tasks, while few studies concentrated on the resting-state abnormalities of FD patients, which might be potentially valuable to provide us with direct information about the neural basis of FD. The main purpose of the current study was thereby to characterize the distinct patterns of resting-state function between FD patients and healthy controls (HCs). Methodology/Principal Findings Thirty FD patients and thirty HCs were enrolled and experienced 5-mintue resting-state scanning. Based on the support vector machine (SVM), we applied multivariate pattern analysis (MVPA) to investigate the differences of resting-state function mapped by regional homogeneity (ReHo). A classifier was designed by using the principal component analysis and the linear SVM. Permutation test was then employed to identify the significant contribution to the final discrimination. The results displayed that the mean classifier accuracy was 86.67%, and highly discriminative brain regions mainly included the prefrontal cortex (PFC), orbitofrontal cortex (OFC), supplementary motor area (SMA), temporal pole (TP), insula, anterior/middle cingulate cortex (ACC/MCC), thalamus, hippocampus (HIPP)/parahippocamus (ParaHIPP) and cerebellum. Correlation analysis revealed significant correlations between ReHo values in certain regions of interest (ROI) and the FD symptom severity and/or duration, including the positive correlations between the dmPFC, pACC and the symptom severity; whereas, the positive correlations between the MCC, OFC, insula, TP and FD duration. Conclusions These findings indicated that significantly distinct patterns existed between FD patients and HCs during the resting-state, which could expand our understanding of the neural basis of FD. Meanwhile, our results possibly showed potential feasibility of functional magnetic resonance imaging diagnostic assay for FD. PMID:23874543

Developing a CD-CBM Anticipatory Approach for Cavitation - Defining a Model Descriptor Consistent Between Processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allgood, G.O.; Dress, W.B.; Kercel, S.W.

1999-05-10

A major problem with cavitation in pumps and other hydraulic devices is that there is no effective method for detecting or predicting its inception. The traditional approach is to declare the pump in cavitation when the total head pressure drops by some arbitrary value (typically 3o/0) in response to a reduction in pump inlet pressure. However, the pump is already cavitating at this point. A method is needed in which cavitation events are captured as they occur and characterized by their process dynamics. The object of this research was to identify specific features of cavitation that could be used asmore » a model-based descriptor in a context-dependent condition-based maintenance (CD-CBM) anticipatory prognostic and health assessment model. This descriptor was based on the physics of the phenomena, capturing the salient features of the process dynamics. An important element of this concept is the development and formulation of the extended process feature vector @) or model vector. Thk model-based descriptor encodes the specific information that describes the phenomena and its dynamics and is formulated as a data structure consisting of several elements. The first is a descriptive model abstracting the phenomena. The second is the parameter list associated with the functional model. The third is a figure of merit, a single number between [0,1] representing a confidence factor that the functional model and parameter list actually describes the observed data. Using this as a basis and applying it to the cavitation problem, any given location in a flow loop will have this data structure, differing in value but not content. The extended process feature vector is formulated as follows: E`> [ , {parameter Iist}, confidence factor]. (1) For this study, the model that characterized cavitation was a chirped-exponentially decaying sinusoid. Using the parameters defined by this model, the parameter list included frequency, decay, and chirp rate. Based on this, the process feature vector has the form: @=> [, {01 = a, ~= b, ~ = c}, cf = 0.80]. (2) In this experiment a reversible catastrophe was examined. The reason for this is that the same catastrophe could be repeated to ensure the statistical significance of the data.« less

Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology

PubMed Central

Nagarajan, Mahesh B.; Huber, Markus B.; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2014-01-01

Objective While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. Methods and Materials We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. Results Of the feature vectors investigated, the best performance was observed with Minkowski functional ’perimeter’ while comparable performance was observed with ’area’. Of the dimension reduction algorithms tested with ’perimeter’, the best performance was observed with Sammon’s mapping (0.84 ± 0.10) while comparable performance was achieved with exploratory observation machine (0.82 ± 0.09) and principal component analysis (0.80 ± 0.10). Conclusions The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction. PMID:24355697

Estimation of regionalized compositions: A comparison of three methods

USGS Publications Warehouse

Pawlowsky, V.; Olea, R.A.; Davis, J.C.

1995-01-01

A regionalized composition is a random vector function whose components are positive and sum to a constant at every point of the sampling region. Consequently, the components of a regionalized composition are necessarily spatially correlated. This spatial dependence-induced by the constant sum constraint-is a spurious spatial correlation and may lead to misinterpretations of statistical analyses. Furthermore, the cross-covariance matrices of the regionalized composition are singular, as is the coefficient matrix of the cokriging system of equations. Three methods of performing estimation or prediction of a regionalized composition at unsampled points are discussed: (1) the direct approach of estimating each variable separately; (2) the basis method, which is applicable only when a random function is available that can he regarded as the size of the regionalized composition under study; (3) the logratio approach, using the additive-log-ratio transformation proposed by J. Aitchison, which allows statistical analysis of compositional data. We present a brief theoretical review of these three methods and compare them using compositional data from the Lyons West Oil Field in Kansas (USA). It is shown that, although there are no important numerical differences, the direct approach leads to invalid results, whereas the basis method and the additive-log-ratio approach are comparable. ?? 1995 International Association for Mathematical Geology.

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Hoang, Nhat-Duc

2017-09-01

In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

Design of 2D time-varying vector fields.

PubMed

Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D; Zhang, Eugene

2012-10-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects.

Adsorption and dissociation of molecular oxygen on α-Pu (0 2 0) surface: A density functional study

NASA Astrophysics Data System (ADS)

Wang, Jianguang; Ray, Asok K.

2011-09-01

Molecular and dissociative oxygen adsorptions on the α-Pu (0 2 0) surface have been systematically studied using the full-potential linearized augmented-plane-wave plus local orbitals (FP-LAPW+lo) basis method and the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. Chemisorption energies have been optimized for the distance of the admolecule from the Pu surface and the bond length of O-O atoms for four adsorption sites and three approaches of O 2 admolecule to the (0 2 0) surface. Chemisorption energies have been calculated at the scalar relativistic level with no spin-orbit coupling (NSOC) and at the fully relativistic level with spin-orbit coupling (SOC). Dissociative adsorptions are found at the two horizontal approaches (O 2 is parallel to the surface and perpendicular/parallel to a lattice vector). Hor2 (O 2 is parallel to the surface and perpendicular to a lattice vector) approach at the one-fold top site is the most stable adsorption site, with chemisorption energies of 8.048 and 8.415 eV for the NSOC and SOC cases, respectively, and an OO separation of 3.70 Å. Molecular adsorption occurs at the Vert (O 2 is vertical to the surface) approach of each adsorption site. The calculated work functions and net spin magnetic moments, respectively, increase and decrease in all cases upon chemisorption compared to the clean surface. The partial charges inside the muffin-tins, the difference charge density distributions, and the local density of states have been used to investigate the Pu-admolecule electronic structures and bonding mechanisms.

The mean-square error optimal linear discriminant function and its application to incomplete data vectors

NASA Technical Reports Server (NTRS)

Walker, H. F.

1979-01-01

In many pattern recognition problems, data vectors are classified although one or more of the data vector elements are missing. This problem occurs in remote sensing when the ground is obscured by clouds. Optimal linear discrimination procedures for classifying imcomplete data vectors are discussed.

Re-engineering adenovirus vector systems to enable high-throughput analyses of gene function.

PubMed

Stanton, Richard J; McSharry, Brian P; Armstrong, Melanie; Tomasec, Peter; Wilkinson, Gavin W G

2008-12-01

With the enhanced capacity of bioinformatics to interrogate extensive banks of sequence data, more efficient technologies are needed to test gene function predictions. Replication-deficient recombinant adenovirus (Ad) vectors are widely used in expression analysis since they provide for extremely efficient expression of transgenes in a wide range of cell types. To facilitate rapid, high-throughput generation of recombinant viruses, we have re-engineered an adenovirus vector (designated AdZ) to allow single-step, directional gene insertion using recombineering technology. Recombineering allows for direct insertion into the Ad vector of PCR products, synthesized sequences, or oligonucleotides encoding shRNAs without requirement for a transfer vector Vectors were optimized for high-throughput applications by making them "self-excising" through incorporating the I-SceI homing endonuclease into the vector removing the need to linearize vectors prior to transfection into packaging cells. AdZ vectors allow genes to be expressed in their native form or with strep, V5, or GFP tags. Insertion of tetracycline operators downstream of the human cytomegalovirus major immediate early (HCMV MIE) promoter permits silencing of transgenes in helper cells expressing the tet repressor thus making the vector compatible with the cloning of toxic gene products. The AdZ vector system is robust, straightforward, and suited to both sporadic and high-throughput applications.

Segmentation of discrete vector fields.

PubMed

Li, Hongyu; Chen, Wenbin; Shen, I-Fan

2006-01-01

In this paper, we propose an approach for 2D discrete vector field segmentation based on the Green function and normalized cut. The method is inspired by discrete Hodge Decomposition such that a discrete vector field can be broken down into three simpler components, namely, curl-free, divergence-free, and harmonic components. We show that the Green Function Method (GFM) can be used to approximate the curl-free and the divergence-free components to achieve our goal of the vector field segmentation. The final segmentation curves that represent the boundaries of the influence region of singularities are obtained from the optimal vector field segmentations. These curves are composed of piecewise smooth contours or streamlines. Our method is applicable to both linear and nonlinear discrete vector fields. Experiments show that the segmentations obtained using our approach essentially agree with human perceptual judgement.

Mobilome and genetic modification of bifidobacteria.

PubMed

Guglielmetti, S; Mayo, B; Álvarez-Martín, P

2013-06-01

Until recently, proper development of molecular studies in Bifidobacterium species has been hampered by growth difficulties, because of their exigent nutritive requirements, oxygen sensitivity and lack of efficient genetic tools. These studies, however, are critical to uncover the cross-talk between bifidobacteria and their hosts' cells and to prove unequivocally the supposed beneficial effects provided through the endogenous bifidobacterial populations or after ingestion as probiotics. The genome sequencing projects of different bifidobacterial strains have provided a wealth of genetic data that will be of much help in deciphering the molecular basis of the physiological properties of bifidobacteria. To this end, the purposeful development of stable cloning and expression vectors based on robust replicons - either from temperate phages or resident plasmids - is still needed. This review addresses the current knowledge on the mobile genetic elements of bifidobacteria (prophages, plasmids and transposons) and summarises the different types of vectors already available, together with the transformation procedures for introducing DNA into the cells. It also covers recent molecular studies performed with such vectors and incipient results on the genetic modification of these organisms, establishing the basis that would allow the use of bifidobacteria for future biotechnological applications.

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

A regularization method for extrapolation of solar potential magnetic fields

NASA Technical Reports Server (NTRS)

Gary, G. A.; Musielak, Z. E.

1992-01-01

The mathematical basis of a Tikhonov regularization method for extrapolating the chromospheric-coronal magnetic field using photospheric vector magnetograms is discussed. The basic techniques show that the Cauchy initial value problem can be formulated for potential magnetic fields. The potential field analysis considers a set of linear, elliptic partial differential equations. It is found that, by introducing an appropriate smoothing of the initial data of the Cauchy potential problem, an approximate Fourier integral solution is found, and an upper bound to the error in the solution is derived. This specific regularization technique, which is a function of magnetograph measurement sensitivities, provides a method to extrapolate the potential magnetic field above an active region into the chromosphere and low corona.

Spectral functions with the density matrix renormalization group: Krylov-space approach for correction vectors

DOE Office of Scientific and Technical Information (OSTI.GOV)

None, None

Frequency-dependent correlations, such as the spectral function and the dynamical structure factor, help illustrate condensed matter experiments. Within the density matrix renormalization group (DMRG) framework, an accurate method for calculating spectral functions directly in frequency is the correction-vector method. The correction vector can be computed by solving a linear equation or by minimizing a functional. Our paper proposes an alternative to calculate the correction vector: to use the Krylov-space approach. This paper also studies the accuracy and performance of the Krylov-space approach, when applied to the Heisenberg, the t-J, and the Hubbard models. The cases we studied indicate that themore » Krylov-space approach can be more accurate and efficient than the conjugate gradient, and that the error of the former integrates best when a Krylov-space decomposition is also used for ground state DMRG.« less

Reconstruction of the domain orientation distribution function of polycrystalline PZT ceramics using vector piezoresponse force microscopy.

PubMed

Kratzer, Markus; Lasnik, Michael; Röhrig, Sören; Teichert, Christian; Deluca, Marco

2018-01-11

Lead zirconate titanate (PZT) is one of the prominent materials used in polycrystalline piezoelectric devices. Since the ferroelectric domain orientation is the most important parameter affecting the electromechanical performance, analyzing the domain orientation distribution is of great importance for the development and understanding of improved piezoceramic devices. Here, vector piezoresponse force microscopy (vector-PFM) has been applied in order to reconstruct the ferroelectric domain orientation distribution function of polished sections of device-ready polycrystalline lead zirconate titanate (PZT) material. A measurement procedure and a computer program based on the software Mathematica have been developed to automatically evaluate the vector-PFM data for reconstructing the domain orientation function. The method is tested on differently in-plane and out-of-plane poled PZT samples, and the results reveal the expected domain patterns and allow determination of the polarization orientation distribution function at high accuracy.

Spectral functions with the density matrix renormalization group: Krylov-space approach for correction vectors

DOE PAGES

None, None

2016-11-21

Frequency-dependent correlations, such as the spectral function and the dynamical structure factor, help illustrate condensed matter experiments. Within the density matrix renormalization group (DMRG) framework, an accurate method for calculating spectral functions directly in frequency is the correction-vector method. The correction vector can be computed by solving a linear equation or by minimizing a functional. Our paper proposes an alternative to calculate the correction vector: to use the Krylov-space approach. This paper also studies the accuracy and performance of the Krylov-space approach, when applied to the Heisenberg, the t-J, and the Hubbard models. The cases we studied indicate that themore » Krylov-space approach can be more accurate and efficient than the conjugate gradient, and that the error of the former integrates best when a Krylov-space decomposition is also used for ground state DMRG.« less

Simple cloning strategy using GFPuv gene as positive/negative indicator.

PubMed

Miura, Hiromi; Inoko, Hidetoshi; Inoue, Ituro; Tanaka, Masafumi; Sato, Masahiro; Ohtsuka, Masato

2011-09-15

Because construction of expression vectors is the first requisite in the functional analysis of genes, development of simple cloning systems is a major requirement during the postgenomic era. In the current study, we developed cloning vectors for gain- or loss-of-function studies by using the GFPuv gene as a positive/negative indicator of cloning. These vectors allow us to easily detect correct clones and obtain expression vectors from a simple procedure by means of the combined use of the GFPuv gene and a type IIS restriction enzyme. Copyright © 2011 Elsevier Inc. All rights reserved.

Stochastic subset selection for learning with kernel machines.

PubMed

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics.

PubMed

Marelli, Marco; Baroni, Marco

2015-07-01

The present work proposes a computational model of morpheme combination at the meaning level. The model moves from the tenets of distributional semantics, and assumes that word meanings can be effectively represented by vectors recording their co-occurrence with other words in a large text corpus. Given this assumption, affixes are modeled as functions (matrices) mapping stems onto derived forms. Derived-form meanings can be thought of as the result of a combinatorial procedure that transforms the stem vector on the basis of the affix matrix (e.g., the meaning of nameless is obtained by multiplying the vector of name with the matrix of -less). We show that this architecture accounts for the remarkable human capacity of generating new words that denote novel meanings, correctly predicting semantic intuitions about novel derived forms. Moreover, the proposed compositional approach, once paired with a whole-word route, provides a new interpretative framework for semantic transparency, which is here partially explained in terms of ease of the combinatorial procedure and strength of the transformation brought about by the affix. Model-based predictions are in line with the modulation of semantic transparency on explicit intuitions about existing words, response times in lexical decision, and morphological priming. In conclusion, we introduce a computational model to account for morpheme combination at the meaning level. The model is data-driven, theoretically sound, and empirically supported, and it makes predictions that open new research avenues in the domain of semantic processing. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Complex Modulation of the Aedes aegypti Transcriptome in Response to Dengue Virus Infection

PubMed Central

Bonizzoni, Mariangela; Dunn, W. Augustine; Campbell, Corey L.; Olson, Ken E.; Marinotti, Osvaldo; James, Anthony A.

2012-01-01

Dengue fever is the most important arboviral disease world-wide, with Aedes aegypti being the major vector. Interactions between the mosquito host and dengue viruses (DENV) are complex and vector competence varies among geographically-distinct Ae. aegypti populations. Additionally, dengue is caused by four antigenically-distinct viral serotypes (DENV1–4), each with multiple genotypes. Each virus genotype interacts differently with vertebrate and invertebrate hosts. Analyses of alterations in mosquito transcriptional profiles during DENV infection are expected to provide the basis for identifying networks of genes involved in responses to viruses and contribute to the molecular-genetic understanding of vector competence. In addition, this knowledge is anticipated to support the development of novel disease-control strategies. RNA-seq technology was used to assess genome-wide changes in transcript abundance at 1, 4 and 14 days following DENV2 infection in carcasses, midguts and salivary glands of the Ae. aegypti Chetumal strain. DENV2 affected the expression of 397 Ae. aegypti genes, most of which were down-regulated by viral infection. Differential accumulation of transcripts was mainly tissue- and time-specific. Comparisons of our data with other published reports reveal conservation of functional classes, but limited concordance of specific mosquito genes responsive to DENV2 infection. These results indicate the necessity of additional studies of mosquito-DENV interactions, specifically those focused on recently-derived mosquito strains with multiple dengue virus serotypes and genotypes. PMID:23209765

The Adenovirus Genome Contributes to the Structural Stability of the Virion

PubMed Central

Saha, Bratati; Wong, Carmen M.; Parks, Robin J.

2014-01-01

Adenovirus (Ad) vectors are currently the most commonly used platform for therapeutic gene delivery in human gene therapy clinical trials. Although these vectors are effective, many researchers seek to further improve the safety and efficacy of Ad-based vectors through detailed characterization of basic Ad biology relevant to its function as a vector system. Most Ad vectors are deleted of key, or all, viral protein coding sequences, which functions to not only prevent virus replication but also increase the cloning capacity of the vector for foreign DNA. However, radical modifications to the genome size significantly decreases virion stability, suggesting that the virus genome plays a role in maintaining the physical stability of the Ad virion. Indeed, a similar relationship between genome size and virion stability has been noted for many viruses. This review discusses the impact of the genome size on Ad virion stability and emphasizes the need to consider this aspect of virus biology in Ad-based vector design. PMID:25254384

Cone-Specific Promoters for Gene Therapy of Achromatopsia and Other Retinal Diseases

PubMed Central

Ye, Guo-Jie; Budzynski, Ewa; Sonnentag, Peter; Nork, T. Michael; Sheibani, Nader; Gurel, Zafer; Boye, Sanford L.; Peterson, James J.; Boye, Shannon E.; Hauswirth, William W.; Chulay, Jeffrey D.

2016-01-01

Adeno-associated viral (AAV) vectors containing cone-specific promoters have rescued cone photoreceptor function in mouse and dog models of achromatopsia, but cone-specific promoters have not been optimized for use in primates. Using AAV vectors administered by subretinal injection, we evaluated a series of promoters based on the human L-opsin promoter, or a chimeric human cone transducin promoter, for their ability to drive gene expression of green fluorescent protein (GFP) in mice and nonhuman primates. Each of these promoters directed high-level GFP expression in mouse photoreceptors. In primates, subretinal injection of an AAV-GFP vector containing a 1.7-kb L-opsin promoter (PR1.7) achieved strong and specific GFP expression in all cone photoreceptors and was more efficient than a vector containing the 2.1-kb L-opsin promoter that was used in AAV vectors that rescued cone function in mouse and dog models of achromatopsia. A chimeric cone transducin promoter that directed strong GFP expression in mouse and dog cone photoreceptors was unable to drive GFP expression in primate cones. An AAV vector expressing a human CNGB3 gene driven by the PR1.7 promoter rescued cone function in the mouse model of achromatopsia. These results have informed the design of an AAV vector for treatment of patients with achromatopsia. PMID:26603570

Alpharetroviral Vector-mediated Gene Therapy for X-CGD: Functional Correction and Lack of Aberrant Splicing

PubMed Central

Kaufmann, Kerstin B.; Brendel, Christian; Suerth, Julia D.; Mueller-Kuller, Uta; Chen-Wichmann, Linping; Schwäble, Joachim; Pahujani, Shweta; Kunkel, Hana; Schambach, Axel; Baum, Christopher; Grez, Manuel

2013-01-01

Comparative integrome analysis has revealed that the most neutral integration pattern among retroviruses is attributed to alpharetroviruses. We chose X-linked chronic granulomatous disease (X-CGD) as model to evaluate the potential of self-inactivating (SIN) alpharetroviral vectors for gene therapy of monogenic diseases. Therefore, we combined the alpharetroviral vector backbone with the elongation factor-1α short promoter, both considered to possess a low genotoxic profile, to drive transgene (gp91phox) expression. Following efficient transduction transgene expression was sustained and provided functional correction of the CGD phenotype in a cell line model at low vector copy number. Further analysis in a murine X-CGD transplantation model revealed gene-marking of bone marrow cells and oxidase positive granulocytes in peripheral blood. Transduction of human X-CGD CD34+ cells provided functional correction up to wild-type levels and long-term expression upon transplantation into a humanized mouse model. In contrast to lentiviral vectors, no aberrantly spliced transcripts containing cellular exons fused to alpharetroviral sequences were found in transduced cells, implying that the safety profile of alpharetroviral vectors may extend beyond their neutral integration profile. Taken together, this highlights the potential of this SIN alpharetroviral system as a platform for new candidate vectors for future gene therapy of hematopoietic disorders. PMID:23207695

Cone-Specific Promoters for Gene Therapy of Achromatopsia and Other Retinal Diseases.

PubMed

Ye, Guo-Jie; Budzynski, Ewa; Sonnentag, Peter; Nork, T Michael; Sheibani, Nader; Gurel, Zafer; Boye, Sanford L; Peterson, James J; Boye, Shannon E; Hauswirth, William W; Chulay, Jeffrey D

2016-01-01

Adeno-associated viral (AAV) vectors containing cone-specific promoters have rescued cone photoreceptor function in mouse and dog models of achromatopsia, but cone-specific promoters have not been optimized for use in primates. Using AAV vectors administered by subretinal injection, we evaluated a series of promoters based on the human L-opsin promoter, or a chimeric human cone transducin promoter, for their ability to drive gene expression of green fluorescent protein (GFP) in mice and nonhuman primates. Each of these promoters directed high-level GFP expression in mouse photoreceptors. In primates, subretinal injection of an AAV-GFP vector containing a 1.7-kb L-opsin promoter (PR1.7) achieved strong and specific GFP expression in all cone photoreceptors and was more efficient than a vector containing the 2.1-kb L-opsin promoter that was used in AAV vectors that rescued cone function in mouse and dog models of achromatopsia. A chimeric cone transducin promoter that directed strong GFP expression in mouse and dog cone photoreceptors was unable to drive GFP expression in primate cones. An AAV vector expressing a human CNGB3 gene driven by the PR1.7 promoter rescued cone function in the mouse model of achromatopsia. These results have informed the design of an AAV vector for treatment of patients with achromatopsia.

Altering the selection capabilities of common cloning vectors via restriction enzyme mediated gene disruption

PubMed Central

2013-01-01

Background The cloning of gene sequences forms the basis for many molecular biological studies. One important step in the cloning process is the isolation of bacterial transformants carrying vector DNA. This involves a vector-encoded selectable marker gene, which in most cases, confers resistance to an antibiotic. However, there are a number of circumstances in which a different selectable marker is required or may be preferable. Such situations can include restrictions to host strain choice, two phase cloning experiments and mutagenesis experiments, issues that result in additional unnecessary cloning steps, in which the DNA needs to be subcloned into a vector with a suitable selectable marker. Results We have used restriction enzyme mediated gene disruption to modify the selectable marker gene of a given vector by cloning a different selectable marker gene into the original marker present in that vector. Cloning a new selectable marker into a pre-existing marker was found to change the selection phenotype conferred by that vector, which we were able to demonstrate using multiple commonly used vectors and multiple resistance markers. This methodology was also successfully applied not only to cloning vectors, but also to expression vectors while keeping the expression characteristics of the vector unaltered. Conclusions Changing the selectable marker of a given vector has a number of advantages and applications. This rapid and efficient method could be used for co-expression of recombinant proteins, optimisation of two phase cloning procedures, as well as multiple genetic manipulations within the same host strain without the need to remove a pre-existing selectable marker in a previously genetically modified strain. PMID:23497512

Combined data mining/NIR spectroscopy for purity assessment of lime juice

NASA Astrophysics Data System (ADS)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

Action Direction of Muscle Synergies in Three-Dimensional Force Space

PubMed Central

Hagio, Shota; Kouzaki, Motoki

2015-01-01

Redundancy in the musculoskeletal system was supposed to be simplified by muscle synergies, which modularly organize muscles. To clarify the underlying mechanisms of motor control using muscle synergies, it is important to examine the spatiotemporal contribution of muscle synergies in the task space. In this study, we quantified the mechanical contribution of muscle synergies as considering spatiotemporal correlation between the activation of muscle synergies and endpoint force fluctuations. Subjects performed isometric force generation in the three-dimensional force space. The muscle-weighting vectors of muscle synergies and their activation traces across different trials were extracted from electromyogram data using decomposing technique. We then estimated mechanical contribution of muscle synergies across each trial based on cross-correlation analysis. The contributing vectors were averaged for all trials, and the averaging was defined as action direction (AD) of muscle synergies. As a result, we extracted approximately five muscle synergies. The ADs of muscle synergies mainly depended on the anatomical functions of their weighting muscles. Furthermore, the AD of each muscle indicated the synchronous activation of muscles, which composed of the same muscle synergy. These results provide the spatiotemporal characteristics of muscle synergies as neural basis. PMID:26618156

Action Direction of Muscle Synergies in Three-Dimensional Force Space.

PubMed

Hagio, Shota; Kouzaki, Motoki

2015-01-01

Redundancy in the musculoskeletal system was supposed to be simplified by muscle synergies, which modularly organize muscles. To clarify the underlying mechanisms of motor control using muscle synergies, it is important to examine the spatiotemporal contribution of muscle synergies in the task space. In this study, we quantified the mechanical contribution of muscle synergies as considering spatiotemporal correlation between the activation of muscle synergies and endpoint force fluctuations. Subjects performed isometric force generation in the three-dimensional force space. The muscle-weighting vectors of muscle synergies and their activation traces across different trials were extracted from electromyogram data using decomposing technique. We then estimated mechanical contribution of muscle synergies across each trial based on cross-correlation analysis. The contributing vectors were averaged for all trials, and the averaging was defined as action direction (AD) of muscle synergies. As a result, we extracted approximately five muscle synergies. The ADs of muscle synergies mainly depended on the anatomical functions of their weighting muscles. Furthermore, the AD of each muscle indicated the synchronous activation of muscles, which composed of the same muscle synergy. These results provide the spatiotemporal characteristics of muscle synergies as neural basis.

Odor Coding in the Maxillary Palp of the Malaria Vector Mosquito Anopheles gambiae

PubMed Central

Lu, Tan; Qiu, Yu Tong; Wang, Guirong; Kwon, Jae Young; Rutzler, Michael; Kwon, Hyung-Wook; Pitts, R. Jason; van Loon, Joop J.A.; Takken, Willem; Carlson, John R.; Zwiebel, Laurence J.

2011-01-01

Summary Background Many species of mosquitoes, including the major malaria vector Anopheles gambiae, utilize carbon dioxide (CO2) and 1-octen-3-ol as olfactory cues in host-seeking behaviors that underlie their vectorial capacity. However, the molecular and cellular basis of such olfactory responses remains largely unknown. Results Here, we use molecular and physiological approaches coupled with systematic functional analyses to define the complete olfactory sensory map of the An. gambiae maxillary palp, an olfactory appendage that mediates the detection of these compounds. In doing so, we identify three olfactory receptor neurons (ORNs) that are organized in stereotyped triads within the maxillary-palp capitate-peg-sensillum population. One ORN is CO2-responsive and characterized by the coexpression of three receptors that confer CO2 responses, whereas the other ORNs express characteristic odorant receptors (AgORs) that are responsible for their in vivo olfactory responses. Conclusions Our results describe a complete and highly concordant map of both the molecular and cellular olfactory components on the maxillary palp of the adult female An. gambiae mosquito. These results also facilitate the understanding of how An. gambiae mosquitoes sense olfactory cues that might be exploited to compromise their ability to transmit malaria. PMID:17764944

Potential of cancer screening with serum surface-enhanced Raman spectroscopy and a support vector machine

NASA Astrophysics Data System (ADS)

Li, S. X.; Zhang, Y. J.; Zeng, Q. Y.; Li, L. F.; Guo, Z. Y.; Liu, Z. M.; Xiong, H. L.; Liu, S. H.

2014-06-01

Cancer is the most common disease to threaten human health. The ability to screen individuals with malignant tumours with only a blood sample would be greatly advantageous to early diagnosis and intervention. This study explores the possibility of discriminating between cancer patients and normal subjects with serum surface-enhanced Raman spectroscopy (SERS) and a support vector machine (SVM) through a peripheral blood sample. A total of 130 blood samples were obtained from patients with liver cancer, colonic cancer, esophageal cancer, nasopharyngeal cancer, gastric cancer, as well as 113 blood samples from normal volunteers. Several diagnostic models were built with the serum SERS spectra using SVM and principal component analysis (PCA) techniques. The results show that a diagnostic accuracy of 85.5% is acquired with a PCA algorithm, while a diagnostic accuracy of 95.8% is obtained using radial basis function (RBF), PCA-SVM methods. The results prove that a RBF kernel PCA-SVM technique is superior to PCA and conventional SVM (C-SVM) algorithms in classification serum SERS spectra. The study demonstrates that serum SERS, in combination with SVM techniques, has great potential for screening cancerous patients with any solid malignant tumour through a peripheral blood sample.

Genetically modified pigs produced with a nonviral episomal vector

PubMed Central

Manzini, Stefano; Vargiolu, Alessia; Stehle, Isa M; Bacci, Maria Laura; Cerrito, Maria Grazia; Giovannoni, Roberto; Zannoni, Augusta; Bianco, Maria Rosaria; Forni, Monica; Donini, Pierluigi; Papa, Michele; Lipps, Hans J; Lavitrano, Marialuisa

2006-01-01

Genetic modification of cells and animals is an invaluable tool for biotechnology and biomedicine. Currently, integrating vectors are used for this purpose. These vectors, however, may lead to insertional mutagenesis and variable transgene expression and can undergo silencing. Scaffold/matrix attachment region-based vectors are nonviral expression systems that replicate autonomously in mammalian cells, thereby making possible safe and reliable genetic modification of higher eukaryotic cells and organisms. In this study, genetically modified pig fetuses were produced with the scaffold/matrix attachment region-based vector pEPI, delivered to embryos by the sperm-mediated gene transfer method. The pEPI vector was detected in 12 of 18 fetuses in the different tissues analyzed and was shown to be retained as an episome. The reporter gene encoded by the pEPI vector was expressed in 9 of 12 genetically modified fetuses. In positive animals, all tissues analyzed expressed the reporter gene; moreover in these tissues, the positive cells were on the average 79%. The high percentage of EGFP-expressing cells and the absence of mosaicism have important implications for biotechnological and biomedical applications. These results are an important step forward in animal transgenesis and can provide the basis for the future development of germ-line gene therapy. PMID:17101993

Adaptation of orientation vectors of otolith-related central vestibular neurons to gravity.

PubMed

Eron, Julia N; Cohen, Bernard; Raphan, Theodore; Yakushin, Sergei B

2008-09-01

Behavioral experiments indicate that central pathways that process otolith-ocular and perceptual information have adaptive capabilities. Because polarization vectors of otolith afferents are directly related to the electro-mechanical properties of the hair cell bundle, it is unlikely that they change their direction of excitation. This indicates that the adaptation must take place in central pathways. Here we demonstrate for the first time that otolith polarization vectors of canal-otolith convergent neurons in the vestibular nuclei have adaptive capability. A total of 10 vestibular-only and vestibular-plus-saccade neurons were recorded extracellularly in two monkeys before and after they were in side-down positions for 2 h. The spatial characteristics of the otolith input were determined from the response vector orientation (RVO), which is the projection of the otolith polarization vector, onto the head horizontal plane. The RVOs had no specific orientation before animals were in side-down positions but moved toward the gravitational axis after the animals were tilted for extended periods. Vector reorientations varied from 0 to 109 degrees and were linearly related to the original deviation of the RVOs from gravity in the position of adaptation. Such reorientation of central polarization vectors could provide the basis for changes in perception and eye movements related to prolonged head tilts relative to gravity or in microgravity.

A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

PubMed Central

Zhang, Rui; Zhu, Shiping; Zhou, Qin

2016-01-01

Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models. PMID:27775660

Efficient gene transfer into nondividing cells by adeno-associated virus-based vectors.

PubMed Central

Podsakoff, G; Wong, K K; Chatterjee, S

1994-01-01

Gene transfer vectors based on adeno-associated virus (AAV) are emerging as highly promising for use in human gene therapy by virtue of their characteristics of wide host range, high transduction efficiencies, and lack of cytopathogenicity. To better define the biology of AAV-mediated gene transfer, we tested the ability of an AAV vector to efficiently introduce transgenes into nonproliferating cell populations. Cells were induced into a nonproliferative state by treatment with the DNA synthesis inhibitors fluorodeoxyuridine and aphidicolin or by contact inhibition induced by confluence and serum starvation. Cells in logarithmic growth or DNA synthesis arrest were transduced with vCWR:beta gal, an AAV-based vector encoding beta-galactosidase under Rous sarcoma virus long terminal repeat promoter control. Under each condition tested, vCWR:beta Gal expression in nondividing cells was at least equivalent to that in actively proliferating cells, suggesting that mechanisms for virus attachment, nuclear transport, virion uncoating, and perhaps some limited second-strand synthesis of AAV vectors were present in nondividing cells. Southern hybridization analysis of vector sequences from cells transduced while in DNA synthetic arrest and expanded after release of the block confirmed ultimate integration of the vector genome into cellular chromosomal DNA. These findings may provide the basis for the use of AAV-based vectors for gene transfer into quiescent cell populations such as totipotent hematopoietic stem cells. Images PMID:8057446

Efficient gene transfer into nondividing cells by adeno-associated virus-based vectors.

PubMed

Podsakoff, G; Wong, K K; Chatterjee, S

1994-09-01

Gene transfer vectors based on adeno-associated virus (AAV) are emerging as highly promising for use in human gene therapy by virtue of their characteristics of wide host range, high transduction efficiencies, and lack of cytopathogenicity. To better define the biology of AAV-mediated gene transfer, we tested the ability of an AAV vector to efficiently introduce transgenes into nonproliferating cell populations. Cells were induced into a nonproliferative state by treatment with the DNA synthesis inhibitors fluorodeoxyuridine and aphidicolin or by contact inhibition induced by confluence and serum starvation. Cells in logarithmic growth or DNA synthesis arrest were transduced with vCWR:beta gal, an AAV-based vector encoding beta-galactosidase under Rous sarcoma virus long terminal repeat promoter control. Under each condition tested, vCWR:beta Gal expression in nondividing cells was at least equivalent to that in actively proliferating cells, suggesting that mechanisms for virus attachment, nuclear transport, virion uncoating, and perhaps some limited second-strand synthesis of AAV vectors were present in nondividing cells. Southern hybridization analysis of vector sequences from cells transduced while in DNA synthetic arrest and expanded after release of the block confirmed ultimate integration of the vector genome into cellular chromosomal DNA. These findings may provide the basis for the use of AAV-based vectors for gene transfer into quiescent cell populations such as totipotent hematopoietic stem cells.

Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data.

PubMed

Kim, Jungmin; Park, Juyong; Lee, Wonjae

2018-01-01

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility.

Graph-state formalism for mutually unbiased bases

NASA Astrophysics Data System (ADS)

Spengler, Christoph; Kraus, Barbara

2013-11-01

A pair of orthonormal bases is called mutually unbiased if all mutual overlaps between any element of one basis and an arbitrary element of the other basis coincide. In case the dimension, d, of the considered Hilbert space is a power of a prime number, complete sets of d+1 mutually unbiased bases (MUBs) exist. Here we present a method based on the graph-state formalism to construct such sets of MUBs. We show that for n p-level systems, with p being prime, one particular graph suffices to easily construct a set of pn+1 MUBs. In fact, we show that a single n-dimensional vector, which is associated with this graph, can be used to generate a complete set of MUBs and demonstrate that this vector can be easily determined. Finally, we discuss some advantages of our formalism regarding the analysis of entanglement structures in MUBs, as well as experimental realizations.

Wide-angle full-vector beam propagation method based on an alternating direction implicit preconditioner

NASA Astrophysics Data System (ADS)

Chui, Siu Lit; Lu, Ya Yan

2004-03-01

Wide-angle full-vector beam propagation methods (BPMs) for three-dimensional wave-guiding structures can be derived on the basis of rational approximants of a square root operator or its exponential (i.e., the one-way propagator). While the less accurate BPM based on the slowly varying envelope approximation can be efficiently solved by the alternating direction implicit (ADI) method, the wide-angle variants involve linear systems that are more difficult to handle. We present an efficient solver for these linear systems that is based on a Krylov subspace method with an ADI preconditioner. The resulting wide-angle full-vector BPM is used to simulate the propagation of wave fields in a Y branch and a taper.

Wide-angle full-vector beam propagation method based on an alternating direction implicit preconditioner.

PubMed

Chui, Siu Lit; Lu, Ya Yan

2004-03-01

Wide-angle full-vector beam propagation methods (BPMs) for three-dimensional wave-guiding structures can be derived on the basis of rational approximants of a square root operator or its exponential (i.e., the one-way propagator). While the less accurate BPM based on the slowly varying envelope approximation can be efficiently solved by the alternating direction implicit (ADI) method, the wide-angle variants involve linear systems that are more difficult to handle. We present an efficient solver for these linear systems that is based on a Krylov subspace method with an ADI preconditioner. The resulting wide-angle full-vector BPM is used to simulate the propagation of wave fields in a Y branch and a taper.

Improved hybrid algorithm with Gaussian basis sets and plane waves: First-principles calculations of ethylene adsorption on β-SiC(001)-(3×2)

NASA Astrophysics Data System (ADS)

Wieferink, Jürgen; Krüger, Peter; Pollmann, Johannes

2006-11-01

We present an algorithm for DFT calculations employing Gaussian basis sets for the wave function and a Fourier basis for the potential representation. In particular, a numerically very efficient calculation of the local potential matrix elements and the charge density is described. Special emphasis is placed on the consequences of periodicity and explicit k -vector dependence. The algorithm is tested by comparison with more straightforward ones for the case of adsorption of ethylene on the silicon-rich SiC(001)-(3×2) surface clearly revealing its substantial advantages. A complete self-consistency cycle is speeded up by roughly one order of magnitude since the calculation of matrix elements and of the charge density are accelerated by factors of 10 and 80, respectively, as compared to their straightforward calculation. Our results for C2H4:SiC(001)-(3×2) show that ethylene molecules preferentially adsorb in on-top positions above Si dimers on the substrate surface saturating both dimer dangling bonds per unit cell. In addition, a twist of the molecules around a surface-perpendicular axis is slightly favored energetically similar to the case of a complete monolayer of ethylene adsorbed on the Si(001)-(2×1) surface.

Real-time restoration of white-light confocal microscope optical sections

PubMed Central

Balasubramanian, Madhusudhanan; Iyengar, S. Sitharama; Beuerman, Roger W.; Reynaud, Juan; Wolenski, Peter

2009-01-01

Confocal microscopes (CM) are routinely used for building 3-D images of microscopic structures. Nonideal imaging conditions in a white-light CM introduce additive noise and blur. The optical section images need to be restored prior to quantitative analysis. We present an adaptive noise filtering technique using Karhunen–Loéve expansion (KLE) by the method of snapshots and a ringing metric to quantify the ringing artifacts introduced in the images restored at various iterations of iterative Lucy–Richardson deconvolution algorithm. The KLE provides a set of basis functions that comprise the optimal linear basis for an ensemble of empirical observations. We show that most of the noise in the scene can be removed by reconstructing the images using the KLE basis vector with the largest eigenvalue. The prefiltering scheme presented is faster and does not require prior knowledge about image noise. Optical sections processed using the KLE prefilter can be restored using a simple inverse restoration algorithm; thus, the methodology is suitable for real-time image restoration applications. The KLE image prefilter outperforms the temporal-average prefilter in restoring CM optical sections. The ringing metric developed uses simple binary morphological operations to quantify the ringing artifacts and confirms with the visual observation of ringing artifacts in the restored images. PMID:20186290

Sowing the seeds of economic entomology: houseflies and the emergence of medical entomology in Britain.

PubMed

Clark, J F M

2008-12-01

The golden age of medical entomology, 1870-1920, is often celebrated for the elucidation of the aetiology of vector-borne diseases within the rubric of the emergent discipline of tropical medicine. Within these triumphal accounts, the origins of vector control science and technology remain curiously underexplored; yet vector control and eradication constituted the basis of the entomologists' expertise within the emergent specialism of medical entomology. New imperial historians have been sensitive to the ideological implications of vector control policies in the colonies and protectorates, but the reciprocal transfer of vector-control knowledge, practices and policies between periphery and core have received little attention. This paper argues that medical entomology arose in Britain as an amalgam of tropical medicine and agricultural entomology under the umbrella of "economic entomology". An examination of early twentieth-century anti-housefly campaigns sheds light on the relative importance of medical entomology as an imperial science for the careers, practices, and policies of economic entomologists working in Britain. Moreover, their sensitivity to vector ecology provides insight into late nineteenth- and early twentieth-century urban environments and environmental conditions of front-line war.

Enhanced green fluorescent protein (egfp) gene expression in Tetraselmis subcordiformis chloroplast with endogenous regulators.

PubMed

Cui, Yulin; Zhao, Jialin; Hou, Shichang; Qin, Song

2016-05-01

On the basis of fundamental genetic transformation technologies, the goal of this study was to optimize Tetraselmis subcordiformis chloroplast transformation through the use of endogenous regulators. The genes rrn16S, rbcL, psbA, and psbC are commonly highly expressed in chloroplasts, and the regulators of these genes are often used in chloroplast transformation. For lack of a known chloroplast genome sequence, the genome-walking method was used here to obtain full sequences of T. subcordiformis endogenous regulators. The resulting regulators, including three promoters, two terminators, and a ribosome combination sequence, were inserted into the previously constructed plasmid pPSC-R, with the egfp gene included as a reporter gene, and five chloroplast expression vectors prepared. These vectors were successfully transformed into T. subcordiformis by particle bombardment and the efficiency of each vector tested by assessing EGFP fluorescence via microscopy. The results showed that these vectors exhibited higher efficiency than the former vector pPSC-G carrying exogenous regulators, and the vector pRFA with Prrn, psbA-5'RE, and TpsbA showed the highest efficiency. This research provides a set of effective endogenous regulators for T. subcordiformis and will facilitate future fundamental studies of this alga.

Pentatomoids as vectors of plant pathogens

USDA-ARS?s Scientific Manuscript database

Vector-borne pathogens can be categorized functionally according to the degree of symbiosis that they acquire with their respective vectors. Three modes of transmission have been broadly described: non-persistent, semi-persistent, and persistent. Originally compiled specifically for viruses transm...

Contrasting Effects of Human, Canine, and Hybrid Adenovirus Vectors on the Phenotypical and Functional Maturation of Human Dendritic Cells: Implications for Clinical Efficacy▿

PubMed Central

Perreau, Matthieu; Mennechet, Franck; Serratrice, Nicolas; Glasgow, Joel N.; Curiel, David T.; Wodrich, Harald; Kremer, Eric J.

2007-01-01

Antipathogen immune responses create a balance between immunity, tolerance, and immune evasion. However, during gene therapy most viral vectors are delivered in substantial doses and are incapable of expressing gene products that reduce the host's ability to detect transduced cells. Gene transfer efficacy is also modified by the in vivo transduction of dendritic cells (DC), which notably increases the immunogenicity of virions and vector-encoded genes. In this study, we evaluated parameters that are relevant to the use of canine adenovirus serotype 2 (CAV-2) vectors in the clinical setting by assaying their effect on human monocyte-derived DC (hMoDC). We compared CAV-2 to human adenovirus (HAd) vectors containing the wild-type virion, functional deletions in the penton base RGD motif, and the CAV-2 fiber knob. In contrast to the HAd type 5 (HAd5)-based vectors, CAV-2 poorly transduced hMoDC, provoked minimal upregulation of major histocompatibility complex class I/II and costimulatory molecules (CD40, CD80, and CD86), and induced negligible morphological changes indicative of DC maturation. Functional maturation assay results (e.g., reduced antigen uptake; tumor necrosis factor alpha, interleukin-1β [IL-1β], gamma interferon [IFN-γ], IL-10, IL-12, and IFN-α/β secretion; and stimulation of heterologous T-cell proliferation) were also significantly lower for CAV-2. Our data suggested that this was due, in part, to the use of an alternative receptor and a block in vesicular escape. Additionally, HAd5 vector-induced hMoDC maturation was independent of the aforementioned cytokines. Paradoxically, an HAd5/CAV-2 hybrid vector induced the greatest phenotypical and functional maturation of hMoDC. Our data suggest that CAV-2 and the HAd5/CAV-2 vector may be the antithesis of Adenoviridae immunogenicity and that each may have specific clinical advantages. PMID:17229706

A multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors for functional gene analysis.

PubMed

Weber, Kristoffer; Bartsch, Udo; Stocking, Carol; Fehse, Boris

2008-04-01

Functional gene analysis requires the possibility of overexpression, as well as downregulation of one, or ideally several, potentially interacting genes. Lentiviral vectors are well suited for this purpose as they ensure stable expression of complementary DNAs (cDNAs), as well as short-hairpin RNAs (shRNAs), and can efficiently transduce a wide spectrum of cell targets when packaged within the coat proteins of other viruses. Here we introduce a multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors designed according to the "building blocks" principle. Using a wide spectrum of different fluorescent markers, including drug-selectable enhanced green fluorescent protein (eGFP)- and dTomato-blasticidin-S resistance fusion proteins, LeGO vectors allow simultaneous analysis of multiple genes and shRNAs of interest within single, easily identifiable cells. Furthermore, each functional module is flanked by unique cloning sites, ensuring flexibility and individual optimization. The efficacy of these vectors for analyzing multiple genes in a single cell was demonstrated in several different cell types, including hematopoietic, endothelial, and neural stem and progenitor cells, as well as hepatocytes. LeGO vectors thus represent a valuable tool for investigating gene networks using conditional ectopic expression and knock-down approaches simultaneously.

USSR and Eastern Europe Scientific Abstracts- Physics - Number 45

DTIC Science & Technology

1978-10-02

compound, a function of the angle between the electrical vector of the ’ light wave and the optical c-axis of the crystal. Heterodiodes have first...of naturally radioactive U, Th and K in a 1-liter sample. USSR A VECTOR MESON IN A QUANTUM ELECTROMAGNETIC FIELD Moscow TEORETICHESKAYA I...arbitrary spin in a classical plane electromagnetic field are used to find the exact wave function of a vector meson in the quantum field of a linearly

Cre-lox Univector acceptor vectors for functional screening in protoplasts: analysis of Arabidopsis donor cDNAs encoding ABSCISIC ACID INSENSITIVE1-Like protein phosphatases

PubMed Central

Jia, Fan; Gampala, Srinivas S.L.; Mittal, Amandeep; Luo, Qingjun; Rock, Christopher D.

2009-01-01

The 14,200 available full length Arabidopsis thaliana cDNAs in the Universal Plasmid System (UPS) donor vector pUNI51 should be applied broadly and efficiently to leverage a “functional map-space” of homologous plant genes. We have engineered Cre-lox UPS host acceptor vectors (pCR701- 705) with N-terminal epitope tags in frame with the loxH site and downstream from the maize Ubiquitin promoter for use in transient protoplast expression assays and particle bombardment transformation of monocots. As an example of the utility of these vectors, we recombined them with several Arabidopsis cDNAs encoding Ser/Thr protein phosphatase type 2C (PP2Cs) known from genetic studies or predicted by hierarchical clustering meta-analysis to be involved in ABA and stress responses. Our functional results in Zea mays mesophyll protoplasts on ABA-inducible expression effects on the Late Embryogenesis Abundant promoter ProEm:GUS reporter were consistent with predictions and resulted in identification of novel activities of some PP2Cs. Deployment of these vectors can facilitate functional genomics and proteomics and identification of novel gene activities. PMID:19499346

EEG complexity as a biomarker for autism spectrum disorder risk

PubMed Central

2011-01-01

Background Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD. Methods Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months. Results Multiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter. Conclusions This proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder. PMID:21342500

Examination of the genetic basis for sexual dimorphism in the Aedes aegypti (dengue vector mosquito) pupal brain

PubMed Central

2014-01-01

Background Most animal species exhibit sexually dimorphic behaviors, many of which are linked to reproduction. A number of these behaviors, including blood feeding in female mosquitoes, contribute to the global spread of vector-borne illnesses. However, knowledge concerning the genetic basis of sexually dimorphic traits is limited in any organism, including mosquitoes, especially with respect to differences in the developing nervous system. Methods Custom microarrays were used to examine global differences in female vs. male gene expression in the developing pupal head of the dengue vector mosquito, Aedes aegypti. The spatial expression patterns of a subset of differentially expressed transcripts were examined in the developing female vs. male pupal brain through in situ hybridization experiments. Small interfering RNA (siRNA)-mediated knockdown studies were used to assess the putative role of Doublesex, a terminal component of the sex determination pathway, in the regulation of sex-specific gene expression observed in the developing pupal brain. Results Transcripts (2,527), many of which were linked to proteolysis, the proteasome, metabolism, catabolic, and biosynthetic processes, ion transport, cell growth, and proliferation, were found to be differentially expressed in A. aegypti female vs. male pupal heads. Analysis of the spatial expression patterns for a subset of dimorphically expressed genes in the pupal brain validated the data set and also facilitated the identification of brain regions with dimorphic gene expression. In many cases, dimorphic gene expression localized to the optic lobe. Sex-specific differences in gene expression were also detected in the antennal lobe and mushroom body. siRNA-mediated gene targeting experiments demonstrated that Doublesex, a transcription factor with consensus binding sites located adjacent to many dimorphically expressed transcripts that function in neural development, is required for regulation of sex-specific gene expression in the developing A. aegypti brain. Conclusions These studies revealed sex-specific gene expression profiles in the developing A. aegypti pupal head and identified Doublesex as a key regulator of sexually dimorphic gene expression during mosquito neural development. PMID:25729562

Toward Brain Tumor Gene Therapy Using Multipotent Mesenchymal Stromal Cell Vectors

PubMed Central

Bexell, Daniel; Scheding, Stefan; Bengzon, Johan

2010-01-01

Gene therapy of solid cancers has been severely restricted by the limited distribution of vectors within tumors. However, cellular vectors have emerged as an effective migratory system for gene delivery to invasive cancers. Implanted and injected multipotent mesenchymal stromal cells (MSCs) have shown tropism for several types of primary tumors and metastases. This capacity of MSCs forms the basis for their use as a gene vector system in neoplasms. Here, we review the tumor-directed migratory potential of MSCs, mechanisms of the migration, and the choice of therapeutic transgenes, with a focus on malignant gliomas as a model system for invasive and highly vascularized tumors. We examine recent findings demonstrating that MSCs share many characteristics with pericytes and that implanted MSCs localize primarily to perivascular niches within tumors, which might have therapeutic implications. The use of MSC vectors in cancer gene therapy raises concerns, however, including a possible MSC contribution to tumor stroma and vasculature, MSC-mediated antitumor immune suppression, and the potential malignant transformation of cultured MSCs. Nonetheless, we highlight the novel prospects of MSC-based tumor therapy, which appears to be a promising approach. PMID:20407426

Singular value description of a digital radiographic detector: Theory and measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kyprianou, Iacovos S.; Badano, Aldo; Gallas, Brandon D.

The H operator represents the deterministic performance of any imaging system. For a linear, digital imaging system, this system operator can be written in terms of a matrix, H, that describes the deterministic response of the system to a set of point objects. A singular value decomposition of this matrix results in a set of orthogonal functions (singular vectors) that form the system basis. A linear combination of these vectors completely describes the transfer of objects through the linear system, where the respective singular values associated with each singular vector describe the magnitude with which that contribution to the objectmore » is transferred through the system. This paper is focused on the measurement, analysis, and interpretation of the H matrix for digital x-ray detectors. A key ingredient in the measurement of the H matrix is the detector response to a single x ray (or infinitestimal x-ray beam). The authors have developed a method to estimate the 2D detector shift-variant, asymmetric ray response function (RRF) from multiple measured line response functions (LRFs) using a modified edge technique. The RRF measurements cover a range of x-ray incident angles from 0 deg. (equivalent location at the detector center) to 30 deg. (equivalent location at the detector edge) for a standard radiographic or cone-beam CT geometric setup. To demonstrate the method, three beam qualities were tested using the inherent, Lu/Er, and Yb beam filtration. The authors show that measures using the LRF, derived from an edge measurement, underestimate the system's performance when compared with the H matrix derived using the RRF. Furthermore, the authors show that edge measurements must be performed at multiple directions in order to capture rotational asymmetries of the RRF. The authors interpret the results of the H matrix SVD and provide correlations with the familiar MTF methodology. Discussion is made about the benefits of the H matrix technique with regards to signal detection theory, and the characterization of shift-variant imaging systems.« less

An age-structured extension to the vectorial capacity model.

PubMed

Novoseltsev, Vasiliy N; Michalski, Anatoli I; Novoseltseva, Janna A; Yashin, Anatoliy I; Carey, James R; Ellis, Alicia M

2012-01-01

Vectorial capacity and the basic reproductive number (R(0)) have been instrumental in structuring thinking about vector-borne pathogen transmission and how best to prevent the diseases they cause. One of the more important simplifying assumptions of these models is age-independent vector mortality. A growing body of evidence indicates that insect vectors exhibit age-dependent mortality, which can have strong and varied affects on pathogen transmission dynamics and strategies for disease prevention. Based on survival analysis we derived new equations for vectorial capacity and R(0) that are valid for any pattern of age-dependent (or age-independent) vector mortality and explore the behavior of the models across various mortality patterns. The framework we present (1) lays the groundwork for an extension and refinement of the vectorial capacity paradigm by introducing an age-structured extension to the model, (2) encourages further research on the actuarial dynamics of vectors in particular and the relationship of vector mortality to pathogen transmission in general, and (3) provides a detailed quantitative basis for understanding the relative impact of reductions in vector longevity compared to other vector-borne disease prevention strategies. Accounting for age-dependent vector mortality in estimates of vectorial capacity and R(0) was most important when (1) vector densities are relatively low and the pattern of mortality can determine whether pathogen transmission will persist; i.e., determines whether R(0) is above or below 1, (2) vector population growth rate is relatively low and there are complex interactions between birth and death that differ fundamentally from birth-death relationships with age-independent mortality, and (3) the vector exhibits complex patterns of age-dependent mortality and R(0) ∼ 1. A limiting factor in the construction and evaluation of new age-dependent mortality models is the paucity of data characterizing vector mortality patterns, particularly for free ranging vectors in the field.

An Age-Structured Extension to the Vectorial Capacity Model

PubMed Central

Novoseltsev, Vasiliy N.; Michalski, Anatoli I.; Novoseltseva, Janna A.; Yashin, Anatoliy I.; Carey, James R.; Ellis, Alicia M.

2012-01-01

Background Vectorial capacity and the basic reproductive number (R0) have been instrumental in structuring thinking about vector-borne pathogen transmission and how best to prevent the diseases they cause. One of the more important simplifying assumptions of these models is age-independent vector mortality. A growing body of evidence indicates that insect vectors exhibit age-dependent mortality, which can have strong and varied affects on pathogen transmission dynamics and strategies for disease prevention. Methodology/Principal Findings Based on survival analysis we derived new equations for vectorial capacity and R0 that are valid for any pattern of age-dependent (or age–independent) vector mortality and explore the behavior of the models across various mortality patterns. The framework we present (1) lays the groundwork for an extension and refinement of the vectorial capacity paradigm by introducing an age-structured extension to the model, (2) encourages further research on the actuarial dynamics of vectors in particular and the relationship of vector mortality to pathogen transmission in general, and (3) provides a detailed quantitative basis for understanding the relative impact of reductions in vector longevity compared to other vector-borne disease prevention strategies. Conclusions/Significance Accounting for age-dependent vector mortality in estimates of vectorial capacity and R0 was most important when (1) vector densities are relatively low and the pattern of mortality can determine whether pathogen transmission will persist; i.e., determines whether R0 is above or below 1, (2) vector population growth rate is relatively low and there are complex interactions between birth and death that differ fundamentally from birth-death relationships with age-independent mortality, and (3) the vector exhibits complex patterns of age-dependent mortality and R0∼1. A limiting factor in the construction and evaluation of new age-dependent mortality models is the paucity of data characterizing vector mortality patterns, particularly for free ranging vectors in the field. PMID:22724022

Adenovirus vector expressing keratinocyte growth factor using CAG promoter impairs pulmonary function of mice with elastase-induced emphysema.

PubMed

Oki, Hiroshi; Yazawa, Takuya; Baba, Yasuko; Kanegae, Yumi; Sato, Hanako; Sakamoto, Seiko; Goto, Takahisa; Saito, Izumu; Kurahashi, Kiyoyasu

2017-07-01

Pulmonary emphysema impairs quality of life and increases mortality. It has previously been shown that administration of adenovirus vector expressing murine keratinocyte growth factor (KGF) before elastase instillation prevents pulmonary emphysema in mice. We therefore hypothesized that therapeutic administration of KGF would restore damage to lungs caused by elastase instillation and thus improve pulmonary function in an animal model. KGF expressing adenovirus vector, which prevented bleomycin-induced pulmonary fibrosis in a previous study, was constructed. Adenovirus vector (1.0 × 10 9 plaque-forming units) was administered intratracheally one week after administration of elastase into mouse lungs. One week after administration of KGF-vector, exercise tolerance testing and blood gas analysis were performed, after which the lungs were removed under deep anesthesia. KGF-positive pneumocytes were more numerous, surfactant protein secretion in the airspace greater and mean linear intercept of lungs shorter in animals that had received KGF than in control animals. Unexpectedly, however, arterial blood oxygenation was worse in the KGF group and maximum running speed, an indicator of exercise capacity, had not improved after KGF in mice with elastase-induced emphysema, indicating that KGF-expressing adenovirus vector impaired pulmonary function in these mice. Notably, vector lacking KGF-expression unit did not induce such impairment, implying that the KGF expression unit itself may cause the damage to alveolar cells. Possible involvement of the CAG promoter used for KGF expression in impairing pulmonary function is discussed. © 2017 The Societies and John Wiley & Sons Australia, Ltd.

Electrotransformation of Lactobacillus delbrueckii subsp. bulgaricus and L. delbrueckii subsp. lactis with Various Plasmids

PubMed Central

Serror, Pascale; Sasaki, Takashi; Ehrlich, S. Dusko; Maguin, Emmanuelle

2002-01-01

We describe, for the first time, a detailed electroporation procedure for Lactobacillus delbrueckii. Three L. delbrueckii strains were successfully transformed. Under optimal conditions, the transformation efficiency was 104 transformants per μg of DNA. Using this procedure, we identified several plasmids able to replicate in L. delbrueckii and integrated an integrative vector based on phage integrative elements into the L. delbrueckii subsp. bulgaricus chromosome. These vectors provide a good basis for developing molecular tools for L. delbrueckii and open the field of genetic studies in L. delbrueckii. PMID:11772607

A vector-dyadic development of the equations of motion for N-coupled rigid bodies and point masses

NASA Technical Reports Server (NTRS)

Frisch, H. P.

1974-01-01

The equations of motion are derived, in vector-dyadic format, for a topological tree of coupled rigid bodies, point masses, and symmetrical momentum wheels. These equations were programmed, and form the basis for the general-purpose digital computer program N-BOD. A complete derivation of the equations of motion is included along with a description of the methods used for kinematics, constraint elimination, and for the inclusion of nongyroscope forces and torques acting external or internal to the system.

Effects of Rock Joints on Failure of Tunnels Subject to Blast Loading

DTIC Science & Technology

2013-11-01

The out of plane component of stress , if present, is denoted by σ33, associated with an orthonormal basis vector e3. The principal directions of stress ...lies within the plane of stress or strain, and forms an angle, θ, with respect to the first principal direction p1. Define the normal vector to the...surface of material failure by the critical angle, θc. For the regime (a), (b), (c)-(d), n is equal to p1, the direction of maximum principal stress

Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

PubMed

Fernandez, Michael; Abreu, Jose I; Shi, Hongqing; Barnard, Amanda S

2016-11-14

The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation vectors using machine learning techniques. Machine learning modeling revealed that the most relevant correlations appear at topological distances in the range of 1 to 42 with prediction accuracy higher than 80%. The data-driven model can statistically discriminate between graphene nanoflakes with different energy gaps on the basis of their molecular topology.

Novel Adeno-Associated Viral Vector Delivering the Utrophin Gene Regulator Jazz Counteracts Dystrophic Pathology in mdx Mice

PubMed Central

Strimpakos, Georgios; Corbi, Nicoletta; Pisani, Cinzia; Di Certo, Maria Grazia; Onori, Annalisa; Luvisetto, Siro; Severini, Cinzia; Gabanella, Francesca; Monaco, Lucia; Mattei, Elisabetta; Passananti, Claudio

2014-01-01

Over-expression of the dystrophin-related gene utrophin represents a promising therapeutic strategy for Duchenne muscular dystrophy (DMD). The strategy is based on the ability of utrophin to functionally replace defective dystrophin. We developed the artificial zinc finger transcription factor “Jazz” that up-regulates both the human and mouse utrophin promoter. We observed a significant recovery of muscle strength in dystrophic Jazz-transgenic mdx mice. Here we demonstrate the efficacy of an experimental gene therapy based on the systemic delivery of Jazz gene in mdx mice by adeno-associated virus (AAV). AAV serotype 8 was chosen on the basis of its high affinity for skeletal muscle. Muscle-specific expression of the therapeutic Jazz gene was enhanced by adding the muscle α-actin promoter to the AAV vector (mAAV). Injection of mAAV8-Jazz viral preparations into mdx mice resulted in muscle-specific Jazz expression coupled with up-regulation of the utrophin gene. We show a significant recovery from the dystrophic phenotype in mAAV8-Jazz-treated mdx mice. Histological and physiological analysis revealed a reduction of fiber necrosis and inflammatory cell infiltration associated with functional recovery in muscle contractile force. The combination of ZF-ATF technology with the AAV delivery can open a new avenue to obtain a therapeutic strategy for treatment of DMD. J. Cell. Physiol. 229: 1283–1291, 2014. © 2014 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals, Inc. PMID:24469912

Petaflops router

DOEpatents

Baker, Zachary Kent; Power, John Fredrick; Tripp, Justin Leonard; Dunham, Mark Edward; Stettler, Matthew W; Jones, John Alexander

2014-10-14

Disclosed is a method and system for performing operations on at least one input data vector in order to produce at least one output vector to permit easy, scalable and fast programming of a petascale equivalent supercomputer. A PetaFlops Router may comprise one or more PetaFlops Nodes, which may be connected to each other and/or external data provider/consumers via a programmable crossbar switch external to the PetaFlops Node. Each PetaFlops Node has a FPGA and a programmable intra-FPGA crossbar switch that permits input and output variables to be configurably connected to various physical operators contained in the FPGA as desired by a user. This allows a user to specify the instruction set of the system on a per-application basis. Further, the intra-FPGA crossbar switch permits the output of one operation to be delivered as an input to a second operation. By configuring the external crossbar switch, the output of a first operation on a first PetaFlops Node may be used as the input for a second operation on a second PetaFlops Node. An embodiment may provide an ability for the system to recognize and generate pipelined functions. Streaming operators may be connected together at run-time and appropriately staged to allow data to flow through a series of functions. This allows the system to provide high throughput and parallelism when possible. The PetaFlops Router may implement the user desired instructions by appropriately configuring the intra-FPGA crossbar switch on each PetaFlops Node and the external crossbar switch.

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

NASA Astrophysics Data System (ADS)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, A. P. P. Abdul; Razali Abdullah, Mohamad; Aizzat Zakaria, Muhammad; Muaz Alim, Muhammad; Arif Mat Jizat, Jessnor; Fauzi Ibrahim, Mohamad

2018-03-01

Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models, i.e. linear and fine radial basis function (RBF) kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

Development of a new vector using Soybean yellow common mosaic virus for gene function study or heterologous protein expression in soybeans.

PubMed

Lim, Seungmo; Nam, Moon; Kim, Kil Hyun; Lee, Su-Heon; Moon, Jung-Kyung; Lim, Hyoun-Sub; Choung, Myoung-Gun; Kim, Sang-Mok; Moon, Jae Sun

2016-02-01

A new vector using Soybean yellow common mosaic virus (SYCMV) was constructed for gene function study or heterologous protein expression in soybeans. The in vitro transcript with a 5' cap analog m7GpppG from an SYCMV full-length infectious vector driven by a T7 promoter infected soybeans (pSYCMVT7-full). The symptoms observed in the soybeans infected with either the sap from SYCMV-infected leaves or pSYCMVT7-full were indistinguishable, suggesting that the vector exhibits equivalent biological activity as the virus itself. To utilize the vector further, a DNA-based vector driven by the Cauliflower mosaic virus (CaMV) 35S promoter was constructed. The complete sequence of the SYCMV genome was inserted into a binary vector flanked by a CaMV 35S promoter at the 5' terminus of the SYCMV genome and a cis-cleaving ribozyme sequence followed by a nopaline synthase terminator at the 3' terminus of the SYCMV genome (pSYCMV-full). The SYCMV-derived vector was tested for use as a virus-induced gene silencing (VIGS) vector for the functional analysis of soybean genes. VIGS constructs containing either a fragment of the Phytoene desaturase (PDS) gene (pSYCMV-PDS1) or a fragment of the small subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase (RbcS) gene (pSYCMV-RbcS2) were constructed. Plants infiltrated with each vector using the Agrobacterium-mediated inoculation method exhibited distinct symptoms, such as photo-bleaching in plants infiltrated with pSYCMV-PDS1 and yellow or pale green coloring in plants infiltrated with pSYCMV-RbcS2. In addition, down-regulation of the transcripts of the two target genes was confirmed via northern blot analysis. Particle bombardment and direct plasmid DNA rubbing were also confirmed as alternative inoculation methods. To determine if the SYCMV vector can be used for the expression of heterologous proteins in soybean plants, the vector encoding amino acids 135-160 of VP1 of Foot-and-mouth disease virus (FMDV) serotype O1 Campos (O1C) was constructed (pSYCMV-FMDV). Plants infiltrated with pSYCMV-FMDV were only detected via western blotting using the O1C antibody. Based on these results, we propose that the SYCMV-derived vector can be used for gene function study or expression of useful heterologous proteins in soybeans. Copyright © 2015 Elsevier B.V. All rights reserved.

A unified development of several techniques for the representation of random vectors and data sets

NASA Technical Reports Server (NTRS)

Bundick, W. T.

1973-01-01

Linear vector space theory is used to develop a general representation of a set of data vectors or random vectors by linear combinations of orthonormal vectors such that the mean squared error of the representation is minimized. The orthonormal vectors are shown to be the eigenvectors of an operator. The general representation is applied to several specific problems involving the use of the Karhunen-Loeve expansion, principal component analysis, and empirical orthogonal functions; and the common properties of these representations are developed.

Viral vector-based tools advance knowledge of basal ganglia anatomy and physiology.

PubMed

Sizemore, Rachel J; Seeger-Armbruster, Sonja; Hughes, Stephanie M; Parr-Brownlie, Louise C

2016-04-01

Viral vectors were originally developed to deliver genes into host cells for therapeutic potential. However, viral vector use in neuroscience research has increased because they enhance interpretation of the anatomy and physiology of brain circuits compared with conventional tract tracing or electrical stimulation techniques. Viral vectors enable neuronal or glial subpopulations to be labeled or stimulated, which can be spatially restricted to a single target nucleus or pathway. Here we review the use of viral vectors to examine the structure and function of motor and limbic basal ganglia (BG) networks in normal and pathological states. We outline the use of viral vectors, particularly lentivirus and adeno-associated virus, in circuit tracing, optogenetic stimulation, and designer drug stimulation experiments. Key studies that have used viral vectors to trace and image pathways and connectivity at gross or ultrastructural levels are reviewed. We explain how optogenetic stimulation and designer drugs used to modulate a distinct pathway and neuronal subpopulation have enhanced our mechanistic understanding of BG function in health and pathophysiology in disease. Finally, we outline how viral vector technology may be applied to neurological and psychiatric conditions to offer new treatments with enhanced outcomes for patients. Copyright © 2016 the American Physiological Society.

Gene silencing in Escherichia coli using antisense RNAs expressed from doxycycline-inducible vectors.

PubMed

Nakashima, N; Tamura, T

2013-06-01

Here, we report on the construction of doxycycline (tetracycline analogue)-inducible vectors that express antisense RNAs in Escherichia coli. Using these vectors, the expression of genes of interest can be silenced conditionally. The expression of antisense RNAs from the vectors was more tightly regulated than the previously constructed isopropyl-β-D-galactopyranoside-inducible vectors. Furthermore, expression levels of antisense RNAs were enhanced by combining the doxycycline-inducible promoter with the T7 promoter-T7 RNA polymerase system; the T7 RNA polymerase gene, under control of the doxycycline-inducible promoter, was integrated into the lacZ locus of the genome without leaving any antibiotic marker. These vectors are useful for investigating gene functions or altering cell phenotypes for biotechnological and industrial applications. A gene silencing method using antisense RNAs in Escherichia coli is described, which facilitates the investigation of bacterial gene function. In particular, the method is suitable for comprehensive analyses or phenotypic analyses of genes essential for growth. Here, we describe expansion of vector variations for expressing antisense RNAs, allowing choice of a vector appropriate for the target genes or experimental purpose. © 2013 The Society for Applied Microbiology.

Viral vector-based tools advance knowledge of basal ganglia anatomy and physiology

PubMed Central

Sizemore, Rachel J.; Seeger-Armbruster, Sonja; Hughes, Stephanie M.

2016-01-01

Viral vectors were originally developed to deliver genes into host cells for therapeutic potential. However, viral vector use in neuroscience research has increased because they enhance interpretation of the anatomy and physiology of brain circuits compared with conventional tract tracing or electrical stimulation techniques. Viral vectors enable neuronal or glial subpopulations to be labeled or stimulated, which can be spatially restricted to a single target nucleus or pathway. Here we review the use of viral vectors to examine the structure and function of motor and limbic basal ganglia (BG) networks in normal and pathological states. We outline the use of viral vectors, particularly lentivirus and adeno-associated virus, in circuit tracing, optogenetic stimulation, and designer drug stimulation experiments. Key studies that have used viral vectors to trace and image pathways and connectivity at gross or ultrastructural levels are reviewed. We explain how optogenetic stimulation and designer drugs used to modulate a distinct pathway and neuronal subpopulation have enhanced our mechanistic understanding of BG function in health and pathophysiology in disease. Finally, we outline how viral vector technology may be applied to neurological and psychiatric conditions to offer new treatments with enhanced outcomes for patients. PMID:26888111

[Study on application of SVM in prediction of coronary heart disease].

PubMed

Zhu, Yue; Wu, Jianghua; Fang, Ying

2013-12-01

Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine (SVM) was applied to identify coronary heart disease (CHD) in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter sigma were optimized by particle swarm optimization (PSO) and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation (BP) model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%, respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.

New KF-PP-SVM classification method for EEG in brain-computer interfaces.

PubMed

Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

2014-01-01

Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fast evaluation of solid harmonic Gaussian integrals for local resolution-of-the-identity methods and range-separated hybrid functionals.

PubMed

Golze, Dorothea; Benedikter, Niels; Iannuzzi, Marcella; Wilhelm, Jan; Hutter, Jürg

2017-01-21

An integral scheme for the efficient evaluation of two-center integrals over contracted solid harmonic Gaussian functions is presented. Integral expressions are derived for local operators that depend on the position vector of one of the two Gaussian centers. These expressions are then used to derive the formula for three-index overlap integrals where two of the three Gaussians are located at the same center. The efficient evaluation of the latter is essential for local resolution-of-the-identity techniques that employ an overlap metric. We compare the performance of our integral scheme to the widely used Cartesian Gaussian-based method of Obara and Saika (OS). Non-local interaction potentials such as standard Coulomb, modified Coulomb, and Gaussian-type operators, which occur in range-separated hybrid functionals, are also included in the performance tests. The speed-up with respect to the OS scheme is up to three orders of magnitude for both integrals and their derivatives. In particular, our method is increasingly efficient for large angular momenta and highly contracted basis sets.

Fast evaluation of solid harmonic Gaussian integrals for local resolution-of-the-identity methods and range-separated hybrid functionals

NASA Astrophysics Data System (ADS)

Golze, Dorothea; Benedikter, Niels; Iannuzzi, Marcella; Wilhelm, Jan; Hutter, Jürg

2017-01-01

An integral scheme for the efficient evaluation of two-center integrals over contracted solid harmonic Gaussian functions is presented. Integral expressions are derived for local operators that depend on the position vector of one of the two Gaussian centers. These expressions are then used to derive the formula for three-index overlap integrals where two of the three Gaussians are located at the same center. The efficient evaluation of the latter is essential for local resolution-of-the-identity techniques that employ an overlap metric. We compare the performance of our integral scheme to the widely used Cartesian Gaussian-based method of Obara and Saika (OS). Non-local interaction potentials such as standard Coulomb, modified Coulomb, and Gaussian-type operators, which occur in range-separated hybrid functionals, are also included in the performance tests. The speed-up with respect to the OS scheme is up to three orders of magnitude for both integrals and their derivatives. In particular, our method is increasingly efficient for large angular momenta and highly contracted basis sets.

Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peng, Bo; Kowalski, Karol

The representation and storage of two-electron integral tensors are vital in large- scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this paper, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition ofmore » the two-electron integral tensor in our implementation. For the size of atomic basis set N_b ranging from ~ 100 up to ~ 2, 000, the observed numerical scaling of our implementation shows O(N_b^{2.5~3}) versus O(N_b^{3~4}) of single CD in most of other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic-orbital (AO) two-electron integral tensor from O(N_b^4) to O(N_b^2 log_{10}(N_b)) with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled- cluster formalism employing single and double excitations (CCSD) on several bench- mark systems including the C_{60} molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10^{-4} to 10^{-3} to give acceptable compromise between efficiency and accuracy.« less

Combining the Complete Active Space Self-Consistent Field Method and the Full Configuration Interaction Quantum Monte Carlo within a Super-CI Framework, with Application to Challenging Metal-Porphyrins.

PubMed

Li Manni, Giovanni; Smart, Simon D; Alavi, Ali

2016-03-08

A novel stochastic Complete Active Space Self-Consistent Field (CASSCF) method has been developed and implemented in the Molcas software package. A two-step procedure is used, in which the CAS configuration interaction secular equations are solved stochastically with the Full Configuration Interaction Quantum Monte Carlo (FCIQMC) approach, while orbital rotations are performed using an approximated form of the Super-CI method. This new method does not suffer from the strong combinatorial limitations of standard MCSCF implementations using direct schemes and can handle active spaces well in excess of those accessible to traditional CASSCF approaches. The density matrix formulation of the Super-CI method makes this step independent of the size of the CI expansion, depending exclusively on one- and two-body density matrices with indices restricted to the relatively small number of active orbitals. No sigma vectors need to be stored in memory for the FCIQMC eigensolver--a substantial gain in comparison to implementations using the Davidson method, which require three or more vectors of the size of the CI expansion. Further, no orbital Hessian is computed, circumventing limitations on basis set expansions. Like the parent FCIQMC method, the present technique is scalable on massively parallel architectures. We present in this report the method and its application to the free-base porphyrin, Mg(II) porphyrin, and Fe(II) porphyrin. In the present study, active spaces up to 32 electrons and 29 orbitals in orbital expansions containing up to 916 contracted functions are treated with modest computational resources. Results are quite promising even without accounting for the correlation outside the active space. The systems here presented clearly demonstrate that large CASSCF calculations are possible via FCIQMC-CASSCF without limitations on basis set size.

Determination Of Constituent Concentration In Fluid Mixtures Using Magnetic Resonance Imaging

NASA Astrophysics Data System (ADS)

Galloway, Robert L.; Collins, Jerry C.; Carroll, Frank E.

1987-01-01

The primary application of magnetic resonance imaging (MRI) has been qualitative and anatomical evaluation of patient status. Recent efforts to analyze image information for quantitative evaluation centered on two relaxation parameters, Tl and T2, as the descriptors for the image data. In our work we have found that relaxation curves for biologic materials cannot be described by a monoexponential function and that, in a spin echo system, calculated Tl values are dependent on repetition time. This finding is not unexpected since, in physiologic imaging, any region of interest (ROI), is composed of a number of distinct substances and the response of that ROI will be a composite of the constituent materials. The purpose of our study was to develop a method by which the relaxation behaviors of a composite of physiological material might be characterized and use that characterization to determine its constituent materials. We created a phantom in which volumes of several "pure" materials (blood, plasma, saline and oil) were available as well as volumes which contained concentric enclosures of the pure materials. Images were formed at a number of repetition times, ranging from 160 milliseconds to 2 seconds. The image data was then transferred to a VAX 11/750 where regions of interest were marked and the mean image intensity for each ROI at each repetition time was calculated. The resultant relaxation curves of the pure materials formed basis vectors for the composite responses and the fractional content of each material was determined by a least-square error fit to the basis vectors. Excellent agreement was seen between known and measured mixture percentages. Ongoing work is centered around optimizing repetition time selection and accounting for the interaction between species in the mixtures.

Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations.

PubMed

Peng, Bo; Kowalski, Karol

2017-09-12

The representation and storage of two-electron integral tensors are vital in large-scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this work, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition of the two-electron integral tensor in our implementation. For the size of the atomic basis set, N b , ranging from ∼100 up to ∼2,000, the observed numerical scaling of our implementation shows [Formula: see text] versus [Formula: see text] cost of performing single CD on the two-electron integral tensor in most of the other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic orbital (AO) two-electron integral tensor from [Formula: see text] to [Formula: see text] with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled cluster formalism employing single and double excitations (CCSD) on several benchmark systems including the C 60 molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10 -4 to 10 -3 to give acceptable compromise between efficiency and accuracy.

Vector dissimilarity and clustering.

PubMed

Lefkovitch, L P

1991-04-01

Based on the description of objects by m attributes, an m-element vector dissimilarity function is defined that, unlike scalar functions, retains the distinction among attributes. This function, which satisfies the conditions for a metric, allows the definition of betweenness, which can then be used for clustering. Applications to the subset-generation phase of conditional clustering and to nearest-neighbor-type algorithms are described.

LINEARIZATION OF EMPIRICAL RHEOLOGICAL DATA FOR USE IN COMPOSITION CONTROL OF MULTICOMPONENT FOODSTUFFS.

PubMed

Drake, Birger; Nádai, Béla

1970-03-01

An empirical measure of viscosity, which is often far from being a linear function of composition, was used together with refractive index to build up a function which bears a linear relationship to the composition of tomato paste-water-sucrose mixtures. The new function can be used directly for rapid composition control by linear vector-vector transformation.

Collinear and vector interaction of light waves in nonlinear optical crystals KTiOPO4("KTP"), Ba2NaNb5O15 ("banana")

NASA Astrophysics Data System (ADS)

Deinekina, N. A.; Korosteleva, I. A.; Kravchenko, O. V.; Faleev, D. S.

2016-11-01

Esents the research results of biaxial crystals with mm2 symmetry class. These crystals were used for determining regularities of nonlinear conversion of broadband optical emission on the basis of collinear and vector light waves interactions of different nature. The quantities of the basis nonlinear optical characteristics of "KTP" (KTiOPO4) and "banana" (Ba2NaNb5O15) crystals were calculated in case of synchronous conversion of broadband emission from the area of 0.8 - 2.8 micron to the visible spectrum of 0.4 - 0.7 micron. The nonlinear optical characteristics of "KTP" crystals are defined by their geometrical structure, the mode of interaction of light waves, and the infra-red spectrum width, that was experimentally confirmed on "KTP" crystal. The quality characteristics β were calculated for the "KTP" crystal. For "banana" crystal the angle of phase synchronism θc changes insignificantly when the observation plane is changed. It can be explained by the fact that the biaxiality of crystal is not strongly expressed, because of the basis refraction indices the conditions nz<=ny≈nx are performed.

Generalized decompositions of dynamic systems and vector Lyapunov functions

NASA Astrophysics Data System (ADS)

Ikeda, M.; Siljak, D. D.

1981-10-01

The notion of decomposition is generalized to provide more freedom in constructing vector Lyapunov functions for stability analysis of nonlinear dynamic systems. A generalized decomposition is defined as a disjoint decomposition of a system which is obtained by expanding the state-space of a given system. An inclusion principle is formulated for the solutions of the expansion to include the solutions of the original system, so that stability of the expansion implies stability of the original system. Stability of the expansion can then be established by standard disjoint decompositions and vector Lyapunov functions. The applicability of the new approach is demonstrated using the Lotka-Volterra equations.

A vector autopilot system. [aircraft attitude determination with three-axis magnetometer

NASA Technical Reports Server (NTRS)

Pietila, R.; Dunn, W. R., Jr.

1976-01-01

Current technology has evolved low cost, highly reliable solid state vector magnetometers with excellent angular resolution. This paper discusses the role of a three-axis magnetometer as a new instrument for aircraft attitude determination. Using flight data acquired by an instrumented aircraft, attitude is calculated using the earth's magnetic field vector and compared to measured attitudes. The magnetic field alone is not adequate to resolve all attitude variations and the need for a second reference angle or vector is discussed. A system combining the functions of heading determination and attitude measurement is presented to show that both functions can be implemented with essentially the same component count required to measure heading alone. It is concluded that with the correlation achieved in calculated and measured attitude there is a potential application of vector magnetometry in attitude measurement systems.

Hopf-algebraic structure of combinatorial objects and differential operators

NASA Technical Reports Server (NTRS)

Grossman, Robert; Larson, Richard G.

1989-01-01

A Hopf-algebraic structure on a vector space which has as basis a family of trees is described. Some applications of this structure to combinatorics and to differential operators are surveyed. Some possible future directions for this work are indicated.

Large-scale FMO-MP3 calculations on the surface proteins of influenza virus, hemagglutinin (HA) and neuraminidase (NA)

NASA Astrophysics Data System (ADS)

Mochizuki, Yuji; Yamashita, Katsumi; Fukuzawa, Kaori; Takematsu, Kazutomo; Watanabe, Hirofumi; Taguchi, Naoki; Okiyama, Yoshio; Tsuboi, Misako; Nakano, Tatsuya; Tanaka, Shigenori

2010-06-01

Two proteins on the influenza virus surface have been well known. One is hemagglutinin (HA) associated with the infection to cells. The fragment molecular orbital (FMO) calculations were performed on a complex consisting of HA trimer and two Fab-fragments at the third-order Møller-Plesset perturbation (MP3) level. The numbers of residues and 6-31G basis functions were 2351 and 201276, and thus a massively parallel-vector computer was utilized to accelerate the processing. This FMO-MP3 job was completed in 5.8 h with 1024 processors. Another protein is neuraminidase (NA) involved in the escape from infected cells. The FMO-MP3 calculation was also applied to analyze the interactions between oseltamivir and surrounding residues in pharmacophore.

The Prediction of Broadband Shock-Associated Noise Including Propagation Effects

NASA Technical Reports Server (NTRS)

Miller, Steven; Morris, Philip J.

2011-01-01

An acoustic analogy is developed based on the Euler equations for broadband shock- associated noise (BBSAN) that directly incorporates the vector Green's function of the linearized Euler equations and a steady Reynolds-Averaged Navier-Stokes solution (SRANS) as the mean flow. The vector Green's function allows the BBSAN propagation through the jet shear layer to be determined. The large-scale coherent turbulence is modeled by two-point second order velocity cross-correlations. Turbulent length and time scales are related to the turbulent kinetic energy and dissipation. An adjoint vector Green's function solver is implemented to determine the vector Green's function based on a locally parallel mean flow at streamwise locations of the SRANS solution. However, the developed acoustic analogy could easily be based on any adjoint vector Green's function solver, such as one that makes no assumptions about the mean flow. The newly developed acoustic analogy can be simplified to one that uses the Green's function associated with the Helmholtz equation, which is consistent with the formulation of Morris and Miller (AIAAJ 2010). A large number of predictions are generated using three different nozzles over a wide range of fully expanded Mach numbers and jet stagnation temperatures. These predictions are compared with experimental data from multiple jet noise labs. In addition, two models for the so-called 'fine-scale' mixing noise are included in the comparisons. Improved BBSAN predictions are obtained relative to other models that do not include the propagation effects, especially in the upstream direction of the jet.

Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery.

PubMed

Xie, Qi; Zhao, Qian; Meng, Deyu; Xu, Zongben

2017-08-02

It is well known that the sparsity/low-rank of a vector/matrix can be rationally measured by nonzero-entries-number ($l_0$ norm)/nonzero- singular-values-number (rank), respectively. However, data from real applications are often generated by the interaction of multiple factors, which obviously cannot be sufficiently represented by a vector/matrix, while a high order tensor is expected to provide more faithful representation to deliver the intrinsic structure underlying such data ensembles. Unlike the vector/matrix case, constructing a rational high order sparsity measure for tensor is a relatively harder task. To this aim, in this paper we propose a measure for tensor sparsity, called Kronecker-basis-representation based tensor sparsity measure (KBR briefly), which encodes both sparsity insights delivered by Tucker and CANDECOMP/PARAFAC (CP) low-rank decompositions for a general tensor. Then we study the KBR regularization minimization (KBRM) problem, and design an effective ADMM algorithm for solving it, where each involved parameter can be updated with closed-form equations. Such an efficient solver makes it possible to extend KBR to various tasks like tensor completion and tensor robust principal component analysis. A series of experiments, including multispectral image (MSI) denoising, MSI completion and background subtraction, substantiate the superiority of the proposed methods beyond state-of-the-arts.

Olfactory basis of floral preference of the malaria vector Anopheles gambiae (Diptera: Culicidae) among common African plants.

PubMed

Nikbakhtzadeh, Mahmood R; Terbot, John W; Otienoburu, Philip E; Foster, Woodbridge A

2014-12-01

Mosquitoes of both sexes feed on plants to obtain sugar. Nocturnal species probably locate the plants primarily by their volatile semiochemicals that also form the basis for the mosquitoes' innate plant-species preferences. To evaluate these olfactory preferences quantitatively, we used a two-choice wind-tunnel olfactometer to measure the upwind orientation of Anopheles gambiae Giles, an important vector of malaria in equatorial Africa, toward odor plumes produced by nine plant species common where this mosquito occurs. These plants are reported to induce feeding behaviors in An. gambiae and to produce floral or extrafloral nectar. Results presented here demonstrated that the volatiles of S. didymobotrya, P. hysterophorus, S. occidentalis, and L. camara, in descending order of numbers of mosquitoes responding, were all attractive, compared to a control plant species, whereas D. stramonium, R. communis, S. bicapsularis, T. stans, and T. diversifolia were not. As expected, chromatographic analysis of the headspace of attractive plants whose volatiles were captured by stir-bar sorptive extraction revealed a wide range of compounds, primarily terpenoids. Once their bioactivity and attractiveness for An. gambiae, alone and in blends, has been firmly established, some of these semiochemicals may have applications in population sampling and control. © 2014 The Society for Vector Ecology.

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy

PubMed Central

Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

2016-01-01

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system. PMID:27835638

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

PubMed

Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

2016-01-01

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

Improved resistivity imaging of groundwater solute plumes using POD-based inversion

NASA Astrophysics Data System (ADS)

Oware, E. K.; Moysey, S. M.; Khan, T.

2012-12-01

We propose a new approach for enforcing physics-based regularization in electrical resistivity imaging (ERI) problems. The approach utilizes a basis-constrained inversion where an optimal set of basis vectors is extracted from training data by Proper Orthogonal Decomposition (POD). The key aspect of the approach is that Monte Carlo simulation of flow and transport is used to generate a training dataset, thereby intrinsically capturing the physics of the underlying flow and transport models in a non-parametric form. POD allows for these training data to be projected onto a subspace of the original domain, resulting in the extraction of a basis for the inversion that captures characteristics of the groundwater flow and transport system, while simultaneously allowing for dimensionality reduction of the original problem in the projected space We use two different synthetic transport scenarios in heterogeneous media to illustrate how the POD-based inversion compares with standard Tikhonov and coupled inversion. The first scenario had a single source zone leading to a unimodal solute plume (synthetic #1), whereas, the second scenario had two source zones that produced a bimodal plume (synthetic #2). For both coupled inversion and the POD approach, the conceptual flow and transport model used considered only a single source zone for both scenarios. Results were compared based on multiple metrics (concentration root-mean square error (RMSE), peak concentration, and total solute mass). In addition, results for POD inversion based on 3 different data densities (120, 300, and 560 data points) and varying number of selected basis images (100, 300, and 500) were compared. For synthetic #1, we found that all three methods provided qualitatively reasonable reproduction of the true plume. Quantitatively, the POD inversion performed best overall for each metric considered. Moreover, since synthetic #1 was consistent with the conceptual transport model, a small number of basis vectors (100) contained enough a priori information to constrain the inversion. Increasing the amount of data or number of selected basis images did not translate into significant improvement in imaging results. For synthetic #2, the RMSE and error in total mass were lowest for the POD inversion. However, the peak concentration was significantly overestimated by the POD approach. Regardless, the POD-based inversion was the only technique that could capture the bimodality of the plume in the reconstructed image, thus providing critical information that could be used to reconceptualize the transport problem. We also found that, in the case of synthetic #2, increasing the number of resistivity measurements and the number of selected basis vectors allowed for significant improvements in the reconstructed images.

Deformation structure analysis of material at fatigue on the basis of the vector field

NASA Astrophysics Data System (ADS)

Kibitkin, Vladimir V.; Solodushkin, Andrey I.; Pleshanov, Vasily S.

2017-12-01

In the paper, spatial distributions of deformation, circulation, and shear amplitudes and shear angles are obtained from the displacement vector field measured by the DIC technique. This vector field and its characteristics of shears and vortices are given as an example of such approach. The basic formulae are also given. The experiment shows that honeycomb deformation structures can arise in the center of a macrovortex at developed plastic flow. The spatial distribution of local circulation and shears is discovered, which coincides with the deformation structure but their amplitudes are different. The analysis proves that the spatial distribution of shear angles is a result of maximum tangential and normal stresses. The anticlockwise circulation of most local vortices obeys the normal Gaussian law in the area of interest.

Verifying the equivalence of representations of the knee joint moment vector from a drop vertical jump task.

PubMed

Nichols, Julia K; O'Reilly, Oliver M

2017-03-01

Biomechanics software programs, such as Visual3D, Nexus, Cortex, and OpenSim, have the capability of generating several distinct component representations for joint moments and forces from motion capture data. These representations include those for orthonormal proximal and distal coordinate systems and a non-orthogonal joint coordinate system. In this article, a method is presented to address the challenging problem of evaluating and verifying the equivalence of these representations. The method accommodates the difficulty that there are two possible sets of non-orthogonal basis vectors that can be used to express a vector in the joint coordinate system and is illuminated using motion capture data from a drop vertical jump task. Copyright © 2016 Elsevier B.V. All rights reserved.

A multi-dimensional Smolyak collocation method in curvilinear coordinates for computing vibrational spectra

DOE Office of Scientific and Technical Information (OSTI.GOV)

Avila, Gustavo, E-mail: Gustavo-Avila@telefonica.net; Carrington, Tucker, E-mail: Tucker.Carrington@queensu.ca

In this paper, we improve the collocation method for computing vibrational spectra that was presented in Avila and Carrington, Jr. [J. Chem. Phys. 139, 134114 (2013)]. Using an iterative eigensolver, energy levels and wavefunctions are determined from values of the potential on a Smolyak grid. The kinetic energy matrix-vector product is evaluated by transforming a vector labelled with (nondirect product) grid indices to a vector labelled by (nondirect product) basis indices. Both the transformation and application of the kinetic energy operator (KEO) scale favorably. Collocation facilitates dealing with complicated KEOs because it obviates the need to calculate integrals of coordinatemore » dependent coefficients of differential operators. The ideas are tested by computing energy levels of HONO using a KEO in bond coordinates.« less

An Improved Brome mosaic virus Silencing Vector: Greater Insert Stability and More Extensive VIGS1[OPEN

PubMed Central

2018-01-01

Virus-induced gene silencing (VIGS) is used extensively for gene function studies in plants. VIGS is inexpensive and rapid compared with silencing conducted through stable transformation, but many virus-silencing vectors, especially in grasses, induce only transient silencing phenotypes. A major reason for transient phenotypes is the instability of the foreign gene fragment (insert) in the vector during VIGS. Here, we report the development of a Brome mosaic virus (BMV)-based vector that better maintains inserts through modification of the original BMV vector RNA sequence. Modification of the BMV RNA3 sequence yielded a vector, BMVCP5, that better maintained phytoene desaturase and heat shock protein70-1 (HSP70-1) inserts in Nicotiana benthamiana and maize (Zea mays). Longer maintenance of inserts was correlated with greater target gene silencing and more extensive visible silencing phenotypes displaying greater tissue penetration and involving more leaves. The modified vector accumulated similarly to the original vector in N. benthamiana after agroinfiltration, thus maintaining a high titer of virus in this intermediate host used to produce virus inoculum for grass hosts. For HSP70, silencing one family member led to a large increase in the expression of another family member, an increase likely related to the target gene knockdown and not a general effect of virus infection. The cause of the increased insert stability in the modified vector is discussed in relationship to its recombination and accumulation potential. The modified vector will improve functional genomic studies in grasses, and the conceptual methods used to improve the vector may be applied to other VIGS vectors. PMID:29127260

Ultra-low background DNA cloning system.

PubMed

Goto, Kenta; Nagano, Yukio

2013-01-01

Yeast-based in vivo cloning is useful for cloning DNA fragments into plasmid vectors and is based on the ability of yeast to recombine the DNA fragments by homologous recombination. Although this method is efficient, it produces some by-products. We have developed an "ultra-low background DNA cloning system" on the basis of yeast-based in vivo cloning, by almost completely eliminating the generation of by-products and applying the method to commonly used Escherichia coli vectors, particularly those lacking yeast replication origins and carrying an ampicillin resistance gene (Amp(r)). First, we constructed a conversion cassette containing the DNA sequences in the following order: an Amp(r) 5' UTR (untranslated region) and coding region, an autonomous replication sequence and a centromere sequence from yeast, a TRP1 yeast selectable marker, and an Amp(r) 3' UTR. This cassette allowed conversion of the Amp(r)-containing vector into the yeast/E. coli shuttle vector through use of the Amp(r) sequence by homologous recombination. Furthermore, simultaneous transformation of the desired DNA fragment into yeast allowed cloning of this DNA fragment into the same vector. We rescued the plasmid vectors from all yeast transformants, and by-products containing the E. coli replication origin disappeared. Next, the rescued vectors were transformed into E. coli and the by-products containing the yeast replication origin disappeared. Thus, our method used yeast- and E. coli-specific "origins of replication" to eliminate the generation of by-products. Finally, we successfully cloned the DNA fragment into the vector with almost 100% efficiency.

Practical utilization of recombinant AAV vector reference standards: focus on vector genomes titration by free ITR qPCR.

PubMed

D'Costa, Susan; Blouin, Veronique; Broucque, Frederic; Penaud-Budloo, Magalie; François, Achille; Perez, Irene C; Le Bec, Christine; Moullier, Philippe; Snyder, Richard O; Ayuso, Eduard

2016-01-01

Clinical trials using recombinant adeno-associated virus (rAAV) vectors have demonstrated efficacy and a good safety profile. Although the field is advancing quickly, vector analytics and harmonization of dosage units are still a limitation for commercialization. AAV reference standard materials (RSMs) can help ensure product safety by controlling the consistency of assays used to characterize rAAV stocks. The most widely utilized unit of vector dosing is based on the encapsidated vector genome. Quantitative polymerase chain reaction (qPCR) is now the most common method to titer vector genomes (vg); however, significant inter- and intralaboratory variations have been documented using this technique. Here, RSMs and rAAV stocks were titered on the basis of an inverted terminal repeats (ITRs) sequence-specific qPCR and we found an artificial increase in vg titers using a widely utilized approach. The PCR error was introduced by using single-cut linearized plasmid as the standard curve. This bias was eliminated using plasmid standards linearized just outside the ITR region on each end to facilitate the melting of the palindromic ITR sequences during PCR. This new "Free-ITR" qPCR delivers vg titers that are consistent with titers obtained with transgene-specific qPCR and could be used to normalize in-house product-specific AAV vector standards and controls to the rAAV RSMs. The free-ITR method, including well-characterized controls, will help to calibrate doses to compare preclinical and clinical data in the field.

Temporo-spatial distribution of insecticide-resistance in Indian malaria vectors in the last quarter-century: Need for regular resistance monitoring and management.

PubMed

Raghavendra, Kamaraju; Velamuri, Poonam Sharma; Verma, Vaishali; Elamathi, Natarajan; Barik, Tapan Kumar; Bhatt, Rajendra Mohan; Dash, Aditya Prasad

2017-01-01

The Indian vector control programme similar to other programmes in the world is still reliant on chemical insecticides. Anopheles culicifacies is the major vector out of six primary malaria vectors in India and alone contributes about 2/3 malaria cases annually; and per se its control is actually control of malaria in India. For effective management of vectors, current information on their susceptibility status to different insecticides is essential. In this review, an attempt was made to compile and present the available data on the susceptibility status of different malaria vector species in India from the last 2.5 decades. Literature search was conducted by different means mainly web and library search; susceptibility data was collated from 62 sources for the nine malaria vector species from 145 districts in 21 states and two union territories between 1991 and 2016. Interpretation of the susceptibility/resistance status was made on basis of the recent WHO criteria. Comprehensive analysis of the data indicated that An. culicifacies, a major vector species was resistant to at least one insecticide in 70% (101/145) of the districts. It was reported mostly resistant to DDT and malathion whereas, its resistant status against deltamethrin varied across the districts. The major threat for the malaria control programmes is multiple-insecticide-resistance in An. culicifacies which needs immediate attention for resistance management in order to sustain the gains achieved so far, as the programmes have targeted malaria elimination by 2030.

Robust support vector regression networks for function approximation with outliers.

PubMed

Chuang, Chen-Chia; Su, Shun-Feng; Jeng, Jin-Tsong; Hsiao, Chih-Ching

2002-01-01

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. In this paper, a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR. In the approach, traditional robust learning approaches are employed to improve the learning performance for any selected parameters. From the simulation results, our RSVR can always improve the performance of the learned systems for all cases. Besides, it can be found that even the training lasted for a long period, the testing errors would not go up. In other words, the overfitting phenomenon is indeed suppressed.

Floral traits influence pollen vectors' choices in higher elevation communities in the Himalaya-Hengduan Mountains.

PubMed

Zhao, Yan-Hui; Ren, Zong-Xin; Lázaro, Amparo; Wang, Hong; Bernhardt, Peter; Li, Hai-Dong; Li, De-Zhu

2016-05-24

How floral traits and community composition influence plant specialization is poorly understood and the existing evidence is restricted to regions where plant diversity is low. Here, we assessed whether plant specialization varied among four species-rich subalpine/alpine communities on the Yulong Mountain, SW China (elevation from 2725 to 3910 m). We analyzed two factors (floral traits and pollen vector community composition: richness and density) to determine the degree of plant specialization across 101 plant species in all four communities. Floral visitors were collected and pollen load analyses were conducted to identify and define pollen vectors. Plant specialization of each species was described by using both pollen vector diversity (Shannon's diversity index) and plant selectiveness (d' index), which reflected how selective a given species was relative to available pollen vectors. Pollen vector diversity tended to be higher in communities at lower elevations, while plant selectiveness was significantly lower in a community with the highest proportion of unspecialized flowers (open flowers and clusters of flowers in open inflorescences). In particular, we found that plant species with large and unspecialized flowers attracted a greater diversity of pollen vectors and showed higher selectiveness in their use of pollen vectors. Plant species with large floral displays and high flower abundance were more selective in their exploitation of pollen vectors. Moreover, there was a negative relationship between plant selectiveness and pollen vector density. These findings suggest that flower shape and flower size can increase pollen vector diversity but they also increased plant selectiveness. This indicated that those floral traits that were more attractive to insects increased the diversity of pollen vectors to plants while decreasing overlap among co-blooming plant species for the same pollen vectors. Furthermore, floral traits had a more important impact on the diversity of pollen vectors than the composition of anthophilous insect communities. Plant selectiveness of pollen vectors was strongly influenced by both floral traits and insect community composition. These findings provide a basis for a better understanding of how floral traits and community context shape interactions between flowers and their pollen vectors in species-rich communities.

A transposase strategy for creating libraries of circularly permuted proteins.

PubMed

Mehta, Manan M; Liu, Shirley; Silberg, Jonathan J

2012-05-01

A simple approach for creating libraries of circularly permuted proteins is described that is called PERMutation Using Transposase Engineering (PERMUTE). In PERMUTE, the transposase MuA is used to randomly insert a minitransposon that can function as a protein expression vector into a plasmid that contains the open reading frame (ORF) being permuted. A library of vectors that express different permuted variants of the ORF-encoded protein is created by: (i) using bacteria to select for target vectors that acquire an integrated minitransposon; (ii) excising the ensemble of ORFs that contain an integrated minitransposon from the selected vectors; and (iii) circularizing the ensemble of ORFs containing integrated minitransposons using intramolecular ligation. Construction of a Thermotoga neapolitana adenylate kinase (AK) library using PERMUTE revealed that this approach produces vectors that express circularly permuted proteins with distinct sequence diversity from existing methods. In addition, selection of this library for variants that complement the growth of Escherichia coli with a temperature-sensitive AK identified functional proteins with novel architectures, suggesting that PERMUTE will be useful for the directed evolution of proteins with new functions.

A transposase strategy for creating libraries of circularly permuted proteins

PubMed Central

Mehta, Manan M.; Liu, Shirley; Silberg, Jonathan J.

2012-01-01

A simple approach for creating libraries of circularly permuted proteins is described that is called PERMutation Using Transposase Engineering (PERMUTE). In PERMUTE, the transposase MuA is used to randomly insert a minitransposon that can function as a protein expression vector into a plasmid that contains the open reading frame (ORF) being permuted. A library of vectors that express different permuted variants of the ORF-encoded protein is created by: (i) using bacteria to select for target vectors that acquire an integrated minitransposon; (ii) excising the ensemble of ORFs that contain an integrated minitransposon from the selected vectors; and (iii) circularizing the ensemble of ORFs containing integrated minitransposons using intramolecular ligation. Construction of a Thermotoga neapolitana adenylate kinase (AK) library using PERMUTE revealed that this approach produces vectors that express circularly permuted proteins with distinct sequence diversity from existing methods. In addition, selection of this library for variants that complement the growth of Escherichia coli with a temperature-sensitive AK identified functional proteins with novel architectures, suggesting that PERMUTE will be useful for the directed evolution of proteins with new functions. PMID:22319214

Lefschetz thimbles in fermionic effective models with repulsive vector-field

NASA Astrophysics Data System (ADS)

Mori, Yuto; Kashiwa, Kouji; Ohnishi, Akira

2018-06-01

We discuss two problems in complexified auxiliary fields in fermionic effective models, the auxiliary sign problem associated with the repulsive vector-field and the choice of the cut for the scalar field appearing from the logarithmic function. In the fermionic effective models with attractive scalar and repulsive vector-type interaction, the auxiliary scalar and vector fields appear in the path integral after the bosonization of fermion bilinears. When we make the path integral well-defined by the Wick rotation of the vector field, the oscillating Boltzmann weight appears in the partition function. This "auxiliary" sign problem can be solved by using the Lefschetz-thimble path-integral method, where the integration path is constructed in the complex plane. Another serious obstacle in the numerical construction of Lefschetz thimbles is caused by singular points and cuts induced by multivalued functions of the complexified scalar field in the momentum integration. We propose a new prescription which fixes gradient flow trajectories on the same Riemann sheet in the flow evolution by performing the momentum integration in the complex domain.

A method of recovering the initial vectors of globally coupled map lattices based on symbolic dynamics

NASA Astrophysics Data System (ADS)

Sun, Li-Sha; Kang, Xiao-Yun; Zhang, Qiong; Lin, Lan-Xin

2011-12-01

Based on symbolic dynamics, a novel computationally efficient algorithm is proposed to estimate the unknown initial vectors of globally coupled map lattices (CMLs). It is proved that not all inverse chaotic mapping functions are satisfied for contraction mapping. It is found that the values in phase space do not always converge on their initial values with respect to sufficient backward iteration of the symbolic vectors in terms of global convergence or divergence (CD). Both CD property and the coupling strength are directly related to the mapping function of the existing CML. Furthermore, the CD properties of Logistic, Bernoulli, and Tent chaotic mapping functions are investigated and compared. Various simulation results and the performances of the initial vector estimation with different signal-to-noise ratios (SNRs) are also provided to confirm the proposed algorithm. Finally, based on the spatiotemporal chaotic characteristics of the CML, the conditions of estimating the initial vectors using symbolic dynamics are discussed. The presented method provides both theoretical and experimental results for better understanding and characterizing the behaviours of spatiotemporal chaotic systems.

Chemical application of diffusion quantum Monte Carlo

NASA Technical Reports Server (NTRS)

Reynolds, P. J.; Lester, W. A., Jr.

1984-01-01

The diffusion quantum Monte Carlo (QMC) method gives a stochastic solution to the Schroedinger equation. This approach is receiving increasing attention in chemical applications as a result of its high accuracy. However, reducing statistical uncertainty remains a priority because chemical effects are often obtained as small differences of large numbers. As an example, the single-triplet splitting of the energy of the methylene molecule CH sub 2 is given. The QMC algorithm was implemented on the CYBER 205, first as a direct transcription of the algorithm running on the VAX 11/780, and second by explicitly writing vector code for all loops longer than a crossover length C. The speed of the codes relative to one another as a function of C, and relative to the VAX, are discussed. The computational time dependence obtained versus the number of basis functions is discussed and this is compared with that obtained from traditional quantum chemistry codes and that obtained from traditional computer architectures.

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Assessment of time-dependent density functional theory with the restricted excitation space approximation for excited state calculations of large systems

NASA Astrophysics Data System (ADS)

Hanson-Heine, Magnus W. D.; George, Michael W.; Besley, Nicholas A.

2018-06-01

The restricted excitation subspace approximation is explored as a basis to reduce the memory storage required in linear response time-dependent density functional theory (TDDFT) calculations within the Tamm-Dancoff approximation. It is shown that excluding the core orbitals and up to 70% of the virtual orbitals in the construction of the excitation subspace does not result in significant changes in computed UV/vis spectra for large molecules. The reduced size of the excitation subspace greatly reduces the size of the subspace vectors that need to be stored when using the Davidson procedure to determine the eigenvalues of the TDDFT equations. Furthermore, additional screening of the two-electron integrals in combination with a reduction in the size of the numerical integration grid used in the TDDFT calculation leads to significant computational savings. The use of these approximations represents a simple approach to extend TDDFT to the study of large systems and make the calculations increasingly tractable using modest computing resources.

A reduced-order model from high-dimensional frictional hysteresis

PubMed Central

Biswas, Saurabh; Chatterjee, Anindya

2014-01-01

Hysteresis in material behaviour includes both signum nonlinearities as well as high dimensionality. Available models for component-level hysteretic behaviour are empirical. Here, we derive a low-order model for rate-independent hysteresis from a high-dimensional massless frictional system. The original system, being given in terms of signs of velocities, is first solved incrementally using a linear complementarity problem formulation. From this numerical solution, to develop a reduced-order model, basis vectors are chosen using the singular value decomposition. The slip direction in generalized coordinates is identified as the minimizer of a dissipation-related function. That function includes terms for frictional dissipation through signum nonlinearities at many friction sites. Luckily, it allows a convenient analytical approximation. Upon solution of the approximated minimization problem, the slip direction is found. A final evolution equation for a few states is then obtained that gives a good match with the full solution. The model obtained here may lead to new insights into hysteresis as well as better empirical modelling thereof. PMID:24910522

Lyme and Dopaminergic Function: Hypothesizing Reduced Reward Deficiency Symptomatology by Regulating Dopamine Transmission

PubMed Central

Blum, Kenneth; Modestino, Edward J; Febo, Marcelo; Steinberg, Bruce; McLaughlin, Thomas; Fried, Lyle; Baron, David; Siwicki, David; Badgaiyan, Rajendra D

2017-01-01

The principal vector of Lyme disease in the United States is Ixodes scapularis: black legged or deer ticks. There is increased evidence that those infected may be plagued by anxiety or depression as well. Researchers have identified transcripts coding for two putative cytosolic sulfotransferases in these ticks, which recognized phenolic monoamines as their substrates. It is hypothesized that protracted Lyme disease sequelae may be due to impairment of dopaminergic function of the brain reward circuitry. The subsequent recombinant proteins exhibited sulfotransferase function against two neurotransmitters: dopamine and octopamine. This, in itself, can reduce dopamine function leading to many Reward Deficiency Syndrome behaviors, including depression and possibly, anxiety. In fact, it was shown that activity of Ixosc Sult 1 and Sult 2 in the Ixodid tick salivary glands might contain inactivation of the salivation signal through sulfonation of either dopamine or octopamine. This infraction results in a number of clinically observed mood changes, such as anxiety and depression. In fact, there are common symptoms observed for both Parkinson and Lyme diseases. The importance of understanding the mechanistic and neurobiological effects of Lyme on the central nervous system (CNS) provides the basis for pro-dopamine regulation as a treatment. WC 195 PMID:28736624

Lyme and Dopaminergic Function: Hypothesizing Reduced Reward Deficiency Symptomatology by Regulating Dopamine Transmission.

PubMed

Blum, Kenneth; Modestino, Edward J; Febo, Marcelo; Steinberg, Bruce; McLaughlin, Thomas; Fried, Lyle; Baron, David; Siwicki, David; Badgaiyan, Rajendra D

2017-05-01

The principal vector of Lyme disease in the United States is Ixodes scapularis: black legged or deer ticks. There is increased evidence that those infected may be plagued by anxiety or depression as well. Researchers have identified transcripts coding for two putative cytosolic sulfotransferases in these ticks, which recognized phenolic monoamines as their substrates. It is hypothesized that protracted Lyme disease sequelae may be due to impairment of dopaminergic function of the brain reward circuitry. The subsequent recombinant proteins exhibited sulfotransferase function against two neurotransmitters: dopamine and octopamine. This, in itself, can reduce dopamine function leading to many Reward Deficiency Syndrome behaviors, including depression and possibly, anxiety. In fact, it was shown that activity of Ixosc Sult 1 and Sult 2 in the Ixodid tick salivary glands might contain inactivation of the salivation signal through sulfonation of either dopamine or octopamine. This infraction results in a number of clinically observed mood changes, such as anxiety and depression. In fact, there are common symptoms observed for both Parkinson and Lyme diseases. The importance of understanding the mechanistic and neurobiological effects of Lyme on the central nervous system (CNS) provides the basis for pro-dopamine regulation as a treatment. WC 195.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Holomorphic projections and Ramanujan’s mock theta functions

PubMed Central

Imamoğlu, Özlem; Raum, Martin; Richter, Olav K.

2014-01-01

We use spectral methods of automorphic forms to establish a holomorphic projection operator for tensor products of vector-valued harmonic weak Maass forms and vector-valued modular forms. We apply this operator to discover simple recursions for Fourier series coefficients of Ramanujan’s mock theta functions. PMID:24591582

Predictiveness of Disease Risk in a Global Outreach Tourist Setting in Thailand Using Meteorological Data and Vector-Borne Disease Incidences

PubMed Central

Ninphanomchai, Suwannapa; Chansang, Chitti; Hii, Yien Ling; Rocklöv, Joacim; Kittayapong, Pattamaporn

2014-01-01

Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate. PMID:25325356

[Entomological study of Trypanosoma cruzi vectors in the rural communities of Sucre state, Venezuela].

PubMed

García-Jordán, Noris; Berrizbeitia, Mariolga; Concepción, Juan Luis; Aldana, Elis; Cáceres, Ana; Quiñones, Wilfredo

2015-01-01

The ecological niche of Reduvidae vectors has been modified due to environmental changes and human encroachment into the rural areas. This study evaluates the current entomological indices of triatomines responsible for Trypanosoma cruzi infection in Sucre State, Venezuela. A cross-sectional and prospective study was conducted in 95 towns and 577 dwellings in the 15 municipalities of the state of Sucre, Venezuela, from August to November, 2008. Triatomine bugs were identified on the basis of morphological characteristics, and their feces examined for T. cruzi infection through direct microscopy. Positive slides were stained with Giemsa and parasites were identified by morphologic characterization. The entomological indices expressing the highest values were dispersion (16.67%) and household colonization (33.33%). The triatomine species captured were: Rhodnius prolixus , Rhodnius main intradomiciliary vector. Despite the low index of vector infection (1.72%), the existence of species with domiciliary and peridomiciliary reproductive success ensures the persistence of the epidemiological chain both for the disease and the parasite.

Prevention of vector transmitted diseases with clove oil insect repellent.

PubMed

Shapiro, Rochel

2012-08-01

Vector repellent is one element in the prevention of vector-borne diseases. Families that neglect protecting their children against vectors risk their children contracting illnesses such as West Nile virus, eastern equine encephalitis, Lyme disease, malaria, dengue hemorrhagic fever, yellow fever, babesiosis, Crimean-Congo hemorrhagic fever, Rocky Mountain spotted fever, Southern tick-associated rash illness, ehrlichiosis, tick-borne relapsing fever, tularemia, and other insect and arthropod related diseases (CDC, 2011). Identification of families at risk includes screening of the underlying basis for reluctance to apply insect repellent. Nurses and physicians can participate in a positive role by assisting families to determine the proper prophylaxis by recommending insect repellent choices that are economical, safe, and easy to use. A holistic alternative might include the suggestion of clove oil in cases where families might have trepidations regarding the use of DEET on children. This article will explore the safety and effectiveness of clove oil and its use as an insect repellent. Copyright © 2012 Elsevier Inc. All rights reserved.

Predictiveness of disease risk in a global outreach tourist setting in Thailand using meteorological data and vector-borne disease incidences.

PubMed

Ninphanomchai, Suwannapa; Chansang, Chitti; Hii, Yien Ling; Rocklöv, Joacim; Kittayapong, Pattamaporn

2014-10-16

Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate.

A real-time TaqMan polymerase chain reaction for the identification of Culex vectors of West Nile and Saint Louis encephalitis viruses in North America.

PubMed

Sanogo, Yibayiri O; Kim, Chang-Hyun; Lampman, Richard; Novak, Robert J

2007-07-01

In North America, West Nile and St. Louis encephalitis viruses have been detected in a wide range of vector species, but the majority of isolations continue to be from pools of mixed mosquitoes in the Culex subgenus Culex. Unfortunately, the morphologic identification of these important disease vectors is often difficult, particularly in regions of sympatry. We developed a sensitive real-time TaqMan polymerase chain reaction assay that allows reliable identification of Culex mosquitoes including Culex pipiens pipiens, Cx. p. quinquefasciatus, Cx. restuans, Cx. salinarius, Cx. nigripalpus, and Cx. tarsalis. Primers and fluorogenic probes specific to each species were designed based on sequences of the acetylcholinesterase gene (Ace2). Both immature and adult mosquitoes were successfully identified as individuals and as mixed species pools. This identification technique provides the basis for a rapid, sensitive, and high-throughput method for expounding the species-specific contribution of vectors to various phases of arbovirus transmission.

Lentiviral Vector Induced Insertional Haploinsufficiency of Ebf1 Causes Murine Leukemia

PubMed Central

Heckl, Dirk; Schwarzer, Adrian; Haemmerle, Reinhard; Steinemann, Doris; Rudolph, Cornelia; Skawran, Britta; Knoess, Sabine; Krause, Johanna; Li, Zhixiong; Schlegelberger, Brigitte; Baum, Christopher; Modlich, Ute

2012-01-01

Integrating vectors developed on the basis of various retroviruses have demonstrated therapeutic potential following genetic modification of long-lived hematopoietic stem and progenitor cells. Lentiviral vectors (LV) are assumed to circumvent genotoxic events previously observed with γ-retroviral vectors, due to their integration bias to transcription units in comparison to the γ-retroviral preference for promoter regions and CpG islands. However, recently several studies have revealed the potential for gene activation by LV insertions. Here, we report a murine acute B-lymphoblastic leukemia (B-ALL) triggered by insertional gene inactivation. LV integration occurred into the 8th intron of Ebf1, a major regulator of B-lymphopoiesis. Various aberrant splice variants could be detected that involved splice donor and acceptor sites of the lentiviral construct, inducing downregulation of Ebf1 full-length message. The transcriptome signature was compatible with loss of this major determinant of B-cell differentiation, with partial acquisition of myeloid markers, including Csf1r (macrophage colony-stimulating factor (M-CSF) receptor). This was accompanied by receptor phosphorylation and STAT5 activation, both most likely contributing to leukemic progression. Our results highlight the risk of intragenic vector integration to initiate leukemia by inducing haploinsufficiency of a tumor suppressor gene. We propose to address this risk in future vector design. PMID:22472950

Preparation for a first-in-man lentivirus trial in patients with cystic fibrosis

PubMed Central

Alton, Eric W F W; Beekman, Jeffery M; Boyd, A Christopher; Brand, June; Carlon, Marianne S; Connolly, Mary M; Chan, Mario; Conlon, Sinead; Davidson, Heather E; Davies, Jane C; Davies, Lee A; Dekkers, Johanna F; Doherty, Ann; Gea-Sorli, Sabrina; Gill, Deborah R; Griesenbach, Uta; Hasegawa, Mamoru; Higgins, Tracy E; Hironaka, Takashi; Hyndman, Laura; McLachlan, Gerry; Inoue, Makoto; Hyde, Stephen C; Innes, J Alastair; Maher, Toby M; Moran, Caroline; Meng, Cuixiang; Paul-Smith, Michael C; Pringle, Ian A; Pytel, Kamila M; Rodriguez-Martinez, Andrea; Schmidt, Alexander C; Stevenson, Barbara J; Sumner-Jones, Stephanie G; Toshner, Richard; Tsugumine, Shu; Wasowicz, Marguerite W; Zhu, Jie

2017-01-01

We have recently shown that non-viral gene therapy can stabilise the decline of lung function in patients with cystic fibrosis (CF). However, the effect was modest, and more potent gene transfer agents are still required. Fuson protein (F)/Hemagglutinin/Neuraminidase protein (HN)-pseudotyped lentiviral vectors are more efficient for lung gene transfer than non-viral vectors in preclinical models. In preparation for a first-in-man CF trial using the lentiviral vector, we have undertaken key translational preclinical studies. Regulatory-compliant vectors carrying a range of promoter/enhancer elements were assessed in mice and human air–liquid interface (ALI) cultures to select the lead candidate; cystic fibrosis transmembrane conductance receptor (CFTR) expression and function were assessed in CF models using this lead candidate vector. Toxicity was assessed and ‘benchmarked’ against the leading non-viral formulation recently used in a Phase IIb clinical trial. Integration site profiles were mapped and transduction efficiency determined to inform clinical trial dose-ranging. The impact of pre-existing and acquired immunity against the vector and vector stability in several clinically relevant delivery devices was assessed. A hybrid promoter hybrid cytosine guanine dinucleotide (CpG)- free CMV enhancer/elongation factor 1 alpha promoter (hCEF) consisting of the elongation factor 1α promoter and the cytomegalovirus enhancer was most efficacious in both murine lungs and human ALI cultures (both at least 2-log orders above background). The efficacy (at least 14% of airway cells transduced), toxicity and integration site profile supports further progression towards clinical trial and pre-existing and acquired immune responses do not interfere with vector efficacy. The lead rSIV.F/HN candidate expresses functional CFTR and the vector retains 90–100% transduction efficiency in clinically relevant delivery devices. The data support the progression of the F/HN-pseudotyped lentiviral vector into a first-in-man CF trial in 2017. PMID:27852956

Magic Pools: Parallel Assessment of Transposon Delivery Vectors in Bacteria

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hualan; Price, Morgan N.; Waters, Robert Jordan

Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach for discovering the functions of bacterial genes. However, the development of a suitable TnSeq strategy for a given bacterium can be costly and time-consuming. To meet this challenge, we describe a part-based strategy for constructing libraries of hundreds of transposon delivery vectors, which we term “magic pools.” Within a magic pool, each transposon vector has a different combination of upstream sequences (promoters and ribosome binding sites) and antibiotic resistance markers as well as a random DNA barcode sequence, which allows the tracking of each vector during mutagenesis experiments. Tomore » identify an efficient vector for a given bacterium, we mutagenize it with a magic pool and sequence the resulting insertions; we then use this efficient vector to generate a large mutant library. We used the magic pool strategy to construct transposon mutant libraries in five genera of bacteria, including three genera of the phylumBacteroidetes. IMPORTANCEMolecular genetics is indispensable for interrogating the physiology of bacteria. However, the development of a functional genetic system for any given bacterium can be time-consuming. Here, we present a streamlined approach for identifying an effective transposon mutagenesis system for a new bacterium. Our strategy first involves the construction of hundreds of different transposon vector variants, which we term a “magic pool.” The efficacy of each vector in a magic pool is monitored in parallel using a unique DNA barcode that is introduced into each vector design. Using archived DNA “parts,” we next reassemble an effective vector for making a whole-genome transposon mutant library that is suitable for large-scale interrogation of gene function using competitive growth assays. Here, we demonstrate the utility of the magic pool system to make mutant libraries in five genera of bacteria.« less

Magic Pools: Parallel Assessment of Transposon Delivery Vectors in Bacteria

DOE PAGES

Liu, Hualan; Price, Morgan N.; Waters, Robert Jordan; ...

2018-01-16

Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach for discovering the functions of bacterial genes. However, the development of a suitable TnSeq strategy for a given bacterium can be costly and time-consuming. To meet this challenge, we describe a part-based strategy for constructing libraries of hundreds of transposon delivery vectors, which we term “magic pools.” Within a magic pool, each transposon vector has a different combination of upstream sequences (promoters and ribosome binding sites) and antibiotic resistance markers as well as a random DNA barcode sequence, which allows the tracking of each vector during mutagenesis experiments. Tomore » identify an efficient vector for a given bacterium, we mutagenize it with a magic pool and sequence the resulting insertions; we then use this efficient vector to generate a large mutant library. We used the magic pool strategy to construct transposon mutant libraries in five genera of bacteria, including three genera of the phylumBacteroidetes. IMPORTANCEMolecular genetics is indispensable for interrogating the physiology of bacteria. However, the development of a functional genetic system for any given bacterium can be time-consuming. Here, we present a streamlined approach for identifying an effective transposon mutagenesis system for a new bacterium. Our strategy first involves the construction of hundreds of different transposon vector variants, which we term a “magic pool.” The efficacy of each vector in a magic pool is monitored in parallel using a unique DNA barcode that is introduced into each vector design. Using archived DNA “parts,” we next reassemble an effective vector for making a whole-genome transposon mutant library that is suitable for large-scale interrogation of gene function using competitive growth assays. Here, we demonstrate the utility of the magic pool system to make mutant libraries in five genera of bacteria.« less

Classification of Alzheimer's disease patients with hippocampal shape wrapper-based feature selection and support vector machine

NASA Astrophysics Data System (ADS)

Young, Jonathan; Ridgway, Gerard; Leung, Kelvin; Ourselin, Sebastien

2012-02-01

It is well known that hippocampal atrophy is a marker of the onset of Alzheimer's disease (AD) and as a result hippocampal volumetry has been used in a number of studies to provide early diagnosis of AD and predict conversion of mild cognitive impairment patients to AD. However, rates of atrophy are not uniform across the hippocampus making shape analysis a potentially more accurate biomarker. This study studies the hippocampi from 226 healthy controls, 148 AD patients and 330 MCI patients obtained from T1 weighted structural MRI images from the ADNI database. The hippocampi are anatomically segmented using the MAPS multi-atlas segmentation method, and the resulting binary images are then processed with SPHARM software to decompose their shapes as a weighted sum of spherical harmonic basis functions. The resulting parameterizations are then used as feature vectors in Support Vector Machine (SVM) classification. A wrapper based feature selection method was used as this considers the utility of features in discriminating classes in combination, fully exploiting the multivariate nature of the data and optimizing the selected set of features for the type of classifier that is used. The leave-one-out cross validated accuracy obtained on training data is 88.6% for classifying AD vs controls and 74% for classifying MCI-converters vs MCI-stable with very compact feature sets, showing that this is a highly promising method. There is currently a considerable fall in accuracy on unseen data indicating that the feature selection is sensitive to the data used, however feature ensemble methods may overcome this.

Landscape movements of Anopheles gambiae malaria vector mosquitoes in rural Gambia.

PubMed

Thomas, Christopher J; Cross, Dónall E; Bøgh, Claus

2013-01-01

For malaria control in Africa it is crucial to characterise the dispersal of its most efficient vector, Anopheles gambiae, in order to target interventions and assess their impact spatially. Our study is, we believe, the first to present a statistical model of dispersal probability against distance from breeding habitat to human settlements for this important disease vector. We undertook post-hoc analyses of mosquito catches made in The Gambia to derive statistical dispersal functions for An. gambiae sensu lato collected in 48 villages at varying distances to alluvial larval habitat along the River Gambia. The proportion dispersing declined exponentially with distance, and we estimated that 90% of movements were within 1.7 km. Although a 'heavy-tailed' distribution is considered biologically more plausible due to active dispersal by mosquitoes seeking blood meals, there was no statistical basis for choosing it over a negative exponential distribution. Using a simple random walk model with daily survival and movements previously recorded in Burkina Faso, we were able to reproduce the dispersal probabilities observed in The Gambia. Our results provide an important quantification of the probability of An. gambiae s.l. dispersal in a rural African setting typical of many parts of the continent. However, dispersal will be landscape specific and in order to generalise to other spatial configurations of habitat and hosts it will be necessary to produce tractable models of mosquito movements for operational use. We show that simple random walk models have potential. Consequently, there is a pressing need for new empirical studies of An. gambiae survival and movements in different settings to drive this development.

The Application of a Technique for Vector Correlation to Problems in Meteorology and Oceanography.

NASA Astrophysics Data System (ADS)

Breaker, L. C.; Gemmill, W. H.; Crosby, D. S.

1994-11-01

In a recent study, Crosby et al. proposed a definition for vector correlation that has not been commonly used in meteorology or oceanography. This definition has both a firm theoretical basis and a rather complete set of desirable statistical properties. In this study, the authors apply the definition to practical problems arising in meteorology and oceanography. In the first of two case studies, vector correlations were calculated between subsurface currents for five locations along the southeastern shore of Lake Erie. Vector correlations for one sample size were calculated for all current meter combinations, first including the seiche frequency and then with the seiche frequency removed. Removal of the seiche frequency, which was easily detected in the current spectra, had only a small effect on the vector correlations. Under reasonable assumptions, the vector correlations were in most cases statistically significant and revealed considerable fine structure in the vector correlation sequences. In some cases, major variations in vector correlation coincided with changes in surface wind. The vector correlations for the various current meter combinations decreased rapidly with increasing spatial separation. For one current meter combination, canonical correlations were also calculated; the first canonical correlation tended to retain the underlying trend, whereas the second canonical correlation retained the peaks in the vector correlations.In the second case study, vector correlations were calculated between marine surface winds derived from the National Meteorological Center's Global Data Assimilation System and observed winds acquired from the network of National Data Buoy Center buoys that are located off the continental United States and in the Gulf of Alaska. Results of this comparison indicated that 1) there was a significant decrease in correlation between the predicted and observed winds with increasing forecast interval out to 72 h, 2) the technique provides a sensitive indicator for detecting bad buoy reports, and 3) there was no obvious seasonal cycle in the monthly vector correlations for the period of observation.

Mathematical modelling of vector-borne diseases and insecticide resistance evolution.

PubMed

Gabriel Kuniyoshi, Maria Laura; Pio Dos Santos, Fernando Luiz

2017-01-01

Vector-borne diseases are important public health issues and, consequently, in silico models that simulate them can be useful. The susceptible-infected-recovered (SIR) model simulates the population dynamics of an epidemic and can be easily adapted to vector-borne diseases, whereas the Hardy-Weinberg model simulates allele frequencies and can be used to study insecticide resistance evolution. The aim of the present study is to develop a coupled system that unifies both models, therefore enabling the analysis of the effects of vector population genetics on the population dynamics of an epidemic. Our model consists of an ordinary differential equation system. We considered the populations of susceptible, infected and recovered humans, as well as susceptible and infected vectors. Concerning these vectors, we considered a pair of alleles, with complete dominance interaction that determined the rate of mortality induced by insecticides. Thus, we were able to separate the vectors according to the genotype. We performed three numerical simulations of the model. In simulation one, both alleles conferred the same mortality rate values, therefore there was no resistant strain. In simulations two and three, the recessive and dominant alleles, respectively, conferred a lower mortality. Our numerical results show that the genetic composition of the vector population affects the dynamics of human diseases. We found that the absolute number of vectors and the proportion of infected vectors are smaller when there is no resistant strain, whilst the ratio of infected people is larger in the presence of insecticide-resistant vectors. The dynamics observed for infected humans in all simulations has a very similar shape to real epidemiological data. The population genetics of vectors can affect epidemiological dynamics, and the presence of insecticide-resistant strains can increase the number of infected people. Based on the present results, the model is a basis for development of other models and for investigating population dynamics.

New Constructions of Orthogonal Product Basis Quantum States

NASA Astrophysics Data System (ADS)

Zuo, Huijuan; Liu, Shuxia; Yang, Yinghui

2018-02-01

An orthogonal basis B9 for the Hilbert space C 3 × C 3 was presented by Bennett et al. (Phys. Rev. A 59, 1070, 1999) which was illustrated in a visual figure in their report. The character of the construction is that each base vector is a product state, thus any distinguishing operator cannot create entanglement. In this paper, we mainly focus on some new constructions of orthogonal product basis quantum states in the high-dimensional quantum systems. Especially, as for the quantum system of (2m + 1) ⊗ (2m + 1), where m ∈ Z and m ≥ 2, we have provided the direct construction in mathematical method.

Absorptive corrections for vector mesons: matching to complex mass scheme and longitudinal corrections

NASA Astrophysics Data System (ADS)

Jiménez Pérez, L. A.; Toledo Sánchez, G.

2017-12-01

Unstable spin-1 particles are properly described by including absorptive corrections to the electromagnetic vertex and propagator, without breaking the electromagnetic gauge invariance. We show that the modified propagator can be set in a complex mass form, provided the mass and width parameters, which are properly defined at the pole, are replaced by energy dependent functions fulfilling the same requirements at the pole. We exemplify the case for the {K}* (892) vector meson, and find that the mass function deviates around 2 MeV from the Kπ threshold to the pole, and that the width function exhibits a different behavior compared to the uncorrected energy dependent width. Considering the {τ }-\\to {K}{{S}}{π }-{ν }τ decay as dominated by the {K}* (892) and {K}{\\prime * }(1410) vectors and one scalar particle, we exhibit the role of the transversal and longitudinal corrections to the vector propagator by obtaining the modified vector and scalar form factors. The modified vector form factor is found to be the same as in the complex mass form, while the scalar form factor receives a modification from the longitudinal correction to the vector propagator. A fit to the experimental Kπ spectrum shows that the phase induced by the presence of this new contribution in the scalar sector improves the description of the experimental data in the troublesome region around 0.7 GeV. Besides that, the correction to the scalar form factor is found to be negligible.

Closed-form integrator for the quaternion (euler angle) kinematics equations

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A. (Inventor)

2000-01-01

The invention is embodied in a method of integrating kinematics equations for updating a set of vehicle attitude angles of a vehicle using 3-dimensional angular velocities of the vehicle, which includes computing an integrating factor matrix from quantities corresponding to the 3-dimensional angular velocities, computing a total integrated angular rate from the quantities corresponding to a 3-dimensional angular velocities, computing a state transition matrix as a sum of (a) a first complementary function of the total integrated angular rate and (b) the integrating factor matrix multiplied by a second complementary function of the total integrated angular rate, and updating the set of vehicle attitude angles using the state transition matrix. Preferably, the method further includes computing a quanternion vector from the quantities corresponding to the 3-dimensional angular velocities, in which case the updating of the set of vehicle attitude angles using the state transition matrix is carried out by (a) updating the quanternion vector by multiplying the quanternion vector by the state transition matrix to produce an updated quanternion vector and (b) computing an updated set of vehicle attitude angles from the updated quanternion vector. The first and second trigonometric functions are complementary, such as a sine and a cosine. The quantities corresponding to the 3-dimensional angular velocities include respective averages of the 3-dimensional angular velocities over plural time frames. The updating of the quanternion vector preserves the norm of the vector, whereby the updated set of vehicle attitude angles are virtually error-free.

Receptor-mediated gene transfer vectors: progress towards genetic pharmaceuticals.

PubMed

Molas, M; Gómez-Valadés, A G; Vidal-Alabró, A; Miguel-Turu, M; Bermudez, J; Bartrons, R; Perales, J C

2003-10-01

Although specific delivery to tissues and unique cell types in vivo has been demonstrated for many non-viral vectors, current methods are still inadequate for human applications, mainly because of limitations on their efficiencies. All the steps required for an efficient receptor-mediated gene transfer process may in principle be exploited to enhance targeted gene delivery. These steps are: DNA/vector binding, internalization, subcellular trafficking, vesicular escape, nuclear import, and unpacking either for transcription or other functions (i.e., antisense, RNA interference, etc.). The large variety of vector designs that are currently available, usually aimed at improving the efficiency of these steps, has complicated the evaluation of data obtained from specific derivatives of such vectors. The importance of the structure of the final vector and the consequences of design decisions at specific steps on the overall efficiency of the vector will be discussed in detail. We emphasize in this review that stability in serum and thus, proper bioavailability of vectors to their specific receptors may be the single greatest limiting factor on the overall gene transfer efficiency in vivo. We discuss current approaches to overcome the intrinsic instability of synthetic vectors in the blood. In this regard, a summary of the structural features of the vectors obtained from current protocols will be presented and their functional characteristics evaluated. Dissecting information on molecular conjugates obtained by such methodologies, when carefully evaluated, should provide important guidelines for the creation of effective, targeted and safe DNA therapeutics.

Vector and Axial-Vector Correlators in AN Instanton-Like Quark Model

NASA Astrophysics Data System (ADS)

Dorokhov, Alexander E.

The behavior of the vector Adler function at spacelike momenta is studied in the framework of a covariant chiral quark model with instanton-like quark-quark interaction. This function describes the transition between the high energy asymptotically free region of almost massless current quarks to the low energy hadronized regime with massive constituent quarks. The model reproduces the Adler function and V-A correlator extracted from the ALEPH and OPAL data on hadronic τ lepton decays, transformed into the Euclidean domain via dispersion relations. The leading order contribution from hadronic part of the photon vacuum polarization to the anomalous magnetic moment of the muon, aμ hvp(1), is estimated.

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

An Improved Brome mosaic virus Silencing Vector: Greater Insert Stability and More Extensive VIGS.

PubMed

Ding, Xin Shun; Mannas, Stephen W; Bishop, Bethany A; Rao, Xiaolan; Lecoultre, Mitchell; Kwon, Soonil; Nelson, Richard S

2018-01-01

Virus-induced gene silencing (VIGS) is used extensively for gene function studies in plants. VIGS is inexpensive and rapid compared with silencing conducted through stable transformation, but many virus-silencing vectors, especially in grasses, induce only transient silencing phenotypes. A major reason for transient phenotypes is the instability of the foreign gene fragment (insert) in the vector during VIGS. Here, we report the development of a Brome mosaic virus (BMV)-based vector that better maintains inserts through modification of the original BMV vector RNA sequence. Modification of the BMV RNA3 sequence yielded a vector, BMVCP5, that better maintained phytoene desaturase and heat shock protein70-1 ( HSP70-1 ) inserts in Nicotiana benthamiana and maize ( Zea mays ). Longer maintenance of inserts was correlated with greater target gene silencing and more extensive visible silencing phenotypes displaying greater tissue penetration and involving more leaves. The modified vector accumulated similarly to the original vector in N. benthamiana after agroinfiltration, thus maintaining a high titer of virus in this intermediate host used to produce virus inoculum for grass hosts. For HSP70 , silencing one family member led to a large increase in the expression of another family member, an increase likely related to the target gene knockdown and not a general effect of virus infection. The cause of the increased insert stability in the modified vector is discussed in relationship to its recombination and accumulation potential. The modified vector will improve functional genomic studies in grasses, and the conceptual methods used to improve the vector may be applied to other VIGS vectors. © 2018 American Society of Plant Biologists. All Rights Reserved.

The inner topological structure and defect control of magnetic skyrmions

NASA Astrophysics Data System (ADS)

Ren, Ji-Rong; Yu, Zhong-Xi

2017-10-01

We prove that the integrand of magnetic skyrmions can be expressed as curvature tensor of Wu-Yang potential. Taking the projection of the normalized magnetization vector on the 2-dim material surface, and according to Duan's decomposition theory of gauge potential, we reveal that every single skyrmion is just characterized by Hopf index and Brouwer degree at the zero point of this vector field. Our theory meet the results that experimental physicists have achieved by many technologies. The inner topological structure expression of skyrmion with Hopf index and Brouwer degree will be indispensable mathematical basis of skyrmion logic gates.

Nucleon and Delta axial-vector couplings in 1/N{sub c}-Baryon Chiral Perturbation Theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goity, Jose Luis; Calle Cordon, Alvaro

In this contribution, baryon axial-vector couplings are studied in the framework of the combined 1/N{sub c} and chiral expansions. This framework is implemented on the basis of the emergent spin-flavor symmetry in baryons at large N{sub c} and HBChPT, and linking both expansions ({xi}-expansion), where 1/N{sub c} is taken to be a quantity order p. The study is carried out including one-loop contributions, which corresponds to order xi to the third for baryon masses and order {xi} square for the axial couplings.

Properties of Vector Preisach Models

NASA Technical Reports Server (NTRS)

Kahler, Gary R.; Patel, Umesh D.; Torre, Edward Della

2004-01-01

This paper discusses rotational anisotropy and rotational accommodation of magnetic particle tape. These effects have a performance impact during the reading and writing of the recording process. We introduce the reduced vector model as the basis for the computations. Rotational magnetization models must accurately compute the anisotropic characteristics of ellipsoidally magnetizable media. An ellipticity factor is derived for these media that computes the two-dimensional magnetization trajectory for all applied fields. An orientation correction must be applied to the computed rotational magnetization. For isotropic materials, an orientation correction has been developed and presented. For anisotropic materials, an orientation correction is introduced.

Optical implementation of inner product neural associative memory

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor)

1995-01-01

An optical implementation of an inner-product neural associative memory is realized with a first spatial light modulator for entering an initial two-dimensional N-tuple vector and for entering a thresholded output vector image after each iteration until convergence is reached, and a second spatial light modulator for entering M weighted vectors of inner-product scalars multiplied with each of the M stored vectors, where the inner-product scalars are produced by multiplication of the initial input vector in the first iterative cycle (and thresholded vectors in subsequent iterative cycles) with each of the M stored vectors, and the weighted vectors are produced by multiplication of the scalars with corresponding ones of the stored vectors. A Hughes liquid crystal light valve is used for the dual function of summing the weighted vectors and thresholding the sum vector. The thresholded vector is then entered through the first spatial light modulator for reiteration of the process cycle until convergence is reached.

A novel upwind stabilized discontinuous finite element angular framework for deterministic dose calculations in magnetic fields.

PubMed

Yang, R; Zelyak, O; Fallone, B G; St-Aubin, J

2018-01-30

Angular discretization impacts nearly every aspect of a deterministic solution to the linear Boltzmann transport equation, especially in the presence of magnetic fields, as modeled by a streaming operator in angle. In this work a novel stabilization treatment of the magnetic field term is developed for an angular finite element discretization on the unit sphere, specifically involving piecewise partitioning of path integrals along curved element edges into uninterrupted segments of incoming and outgoing flux, with outgoing components updated iteratively. Correct order-of-accuracy for this angular framework is verified using the method of manufactured solutions for linear, quadratic, and cubic basis functions in angle. Higher order basis functions were found to reduce the error especially in strong magnetic fields and low density media. We combine an angular finite element mesh respecting octant boundaries on the unit sphere to spatial Cartesian voxel elements to guarantee an unambiguous transport sweep ordering in space. Accuracy for a dosimetrically challenging scenario involving bone and air in the presence of a 1.5 T parallel magnetic field is validated against the Monte Carlo package GEANT4. Accuracy and relative computational efficiency were investigated for various angular discretization parameters. 32 angular elements with quadratic basis functions yielded a reasonable compromise, with gamma passing rates of 99.96% (96.22%) for a 2%/2 mm (1%/1 mm) criterion. A rotational transformation of the spatial calculation geometry is performed to orient an arbitrary magnetic field vector to be along the z-axis, a requirement for a constant azimuthal angular sweep ordering. Working on the unit sphere, we apply the same rotational transformation to the angular domain to align its octants with the rotated Cartesian mesh. Simulating an oblique 1.5 T magnetic field against GEANT4 yielded gamma passing rates of 99.42% (95.45%) for a 2%/2 mm (1%/1 mm) criterion.

A novel upwind stabilized discontinuous finite element angular framework for deterministic dose calculations in magnetic fields

NASA Astrophysics Data System (ADS)

Yang, R.; Zelyak, O.; Fallone, B. G.; St-Aubin, J.

2018-02-01

Angular discretization impacts nearly every aspect of a deterministic solution to the linear Boltzmann transport equation, especially in the presence of magnetic fields, as modeled by a streaming operator in angle. In this work a novel stabilization treatment of the magnetic field term is developed for an angular finite element discretization on the unit sphere, specifically involving piecewise partitioning of path integrals along curved element edges into uninterrupted segments of incoming and outgoing flux, with outgoing components updated iteratively. Correct order-of-accuracy for this angular framework is verified using the method of manufactured solutions for linear, quadratic, and cubic basis functions in angle. Higher order basis functions were found to reduce the error especially in strong magnetic fields and low density media. We combine an angular finite element mesh respecting octant boundaries on the unit sphere to spatial Cartesian voxel elements to guarantee an unambiguous transport sweep ordering in space. Accuracy for a dosimetrically challenging scenario involving bone and air in the presence of a 1.5 T parallel magnetic field is validated against the Monte Carlo package GEANT4. Accuracy and relative computational efficiency were investigated for various angular discretization parameters. 32 angular elements with quadratic basis functions yielded a reasonable compromise, with gamma passing rates of 99.96% (96.22%) for a 2%/2 mm (1%/1 mm) criterion. A rotational transformation of the spatial calculation geometry is performed to orient an arbitrary magnetic field vector to be along the z-axis, a requirement for a constant azimuthal angular sweep ordering. Working on the unit sphere, we apply the same rotational transformation to the angular domain to align its octants with the rotated Cartesian mesh. Simulating an oblique 1.5 T magnetic field against GEANT4 yielded gamma passing rates of 99.42% (95.45%) for a 2%/2 mm (1%/1 mm) criterion.

Multiresolution Wavelet Based Adaptive Numerical Dissipation Control for Shock-Turbulence Computations

NASA Technical Reports Server (NTRS)

Sjoegreen, B.; Yee, H. C.

2001-01-01

The recently developed essentially fourth-order or higher low dissipative shock-capturing scheme of Yee, Sandham and Djomehri (1999) aimed at minimizing nu- merical dissipations for high speed compressible viscous flows containing shocks, shears and turbulence. To detect non smooth behavior and control the amount of numerical dissipation to be added, Yee et al. employed an artificial compression method (ACM) of Harten (1978) but utilize it in an entirely different context than Harten originally intended. The ACM sensor consists of two tuning parameters and is highly physical problem dependent. To minimize the tuning of parameters and physical problem dependence, new sensors with improved detection properties are proposed. The new sensors are derived from utilizing appropriate non-orthogonal wavelet basis functions and they can be used to completely switch to the extra numerical dissipation outside shock layers. The non-dissipative spatial base scheme of arbitrarily high order of accuracy can be maintained without compromising its stability at all parts of the domain where the solution is smooth. Two types of redundant non-orthogonal wavelet basis functions are considered. One is the B-spline wavelet (Mallat & Zhong 1992) used by Gerritsen and Olsson (1996) in an adaptive mesh refinement method, to determine regions where re nement should be done. The other is the modification of the multiresolution method of Harten (1995) by converting it to a new, redundant, non-orthogonal wavelet. The wavelet sensor is then obtained by computing the estimated Lipschitz exponent of a chosen physical quantity (or vector) to be sensed on a chosen wavelet basis function. Both wavelet sensors can be viewed as dual purpose adaptive methods leading to dynamic numerical dissipation control and improved grid adaptation indicators. Consequently, they are useful not only for shock-turbulence computations but also for computational aeroacoustics and numerical combustion. In addition, these sensors are scheme independent and can be stand alone options for numerical algorithm other than the Yee et al. scheme.

Quantization of Electromagnetic Fields in Cavities

NASA Technical Reports Server (NTRS)

Kakazu, Kiyotaka; Oshiro, Kazunori

1996-01-01

A quantization procedure for the electromagnetic field in a rectangular cavity with perfect conductor walls is presented, where a decomposition formula of the field plays an essential role. All vector mode functions are obtained by using the decomposition. After expanding the field in terms of the vector mode functions, we get the quantized electromagnetic Hamiltonian.

Amiloride-enhanced gene transfection of octa-arginine functionalized calcium phosphate nanoparticles.

PubMed

Vanegas Sáenz, Juan Ramón; Tenkumo, Taichi; Kamano, Yuya; Egusa, Hiroshi; Sasaki, Keiichi

2017-01-01

Nanoparticles represent promising gene delivery systems in biomedicine to facilitate prolonged gene expression with low toxicity compared to viral vectors. Specifically, nanoparticles of calcium phosphate (nCaP), the main inorganic component of human bone, exhibit high biocompatibility and good biodegradability and have been reported to have high affinity for protein or DNA, having thus been used as gene transfer vectors. On the other hand, Octa-arginine (R8), which has a high permeability to cell membrane, has been reported to improve intracellular delivery systems. Here, we present an optimized method for nCaP-mediated gene delivery using an octa-arginine (R8)-functionalized nCaP vector containing a marker or functional gene construct. nCaP particle size was between 220-580 nm in diameter and all R8-functionalized nCaPs carried a positive charge. R8 concentration significantly improved nCaP transfection efficiency with high cell compatibility in human mesenchymal stem cells (hMSC) and human osteoblasts (hOB) in particular, suggesting nCaPs as a good option for non-viral vector gene delivery. Furthermore, pre-treatment with different endocytosis inhibitors identified that the endocytic pathway differed among cell lines and functionalized nanoparticles, with amiloride increasing transfection efficiency of R8-functionalized nCaPs in hMSC and hOB.

Adenovirus Vectors Target Several Cell Subtypes of Mammalian Inner Ear In Vivo

PubMed Central

Li, Wenyan; Shen, Jun

2016-01-01

Mammalian inner ear harbors diverse cell types that are essential for hearing and balance. Adenovirus is one of the major vectors to deliver genes into the inner ear for functional studies and hair cell regeneration. To identify adenovirus vectors that target specific cell subtypes in the inner ear, we studied three adenovirus vectors, carrying a reporter gene encoding green fluorescent protein (GFP) from two vendors or with a genome editing gene Cre recombinase (Cre), by injection into postnatal days 0 (P0) and 4 (P4) mouse cochlea through scala media by cochleostomy in vivo. We found three adenovirus vectors transduced mouse inner ear cells with different specificities and expression levels, depending on the type of adenoviral vectors and the age of mice. The most frequently targeted region was the cochlear sensory epithelium, including auditory hair cells and supporting cells. Adenovirus with GFP transduced utricular supporting cells as well. This study shows that adenovirus vectors are capable of efficiently and specifically transducing different cell types in the mammalian inner ear and provides useful tools to study inner ear gene function and to evaluate gene therapy to treat hearing loss and vestibular dysfunction. PMID:28116172

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, X; Yang, D

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was usedmore » to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.« less

Prediction of subcellular localization of eukaryotic proteins using position-specific profiles and neural network with weighted inputs.

PubMed

Zou, Lingyun; Wang, Zhengzhi; Huang, Jiaomin

2007-12-01

Subcellular location is one of the key biological characteristics of proteins. Position-specific profiles (PSP) have been introduced as important characteristics of proteins in this article. In this study, to obtain position-specific profiles, the Position Specific Iterative-Basic Local Alignment Search Tool (PSI-BLAST) has been used to search for protein sequences in a database. Position-specific scoring matrices are extracted from the profiles as one class of characteristics. Four-part amino acid compositions and 1st-7th order dipeptide compositions have also been calculated as the other two classes of characteristics. Therefore, twelve characteristic vectors are extracted from each of the protein sequences. Next, the characteristic vectors are weighed by a simple weighing function and inputted into a BP neural network predictor named PSP-Weighted Neural Network (PSP-WNN). The Levenberg-Marquardt algorithm is employed to adjust the weight matrices and thresholds during the network training instead of the error back propagation algorithm. With a jackknife test on the RH2427 dataset, PSP-WNN has achieved a higher overall prediction accuracy of 88.4% rather than the prediction results by the general BP neural network, Markov model, and fuzzy k-nearest neighbors algorithm on this dataset. In addition, the prediction performance of PSP-WNN has been evaluated with a five-fold cross validation test on the PK7579 dataset and the prediction results have been consistently better than those of the previous method on the basis of several support vector machines, using compositions of both amino acids and amino acid pairs. These results indicate that PSP-WNN is a powerful tool for subcellular localization prediction. At the end of the article, influences on prediction accuracy using different weighting proportions among three characteristic vector categories have been discussed. An appropriate proportion is considered by increasing the prediction accuracy.

Diversity and conservation in maize pollen: Phenotypes and transcripts

EPA Science Inventory

In addition to its crucial role in seed production, pollen serves as a vector for gene flow between plant populations. Recently, pollen was identified as a mechanism for introduction of transgenes into non-transgenic populations. To investigate the genetic basis for pollen fitn...

Geometric Representations of Condition Queries on Three-Dimensional Vector Fields

NASA Technical Reports Server (NTRS)

Henze, Chris

1999-01-01

Condition queries on distributed data ask where particular conditions are satisfied. It is possible to represent condition queries as geometric objects by plotting field data in various spaces derived from the data, and by selecting loci within these derived spaces which signify the desired conditions. Rather simple geometric partitions of derived spaces can represent complex condition queries because much complexity can be encapsulated in the derived space mapping itself A geometric view of condition queries provides a useful conceptual unification, allowing one to intuitively understand many existing vector field feature detection algorithms -- and to design new ones -- as variations on a common theme. A geometric representation of condition queries also provides a simple and coherent basis for computer implementation, reducing a wide variety of existing and potential vector field feature detection techniques to a few simple geometric operations.

Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data

PubMed Central

Park, Juyong

2018-01-01

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility. PMID:29432440

System for Automated Calibration of Vector Modulators

NASA Technical Reports Server (NTRS)

Lux, James; Boas, Amy; Li, Samuel

2009-01-01

Vector modulators are used to impose baseband modulation on RF signals, but non-ideal behavior limits the overall performance. The non-ideal behavior of the vector modulator is compensated using data collected with the use of an automated test system driven by a LabVIEW program that systematically applies thousands of control-signal values to the device under test and collects RF measurement data. The technology innovation automates several steps in the process. First, an automated test system, using computer controlled digital-to-analog converters (DACs) and a computer-controlled vector network analyzer (VNA) systematically can apply different I and Q signals (which represent the complex number by which the RF signal is multiplied) to the vector modulator under test (VMUT), while measuring the RF performance specifically, gain and phase. The automated test system uses the LabVIEW software to control the test equipment, collect the data, and write it to a file. The input to the Lab - VIEW program is either user-input for systematic variation, or is provided in a file containing specific test values that should be fed to the VMUT. The output file contains both the control signals and the measured data. The second step is to post-process the file to determine the correction functions as needed. The result of the entire process is a tabular representation, which allows translation of a desired I/Q value to the required analog control signals to produce a particular RF behavior. In some applications, corrected performance is needed only for a limited range. If the vector modulator is being used as a phase shifter, there is only a need to correct I and Q values that represent points on a circle, not the entire plane. This innovation has been used to calibrate 2-GHz MMIC (monolithic microwave integrated circuit) vector modulators in the High EIRP Cluster Array project (EIRP is high effective isotropic radiated power). These calibrations were then used to create correction tables to allow the commanding of the phase shift in each of four channels used as a phased array for beam steering of a Ka-band (32-GHz) signal. The system also was the basis of a breadboard electronic beam steering system. In this breadboard, the goal was not to make systematic measurements of the properties of a vector modulator, but to drive the breadboard with a series of test patterns varying in phase and amplitude. This is essentially the same calibration process, but with the difference that the data collection process is oriented toward collecting breadboard performance, rather than the measurement of output from a network analyzer.

An optical flow-based method for velocity field of fluid flow estimation

NASA Astrophysics Data System (ADS)

Głomb, Grzegorz; Świrniak, Grzegorz; Mroczka, Janusz

2017-06-01

The aim of this paper is to present a method for estimating flow-velocity vector fields using the Lucas-Kanade algorithm. The optical flow measurements are based on the Particle Image Velocimetry (PIV) technique, which is commonly used in fluid mechanics laboratories in both research institutes and industry. Common approaches for an optical characterization of velocity fields base on computation of partial derivatives of the image intensity using finite differences. Nevertheless, the accuracy of velocity field computations is low due to the fact that an exact estimation of spatial derivatives is very difficult in presence of rapid intensity changes in the PIV images, caused by particles having small diameters. The method discussed in this paper solves this problem by interpolating the PIV images using Gaussian radial basis functions. This provides a significant improvement in the accuracy of the velocity estimation but, more importantly, allows for the evaluation of the derivatives in intermediate points between pixels. Numerical analysis proves that the method is able to estimate even a separate vector for each particle with a 5× 5 px2 window, whereas a classical correlation-based method needs at least 4 particle images. With the use of a specialized multi-step hybrid approach to data analysis the method improves the estimation of the particle displacement far above 1 px.

Computing the modal mass from the state space model in combined experimental-operational modal analysis

NASA Astrophysics Data System (ADS)

Cara, Javier

2016-05-01

Modal parameters comprise natural frequencies, damping ratios, modal vectors and modal masses. In a theoretic framework, these parameters are the basis for the solution of vibration problems using the theory of modal superposition. In practice, they can be computed from input-output vibration data: the usual procedure is to estimate a mathematical model from the data and then to compute the modal parameters from the estimated model. The most popular models for input-output data are based on the frequency response function, but in recent years the state space model in the time domain has become popular among researchers and practitioners of modal analysis with experimental data. In this work, the equations to compute the modal parameters from the state space model when input and output data are available (like in combined experimental-operational modal analysis) are derived in detail using invariants of the state space model: the equations needed to compute natural frequencies, damping ratios and modal vectors are well known in the operational modal analysis framework, but the equation needed to compute the modal masses has not generated much interest in technical literature. These equations are applied to both a numerical simulation and an experimental study in the last part of the work.

Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir

NASA Astrophysics Data System (ADS)

Ansari, Hamid Reza

2014-09-01

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ε-support vector regression (ε-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system.

New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.

Time-to-Passage Judgments in Nonconstant Optical Flow Fields

NASA Technical Reports Server (NTRS)

Kaiser, Mary K.; Hecht, Heiko

1995-01-01

The time until an approaching object will pass an observer (time to passage, or TTP) is optically specified by a global flow field even in the absence of local expansion or size cues. Kaiser and Mowafy have demonstrated that observers are in fact sensitive to this global flow information. The present studies investigate two factors that are usually ignored in work related to TTP: (1) non-constant motion functions and (2) concomitant eye rotation. Non-constant velocities violate an assumption of some TTP derivations, and eye rotations may complicate heading extraction. Such factors have practical significance, for example, in the case of a pilot accelerating an aircraft or executing a roll. In our studies, a flow field of constant-sized stars was presented monocularly on a large screen. TIP judgments had to be made on the basis of one target star. The flow field varied in its acceleration pattern and its roll component. Observers did not appear to utilize acceleration information. In particular, TTP with decelerating motion were consistently underestimated. TTP judgments were fairly robust with respect to roll, even when roll axis and track vector were decoupled. However, substantial decoupling between heading and track vector led to a decrement in performance, in both the presence and the absence of roll.

Identification of potentially hazardous human gene products in GMO risk assessment.

PubMed

Bergmans, Hans; Logie, Colin; Van Maanen, Kees; Hermsen, Harm; Meredyth, Michelle; Van Der Vlugt, Cécile

2008-01-01

Genetically modified organisms (GMOs), e.g. viral vectors, could threaten the environment if by their release they spread hazardous gene products. Even in contained use, to prevent adverse consequences, viral vectors carrying genes from mammals or humans should be especially scrutinized as to whether gene products that they synthesize could be hazardous in their new context. Examples of such potentially hazardous gene products (PHGPs) are: protein toxins, products of dominant alleles that have a role in hereditary diseases, gene products and sequences involved in genome rearrangements, gene products involved in immunomodulation or with an endocrine function, gene products involved in apoptosis, activated proto-oncogenes. For contained use of a GMO that carries a construct encoding a PHGP, the precautionary principle dictates that safety measures should be applied on a "worst case" basis, until the risks of the specific case have been assessed. The potential hazard of cloned genes can be estimated before empirical data on the actual GMO become available. Preliminary data may be used to focus hazard identification and risk assessment. Both predictive and empirical data may also help to identify what further information is needed to assess the risk of the GMO. A two-step approach, whereby a PHGP is evaluated for its conceptual dangers, then checked by data bank searches, is delineated here.

Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

PubMed

Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi

2016-06-21

Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Video data compression using artificial neural network differential vector quantization

NASA Technical Reports Server (NTRS)

Krishnamurthy, Ashok K.; Bibyk, Steven B.; Ahalt, Stanley C.

1991-01-01

An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

Diversity of breeding habitats of anophelines (Diptera: Culicidae) in Ramgarh district, Jharkhand, India.

PubMed

Pandey, Siddharth; Das, M K; Dhiman, Ramesh C

2016-01-01

The Ramgarh district of Jharkhand state, India is highly malarious owing to abundance of different malaria vector species, namely Anopheles culicifacies, An. fluviatilis and An. annularis. In spite of high prevalence of malaria vectors in Ramgarh, their larval ecology and climatic conditions affecting malaria dynamics have never been studied. Therefore, the objective of this study was to identify the diversity of potential breeding habitats and breeding preferences of anopheline vectors in the Ramgarh district. Anopheles immature collection was carried out at potential aquatic habitats in Ramgarh and Gola sites using the standard dipper on fortnightly basis from August 2012 to July 2013. The immatures were reared till adult emergence and further identified using standard keys. Temperature of outdoor and water bodies was recorded through temperature data loggers, and rainfall through standard rain gauges installed at each site. A total of 6495 immature specimens representing 17 Anopheles species including three malaria vectors, viz. An. culicifacies, An. fluviatilis and An. annularis were collected from 11 types of breeding habitats. The highly preferred breeding habitats of vector anophelines were river bed pools, rivulets, wells, ponds, river margins, ditches and irrigation channels. Larval abundance of vector species showed site-specific variation with temperature and rainfall patterns throughout the year. The Shannon-Weiner diversity index ranged from 0.19 to 1.94 at Ramgarh site and 0.16 to 1.76 at Gola site. The study revealed that malaria vector species have been adapted to breed in a wide range of water bodies. The regular monitoring of such specific vector breeding sites under changing ecological and environmental conditions will be useful in guiding larval control operations selectively for effective vector/ malaria control.

Impact of vectorborne parasitic neglected tropical diseases on child health.

PubMed

Barry, Meagan A; Murray, Kristy O; Hotez, Peter J; Jones, Kathryn M

2016-07-01

Chagas disease, leishmaniasis, onchocerciasis and lymphatic filariasis are all vectorborne neglected tropical diseases (NTDs) that are responsible for significant disease burden in impoverished children and adults worldwide. As vectorborne parasitic diseases, they can all be targeted for elimination through vector control strategies. Examples of successful vector control programmes for these diseases over the past two decades have included the Southern Cone Initiative against Chagas disease, the Kala-azar Control Scheme against leishmaniasis, the Onchocerciasis Control Programme and the lymphatic filariasis control programme in The Gambia. A common vector control component in all of these programmes is the use of adulticides including dichlorodiphenyltrichloroethane and newer synthetic pyrethroid insecticides against the insect vectors of disease. Household spraying has been used against Chagas disease and leishmaniasis, and insecticide-treated bed nets have helped prevent leishmaniasis and lymphatic filariasis. Recent trends in vector control focus on collaborations between programmes and sectors to achieve integrated vector management that addresses the holistic vector control needs of a community rather than approaching it on a disease-by-disease basis, with the goals of increased efficacy, sustainability and cost-effectiveness. As evidence of vector resistance to currently used insecticide regimens emerges, research to develop new and improved insecticides and novel control strategies will be critical in reducing disease burden. In the quest to eliminate these vectorborne NTDs, efforts need to be made to continue existing control programmes, further implement integrated vector control strategies and stimulate research into new insecticides and control methods. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Reducing vector-borne disease by empowering farmers in integrated vector management.

PubMed

van den Berg, Henk; von Hildebrand, Alexander; Ragunathan, Vaithilingam; Das, Pradeep K

2007-07-01

Irrigated agriculture exposes rural people to health risks associated with vector-borne diseases and pesticides used in agriculture and for public health protection. Most developing countries lack collaboration between the agricultural and health sectors to jointly address these problems. We present an evaluation of a project that uses the "farmer field school" method to teach farmers how to manage vector-borne diseases and how to improve rice yields. Teaching farmers about these two concepts together is known as "integrated pest and vector management". An intersectoral project targeting rice irrigation systems in Sri Lanka. Project partners developed a new curriculum for the field school that included a component on vector-borne diseases. Rice farmers in intervention villages who graduated from the field school took vector-control actions as well as improving environmental sanitation and their personal protection measures against disease transmission. They also reduced their use of agricultural pesticides, especially insecticides. The intervention motivated and enabled rural people to take part in vector-management activities and to reduce several environmental health risks. There is scope for expanding the curriculum to include information on the harmful effects of pesticides on human health and to address other public health concerns. Benefits of this approach for community-based health programmes have not yet been optimally assessed. Also, the institutional basis of the integrated management approach needs to be broadened so that people from a wider range of organizations take part. A monitoring and evaluation system needs to be established to measure the performance of integrated management initiatives.

Vector control for malaria and other mosquito-borne diseases. Report of a WHO study group.

PubMed

1995-01-01

Since the Ministerial Conference on Malaria in 1992, which acknowledged the urgent need for worldwide commitment to malaria control, efforts have been directed to implementation of a Global Malaria Control Strategy. Vector control, an essential component of malaria control, has become less effective in recent years, partly as a result of poor use of alternative control tools, inappropriate use of insecticides, lack of an epidemiological basis for interventions, inadequate resources and infrastructure, and weak management. Changing environmental conditions, the behavioural characteristics of certain vectors, and resistance to insecticides have added to the difficulties. This report of a WHO Study Group provides guidelines for the planning, implementation and evaluation of cost-effective and sustainable vector control in the context of the Global Malaria Control Strategy. It reviews the available methods - indoor residual spraying, personal protection, larval control and environmental management - stressing the need for selective and flexible use of interventions according to local conditions. Requirements for data collection and the appropriate use of entomological parameters and techniques are discussed and priorities identified for the development of local capacity for vector control and for operational research. Emphasis is placed both on the monitoring and evaluation of vector control to ensure cost-effectiveness and on the development of strong managerial structures, which can support community participation and intersectoral collaboration and accommodate the control of other vector-borne diseases. The report concludes with recommendations aimed at promoting the targeted and efficient use of vector control in preventing and controlling malaria, thereby reducing the threat to health and socioeconomic development in many tropical countries.

A Geomorphological Approach To Landslide Susceptibility Assessment and Its Application To The Virginio River Basin

NASA Astrophysics Data System (ADS)

Casagli, N.; Catani, F.; Delmonaco, G.; Ermini, L.; Margottini, C.; Puglisi, C.

The present note presents the results of a research project aimed at developing a ge- omorphology based method for the assessment of landslide susceptibility. The work has been carried out in the Virginio River basin, a tributary of the Arno river with confluence located 20 km downstream from Florence (Italy). Starting from a detailed landslide inventory, essentially carried out by aerial-photograph survey, the first phase of the research was directed to geomorphic field investigations, aimed at determining those hillslope factors influencing landslide triggering. This analysis was carried out for a significant sub-sample of the inventoried landslides, allowing the distinction of the most relevant hillslope factors affecting each type of mass movement. Selected factors are a) the past slope gradient in which landslide originated, b) litology and 3) land use. Once defined as thematic vector data, these factors have been handled by GIS overlay mapping, allowing to single out, for the entire Virginio River basin, homogeneous domains (Unique Condition Units) that contain, for each landslide ty- pology, unique combinations of the selected hillslope factors. Those domains are the basic Terrain Units for the following phase of landslide susceptibility assessment and mapping. The hillslope factors not selected in the first phase, however playing an im- portant role in contributing to the activation of the mass movements, have been taken into account for the definition of a landslide susceptibility function. This function is es- sentially a logic function based on the presence/absence of preparatory factors within the previous selected Unique Condition Terrain Units. The final mapping of the ar- eas characterized by different landslide susceptibility levels was performed by vector and raster based GIS techniques on the basis of the number and rank of predisposing factors. These landslide susceptibility classes, defined by a logic and easily replicable function, can be useful in the decision making procedures connected with territorial planning.

Electroweak splitting functions and high energy showering

NASA Astrophysics Data System (ADS)

Chen, Junmou; Han, Tao; Tweedie, Brock

2017-11-01

We derive the electroweak (EW) collinear splitting functions for the Standard Model, including the massive fermions, gauge bosons and the Higgs boson. We first present the splitting functions in the limit of unbroken SU(2) L × U(1) Y and discuss their general features in the collinear and soft-collinear regimes. These are the leading contributions at a splitting scale ( k T ) far above the EW scale ( v). We then systematically incorporate EW symmetry breaking (EWSB), which leads to the emergence of additional "ultra-collinear" splitting phenomena and naive violations of the Goldstone-boson Equivalence Theorem. We suggest a particularly convenient choice of non-covariant gauge (dubbed "Goldstone Equivalence Gauge") that disentangles the effects of Goldstone bosons and gauge fields in the presence of EWSB, and allows trivial book-keeping of leading power corrections in v/ k T . We implement a comprehensive, practical EW showering scheme based on these splitting functions using a Sudakov evolution formalism. Novel features in the implementation include a complete accounting of ultra-collinear effects, matching between shower and decay, kinematic back-reaction corrections in multi-stage showers, and mixed-state evolution of neutral bosons ( γ/ Z/ h) using density-matrices. We employ the EW showering formalism to study a number of important physical processes at O (1-10 TeV) energies. They include (a) electroweak partons in the initial state as the basis for vector-boson-fusion; (b) the emergence of "weak jets" such as those initiated by transverse gauge bosons, with individual splitting probabilities as large as O (35%); (c) EW showers initiated by top quarks, including Higgs bosons in the final state; (d) the occurrence of O (1) interference effects within EW showers involving the neutral bosons; and (e) EW corrections to new physics processes, as illustrated by production of a heavy vector boson ( W ') and the subsequent showering of its decay products.

Measurements of wind vectors, eddy momentum transports, and energy conversions in Jupiter's atmosphere from Voyager 1 images

NASA Astrophysics Data System (ADS)

Beebe, R. F.; Ingersoll, A. P.; Hunt, G. E.; Mitchell, J. L.; Muller, J.-P.

1980-01-01

Voyager 1 narrow-angle images were used to obtain displacements of features down to 100 to 200 km in size over intervals of 10 hours. A global map of velocity vectors and longitudinally averaged zonal wind vectors as functions of the latitude, is presented and discussed

Correction of the enzymatic and functional deficits in a model of Pompe disease using adeno-associated virus vectors.

PubMed

Fraites, Thomas J; Schleissing, Mary R; Shanely, R Andrew; Walter, Glenn A; Cloutier, Denise A; Zolotukhin, Irene; Pauly, Daniel F; Raben, Nina; Plotz, Paul H; Powers, Scott K; Kessler, Paul D; Byrne, Barry J

2002-05-01

Pompe disease is a lysosomal storage disease caused by the absence of acid alpha-1,4 glucosidase (GAA). The pathophysiology of Pompe disease includes generalized myopathy of both cardiac and skeletal muscle. We sought to use recombinant adeno-associated virus (rAAV) vectors to deliver functional GAA genes in vitro and in vivo. Myotubes and fibroblasts from Pompe patients were transduced in vitro with rAAV2-GAA. At 14 days postinfection, GAA activities were at least fourfold higher than in their respective untransduced controls, with a 10-fold increase observed in GAA-deficient myotubes. BALB/c and Gaa(-/-) mice were also treated with rAAV vectors. Persistent expression of vector-derived human GAA was observed in BALB/c mice up to 6 months after treatment. In Gaa(-/-) mice, intramuscular and intramyocardial delivery of rAAV2-Gaa (carrying the mouse Gaa cDNA) resulted in near-normal enzyme activities. Skeletal muscle contractility was partially restored in the soleus muscles of treated Gaa(-/-) mice, indicating the potential for vector-mediated restoration of both enzymatic activity and muscle function. Furthermore, intramuscular treatment with a recombinant AAV serotype 1 vector (rAAV1-Gaa) led to nearly eight times normal enzymatic activity in Gaa(-/-) mice, with concomitant glycogen clearance as assessed in vitro and by proton magnetic resonance spectroscopy.

Gene Delivery to Adipose Tissue Using Transcriptionally Targeted rAAV8 Vectors

PubMed Central

Uhrig-Schmidt, Silke; Geiger, Matthias; Luippold, Gerd; Birk, Gerald; Mennerich, Detlev; Neubauer, Heike; Grimm, Dirk; Wolfrum, Christian; Kreuz, Sebastian

2014-01-01

In recent years, the increasing prevalence of obesity and obesity-related co-morbidities fostered intensive research in the field of adipose tissue biology. To further unravel molecular mechanisms of adipose tissue function, genetic tools enabling functional studies in vitro and in vivo are essential. While the use of transgenic animals is well established, attempts using viral and non-viral vectors to genetically modify adipocytes in vivo are rare. Therefore, we here characterized recombinant Adeno-associated virus (rAAV) vectors regarding their potency as gene transfer vehicles for adipose tissue. Our results demonstrate that a single dose of systemically applied rAAV8-CMV-eGFP can give rise to remarkable transgene expression in murine adipose tissues. Upon transcriptional targeting of the rAAV8 vector to adipocytes using a 2.2 kb fragment of the murine adiponectin (mAP2.2) promoter, eGFP expression was significantly decreased in off-target tissues while efficient transduction was maintained in subcutaneous and visceral fat depots. Moreover, rAAV8-mAP2.2-mediated expression of perilipin A – a lipid-droplet-associated protein – resulted in significant changes in metabolic parameters only three weeks post vector administration. Taken together, our findings indicate that rAAV vector technology is applicable as a flexible tool to genetically modify adipocytes for functional proof-of-concept studies and the assessment of putative therapeutic targets in vivo. PMID:25551639

Vector-based genetically modified vaccines: Exploiting Jenner's legacy.

PubMed

Ramezanpour, Bahar; Haan, Ingrid; Osterhaus, Ab; Claassen, Eric

2016-12-07

The global vaccine market is diverse while facing a plethora of novel developments. Genetic modification (GM) techniques facilitate the design of 'smarter' vaccines. For many of the major infectious diseases of humans, like AIDS and malaria, but also for most human neoplastic disorders, still no vaccines are available. It may be speculated that novel GM technologies will significantly contribute to their development. While a promising number of studies is conducted on GM vaccines and GM vaccine technologies, the contribution of GM technology to newly introduced vaccines on the market is disappointingly limited. In this study, the field of vector-based GM vaccines is explored. Data on currently available, actually applied, and newly developed vectors is retrieved from various sources, synthesised and analysed, in order to provide an overview on the use of vector-based technology in the field of GM vaccine development. While still there are only two vector-based vaccines on the human vaccine market, there is ample activity in the fields of patenting, preclinical research, and different stages of clinical research. Results of this study revealed that vector-based vaccines comprise a significant part of all GM vaccines in the pipeline. This study further highlights that poxviruses and adenoviruses are among the most prominent vectors in GM vaccine development. After the approval of the first vectored human vaccine, based on a flavivirus vector, vaccine vector technology, especially based on poxviruses and adenoviruses, holds great promise for future vaccine development. It may lead to cheaper methods for the production of safe vaccines against diseases for which no or less perfect vaccines exist today, thus catering for an unmet medical need. After the introduction of Jenner's vaccinia virus as the first vaccine more than two centuries ago, which eventually led to the recent eradication of smallpox, this and other viruses may now be the basis for constructing vectors that may help us control other major scourges of mankind. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tenney, Rebeca M.; Bell, Christie L.; Wilson, James M., E-mail: wilsonjm@mail.med.upenn.edu

Adeno-associated virus serotype 8 (AAV8) is a promising vector for liver-directed gene therapy. Although efficient uncoating of viral capsids has been implicated in AAV8's robust liver transduction, much about the biology of AAV8 hepatotropism remains unclear. Our study investigated the structural basis of AAV8 liver transduction efficiency by constructing chimeric vector capsids containing sequences derived from AAV8 and AAV2 – a highly homologous yet poorly hepatotropic serotype. Engineered vectors containing capsid variable regions (VR) VII and IX from AAV8 in an AAV2 backbone mediated near AAV8-like transduction in mouse liver, with higher numbers of chimeric genomes detected in whole livermore » cells and isolated nuclei. Interestingly, chimeric capsids within liver nuclei also uncoated similarly to AAV8 by 6 weeks after administration, in contrast with AAV2, of which a significantly smaller proportion were uncoated. This study links specific AAV capsid regions to the transduction ability of a clinically relevant AAV serotype. - Highlights: • We construct chimeric vectors to identify determinants of AAV8 liver transduction. • An AAV2-based vector with 17 AAV8 residues exhibited high liver transduction in mice. • This vector also surpassed AAV2 in cell entry, nuclear entry and onset of expression. • Most chimeric vector particles were uncoated at 6 weeks, like AAV8 and unlike AAV2. • Chimera retained heparin binding and was antigenically distinct from AAV2 and AAV8.« less

QuickMap: a public tool for large-scale gene therapy vector insertion site mapping and analysis.

PubMed

Appelt, J-U; Giordano, F A; Ecker, M; Roeder, I; Grund, N; Hotz-Wagenblatt, A; Opelz, G; Zeller, W J; Allgayer, H; Fruehauf, S; Laufs, S

2009-07-01

Several events of insertional mutagenesis in pre-clinical and clinical gene therapy studies have created intense interest in assessing the genomic insertion profiles of gene therapy vectors. For the construction of such profiles, vector-flanking sequences detected by inverse PCR, linear amplification-mediated-PCR or ligation-mediated-PCR need to be mapped to the host cell's genome and compared to a reference set. Although remarkable progress has been achieved in mapping gene therapy vector insertion sites, public reference sets are lacking, as are the possibilities to quickly detect non-random patterns in experimental data. We developed a tool termed QuickMap, which uniformly maps and analyzes human and murine vector-flanking sequences within seconds (available at www.gtsg.org). Besides information about hits in chromosomes and fragile sites, QuickMap automatically determines insertion frequencies in +/- 250 kb adjacency to genes, cancer genes, pseudogenes, transcription factor and (post-transcriptional) miRNA binding sites, CpG islands and repetitive elements (short interspersed nuclear elements (SINE), long interspersed nuclear elements (LINE), Type II elements and LTR elements). Additionally, all experimental frequencies are compared with the data obtained from a reference set, containing 1 000 000 random integrations ('random set'). Thus, for the first time a tool allowing high-throughput profiling of gene therapy vector insertion sites is available. It provides a basis for large-scale insertion site analyses, which is now urgently needed to discover novel gene therapy vectors with 'safe' insertion profiles.

Orthonormal vector general polynomials derived from the Cartesian gradient of the orthonormal Zernike-based polynomials.

PubMed

Mafusire, Cosmas; Krüger, Tjaart P J

2018-06-01

The concept of orthonormal vector circle polynomials is revisited by deriving a set from the Cartesian gradient of Zernike polynomials in a unit circle using a matrix-based approach. The heart of this model is a closed-form matrix equation of the gradient of Zernike circle polynomials expressed as a linear combination of lower-order Zernike circle polynomials related through a gradient matrix. This is a sparse matrix whose elements are two-dimensional standard basis transverse Euclidean vectors. Using the outer product form of the Cholesky decomposition, the gradient matrix is used to calculate a new matrix, which we used to express the Cartesian gradient of the Zernike circle polynomials as a linear combination of orthonormal vector circle polynomials. Since this new matrix is singular, the orthonormal vector polynomials are recovered by reducing the matrix to its row echelon form using the Gauss-Jordan elimination method. We extend the model to derive orthonormal vector general polynomials, which are orthonormal in a general pupil by performing a similarity transformation on the gradient matrix to give its equivalent in the general pupil. The outer form of the Gram-Schmidt procedure and the Gauss-Jordan elimination method are then applied to the general pupil to generate the orthonormal vector general polynomials from the gradient of the orthonormal Zernike-based polynomials. The performance of the model is demonstrated with a simulated wavefront in a square pupil inscribed in a unit circle.

The New Self-Inactivating Lentiviral Vector for Thalassemia Gene Therapy Combining Two HPFH Activating Elements Corrects Human Thalassemic Hematopoietic Stem Cells

PubMed Central

Papanikolaou, Eleni; Georgomanoli, Maria; Stamateris, Evangelos; Panetsos, Fottes; Karagiorga, Markisia; Tsaftaridis, Panagiotis; Graphakos, Stelios

2012-01-01

Abstract To address how low titer, variable expression, and gene silencing affect gene therapy vectors for hemoglobinopathies, in a previous study we successfully used the HPFH (hereditary persistence of fetal hemoglobin)-2 enhancer in a series of oncoretroviral vectors. On the basis of these data, we generated a novel insulated self-inactivating (SIN) lentiviral vector, termed GGHI, carrying the Aγ-globin gene with the −117 HPFH point mutation and the HPFH-2 enhancer and exhibiting a pancellular pattern of Aγ-globin gene expression in MEL-585 clones. To assess the eventual clinical feasibility of this vector, GGHI was tested on CD34+ hematopoietic stem cells from nonmobilized peripheral blood or bone marrow from 20 patients with β-thalassemia. Our results show that GGHI increased the production of γ-globin by 32.9% as measured by high-performance liquid chromatography (p=0.001), with a mean vector copy number per cell of 1.1 and a mean transduction efficiency of 40.3%. Transduced populations also exhibited a lower rate of apoptosis and resulted in improvement of erythropoiesis with a higher percentage of orthochromatic erythroblasts. This is the first report of a locus control region (LCR)-free SIN insulated lentiviral vector that can be used to efficiently produce the anticipated therapeutic levels of γ-globin protein in the erythroid progeny of primary human thalassemic hematopoietic stem cells in vitro. PMID:21875313

A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging.

PubMed

Long, Zhuqing; Jing, Bin; Yan, Huagang; Dong, Jianxin; Liu, Han; Mo, Xiao; Han, Ying; Li, Haiyun

2016-09-07

Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer's disease (AD). Non-invasive diagnostic methods are desirable to identify MCI for early therapeutic interventions. In this study, we proposed a support vector machine (SVM)-based method to discriminate between MCI patients and normal controls (NCs) using multi-level characteristics of magnetic resonance imaging (MRI). This method adopted a radial basis function (RBF) as the kernel function, and a grid search method to optimize the two parameters of SVM. The calculated characteristics, i.e., the Hurst exponent (HE), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and gray matter density (GMD), were adopted as the classification features. A leave-one-out cross-validation (LOOCV) was used to evaluate the classification performance of the method. Applying the proposed method to the experimental data from 29 MCI patients and 33 healthy subjects, we achieved a classification accuracy of up to 96.77%, with a sensitivity of 93.10% and a specificity of 100%, and the area under the curve (AUC) yielded up to 0.97. Furthermore, the most discriminative features for classification were found to predominantly involve default-mode regions, such as hippocampus (HIP), parahippocampal gyrus (PHG), posterior cingulate gyrus (PCG) and middle frontal gyrus (MFG), and subcortical regions such as lentiform nucleus (LN) and amygdala (AMYG). Therefore, our method is promising in distinguishing MCI patients from NCs and may be useful for the diagnosis of MCI. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Virus induced gene silencing (VIGS) for functional analysis of wheat genes involved in Zymoseptoria tritici susceptibility and resistance.

PubMed

Lee, Wing-Sham; Rudd, Jason J; Kanyuka, Kostya

2015-06-01

Virus-induced gene silencing (VIGS) has emerged as a powerful reverse genetic technology in plants supplementary to stable transgenic RNAi and, in certain species, as a viable alternative approach for gene functional analysis. The RNA virus Barley stripe mosaic virus (BSMV) was developed as a VIGS vector in the early 2000s and since then it has been used to study the function of wheat genes. Several variants of BSMV vectors are available, with some requiring in vitro transcription of infectious viral RNA, while others rely on in planta production of viral RNA from DNA-based vectors delivered to plant cells either by particle bombardment or Agrobacterium tumefaciens. We adapted the latest generation of binary BSMV VIGS vectors for the identification and study of wheat genes of interest involved in interactions with Zymoseptoria tritici and here present detailed and the most up-to-date protocols. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Shape Sensing Using a Multi-Core Optical Fiber Having an Arbitrary Initial Shape in the Presence of Extrinsic Forces

NASA Technical Reports Server (NTRS)

Rogge, Matthew D. (Inventor); Moore, Jason P. (Inventor)

2014-01-01

Shape of a multi-core optical fiber is determined by positioning the fiber in an arbitrary initial shape and measuring strain over the fiber's length using strain sensors. A three-coordinate p-vector is defined for each core as a function of the distance of the corresponding cores from a center point of the fiber and a bending angle of the cores. The method includes calculating, via a controller, an applied strain value of the fiber using the p-vector and the measured strain for each core, and calculating strain due to bending as a function of the measured and the applied strain values. Additionally, an apparent local curvature vector is defined for each core as a function of the calculated strain due to bending. Curvature and bend direction are calculated using the apparent local curvature vector, and fiber shape is determined via the controller using the calculated curvature and bend direction.

Renninger's Gedankenexperiment, the collapse of the wave function in a rigid quantum metamaterial and the reality of the quantum state vector.

PubMed

Savel'ev, Sergey E; Zagoskin, Alexandre M

2018-06-25

A popular interpretation of the "collapse" of the wave function is as being the result of a local interaction ("measurement") of the quantum system with a macroscopic system ("detector"), with the ensuing loss of phase coherence between macroscopically distinct components of its quantum state vector. Nevetheless as early as in 1953 Renninger suggested a Gedankenexperiment, in which the collapse is triggered by non-observation of one of two mutually exclusive outcomes of the measurement, i.e., in the absence of interaction of the quantum system with the detector. This provided a powerful argument in favour of "physical reality" of (nonlocal) quantum state vector. In this paper we consider a possible version of Renninger's experiment using the light propagation through a birefringent quantum metamaterial. Its realization would provide a clear visualization of a wave function collapse produced by a "non-measurement", and make the concept of a physically real quantum state vector more acceptable.

Quantized Vector Potential and the Photon Wave-function

NASA Astrophysics Data System (ADS)

Meis, C.; Dahoo, P. R.

2017-12-01

The vector potential function {\\overrightarrow{α }}kλ (\\overrightarrow{r},t) for a k-mode and λ-polarization photon, with the quantized amplitude α 0k (ω k ) = ξω k , satisfies the classical wave propagation equation as well as the Schrodinger’s equation with the relativistic massless Hamiltonian \\mathop{H}\\limits∼ =-i\\hslash c\\overrightarrow{\

Jacobian projection reduced-order models for dynamic systems with contact nonlinearities

NASA Astrophysics Data System (ADS)

Gastaldi, Chiara; Zucca, Stefano; Epureanu, Bogdan I.

2018-02-01

In structural dynamics, the prediction of the response of systems with localized nonlinearities, such as friction dampers, is of particular interest. This task becomes especially cumbersome when high-resolution finite element models are used. While state-of-the-art techniques such as Craig-Bampton component mode synthesis are employed to generate reduced order models, the interface (nonlinear) degrees of freedom must still be solved in-full. For this reason, a new generation of specialized techniques capable of reducing linear and nonlinear degrees of freedom alike is emerging. This paper proposes a new technique that exploits spatial correlations in the dynamics to compute a reduction basis. The basis is composed of a set of vectors obtained using the Jacobian of partial derivatives of the contact forces with respect to nodal displacements. These basis vectors correspond to specifically chosen boundary conditions at the contacts over one cycle of vibration. The technique is shown to be effective in the reduction of several models studied using multiple harmonics with a coupled static solution. In addition, this paper addresses another challenge common to all reduction techniques: it presents and validates a novel a posteriori error estimate capable of evaluating the quality of the reduced-order solution without involving a comparison with the full-order solution.

Teleparallelism as a universal connection on null hypersurfaces in general relativity

NASA Technical Reports Server (NTRS)

Mazur, P. O.; Sokolowski, L. M.

1986-01-01

It is shown that a close relationship between the inner geometry of a null hypersurface N3 and the Newman-Penrose (NP) (1962, 1963) spin coefficient formalism exists. Projecting the null complex NP tetrad onto N3, two triads of basis vectors in N3 are obtained. The inner geometry of N3 is based on the assumption that these vectors are parallelly transported along the surface; this gives rise to the teleparallel connection as a metric nonsymmetric affine connection. The gauge freedom for the choice of the basis triads is given by the isotropy subgroup of the local Lorentz group leaving invariant the direction of the null generators of N3, and teleparallelism is determined by the equivalence class of the basis triads with respect to the global gauge group. Nine of the twelve NP coefficients are identified as the triad components of the torsion and the second fundamental form of N3. The resulting generalized Gauss-Codazzi equations are identical to nine of the NP equations, i.e., to the half of the Ricci identities. This result gives a geometrical meaning to the entire formalism. Finally a general proof of Penrose's theorem that the shear of the null generators of N3 is the only initial null datum for a gravitational field on N3 is presented.

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

PubMed Central

Ramirez-Villegas, Juan F.; Lam-Espinosa, Eric; Ramirez-Moreno, David F.; Calvo-Echeverry, Paulo C.; Agredo-Rodriguez, Wilfredo

2011-01-01

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis. PMID:21386966

Design type air engine Di Pietro

NASA Astrophysics Data System (ADS)

Zwierzchowski, Jaroslaw

The article presents a pneumatic engine constructed by Angelo Di Pietro. 3D solid models of pneumatic engine components were presented therein. A directional valve is a key element of the control system. The valve functions as a camshaft distributing air to particular engine chambers. The construction designed by Angelo Di Pietro is modern and innovative. A pneumatic engine requires low pressure to start rotary movement. With the use of CFD software, the fields of velocity vectors' distribution were determined. Moreover, the author determined the distribution of pressure values in engine inlet and outlet channels. CFD model studies on engine operation were conducted for chosen stages of operating cycles. On the basis of simulation tests that were conducted, the values of flow rates for the engine were determined. The distribution of pressure values made it possible to evaluate the torque value on the rotating shaft.

Efficient time-dependent density functional theory approximations for hybrid density functionals: analytical gradients and parallelization.

PubMed

Petrenko, Taras; Kossmann, Simone; Neese, Frank

2011-02-07

In this paper, we present the implementation of efficient approximations to time-dependent density functional theory (TDDFT) within the Tamm-Dancoff approximation (TDA) for hybrid density functionals. For the calculation of the TDDFT/TDA excitation energies and analytical gradients, we combine the resolution of identity (RI-J) algorithm for the computation of the Coulomb terms and the recently introduced "chain of spheres exchange" (COSX) algorithm for the calculation of the exchange terms. It is shown that for extended basis sets, the RIJCOSX approximation leads to speedups of up to 2 orders of magnitude compared to traditional methods, as demonstrated for hydrocarbon chains. The accuracy of the adiabatic transition energies, excited state structures, and vibrational frequencies is assessed on a set of 27 excited states for 25 molecules with the configuration interaction singles and hybrid TDDFT/TDA methods using various basis sets. Compared to the canonical values, the typical error in transition energies is of the order of 0.01 eV. Similar to the ground-state results, excited state equilibrium geometries differ by less than 0.3 pm in the bond distances and 0.5° in the bond angles from the canonical values. The typical error in the calculated excited state normal coordinate displacements is of the order of 0.01, and relative error in the calculated excited state vibrational frequencies is less than 1%. The errors introduced by the RIJCOSX approximation are, thus, insignificant compared to the errors related to the approximate nature of the TDDFT methods and basis set truncation. For TDDFT/TDA energy and gradient calculations on Ag-TB2-helicate (156 atoms, 2732 basis functions), it is demonstrated that the COSX algorithm parallelizes almost perfectly (speedup ~26-29 for 30 processors). The exchange-correlation terms also parallelize well (speedup ~27-29 for 30 processors). The solution of the Z-vector equations shows a speedup of ~24 on 30 processors. The parallelization efficiency for the Coulomb terms can be somewhat smaller (speedup ~15-25 for 30 processors), but their contribution to the total calculation time is small. Thus, the parallel program completes a Becke3-Lee-Yang-Parr energy and gradient calculation on the Ag-TB2-helicate in less than 4 h on 30 processors. We also present the necessary extension of the Lagrangian formalism, which enables the calculation of the TDDFT excited state properties in the frozen-core approximation. The algorithms described in this work are implemented into the ORCA electronic structure system.

The morphing of geographical features by Fourier transformation.

PubMed

Li, Jingzhong; Liu, Pengcheng; Yu, Wenhao; Cheng, Xiaoqiang

2018-01-01

This paper presents a morphing model of vector geographical data based on Fourier transformation. This model involves three main steps. They are conversion from vector data to Fourier series, generation of intermediate function by combination of the two Fourier series concerning a large scale and a small scale, and reverse conversion from combination function to vector data. By mirror processing, the model can also be used for morphing of linear features. Experimental results show that this method is sensitive to scale variations and it can be used for vector map features' continuous scale transformation. The efficiency of this model is linearly related to the point number of shape boundary and the interceptive value n of Fourier expansion. The effect of morphing by Fourier transformation is plausible and the efficiency of the algorithm is acceptable.

What does not kill them makes them stronger: larval environment and infectious dose alter mosquito potential to transmit filarial worms.

PubMed

Breaux, Jennifer A; Schumacher, Molly K; Juliano, Steven A

2014-07-07

For organisms with complex life cycles, larval environments can modify adult phenotypes. For mosquitoes and other vectors, when physiological impacts of stressors acting on larvae carry over into the adult stage they may interact with infectious dose of a vector-borne pathogen, producing a range of phenotypes for vector potential. Investigation of impacts of a common source of stress, larval crowding and intraspecific competition, on adult vector interactions with pathogens may increase our understanding of the dynamics of pathogen transmission by mosquito vectors. Using Aedes aegypti and the nematode parasite Brugia pahangi, we demonstrate dose dependency of fitness effects of B. pahangi infection on the mosquito, as well as interactions between competitive stress among larvae and infectious dose for resulting adults that affect the physiological and functional ability of mosquitoes to act as vectors. Contrary to results from studies on mosquito-arbovirus interactions, our results suggest that adults from crowded larvae may limit infection better than do adults from uncrowded controls, and that mosquitoes from high-quality larval environments are more physiologically and functionally capable vectors of B. pahangi. Our results provide another example of how the larval environment can have profound effects on vector potential of resulting adults. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.

PubMed

Schaid, Daniel J

2010-01-01

Measures of genomic similarity are the basis of many statistical analytic methods. We review the mathematical and statistical basis of similarity methods, particularly based on kernel methods. A kernel function converts information for a pair of subjects to a quantitative value representing either similarity (larger values meaning more similar) or distance (smaller values meaning more similar), with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This review emphasizes the wide range of statistical methods and software that can be used when similarity is based on kernel methods, such as nonparametric regression, linear mixed models and generalized linear mixed models, hierarchical models, score statistics, and support vector machines. The mathematical rigor for these methods is summarized, as is the mathematical framework for making kernels. This review provides a framework to move from intuitive and heuristic approaches to define genomic similarities to more rigorous methods that can take advantage of powerful statistical modeling and existing software. A companion paper reviews novel approaches to creating kernels that might be useful for genomic analyses, providing insights with examples [1]. Copyright © 2010 S. Karger AG, Basel.

3D acoustic atmospheric tomography

NASA Astrophysics Data System (ADS)

Rogers, Kevin; Finn, Anthony

2014-10-01

This paper presents a method for tomographically reconstructing spatially varying 3D atmospheric temperature profiles and wind velocity fields based. Measurements of the acoustic signature measured onboard a small Unmanned Aerial Vehicle (UAV) are compared to ground-based observations of the same signals. The frequency-shifted signal variations are then used to estimate the acoustic propagation delay between the UAV and the ground microphones, which are also affected by atmospheric temperature and wind speed vectors along each sound ray path. The wind and temperature profiles are modelled as the weighted sum of Radial Basis Functions (RBFs), which also allow local meteorological measurements made at the UAV and ground receivers to supplement any acoustic observations. Tomography is used to provide a full 3D reconstruction/visualisation of the observed atmosphere. The technique offers observational mobility under direct user control and the capacity to monitor hazardous atmospheric environments, otherwise not justifiable on the basis of cost or risk. This paper summarises the tomographic technique and reports on the results of simulations and initial field trials. The technique has practical applications for atmospheric research, sound propagation studies, boundary layer meteorology, air pollution measurements, analysis of wind shear, and wind farm surveys.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jibben, Zechariah Joel; Herrmann, Marcus

Here, we present a Runge-Kutta discontinuous Galerkin method for solving conservative reinitialization in the context of the conservative level set method. This represents an extension of the method recently proposed by Owkes and Desjardins [21], by solving the level set equations on the refined level set grid and projecting all spatially-dependent variables into the full basis used by the discontinuous Galerkin discretization. By doing so, we achieve the full k+1 order convergence rate in the L1 norm of the level set field predicted for RKDG methods given kth degree basis functions when the level set profile thickness is held constantmore » with grid refinement. Shape and volume errors for the 0.5-contour of the level set, on the other hand, are found to converge between first and second order. We show a variety of test results, including the method of manufactured solutions, reinitialization of a circle and sphere, Zalesak's disk, and deforming columns and spheres, all showing substantial improvements over the high-order finite difference traditional level set method studied for example by Herrmann. We also demonstrate the need for kth order accurate normal vectors, as lower order normals are found to degrade the convergence rate of the method.« less

EEG-distributed inverse solutions for a spherical head model

NASA Astrophysics Data System (ADS)

Riera, J. J.; Fuentes, M. E.; Valdés, P. A.; Ohárriz, Y.

1998-08-01

The theoretical study of the minimum norm solution to the MEG inverse problem has been carried out in previous papers for the particular case of spherical symmetry. However, a similar study for the EEG is remarkably more difficult due to the very complicated nature of the expression relating the voltage differences on the scalp to the primary current density (PCD) even for this simple symmetry. This paper introduces the use of the electric lead field (ELF) on the dyadic formalism in the spherical coordinate system to overcome such a drawback using an expansion of the ELF in terms of longitudinal and orthogonal vector fields. This approach allows us to represent EEG Fourier coefficients on a 2-sphere in terms of a current multipole expansion. The choice of a suitable basis for the Hilbert space of the PCDs on the brain region allows the current multipole moments to be related by spatial transfer functions to the PCD spectral coefficients. Properties of the most used distributed inverse solutions are explored on the basis of these results. Also, a part of the ELF null space is completely characterized and those spherical components of the PCD which are possible silent candidates are discussed.