Using support vector machine ensembles for target audience classification on Twitter.

PubMed

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

Ontology for Vector Surveillance and Management

PubMed Central

LOZANO-FUENTES, SAUL; BANDYOPADHYAY, ARITRA; COWELL, LINDSAY G.; GOLDFAIN, ALBERT; EISEN, LARS

2013-01-01

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an “umbrella” for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a “term tree” to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has_vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO *.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use. PMID:23427646

Ontology for vector surveillance and management.

PubMed

Lozano-Fuentes, Saul; Bandyopadhyay, Aritra; Cowell, Lindsay G; Goldfain, Albert; Eisen, Lars

2013-01-01

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an "umbrella" for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a "term tree" to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO*.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use.

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

Combining information from 3 anatomic regions in the diagnosis of glaucoma with time-domain optical coherence tomography.

PubMed

Wang, Mingwu; Lu, Ake Tzu-Hui; Varma, Rohit; Schuman, Joel S; Greenfield, David S; Huang, David

2014-03-01

To improve the diagnosis of glaucoma by combining time-domain optical coherence tomography (TD-OCT) measurements of the optic disc, circumpapillary retinal nerve fiber layer (RNFL), and macular retinal thickness. Ninety-six age-matched normal and 96 perimetric glaucoma participants were included in this observational, cross-sectional study. Or-logic, support vector machine, relevance vector machine, and linear discrimination function were used to analyze the performances of combined TD-OCT diagnostic variables. The area under the receiver-operating curve (AROC) was used to evaluate the diagnostic accuracy and to compare the diagnostic performance of single and combined anatomic variables. The best RNFL thickness variables were the inferior (AROC=0.900), overall (AROC=0.892), and superior quadrants (AROC=0.850). The best optic disc variables were horizontal integrated rim width (AROC=0.909), vertical integrated rim area (AROC=0.908), and cup/disc vertical ratio (AROC=0.890). All macular retinal thickness variables had AROCs of 0.829 or less. Combining the top 3 RNFL and optic disc variables in optimizing glaucoma diagnosis, support vector machine had the highest AROC, 0.954, followed by or-logic (AROC=0.946), linear discrimination function (AROC=0.946), and relevance vector machine (AROC=0.943). All combination diagnostic variables had significantly larger AROCs than any single diagnostic variable. There are no significant differences among the combination diagnostic indices. With TD-OCT, RNFL and optic disc variables had better diagnostic accuracy than macular retinal variables. Combining top RNFL and optic disc variables significantly improved diagnostic performance. Clinically, or-logic classification was the most practical analytical tool with sufficient accuracy to diagnose early glaucoma.

Ubiquitin fusion constructs allow the expression and purification of multi-KOW domain complexes of the Saccharomyces cerevisiae transcription elongation factor Spt4/5.

PubMed

Blythe, Amanda; Gunasekara, Sanjika; Walshe, James; Mackay, Joel P; Hartzog, Grant A; Vrielink, Alice

2014-08-01

Spt4/5 is a hetero-dimeric transcription elongation factor that can both inhibit and promote transcription elongation by RNA polymerase II (RNAPII). However, Spt4/5's mechanism of action remains elusive. Spt5 is an essential protein and the only universally-conserved RNAP-associated transcription elongation factor. The protein contains multiple Kyrpides, Ouzounis and Woese (KOW) domains. These domains, in other proteins, are thought to bind RNA although there is little direct evidence in the literature to support such a function in Spt5. This could be due, at least in part, to difficulties in expressing and purifying recombinant Spt5. When expressed in Escherichia coli (E. coli), Spt5 is innately insoluble. Here we report a new approach for the successful expression and purification of milligram quantities of three different multi-KOW domain complexes of Saccharomyces cerevisiae Spt4/5 for use in future functional studies. Using the E. coli strain Rosetta2 (DE3) we have developed strategies for co-expression of Spt4 and multi-KOW domain Spt5 complexes from the bi-cistronic pET-Duet vector. In a second strategy, Spt4/5 was expressed via co-transformation of Spt4 in the vector pET-M11 with Spt5 ubiquitin fusion constructs in the vector pHUE. We characterized the multi-KOW domain Spt4/5 complexes by Western blot, limited proteolysis, circular dichroism, SDS-PAGE and size exclusion chromatography-multiangle light scattering and found that the proteins are folded with a Spt4:Spt5 hetero-dimeric stoichiometry of 1:1. These expression constructs encompass a larger region of Spt5 than has previously been reported, and will provide the opportunity to elucidate the biological function of the multi-KOW containing Spt5. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

PubMed Central

2011-01-01

Background Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors. Results This study proposes a systematic approach (named Auto-IDPCPs) to automatically identify a set of physicochemical and biochemical properties in the AAindex database to design SVM-based classifiers for predicting and analyzing DNA-binding domains/proteins. Auto-IDPCPs consists of 1) clustering 531 amino acid indices in AAindex into 20 clusters using a fuzzy c-means algorithm, 2) utilizing an efficient genetic algorithm based optimization method IBCGA to select an informative feature set of size m to represent sequences, and 3) analyzing the selected features to identify related physicochemical properties which may affect the binding mechanism of DNA-binding domains/proteins. The proposed Auto-IDPCPs identified m=22 features of properties belonging to five clusters for predicting DNA-binding domains with a five-fold cross-validation accuracy of 87.12%, which is promising compared with the accuracy of 86.62% of the existing method PSSM-400. For predicting DNA-binding sequences, the accuracy of 75.50% was obtained using m=28 features, where PSSM-400 has an accuracy of 74.22%. Auto-IDPCPs and PSSM-400 have accuracies of 80.73% and 82.81%, respectively, applied to an independent test data set of DNA-binding domains. Some typical physicochemical properties discovered are hydrophobicity, secondary structure, charge, solvent accessibility, polarity, flexibility, normalized Van Der Waals volume, pK (pK-C, pK-N, pK-COOH and pK-a(RCOOH)), etc. Conclusions The proposed approach Auto-IDPCPs would help designers to investigate informative physicochemical and biochemical properties by considering both prediction accuracy and analysis of binding mechanism simultaneously. The approach Auto-IDPCPs can be also applicable to predict and analyze other protein functions from sequences. PMID:21342579

Algebraic and radical potential fields. Stability domains in coordinate and parametric space

NASA Astrophysics Data System (ADS)

Uteshev, Alexei Yu.

2018-05-01

A dynamical system d X/d t = F(X; A) is treated where F(X; A) is a polynomial (or some general type of radical contained) function in the vectors of state variables X ∈ ℝn and parameters A ∈ ℝm. We are looking for stability domains in both spaces, i.e. (a) domain ℙ ⊂ ℝm such that for any parameter vector specialization A ∈ ℙ, there exists a stable equilibrium for the dynamical system, and (b) domain 𝕊 ⊂ ℝn such that any point X* ∈ 𝕊 could be made a stable equilibrium by a suitable specialization of the parameter vector A.

Domain walls of linear polarization in isotropic Kerr media

NASA Astrophysics Data System (ADS)

Louis, Y.; Sheppard, A. P.; Haelterman, M.

1997-09-01

We present a new type of domain-wall vector solitary waves in isotropic self-defocusing Kerr media. These domain walls consist of localized structures separating uniform field domains of orthogonal linear polarizations. They result from the interplay between diffraction, self-phase modulation and cross-phase modulation in cases where the nonlinear birefringence coefficient B = {χxyyx(3)}/{χxxxx(3)} is negative. Numerical simulations show that these new vector solitary waves are stable.

Stochastic Navier-Stokes Equations in Unbounded Channel Domains (Open Source)

DTIC Science & Technology

2014-09-17

0 (Θ) = The space of all infinitely differentiable vector fields with compact support in Θ, W0(Θ) = The completion of C∞0 (Θ) vector fields in the...us use the differentiability of K(y, t) in time t. For |h| < η, we have E [( w1(y, t+ h, ω)− w1(y, t, ω) h − w1t(y, t, ω) )2 ] = E [∫ t 0 ( K(y, t+ h...s, ω) → K(y, 0)f(t, ω) = f(t, ω). Thus by the Lebesgue’s differentiation theorem (Theorem 6, Appendix E.4 of Evans [21]), the last term of the right

Generalized vector calculus on convex domain

NASA Astrophysics Data System (ADS)

Agrawal, Om P.; Xu, Yufeng

2015-06-01

In this paper, we apply recently proposed generalized integral and differential operators to develop generalized vector calculus and generalized variational calculus for problems defined over a convex domain. In particular, we present some generalization of Green's and Gauss divergence theorems involving some new operators, and apply these theorems to generalized variational calculus. For fractional power kernels, the formulation leads to fractional vector calculus and fractional variational calculus for problems defined over a convex domain. In special cases, when certain parameters take integer values, we obtain formulations for integer order problems. Two examples are presented to demonstrate applications of the generalized variational calculus which utilize the generalized vector calculus developed in the paper. The first example leads to a generalized partial differential equation and the second example leads to a generalized eigenvalue problem, both in two dimensional convex domains. We solve the generalized partial differential equation by using polynomial approximation. A special case of the second example is a generalized isoperimetric problem. We find an approximate solution to this problem. Many physical problems containing integer order integrals and derivatives are defined over arbitrary domains. We speculate that future problems containing fractional and generalized integrals and derivatives in fractional mechanics will be defined over arbitrary domains, and therefore, a general variational calculus incorporating a general vector calculus will be needed for these problems. This research is our first attempt in that direction.

Systematic characterization of the specificity of the SH2 domains of cytoplasmic tyrosine kinases.

PubMed

Zhao, Bing; Tan, Pauline H; Li, Shawn S C; Pei, Dehua

2013-04-09

Cytoplasmic tyrosine kinases (CTK) generally contain a Src-homology 2 (SH2) domain, whose role in the CTK family is not fully understood. Here we report the determination of the specificity of 25 CTK SH2 domains by screening one-bead-one-compound (OBOC) peptide libraries. Based on the peptide sequences selected by the SH2 domains, we built Support Vector Machine (SVM) models for the prediction of binding ligands for the SH2 domains. These models yielded support for the progressive phosphorylation model for CTKs in which the overlapping specificity of the CTK SH2 and kinase domains has been proposed to facilitate targeting of the CTK substrates with at least two potential phosphotyrosine (pTyr) sites. We curated 93 CTK substrates with at least two pTyr sites catalyzed by the same CTK, and showed that 71% of these substrates had at least two pTyr sites predicted to bind a common CTK SH2 domain. More importantly, we found 34 instances where there was at least one pTyr site predicted to be recognized by the SH2 domain of the same CTK, suggesting that the SH2 and kinase domains of the CTKs may cooperate to achieve progressive phosphorylation of a protein substrate. This article is part of a Special Issue entitled: From protein structures to clinical applications. Copyright © 2012 Elsevier B.V. All rights reserved.

Solvability and Regularity for an Elliptic System Prescribing the Curl, Divergence, and Partial Trace of a Vector Field on Sobolev-Class Domains

NASA Astrophysics Data System (ADS)

Cheng, C. H. Arthur; Shkoller, Steve

2017-09-01

We provide a self-contained proof of the solvability and regularity of a Hodge-type elliptic system, wherein the divergence and curl of a vector field u are prescribed in an open, bounded, Sobolev-class domain {Ω \\subseteq R^n}, and either the normal component {{u} \\cdot {N}} or the tangential components of the vector field {{u} × {N}} are prescribed on the boundary {partial Ω}. For {k > n/2}, we prove that u is in the Sobolev space {H^k+1(Ω)} if {Ω} is an {H^k+1}-domain, and the divergence, curl, and either the normal or tangential trace of u has sufficient regularity. The proof is based on a regularity theory for vector elliptic equations set on Sobolev-class domains and with Sobolev-class coefficients, and with a rather general set of Dirichlet and Neumann boundary conditions. The resulting regularity theory for the vector u is fundamental in the analysis of free-boundary and moving interface problems in fluid dynamics.

On the domain of the Nelson Hamiltonian

NASA Astrophysics Data System (ADS)

Griesemer, M.; Wünsch, A.

2018-04-01

The Nelson Hamiltonian is unitarily equivalent to a Hamiltonian defined through a closed, semibounded quadratic form, the unitary transformation being explicitly known and due to Gross. In this paper, we study the mapping properties of the Gross-transform in order to characterize the regularity properties of vectors in the form domain of the Nelson Hamiltonian. Since the operator domain is a subset of the form domain, our results apply to vectors in the domain of the Hamiltonian as well. This work is a continuation of our previous work on the Fröhlich Hamiltonian.

An analysis of random projection for changeable and privacy-preserving biometric verification.

PubMed

Wang, Yongjin; Plataniotis, Konstantinos N

2010-10-01

Changeability and privacy protection are important factors for widespread deployment of biometrics-based verification systems. This paper presents a systematic analysis of a random-projection (RP)-based method for addressing these problems. The employed method transforms biometric data using a random matrix with each entry an independent and identically distributed Gaussian random variable. The similarity- and privacy-preserving properties, as well as the changeability of the biometric information in the transformed domain, are analyzed in detail. Specifically, RP on both high-dimensional image vectors and dimensionality-reduced feature vectors is discussed and compared. A vector translation method is proposed to improve the changeability of the generated templates. The feasibility of the introduced solution is well supported by detailed theoretical analyses. Extensive experimentation on a face-based biometric verification problem shows the effectiveness of the proposed method.

On the fakeness of fake supergravity

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

Celi, Alessio; Proeyen, Antoine van; Ceresole, Anna

2005-02-15

We revisit and complete the study of curved BPS-domain walls in matter-coupled 5D, N=2 supergravity and carefully analyze the relation to gravitational theories known as ''fake supergravities.'' We first show that curved BPS-domain walls require the presence of nontrivial hypermultiplet scalars, whereas walls that are solely supported by vector multiplet scalars are necessarily flat, due to the constraints from very special geometry. We then recover fake supergravity as the effective description of true supergravity where one restricts the attention to the flowing scalar field of a given BPS-domain wall. In general, however, true supergravity can be simulated by fake supergravitymore » at most locally, based upon two choices: (i) a suitable adapted coordinate system on the scalar manifold, such that only one scalar field plays a dynamical role, and (ii) a gauge fixing of the SU(2) connection on the quaternionic-Kaehler manifold, as this connection does not fit the simple formalism of fake supergravity. Employing these gauge and coordinate choices, the BPS-equations for both vector and hypermultiplet scalars become identical to the fake supergravity equations, once the line of flow is determined by the full supergravity equations.« less

Observation of polarization domain wall solitons in weakly birefringent cavity fiber lasers

NASA Astrophysics Data System (ADS)

Zhang, H.; Tang, D. Y.; Zhao, L. M.; Wu, X.

2009-08-01

We report on the experimental observation of two types of phase-locked vector soliton in weakly birefringent cavity erbium-doped fiber lasers. While a phase-locked dark-dark vector soliton was only observed in fiber lasers of positive dispersion, a phase-locked dark-bright vector soliton was obtained in fiber lasers of either positive or negative dispersion. Numerical simulations confirmed the experimental observations and further showed that the observed vector solitons are the two types of phase-locked polarization domain wall solitons theoretically predicted.

Development of a domain-specific genetic language to design Chlamydomonas reinhardtii expression vectors.

PubMed

Wilson, Mandy L; Okumoto, Sakiko; Adam, Laura; Peccoud, Jean

2014-01-15

Expression vectors used in different biotechnology applications are designed with domain-specific rules. For instance, promoters, origins of replication or homologous recombination sites are host-specific. Similarly, chromosomal integration or viral delivery of an expression cassette imposes specific structural constraints. As de novo gene synthesis and synthetic biology methods permeate many biotechnology specialties, the design of application-specific expression vectors becomes the new norm. In this context, it is desirable to formalize vector design strategies applicable in different domains. Using the design of constructs to express genes in the chloroplast of Chlamydomonas reinhardtii as an example, we show that a vector design strategy can be formalized as a domain-specific language. We have developed a graphical editor of context-free grammars usable by biologists without prior exposure to language theory. This environment makes it possible for biologists to iteratively improve their design strategies throughout the course of a project. It is also possible to ensure that vectors designed with early iterations of the language are consistent with the latest iteration of the language. The context-free grammar editor is part of the GenoCAD application. A public instance of GenoCAD is available at http://www.genocad.org. GenoCAD source code is available from SourceForge and licensed under the Apache v2.0 open source license.

An efficient scheme for automatic web pages categorization using the support vector machine

NASA Astrophysics Data System (ADS)

Bhalla, Vinod Kumar; Kumar, Neeraj

2016-07-01

In the past few years, with an evolution of the Internet and related technologies, the number of the Internet users grows exponentially. These users demand access to relevant web pages from the Internet within fraction of seconds. To achieve this goal, there is a requirement of an efficient categorization of web page contents. Manual categorization of these billions of web pages to achieve high accuracy is a challenging task. Most of the existing techniques reported in the literature are semi-automatic. Using these techniques, higher level of accuracy cannot be achieved. To achieve these goals, this paper proposes an automatic web pages categorization into the domain category. The proposed scheme is based on the identification of specific and relevant features of the web pages. In the proposed scheme, first extraction and evaluation of features are done followed by filtering the feature set for categorization of domain web pages. A feature extraction tool based on the HTML document object model of the web page is developed in the proposed scheme. Feature extraction and weight assignment are based on the collection of domain-specific keyword list developed by considering various domain pages. Moreover, the keyword list is reduced on the basis of ids of keywords in keyword list. Also, stemming of keywords and tag text is done to achieve a higher accuracy. An extensive feature set is generated to develop a robust classification technique. The proposed scheme was evaluated using a machine learning method in combination with feature extraction and statistical analysis using support vector machine kernel as the classification tool. The results obtained confirm the effectiveness of the proposed scheme in terms of its accuracy in different categories of web pages.

A consensus for the development of a vector model to assess clinical complexity.

PubMed

Corazza, Gino Roberto; Klersy, Catherine; Formagnana, Pietro; Lenti, Marco Vincenzo; Padula, Donatella

2017-12-01

The progressive rise in multimorbidity has made management of complex patients one of the most topical and challenging issues in medicine, both in clinical practice and for healthcare organizations. To make this easier, a score of clinical complexity (CC) would be useful. A vector model to evaluate biological and extra-biological (socio-economic, cultural, behavioural, environmental) domains of CC was proposed a few years ago. However, given that the variables that grade each domain had never been defined, this model has never been used in clinical practice. To overcome these limits, a consensus meeting was organised to grade each domain of CC, and to establish the hierarchy of the domains. A one-day consensus meeting consisting of a multi-professional panel of 25 people was held at our Hospital. In a preliminary phase, the proponents selected seven variables as qualifiers for each of the five above-mentioned domains. In the course of the meeting, the panel voted for five variables considered to be the most representative for each domain. Consensus was established with 2/3 agreement, and all variables were dichotomised. Finally, the various domains were parametrized and ranked within a feasible vector model. A Clinical Complexity Index was set up using the chosen variables. All the domains were graphically represented through a vector model: the biological domain was chosen as the most significant (highest slope), followed by the behavioural and socio-economic domains (intermediate slope), and lastly by the cultural and environmental ones (lowest slope). A feasible and comprehensive tool to evaluate CC in clinical practice is proposed herein.

Type-II domains in ferroelectric gadolinium molybdate (in German)

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

Bohm, J.; Kuersten, H.D.

Etching (001)-faces of gadolinium molybdate (GMO) reveals new kinds of domains. They are created by a translation, that leaves the spontaneous polarization and the transition parameter invariant. The translation vector is a part of a lattice vector, similar to stacking faults. (auth)

Energy-exchange collisions of dark-bright-bright vector solitons.

PubMed

Radhakrishnan, R; Manikandan, N; Aravinthan, K

2015-12-01

We find a dark component guiding the practically interesting bright-bright vector one-soliton to two different parametric domains giving rise to different physical situations by constructing a more general form of three-component dark-bright-bright mixed vector one-soliton solution of the generalized Manakov model with nine free real parameters. Moreover our main investigation of the collision dynamics of such mixed vector solitons by constructing the multisoliton solution of the generalized Manakov model with the help of Hirota technique reveals that the dark-bright-bright vector two-soliton supports energy-exchange collision dynamics. In particular the dark component preserves its initial form and the energy-exchange collision property of the bright-bright vector two-soliton solution of the Manakov model during collision. In addition the interactions between bound state dark-bright-bright vector solitons reveal oscillations in their amplitudes. A similar kind of breathing effect was also experimentally observed in the Bose-Einstein condensates. Some possible ways are theoretically suggested not only to control this breathing effect but also to manage the beating, bouncing, jumping, and attraction effects in the collision dynamics of dark-bright-bright vector solitons. The role of multiple free parameters in our solution is examined to define polarization vector, envelope speed, envelope width, envelope amplitude, grayness, and complex modulation of our solution. It is interesting to note that the polarization vector of our mixed vector one-soliton evolves in sphere or hyperboloid depending upon the initial parametric choices.

Adaptive mesh refinement for time-domain electromagnetics using vector finite elements :a feasibility study.

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

Turner, C. David; Kotulski, Joseph Daniel; Pasik, Michael Francis

This report investigates the feasibility of applying Adaptive Mesh Refinement (AMR) techniques to a vector finite element formulation for the wave equation in three dimensions. Possible error estimators are considered first. Next, approaches for refining tetrahedral elements are reviewed. AMR capabilities within the Nevada framework are then evaluated. We summarize our conclusions on the feasibility of AMR for time-domain vector finite elements and identify a path forward.

Median filtering detection using variation of neighboring line pairs for image forensics

NASA Astrophysics Data System (ADS)

Rhee, Kang Hyeon

2016-09-01

Attention to tampering by median filtering (MF) has recently increased in digital image forensics. For the MF detection (MFD), this paper presents a feature vector that is extracted from two kinds of variations between the neighboring line pairs: the row and column directions. Of these variations in the proposed method, one is defined by a gradient difference of the intensity values between the neighboring line pairs, and the other is defined by a coefficient difference of the Fourier transform (FT) between the neighboring line pairs. Subsequently, the constructed 19-dimensional feature vector is composed of these two parts. One is the extracted 9-dimensional from the space domain of an image and the other is the 10-dimensional from the frequency domain of an image. The feature vector is trained in a support vector machine classifier for MFD in the altered images. As a result, in the measured performances of the experimental items, the area under the receiver operating characteristic curve (AUC, ROC) by the sensitivity (PTP: the true positive rate) and 1-specificity (PFP: the false-positive rate) are above 0.985 and the classification ratios are also above 0.979. Pe (a minimal average decision error) ranges from 0 to 0.024, and PTP at PFP=0.01 ranges from 0.965 to 0.996. It is confirmed that the grade evaluation of the proposed variation-based MF detection method is rated as "Excellent (A)" by AUC is above 0.9.

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-08-01

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

A 2D Daubechies finite wavelet domain method for transient wave response analysis in shear deformable laminated composite plates

NASA Astrophysics Data System (ADS)

Nastos, C. V.; Theodosiou, T. C.; Rekatsinas, C. S.; Saravanos, D. A.

2018-03-01

An efficient numerical method is developed for the simulation of dynamic response and the prediction of the wave propagation in composite plate structures. The method is termed finite wavelet domain method and takes advantage of the outstanding properties of compactly supported 2D Daubechies wavelet scaling functions for the spatial interpolation of displacements in a finite domain of a plate structure. The development of the 2D wavelet element, based on the first order shear deformation laminated plate theory is described and equivalent stiffness, mass matrices and force vectors are calculated and synthesized in the wavelet domain. The transient response is predicted using the explicit central difference time integration scheme. Numerical results for the simulation of wave propagation in isotropic, quasi-isotropic and cross-ply laminated plates are presented and demonstrate the high spatial convergence and problem size reduction obtained by the present method.

Using a Relational Database to Index Infectious Disease Information

PubMed Central

Brown, Jay A.

2010-01-01

Mapping medical knowledge into a relational database became possible with the availability of personal computers and user-friendly database software in the early 1990s. To create a database of medical knowledge, the domain expert works like a mapmaker to first outline the domain and then add the details, starting with the most prominent features. The resulting “intelligent database” can support the decisions of healthcare professionals. The intelligent database described in this article contains profiles of 275 infectious diseases. Users can query the database for all diseases matching one or more specific criteria (symptom, endemic region of the world, or epidemiological factor). Epidemiological factors include sources (patients, water, soil, or animals), routes of entry, and insect vectors. Medical and public health professionals could use such a database as a decision-support software tool. PMID:20623018

Effective Moment Feature Vectors for Protein Domain Structures

PubMed Central

Shi, Jian-Yu; Yiu, Siu-Ming; Zhang, Yan-Ning; Chin, Francis Yuk-Lun

2013-01-01

Imaging processing techniques have been shown to be useful in studying protein domain structures. The idea is to represent the pairwise distances of any two residues of the structure in a 2D distance matrix (DM). Features and/or submatrices are extracted from this DM to represent a domain. Existing approaches, however, may involve a large number of features (100–400) or complicated mathematical operations. Finding fewer but more effective features is always desirable. In this paper, based on some key observations on DMs, we are able to decompose a DM image into four basic binary images, each representing the structural characteristics of a fundamental secondary structure element (SSE) or a motif in the domain. Using the concept of moments in image processing, we further derive 45 structural features based on the four binary images. Together with 4 features extracted from the basic images, we represent the structure of a domain using 49 features. We show that our feature vectors can represent domain structures effectively in terms of the following. (1) We show a higher accuracy for domain classification. (2) We show a clear and consistent distribution of domains using our proposed structural vector space. (3) We are able to cluster the domains according to our moment features and demonstrate a relationship between structural variation and functional diversity. PMID:24391828

Recombination–deletion between homologous cassettes in retrovirus is suppressed via a strategy of degenerate codon substitution

PubMed Central

Im, Eung Jun; Bais, Anthony J; Yang, Wen; Ma, Qiangzhong; Guo, Xiuyang; Sepe, Steven M; Junghans, Richard P

2014-01-01

Transduction and expression procedures in gene therapy protocols may optimally transfer more than a single gene to correct a defect and/or transmit new functions to recipient cells or organisms. This may be accomplished by transduction with two (or more) vectors, or, more efficiently, in a single vector. Occasionally, it may be useful to coexpress homologous genes or chimeric proteins with regions of shared homology. Retroviridae include the dominant vector systems for gene transfer (e.g., gamma-retro and lentiviruses) and are capable of such multigene expression. However, these same viruses are known for efficient recombination–deletion when domains are duplicated within the viral genome. This problem can be averted by resorting to two-vector strategies (two-chain two-vector), but at a penalty to cost, convenience, and efficiency. Employing a chimeric antigen receptor system as an example, we confirm that coexpression of two genes with homologous domains in a single gamma-retroviral vector (two-chain single-vector) leads to recombination–deletion between repeated sequences, excising the equivalent of one of the chimeric antigen receptors. Here, we show that a degenerate codon substitution strategy in the two-chain single-vector format efficiently suppressed intravector deletional loss with rescue of balanced gene coexpression by minimizing sequence homology between repeated domains and preserving the final protein sequence. PMID:25419532

Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression

NASA Astrophysics Data System (ADS)

Dougherty, Andrew W.

Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor responses in the time, gas and temperature domains, and the dual representation of the support vector regression solution is shown to provide insight into the sensor's sensitivity and potential orthogonality. Finally, the dual weights of the support vector regression solution to the sensor's response are suggested as a fitness function for a genetic algorithm, or some other method for efficiently searching large parameter spaces.

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

PubMed Central

Srinivasan, Pratul P.; Kim, Leo A.; Mettu, Priyatham S.; Cousins, Scott W.; Comer, Grant M.; Izatt, Joseph A.; Farsiu, Sina

2014-01-01

We present a novel fully automated algorithm for the detection of retinal diseases via optical coherence tomography (OCT) imaging. Our algorithm utilizes multiscale histograms of oriented gradient descriptors as feature vectors of a support vector machine based classifier. The spectral domain OCT data sets used for cross-validation consisted of volumetric scans acquired from 45 subjects: 15 normal subjects, 15 patients with dry age-related macular degeneration (AMD), and 15 patients with diabetic macular edema (DME). Our classifier correctly identified 100% of cases with AMD, 100% cases with DME, and 86.67% cases of normal subjects. This algorithm is a potentially impactful tool for the remote diagnosis of ophthalmic diseases. PMID:25360373

A vector scanning processing technique for pulsed laser velocimetry

NASA Technical Reports Server (NTRS)

Wernet, Mark P.; Edwards, Robert V.

1989-01-01

Pulsed laser sheet velocimetry yields nonintrusive measurements of two-dimensional velocity vectors across an extended planar region of a flow. Current processing techniques offer high precision (1 pct) velocity estimates, but can require several hours of processing time on specialized array processors. Under some circumstances, a simple, fast, less accurate (approx. 5 pct), data reduction technique which also gives unambiguous velocity vector information is acceptable. A direct space domain processing technique was examined. The direct space domain processing technique was found to be far superior to any other techniques known, in achieving the objectives listed above. It employs a new data coding and reduction technique, where the particle time history information is used directly. Further, it has no 180 deg directional ambiguity. A complex convection vortex flow was recorded and completely processed in under 2 minutes on an 80386 based PC, producing a 2-D velocity vector map of the flow field. Hence, using this new space domain vector scanning (VS) technique, pulsed laser velocimetry data can be reduced quickly and reasonably accurately, without specialized array processing hardware.

Different polarization dynamic states in a vector Yb-doped fiber laser.

PubMed

Li, Xingliang; Zhang, Shumin; Han, Huiyun; Han, Mengmeng; Zhang, Huaxing; Zhao, Luming; Wen, Fang; Yang, Zhenjun

2015-04-20

Different polarization dynamic states in an unidirectional, vector, Yb-doped fiber ring laser have been observed. A rich variety of dynamic states, including group velocity locked polarization domains and their splitting into regularly distributed multiple domains, polarization locked square pulses and their harmonic mode locking counterparts, and dissipative soliton resonances have all been observed with different operating parameters. We have also shown experimentally details of the conditions under which polarization-domain-wall dark pulses and bright square pulses form.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line.

PubMed

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-09-16

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF₂) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line

PubMed Central

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-01-01

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach. PMID:28926953

A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis

PubMed Central

Rahman, M. M.; Antani, S. K.; Thoma, G. R.

2011-01-01

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall. PMID:21822350

Problems and Mitigation Strategies for Developing and Validating Statistical Cyber Defenses

DTIC Science & Technology

2014-04-01

Clustering Support  Vector  Machine   (SVM)  Classification Netflow Twitter   Training  Datasets Trained  SVMs Enriched  Feature   State...requests. • Netflow data for TCP connections • E-mail data from SMTP logs • Chat data from XMPP logs • Microtext data (from Twitter message archives...summary data from Bro and Netflow data captured on the BBN network over the period of 1 month, plus simulated attacks WHOIS Domain name record

A Unified Approach to Abductive Inference

DTIC Science & Technology

2014-09-30

learning in “ Big data ” domains. COMBINING MARKOV LOGIC AND SUPPORT VECTOR MACHINES FOR EVENT EXTRACTION Event extraction is the task of...and                          achieves state­of­the­art performance. This makes it an ideal candidate for learning in “ Big data ...including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the

Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine

PubMed Central

Amiri, Amir Mohammad; Abtahi, Mohammadreza; Constant, Nick; Mankodiya, Kunal

2017-01-01

Phonocardiogram (PCG) monitoring on newborns is one of the most important and challenging tasks in the heart assessment in the early ages of life. In this paper, we present a novel approach for cardiac monitoring applied in PCG data. This basic system coupled with denoising, segmentation, cardiac cycle selection and classification of heart sound can be used widely for a large number of the data. This paper describes the problems and additional advantages of the PCG method including the possibility of recording heart sound at home, removing unwanted noises and data reduction on a mobile device, and an intelligent system to diagnose heart diseases on the cloud server. A wide range of physiological features from various analysis domains, including modeling, time/frequency domain analysis, an algorithm, etc., is proposed in order to extract features which will be considered as inputs for the classifier. In order to record the PCG data set from multiple subjects over one year, an electronic stethoscope was used for collecting data that was connected to a mobile device. In this study, we used different types of classifiers in order to distinguish between healthy and pathological heart sounds, and a comparison on the performances revealed that support vector machine (SVM) provides 92.2% accuracy and AUC = 0.98 in a time of 1.14 seconds for training, on a dataset of 116 samples. PMID:28335471

Analysis of an Environmental Exposure Health Questionnaire in a Metropolitan Minority Population Utilizing Logistic Regression and Support Vector Machines

PubMed Central

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D.; Hood, Darryl B.; Skelton, Tyler

2014-01-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire. PMID:23395953

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

PubMed

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity.

PubMed

Diykh, Mohammed; Li, Yan; Wen, Peng

2016-11-01

The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high classification accuracy. In this paper, the statistical features in time domain, the structural graph similarity and the K-means (SGSKM) are combined to identify six sleep stages using single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted, sorted into different sets of features and forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relationships between sleep stages and the time domain features of the EEG data used in this paper. The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by using the proposed method.

Discontinuous finite element method for vector radiative transfer

NASA Astrophysics Data System (ADS)

Wang, Cun-Hai; Yi, Hong-Liang; Tan, He-Ping

2017-03-01

The discontinuous finite element method (DFEM) is applied to solve the vector radiative transfer in participating media. The derivation in a discrete form of the vector radiation governing equations is presented, in which the angular space is discretized by the discrete-ordinates approach with a local refined modification, and the spatial domain is discretized into finite non-overlapped discontinuous elements. The elements in the whole solution domain are connected by modelling the boundary numerical flux between adjacent elements, which makes the DFEM numerically stable for solving radiative transfer equations. Several various problems of vector radiative transfer are tested to verify the performance of the developed DFEM, including vector radiative transfer in a one-dimensional parallel slab containing a Mie/Rayleigh/strong forward scattering medium and a two-dimensional square medium. The fact that DFEM results agree very well with the benchmark solutions in published references shows that the developed DFEM in this paper is accurate and effective for solving vector radiative transfer problems.

An improved exact inversion formula for solenoidal fields in cone beam vector tomography

NASA Astrophysics Data System (ADS)

Katsevich, Alexander; Rothermel, Dimitri; Schuster, Thomas

2017-06-01

In this paper we present an improved inversion formula for the 3D cone beam transform of vector fields supported in the unit ball which is exact for solenoidal fields. It is well known that only the solenoidal part of a vector field can be determined from the longitudinal ray transform of a vector field in cone beam geometry. The inversion formula, as it was developed in Katsevich and Schuster (2013 An exact inversion formula for cone beam vector tomography Inverse Problems 29 065013), consists of two parts. The first part is of the filtered backprojection type, whereas the second part is a costly 4D integration and very inefficient. In this article we tackle this second term and obtain an improved formula, which is easy to implement and saves one order of integration. We also show that the first part contains all information about the curl of the field, whereas the second part has information about the boundary values. More precisely, the second part vanishes if the solenoidal part of the original field is tangential at the boundary. A number of numerical tests presented in the paper confirm the theoretical results and the exactness of the formula. Also, we obtain an inversion algorithm that works for general convex domains.

The influence of thermal and conductive temperatures in a nanoscale resonator

NASA Astrophysics Data System (ADS)

Hobiny, Aatef; Abbas, Ibrahim A.

2018-06-01

In this work, the thermoelastic interaction in a nano-scale resonator based on two-temperature Green-Naghdi model is established. The nanoscale resonator ends were simply supported. In the Laplace's domain, the analytical solution of conductivity temperature and thermodynamic temperature, the displacement and the stress components are obtained. The eigenvalue approach resorted to for solutions. In the vector-matrix differential equations form, the essential equations were written. The numerical results for all variables are presented and are illustrated graphically.

Orthonormal vector polynomials in a unit circle, Part I: Basis set derived from gradients of Zernike polynomials.

PubMed

Zhao, Chunyu; Burge, James H

2007-12-24

Zernike polynomials provide a well known, orthogonal set of scalar functions over a circular domain, and are commonly used to represent wavefront phase or surface irregularity. A related set of orthogonal functions is given here which represent vector quantities, such as mapping distortion or wavefront gradient. These functions are generated from gradients of Zernike polynomials, made orthonormal using the Gram- Schmidt technique. This set provides a complete basis for representing vector fields that can be defined as a gradient of some scalar function. It is then efficient to transform from the coefficients of the vector functions to the scalar Zernike polynomials that represent the function whose gradient was fit. These new vector functions have immediate application for fitting data from a Shack-Hartmann wavefront sensor or for fitting mapping distortion for optical testing. A subsequent paper gives an additional set of vector functions consisting only of rotational terms with zero divergence. The two sets together provide a complete basis that can represent all vector distributions in a circular domain.

Tracking and recognition of multiple human targets moving in a wireless pyroelectric infrared sensor network.

PubMed

Xiong, Ji; Li, Fangmin; Zhao, Ning; Jiang, Na

2014-04-22

With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.

Phase Domain Walls in Weakly Nonlinear Deep Water Surface Gravity Waves.

PubMed

Tsitoura, F; Gietz, U; Chabchoub, A; Hoffmann, N

2018-06-01

We report a theoretical derivation, an experimental observation and a numerical validation of nonlinear phase domain walls in weakly nonlinear deep water surface gravity waves. The domain walls presented are connecting homogeneous zones of weakly nonlinear plane Stokes waves of identical amplitude and wave vector but differences in phase. By exploiting symmetry transformations within the framework of the nonlinear Schrödinger equation we demonstrate the existence of exact analytical solutions representing such domain walls in the weakly nonlinear limit. The walls are in general oblique to the direction of the wave vector and stationary in moving reference frames. Experimental and numerical studies confirm and visualize the findings. Our present results demonstrate that nonlinear domain walls do exist in the weakly nonlinear regime of general systems exhibiting dispersive waves.

Phase Domain Walls in Weakly Nonlinear Deep Water Surface Gravity Waves

NASA Astrophysics Data System (ADS)

Tsitoura, F.; Gietz, U.; Chabchoub, A.; Hoffmann, N.

2018-06-01

We report a theoretical derivation, an experimental observation and a numerical validation of nonlinear phase domain walls in weakly nonlinear deep water surface gravity waves. The domain walls presented are connecting homogeneous zones of weakly nonlinear plane Stokes waves of identical amplitude and wave vector but differences in phase. By exploiting symmetry transformations within the framework of the nonlinear Schrödinger equation we demonstrate the existence of exact analytical solutions representing such domain walls in the weakly nonlinear limit. The walls are in general oblique to the direction of the wave vector and stationary in moving reference frames. Experimental and numerical studies confirm and visualize the findings. Our present results demonstrate that nonlinear domain walls do exist in the weakly nonlinear regime of general systems exhibiting dispersive waves.

Polypeptides having xylanase activity and polynucleotides encoding same

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

Spodsberg, Nikolaj; Shaghasi, Tarana

The present invention relates to polypeptides having xylanase activity, catalytic domains, and carbohydrate binding domains, and polynucleotides encoding the polypeptides, catalytic domains, and carbohydrate binding domains. The present invention also relates to nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides, catalytic domains, and carbohydrate binding domains.

Polypeptides having endoglucanase activity and polynucleotides encoding same

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

Spodsberg, Nikolaj; Shagasi, Tarana

The present invention relates to isolated polypeptides having endoglucanase activity, catalytic domains, cellulose binding domains and polynucleotides encoding the polypeptides, catalytic domains or cellulose binding domains. The invention also relates to nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides, catalytic domains or cellulose binding domains.

Polypeptides having endoglucanase activity and polynucleotides encoding same

DOEpatents

Spodsberg, Nikolaj; Shagasi, Tarana

2015-06-30

The present invention relates to isolated polypeptides having endoglucanase activity, catalytic domains, cellulose binding domains and polynucleotides encoding the polypeptides, catalytic domains or cellulose binding domains. The invention also relates to nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides, catalytic domains or cellulose binding domains.

Polypeptides having cellobiohydrolase activity and polynucleotides encoding same

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

Stringer, Mary Ann; McBrayer, Brett

2016-11-29

The present invention relates to isolated polypeptides having cellobiohydrolase activity, catalytic domains, and cellulose binding domains and polynucleotides encoding the polypeptides, catalytic domains, and cellulose binding domains. The invention also relates to nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides, catalytic domains, or cellulose binding domains.

Insertion of targeting domains into the envelope glycoprotein of Moloney murine leukemia virus (MoMLV)-based vectors modulates the route of mCAT-1-mediated viral entry.

PubMed

Viejo-Borbolla, A; Pizzato, M; Blair, E D; Schulz, T F

2005-03-01

Several groups have inserted targeting domains into the envelope glycoprotein (Env) of Moloney murine leukemia virus (MoMLV) in an attempt to produce targeted retroviral vectors for human gene therapy. While binding of these modified Envs to the target molecule expressed on the surface of human cells was observed, specific high-titer infection of human cells expressing the target molecule was not achieved. Here we investigate the initial steps in the entry process of targeted MoMLV vectors both in murine and human cells expressing the MoMLV receptor, the mouse cationic amino acid transporter-1 (mCAT-1). We show that insertion of a small ligand targeted to E-selectin and of a single chain antibody (scFv) targeted to folate-binding protein (FBP) into the N-terminus of MoMLV Env results in the reduction of the infectivity and the kinetics of entry of the MoMLV vectors. The use of soluble receptor-binding domain (sRBD), bafilomycin A1 (BafA1) and methyl-beta-cyclodextrin (MbetaC) increase the infectivity of the MoMLV vectors targeted to FBP (MoMLV-FBP) suggesting that the scFv targeted to FBP increases the threshold for fusion and might re-route entry of the targeted MoMLV-FBP vector towards an endocytic, non-productive pathway.

Reconstruction of Vectorial Acoustic Sources in Time-Domain Tomography

PubMed Central

Xia, Rongmin; Li, Xu; He, Bin

2009-01-01

A new theory is proposed for the reconstruction of curl-free vector field, whose divergence serves as acoustic source. The theory is applied to reconstruct vector acoustic sources from the scalar acoustic signals measured on a surface enclosing the source area. It is shown that, under certain conditions, the scalar acoustic measurements can be vectorized according to the known measurement geometry and subsequently be used to reconstruct the original vector field. Theoretically, this method extends the application domain of the existing acoustic reciprocity principle from a scalar field to a vector field, indicating that the stimulating vectorial source and the transmitted acoustic pressure vector (acoustic pressure vectorized according to certain measurement geometry) are interchangeable. Computer simulation studies were conducted to evaluate the proposed theory, and the numerical results suggest that reconstruction of a vector field using the proposed theory is not sensitive to variation in the detecting distance. The present theory may be applied to magnetoacoustic tomography with magnetic induction (MAT-MI) for reconstructing current distribution from acoustic measurements. A simulation on MAT-MI shows that, compared to existing methods, the present method can give an accurate estimation on the source current distribution and a better conductivity reconstruction. PMID:19211344

Increased proliferation of endothelial cells with overexpression of soluble TNF-alpha receptor I gene.

PubMed

Sugano, Masahiro; Tsuchida, Keiko; Tomita, Hideharu; Makino, Naoki

2002-05-01

Vascular endothelial growth factor (VEGF) can overcome a potential anti-angiogenic effect of TNF-alpha by inhibiting endothelial apoptosis induced by this cytokine. Soluble TNF-alpha receptor I (sTNFRI) is an extracellular domain of TNFRI and antagonizes the activity of TNF-alpha. Here we report that sTNFRI is able to stimulate the growth of endothelial cells not by antagonizing TNF-alpha. Exogenously added recombinant human sTNFRI stimulated significantly more cell growth of human umbilical venous endothelial cells (HUVEC) with a low dose (50-200 pg/ml) compared with smooth muscle cells. In contrast, monoclonal antibody against TNF-alpha did not stimulate growth of human HUVEC. The sTNFRI expression plasmid (pcDNA3.1 plasmid) was introduced into the cell culture using OPTI-MEM, lipofectin and transferrin. Growth of HUVEC transfected with sTNFRI vector also increased significantly compared with those transfected with control vector. HUVEC transfected with sTNFRI vector increased the extracellular domain of TNFRI mRNA levels, but did not affect the intracellular domain of TNFRI mRNA levels. Accumulation of sTNFRI significantly increased in conditioned medium from HUVEC transfected with sTNFRI vector compared with those transfected with control vector. HUVEC transfected with sTNFRI vector not only increased sTNFRI but also prevented shedding of sTNFRI from TNFRI. The TNF-alpha -induced internucleosomic fragmentation was also significantly prevented in HUVEC transfected with sTNFRI vector compared with those transfected with control vector. These results suggest that instead of growth factors such as VEGF, local transfection of the sTNFRI gene may have potential therapeutic value in vascular diseases in which TNF-alpha is also usually highly expressed.

BDDC algorithms with deluxe scaling and adaptive selection of primal constraints for Raviart-Thomas vector fields

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

Oh, Duk -Soon; Widlund, Olof B.; Zampini, Stefano

Here, a BDDC domain decomposition preconditioner is defined by a coarse component, expressed in terms of primal constraints, a weighted average across the interface between the subdomains, and local components given in terms of solvers of local subdomain problems. BDDC methods for vector field problems discretized with Raviart-Thomas finite elements are introduced. The methods are based on a new type of weighted average an adaptive selection of primal constraints developed to deal with coefficients with high contrast even inside individual subdomains. For problems with very many subdomains, a third level of the preconditioner is introduced. Assuming that the subdomains aremore » all built from elements of a coarse triangulation of the given domain, and that in each subdomain the material parameters are consistent, one obtains a bound for the preconditioned linear system's condition number which is independent of the values and jumps of these parameters across the subdomains' interface. Numerical experiments, using the PETSc library, are also presented which support the theory and show the algorithms' effectiveness even for problems not covered by the theory. Also included are experiments with Brezzi-Douglas-Marini finite-element approximations.« less

BDDC algorithms with deluxe scaling and adaptive selection of primal constraints for Raviart-Thomas vector fields

DOE PAGES

Oh, Duk -Soon; Widlund, Olof B.; Zampini, Stefano; ...

2017-06-21

Here, a BDDC domain decomposition preconditioner is defined by a coarse component, expressed in terms of primal constraints, a weighted average across the interface between the subdomains, and local components given in terms of solvers of local subdomain problems. BDDC methods for vector field problems discretized with Raviart-Thomas finite elements are introduced. The methods are based on a new type of weighted average an adaptive selection of primal constraints developed to deal with coefficients with high contrast even inside individual subdomains. For problems with very many subdomains, a third level of the preconditioner is introduced. Assuming that the subdomains aremore » all built from elements of a coarse triangulation of the given domain, and that in each subdomain the material parameters are consistent, one obtains a bound for the preconditioned linear system's condition number which is independent of the values and jumps of these parameters across the subdomains' interface. Numerical experiments, using the PETSc library, are also presented which support the theory and show the algorithms' effectiveness even for problems not covered by the theory. Also included are experiments with Brezzi-Douglas-Marini finite-element approximations.« less

[Preparation and characterization of monoclonal antibodies against Micrococcus luteus Rpf domain].

PubMed

Fan, Ai-lin; Shi, Chang-hong; Su, Ming-quan; Ma, Jing; Bai, Yin-lan; Cheng, Xiao-dong; Xu, Zhi-kai; Hao, Xiao-ke

2008-05-01

To express Micrococcus luteus Rpf domain in prokaryotic cells and prepare monoclonal antibodies against Rpf domain. The gene encoding Micrococcus luteus Rpf domain was amplified from genome of Micrococcus luteus by polymerase chain reaction(PCR), and inserted into cloning vector pUC-19. After sequenced, Micrococcus luteus Rpf domain gene was subcloned into the expression vector pPro-EXHT and transfected into E.coli DH5alpha. After induced by IPTG, the bacteria controlled by T7 promoter expressed the fused Micrococcus luteus Rpf domain protein with a hexahistidine tail at its N-terminal and the target protein was purified under denaturing conditions. Using this protein as antigen to immunize the BALB/c mice and prepare monoclonal antibodies against Micrococcus luteus Rpf domain. Then specifities and relative affinities of mAbs were identified by ELISA. The fusion protein was purified by metal chelate affinity chromatography under denaturing condition. Three cloned mAbs were prepared from the mice immunized by Rpf domain. All of them could recognize Rpf domain. specifically. The prepared mAbs against Rpf domain have strong specificity with high titers, which provides useful tools for further study of the function of Rpf domain in TB prevention.

[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.

Why Johnny Struggles When Familiar Concepts Are Taken to a New Mathematical Domain: Towards a Polysemous Approach

ERIC Educational Resources Information Center

Kontorovich, Igor'

2018-01-01

This article is concerned with cognitive aspects of students' struggles in situations in which familiar concepts are reconsidered in a new mathematical domain. Examples of such cross-curricular concepts are divisibility in the domain of integers and in the domain of polynomials, multiplication in the domain of numbers and in the domain of vectors,…

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.

A novel high-throughput (HTP) cloning strategy for site-directed designed chimeragenesis and mutation using the Gateway cloning system

PubMed Central

Suzuki, Yasuhiro; Kagawa, Naoko; Fujino, Toru; Sumiya, Tsuyoshi; Andoh, Taichi; Ishikawa, Kumiko; Kimura, Rie; Kemmochi, Kiyokazu; Ohta, Tsutomu; Tanaka, Shigeo

2005-01-01

There is an increasing demand for easy, high-throughput (HTP) methods for protein engineering to support advances in the development of structural biology, bioinformatics and drug design. Here, we describe an N- and C-terminal cloning method utilizing Gateway cloning technology that we have adopted for chimeric and mutant genes production as well as domain shuffling. This method involves only three steps: PCR, in vitro recombination and transformation. All three processes consist of simple handling, mixing and incubation steps. We have characterized this novel HTP method on 96 targets with >90% success. Here, we also discuss an N- and C-terminal cloning method for domain shuffling and a combination of mutation and chimeragenesis with two types of plasmid vectors. PMID:16009811

Tracking and Recognition of Multiple Human Targets Moving in a Wireless Pyroelectric Infrared Sensor Network

PubMed Central

Xiong, Ji; Li, Fangmin; Zhao, Ning; Jiang, Na

2014-01-01

With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%. PMID:24759117

Using domain decomposition in the multigrid NAS parallel benchmark on the Fujitsu VPP500

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

Wang, J.C.H.; Lung, H.; Katsumata, Y.

1995-12-01

In this paper, we demonstrate how domain decomposition can be applied to the multigrid algorithm to convert the code for MPP architectures. We also discuss the performance and scalability of this implementation on the new product line of Fujitsu`s vector parallel computer, VPP500. This computer has Fujitsu`s well-known vector processor as the PE each rated at 1.6 C FLOPS. The high speed crossbar network rated at 800 MB/s provides the inter-PE communication. The results show that the physical domain decomposition is the best way to solve MG problems on VPP500.

Hypoxia-inducible vascular endothelial growth factor gene therapy using the oxygen-dependent degradation domain in myocardial ischemia.

PubMed

Kim, Hyun Ah; Lim, Soyeon; Moon, Hyung-Ho; Kim, Sung Wan; Hwang, Ki-Chul; Lee, Minhyung; Kim, Sun Hwa; Choi, Donghoon

2010-10-01

A hypoxia-inducible VEGF expression system with the oxygen-dependent degradation (ODD) domain was constructed and tested to be used in gene therapy for ischemic myocardial disease. Luciferase and VEGF expression vector systems were constructed with or without the ODD domain: pEpo-SV-Luc (or pEpo-SV-VEGF) and pEpo-SV-Luc-ODD (or pEpo-SV-VEGF-ODD). In vitro gene expression efficiency of each vector type was evaluated in HEK 293 cells under both hypoxic and normoxic conditions. The amount of VEGF protein was estimated by ELISA. The VEGF expression vectors with or without the ODD domain were injected into ischemic rat myocardium. Fibrosis, neovascularization, and cardiomyocyte apoptosis were assessed using Masson's trichrome staining, α-smooth muscle actin (α-SMA) immunostaining, and the TUNEL assay, respectively. The plasmid vectors containing ODD significantly improved the expression level of VEGF protein in hypoxic conditions. The enhancement of VEGF protein production was attributed to increased protein stability due to oxygen deficiency. In a rat model of myocardial ischemia, the pEpo-SV-VEGF-ODD group exhibited less myocardial fibrosis, higher microvessel density, and less cardiomyocyte apoptosis compared to the control groups (saline and pEpo-SV-VEGF treatments). An ODD-mediated VEGF expression system that facilitates VEGF-production under hypoxia may be useful in the treatment of ischemic heart disease.

Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

NASA Astrophysics Data System (ADS)

Kachach, Redouane; Cañas, José María

2016-05-01

Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

[New strategy for RNA vectorization in mammalian cells. Use of a peptide vector].

PubMed

Vidal, P; Morris, M C; Chaloin, L; Heitz, F; Divita, G

1997-04-01

A major barrier for gene delivery is the low permeability of nucleic acids to cellular membranes. The development of antisenses and gene therapy has focused mainly on improving methods of oligonucleotide or gene delivery to the cell. In this report we described a new strategy for RNA cell delivery, based on a short single peptide. This peptide vector is derived from both the fusion domain of the gp41 protein of HIV and the nuclear localization sequence of the SV40 large T antigen. This peptide vector localizes rapidly to the cytoplasm then to the nucleus of human fibroblasts (HS-68) within a few minutes and exhibits a high affinity for a single-stranded mRNA encoding the p66 subunit of the HIV-1 reverse transcriptase (in a 100 nM range). The peptide/RNA complex formation involves mainly electrostatic interactions between the basic residues of the peptide and the charges on the phosphate group of the RNA. In the presence of the peptide-vector fluorescently-labelled mRNA is delivered into the cytoplasm of mammalian cells (HS68 human fibroblasts) in less than 1 h with a relatively high efficiency (80%). This new concept based on a peptide-derived vector offers several advantages compared to other compounds commonly used in gene delivery. This vector is highly soluble and exhibits no cytotoxicity at the concentrations used for optimal gene delivery. This result clearly supports the fact that this peptide vector is a powerful tool and that it can be used widely, as much for laboratory research as for new applications and development in gene and/or antisense therapy.

Seminal quality prediction using data mining methods.

PubMed

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of fertility rate. In this paper, eight feature selection methods are applied on fertility dataset to find out a set of good features. The investigational results shows that childish diseases (0.079) and high fever features (0.057) has less impact on fertility rate while age (0.8685), season (0.843), surgical intervention (0.7683), alcohol consumption (0.5992), smoking habit (0.575), number of hours spent on setting (0.4366) and accident (0.5973) features have more impact. It is also observed that feature selection methods increase the accuracy of above mentioned techniques (multilayer perceptron 92%, support vector machine 91%, SVM+PSO 94%, Navie Bayes (Kernel) 89% and decision tree 89%) as compared to without feature selection methods (multilayer perceptron 86%, support vector machine 86%, SVM+PSO 85%, Navie Bayes (Kernel) 83% and decision tree 84%) which shows the applicability of feature selection methods in prediction. This paper lightens the application of artificial techniques in medical domain. From this paper, it can be concluded that data mining methods can be used to predict a person with or without disease based on environmental and lifestyle parameters/features rather than undergoing various medical test. In this paper, five data mining techniques are used to predict the fertility rate and among which SVM+PSO provide more accurate results than support vector machine and decision tree.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

Detecting double compressed MPEG videos with the same quantization matrix and synchronized group of pictures structure

NASA Astrophysics Data System (ADS)

Aghamaleki, Javad Abbasi; Behrad, Alireza

2018-01-01

Double compression detection is a crucial stage in digital image and video forensics. However, the detection of double compressed videos is challenging when the video forger uses the same quantization matrix and synchronized group of pictures (GOP) structure during the recompression history to conceal tampering effects. A passive approach is proposed for detecting double compressed MPEG videos with the same quantization matrix and synchronized GOP structure. To devise the proposed algorithm, the effects of recompression on P frames are mathematically studied. Then, based on the obtained guidelines, a feature vector is proposed to detect double compressed frames on the GOP level. Subsequently, sparse representations of the feature vectors are used for dimensionality reduction and enrich the traces of recompression. Finally, a support vector machine classifier is employed to detect and localize double compression in temporal domain. The experimental results show that the proposed algorithm achieves the accuracy of more than 95%. In addition, the comparisons of the results of the proposed method with those of other methods reveal the efficiency of the proposed algorithm.

Solid-phase-assisted synthesis of targeting peptide-PEG-oligo(ethane amino)amides for receptor-mediated gene delivery.

PubMed

Martin, Irene; Dohmen, Christian; Mas-Moruno, Carlos; Troiber, Christina; Kos, Petra; Schaffert, David; Lächelt, Ulrich; Teixidó, Meritxell; Günther, Michael; Kessler, Horst; Giralt, Ernest; Wagner, Ernst

2012-04-28

In the forthcoming era of cancer gene therapy, efforts will be devoted to the development of new efficient and non-toxic gene delivery vectors. In this regard, the use of Fmoc/Boc-protected oligo(ethane amino)acids as building blocks for solid-phase-supported assembly represents a novel promising approach towards fully controlled syntheses of effective gene vectors. Here we report on the synthesis of defined polymers containing the following: (i) a plasmid DNA (pDNA) binding domain of eight succinoyl-tetraethylenpentamine (Stp) units and two terminal cysteine residues; (ii) a central polyethylene glycol (PEG) chain (with twenty-four oxyethylene units) for shielding; and (iii) specific peptides for targeting towards cancer cells. Peptides B6 and c(RGDfK), which bind transferrin receptor and α(v)β(3) integrin, respectively, were chosen because of the high expression of these receptors in many tumoral cells. This study shows the feasibility of designing these kinds of fully controlled vectors and their success for targeted pDNA-based gene transfer. This journal is © The Royal Society of Chemistry 2012

Inclusive τ lepton hadronic decay in vector and axial-vector channels within dispersive approach to QCD

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

Nesterenko, A. V.

The dispersive approach to QCD, which properly embodies the intrinsically nonperturbative constraints originating in the kinematic restrictions on relevant physical processes and extends the applicability range of perturbation theory towards the infrared domain, is briefly overviewed. The study of OPAL (update 2012) and ALEPH (update 2014) experimental data on inclusive τ lepton hadronic decay in vector and axial-vector channels within dispersive approach is presented.

Recombinant soluble adenovirus receptor

DOEpatents

Freimuth, Paul I.

2002-01-01

Disclosed are isolated polypeptides from human CAR (coxsackievirus and adenovirus receptor) protein which bind adenovirus. Specifically disclosed are amino acid sequences which corresponds to adenovirus binding domain D1 and the entire extracellular domain of human CAR protein comprising D1 and D2. In other aspects, the disclosure relates to nucleic acid sequences encoding these domains as well as expression vectors which encode the domains and bacterial cells containing such vectors. Also disclosed is an isolated fusion protein comprised of the D1 polypeptide sequence fused to a polypeptide sequence which facilitates folding of D1 into a functional, soluble domain when expressed in bacteria. The functional D1 domain finds application for example in a therapeutic method for treating a patient infected with a virus which binds to D1, and also in a method for identifying an antiviral compound which interferes with viral attachment. Also included is a method for specifically targeting a cell for infection by a virus which binds to D1.

Nucleic acid sequences encoding D1 and D1/D2 domains of human coxsackievirus and adenovirus receptor (CAR)

DOEpatents

Freimuth, Paul I.

2010-04-06

The invention provides recombinant human CAR (coxsackievirus and adenovirus receptor) polypeptides which bind adenovirus. Specifically, polypeptides corresponding to adenovirus binding domain D1 and the entire extracellular domain of human CAR protein comprising D1 and D2 are provided. In another aspect, the invention provides nucleic acid sequences encoding these domains and expression vectors for producing the domains and bacterial cells containing such vectors. The invention also includes an isolated fusion protein comprised of the D1 polypeptide fused to a polypeptide which facilitates folding of D1 when expressed in bacteria. The functional D1 domain finds application in a therapeutic method for treating a patient infected with a CAR D1-binding virus, and also in a method for identifying an antiviral compound which interferes with viral attachment. The invention also provides a method for specifically targeting a cell for infection by a virus which binds to D1.

Feasibility of Bioelectrical Impedance Spectroscopy Measurement before and after Thoracentesis

PubMed Central

Weyer, Sören; Pauly, Karolin; Napp, Andreas; Dreher, Michael; Leonhardt, Steffen; Marx, Nikolaus; Schauerte, Patrick; Mischke, Karl

2015-01-01

Background. Bioelectrical impedance spectroscopy is applied to measure changes in tissue composition. The aim of this study was to evaluate its feasibility in measuring the fluid shift after thoracentesis in patients with pleural effusion. Methods. 45 participants (21 with pleural effusion and 24 healthy subjects) were included. Bioelectrical impedance was analyzed for “Transthoracic,” “Foot to Foot,” “Foot to Hand,” and “Hand to Hand” vectors in low and high frequency domain before and after thoracentesis. Healthy subjects were measured at a single time point. Results. The mean volume of removed pleural effusion was 1169 ± 513 mL. The “Foot to Foot,” “Hand to Hand,” and “Foot to Hand” vector indicated a trend for increased bioelectrical impedance after thoracentesis. Values for the low frequency domain in the “Transthoracic” vector increased significantly (P < 0.001). A moderate correlation was observed between the amount of removed fluid and impedance change in the low frequency domain using the “Foot to Hand” vector (r = −0.7). Conclusion. Bioelectrical impedance changes in correlation with the thoracic fluid level. It was feasible to monitor significant fluid shifts and loss after thoracentesis in the “Transthoracic” vector by means of bioelectrical impedance spectroscopy. The trial is registered with Registration Numbers IRB EK206/11 and NCT01778270. PMID:25861647

Specific insertions of zinc finger domains into Gag-Pol yield engineered retroviral vectors with selective integration properties

PubMed Central

Lim, Kwang-il; Klimczak, Ryan; Yu, Julie H.; Schaffer, David V.

2010-01-01

Retroviral vectors offer benefits of efficient delivery and stable gene expression; however, their clinical use raises the concerns of insertional mutagenesis and potential oncogenesis due to genomic integration preferences in transcriptional start sites (TSS). We have shifted the integration preferences of retroviral vectors by generating a library of viral variants with a DNA-binding domain inserted at random positions throughout murine leukemia virus Gag-Pol, then selecting for variants that are viable and exhibit altered integration properties. We found seven permissive zinc finger domain (ZFD) insertion sites throughout Gag-Pol, including within p12, reverse transcriptase, and integrase. Comprehensive genome integration analysis showed that several ZFD insertions yielded retroviral vector variants with shifted integration patterns that did not favor TSS. Furthermore, integration site analysis revealed selective integration for numerous mutants. For example, two retroviral variants with a given ZFD at appropriate positions in Gag-Pol strikingly integrated primarily into four common sites out of 3.1 × 109 possible human genome locations (P = 4.6 × 10-29). Our findings demonstrate that insertion of DNA-binding motifs into multiple locations in Gag-Pol can make considerable progress toward engineering safer retroviral vectors that integrate into a significantly narrowed pool of sites on human genome and overcome the preference for TSS. PMID:20616052

Identification of candidate transmission-blocking antigen genes in Theileria annulata and related vector-borne apicomplexan parasites.

PubMed

Lempereur, Laetitia; Larcombe, Stephen D; Durrani, Zeeshan; Karagenc, Tulin; Bilgic, Huseyin Bilgin; Bakirci, Serkan; Hacilarlioglu, Selin; Kinnaird, Jane; Thompson, Joanne; Weir, William; Shiels, Brian

2017-06-05

Vector-borne apicomplexan parasites are a major cause of mortality and morbidity to humans and livestock globally. The most important disease syndromes caused by these parasites are malaria, babesiosis and theileriosis. Strategies for control often target parasite stages in the mammalian host that cause disease, but this can result in reservoir infections that promote pathogen transmission and generate economic loss. Optimal control strategies should protect against clinical disease, block transmission and be applicable across related genera of parasites. We have used bioinformatics and transcriptomics to screen for transmission-blocking candidate antigens in the tick-borne apicomplexan parasite, Theileria annulata. A number of candidate antigen genes were identified which encoded amino acid domains that are conserved across vector-borne Apicomplexa (Babesia, Plasmodium and Theileria), including the Pfs48/45 6-cys domain and a novel cysteine-rich domain. Expression profiling confirmed that selected candidate genes are expressed by life cycle stages within infected ticks. Additionally, putative B cell epitopes were identified in the T. annulata gene sequences encoding the 6-cys and cysteine rich domains, in a gene encoding a putative papain-family cysteine peptidase, with similarity to the Plasmodium SERA family, and the gene encoding the T. annulata major merozoite/piroplasm surface antigen, Tams1. Candidate genes were identified that encode proteins with similarity to known transmission blocking candidates in related parasites, while one is a novel candidate conserved across vector-borne apicomplexans and has a potential role in the sexual phase of the life cycle. The results indicate that a 'One Health' approach could be utilised to develop a transmission-blocking strategy effective against vector-borne apicomplexan parasites of animals and humans.

Recognising discourse causality triggers in the biomedical domain.

PubMed

Mihăilă, Claudiu; Ananiadou, Sophia

2013-12-01

Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types.

Selective targeting of human cells by a chimeric adenovirus vector containing a modified fiber protein.

PubMed Central

Stevenson, S C; Rollence, M; Marshall-Neff, J; McClelland, A

1997-01-01

The adenovirus fiber protein is responsible for attachment of the virion to unidentified cell surface receptors. There are at least two distinct adenovirus fiber receptors which interact with the group B (Ad3) and group C (Ad5) adenoviruses. We have previously shown by using expressed adenovirus fiber proteins that it is possible to change the specificity of the fiber protein by exchanging the head domain with another serotype which recognizes a different receptor (S. C. Stevenson et al., J. Virol. 69:2850-2857, 1995). A chimeric fiber cDNA containing the Ad3 fiber head domain fused to the Ad5 fiber tail and shaft was incorporated into the genome of an adenovirus vector with E1 and E3 deleted encoding beta-galactosidase to generate Av9LacZ4, an adenovirus particle which contains a chimeric fiber protein. Western blot analysis of the chimeric fiber vector confirmed expression of the chimeric fiber protein and its association with the adenovirus capsid. Transduction experiments with fiber protein competitors demonstrated the altered receptor tropism of the chimeric fiber vector compared to that of the parental Av1LacZ4 vector. Transduction of a panel of human cell lines with the chimeric and parental vectors provided evidence for a different cellular distribution of the Ad5 and Ad3 receptors. Three cell lines (THP-1, MRC-5, and FaDu) were more efficiently transduced by the vector containing the Ad3 fiber head than by the Ad5 fiber vector. In contrast, human coronary artery endothelial cells were transduced more readily with the vector containing the Ad5 fiber than with the chimeric fiber vector. HeLa and human umbilical vein endothelial cells were transduced at equivalent levels compared with human diploid fibroblasts, which were refractory to transduction with both vectors. These results provide evidence for the differential expression of the Ad5 and Ad3 receptors on human cell lines derived from clinically relevant target tissues. Furthermore, we show that exchange of the fiber head domain is a viable approach to the production of adenovirus vectors with cell-type-selective transduction properties. It may be possible to extend this approach to the use of ligands for a range of different cellular receptors in order to target gene transfer to specific cell types at the level of transduction. PMID:9151872

Group-velocity-locked vector soliton molecules in fiber lasers.

PubMed

Luo, Yiyang; Cheng, Jianwei; Liu, Bowen; Sun, Qizhen; Li, Lei; Fu, Songnian; Tang, Dingyuan; Zhao, Luming; Liu, Deming

2017-05-24

Physics phenomena of multi-soliton complexes have enriched the life of dissipative solitons in fiber lasers. By developing a birefringence-enhanced fiber laser, we report the first experimental observation of group-velocity-locked vector soliton (GVLVS) molecules. The birefringence-enhanced fiber laser facilitates the generation of GVLVSs, where the two orthogonally polarized components are coupled together to form a multi-soliton complex. Moreover, the interaction of repulsive and attractive forces between multiple pulses binds the particle-like GVLVSs together in time domain to further form compound multi-soliton complexes, namely GVLVS molecules. By adopting the polarization-resolved measurement, we show that the two orthogonally polarized components of the GVLVS molecules are both soliton molecules supported by the strongly modulated spectral fringes and the double-humped intensity profiles. Additionally, GVLVS molecules with various soliton separations are also observed by adjusting the pump power and the polarization controller.

Identification of Immunogenic Hot Spots within Plum Pox Potyvirus Capsid Protein for Efficient Antigen Presentation

PubMed Central

Fernández-Fernández, M. Rosario; Martínez-Torrecuadrada, Jorge L.; Roncal, Fernando; Domínguez, Elvira; García, Juan Antonio

2002-01-01

PEPSCAN analysis has been used to characterize the immunogenic regions of the capsid protein (CP) in virions of plum pox potyvirus (PPV). In addition to the well-known highly immunogenic N- and C-terminal domains of CP, regions within the core domain of the protein have also shown high immunogenicity. Moreover, the N terminus of CP is not homogeneously immunogenic, alternatively showing regions frequently recognized by antibodies and others that are not recognized at all. These results have helped us to design efficient antigen presentation vectors based on PPV. As predicted by PEPSCAN analysis, a small displacement of the insertion site in a previously constructed vector, PPV-γ, turned the derived chimeras into efficient immunogens. Vectors expressing foreign peptides at different positions within a highly immunogenic region (amino acids 43 to 52) in the N-terminal domain of CP were the most effective at inducing specific antibody responses against the foreign sequence. PMID:12438590

On the estimation of the domain of attraction for discrete-time switched and hybrid nonlinear systems

NASA Astrophysics Data System (ADS)

Kit Luk, Chuen; Chesi, Graziano

2015-11-01

This paper addresses the estimation of the domain of attraction for discrete-time nonlinear systems where the vector field is subject to changes. First, the paper considers the case of switched systems, where the vector field is allowed to arbitrarily switch among the elements of a finite family. Second, the paper considers the case of hybrid systems, where the state space is partitioned into several regions described by polynomial inequalities, and the vector field is defined on each region independently from the other ones. In both cases, the problem consists of computing the largest sublevel set of a Lyapunov function included in the domain of attraction. An approach is proposed for solving this problem based on convex programming, which provides a guaranteed inner estimate of the sought sublevel set. The conservatism of the provided estimate can be decreased by increasing the size of the optimisation problem. Some numerical examples illustrate the proposed approach.

Full Wave Analysis of RF Signal Attenuation in a Lossy Cave using a High Order Time Domain Vector Finite Element Method

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

Pingenot, J; Rieben, R; White, D

2004-12-06

We present a computational study of signal propagation and attenuation of a 200 MHz dipole antenna in a cave environment. The cave is modeled as a straight and lossy random rough wall. To simulate a broad frequency band, the full wave Maxwell equations are solved directly in the time domain via a high order vector finite element discretization using the massively parallel CEM code EMSolve. The simulation is performed for a series of random meshes in order to generate statistical data for the propagation and attenuation properties of the cave environment. Results for the power spectral density and phase ofmore » the electric field vector components are presented and discussed.« less

Frequency-domain-independent vector analysis for mode-division multiplexed transmission

NASA Astrophysics Data System (ADS)

Liu, Yunhe; Hu, Guijun; Li, Jiao

2018-04-01

In this paper, we propose a demultiplexing method based on frequency-domain independent vector analysis (FD-IVA) algorithm for mode-division multiplexing (MDM) system. FD-IVA extends frequency-domain independent component analysis (FD-ICA) from unitary variable to multivariate variables, and provides an efficient method to eliminate the permutation ambiguity. In order to verify the performance of FD-IVA algorithm, a 6 ×6 MDM system is simulated. The simulation results show that the FD-IVA algorithm has basically the same bit-error-rate(BER) performance with the FD-ICA algorithm and frequency-domain least mean squares (FD-LMS) algorithm. Meanwhile, the convergence speed of FD-IVA algorithm is the same as that of FD-ICA. However, compared with the FD-ICA and the FD-LMS, the FD-IVA has an obviously lower computational complexity.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry.

PubMed

Silva, Fabrício R; Vidotti, Vanessa G; Cremasco, Fernanda; Dias, Marcelo; Gomi, Edson S; Costa, Vital P

2013-01-01

To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Flow Charts: Visualization of Vector Fields on Arbitrary Surfaces

PubMed Central

Li, Guo-Shi; Tricoche, Xavier; Weiskopf, Daniel; Hansen, Charles

2009-01-01

We introduce a novel flow visualization method called Flow Charts, which uses a texture atlas approach for the visualization of flows defined over curved surfaces. In this scheme, the surface and its associated flow are segmented into overlapping patches, which are then parameterized and packed in the texture domain. This scheme allows accurate particle advection across multiple charts in the texture domain, providing a flexible framework that supports various flow visualization techniques. The use of surface parameterization enables flow visualization techniques requiring the global view of the surface over long time spans, such as Unsteady Flow LIC (UFLIC), particle-based Unsteady Flow Advection Convolution (UFAC), or dye advection. It also prevents visual artifacts normally associated with view-dependent methods. Represented as textures, Flow Charts can be naturally integrated into hardware accelerated flow visualization techniques for interactive performance. PMID:18599918

Polarization Control with Plasmonic Antenna Tips: A Universal Approach to Optical Nanocrystallography and Vector-Field Imaging

NASA Astrophysics Data System (ADS)

Park, Kyoung-Duck; Raschke, Markus B.

2018-05-01

Controlling the propagation and polarization vectors in linear and nonlinear optical spectroscopy enables to probe the anisotropy of optical responses providing structural symmetry selective contrast in optical imaging. Here we present a novel tilted antenna-tip approach to control the optical vector-field by breaking the axial symmetry of the nano-probe in tip-enhanced near-field microscopy. This gives rise to a localized plasmonic antenna effect with significantly enhanced optical field vectors with control of both \\textit{in-plane} and \\textit{out-of-plane} components. We use the resulting vector-field specificity in the symmetry selective nonlinear optical response of second-harmonic generation (SHG) for a generalized approach to optical nano-crystallography and -imaging. In tip-enhanced SHG imaging of monolayer MoS$_2$ films and single-crystalline ferroelectric YMnO$_3$, we reveal nano-crystallographic details of domain boundaries and domain topology with enhanced sensitivity and nanoscale spatial resolution. The approach is applicable to any anisotropic linear and nonlinear optical response, and provides for optical nano-crystallographic imaging of molecular or quantum materials.

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

Digital Biological Converter

DTIC Science & Technology

2013-06-28

of cuts that each fragment should be cut into so the fragments are no greater than a specific length threshold. Additionally, vector sequences and...restriction sites are attached to each fragment while ensuring the restriction sites are unique to each sequence. The vector sequences serve as hooks...for assembly into vector for cloning purposes, and also as primer binding domains for PCR ampl ification. The restriction sites are added to

Correction of murine hemophilia A following nonmyeloablative transplantation of hematopoietic stem cells engineered to encode an enhanced human factor VIII variant using a safety-augmented retroviral vector

PubMed Central

Ramezani, Ali

2009-01-01

Insertional mutagenesis by retroviral vectors is a major impediment to the clinical application of hematopoietic stem cell gene transfer for the treatment of hematologic disorders. We recently developed an insulated self-inactivating gammaretroviral vector, RMSinOFB, which uses a novel enhancer-blocking element that significantly decreases genotoxicity of retroviral integration. In this study, we used the RMSinOFB vector to evaluate the efficacy of a newly bioengineered factor VIII (fVIII) variant (efVIII)—containing a combination of A1 domain point mutations (L303E/F309S) and an extended partial B domain for improved secretion plus A2 domain mutations (R484A/R489A/P492A) for reduced immunogenicity—toward successful treatment of murine hemophilia A. In cell lines, efVIII was secreted at up to 6-fold higher levels than an L303E/F309S A1 domain–only fVIII variant (sfVIIIΔB). Most important, when compared with a conventional gammaretroviral vector expressing sfVIIIΔB, lower doses of RMSin-efVIII-OFB–transduced hematopoietic stem cells were needed to generate comparable curative fVIII levels in hemophilia A BALB/c mice after reduced-intensity total body irradiation or nonmyeloablative chemotherapy conditioning regimens. These data suggest that the safety-augmented RMSin-efVIII-OFB platform represents an encouraging step in the development of a clinically appropriate gene addition therapy for hemophilia A. PMID:19470695

New ligation independent cloning vectors for expression of recombinant proteins with a self-cleaving CPD/6xHis-tag.

PubMed

Biancucci, Marco; Dolores, Jazel S; Wong, Jennifer; Grimshaw, Sarah; Anderson, Wayne F; Satchell, Karla J F; Kwon, Keehwan

2017-01-05

Recombinant protein purification is a crucial step for biochemistry and structural biology fields. Rapid robust purification methods utilize various peptide or protein tags fused to the target protein for affinity purification using corresponding matrices and to enhance solubility. However, affinity/solubility-tags often need to be removed in order to conduct functional and structural studies, adding complexities to purification protocols. In this work, the Vibrio cholerae MARTX toxin Cysteine Protease Domain (CPD) was inserted in a ligation-independent cloning (LIC) vector to create a C-terminal 6xHis-tagged inducible autoprocessing enzyme tag, called "the CPD-tag". The pCPD and alternative pCPD/ccdB cloning vectors allow for easy insertion of DNA and expression of the target protein fused to the CPD-tag, which is removed at the end of the purification step by addition of the inexpensive small molecule inositol hexakisphosphate to induce CPD autoprocessing. This process is demonstrated using a small bacterial membrane localization domain and for high yield purification of the eukaryotic small GTPase KRas. Subsequently, pCPD was tested with 40 proteins or sub-domains selected from a high throughput crystallization pipeline. pCPD vectors are easily used LIC compatible vectors for expression of recombinant proteins with a C-terminal CPD/6xHis-tag. Although intended only as a strategy for rapid tag removal, this pilot study revealed the CPD-tag may also increase expression and solubility of some recombinant proteins.

Vector tomography for reconstructing electric fields with non-zero divergence in bounded domains

NASA Astrophysics Data System (ADS)

Koulouri, Alexandra; Brookes, Mike; Rimpiläinen, Ville

2017-01-01

In vector tomography (VT), the aim is to reconstruct an unknown multi-dimensional vector field using line integral data. In the case of a 2-dimensional VT, two types of line integral data are usually required. These data correspond to integration of the parallel and perpendicular projection of the vector field along the integration lines and are called the longitudinal and transverse measurements, respectively. In most cases, however, the transverse measurements cannot be physically acquired. Therefore, the VT methods are typically used to reconstruct divergence-free (or source-free) velocity and flow fields that can be reconstructed solely from the longitudinal measurements. In this paper, we show how vector fields with non-zero divergence in a bounded domain can also be reconstructed from the longitudinal measurements without the need of explicitly evaluating the transverse measurements. To the best of our knowledge, VT has not previously been used for this purpose. In particular, we study low-frequency, time-harmonic electric fields generated by dipole sources in convex bounded domains which arise, for example, in electroencephalography (EEG) source imaging. We explain in detail the theoretical background, the derivation of the electric field inverse problem and the numerical approximation of the line integrals. We show that fields with non-zero divergence can be reconstructed from the longitudinal measurements with the help of two sparsity constraints that are constructed from the transverse measurements and the vector Laplace operator. As a comparison to EEG source imaging, we note that VT does not require mathematical modeling of the sources. By numerical simulations, we show that the pattern of the electric field can be correctly estimated using VT and the location of the source activity can be determined accurately from the reconstructed magnitudes of the field.

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

PubMed Central

2014-01-01

Background Protein complexes play important roles in biological systems such as gene regulatory networks and metabolic pathways. Most methods for predicting protein complexes try to find protein complexes with size more than three. It, however, is known that protein complexes with smaller sizes occupy a large part of whole complexes for several species. In our previous work, we developed a method with several feature space mappings and the domain composition kernel for prediction of heterodimeric protein complexes, which outperforms existing methods. Results We propose methods for prediction of heterotrimeric protein complexes by extending techniques in the previous work on the basis of the idea that most heterotrimeric protein complexes are not likely to share the same protein with each other. We make use of the discriminant function in support vector machines (SVMs), and design novel feature space mappings for the second phase. As the second classifier, we examine SVMs and relevance vector machines (RVMs). We perform 10-fold cross-validation computational experiments. The results suggest that our proposed two-phase methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler for prediction of heterotrimeric protein complexes. Conclusions We propose two-phase prediction methods with the extended features, the domain composition kernel, SVMs and RVMs. The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes. PMID:24564744

On the use of feature selection to improve the detection of sea oil spills in SAR images

NASA Astrophysics Data System (ADS)

Mera, David; Bolon-Canedo, Veronica; Cotos, J. M.; Alonso-Betanzos, Amparo

2017-03-01

Fast and effective oil spill detection systems are crucial to ensure a proper response to environmental emergencies caused by hydrocarbon pollution on the ocean's surface. Typically, these systems uncover not only oil spills, but also a high number of look-alikes. The feature extraction is a critical and computationally intensive phase where each detected dark spot is independently examined. Traditionally, detection systems use an arbitrary set of features to discriminate between oil spills and look-alikes phenomena. However, Feature Selection (FS) methods based on Machine Learning (ML) have proved to be very useful in real domains for enhancing the generalization capabilities of the classifiers, while discarding the existing irrelevant features. In this work, we present a generic and systematic approach, based on FS methods, for choosing a concise and relevant set of features to improve the oil spill detection systems. We have compared five FS methods: Correlation-based feature selection (CFS), Consistency-based filter, Information Gain, ReliefF and Recursive Feature Elimination for Support Vector Machine (SVM-RFE). They were applied on a 141-input vector composed of features from a collection of outstanding studies. Selected features were validated via a Support Vector Machine (SVM) classifier and the results were compared with previous works. Test experiments revealed that the classifier trained with the 6-input feature vector proposed by SVM-RFE achieved the best accuracy and Cohen's kappa coefficient (87.1% and 74.06% respectively). This is a smaller feature combination with similar or even better classification accuracy than previous works. The presented finding allows to speed up the feature extraction phase without reducing the classifier accuracy. Experiments also confirmed the significance of the geometrical features since 75.0% of the different features selected by the applied FS methods as well as 66.67% of the proposed 6-input feature vector belong to this category.

Chi-square-based scoring function for categorization of MEDLINE citations.

PubMed

Kastrin, A; Peterlin, B; Hristovski, D

2010-01-01

Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain. Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine-learning algorithms (support vector machines, decision trees, naïve Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine-learning algorithms. We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.

T Cell Responses Induced by Adenoviral Vectored Vaccines Can Be Adjuvanted by Fusion of Antigen to the Oligomerization Domain of C4b-Binding Protein

PubMed Central

Forbes, Emily K.; de Cassan, Simone C.; Llewellyn, David; Biswas, Sumi; Goodman, Anna L.; Cottingham, Matthew G.; Long, Carole A.; Pleass, Richard J.; Hill, Adrian V. S.; Hill, Fergal; Draper, Simon J.

2012-01-01

Viral vectored vaccines have been shown to induce both T cell and antibody responses in animals and humans. However, the induction of even higher level T cell responses may be crucial in achieving vaccine efficacy against difficult disease targets, especially in humans. Here we investigate the oligomerization domain of the α-chain of C4b-binding protein (C4 bp) as a candidate T cell “molecular adjuvant” when fused to malaria antigens expressed by human adenovirus serotype 5 (AdHu5) vectored vaccines in BALB/c mice. We demonstrate that i) C-terminal fusion of an oligomerization domain can enhance the quantity of antigen-specific CD4+ and CD8+ T cell responses induced in mice after only a single immunization of recombinant AdHu5, and that the T cells maintain similar functional cytokine profiles; ii) an adjuvant effect is observed for AdHu5 vectors expressing either the 42 kDa C-terminal domain of Plasmodium yoelii merozoite surface protein 1 (PyMSP142) or the 83 kDa ectodomain of P. falciparum strain 3D7 apical membrane antigen 1 (PfAMA1), but not a candidate 128kDa P. falciparum MSP1 biallelic fusion antigen; iii) following two homologous immunizations of AdHu5 vaccines, antigen-specific T cell responses are further enhanced, however, in both BALB/c mice and New Zealand White rabbits no enhancement of functional antibody responses is observed; and iv) that the T cell adjuvant activity of C4 bp is not dependent on a functional Fc-receptor γ-chain in the host, but is associated with the oligomerization of small (<80 kDa) antigens expressed by recombinant AdHu5. The oligomerization domain of C4 bp can thus adjuvant T cell responses induced by AdHu5 vectors against selected antigens and its clinical utility as well as mechanism of action warrant further investigation. PMID:22984589

A vectorization of the Jameson-Caughey NYU transonic swept-wing computer program FLO-22-V1 for the STAR-100 computer

NASA Technical Reports Server (NTRS)

Smith, R. E.; Pitts, J. I.; Lambiotte, J. J., Jr.

1978-01-01

The computer program FLO-22 for analyzing inviscid transonic flow past 3-D swept-wing configurations was modified to use vector operations and run on the STAR-100 computer. The vectorized version described herein was called FLO-22-V1. Vector operations were incorporated into Successive Line Over-Relaxation in the transformed horizontal direction. Vector relational operations and control vectors were used to implement upwind differencing at supersonic points. A high speed of computation and extended grid domain were characteristics of FLO-22-V1. The new program was not the optimal vectorization of Successive Line Over-Relaxation applied to transonic flow; however, it proved that vector operations can readily be implemented to increase the computation rate of the algorithm.

Antibody-mediated targeting of replication-competent retroviral vectors.

PubMed

Tai, Chien-Kuo; Logg, Christopher R; Park, Jinha M; Anderson, W French; Press, Michael F; Kasahara, Noriyuki

2003-05-20

Replication-competent murine leukemia virus (MLV) vectors can be engineered to achieve high efficiency gene transfer to solid tumors in vivo and tumor-restricted replication, however their safety can be further enhanced by redirecting tropism of the virus envelope. We have therefore tested the targeting capability and replicative stability of ecotropic and amphotropic replication-competent retrovirus (RCR) vectors containing two tandem repeats from the immunoglobulin G-binding domain of Staphylococcal protein A inserted into the proline-rich "hinge" region of the envelope, which enables modular use of antibodies of various specificities for vector targeting. The modified envelopes were efficiently expressed and incorporated into virions, were capable of capturing monoclonal anti-HER2 antibodies, and mediated efficient binding of the virus-antibody complex to HER2-positive target cells. While infectivity was markedly reduced by pseudotyping with targeted envelopes alone, coexpression of wild-type envelope rescued efficient cellular entry. Both ecotropic and amphotropic RCR vector/anti-HER2 antibody complexes achieved significant enhancement of transduction on murine target cells overexpressing HER2, which could be competed by preincubation with excess free antibodies. Interestingly, HER2-expressing human breast cancer cells did not show enhancement of transduction despite efficient antibody-mediated cell surface binding, suggesting that target cell-specific parameters markedly affect the efficiency of post-binding entry processes. Serial replication of targeted vectors resulted in selection of Z domain deletion variants, but reduction of the overall size of the vector genome enhanced its stability. Application of antibody-mediated targeting to the initial localization of replication-competent virus vectors to tumor sites will thus require optimized target selection and vector design.

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

PubMed Central

Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong

2018-01-01

The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis. PMID:29401730

Adenovirus Type 5 Viral Particles Pseudotyped with Mutagenized Fiber Proteins Show Diminished Infectivity of Coxsackie B-Adenovirus Receptor-Bearing Cells

PubMed Central

Jakubczak, John L.; Rollence, Michele L.; Stewart, David A.; Jafari, Jonathon D.; Von Seggern, Dan J.; Nemerow, Glen R.; Stevenson, Susan C.; Hallenbeck, Paul L.

2001-01-01

A major limitation of adenovirus type 5 (Ad5)-based gene therapy, the inability to target therapeutic genes to selected cell types, is attributable to the natural tropism of the virus for the widely expressed coxsackievirus-adenovirus receptor (CAR) protein. Modifications of the Ad5 fiber knob domain have been shown to alter the tropism of the virus. We have developed a novel system to rapidly evaluate the function of modified fiber proteins in their most relevant context, the adenoviral capsid. This transient transfection/infection system combines transfection of cells with plasmids that express high levels of the modified fiber protein and infection with Ad5.βgal.ΔF, an E1-, E3-, and fiber-deleted adenoviral vector encoding β-galactosidase. We have used this system to test the adenoviral transduction efficiency mediated by a panel of fiber protein mutants that were proposed to influence CAR interaction. A series of amino acid modifications were incorporated via mutagenesis into the fiber expression plasmid, and the resulting fiber proteins were subsequently incorporated onto adenoviral particles. Mutations located in the fiber knob AB and CD loops demonstrated the greatest reduction in fiber-mediated gene transfer in HeLa cells. We also observed effects on transduction efficiency with mutations in the FG loop, indicating that the binding site may extend to the adjacent monomer in the fiber trimer and in the HI loop. These studies support the concept that modification of the fiber knob domain to diminish or ablate CAR interaction should result in a detargeted adenoviral vector that can be combined simultaneously with novel ligands for the development of a systemically administered, targeted adenoviral vector. PMID:11222722

Efficient Sample Tracking With OpenLabFramework

PubMed Central

List, Markus; Schmidt, Steffen; Trojnar, Jakub; Thomas, Jochen; Thomassen, Mads; Kruse, Torben A.; Tan, Qihua; Baumbach, Jan; Mollenhauer, Jan

2014-01-01

The advance of new technologies in biomedical research has led to a dramatic growth in experimental throughput. Projects therefore steadily grow in size and involve a larger number of researchers. Spreadsheets traditionally used are thus no longer suitable for keeping track of the vast amounts of samples created and need to be replaced with state-of-the-art laboratory information management systems. Such systems have been developed in large numbers, but they are often limited to specific research domains and types of data. One domain so far neglected is the management of libraries of vector clones and genetically engineered cell lines. OpenLabFramework is a newly developed web-application for sample tracking, particularly laid out to fill this gap, but with an open architecture allowing it to be extended for other biological materials and functional data. Its sample tracking mechanism is fully customizable and aids productivity further through support for mobile devices and barcoded labels. PMID:24589879

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

PubMed

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-12-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

PubMed

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-09-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

NASA Astrophysics Data System (ADS)

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-12-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

NASA Astrophysics Data System (ADS)

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-09-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Diagnosis of oral lichen planus from analysis of saliva samples using terahertz time-domain spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Kistenev, Yury V.; Borisov, Alexey V.; Titarenko, Maria A.; Baydik, Olga D.; Shapovalov, Alexander V.

2018-04-01

The ability to diagnose oral lichen planus (OLP) based on saliva analysis using THz time-domain spectroscopy and chemometrics is discussed. The study involved 30 patients (2 male and 28 female) with OLP. This group consisted of two subgroups with the erosive form of OLP (n = 15) and with the reticular and papular forms of OLP (n = 15). The control group consisted of six healthy volunteers (one male and five females) without inflammation in the mucous membrane in the oral cavity and without periodontitis. Principal component analysis was used to reveal informative features in the experimental data. The one-versus-one multiclass classifier using support vector machine binary classifiers was used. The two-stage classification approach using several absorption spectra scans for an individual saliva sample provided 100% accuracy of differential classification between OLP subgroups and control group.

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.

PubMed

Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong

2018-05-24

This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

A support vector machine for predicting defibrillation outcomes from waveform metrics.

PubMed

Howe, Andrew; Escalona, Omar J; Di Maio, Rebecca; Massot, Bertrand; Cromie, Nick A; Darragh, Karen M; Adgey, Jennifer; McEneaney, David J

2014-03-01

Algorithms to predict shock success based on VF waveform metrics could significantly enhance resuscitation by optimising the timing of defibrillation. To investigate robust methods of predicting defibrillation success in VF cardiac arrest patients, by using a support vector machine (SVM) optimisation approach. Frequency-domain (AMSA, dominant frequency and median frequency) and time-domain (slope and RMS amplitude) VF waveform metrics were calculated in a 4.1Y window prior to defibrillation. Conventional prediction test validity of each waveform parameter was conducted and used AUC>0.6 as the criterion for inclusion as a corroborative attribute processed by the SVM classification model. The latter used a Gaussian radial-basis-function (RBF) kernel and the error penalty factor C was fixed to 1. A two-fold cross-validation resampling technique was employed. A total of 41 patients had 115 defibrillation instances. AMSA, slope and RMS waveform metrics performed test validation with AUC>0.6 for predicting termination of VF and return-to-organised rhythm. Predictive accuracy of the optimised SVM design for termination of VF was 81.9% (± 1.24 SD); positive and negative predictivity were respectively 84.3% (± 1.98 SD) and 77.4% (± 1.24 SD); sensitivity and specificity were 87.6% (± 2.69 SD) and 71.6% (± 9.38 SD) respectively. AMSA, slope and RMS were the best VF waveform frequency-time parameters predictors of termination of VF according to test validity assessment. This a priori can be used for a simplified SVM optimised design that combines the predictive attributes of these VF waveform metrics for improved prediction accuracy and generalisation performance without requiring the definition of any threshold value on waveform metrics. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

PubMed

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

On feature augmentation for semantic argument classification of the Quran English translation using support vector machine

NASA Astrophysics Data System (ADS)

Khaira Batubara, Dina; Arif Bijaksana, Moch; Adiwijaya

2018-03-01

Research on the semantic argument classification requires semantically labeled data in large numbers, called corpus. Because building a corpus is costly and time-consuming, recently many studies have used existing corpus as the training data to conduct semantic argument classification research on new domain. But previous studies have proven that there is a significant decrease in performance when classifying semantic arguments on different domain between the training and the testing data. The main problem is when there is a new argument that found in the testing data but it is not found in the training data. This research carries on semantic argument classification on a new domain that is Quran English Translation by utilizing Propbank corpus as the training data. To recognize the new argument in the training data, this research proposes four new features for extending the argument features in the training data. By using SVM Linear, the experiment has proven that augmenting the proposed features to the baseline system with some combinations option improve the performance of semantic argument classification on Quran data using Propbank Corpus as training data.

EEG Responses to Auditory Stimuli for Automatic Affect Recognition

PubMed Central

Hettich, Dirk T.; Bolinger, Elaina; Matuz, Tamara; Birbaumer, Niels; Rosenstiel, Wolfgang; Spüler, Martin

2016-01-01

Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies. PMID:27375410

Electric-field-induced domain intersection in BaTiO3 single crystal

NASA Astrophysics Data System (ADS)

He, Ming; Wang, Mengxia; Zhang, Zhihua

2017-03-01

Large-angle convergent beam electron diffraction was used to determine the directions of polarization vectors in a BaTiO3 single crystal. Domain intersections driven by an electric field were investigated by in situ transmission electron microscopy. The dark triangles observed in the domain intersection region can be accounted for by dislocations and the strain field. Domains nucleate at the domain tip depending on the dislocations and strain field to relieve the accumulated stress. Schematic representations of the intersecting domains and the microscopic structure are given, clarifying the special electric-field-induced domain structure.

SYMBIOmatics: synergies in Medical Informatics and Bioinformatics--exploring current scientific literature for emerging topics.

PubMed

Rebholz-Schuhman, Dietrich; Cameron, Graham; Clark, Dominic; van Mulligen, Erik; Coatrieux, Jean-Louis; Del Hoyo Barbolla, Eva; Martin-Sanchez, Fernando; Milanesi, Luciano; Porro, Ivan; Beltrame, Francesco; Tollis, Ioannis; Van der Lei, Johan

2007-03-08

The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to http://www.symbiomatics.org). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000-2005 ("recent") and 1990-1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science.

SYMBIOmatics: Synergies in Medical Informatics and Bioinformatics – exploring current scientific literature for emerging topics

PubMed Central

Rebholz-Schuhman, Dietrich; Cameron, Graham; Clark, Dominic; van Mulligen, Erik; Coatrieux, Jean-Louis; Del Hoyo Barbolla, Eva; Martin-Sanchez, Fernando; Milanesi, Luciano; Porro, Ivan; Beltrame, Francesco; Tollis, Ioannis; Van der Lei, Johan

2007-01-01

Background The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to ). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. Results This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000–2005 ("recent") and 1990–1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. Conclusion We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science. PMID:17430562

Mining protein function from text using term-based support vector machines

PubMed Central

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Objective grading of facial paralysis using Local Binary Patterns in video processing.

PubMed

He, Shu; Soraghan, John J; O'Reilly, Brian F

2008-01-01

This paper presents a novel framework for objective measurement of facial paralysis in biomedial videos. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the Local Binary Patterns (LBP) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of block schemes. A multi-resolution extension of uniform LBP is proposed to efficiently combine the micro-patterns and large-scale patterns into a feature vector, which increases the algorithmic robustness and reduces noise effects while still retaining computational simplicity. The symmetry of facial movements is measured by the Resistor-Average Distance (RAD) between LBP features extracted from the two sides of the face. Support Vector Machine (SVM) is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) Scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

A Predictive Model of Anesthesia Depth Based on SVM in the Primary Visual Cortex

PubMed Central

Shi, Li; Li, Xiaoyuan; Wan, Hong

2013-01-01

In this paper, a novel model for predicting anesthesia depth is put forward based on local field potentials (LFPs) in the primary visual cortex (V1 area) of rats. The model is constructed using a Support Vector Machine (SVM) to realize anesthesia depth online prediction and classification. The raw LFP signal was first decomposed into some special scaling components. Among these components, those containing higher frequency information were well suited for more precise analysis of the performance of the anesthetic depth by wavelet transform. Secondly, the characteristics of anesthetized states were extracted by complexity analysis. In addition, two frequency domain parameters were selected. The above extracted features were used as the input vector of the predicting model. Finally, we collected the anesthesia samples from the LFP recordings under the visual stimulus experiments of Long Evans rats. Our results indicate that the predictive model is accurate and computationally fast, and that it is also well suited for online predicting. PMID:24044024

Immunogenic Subviral Particles Displaying Domain III of Dengue 2 Envelope Protein Vectored by Measles Virus

PubMed Central

Harahap-Carrillo, Indira S.; Ceballos-Olvera, Ivonne; Reyes-del Valle, Jorge

2015-01-01

Vaccines against dengue virus (DV) are commercially nonexistent. A subunit vaccination strategy may be of value, especially if a safe viral vector acts as biologically active adjuvant. In this paper, we focus on an immunoglobulin-like, independently folded domain III (DIII) from DV 2 envelope protein (E), which contains epitopes that elicits highly specific neutralizing antibodies. We modified the hepatitis B small surface antigen (HBsAg, S) in order to display DV 2 DIII on a virus-like particle (VLP), thus generating the hybrid antigen DIII-S. Two varieties of measles virus (MV) vectors were developed to express DIII-S. The first expresses the hybrid antigen from an additional transcription unit (ATU) and the second additionally expresses HBsAg from a separate ATU. We found that this second MV vectoring the hybrid VLPs displaying DIII-S on an unmodified HBsAg scaffold were immunogenic in MV-susceptible mice (HuCD46Ge-IFNarko), eliciting robust neutralizing responses (averages) against MV (1:1280 NT90), hepatitis B virus (787 mIU/mL), and DV2 (1:160 NT50) in all of the tested animals. Conversely, the MV vector expressing only DIII-S induced immunity against MV alone. In summary, DV2 neutralizing responses can be generated by displaying E DIII on a scaffold of HBsAg-based VLPs, vectored by MV. PMID:26350592

Subspace-based interference removal methods for a multichannel biomagnetic sensor array.

PubMed

Sekihara, Kensuke; Nagarajan, Srikantan S

2017-10-01

In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.

Subspace-based interference removal methods for a multichannel biomagnetic sensor array

NASA Astrophysics Data System (ADS)

Sekihara, Kensuke; Nagarajan, Srikantan S.

2017-10-01

Objective. In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. Approach. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.

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.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Currency crisis indication by using ensembles of support vector machine classifiers

NASA Astrophysics Data System (ADS)

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

2014-07-01

There are many methods that had been experimented in the analysis of currency crisis. However, not all methods could provide accurate indications. This paper introduces an ensemble of classifiers by using Support Vector Machine that's never been applied in analyses involving currency crisis before with the aim of increasing the indication accuracy. The proposed ensemble classifiers' performances are measured using percentage of accuracy, root mean squared error (RMSE), area under the Receiver Operating Characteristics (ROC) curve and Type II error. The performances of an ensemble of Support Vector Machine classifiers are compared with the single Support Vector Machine classifier and both of classifiers are tested on the data set from 27 countries with 12 macroeconomic indicators for each country. From our analyses, the results show that the ensemble of Support Vector Machine classifiers outperforms single Support Vector Machine classifier on the problem involving indicating a currency crisis in terms of a range of standard measures for comparing the performance of classifiers.

Axial U(1) current in Grabowska and Kaplan's formulation

NASA Astrophysics Data System (ADS)

Hamada, Yu; Kawai, Hikaru

2017-06-01

Recently, Grabowska and Kaplan [Phys. Rev. Lett. 116, 211602 (2016); Phys. Rev. D 94, 114504 (2016)] suggested a nonperturbative formulation of a chiral gauge theory, which consists of the conventional domain-wall fermion and a gauge field that evolves by gradient flow from one domain wall to the other. We introduce two sets of domain-wall fermions belonging to complex conjugate representations so that the effective theory is a 4D vector-like gauge theory. Then, as a natural definition of the axial-vector current, we consider a current that generates simultaneous phase transformations for the massless modes in 4 dimensions. However, this current is exactly conserved and does not reproduce the correct anomaly. In order to investigate this point precisely, we consider the mechanism of the conservation. We find that this current includes not only the axial current on the domain wall but also a contribution from the bulk, which is nonlocal in the sense of 4D fields. Therefore, the local current is obtained by subtracting the bulk contribution from it.

Three-dimensional study of the vector potential of magnetic structures.

PubMed

Phatak, Charudatta; Petford-Long, Amanda K; De Graef, Marc

2010-06-25

The vector potential is central to a number of areas of condensed matter physics, such as superconductivity and magnetism. We have used a combination of electron wave phase reconstruction and electron tomographic reconstruction to experimentally measure and visualize the three-dimensional vector potential in and around a magnetic Permalloy structure. The method can probe the vector potential of the patterned structures with a resolution of about 13 nm. A transmission electron microscope operated in the Lorentz mode is used to record four tomographic tilt series. Measurements for a square Permalloy structure with an internal closure domain configuration are presented.

Robust and accurate vectorization of line drawings.

PubMed

Hilaire, Xavier; Tombre, Karl

2006-06-01

This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.

High performance geospatial and climate data visualization using GeoJS

NASA Astrophysics Data System (ADS)

Chaudhary, A.; Beezley, J. D.

2015-12-01

GeoJS (https://github.com/OpenGeoscience/geojs) is an open-source library developed to support interactive scientific and geospatial visualization of climate and earth science datasets in a web environment. GeoJS has a convenient application programming interface (API) that enables users to harness the fast performance of WebGL and Canvas 2D APIs with sophisticated Scalable Vector Graphics (SVG) features in a consistent and convenient manner. We started the project in response to the need for an open-source JavaScript library that can combine traditional geographic information systems (GIS) and scientific visualization on the web. Many libraries, some of which are open source, support mapping or other GIS capabilities, but lack the features required to visualize scientific and other geospatial datasets. For instance, such libraries are not be capable of rendering climate plots from NetCDF files, and some libraries are limited in regards to geoinformatics (infovis in a geospatial environment). While libraries such as d3.js are extremely powerful for these kinds of plots, in order to integrate them into other GIS libraries, the construction of geoinformatics visualizations must be completed manually and separately, or the code must somehow be mixed in an unintuitive way.We developed GeoJS with the following motivations:• To create an open-source geovisualization and GIS library that combines scientific visualization with GIS and informatics• To develop an extensible library that can combine data from multiple sources and render them using multiple backends• To build a library that works well with existing scientific visualizations tools such as VTKWe have successfully deployed GeoJS-based applications for multiple domains across various projects. The ClimatePipes project funded by the Department of Energy, for example, used GeoJS to visualize NetCDF datasets from climate data archives. Other projects built visualizations using GeoJS for interactively exploring data and analysis regarding 1) the human trafficking domain, 2) New York City taxi drop-offs and pick-ups, and 3) the Ebola outbreak. GeoJS supports advanced visualization features such as picking and selecting, as well as clustering. It also supports 2D contour plots, vector plots, heat maps, and geospatial graphs.

Steganalysis using logistic regression

NASA Astrophysics Data System (ADS)

Lubenko, Ivans; Ker, Andrew D.

2011-02-01

We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

Numerical Simulation of Partially-Coherent Broadband Optical Imaging Using the FDTD Method

PubMed Central

Çapoğlu, İlker R.; White, Craig A.; Rogers, Jeremy D.; Subramanian, Hariharan; Taflove, Allen; Backman, Vadim

2012-01-01

Rigorous numerical modeling of optical systems has attracted interest in diverse research areas ranging from biophotonics to photolithography. We report the full-vector electromagnetic numerical simulation of a broadband optical imaging system with partially-coherent and unpolarized illumination. The scattering of light from the sample is calculated using the finite-difference time-domain (FDTD) numerical method. Geometrical optics principles are applied to the scattered light to obtain the intensity distribution at the image plane. Multilayered object spaces are also supported by our algorithm. For the first time, numerical FDTD calculations are directly compared to and shown to agree well with broadband experimental microscopy results. PMID:21540939

Gene Transduction and Cell Entry Pathway of Fiber-Modified Adenovirus Type 5 Vectors Carrying Novel Endocytic Peptide Ligands Selected on Human Tracheal Glandular Cells

PubMed Central

Gaden, Florence; Franqueville, Laure; Magnusson, Maria K.; Hong, Saw See; Merten, Marc D.; Lindholm, Leif; Boulanger, Pierre

2004-01-01

Monolayers of cystic fibrosis transmembrane conductance regulator (CFTR)-deficient human tracheal glandular cells (CF-KM4) were subjected to phage biopanning, and cell-internalized phages were isolated and sequenced, in order to identify CF-KM4-specific peptide ligands that would confer upon adenovirus type 5 (Ad5) vector a novel cell target specificity and/or higher efficiency of gene delivery into airway cells of patients with cystic fibrosis (CF). Three different ligands, corresponding to prototypes of the most represented families of phagotopes recovered from intracellular phages, were designed and individually inserted into Ad5-green fluorescent protein (GFP) (AdGFP) vectors at the extremities of short fiber shafts (seven repeats [R7]) terminated by scissile knobs. Only one vector, carrying the decapeptide GHPRQMSHVY (abbreviated as QM10), showed an enhanced gene transduction of CF-KM4 cells compared to control nonliganded vector with fibers of the same length (AdGFP-R7-knob). The enhancement in gene transfer efficiency was not specific to CF-KM4 cells but was observed in other mammalian cell lines tested. The QM10-liganded vector was referred to as AdGFP-QM10-knob in its knobbed version and as AdGFP-QM10 in its proteolytically deknobbed version. AdGFP-QM10 was found to transduce cells with a higher efficiency than its knob-bearing version, AdGFP-QM10-knob. Consistent with this, competition experiments indicated that the presence of knob domains was not an absolute requirement for cell attachment of the QM10-liganded vector and that the knobless AdGFP-QM10 used alternative cell-binding domains on its capsid, including penton base capsomer, via a site(s) different from its RGD motifs. The QM10-mediated effect on gene transduction seemed to take place at the step of endocytosis in both quantitative and qualitative manners. Virions of AdGFP-QM10 were endocytosed in higher numbers than virions of the control vector and were directed to a compartment different from the early endosomes targeted by members of species C Ad. AdGFP-QM10 was found to accumulate in late endosomal and low-pH compartments, suggesting that QM10 acted as an endocytic ligand of the lysosomal pathway. These results validated the concept of detargeting and retargeting Ad vectors via our deknobbing system and redirecting Ad vectors to an alternative endocytic pathway via a peptide ligand inserted in the fiber shaft domain. PMID:15194799

TWSVR: Regression via Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh

2016-02-01

Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

Magnonic band structure in a Co/Pd stripe domain system investigated by Brillouin light scattering and micromagnetic simulations

NASA Astrophysics Data System (ADS)

Banerjee, Chandrima; Gruszecki, Pawel; Klos, Jaroslaw W.; Hellwig, Olav; Krawczyk, Maciej; Barman, Anjan

2017-07-01

By combining Brillouin light scattering and micromagnetic simulations, we studied the spin-wave (SW) dynamics of a Co/Pd thin film multilayer, which features a stripe domain structure at remanence. The periodic up and down domains are separated by corkscrew type domain walls. The existence of these domains causes a scattering of the otherwise bulk and surface SW modes, which form mode families, similar to a one-dimensional magnonic crystal. The dispersion relation and mode profiles of SWs are measured for the transferred wave vector parallel and perpendicular to the domain axis.

Attitude Control for an Aero-Vehicle Using Vector Thrusting and Variable Speed Control Moment Gyros

NASA Technical Reports Server (NTRS)

Shin, Jong-Yeob; Lim, K. B.; Moerder, D. D.

2005-01-01

Stabilization of passively unstable thrust-levitated vehicles can require significant control inputs. Although thrust vectoring is a straightforward choice for realizing these inputs, this may lead to difficulties discussed in the paper. This paper examines supplementing thrust vectoring with Variable-Speed Control Moment Gyroscopes (VSCMGs). The paper describes how to allocate VSCMGs and the vectored thrust mechanism for attitude stabilization in frequency domain and also shows trade-off between vectored thrust and VSCMGs. Using an H2 control synthesis methodology in LMI optimization, a feedback control law is designed for a thrust-levitated research vehicle and is simulated with the full nonlinear model. It is demonstrated that VSCMGs can reduce the use of vectored thrust variation for stabilizing the hovering platform in the presence of strong wind gusts.

Collection of Medical Original Data with Search Engine for Decision Support.

PubMed

Orthuber, Wolfgang

2016-01-01

Medicine is becoming more and more complex and humans can capture total medical knowledge only partially. For specific access a high resolution search engine is demonstrated, which allows besides conventional text search also search of precise quantitative data of medical findings, therapies and results. Users can define metric spaces ("Domain Spaces", DSs) with all searchable quantitative data ("Domain Vectors", DSs). An implementation of the search engine is online in http://numericsearch.com. In future medicine the doctor could make first a rough diagnosis and check which fine diagnostics (quantitative data) colleagues had collected in such a case. Then the doctor decides about fine diagnostics and results are sent (half automatically) to the search engine which filters a group of patients which best fits to these data. In this specific group variable therapies can be checked with associated therapeutic results, like in an individual scientific study for the current patient. The statistical (anonymous) results could be used for specific decision support. Reversely the therapeutic decision (in the best case with later results) could be used to enhance the collection of precise pseudonymous medical original data which is used for better and better statistical (anonymous) search results.

Vector tomography for reconstructing electric fields with non-zero divergence in bounded domains

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

Koulouri, Alexandra, E-mail: koulouri@uni-muenster.de; Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, London SW7 2BT; Brookes, Mike

In vector tomography (VT), the aim is to reconstruct an unknown multi-dimensional vector field using line integral data. In the case of a 2-dimensional VT, two types of line integral data are usually required. These data correspond to integration of the parallel and perpendicular projection of the vector field along the integration lines and are called the longitudinal and transverse measurements, respectively. In most cases, however, the transverse measurements cannot be physically acquired. Therefore, the VT methods are typically used to reconstruct divergence-free (or source-free) velocity and flow fields that can be reconstructed solely from the longitudinal measurements. In thismore » paper, we show how vector fields with non-zero divergence in a bounded domain can also be reconstructed from the longitudinal measurements without the need of explicitly evaluating the transverse measurements. To the best of our knowledge, VT has not previously been used for this purpose. In particular, we study low-frequency, time-harmonic electric fields generated by dipole sources in convex bounded domains which arise, for example, in electroencephalography (EEG) source imaging. We explain in detail the theoretical background, the derivation of the electric field inverse problem and the numerical approximation of the line integrals. We show that fields with non-zero divergence can be reconstructed from the longitudinal measurements with the help of two sparsity constraints that are constructed from the transverse measurements and the vector Laplace operator. As a comparison to EEG source imaging, we note that VT does not require mathematical modeling of the sources. By numerical simulations, we show that the pattern of the electric field can be correctly estimated using VT and the location of the source activity can be determined accurately from the reconstructed magnitudes of the field. - Highlights: • Vector tomography is used to reconstruct electric fields generated by dipole sources. • Inverse solutions are based on longitudinal and transverse line integral measurements. • Transverse line integral measurements are used as a sparsity constraint. • Numerical procedure to approximate the line integrals is described in detail. • Patterns of the studied electric fields are correctly estimated.« less

[Expression in E.coli and bioactivity assay of Micrococcus luteus resuscitation promoting factor domain and its mutants].

PubMed

Yue, Chen-Li; Shi, Jie-Ran; Shi, Chang-Hong; Zhang, Hai; Zhao, Lei; Zhang, Ting-Fen; Zhao, Yong; Xi, Li

2008-10-01

To express Micrococcus luteus resuscitation promoting factor (Rpf) domain and its mutants in prokaryotic cells, and to investigate their bioactivity. The gene of Rpf domain and its mutants (E54K, E54A) were amplified by polymerase chain reaction (PCR) from the genome of Micrococcus luteus and cloned into pMD18-T vector. After sequenced, the Rpf domain and its mutant gene were subcloned into expression vector PGEX-4T-1, and transfected into E. coli DH5alpha. The expressed product was purified by affinity chromatography using GST Fusion Protein Purification bead. The aim proteins were identified by SDS-PAGE analysis and by Western blot with monoclonal antibodies against Rpf domain (mAb). The bioactivity of the proteins was analyzed by stimulating the resuscitation of Mycobacterium smegmatis. The sequences of the PCR products were identical to those of the Rpf domain and its mutant gene in GenBank. The relative molecular mass identified by SDS-PAGE analysis was consistent with that had been reported, which was also confirmed by Western blot analysis that there were specific bindings at 32 000 with Rpf domain mAb. The purified GST-Rpf domain could stimulate resuscitation of Mycobacterium smegmatis. Replacements E54A and especially E54K resulted in inhibition of Rpf resuscitation activity. Rpf domain and two kinds of its mutant protein were obtained, and its effects on the resuscitation of dormant Mycobacterium smegmatis were clarified.

A Code Generation Approach for Auto-Vectorization in the Spade Compiler

NASA Astrophysics Data System (ADS)

Wang, Huayong; Andrade, Henrique; Gedik, Buğra; Wu, Kun-Lung

We describe an auto-vectorization approach for the Spade stream processing programming language, comprising two ideas. First, we provide support for vectors as a primitive data type. Second, we provide a C++ library with architecture-specific implementations of a large number of pre-vectorized operations as the means to support language extensions. We evaluate our approach with several stream processing operators, contrasting Spade's auto-vectorization with the native auto-vectorization provided by the GNU gcc and Intel icc compilers.

Systematic Analysis of Primary Sequence Domain Segments for the Discrimination Between Class C GPCR Subtypes.

PubMed

König, Caroline; Alquézar, René; Vellido, Alfredo; Giraldo, Jesús

2018-03-01

G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate Class C, a member of this super-family that has attracted much attention in pharmacology. The limited knowledge about the complete 3D crystal structure of Class C receptors makes necessary the use of their primary amino acid sequences for analytical purposes. Here, we provide a systematic analysis of distinct receptor sequence segments with regard to their ability to differentiate between seven class C GPCR subtypes according to their topological location in the extracellular, transmembrane, or intracellular domains. We build on the results from the previous research that provided preliminary evidence of the potential use of separated domains of complete class C GPCR sequences as the basis for subtype classification. The use of the extracellular N-terminus domain alone was shown to result in a minor decrease in subtype discrimination in comparison with the complete sequence, despite discarding much of the sequence information. In this paper, we describe the use of Support Vector Machine-based classification models to evaluate the subtype-discriminating capacity of the specific topological sequence segments.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors.

PubMed

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H; Gambhir, Manoj; Fu, Joshua S; Liu, Yang; Remais, Justin V

2013-09-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis , the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001-2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057-2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses-including altered phenology-of disease vectors to altered climate.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors

PubMed Central

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H.; Gambhir, Manoj; Fu, Joshua S.; Liu, Yang; Remais, Justin V.

2014-01-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001–2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057–2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses—including altered phenology—of disease vectors to altered climate. PMID:24772388

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Research on computer systems benchmarking

NASA Technical Reports Server (NTRS)

Smith, Alan Jay (Principal Investigator)

1996-01-01

This grant addresses the topic of research on computer systems benchmarking and is more generally concerned with performance issues in computer systems. This report reviews work in those areas during the period of NASA support under this grant. The bulk of the work performed concerned benchmarking and analysis of CPUs, compilers, caches, and benchmark programs. The first part of this work concerned the issue of benchmark performance prediction. A new approach to benchmarking and machine characterization was reported, using a machine characterizer that measures the performance of a given system in terms of a Fortran abstract machine. Another report focused on analyzing compiler performance. The performance impact of optimization in the context of our methodology for CPU performance characterization was based on the abstract machine model. Benchmark programs are analyzed in another paper. A machine-independent model of program execution was developed to characterize both machine performance and program execution. By merging these machine and program characterizations, execution time can be estimated for arbitrary machine/program combinations. The work was continued into the domain of parallel and vector machines, including the issue of caches in vector processors and multiprocessors. All of the afore-mentioned accomplishments are more specifically summarized in this report, as well as those smaller in magnitude supported by this grant.

Hidden Markov Model and Support Vector Machine based decoding of finger movements using Electrocorticography

PubMed Central

Wissel, Tobias; Pfeiffer, Tim; Frysch, Robert; Knight, Robert T.; Chang, Edward F.; Hinrichs, Hermann; Rieger, Jochem W.; Rose, Georg

2013-01-01

Objective Support Vector Machines (SVM) have developed into a gold standard for accurate classification in Brain-Computer-Interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of Hidden Markov Models (HMM)for online BCIs and discuss strategies to improve their performance. Approach We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from the Electrocorticograms of four subjects doing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online brain-computer interfaces. PMID:24045504

Magnetic domain walls as reconfigurable spin-wave nano-channels

NASA Astrophysics Data System (ADS)

Wagner, Kai

Research efforts to utilize spin waves as information carriers for wave based logic in micro- and nano-structured ferromagnetic materials have increased tremendously over the recent years. However, finding efficient means of tailoring and downscaling guided spin-wave propagation in two dimensions, while maintaining energy efficiency and reconfigurability, still remains a delicate challenge. Here we target these challenges by spin-wave transport inside nanometer-scaled potential wells formed along magnetic domain walls. For this, we investigate the magnetization dynamics of a rectangular-like element in a Landau state exhibiting a so called 180° Néel wall along its center. By microwave antennae the rf-excitation is constricted to one end of the domain wall and the spin-wave intensities are recorded by means of Brillouin-Light Scattering microscopy revealing channeled transport. Additional micromagnetic simulations with pulsed as well as cw-excitation are performed to yield further insight into this class of modes. We find several spin-wave modes quantized along the width of the domain wall yet with well defined wave vectors along the wall, exhibiting positive dispersion. In a final step, we demonstrate the flexibility of these spin-wave nano-channels based on domain walls. In contrast to wave guides realised by fixed geometries, domain walls can be easily manipulated. Here we utilize small external fields to control its position with nanometer precision over a micrometer range, while still enabling transport. Domain walls thus, open the perspective for reprogrammable and yet non-volatile spin-wave waveguides of nanometer width. Financial support by the Deutsche Forschungsgemeinschaft within project SCHU2922/1-1 is gratefully acknowledged.

DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS

DTIC Science & Technology

2015-05-29

DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS Elliot Singer and Douglas Reynolds Massachusetts Institute of...development data is assumed to be unavailable. The method is based on a generalization of data whitening used in association with i-vector length...normalization and utilizes a library of whitening transforms trained at system development time using strictly out-of-domain data. The approach is

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.

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Nucleon form factors with 2+1 flavor dynamical domain-wall fermions

NASA Astrophysics Data System (ADS)

Yamazaki, Takeshi; Aoki, Yasumichi; Blum, Tom; Lin, Huey-Wen; Ohta, Shigemi; Sasaki, Shoichi; Tweedie, Robert; Zanotti, James

2009-06-01

We report our numerical lattice QCD calculations of the isovector nucleon form factors for the vector and axial-vector currents: the vector, induced tensor, axial-vector, and induced pseudoscalar form factors. The calculation is carried out with the gauge configurations generated with Nf=2+1 dynamical domain-wall fermions and Iwasaki gauge actions at β=2.13, corresponding to a cutoff a-1=1.73GeV, and a spatial volume of (2.7fm)3. The up and down-quark masses are varied so the pion mass lies between 0.33 and 0.67 GeV while the strange quark mass is about 12% heavier than the physical one. We calculate the form factors in the range of momentum transfers, 0.26 is required to ensure that finite-volume effects are below 1%.

Predicting PDZ domain mediated protein interactions from structure

PubMed Central

2013-01-01

Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. PMID:23336252

Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy.

PubMed

Wang, Zhi-Long; Zhou, Zhi-Guo; Chen, Ying; Li, Xiao-Ting; Sun, Ying-Shi

The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography. A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis. Support vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively. The support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy.

Research on polarization vector characteristics in a microfiber-based graphene fiber laser

NASA Astrophysics Data System (ADS)

Han, Mengmeng; Zhang, Shumin; Li, Xingliang; Han, Huiyun; Liu, Jingmin; Yan, Dan

2016-11-01

We experimentally investigated the polarization vector characteristics in an Er-doped fiber laser based on graphene that was deposited on microfiber. A variety of dynamic states, including polarization locked fundamental soliton, and polarization domain wall square pulses and their harmonic mode locked counterparts have all been observed with different pump powers and polarization states. These results indicated that the microfiber-based graphene not only could act as a saturable absorber but also could provide high nonlinearity, which is favorable for the cross coupling between the two orthogonal polarization components. It was worth to mention that it is the first time to obtain the polarization domain wall solitons in a mode locked fiber laser.

Wave-vector and polarization dependent impedance model for a hexagonal periodic metasurface exemplified through finite-difference time-domain simulations.

PubMed

Ding, Yi S; He, Yang

2017-08-21

An isotropic impedance sheet model is proposed for a loop-type hexagonal periodic metasurface. Both frequency and wave-vector dispersion are considered near the resonance frequency. Therefore both the angle and polarization dependences of the metasurface impedance can be properly and simultaneously described in our model. The constitutive relation of this model is transformed into auxiliary differential equations which are integrated into the finite-difference time-domain algorithm. Finally, a finite large metasurface sample under oblique illumination is used to test the model and the algorithm. Our model and algorithm can significantly increase the accuracy of the homogenization methods for modeling periodic metasurfaces.

RI/MOM and RI/SMOM renormalization of overlap quark bilinears on domain wall fermion configurations

NASA Astrophysics Data System (ADS)

Bi, Yujiang; Cai, Hao; Chen, Ying; Gong, Ming; Liu, Keh-Fei; Liu, Zhaofeng; Yang, Yi-Bo; χ QCD Collaboration

2018-05-01

Renormalization constants (RCs) of overlap quark bilinear operators on 2 +1 -flavor domain wall fermion configurations are calculated by using the RI/MOM and RI/SMOM schemes. The scale independent RC for the axial vector current is computed by using a Ward identity. Then the RCs for the quark field and the vector, tensor, scalar, and pseudoscalar operators are calculated in both the RI/MOM and RI/SMOM schemes. The RCs are converted to the MS ¯ scheme and we compare the numerical results from using the two intermediate schemes. The lattice size is 4 83×96 and the inverse spacing 1 /a =1.730 (4 ) GeV .

Thermofield duality for higher spin Rindler Gravity

DOE PAGES

Jevicki, Antal; Suzuki, Kenta

2016-02-15

In this paper, we study the Thermo-field realization of the duality between the Rindler-AdS higher spin theory and O(N) vector theory. The CFT represents a decoupled pair of free O(N) vector field theories. It is shown how this decoupled domain CFT is capable of generating the connected Rindler-AdS background with the full set of Higher Spin fields.

VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases

PubMed Central

Giraldo-Calderón, Gloria I.; Emrich, Scott J.; MacCallum, Robert M.; Maslen, Gareth; Dialynas, Emmanuel; Topalis, Pantelis; Ho, Nicholas; Gesing, Sandra; Madey, Gregory; Collins, Frank H.; Lawson, Daniel

2015-01-01

VectorBase is a National Institute of Allergy and Infectious Diseases supported Bioinformatics Resource Center (BRC) for invertebrate vectors of human pathogens. Now in its 11th year, VectorBase currently hosts the genomes of 35 organisms including a number of non-vectors for comparative analysis. Hosted data range from genome assemblies with annotated gene features, transcript and protein expression data to population genetics including variation and insecticide-resistance phenotypes. Here we describe improvements to our resource and the set of tools available for interrogating and accessing BRC data including the integration of Web Apollo to facilitate community annotation and providing Galaxy to support user-based workflows. VectorBase also actively supports our community through hands-on workshops and online tutorials. All information and data are freely available from our website at https://www.vectorbase.org/. PMID:25510499

The C Terminus of the Polerovirus P5 Readthrough Domain Limits Virus Infection to the Phloem▿

PubMed Central

Peter, Kari A.; Gildow, Frederick; Palukaitis, Peter; Gray, Stewart M.

2009-01-01

Poleroviruses are restricted to vascular phloem tissues from which they are transmitted by their aphid vectors and are not transmissible mechanically. Phloem limitation has been attributed to the absence of virus proteins either facilitating movement or counteracting plant defense. The polerovirus capsid is composed of two forms of coat protein, the major P3 protein and the minor P3/P5 protein, a translational readthrough of P3. P3/P5 is required for insect transmission and acts in trans to facilitate long-distance virus movement in phloem tissue. Specific potato leafroll virus mutants lacking part or all of the P5 domain moved into and infected nonvascular mesophyll tissue when the source-sink relationship of the plant (Solanum sarrachoides) was altered by pruning, with the progeny virus now being transmissible mechanically. However, in a period of months, a phloem-specific distribution of the virus was reestablished in the absence of aphid transmission. Virus from the new phloem-limited infection showed compensatory mutations that would be expected to restore the production of full-length P3/P5 as well as the loss of mechanical transmissibility. The data support our hypothesis that phloem limitation in poleroviruses presumably does not result from a deficiency in the repertoire of virus genes but rather results from P3/P5 accumulation under selection in the infected plant, with the colateral effect of facilitating transmission by phloem-feeding aphid vectors. PMID:19297484

The C terminus of the polerovirus p5 readthrough domain limits virus infection to the phloem.

PubMed

Peter, Kari A; Gildow, Frederick; Palukaitis, Peter; Gray, Stewart M

2009-06-01

Poleroviruses are restricted to vascular phloem tissues from which they are transmitted by their aphid vectors and are not transmissible mechanically. Phloem limitation has been attributed to the absence of virus proteins either facilitating movement or counteracting plant defense. The polerovirus capsid is composed of two forms of coat protein, the major P3 protein and the minor P3/P5 protein, a translational readthrough of P3. P3/P5 is required for insect transmission and acts in trans to facilitate long-distance virus movement in phloem tissue. Specific potato leafroll virus mutants lacking part or all of the P5 domain moved into and infected nonvascular mesophyll tissue when the source-sink relationship of the plant (Solanum sarrachoides) was altered by pruning, with the progeny virus now being transmissible mechanically. However, in a period of months, a phloem-specific distribution of the virus was reestablished in the absence of aphid transmission. Virus from the new phloem-limited infection showed compensatory mutations that would be expected to restore the production of full-length P3/P5 as well as the loss of mechanical transmissibility. The data support our hypothesis that phloem limitation in poleroviruses presumably does not result from a deficiency in the repertoire of virus genes but rather results from P3/P5 accumulation under selection in the infected plant, with the colateral effect of facilitating transmission by phloem-feeding aphid vectors.

An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal.

PubMed

Houshyarifar, Vahid; Chehel Amirani, Mehdi

2016-08-12

In this paper we present a method to predict Sudden Cardiac Arrest (SCA) with higher order spectral (HOS) and linear (Time) features extracted from heart rate variability (HRV) signal. Predicting the occurrence of SCA is important in order to avoid the probability of Sudden Cardiac Death (SCD). This work is a challenge to predict five minutes before SCA onset. The method consists of four steps: pre-processing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In second step, bispectrum features of HRV signal and time-domain features are obtained. Six features are extracted from bispectrum and two features from time-domain. In the next step, these features are reduced to one feature by the linear discriminant analysis (LDA) technique. Finally, KNN and support vector machine-based classifiers are used to classify the HRV signals. We used two database named, MIT/BIH Sudden Cardiac Death (SCD) Database and Physiobank Normal Sinus Rhythm (NSR). In this work we achieved prediction of SCD occurrence for six minutes before the SCA with the accuracy over 91%.

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

NASA Astrophysics Data System (ADS)

Jia, Huizhen; Sun, Quansen; Ji, Zexuan; Wang, Tonghan; Chen, Qiang

2014-11-01

The goal of no-reference/blind image quality assessment (NR-IQA) is to devise a perceptual model that can accurately predict the quality of a distorted image as human opinions, in which feature extraction is an important issue. However, the features used in the state-of-the-art "general purpose" NR-IQA algorithms are usually natural scene statistics (NSS) based or are perceptually relevant; therefore, the performance of these models is limited. To further improve the performance of NR-IQA, we propose a general purpose NR-IQA algorithm which combines NSS-based features with perceptually relevant features. The new method extracts features in both the spatial and gradient domains. In the spatial domain, we extract the point-wise statistics for single pixel values which are characterized by a generalized Gaussian distribution model to form the underlying features. In the gradient domain, statistical features based on neighboring gradient magnitude similarity are extracted. Then a mapping is learned to predict quality scores using a support vector regression. The experimental results on the benchmark image databases demonstrate that the proposed algorithm correlates highly with human judgments of quality and leads to significant performance improvements over state-of-the-art methods.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis.

PubMed

Couceiro, R; Carvalho, P; Paiva, R P; Henriques, J; Muehlsteff, J

2014-12-01

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Machine learning models for lipophilicity and their domain of applicability.

PubMed

Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Laak, Antonius Ter; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-01-01

Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting lipophilicity usually have been trained on small and neutral molecules, and are thus often unable to accurately predict in-house data. Using a modern Bayesian machine learning algorithm--a Gaussian process model--this study constructs a log D7 model based on 14,556 drug discovery compounds of Bayer Schering Pharma. Performance is compared with support vector machines, decision trees, ridge regression, and four commercial tools. In a blind test on 7013 new measurements from the last months (including compounds from new projects) 81% were predicted correctly within 1 log unit, compared to only 44% achieved by commercial software. Additional evaluations using public data are presented. We consider error bars for each method (model based error bars, ensemble based, and distance based approaches), and investigate how well they quantify the domain of applicability of each model.

Libpsht - algorithms for efficient spherical harmonic transforms

NASA Astrophysics Data System (ADS)

Reinecke, M.

2011-02-01

Libpsht (or "library for performant spherical harmonic transforms") is a collection of algorithms for efficient conversion between spatial-domain and spectral-domain representations of data defined on the sphere. The package supports both transforms of scalars and spin-1 and spin-2 quantities, and can be used for a wide range of pixelisations (including HEALPix, GLESP, and ECP). It will take advantage of hardware features such as multiple processor cores and floating-point vector operations, if available. Even without this additional acceleration, the employed algorithms are among the most efficient (in terms of CPU time, as well as memory consumption) currently being used in the astronomical community. The library is written in strictly standard-conforming C90, ensuring portability to many different hard- and software platforms, and allowing straightforward integration with codes written in various programming languages like C, C++, Fortran, Python etc. Libpsht is distributed under the terms of the GNU General Public License (GPL) version 2 and can be downloaded from .

Libpsht: Algorithms for Efficient Spherical Harmonic Transforms

NASA Astrophysics Data System (ADS)

Reinecke, Martin

2010-10-01

Libpsht (or "library for Performing Spherical Harmonic Transforms") is a collection of algorithms for efficient conversion between spatial-domain and spectral-domain representations of data defined on the sphere. The package supports transforms of scalars as well as spin-1 and spin-2 quantities, and can be used for a wide range of pixelisations (including HEALPix, GLESP and ECP). It will take advantage of hardware features like multiple processor cores and floating-point vector operations, if available. Even without this additional acceleration, the employed algorithms are among the most efficient (in terms of CPU time as well as memory consumption) currently being used in the astronomical community. The library is written in strictly standard-conforming C90, ensuring portability to many different hard- and software platforms, and allowing straightforward integration with codes written in various programming languages like C, C++, Fortran, Python etc. Libpsht is distributed under the terms of the GNU General Public License (GPL) version 2. Development on this project has ended; its successor is libsharp (ascl:1402.033).

LocTree2 predicts localization for all domains of life

PubMed Central

Goldberg, Tatyana; Hamp, Tobias; Rost, Burkhard

2012-01-01

Motivation: Subcellular localization is one aspect of protein function. Despite advances in high-throughput imaging, localization maps remain incomplete. Several methods accurately predict localization, but many challenges remain to be tackled. Results: In this study, we introduced a framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data. Availability: Online through PredictProtein (predictprotein.org); as standalone version at http://www.rostlab.org/services/loctree2. Contact: localization@rostlab.org Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:22962467

Simulations of incompressible Navier Stokes equations on curved surfaces using discrete exterior calculus

NASA Astrophysics Data System (ADS)

Samtaney, Ravi; Mohamed, Mamdouh; Hirani, Anil

2015-11-01

We present examples of numerical solutions of incompressible flow on 2D curved domains. The Navier-Stokes equations are first rewritten using the exterior calculus notation, replacing vector calculus differential operators by the exterior derivative, Hodge star and wedge product operators. A conservative discretization of Navier-Stokes equations on simplicial meshes is developed based on discrete exterior calculus (DEC). The discretization is then carried out by substituting the corresponding discrete operators based on the DEC framework. By construction, the method is conservative in that both the discrete divergence and circulation are conserved up to machine precision. The relative error in kinetic energy for inviscid flow test cases converges in a second order fashion with both the mesh size and the time step. Numerical examples include Taylor vortices on a sphere, Stuart vortices on a sphere, and flow past a cylinder on domains with varying curvature. Supported by the KAUST Office of Competitive Research Funds under Award No. URF/1/1401-01.

A plane wave generation method by wave number domain point focusing.

PubMed

Chang, Ji-Ho; Choi, Jung-Woo; Kim, Yang-Hann

2010-11-01

A method for generation of a wave-field that is a plane wave is described. This method uses an array of loudspeakers phased so that the field in the wave-number domain is nearly concentrated at a point, this point being at the wave-number vector of the desired plane wave. The method described here for such a wave-number concentration makes use of an expansion in spherical harmonics, and requires a relatively small number of measurement points for a good approximate achievement of a plane wave. The measurement points are on a spherical surface surrounding the array of loudspeakers. The input signals for the individual loudspeakers can be derived without a matrix inversion or without explicit assumptions about the loudspeakers. The mathematical development involves spherical harmonics and three-dimensional Fourier transforms. Some numerical examples are given, with various assumptions concerning the nature of the loudspeakers, that support the premise that the method described in the present paper may be useful in applications.

Diquark mass differences from unquenched lattice QCD

NASA Astrophysics Data System (ADS)

Bi, Yujiang; Cai, Hao; Chen, Ying; Gong, Ming; Liu, Zhaofeng; Qiao, Hao-Xue; Yang, Yi-Bo

2016-07-01

We calculate diquark correlation functions in the Landau gauge on the lattice using overlap valence quarks and 2+1-flavor domain wall fermion configurations. Quark masses are extracted from the scalar part of quark propagators in the Landau gauge. The scalar diquark quark mass difference and axial vector scalar diquark mass difference are obtained for diquarks composed of two light quarks and of a strange and a light quark. The light sea quark mass dependence of the results is examined. Two lattice spacings are used to check the discretization effects. The coarse and fine lattices are of sizes 243 × 64 and 323 × 64 with inverse spacings 1/a = 1.75(4) GeV and 2.33(5) GeV, respectively. Supported by National Science Foundation of China (11575197, 10835002, 11405178, 11335001), joint funds of NSFC (U1232109), MG and ZL are partially supported by the Youth Innovation Promotion Association of CAS (2015013, 2011013), YC and ZL acknowledge support of NSFC and DFG (CRC110)

Recombinant adeno-associated virus vector carrying the thrombomodulin lectin-like domain for the treatment of abdominal aortic aneurysm.

PubMed

Lai, Chao-Han; Wang, Kuan-Chieh; Kuo, Cheng-Hsiang; Lee, Fang-Tzu; Cheng, Tsung-Lin; Chang, Bi-Ing; Yang, Yu-Jen; Shi, Guey-Yueh; Wu, Hua-Lin

2017-07-01

Thrombomodulin (TM), through its lectin-like domain (TMD1), sequesters proinflammatory high-mobility group box 1 (HMGB1) to prevent it from engaging the receptor for advanced glycation end product (RAGE) that sustains inflammation and tissue damage. Our previous study demonstrated that short-term treatment with recombinant TM containing all the extracellular domains (i.e., rTMD123) inhibits HMGB1-RAGE signaling and confers protection against CaCl 2 -induced AAA formation. In this study, we attempted to further optimize TM domains, as a potential therapeutic agent for AAA, using the recombinant adeno-associated virus (AAV) vector. The therapeutic effects of recombinant TMD1 (rTMD1) and recombinant AAV vectors carrying the lectin-like domain of TM (rAAV-TMD1) were evaluated in the CaCl 2 -induced AAA model and angiotensin II-infused AAA model, respectively. In the CaCl 2 -induced model, treatment with rTMD1 suppressed the tissue levels of HMGB1 and RAGE, macrophage accumulation, elastin destruction and AAA formation, and the effects were comparable to a mole-equivalent dosage of rTMD123. In the angiotensin II-infused model, a single intravenous injection of rAAV-TMD1 (10 11 genome copies), which resulted in a persistently high serum level of TMD1 for at least 12 weeks, effectively attenuated AAA formation with suppression of HMGB1 and RAGE levels and inhibition of proinflammatory cytokine production, macrophage accumulation, matrix metalloproteinase activities and oxidative stress in the aortic wall. These findings corroborate the therapeutic potential of the TM lectin-like domain in AAA. The attenuation of angiotensin II-infused AAA by one-time delivery of rAAV-TMD1 provides a proof-of-concept validation of its application as potential gene therapy for aneurysm development. Copyright © 2017 Elsevier B.V. All rights reserved.

Vaccines against Botulism.

PubMed

Sundeen, Grace; Barbieri, Joseph T

2017-09-02

Botulinum neurotoxins (BoNT) cause the flaccid paralysis of botulism by inhibiting the release of acetylcholine from motor neurons. There are seven serotypes of BoNT (A-G), with limited therapies, and no FDA approved vaccine for botulism. An investigational formalin-inactivated penta-serotype-BoNT/A-E toxoid vaccine was used to vaccinate people who are at high risk of contracting botulism. However, this formalin-inactivated penta-serotype-BoNT/A-E toxoid vaccine was losing potency and was discontinued. This article reviews the different vaccines being developed to replace the discontinued toxoid vaccine. These vaccines include DNA-based, viral vector-based, and recombinant protein-based vaccines. DNA-based vaccines include plasmids or viral vectors containing the gene encoding one of the BoNT heavy chain receptor binding domains (HC). Viral vectors reviewed are adenovirus, influenza virus, rabies virus, Semliki Forest virus, and Venezuelan Equine Encephalitis virus. Among the potential recombinant protein vaccines reviewed are HC, light chain-heavy chain translocation domain, and chemically or genetically inactivated holotoxin.

Vector intensity reconstruction using the data completion method.

PubMed

Langrenne, Christophe; Garcia, Alexandre

2013-04-01

This paper presents an application of the data completion method (DCM) for vector intensity reconstructions. A mobile array of 36 pressure-pressure probes (72 microphones) is used to perform measurements near a planar surface. Nevertheless, since the proposed method is based on integral formulations, DCM can be applied with any kind of geometry. This method requires the knowledge of Cauchy data (pressure and velocity) on a part of the boundary of an empty domain in order to evaluate pressure and velocity on the remaining part of the boundary. Intensity vectors are calculated in the interior domain surrounded by the measurement array. This inverse acoustic problem requires the use of a regularization method to obtain a realistic solution. An experiment in a closed wooden car trunk mock-up excited by a shaker and two loudspeakers is presented. In this case, where the volume of the mock-up is small (0.61 m(3)), standing-waves and fluid structure interactions appear and show that DCM is a powerful tool to identify sources in a confined space.

Vaccines against Botulism

PubMed Central

Sundeen, Grace; Barbieri, Joseph T.

2017-01-01

Botulinum neurotoxins (BoNT) cause the flaccid paralysis of botulism by inhibiting the release of acetylcholine from motor neurons. There are seven serotypes of BoNT (A-G), with limited therapies, and no FDA approved vaccine for botulism. An investigational formalin-inactivated penta-serotype-BoNT/A-E toxoid vaccine was used to vaccinate people who are at high risk of contracting botulism. However, this formalin-inactivated penta-serotype-BoNT/A-E toxoid vaccine was losing potency and was discontinued. This article reviews the different vaccines being developed to replace the discontinued toxoid vaccine. These vaccines include DNA-based, viral vector-based, and recombinant protein-based vaccines. DNA-based vaccines include plasmids or viral vectors containing the gene encoding one of the BoNT heavy chain receptor binding domains (HC). Viral vectors reviewed are adenovirus, influenza virus, rabies virus, Semliki Forest virus, and Venezuelan Equine Encephalitis virus. Among the potential recombinant protein vaccines reviewed are HC, light chain-heavy chain translocation domain, and chemically or genetically inactivated holotoxin. PMID:28869493

Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains.

PubMed

Muthu Krishnan, S

2018-05-14

The receptor-associated protein (RAP) is an inhibitor of endocytic receptors that belong to the lipoprotein receptor gene family. In this study, a computational approach was tried to find the evolutionarily related fold of the RAP proteins. Through the structural and sequence-based analysis, found various protein folds that are very close to the RAP folds. Remote homolog datasets were used potentially to develop a different support vector machine (SVM) methods to recognize the homologous RAP fold. This study helps in understanding the relationship of RAP homologs folds based on the structure, function and evolutionary history. Copyright © 2018 Elsevier Ltd. All rights reserved.

Vector Potential Generation for Numerical Relativity Simulations

NASA Astrophysics Data System (ADS)

Silberman, Zachary; Faber, Joshua; Adams, Thomas; Etienne, Zachariah; Ruchlin, Ian

2017-01-01

Many different numerical codes are employed in studies of highly relativistic magnetized accretion flows around black holes. Based on the formalisms each uses, some codes evolve the magnetic field vector B, while others evolve the magnetic vector potential A, the two being related by the curl: B=curl(A). Here, we discuss how to generate vector potentials corresponding to specified magnetic fields on staggered grids, a surprisingly difficult task on finite cubic domains. The code we have developed solves this problem in two ways: a brute-force method, whose scaling is nearly linear in the number of grid cells, and a direct linear algebra approach. We discuss the success both algorithms have in generating smooth vector potential configurations and how both may be extended to more complicated cases involving multiple mesh-refinement levels. NSF ACI-1550436

Disentangling Vector-Borne Transmission Networks: A Universal DNA Barcoding Method to Identify Vertebrate Hosts from Arthropod Bloodmeals

PubMed Central

Alcaide, Miguel; Rico, Ciro; Ruiz, Santiago; Soriguer, Ramón; Muñoz, Joaquín; Figuerola, Jordi

2009-01-01

Emerging infectious diseases represent a challenge for global economies and public health. About one fourth of the last pandemics have been originated by the spread of vector-borne pathogens. In this sense, the advent of modern molecular techniques has enhanced our capabilities to understand vector-host interactions and disease ecology. However, host identification protocols have poorly profited of international DNA barcoding initiatives and/or have focused exclusively on a limited array of vector species. Therefore, ascertaining the potential afforded by DNA barcoding tools in other vector-host systems of human and veterinary importance would represent a major advance in tracking pathogen life cycles and hosts. Here, we show the applicability of a novel and efficient molecular method for the identification of the vertebrate host's DNA contained in the midgut of blood-feeding arthropods. To this end, we designed a eukaryote-universal forward primer and a vertebrate-specific reverse primer to selectively amplify 758 base pairs (bp) of the vertebrate mitochondrial Cytochrome c Oxidase Subunit I (COI) gene. Our method was validated using both extensive sequence surveys from the public domain and Polymerase Chain Reaction (PCR) experiments carried out over specimens from different Classes of vertebrates (Mammalia, Aves, Reptilia and Amphibia) and invertebrate ectoparasites (Arachnida and Insecta). The analysis of mosquito, culicoid, phlebotomie, sucking bugs, and tick bloodmeals revealed up to 40 vertebrate hosts, including 23 avian, 16 mammalian and one reptilian species. Importantly, the inspection and analysis of direct sequencing electropherograms also assisted the resolving of mixed bloodmeals. We therefore provide a universal and high-throughput diagnostic tool for the study of the ecology of haematophagous invertebrates in relation to their vertebrate hosts. Such information is crucial to support the efficient management of initiatives aimed at reducing epidemiologic risks of arthropod vector-borne pathogens, a priority for public health. PMID:19768113

Examining Cybersecurity of Cyberphysical Systems for Critical Infrastructures Through Work Domain Analysis.

PubMed

Wang, Hao; Lau, Nathan; Gerdes, Ryan M

2018-04-01

The aim of this study was to apply work domain analysis for cybersecurity assessment and design of supervisory control and data acquisition (SCADA) systems. Adoption of information and communication technology in cyberphysical systems (CPSs) for critical infrastructures enables automated and distributed control but introduces cybersecurity risk. Many CPSs employ SCADA industrial control systems that have become the target of cyberattacks, which inflict physical damage without use of force. Given that absolute security is not feasible for complex systems, cyberintrusions that introduce unanticipated events will occur; a proper response will in turn require human adaptive ability. Therefore, analysis techniques that can support security assessment and human factors engineering are invaluable for defending CPSs. We conducted work domain analysis using the abstraction hierarchy (AH) to model a generic SCADA implementation to identify the functional structures and means-ends relations. We then adopted a case study approach examining the Stuxnet cyberattack by developing and integrating AHs for the uranium enrichment process, SCADA implementation, and malware to investigate the interactions between the three aspects of cybersecurity in CPSs. The AHs for modeling a generic SCADA implementation and studying the Stuxnet cyberattack are useful for mapping attack vectors, identifying deficiencies in security processes and features, and evaluating proposed security solutions with respect to system objectives. Work domain analysis is an effective analytical method for studying cybersecurity of CPSs for critical infrastructures in a psychologically relevant manner. Work domain analysis should be applied to assess cybersecurity risk and inform engineering and user interface design.

An expert support system for breast cancer diagnosis using color wavelet features.

PubMed

Issac Niwas, S; Palanisamy, P; Chibbar, Rajni; Zhang, W J

2012-10-01

Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

High performance magnetic bearing systems using high temperature superconductors

DOEpatents

Abboud, Robert G.

1998-01-01

A magnetic bearing apparatus and a method for providing at least one stabilizing force in a magnetic bearing structure with a superconducting magnetic assembly and a magnetic assembly, by providing a superconducting magnetic member in the superconducting magnetic assembly with a plurality of domains and arranging said superconducting magnetic member such that at least one domain has a domain C-axis vector alignment angularly disposed relative to a reference axis of the magnetic member in the magnetic assembly.

Continuous EEG signal analysis for asynchronous BCI application.

PubMed

Hsu, Wei-Yen

2011-08-01

In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.

Quantitative analysis of facial paralysis using local binary patterns in biomedical videos.

PubMed

He, Shu; Soraghan, John J; O'Reilly, Brian F; Xing, Dongshan

2009-07-01

Facial paralysis is the loss of voluntary muscle movement of one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents a novel framework for objective measurement of facial paralysis. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBPs) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of novel block processing schemes. A multiresolution extension of uniform LBP is proposed to efficiently combine the micropatterns and large-scale patterns into a feature vector. The symmetry of facial movements is measured by the resistor-average distance (RAD) between LBP features extracted from the two sides of the face. Support vector machine is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

Switching behavior and novel stable states of magnetic hexagonal nanorings

NASA Astrophysics Data System (ADS)

Yasir Rafique, M.; Pan, Liqing; Guo, Zhengang

2017-06-01

Micromagnetic simulations for Cobalt hexagonal shape nanorings show onion (O) and vortex state (V) along with new state named "tri-domain state". The tri-domain state is observed in sufficiently large width of ring. The magnetic reversible mechanism and transition of states are explained with help of vector field display. The transitions from one state to other occur by propagation of domain wall. The vertical parts of hexagonal rings play important role in developing the new "tri-domain" state. The behaviors of switching fields from onion to tri-domain (HO-Tr), tri-domain to vortex state (HTr-V) and vortex to onion state and "states size" are discussed in term of geometrical parameter of ring.

Ontology patterns for tabular representations of biomedical knowledge on neglected tropical diseases

PubMed Central

Santana, Filipe; Schober, Daniel; Medeiros, Zulma; Freitas, Fred; Schulz, Stefan

2011-01-01

Motivation: Ontology-like domain knowledge is frequently published in a tabular format embedded in scientific publications. We explore the re-use of such tabular content in the process of building NTDO, an ontology of neglected tropical diseases (NTDs), where the representation of the interdependencies between hosts, pathogens and vectors plays a crucial role. Results: As a proof of concept we analyzed a tabular compilation of knowledge about pathogens, vectors and geographic locations involved in the transmission of NTDs. After a thorough ontological analysis of the domain of interest, we formulated a comprehensive design pattern, rooted in the biomedical domain upper level ontology BioTop. This pattern was implemented in a VBA script which takes cell contents of an Excel spreadsheet and transforms them into OWL-DL. After minor manual post-processing, the correctness and completeness of the ontology was tested using pre-formulated competence questions as description logics (DL) queries. The expected results could be reproduced by the ontology. The proposed approach is recommended for optimizing the acquisition of ontological domain knowledge from tabular representations. Availability and implementation: Domain examples, source code and ontology are freely available on the web at http://www.cin.ufpe.br/~ntdo. Contact: fss3@cin.ufpe.br PMID:21685092

Multiclass Reduced-Set Support Vector Machines

NASA Technical Reports Server (NTRS)

Tang, Benyang; Mazzoni, Dominic

2006-01-01

There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

Multivariate models for prediction of human skin sensitization hazard.

PubMed

Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

2017-03-01

One of the Interagency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens™ assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy 88%), any of the alternative methods alone (accuracy 63-79%) or test batteries combining data from the individual methods (accuracy 75%). These results suggest that computational methods are promising tools to identify effectively the potential human skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

CpG Methylation Signature Predicts Recurrence in Early-Stage Hepatocellular Carcinoma: Results From a Multicenter Study.

PubMed

Qiu, Jiliang; Peng, Baogang; Tang, Yunqiang; Qian, Yeben; Guo, Pi; Li, Mengfeng; Luo, Junhang; Chen, Bin; Tang, Hui; Lu, Canliang; Cai, Muyan; Ke, Zunfu; He, Wei; Zheng, Yun; Xie, Dan; Li, Binkui; Yuan, Yunfei

2017-03-01

Purpose Early-stage hepatocellular carcinoma (E-HCC) is being diagnosed increasingly, and in one half of diagnosed patients, recurrence will develop. Thus, it is urgent to identify recurrence-related markers. We investigated the effectiveness of CpG methylation in predicting recurrence for patients with E-HCCs. Patients and Methods In total, 576 patients with E-HCC from four independent centers were sorted by three phases. In the discovery phase, 66 tumor samples were analyzed using the Illumina Methylation 450k Beadchip. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. In the training phase, penalized Cox regression was used to further narrow CpGs into 140 samples. In the validation phase, candidate CpGs were validated using an internal cohort (n = 141) and two external cohorts (n = 191 and n =104). Results After combining the 46 CpGs selected by the Least Absolute Shrinkage and Selector Operation and the Support Vector Machine-Recursive Feature Elimination algorithms, three CpGs corresponding to SCAN domain containing 3, Src homology 3-domain growth factor receptor-bound 2-like interacting protein 1, and peptidase inhibitor 3 were highlighted as candidate predictors in the training phase. On the basis of the three CpGs, a methylation signature for E-HCC (MSEH) was developed to classify patients into high- and low-risk recurrence groups in the training cohort ( P < .001). The performance of MSEH was validated in the internal cohort ( P < .001) and in the two external cohorts ( P < .001; P = .002). Furthermore, a nomogram comprising MSEH, tumor differentiation, cirrhosis, hepatitis B virus surface antigen, and antivirus therapy was generated to predict the 5-year recurrence-free survival in the training cohort, and it performed well in the three validation cohorts (concordance index: 0.725, 0.697, and 0.693, respectively). Conclusion MSEH, a three-CpG-based signature, is useful in predicting recurrence for patients with E-HCC.

A Subdivision-Based Representation for Vector Image Editing.

PubMed

Liao, Zicheng; Hoppe, Hugues; Forsyth, David; Yu, Yizhou

2012-11-01

Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

Incremental Support Vector Machine Framework for Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Awad, Mariette; Jiang, Xianhua; Motai, Yuichi

2006-12-01

Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

High performance magnetic bearing systems using high temperature superconductors

DOEpatents

Abboud, R.G.

1998-05-05

Disclosed are a magnetic bearing apparatus and a method for providing at least one stabilizing force in a magnetic bearing structure with a superconducting magnetic assembly and a magnetic assembly, by providing a superconducting magnetic member in the superconducting magnetic assembly with a plurality of domains and arranging said superconducting magnetic member such that at least one domain has a domain C-axis vector alignment angularly disposed relative to a reference axis of the magnetic member in the magnetic assembly. 7 figs.

Laser transit anemometer measurements of a JANNAF nozzle base velocity flow field

NASA Technical Reports Server (NTRS)

Hunter, William W., Jr.; Russ, C. E., Jr.; Clemmons, J. I., Jr.

1990-01-01

Velocity flow fields of a nozzle jet exhausting into a supersonic flow were surveyed. The measurements were obtained with a laser transit anemometer (LTA) system in the time domain with a correlation instrument. The LTA data is transformed into the velocity domain to remove the error that occurs when the data is analyzed in the time domain. The final data is shown in velocity vector plots for positions upstream, downstream, and in the exhaust plane of the jet nozzle.

Insect cell transformation vectors that support high level expression and promoter assessment in insect cell culture

USDA-ARS?s Scientific Manuscript database

A somatic transformation vector, pDP9, was constructed that provides a simplified means of producing permanently transformed cultured insect cells that support high levels of protein expression of foreign genes. The pDP9 plasmid vector incorporates DNA sequences from the Junonia coenia densovirus th...

Connection between the two branches of the quantum two-stream instability across the k space

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

Bret, A.; Haas, F.

2010-05-15

The stability of two quantum counterstreaming electron beams is investigated within the quantum plasma fluid equations for arbitrarily oriented wave vectors k. The analysis reveals that the two quantum two-stream unstable branches are indeed connected by a continuum of unstable modes with oblique wave vectors. Using the longitudinal approximation, the stability domain for any k is analytically explained, together with the growth rate.

Computational mechanics analysis tools for parallel-vector supercomputers

NASA Technical Reports Server (NTRS)

Storaasli, Olaf O.; Nguyen, Duc T.; Baddourah, Majdi; Qin, Jiangning

1993-01-01

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigensolution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization search analysis and domain decomposition. The source code for many of these algorithms is available.

Improved Dual-Luciferase Reporter Assays for Nuclear Receptors

PubMed Central

Paguio, Aileen; Stecha, Pete; Wood, Keith V; Fan, Frank

2010-01-01

Nuclear receptors play important roles in many cellular functions through control of gene transcription. It is also a large target class for drug discovery. Luciferase reporter assays are frequently used to study nuclear receptor function because of their wide dynamic range, low endogenous activity, and ease of use. Recent improvements of luciferase genes and vectors have further enhanced their utilities. Here we applied these improvements to two reporter formats for studying nuclear receptors. The first assay contains a Murine Mammary Tumor Virus promoter upstream of a destabilized luciferase. The presence of response elements for nuclear hormone receptor in this promoter allows the studies of endogenous and/or exogenous full length receptors. The second assay contains a ligand binding domain (LBD) of a nuclear receptor fused to the GAL4 DNA binding domain (DBD) on one vector and multiple Gal4 Upstream Activator Sequences (UAS) upstream of luciferase reporter on another vector. We showed that codon optimization of luciferase reporter genes increased expression levels in conjunction with the incorporation of protein destabilizing sequences into luciferase led to a larger assay dynamic range in both formats. The optimum number of UAS to generate the best response was determined. The expression vector for nuclear receptor LBD/GAL4 DBD fusion also constitutively expresses a Renilla luciferase-neoR fusion protein, which provides selection capability (G418 resistance, neoR) as well as an internal control (Renilla luciferase). This dual-luciferase format allowed detecting compound cytotoxicity or off-target change in expression during drug screening, therefore improved data quality. These luciferase reporter assays provided better research and drug discovery tools for studying the functions of full length nuclear receptors and ligand binding domains. PMID:21687560

Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields

PubMed Central

Zhao, Henan; Bryant, Garnett W.; Griffin, Wesley; Terrill, Judith E.; Chen, Jian

2017-01-01

We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks. PMID:28113469

Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields.

PubMed

Henan Zhao; Bryant, Garnett W; Griffin, Wesley; Terrill, Judith E; Jian Chen

2017-06-01

We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks.

Direct observation of interlocked domain walls and topological four-state vortex-like domain patterns in multiferroic YMnO{sub 3} single crystal

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

Tian, Lei; School of Materials Science and Engineering, Dalian Jiaotong University, Dalian, Liaoning 116028; Wang, Yumei, E-mail: wangym@iphy.ac.cn

2015-03-16

Using the advanced spherical aberration-corrected high angle annular dark field scanning transmission electron microscope imaging techniques, we investigated atomic-scale structural features of domain walls and domain patterns in YMnO{sub 3} single crystal. Three different types of interlocked ferroelectric-antiphase domain walls and two abnormal topological four-state vortex-like domain patterns are identified. Each ferroelectric domain wall is accompanied by a translation vector, i.e., 1/6[210] or −1/6[210], demonstrating its interlocked nature. Different from the four-state vortex domain patterns caused by a partial edge dislocation, two four-state vortex-like domain configurations have been obtained at atomic level. These observed phenomena can further extend our understandingmore » of the fascinating vortex domain patterns in multiferroic hexagonal rare-earth manganites.« less

geomIO: A tool for geodynamicists to turn 2D cross-sections into 3D geometries

NASA Astrophysics Data System (ADS)

Baumann, Tobias; Bauville, Arthur

2016-04-01

In numerical deformation models, material properties are usually defined on elements (e.g., in body-fitted finite elements), or on a set of Lagrangian markers (Eulerian, ALE or mesh-free methods). In any case, geometrical constraints are needed to assign different material properties to the model domain. Whereas simple geometries such as spheres, layers or cuboids can easily be programmed, it quickly gets complex and time-consuming to create more complicated geometries for numerical model setups, especially in three dimensions. geomIO (geometry I/O, http://geomio.bitbucket.org/) is a MATLAB-based library that has two main functionalities. First, it can be used to create 3D volumes based on series of 2D vector drawings similar to a CAD program; and second, it uses these 3D volumes to assign material properties to the numerical model domain. The drawings can conveniently be created using the open-source vector graphics software Inkscape. Adobe Illustrator is also partially supported. The drawings represent a series of cross-sections in the 3D model domain, for example, cross-sectional interpretations of seismic tomography. geomIO is then used to read the drawings and to create 3D volumes by interpolating between the cross-sections. In the second part, the volumes are used to assign material phases to markers inside the volumes. Multiple volumes can be created at the same time and, depending on the order of assignment, unions or intersections can be built to assign additional material phases. geomIO also offers the possibility to create 3D temperature structures for geodynamic models based on depth dependent parameterisations, for example the half space cooling model. In particular, this can be applied to geometries of subducting slabs of arbitrary shape. Yet, geomIO is held very general, and can be used for a variety of applications. We present examples of setup generation from pictures of micro-scale tectonics and lithospheric scale setups of 3D present-day model geometries.

[Support vector machine?assisted diagnosis of human malignant gastric tissues based on dielectric properties].

PubMed

Zhang, Sa; Li, Zhou; Xin, Xue-Gang

2017-12-20

To achieve differential diagnosis of normal and malignant gastric tissues based on discrepancies in their dielectric properties using support vector machine. The dielectric properties of normal and malignant gastric tissues at the frequency ranging from 42.58 to 500 MHz were measured by coaxial probe method, and the Cole?Cole model was used to fit the measured data. Receiver?operating characteristic (ROC) curve analysis was used to evaluate the discrimination capability with respect to permittivity, conductivity, and Cole?Cole fitting parameters. Support vector machine was used for discriminating normal and malignant gastric tissues, and the discrimination accuracy was calculated using k?fold cross? The area under the ROC curve was above 0.8 for permittivity at the 5 frequencies at the lower end of the measured frequency range. The combination of the support vector machine with the permittivity at all these 5 frequencies combined achieved the highest discrimination accuracy of 84.38% with a MATLAB runtime of 3.40 s. The support vector machine?assisted diagnosis is feasible for human malignant gastric tissues based on the dielectric properties.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Gene transfer of high-mobility group box 1 box-A domain in a rat acute liver failure model.

PubMed

Tanaka, Masayuki; Shinoda, Masahiro; Takayanagi, Atsushi; Oshima, Go; Nishiyama, Ryo; Fukuda, Kazumasa; Yagi, Hiroshi; Hayashida, Tetsu; Masugi, Yohei; Suda, Koichi; Yamada, Shingo; Miyasho, Taku; Hibi, Taizo; Abe, Yuta; Kitago, Minoru; Obara, Hideaki; Itano, Osamu; Takeuchi, Hiroya; Sakamoto, Michiie; Tanabe, Minoru; Maruyama, Ikuro; Kitagawa, Yuko

2015-04-01

High-mobility group box 1 (HMGB1) has recently been identified as an important mediator of various kinds of acute and chronic inflammation. The protein encoded by the box-A domain of the HMGB1 gene is known to act as a competitive inhibitor of HMGB1. In this study, we investigated whether box-A gene transfer results in box-A protein production in rats and assessed therapeutic efficacy in vivo using an acute liver failure (ALF) model. Three types of adenovirus vectors were constructed-a wild type and two mutants-and a mutant vector was then selected based on the secretion from HeLa cells. The secreted protein was subjected to a tumor necrosis factor (TNF) production inhibition test in vitro. The vector was injected via the portal vein in healthy Wistar rats to confirm box-A protein production in the liver. The vector was then injected via the portal vein in rats with ALF. Western blot analysis showed enhanced expression of box-A protein in HeLa cells transfected with one of the mutant vectors. The culture supernatant from HeLa cells transfected with the vector inhibited TNF-α production from macrophages. Expression of box-A protein was confirmed in the transfected liver at 72 h after transfection. Transfected rats showed decreased hepatic enzymes, plasma HMGB1, and hepatic TNF-α messenger RNA levels, and histologic findings and survival were significantly improved. HMGB1 box-A gene transfer results in box-A protein production in the liver and appears to have a beneficial effect on ALF in rats. Copyright © 2015 Elsevier Inc. All rights reserved.

Prediction and Identification of Krüppel-Like Transcription Factors by Machine Learning Method.

PubMed

Liao, Zhijun; Wang, Xinrui; Chen, Xingyong; Zou, Quan

2017-01-01

The Krüppel-like factors (KLFs) are a family of containing Zn finger(ZF) motif transcription factors with 18 members in human genome, among them, KLF18 is predicted by bioinformatics. KLFs possess various physiological function involving in a number of cancers and other diseases. Here we perform a binary-class classification of KLFs and non-KLFs by machine learning methods. The protein sequences of KLFs and non-KLFs were searched from UniProt and randomly separate them into training dataset(containing positive and negative sequences) and test dataset(containing only negative sequences), after extracting the 188-dimensional(188D) feature vectors we carry out category with four classifiers(GBDT, libSVM, RF, and k-NN). On the human KLFs, we further dig into the evolutionary relationship and motif distribution, and finally we analyze the conserved amino acid residue of three zinc fingers. The classifier model from training dataset were well constructed, and the highest specificity(Sp) was 99.83% from a library for support vector machine(libSVM) and all the correctly classified rates were over 70% for 10-fold cross-validation on test dataset. The 18 human KLFs can be further divided into 7 groups and the zinc finger domains were located at the carboxyl terminus, and many conserved amino acid residues including Cysteine and Histidine, and the span and interval between them were consistent in the three ZF domains. Two classification models for KLFs prediction have been built by novel machine learning methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

NASA Marshall Space Flight Center Controls Systems Design and Analysis Branch

NASA Technical Reports Server (NTRS)

Gilligan, Eric

2014-01-01

Marshall Space Flight Center maintains a critical national capability in the analysis of launch vehicle flight dynamics and flight certification of GN&C algorithms. MSFC analysts are domain experts in the areas of flexible-body dynamics and control-structure interaction, thrust vector control, sloshing propellant dynamics, and advanced statistical methods. Marshall's modeling and simulation expertise has supported manned spaceflight for over 50 years. Marshall's unparalleled capability in launch vehicle guidance, navigation, and control technology stems from its rich heritage in developing, integrating, and testing launch vehicle GN&C systems dating to the early Mercury-Redstone and Saturn vehicles. The Marshall team is continuously developing novel methods for design, including advanced techniques for large-scale optimization and analysis.

On three-dimensional misorientation spaces.

PubMed

Krakow, Robert; Bennett, Robbie J; Johnstone, Duncan N; Vukmanovic, Zoja; Solano-Alvarez, Wilberth; Lainé, Steven J; Einsle, Joshua F; Midgley, Paul A; Rae, Catherine M F; Hielscher, Ralf

2017-10-01

Determining the local orientation of crystals in engineering and geological materials has become routine with the advent of modern crystallographic mapping techniques. These techniques enable many thousands of orientation measurements to be made, directing attention towards how such orientation data are best studied. Here, we provide a guide to the visualization of misorientation data in three-dimensional vector spaces, reduced by crystal symmetry, to reveal crystallographic orientation relationships. Domains for all point group symmetries are presented and an analysis methodology is developed and applied to identify crystallographic relationships, indicated by clusters in the misorientation space, in examples from materials science and geology. This analysis aids the determination of active deformation mechanisms and evaluation of cluster centres and spread enables more accurate description of transformation processes supporting arguments regarding provenance.

On three-dimensional misorientation spaces

NASA Astrophysics Data System (ADS)

Krakow, Robert; Bennett, Robbie J.; Johnstone, Duncan N.; Vukmanovic, Zoja; Solano-Alvarez, Wilberth; Lainé, Steven J.; Einsle, Joshua F.; Midgley, Paul A.; Rae, Catherine M. F.; Hielscher, Ralf

2017-10-01

Determining the local orientation of crystals in engineering and geological materials has become routine with the advent of modern crystallographic mapping techniques. These techniques enable many thousands of orientation measurements to be made, directing attention towards how such orientation data are best studied. Here, we provide a guide to the visualization of misorientation data in three-dimensional vector spaces, reduced by crystal symmetry, to reveal crystallographic orientation relationships. Domains for all point group symmetries are presented and an analysis methodology is developed and applied to identify crystallographic relationships, indicated by clusters in the misorientation space, in examples from materials science and geology. This analysis aids the determination of active deformation mechanisms and evaluation of cluster centres and spread enables more accurate description of transformation processes supporting arguments regarding provenance.

Ranking of stopping criteria for log domain diffeomorphic demons application in clinical radiation therapy.

PubMed

Peroni, M; Golland, P; Sharp, G C; Baroni, G

2011-01-01

Deformable Image Registration is a complex optimization algorithm with the goal of modeling a non-rigid transformation between two images. A crucial issue in this field is guaranteeing the user a robust but computationally reasonable algorithm. We rank the performances of four stopping criteria and six stopping value computation strategies for a log domain deformable registration. The stopping criteria we test are: (a) velocity field update magnitude, (b) vector field Jacobian, (c) mean squared error, and (d) harmonic energy. Experiments demonstrate that comparing the metric value over the last three iterations with the metric minimum of between four and six previous iterations is a robust and appropriate strategy. The harmonic energy and vector field update magnitude metrics give the best results in terms of robustness and speed of convergence.

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

NASA Astrophysics Data System (ADS)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

Wavefront analysis from its slope data

NASA Astrophysics Data System (ADS)

Mahajan, Virendra N.; Acosta, Eva

2017-08-01

In the aberration analysis of a wavefront over a certain domain, the polynomials that are orthogonal over and represent balanced wave aberrations for this domain are used. For example, Zernike circle polynomials are used for the analysis of a circular wavefront. Similarly, the annular polynomials are used to analyze the annular wavefronts for systems with annular pupils, as in a rotationally symmetric two-mirror system, such as the Hubble space telescope. However, when the data available for analysis are the slopes of a wavefront, as, for example, in a Shack- Hartmann sensor, we can integrate the slope data to obtain the wavefront data, and then use the orthogonal polynomials to obtain the aberration coefficients. An alternative is to find vector functions that are orthogonal to the gradients of the wavefront polynomials, and obtain the aberration coefficients directly as the inner products of these functions with the slope data. In this paper, we show that an infinite number of vector functions can be obtained in this manner. We show further that the vector functions that are irrotational are unique and propagate minimum uncorrelated additive random noise from the slope data to the aberration coefficients.

The Design of a Templated C++ Small Vector Class for Numerical Computing

NASA Technical Reports Server (NTRS)

Moran, Patrick J.

2000-01-01

We describe the design and implementation of a templated C++ class for vectors. The vector class is templated both for vector length and vector component type; the vector length is fixed at template instantiation time. The vector implementation is such that for a vector of N components of type T, the total number of bytes required by the vector is equal to N * size of (T), where size of is the built-in C operator. The property of having a size no bigger than that required by the components themselves is key in many numerical computing applications, where one may allocate very large arrays of small, fixed-length vectors. In addition to the design trade-offs motivating our fixed-length vector design choice, we review some of the C++ template features essential to an efficient, succinct implementation. In particular, we highlight some of the standard C++ features, such as partial template specialization, that are not supported by all compilers currently. This report provides an inventory listing the relevant support currently provided by some key compilers, as well as test code one can use to verify compiler capabilities.

An Efficient Wait-Free Vector

DOE PAGES

Feldman, Steven; Valera-Leon, Carlos; Dechev, Damian

2016-03-01

The vector is a fundamental data structure, which provides constant-time access to a dynamically-resizable range of elements. Currently, there exist no wait-free vectors. The only non-blocking version supports only a subset of the sequential vector API and exhibits significant synchronization overhead caused by supporting opposing operations. Since many applications operate in phases of execution, wherein each phase only a subset of operations are used, this overhead is unnecessary for the majority of the application. To address the limitations of the non-blocking version, we present a new design that is wait-free, supports more of the operations provided by the sequential vector,more » and provides alternative implementations of key operations. These alternatives allow the developer to balance the performance and functionality of the vector as requirements change throughout execution. Compared to the known non-blocking version and the concurrent vector found in Intel’s TBB library, our design outperforms or provides comparable performance in the majority of tested scenarios. Over all tested scenarios, the presented design performs an average of 4.97 times more operations per second than the non-blocking vector and 1.54 more than the TBB vector. In a scenario designed to simulate the filling of a vector, performance improvement increases to 13.38 and 1.16 times. This work presents the first ABA-free non-blocking vector. Finally, unlike the other non-blocking approach, all operations are wait-free and bounds-checked and elements are stored contiguously in memory.« less

Higher-order fluctuation-dissipation relations in plasma physics: Binary Coulomb systems

NASA Astrophysics Data System (ADS)

Golden, Kenneth I.

2018-05-01

A recent approach that led to compact frequency domain formulations of the cubic and quartic fluctuation-dissipation theorems (FDTs) for the classical one-component plasma (OCP) [Golden and Heath, J. Stat. Phys. 162, 199 (2016), 10.1007/s10955-015-1395-6] is generalized to accommodate binary ionic mixtures. Paralleling the procedure followed for the OCP, the basic premise underlying the present approach is that a (k ,ω ) 4-vector rotational symmetry, known to be a pivotal feature in the frequency domain architectures of the linear and quadratic fluctuation-dissipation relations for a variety of Coulomb plasmas [Golden et al., J. Stat. Phys. 6, 87 (1972), 10.1007/BF01023681; J. Stat. Phys. 29, 281 (1982), 10.1007/BF01020787; Golden, Phys. Rev. E 59, 228 (1999), 10.1103/PhysRevE.59.228], is expected to be a pivotal feature of the frequency domain architectures of the higher-order members of the FDT hierarchy. On this premise, each member, in its most tractable form, connects a single (p +1 )-point dynamical structure function to a linear combination of (p +1 )-order p density response functions; by definition, such a combination must also remain invariant under rotation of their (k1,ω1) ,(k2,ω2) ,...,(kp,ωp) , (k1+k2+⋯+kp,ω1+ω2+⋯+ωp) 4-vector arguments. Assigned to each 4-vector is a species index that corotates in lock step. Consistency is assured by matching the static limits of the resulting frequency domain cubic and quartic FDTs to their exact static counterparts independently derived in the present work via a conventional time-independent perturbation expansion of the Liouville distribution function in its macrocanonical form. The proposed procedure entirely circumvents the daunting issues of entangled Liouville space paths and nested Poisson brackets that one would encounter if one attempted to use the conventional time-dependent perturbation-theoretic Kubo approach to establish the frequency domain FDTs beyond quadratic order.

Computational mechanics analysis tools for parallel-vector supercomputers

NASA Technical Reports Server (NTRS)

Storaasli, O. O.; Nguyen, D. T.; Baddourah, M. A.; Qin, J.

1993-01-01

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigen-solution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization algorithm and domain decomposition. The source code for many of these algorithms is available from NASA Langley.

A Distributed Sensor Network Architecture for Defense Against the Ship as a Weapon in the Maritime Domain

DTIC Science & Technology

2011-06-01

time delays, and even insurance premiums [3]. Piracy has plagued the straits of Malacca and Singapore for many years. Though the number of...Island while traversing west to east, it will attract considerable attention when it cuts across the TSS before heading towards Jurong Island (see the...delimited vectors), ’cutvector’ % (NaN-clipped vectors with cuts connecting holes to the % exterior of the polygon

Unsupervised domain adaptation for early detection of drought stress in hyperspectral images

NASA Astrophysics Data System (ADS)

Schmitter, P.; Steinrücken, J.; Römer, C.; Ballvora, A.; Léon, J.; Rascher, U.; Plümer, L.

2017-09-01

Hyperspectral images can be used to uncover physiological processes in plants if interpreted properly. Machine Learning methods such as Support Vector Machines (SVM) and Random Forests have been applied to estimate development of biomass and detect and predict plant diseases and drought stress. One basic requirement of machine learning implies, that training and testing is done in the same domain and the same distribution. Different genotypes, environmental conditions, illumination and sensors violate this requirement in most practical circumstances. Here, we present an approach, which enables the detection of physiological processes by transferring the prior knowledge within an existing model into a related target domain, where no label information is available. We propose a two-step transformation of the target features, which enables a direct application of an existing model. The transformation is evaluated by an objective function including additional prior knowledge about classification and physiological processes in plants. We have applied the approach to three sets of hyperspectral images, which were acquired with different plant species in different environments observed with different sensors. It is shown, that a classification model, derived on one of the sets, delivers satisfying classification results on the transformed features of the other data sets. Furthermore, in all cases early non-invasive detection of drought stress was possible.

Exo-endo cellulase fusion protein

DOEpatents

Bower, Benjamin S [Palo Alto, CA; Larenas, Edmund A [Palo Alto, CA; Mitchinson, Colin [Palo Alto, CA

2012-01-17

The present invention relates to a heterologous exo-endo cellulase fusion construct, which encodes a fusion protein having cellulolytic activity comprising a catalytic domain derived from a fungal exo-cellobiohydrolase and a catalytic domain derived from an endoglucanase. The invention also relates to vectors and fungal host cells comprising the heterologous exo-endo cellulase fusion construct as well as methods for producing a cellulase fusion protein and enzymatic cellulase compositions.

Domain Derivatives in Dielectric Rough Surface Scattering

DTIC Science & Technology

2015-01-01

and require the gradient of the objective function in the unknown model parameter vector at each stage of iteration. For large N, finite...differencing becomes numerically intensive, and an efficient alternative is domain differentiation in which the full gradient is obtained by solving a single...derivative calculation of the gradient for a locally perturbed dielectric interface. The method is non-variational, and algebraic in nature in that it

Biot-Savart helicity versus physical helicity: A topological description of ideal flows

NASA Astrophysics Data System (ADS)

Sahihi, Taliya; Eshraghi, Homayoon

2014-08-01

For an isentropic (thus compressible) flow, fluid trajectories are considered as orbits of a family of one parameter, smooth, orientation-preserving, and nonsingular diffeomorphisms on a compact and smooth-boundary domain in the Euclidian 3-space which necessarily preserve a finite measure, later interpreted as the fluid mass. Under such diffeomorphisms the Biot-Savart helicity of the pushforward of a divergence-free and tangent to the boundary vector field is proved to be conserved and since these circumstances present an isentropic flow, the conservation of the "Biot-Savart helicity" is established for such flows. On the other hand, the well known helicity conservation in ideal flows which here we call it "physical helicity" is found to be an independent constant with respect to the Biot-Savart helicity. The difference between these two helicities reflects some topological features of the domain as well as the velocity and vorticity fields which is discussed and is shown for simply connected domains the two helicities coincide. The energy variation of the vorticity field is shown to be formally the same as for the incompressible flow obtained before. For fluid domains consisting of several disjoint solid tori, at each time, the harmonic knot subspace of smooth vector fields on the fluid domain is found to have two independent base sets with a special type of orthogonality between these two bases by which a topological description of the vortex and velocity fields depending on the helicity difference is achieved since this difference is shown to depend only on the harmonic knot parts of velocity, vorticity, and its Biot-Savart vector field. For an ideal magnetohydrodynamics (MHD) flow three independent constant helicities are reviewed while the helicity of magnetic potential is generalized for non-simply connected domains by inserting a special harmonic knot field in the dynamics of the magnetic potential. It is proved that the harmonic knot part of the vorticity in hydrodynamics and the magnetic field in MHD is presented by constant coefficients (fluxes) when expanded in terms of one of the time dependent base functions.

Nucleon form factors from quenched lattice QCD with domain wall fermions

NASA Astrophysics Data System (ADS)

Sasaki, Shoichi; Yamazaki, Takeshi

2008-07-01

We present a quenched lattice calculation of the weak nucleon form factors: vector [FV(q2)], induced tensor [FT(q2)], axial vector [FA(q2)] and induced pseudoscalar [FP(q2)] form factors. Our simulations are performed on three different lattice sizes L3×T=243×32, 163×32, and 123×32 with a lattice cutoff of a-1≈1.3GeV and light quark masses down to about 1/4 the strange quark mass (mπ≈390MeV) using a combination of the DBW2 gauge action and domain wall fermions. The physical volume of our largest lattice is about (3.6fm)3, where the finite volume effects on form factors become negligible and the lower momentum transfers (q2≈0.1GeV2) are accessible. The q2 dependences of form factors in the low q2 region are examined. It is found that the vector, induced tensor, and axial-vector form factors are well described by the dipole form, while the induced pseudoscalar form factor is consistent with pion-pole dominance. We obtain the ratio of axial to vector coupling gA/gV=FA(0)/FV(0)=1.219(38) and the pseudoscalar coupling gP=mμFP(0.88mμ2)=8.15(54), where the errors are statistical errors only. These values agree with experimental values from neutron β decay and muon capture on the proton. However, the root mean-squared radii of the vector, induced tensor, and axial vector underestimate the known experimental values by about 20%. We also calculate the pseudoscalar nucleon matrix element in order to verify the axial Ward-Takahashi identity in terms of the nucleon matrix elements, which may be called as the generalized Goldberger-Treiman relation.

Degeneracy of vector-channel spatial correlators in high temperature QCD

NASA Astrophysics Data System (ADS)

Rohrhofer, Christian; Aoki, Yasumichi; Cossu, Guido; Fukaya, Hidenori; Glozman, Leonid; Hashimoto, Shoji; Lang, Christian B.; Prelovsek, Sasa

2018-03-01

We study spatial isovector meson correlators in Nf = 2 QCD with dynamical domain-wall fermions on 323 × 8 lattices at temperatures up to 380 MeV with various quark masses. We measure the correlators of spin-one isovector operators including vector, axial-vector, tensor and axial-tensor. At temperatures above Tc we observe an approximate degeneracy of the correlators in these channels, which is unexpected because some of them are not related under SU(2)L×SU(2)R nor U(1)A symmetries. The observed approximate degeneracy suggests emergent SU(2)CS (chiral-spin) and SU(4) symmetries at high T.

Approximate degeneracy of J =1 spatial correlators in high temperature QCD

NASA Astrophysics Data System (ADS)

Rohrhofer, C.; Aoki, Y.; Cossu, G.; Fukaya, H.; Glozman, L. Ya.; Hashimoto, S.; Lang, C. B.; Prelovsek, S.

2017-11-01

We study spatial isovector meson correlators in Nf=2 QCD with dynamical domain-wall fermions on 3 23×8 lattices at temperatures T =220 - 380 MeV . We measure the correlators of spin-one (J =1 ) operators including vector, axial-vector, tensor and axial-tensor. Restoration of chiral U (1 )A and S U (2 )L×S U (2 )R symmetries of QCD implies degeneracies in vector-axial-vector (S U (2 )L×S U (2 )R) and tensor-axial-tensor (U (1 )A) pairs, which are indeed observed at temperatures above Tc. Moreover, we observe an approximate degeneracy of all J =1 correlators with increasing temperature. This approximate degeneracy suggests emergent S U (2 )CS and S U (4 ) symmetries at high temperatures, that mix left- and right-handed quarks.

Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

NASA Astrophysics Data System (ADS)

Li, Tao

2018-06-01

The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

Investigation of ferroelectric domains in thin films of vinylidene fluoride oligomers

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

Sharma, Pankaj, E-mail: psharma@huskers.unl.edu; Poddar, Shashi; Ducharme, Stephen

2014-07-14

High-resolution vector piezoresponse force microscopy (PFM) has been used to investigate ferroelectric domains in thin vinylidene fluoride oligomer films fabricated by the Langmuir-Blodgett deposition technique. Molecular chains are found to be preferentially oriented normal to the substrate, and PFM imaging shows that the films are in ferroelectric β-phase with a predominantly in-plane polarization, in agreement with infrared spectroscopic ellipsometry and X-ray diffraction measurements. The fractal analysis of domain structure has yielded the Hausdorff dimension (D) in the range of ∼1.3–1.5 indicating a random-bond nature of the disorder potential, with domain size exhibiting Landau-Lifshitz-Kittel scaling.

Polarization domain wall pulses in a microfiber-based topological insulator fiber laser

PubMed Central

Liu, Jingmin; Li, Xingliang; Zhang, Shumin; Zhang, Han; Yan, Peiguang; Han, Mengmeng; Pang, Zhaoguang; Yang, Zhenjun

2016-01-01

Topological insulators (TIs), are novel two-dimension materials, which can act as effective saturable absorbers (SAs) in a fiber laser. Moreover, based on the evanescent wave interaction, deposition of the TI on microfiber would create an effective SA, which has combined advantages from the strong nonlinear optical response in TI material together with the sufficiently-long-range interaction length in fiber taper. By using this type of TI SA, various scalar solitons have been obtained in fiber lasers. However, a single mode fiber always exhibits birefringence, and hence can support two orthogonal degenerate modes. Here we investigate experimentally the vector characters of a TI SA fiber laser. Using the saturated absorption and the high nonlinearity of the TI SA, a rich variety of dynamic states, including polarization-locked dark pulses and their harmonic mode locked counterparts, polarization-locked noise-like pulses and their harmonic mode locked counterparts, incoherently coupled polarization domain wall pulses, including bright square pulses, bright-dark pulse pairs, dark pulses and bright square pulse-dark pulse pairs are all observed with different pump powers and polarization states. PMID:27381942

Polarization domain wall pulses in a microfiber-based topological insulator fiber laser

NASA Astrophysics Data System (ADS)

Liu, Jingmin; Li, Xingliang; Zhang, Shumin; Zhang, Han; Yan, Peiguang; Han, Mengmeng; Pang, Zhaoguang; Yang, Zhenjun

2016-07-01

Topological insulators (TIs), are novel two-dimension materials, which can act as effective saturable absorbers (SAs) in a fiber laser. Moreover, based on the evanescent wave interaction, deposition of the TI on microfiber would create an effective SA, which has combined advantages from the strong nonlinear optical response in TI material together with the sufficiently-long-range interaction length in fiber taper. By using this type of TI SA, various scalar solitons have been obtained in fiber lasers. However, a single mode fiber always exhibits birefringence, and hence can support two orthogonal degenerate modes. Here we investigate experimentally the vector characters of a TI SA fiber laser. Using the saturated absorption and the high nonlinearity of the TI SA, a rich variety of dynamic states, including polarization-locked dark pulses and their harmonic mode locked counterparts, polarization-locked noise-like pulses and their harmonic mode locked counterparts, incoherently coupled polarization domain wall pulses, including bright square pulses, bright-dark pulse pairs, dark pulses and bright square pulse-dark pulse pairs are all observed with different pump powers and polarization states.

Semantic similarity measure in biomedical domain leverage web search engine.

PubMed

Chen, Chi-Huang; Hsieh, Sheau-Ling; Weng, Yung-Ching; Chang, Wen-Yung; Lai, Feipei

2010-01-01

Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.

Domain engineering of the metastable domains in the 4f-uniaxial-ferromagnet CeRu2Ga2B

NASA Astrophysics Data System (ADS)

Wulferding, D.; Kim, H.; Yang, I.; Jeong, J.; Barros, K.; Kato, Y.; Martin, I.; Ayala-Valenzuela, O. E.; Lee, M.; Choi, H. C.; Ronning, F.; Civale, L.; Baumbach, R. E.; Bauer, E. D.; Thompson, J. D.; Movshovich, R.; Kim, Jeehoon

2017-04-01

In search of novel, improved materials for magnetic data storage and spintronic devices, compounds that allow a tailoring of magnetic domain shapes and sizes are essential. Good candidates are materials with intrinsic anisotropies or competing interactions, as they are prone to host various domain phases that can be easily and precisely selected by external tuning parameters such as temperature and magnetic field. Here, we utilize vector magnetic fields to visualize directly the magnetic anisotropy in the uniaxial ferromagnet CeRu2Ga2B. We demonstrate a feasible control both globally and locally of domain shapes and sizes by the external field as well as a smooth transition from single stripe to bubble domains, which opens the door to future applications based on magnetic domain tailoring.

Domain engineering of the metastable domains in the 4f-uniaxial-ferromagnet CeRu 2Ga 2B

DOE PAGES

Wulferding, Dirk; Kim, Hoon; Yang, Ilkyu; ...

2017-04-10

In search of novel, improved materials for magnetic data storage and spintronic devices, compounds that allow a tailoring of magnetic domain shapes and sizes are essential. Good candidates are materials with intrinsic anisotropies or competing interactions, as they are prone to host various domain phases that can be easily and precisely selected by external tuning parameters such as temperature and magnetic field. Here, we utilize vector magnetic fields to visualize directly the magnetic anisotropy in the uniaxial ferromagnet CeRu 2Ga 2B. We demonstrate a feasible control both globally and locally of domain shapes and sizes by the external field asmore » well as a smooth transition from single stripe to bubble domains, which opens the door to future applications based on magnetic domain tailoring.« less

Techniques for determining physical zones of influence

DOEpatents

Hamann, Hendrik F; Lopez-Marrero, Vanessa

2013-11-26

Techniques for analyzing flow of a quantity in a given domain are provided. In one aspect, a method for modeling regions in a domain affected by a flow of a quantity is provided which includes the following steps. A physical representation of the domain is provided. A grid that contains a plurality of grid-points in the domain is created. Sources are identified in the domain. Given a vector field that defines a direction of flow of the quantity within the domain, a boundary value problem is defined for each of one or more of the sources identified in the domain. Each of the boundary value problems is solved numerically to obtain a solution for the boundary value problems at each of the grid-points. The boundary problem solutions are post-processed to model the regions affected by the flow of the quantity on the physical representation of the domain.

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

Feldman, Steven; Valera-Leon, Carlos; Dechev, Damian

The vector is a fundamental data structure, which provides constant-time access to a dynamically-resizable range of elements. Currently, there exist no wait-free vectors. The only non-blocking version supports only a subset of the sequential vector API and exhibits significant synchronization overhead caused by supporting opposing operations. Since many applications operate in phases of execution, wherein each phase only a subset of operations are used, this overhead is unnecessary for the majority of the application. To address the limitations of the non-blocking version, we present a new design that is wait-free, supports more of the operations provided by the sequential vector,more » and provides alternative implementations of key operations. These alternatives allow the developer to balance the performance and functionality of the vector as requirements change throughout execution. Compared to the known non-blocking version and the concurrent vector found in Intel’s TBB library, our design outperforms or provides comparable performance in the majority of tested scenarios. Over all tested scenarios, the presented design performs an average of 4.97 times more operations per second than the non-blocking vector and 1.54 more than the TBB vector. In a scenario designed to simulate the filling of a vector, performance improvement increases to 13.38 and 1.16 times. This work presents the first ABA-free non-blocking vector. Finally, unlike the other non-blocking approach, all operations are wait-free and bounds-checked and elements are stored contiguously in memory.« less

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis.

PubMed

Li, Zhan-Chao; Zhou, Xi-Bin; Dai, Zong; Zou, Xiao-Yong

2009-07-01

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou's pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246-255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Hormone treatment enhances WT1 activation of Renilla luciferase constructs in LNCaP cells.

PubMed

Hanson, Julie; Reese, Jennifer; Gorman, Jacquelyn; Cash, Jennifer; Fraizer, Gail

2007-01-01

The zinc finger transcription factor, WT1, regulates many growth control genes, repressing or activating transcription depending on the gene and cell type. Based on earlier analyses of the effect of WT1 on androgen responsive genes, we hypothesized that there may be an interaction between the androgen signaling pathway and WT1, such that the commonly used Renilla luciferase control vectors were activated in LNCaP prostate cancer cells. Using cotransfection assays we tested the effects of WT1 and/or the androgen analog, R1881, on two Renilla luciferase vectors, pRL-SV40 and the promoter-less pRL-null. To determine whether the zinc finger DNA binding domain was required, the zinc finger mutant DDS-WT1 (R394W) was tested; but it had no significant effect on the Renilla luciferase vectors. To determine whether the androgen signaling pathway was required, WT1 was co-transfected with Renilla vectors in cells with varied hormone responsiveness. The WT1 effect on pRL-null varied from no significant effect in 293 and PC3 cells to very strong enhancement in LNCaP cells treated with 5 nM R1881. Overall, these results suggest that hormone enhanced WT1 mediated activation of Renilla luciferase and that these interactions require an intact WT1 zinc finger DNA binding domain.

Towards a Safer, More Randomized Lentiviral Vector Integration Profile Exploring Artificial LEDGF Chimeras

PubMed Central

Vranckx, Lenard S.; Demeulemeester, Jonas; Debyser, Zeger

2016-01-01

The capacity to integrate transgenes into the host cell genome makes retroviral vectors an interesting tool for gene therapy. Although stable insertion resulted in successful correction of several monogenic disorders, it also accounts for insertional mutagenesis, a major setback in otherwise successful clinical gene therapy trials due to leukemia development in a subset of treated patients. Despite improvements in vector design, their use is still not risk-free. Lentiviral vector (LV) integration is directed into active transcription units by LEDGF/p75, a host-cell protein co-opted by the viral integrase. We engineered LEDGF/p75-based hybrid tethers in an effort to elicit a more random integration pattern to increase biosafety, and potentially reduce proto-oncogene activation. We therefore truncated LEDGF/p75 by deleting the N-terminal chromatin-reading PWWP-domain, and replaced this domain with alternative pan-chromatin binding peptides. Expression of these LEDGF-hybrids in LEDGF-depleted cells efficiently rescued LV transduction and resulted in LV integrations that distributed more randomly throughout the host-cell genome. In addition, when considering safe harbor criteria, LV integration sites for these LEDGF-hybrids distributed more safely compared to LEDGF/p75-mediated integration in wild-type cells. This approach should be broadly applicable to introduce therapeutic or suicide genes for cell therapy, such as patient-specific iPS cells. PMID:27788138

A Parallel Vector Machine for the PM Programming Language

NASA Astrophysics Data System (ADS)

Bellerby, Tim

2016-04-01

PM is a new programming language which aims to make the writing of computational geoscience models on parallel hardware accessible to scientists who are not themselves expert parallel programmers. It is based around the concept of communicating operators: language constructs that enable variables local to a single invocation of a parallelised loop to be viewed as if they were arrays spanning the entire loop domain. This mechanism enables different loop invocations (which may or may not be executing on different processors) to exchange information in a manner that extends the successful Communicating Sequential Processes idiom from single messages to collective communication. Communicating operators avoid the additional synchronisation mechanisms, such as atomic variables, required when programming using the Partitioned Global Address Space (PGAS) paradigm. Using a single loop invocation as the fundamental unit of concurrency enables PM to uniformly represent different levels of parallelism from vector operations through shared memory systems to distributed grids. This paper describes an implementation of PM based on a vectorised virtual machine. On a single processor node, concurrent operations are implemented using masked vector operations. Virtual machine instructions operate on vectors of values and may be unmasked, masked using a Boolean field, or masked using an array of active vector cell locations. Conditional structures (such as if-then-else or while statement implementations) calculate and apply masks to the operations they control. A shift in mask representation from Boolean to location-list occurs when active locations become sufficiently sparse. Parallel loops unfold data structures (or vectors of data structures for nested loops) into vectors of values that may additionally be distributed over multiple computational nodes and then split into micro-threads compatible with the size of the local cache. Inter-node communication is accomplished using standard OpenMP and MPI. Performance analyses of the PM vector machine, demonstrating its scaling properties with respect to domain size and the number of processor nodes will be presented for a range of hardware configurations. The PM software and language definition are being made available under unrestrictive MIT and Creative Commons Attribution licenses respectively: www.pm-lang.org.

Angle-domain common imaging gather extraction via Kirchhoff prestack depth migration based on a traveltime table in transversely isotropic media

NASA Astrophysics Data System (ADS)

Liu, Shaoyong; Gu, Hanming; Tang, Yongjie; Bingkai, Han; Wang, Huazhong; Liu, Dingjin

2018-04-01

Angle-domain common image-point gathers (ADCIGs) can alleviate the limitations of common image-point gathers in an offset domain, and have been widely used for velocity inversion and amplitude variation with angle (AVA) analysis. We propose an effective algorithm for generating ADCIGs in transversely isotropic (TI) media based on the gradient of traveltime by Kirchhoff pre-stack depth migration (KPSDM), as the dynamic programming method for computing the traveltime in TI media would not suffer from the limitation of shadow zones and traveltime interpolation. Meanwhile, we present a specific implementation strategy for ADCIG extraction via KPSDM. Three major steps are included in the presented strategy: (1) traveltime computation using a dynamic programming approach in TI media; (2) slowness vector calculation by the gradient of a traveltime table calculated previously; (3) construction of illumination vectors and subsurface angles in the migration process. Numerical examples are included to demonstrate the effectiveness of our approach, which henceforce shows its potential application for subsequent tomographic velocity inversion and AVA.

Community detection in complex networks using proximate support vector clustering

NASA Astrophysics Data System (ADS)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-03-01

Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

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.

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

PubMed Central

Morison, Gordon; Boreham, Philip

2018-01-01

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring. PMID:29385030

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Multimedia Classifier

NASA Astrophysics Data System (ADS)

Costache, G. N.; Gavat, I.

2004-09-01

Along with the aggressive growing of the amount of digital data available (text, audio samples, digital photos and digital movies joined all in the multimedia domain) the need for classification, recognition and retrieval of this kind of data became very important. In this paper will be presented a system structure to handle multimedia data based on a recognition perspective. The main processing steps realized for the interesting multimedia objects are: first, the parameterization, by analysis, in order to obtain a description based on features, forming the parameter vector; second, a classification, generally with a hierarchical structure to make the necessary decisions. For audio signals, both speech and music, the derived perceptual features are the melcepstral (MFCC) and the perceptual linear predictive (PLP) coefficients. For images, the derived features are the geometric parameters of the speaker mouth. The hierarchical classifier consists generally in a clustering stage, based on the Kohonnen Self-Organizing Maps (SOM) and a final stage, based on a powerful classification algorithm called Support Vector Machines (SVM). The system, in specific variants, is applied with good results in two tasks: the first, is a bimodal speech recognition which uses features obtained from speech signal fused to features obtained from speaker's image and the second is a music retrieval from large music database.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

PubMed

Bisenius, Sandrine; Mueller, Karsten; Diehl-Schmid, Janine; Fassbender, Klaus; Grimmer, Timo; Jessen, Frank; Kassubek, Jan; Kornhuber, Johannes; Landwehrmeyer, Bernhard; Ludolph, Albert; Schneider, Anja; Anderl-Straub, Sarah; Stuke, Katharina; Danek, Adrian; Otto, Markus; Schroeter, Matthias L

2017-01-01

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

An adenovirus prime/plasmid boost strategy for induction of equipotent immune responses to two dengue virus serotypes.

PubMed

Khanam, Saima; Rajendra, Pilankatta; Khanna, Navin; Swaminathan, Sathyamangalam

2007-02-15

Dengue is a public health problem of global significance for which there is neither an effective antiviral therapy nor a preventive vaccine. It is a mosquito-borne viral disease, caused by dengue (DEN) viruses, which are members of the Flaviviridae family. There are four closely related serotypes, DEN-1, DEN-2, DEN-3 and DEN-4, each of which is capable of causing disease. As immunity to any one serotype can potentially sensitize an individual to severe disease during exposure to a heterologous serotype, the general consensus is that an effective vaccine should be tetravalent, that is, it must be capable of affording protection against all four serotypes. The current strategy of creating tetravalent vaccine formulations by mixing together four monovalent live attenuated vaccine viruses has revealed the phenomenon of viral interference leading to the manifestation of immune responses biased towards a single serotype. This work stems from the emergence of (i) the DEN virus envelope (E) domain III (EDIII) as the most important region of the molecule from a vaccine perspective and (ii) the adenovirus (Ad) as a promising vaccine vector platform. We describe the construction of a recombinant, replication-defective Ad (rAd) vector encoding a chimeric antigen made of in-frame linked EDIIIs of DEN virus serotypes 2 and 4. Using this rAd vector, in conjunction with a plasmid vector encoding the same chimeric bivalent antigen, in a prime-boost strategy, we show that it is possible to elicit equipotent neutralizing and T cell responses specific to both DEN serotypes 2 and 4. Our data support the hypothesis that a DEN vaccine targeting more than one serotype may be based on a single DNA-based vector to circumvent viral interference. This work lays the foundation for developing a single Ad vector encoding EDIIIs of all four DEN serotypes to evoke a balanced immune response against each one of them. Thus, this work has implications for the development of safe and effective tetravalent dengue vaccines.

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).

Inference of the oxidative stress network in Anopheles stephensi upon Plasmodium infection.

PubMed

Shrinet, Jatin; Nandal, Umesh Kumar; Adak, Tridibes; Bhatnagar, Raj K; Sunil, Sujatha

2014-01-01

Ookinete invasion of Anopheles midgut is a critical step for malaria transmission; the parasite numbers drop drastically and practically reach a minimum during the parasite's whole life cycle. At this stage, the parasite as well as the vector undergoes immense oxidative stress. Thereafter, the vector undergoes oxidative stress at different time points as the parasite invades its tissues during the parasite development. The present study was undertaken to reconstruct the network of differentially expressed genes involved in oxidative stress in Anopheles stephensi during Plasmodium development and maturation in the midgut. Using high throughput next generation sequencing methods, we generated the transcriptome of the An. stephensi midgut during Plasmodium vinckei petteri oocyst invasion of the midgut epithelium. Further, we utilized large datasets available on public domain on Anopheles during Plasmodium ookinete invasion and Drosophila datasets and arrived upon clusters of genes that may play a role in oxidative stress. Finally, we used support vector machines for the functional prediction of the un-annotated genes of An. stephensi. Integrating the results from all the different data analyses, we identified a total of 516 genes that were involved in oxidative stress in An. stephensi during Plasmodium development. The significantly regulated genes were further extracted from this gene cluster and used to infer an oxidative stress network of An. stephensi. Using system biology approaches, we have been able to ascertain the role of several putative genes in An. stephensi with respect to oxidative stress. Further experimental validations of these genes are underway.

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Support vector machines

NASA Technical Reports Server (NTRS)

Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

2004-01-01

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

Lysine acetylation sites prediction using an ensemble of support vector machine classifiers.

PubMed

Xu, Yan; Wang, Xiao-Bo; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang

2010-05-07

Lysine acetylation is an essentially reversible and high regulated post-translational modification which regulates diverse protein properties. Experimental identification of acetylation sites is laborious and expensive. Hence, there is significant interest in the development of computational methods for reliable prediction of acetylation sites from amino acid sequences. In this paper we use an ensemble of support vector machine classifiers to perform this work. The experimentally determined acetylation lysine sites are extracted from Swiss-Prot database and scientific literatures. Experiment results show that an ensemble of support vector machine classifiers outperforms single support vector machine classifier and other computational methods such as PAIL and LysAcet on the problem of predicting acetylation lysine sites. The resulting method has been implemented in EnsemblePail, a web server for lysine acetylation sites prediction available at http://www.aporc.org/EnsemblePail/. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Product Quality Modelling Based on Incremental Support Vector Machine

NASA Astrophysics Data System (ADS)

Wang, J.; Zhang, W.; Qin, B.; Shi, W.

2012-05-01

Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

NASA Technical Reports Server (NTRS)

Forman, Barton A.; Reichle, Rolf Helmut

2014-01-01

A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

Making the most of cloud storage - a toolkit for exploitation by WLCG experiments

NASA Astrophysics Data System (ADS)

Alvarez Ayllon, Alejandro; Arsuaga Rios, Maria; Bitzes, Georgios; Furano, Fabrizio; Keeble, Oliver; Manzi, Andrea

2017-10-01

Understanding how cloud storage can be effectively used, either standalone or in support of its associated compute, is now an important consideration for WLCG. We report on a suite of extensions to familiar tools targeted at enabling the integration of cloud object stores into traditional grid infrastructures and workflows. Notable updates include support for a number of object store flavours in FTS3, Davix and gfal2, including mitigations for lack of vector reads; the extension of Dynafed to operate as a bridge between grid and cloud domains; protocol translation in FTS3; the implementation of extensions to DPM (also implemented by the dCache project) to allow 3rd party transfers over HTTP. The result is a toolkit which facilitates data movement and access between grid and cloud infrastructures, broadening the range of workflows suitable for cloud. We report on deployment scenarios and prototype experience, explaining how, for example, an Amazon S3 or Azure allocation can be exploited by grid workflows.

Multiple image encryption scheme based on pixel exchange operation and vector decomposition

NASA Astrophysics Data System (ADS)

Xiong, Y.; Quan, C.; Tay, C. J.

2018-02-01

We propose a new multiple image encryption scheme based on a pixel exchange operation and a basic vector decomposition in Fourier domain. In this algorithm, original images are imported via a pixel exchange operator, from which scrambled images and pixel position matrices are obtained. Scrambled images encrypted into phase information are imported using the proposed algorithm and phase keys are obtained from the difference between scrambled images and synthesized vectors in a charge-coupled device (CCD) plane. The final synthesized vector is used as an input in a random phase encoding (DRPE) scheme. In the proposed encryption scheme, pixel position matrices and phase keys serve as additional private keys to enhance the security of the cryptosystem which is based on a 4-f system. Numerical simulations are presented to demonstrate the feasibility and robustness of the proposed encryption scheme.

A vector scanning processing technique for pulsed laser velocimetry

NASA Technical Reports Server (NTRS)

Wernet, Mark P.; Edwards, Robert V.

1989-01-01

Pulsed-laser-sheet velocimetry yields two-dimensional velocity vectors across an extended planar region of a flow. Current processing techniques offer high-precision (1-percent) velocity estimates, but can require hours of processing time on specialized array processors. Sometimes, however, a less accurate (about 5 percent) data-reduction technique which also gives unambiguous velocity vector information is acceptable. Here, a direct space-domain processing technique is described and shown to be far superior to previous methods in achieving these objectives. It uses a novel data coding and reduction technique and has no 180-deg directional ambiguity. A complex convection vortex flow was recorded and completely processed in under 2 min on an 80386-based PC, producing a two-dimensional velocity-vector map of the flowfield. Pulsed-laser velocimetry data can thus be reduced quickly and reasonably accurately, without specialized array processing hardware.

On three-dimensional misorientation spaces

PubMed Central

Bennett, Robbie J.; Vukmanovic, Zoja; Solano-Alvarez, Wilberth; Lainé, Steven J.; Einsle, Joshua F.; Midgley, Paul A.; Rae, Catherine M. F.; Hielscher, Ralf

2017-01-01

Determining the local orientation of crystals in engineering and geological materials has become routine with the advent of modern crystallographic mapping techniques. These techniques enable many thousands of orientation measurements to be made, directing attention towards how such orientation data are best studied. Here, we provide a guide to the visualization of misorientation data in three-dimensional vector spaces, reduced by crystal symmetry, to reveal crystallographic orientation relationships. Domains for all point group symmetries are presented and an analysis methodology is developed and applied to identify crystallographic relationships, indicated by clusters in the misorientation space, in examples from materials science and geology. This analysis aids the determination of active deformation mechanisms and evaluation of cluster centres and spread enables more accurate description of transformation processes supporting arguments regarding provenance. PMID:29118660

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.

Filamentation instability in a quantum plasma

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

Bret, A.

2007-08-15

The growth rate of the filamentation instability triggered when a diluted cold electron beam passes through a cold plasma is evaluated using the quantum hydrodynamic equations. Compared with a cold fluid model, quantum effects reduce both the unstable wave vector domain and the maximum growth rate. Stabilization of large wave vector modes is always achieved, but significant reduction of the maximum growth rate depends on a dimensionless parameter that is provided. Although calculations are extended to the relativistic regime, they are mostly relevant to the nonrelativistic one.

Acoustic 3D modeling by the method of integral equations

NASA Astrophysics Data System (ADS)

Malovichko, M.; Khokhlov, N.; Yavich, N.; Zhdanov, M.

2018-02-01

This paper presents a parallel algorithm for frequency-domain acoustic modeling by the method of integral equations (IE). The algorithm is applied to seismic simulation. The IE method reduces the size of the problem but leads to a dense system matrix. A tolerable memory consumption and numerical complexity were achieved by applying an iterative solver, accompanied by an effective matrix-vector multiplication operation, based on the fast Fourier transform (FFT). We demonstrate that, the IE system matrix is better conditioned than that of the finite-difference (FD) method, and discuss its relation to a specially preconditioned FD matrix. We considered several methods of matrix-vector multiplication for the free-space and layered host models. The developed algorithm and computer code were benchmarked against the FD time-domain solution. It was demonstrated that, the method could accurately calculate the seismic field for the models with sharp material boundaries and a point source and receiver located close to the free surface. We used OpenMP to speed up the matrix-vector multiplication, while MPI was used to speed up the solution of the system equations, and also for parallelizing across multiple sources. The practical examples and efficiency tests are presented as well.

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

NASA Astrophysics Data System (ADS)

Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

2017-04-01

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

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.

PubMed

Lei, Tailong; Sun, Huiyong; Kang, Yu; Zhu, Feng; Liu, Hui; Zhou, Wenfang; Wang, Zhe; Li, Dan; Li, Youyong; Hou, Tingjun

2017-11-06

Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure-activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (q ext 2 = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity.

The Polerovirus Minor Capsid Protein Determines Vector Specificity and Intestinal Tropism in the Aphid

PubMed Central

Brault, Véronique; Périgon, Sophie; Reinbold, Catherine; Erdinger, Monique; Scheidecker, Danièle; Herrbach, Etienne; Richards, Ken; Ziegler-Graff, Véronique

2005-01-01

Aphid transmission of poleroviruses is highly specific, but the viral determinants governing this specificity are unknown. We used a gene exchange strategy between two poleroviruses with different vectors, Beet western yellows virus (BWYV) and Cucurbit aphid-borne yellows virus (CABYV), to analyze the role of the major and minor capsid proteins in vector specificity. Virus recombinants obtained by exchanging the sequence of the readthrough domain (RTD) between the two viruses replicated in plant protoplasts and in whole plants. The hybrid readthrough protein of chimeric viruses was incorporated into virions. Aphid transmission experiments using infected plants or purified virions revealed that vector specificity is driven by the nature of the RTD. BWYV and CABYV have specific intestinal sites in the vectors for endocytosis: the midgut for BWYV and both midgut and hindgut for CABYV. Localization of hybrid virions in aphids by transmission electron microscopy revealed that gut tropism is also determined by the viral origin of the RTD. PMID:16014930

Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems

DTIC Science & Technology

2013-05-28

those of the support vector machine and relevance vector machine, and the model runs more quickly than the other algorithms . When one class occurs...incremental support vector machine algorithm for online learning when fewer than 50 data points are available. (a) Papers published in peer-reviewed journals...learning environments, where data processing occurs one observation at a time and the classification algorithm improves over time with new

A Split-GFP Gateway Cloning System for Topology Analyses of Membrane Proteins in Plants.

PubMed

Xie, Wenjun; Nielsen, Mads Eggert; Pedersen, Carsten; Thordal-Christensen, Hans

2017-01-01

To understand the function of membrane proteins, it is imperative to know their topology. For such studies, a split green fluorescent protein (GFP) method is useful. GFP is barrel-shaped, consisting of 11 β-sheets. When the first ten β-sheets (GFP1-10) and the 11th β-sheet (GFP11) are expressed from separate genes they will self-assembly and reconstitute a fluorescent GFP protein. However, this will only occur when the two domains co-localize in the same cellular compartment. We have developed an easy-to-use Gateway vector set for determining on which side of the membrane the N- and C-termini are located. Two vectors were designed for making N- and C-terminal fusions between the membrane proteins-of-interest and GFP11, while another three plasmids were designed to express GFP1-10 in either the cytosol, the endoplasmic reticulum (ER) lumen or the apoplast. We tested functionality of the system by applying the vector set for the transmembrane domain, CNXTM, of the ER membrane protein, calnexin, after transient expression in Nicotiana benthamiana leaves. We observed GFP signal from the ER when we reciprocally co-expressed GFP11-CNXTM with GFP1-10-HDEL and CNXTM-GFP with cytosolic GFP1-10. The opposite combinations did not result in GFP signal emission. This test using the calnexin ER-membrane domain demonstrated its C-terminus to be in the cytosol and its N-terminus in the ER lumen. This result confirmed the known topology of calnexin, and we therefore consider this split-GFP system highly useful for ER membrane topology studies. Furthermore, the vector set provided is useful for detecting the topology of proteins on other membranes in the cell, which we confirmed for a plasma membrane syntaxin. The set of five Ti-plasmids are easily and efficiently used for Gateway cloning and transient transformation of N. benthamiana leaves.

Full Wave Analysis of RF Signal Attenuation in a Lossy Rough Surface Cave using a High Order Time Domain Vector Finite Element Method

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

Pingenot, J; Rieben, R; White, D

2005-10-31

We present a computational study of signal propagation and attenuation of a 200 MHz planar loop antenna in a cave environment. The cave is modeled as a straight and lossy random rough wall. To simulate a broad frequency band, the full wave Maxwell equations are solved directly in the time domain via a high order vector finite element discretization using the massively parallel CEM code EMSolve. The numerical technique is first verified against theoretical results for a planar loop antenna in a smooth lossy cave. The simulation is then performed for a series of random rough surface meshes in ordermore » to generate statistical data for the propagation and attenuation properties of the antenna in a cave environment. Results for the mean and variance of the power spectral density of the electric field are presented and discussed.« less

Implicit flux-split schemes for the Euler equations

NASA Technical Reports Server (NTRS)

Thomas, J. L.; Walters, R. W.; Van Leer, B.

1985-01-01

Recent progress in the development of implicit algorithms for the Euler equations using the flux-vector splitting method is described. Comparisons of the relative efficiency of relaxation and spatially-split approximately factored methods on a vector processor for two-dimensional flows are made. For transonic flows, the higher convergence rate per iteration of the Gauss-Seidel relaxation algorithms, which are only partially vectorizable, is amply compensated for by the faster computational rate per iteration of the approximately factored algorithm. For supersonic flows, the fully-upwind line-relaxation method is more efficient since the numerical domain of dependence is more closely matched to the physical domain of dependence. A hybrid three-dimensional algorithm using relaxation in one coordinate direction and approximate factorization in the cross-flow plane is developed and applied to a forebody shape at supersonic speeds and a swept, tapered wing at transonic speeds.

Vector-model-supported approach in prostate plan optimization

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

Liu, Eva Sau Fan; Department of Health Technology and Informatics, The Hong Kong Polytechnic University; Wu, Vincent Wing Cheung

Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100more » previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number without compromising the plan quality.« less

A helper virus-free HSV-1 vector containing the vesicular glutamate transporter-1 promoter supports expression preferentially in VGLUT1-containing glutamatergic neurons.

PubMed

Zhang, Guo-rong; Geller, Alfred I

2010-05-17

Multiple potential uses of direct gene transfer into neurons require restricting expression to specific classes of glutamatergic neurons. Thus, it is desirable to develop vectors containing glutamatergic class-specific promoters. The three vesicular glutamate transporters (VGLUTs) are expressed in distinct populations of neurons, and VGLUT1 is the predominant VGLUT in the neocortex, hippocampus, and cerebellar cortex. We previously reported a plasmid (amplicon) Herpes Simplex Virus (HSV-1) vector that placed the Lac Z gene under the regulation of the VGLUT1 promoter (pVGLUT1lac). Using helper virus-free vector stocks, we showed that this vector supported approximately 90% glutamatergic neuron-specific expression in postrhinal (POR) cortex, in rats sacrificed at either 4 days or 2 months after gene transfer. We now show that pVGLUT1lac supports expression preferentially in VGLUT1-containing glutamatergic neurons. pVGLUT1lac vector stock was injected into either POR cortex, which contains primarily VGLUT1-containing glutamatergic neurons, or into the ventral medial hypothalamus (VMH), which contains predominantly VGLUT2-containing glutamatergic neurons. Rats were sacrificed at 4 days after gene transfer, and the types of cells expressing ss-galactosidase were determined by immunofluorescent costaining. Cell counts showed that pVGLUT1lac supported expression in approximately 10-fold more cells in POR cortex than in the VMH, whereas a control vector supported expression in similar numbers of cells in these two areas. Further, in POR cortex, pVGLUT1lac supported expression predominately in VGLUT1-containing neurons, and, in the VMH, pVGLUT1lac showed an approximately 10-fold preference for the rare VGLUT1-containing neurons. VGLUT1-specific expression may benefit specific experiments on learning or specific gene therapy approaches, particularly in the neocortex. Copyright 2010 Elsevier B.V. All rights reserved.

MEVA--An Interactive Visualization Application for Validation of Multifaceted Meteorological Data with Multiple 3D Devices.

PubMed

Helbig, Carolin; Bilke, Lars; Bauer, Hans-Stefan; Böttinger, Michael; Kolditz, Olaf

2015-01-01

To achieve more realistic simulations, meteorologists develop and use models with increasing spatial and temporal resolution. The analyzing, comparing, and visualizing of resulting simulations becomes more and more challenging due to the growing amounts and multifaceted character of the data. Various data sources, numerous variables and multiple simulations lead to a complex database. Although a variety of software exists suited for the visualization of meteorological data, none of them fulfills all of the typical domain-specific requirements: support for quasi-standard data formats and different grid types, standard visualization techniques for scalar and vector data, visualization of the context (e.g., topography) and other static data, support for multiple presentation devices used in modern sciences (e.g., virtual reality), a user-friendly interface, and suitability for cooperative work. Instead of attempting to develop yet another new visualization system to fulfill all possible needs in this application domain, our approach is to provide a flexible workflow that combines different existing state-of-the-art visualization software components in order to hide the complexity of 3D data visualization tools from the end user. To complete the workflow and to enable the domain scientists to interactively visualize their data without advanced skills in 3D visualization systems, we developed a lightweight custom visualization application (MEVA - multifaceted environmental data visualization application) that supports the most relevant visualization and interaction techniques and can be easily deployed. Specifically, our workflow combines a variety of different data abstraction methods provided by a state-of-the-art 3D visualization application with the interaction and presentation features of a computer-games engine. Our customized application includes solutions for the analysis of multirun data, specifically with respect to data uncertainty and differences between simulation runs. In an iterative development process, our easy-to-use application was developed in close cooperation with meteorologists and visualization experts. The usability of the application has been validated with user tests. We report on how this application supports the users to prove and disprove existing hypotheses and discover new insights. In addition, the application has been used at public events to communicate research results.

MEVA - An Interactive Visualization Application for Validation of Multifaceted Meteorological Data with Multiple 3D Devices

PubMed Central

Helbig, Carolin; Bilke, Lars; Bauer, Hans-Stefan; Böttinger, Michael; Kolditz, Olaf

2015-01-01

Background To achieve more realistic simulations, meteorologists develop and use models with increasing spatial and temporal resolution. The analyzing, comparing, and visualizing of resulting simulations becomes more and more challenging due to the growing amounts and multifaceted character of the data. Various data sources, numerous variables and multiple simulations lead to a complex database. Although a variety of software exists suited for the visualization of meteorological data, none of them fulfills all of the typical domain-specific requirements: support for quasi-standard data formats and different grid types, standard visualization techniques for scalar and vector data, visualization of the context (e.g., topography) and other static data, support for multiple presentation devices used in modern sciences (e.g., virtual reality), a user-friendly interface, and suitability for cooperative work. Methods and Results Instead of attempting to develop yet another new visualization system to fulfill all possible needs in this application domain, our approach is to provide a flexible workflow that combines different existing state-of-the-art visualization software components in order to hide the complexity of 3D data visualization tools from the end user. To complete the workflow and to enable the domain scientists to interactively visualize their data without advanced skills in 3D visualization systems, we developed a lightweight custom visualization application (MEVA - multifaceted environmental data visualization application) that supports the most relevant visualization and interaction techniques and can be easily deployed. Specifically, our workflow combines a variety of different data abstraction methods provided by a state-of-the-art 3D visualization application with the interaction and presentation features of a computer-games engine. Our customized application includes solutions for the analysis of multirun data, specifically with respect to data uncertainty and differences between simulation runs. In an iterative development process, our easy-to-use application was developed in close cooperation with meteorologists and visualization experts. The usability of the application has been validated with user tests. We report on how this application supports the users to prove and disprove existing hypotheses and discover new insights. In addition, the application has been used at public events to communicate research results. PMID:25915061

Role of MUC4-NIDO domain in the MUC4-mediated metastasis of pancreatic cancer cells

PubMed Central

Senapati, Shantibhusan; Gnanapragassam, Vinayaga Srinivasan; Moniaux, Nicolas; Momi, Navneet; Batra, Surinder K.

2011-01-01

MUC4 is a large transmembrane type I glycoprotein that is overexpressed in pancreatic cancer (PC) and has been shown to be associated with its progression and metastasis. However, the exact cellular and molecular mechanism(s) through which MUC4 promotes metastasis of PC cells has been sparsely studied. Here we showed that the NIDO domain of MUC4, which is similar to the G1-domain present in the nidogen or entactin (an extracellular matrix protein), contributes to the protein-protein interaction property of MUC4. By this interaction, MUC4 promotes breaching of basement membrane integrity, and spreading of cancer cells. These observations are corroborated with the data from our study using an engineered MUC4 protein without the NIDO domain, which was ectopically expressed in the MiaPaCa PC cells, lacking endogenous MUC4 and nidogen protein. The in vitro studies demonstrated an enhanced invasiveness of MiaPaCa cells expressing MUC4 (MiaPaCa-MUC4) compared to vector-transfected cells (MiaPaCa-Vec; p=0.003) or cells expressing MUC4 without the NIDO domain (MiaPaCa-MUC4-NIDOΔ; p=0.03). However, the absence of NIDO-domain has no significant role on cell growth and motility (p=0.93). In the in-vivo studies, all the mice orthotopically implanted with MiPaCa-MUC4 cells developed metastasis to the liver as compared to MiaPaCa-Vec or the MiaPaCa-MUC4-NIDOΔ group, hence, supporting our in vitro observations. Additionally, a reduced binding (p=0.0004) of MiaPaCa-MUC4-NIDOΔ cells to the fibulin-2 coated plates compared to MiaPaCa-MUC4 cells indicated a possible interaction between the MUC4-NIDO domain and fibulin-2, a nidogen-interacting protein. Furthermore, in PC tissue samples, MUC4 colocalized with the fibulin-2 present in the basement membrane. Altogether, our findings demonstrate that the MUC4-NIDO domain significantly contributes to the MUC4-mediated metastasis of PC cells. This may be partly due to the interaction between the MUC4-NIDO domain and fibulin-2. PMID:22105367

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

NASA Astrophysics Data System (ADS)

Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

2017-07-01

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

Vector quantizer based on brightness maps for image compression with the polynomial transform

NASA Astrophysics Data System (ADS)

Escalante-Ramirez, Boris; Moreno-Gutierrez, Mauricio; Silvan-Cardenas, Jose L.

2002-11-01

We present a vector quantization scheme acting on brightness fields based on distance/distortion criteria correspondent with psycho-visual aspects. These criteria quantify sensorial distortion between vectors that represent either portions of a digital image or alternatively, coefficients of a transform-based coding system. In the latter case, we use an image representation model, namely the Hermite transform, that is based on some of the main perceptual characteristics of the human vision system (HVS) and in their response to light stimulus. Energy coding in the brightness domain, determination of local structure, code-book training and local orientation analysis are all obtained by means of the Hermite transform. This paper, for thematic reasons, is divided in four sections. The first one will shortly highlight the importance of having newer and better compression algorithms. This section will also serve to explain briefly the most relevant characteristics of the HVS, advantages and disadvantages related with the behavior of our vision in front of ocular stimulus. The second section shall go through a quick review of vector quantization techniques, focusing their performance on image treatment, as a preview for the image vector quantizer compressor actually constructed in section 5. Third chapter was chosen to concentrate the most important data gathered on brightness models. The building of this so-called brightness maps (quantification of the human perception on the visible objects reflectance), in a bi-dimensional model, will be addressed here. The Hermite transform, a special case of polynomial transforms, and its usefulness, will be treated, in an applicable discrete form, in the fourth chapter. As we have learned from previous works 1, Hermite transform has showed to be a useful and practical solution to efficiently code the energy within an image block, deciding which kind of quantization is to be used upon them (whether scalar or vector). It will also be a unique tool to structurally classify the image block within a given lattice. This particular operation intends to be one of the main contributions of this work. The fifth section will fuse the proposals derived from the study of the three main topics- addressed in the last sections- in order to propose an image compression model that takes advantage of vector quantizers inside the brightness transformed domain to determine the most important structures, finding the energy distribution inside the Hermite domain. Sixth and last section will show some results obtained while testing the coding-decoding model. The guidelines to evaluate the image compressing performance were the compression ratio, SNR and psycho-visual quality. Some conclusions derived from the research and possible unexplored paths will be shown on this section as well.

Signal processing applications of massively parallel charge domain computing devices

NASA Technical Reports Server (NTRS)

Fijany, Amir (Inventor); Barhen, Jacob (Inventor); Toomarian, Nikzad (Inventor)

1999-01-01

The present invention is embodied in a charge coupled device (CCD)/charge injection device (CID) architecture capable of performing a Fourier transform by simultaneous matrix vector multiplication (MVM) operations in respective plural CCD/CID arrays in parallel in O(1) steps. For example, in one embodiment, a first CCD/CID array stores charge packets representing a first matrix operator based upon permutations of a Hartley transform and computes the Fourier transform of an incoming vector. A second CCD/CID array stores charge packets representing a second matrix operator based upon different permutations of a Hartley transform and computes the Fourier transform of an incoming vector. The incoming vector is applied to the inputs of the two CCD/CID arrays simultaneously, and the real and imaginary parts of the Fourier transform are produced simultaneously in the time required to perform a single MVM operation in a CCD/CID array.

On load paths and load bearing topology from finite element analysis

NASA Astrophysics Data System (ADS)

Kelly, D.; Reidsema, C.; Lee, M.

2010-06-01

Load paths can be mapped from vector plots of 'pointing stress vectors'. They define a path along which a component of load remains constant as it traverses the solution domain. In this paper the theory for the paths is first defined. Properties of the plots that enable a designer to interpret the structural behavior from the contours are then identified. Because stress is a second order tensor defined on an orthogonal set of axes, the vector plots define separate paths for load transfer in each direction of the set of axes. An algorithm is therefore presented that combines the vectors to define a topology to carry the loads. The algorithm is shown to straighten the paths reducing bending moments and removing stress concentration. Application to a bolted joint, a racing car body and a yacht hull demonstrate the usefulness of the plots.

A Domain Decomposition Parallelization of the Fast Marching Method

NASA Technical Reports Server (NTRS)

Herrmann, M.

2003-01-01

In this paper, the first domain decomposition parallelization of the Fast Marching Method for level sets has been presented. Parallel speedup has been demonstrated in both the optimal and non-optimal domain decomposition case. The parallel performance of the proposed method is strongly dependent on load balancing separately the number of nodes on each side of the interface. A load imbalance of nodes on either side of the domain leads to an increase in communication and rollback operations. Furthermore, the amount of inter-domain communication can be reduced by aligning the inter-domain boundaries with the interface normal vectors. In the case of optimal load balancing and aligned inter-domain boundaries, the proposed parallel FMM algorithm is highly efficient, reaching efficiency factors of up to 0.98. Future work will focus on the extension of the proposed parallel algorithm to higher order accuracy. Also, to further enhance parallel performance, the coupling of the domain decomposition parallelization to the G(sub 0)-based parallelization will be investigated.

Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology.

PubMed

Hipp, Jason D; Cheng, Jerome Y; Toner, Mehmet; Tompkins, Ronald G; Balis, Ulysses J

2011-02-26

HISTORICALLY, EFFECTIVE CLINICAL UTILIZATION OF IMAGE ANALYSIS AND PATTERN RECOGNITION ALGORITHMS IN PATHOLOGY HAS BEEN HAMPERED BY TWO CRITICAL LIMITATIONS: 1) the availability of digital whole slide imagery data sets and 2) a relative domain knowledge deficit in terms of application of such algorithms, on the part of practicing pathologists. With the advent of the recent and rapid adoption of whole slide imaging solutions, the former limitation has been largely resolved. However, with the expectation that it is unlikely for the general cohort of contemporary pathologists to gain advanced image analysis skills in the short term, the latter problem remains, thus underscoring the need for a class of algorithm that has the concurrent properties of image domain (or organ system) independence and extreme ease of use, without the need for specialized training or expertise. In this report, we present a novel, general case pattern recognition algorithm, Spatially Invariant Vector Quantization (SIVQ), that overcomes the aforementioned knowledge deficit. Fundamentally based on conventional Vector Quantization (VQ) pattern recognition approaches, SIVQ gains its superior performance and essentially zero-training workflow model from its use of ring vectors, which exhibit continuous symmetry, as opposed to square or rectangular vectors, which do not. By use of the stochastic matching properties inherent in continuous symmetry, a single ring vector can exhibit as much as a millionfold improvement in matching possibilities, as opposed to conventional VQ vectors. SIVQ was utilized to demonstrate rapid and highly precise pattern recognition capability in a broad range of gross and microscopic use-case settings. With the performance of SIVQ observed thus far, we find evidence that indeed there exist classes of image analysis/pattern recognition algorithms suitable for deployment in settings where pathologists alone can effectively incorporate their use into clinical workflow, as a turnkey solution. We anticipate that SIVQ, and other related class-independent pattern recognition algorithms, will become part of the overall armamentarium of digital image analysis approaches that are immediately available to practicing pathologists, without the need for the immediate availability of an image analysis expert.

A hybrid approach to select features and classify diseases based on medical data

NASA Astrophysics Data System (ADS)

AbdelLatif, Hisham; Luo, Jiawei

2018-03-01

Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms

Alpharetroviral Self-inactivating Vectors: Long-term Transgene Expression in Murine Hematopoietic Cells and Low Genotoxicity

PubMed Central

Suerth, Julia D; Maetzig, Tobias; Brugman, Martijn H; Heinz, Niels; Appelt, Jens-Uwe; Kaufmann, Kerstin B; Schmidt, Manfred; Grez, Manuel; Modlich, Ute; Baum, Christopher; Schambach, Axel

2012-01-01

Comparative integrome analyses have highlighted alpharetroviral vectors with a relatively neutral, and thus favorable, integration spectrum. However, previous studies used alpharetroviral vectors harboring viral coding sequences and intact long-terminal repeats (LTRs). We recently developed self-inactivating (SIN) alpharetroviral vectors with an advanced split-packaging design. In a murine bone marrow (BM) transplantation model we now compared alpharetroviral, gammaretroviral, and lentiviral SIN vectors and showed that all vectors transduced hematopoietic stem cells (HSCs), leading to comparable, sustained multilineage transgene expression in primary and secondary transplanted mice. Alpharetroviral integrations were decreased near transcription start sites, CpG islands, and potential cancer genes compared with gammaretroviral, and decreased in genes compared with lentiviral integrations. Analyzing the transcriptome and intragenic integrations in engrafting cells, we observed stronger correlations between in-gene integration targeting and transcriptional activity for gammaretroviral and lentiviral vectors than for alpharetroviral vectors. Importantly, the relatively “extragenic” alpharetroviral integration pattern still supported long-term transgene expression upon serial transplantation. Furthermore, sensitive genotoxicity studies revealed a decreased immortalization incidence compared with gammaretroviral and lentiviral SIN vectors. We conclude that alpharetroviral SIN vectors have a favorable integration pattern which lowers the risk of insertional mutagenesis while supporting long-term transgene expression in the progeny of transplanted HSCs. PMID:22334016

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.

Symmetry breaking and electrical frustration during tip-induced polarization switching in the non-polar cut of lithium niobate single crystals

DOE PAGES

Ievlev, Anton; Alikin, Denis O.; Morozovska, A. N.; ...

2014-12-15

Polarization switching in ferroelectric materials is governed by a delicate interplay between bulk polarization dynamics and screening processes at surfaces and domain walls. Here we explore the mechanism of tip-induced polarization switching in the non-polar cuts of uniaxial ferroelectrics. In this case, in-plane component of polarization vector switches, allowing for detailed observations of resultant domain morphologies. We observe surprising variability of resultant domain morphologies stemming from fundamental instability of formed charged domain wall and associated electric frustration. In particular, we demonstrate that controlling vertical tip position allows the polarity of the switching to be controlled. This represents very unusual formmore » of symmetry breaking where mechanical motion in vertical direction controls the lateral domain growth. The implication of these studies for ferroelectric devices and domain wall electronics are discussed.« less

Electron backscatter diffraction as a domain analysis technique in BiFeO(3)-PbTiO(3) single crystals.

PubMed

Burnett, T L; Comyn, T P; Merson, E; Bell, A J; Mingard, K; Hegarty, T; Cain, M

2008-05-01

xBiFeO(3)-(1-x)PbTiO(3) single crystals were grown via a flux method for a range of compositions. Presented here is a study of the domain configuration in the 0.5BiFeO(3)-0.5PbTiO(3) composition using electron backscatter diffraction to demonstrate the ability of the technique to map ferroelastic domain structures at the micron and submicron scale. The micron-scale domains exhibit an angle of approximately 85 degrees between each variant, indicative of a ferroelastic domain wall in a tetragonal system with a spontaneous strain, c/a - 1 of 0.10, in excellent agreement with the lattice parameters derived from x-ray diffraction. Contrast seen in forescatter images is attributed to variations in the direction of the electrical polarization vector, providing images of ferroelectric domain patterns.

Conserved Receptor-Binding Domains of Lake Victoria Marburgvirus and Zaire Ebolavirus Bind a Shared Receptor

DTIC Science & Technology

2006-04-14

virion, because of the functional importance of and limited variation in this region (44, 45). In some cases, such as murine and feline leukemia viruses ...murine leukemia virus ; PBS, phos- phate-buffered saline; RBD, receptor-binding domain; SARS, severe acute respiratory syndrome; VSV, vesicular stomatitis...entryofpseudotypedret- roviruses. A Moloney murine leukemia virus vector expressing GFP was pseudotyped with the GP1,2 of MARV-Mus (MARV/MLV), a mucin-like

Targeting of GLUT1-GLUT5 chimeric proteins in the polarized cell line Caco-2.

PubMed

Inukai, K; Takata, K; Asano, T; Katagiri, H; Ishihara, H; Nakazaki, M; Fukushima, Y; Yazaki, Y; Kikuchi, M; Oka, Y

1997-04-01

Caco-2, a human differentiated intestinal epithelial cell line, is a promising model for investigating the mechanism of polarized targeting of apical and basolateral membrane proteins. We stably transfected rat GLUT5 cDNA and rabbit GLUT1 cDNA into Caco-2 cells with an expression vector. Immunohistochemical study revealed that the GLUT5 protein expressed was localized at apical membranes and that the GLUT1 expressed was present primarily in the basolateral membranes of cells grown on permeable support. Next, to investigate the domain responsible for determining apical vs. basolateral sorting in glucose transporters, we prepared several GLUT1-GLUT5 chimeric cDNAs and transfected them into Caco-2 cells. A GLUT1 [N terminus approximately sixth transmembrane domain (TM6)]-GLUT5 [intracellular loop (IL) approximately C terminus] chimera was observed exclusively at the apical membrane, while GLUT1 (N terminus approximately IL)-GLUT5 (TM7 approximately C terminus) and GLUT1 (N terminus approximately TM12)-GLUT5 (C-terminal domain) chimeras were observed mainly at the basolateral membrane, a localization similar to that of GLUT1. Moreover, using a recombinant adenovirus expression system, we expressed a GLUT5 (N terminus approximately TM6)-GLUT1(IL)-GLUT5(TM7 approximately C-terminus) chimera, which was observed at the basolateral membrane. Based on these results, the C-terminal domain does not determine isoform-specific targeting of GLUT1 and GLUT5. Rather, it is the intracellular loop in glucose transporters that appears to play a pivotal role in apical-basolateral sorting signals in Caco-2 cells.

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

PubMed

Jauregi Unanue, Iñigo; Zare Borzeshi, Ehsan; Piccardi, Massimo

2017-12-01

Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings". (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluation of a wave-vector-frequency-domain method for nonlinear wave propagation

PubMed Central

Jing, Yun; Tao, Molei; Clement, Greg T.

2011-01-01

A wave-vector-frequency-domain method is presented to describe one-directional forward or backward acoustic wave propagation in a nonlinear homogeneous medium. Starting from a frequency-domain representation of the second-order nonlinear acoustic wave equation, an implicit solution for the nonlinear term is proposed by employing the Green’s function. Its approximation, which is more suitable for numerical implementation, is used. An error study is carried out to test the efficiency of the model by comparing the results with the Fubini solution. It is shown that the error grows as the propagation distance and step-size increase. However, for the specific case tested, even at a step size as large as one wavelength, sufficient accuracy for plane-wave propagation is observed. A two-dimensional steered transducer problem is explored to verify the nonlinear acoustic field directional independence of the model. A three-dimensional single-element transducer problem is solved to verify the forward model by comparing it with an existing nonlinear wave propagation code. Finally, backward-projection behavior is examined. The sound field over a plane in an absorptive medium is backward projected to the source and compared with the initial field, where good agreement is observed. PMID:21302985

Efficient combination of a 3D Quasi-Newton inversion algorithm and a vector dual-primal finite element tearing and interconnecting method

NASA Astrophysics Data System (ADS)

Voznyuk, I.; Litman, A.; Tortel, H.

2015-08-01

A Quasi-Newton method for reconstructing the constitutive parameters of three-dimensional (3D) penetrable scatterers from scattered field measurements is presented. This method is adapted for handling large-scale electromagnetic problems while keeping the memory requirement and the time flexibility as low as possible. The forward scattering problem is solved by applying the finite-element tearing and interconnecting full-dual-primal (FETI-FDP2) method which shares the same spirit as the domain decomposition methods for finite element methods. The idea is to split the computational domain into smaller non-overlapping sub-domains in order to simultaneously solve local sub-problems. Various strategies are proposed in order to efficiently couple the inversion algorithm with the FETI-FDP2 method: a separation into permanent and non-permanent subdomains is performed, iterative solvers are favorized for resolving the interface problem and a marching-on-in-anything initial guess selection further accelerates the process. The computational burden is also reduced by applying the adjoint state vector methodology. Finally, the inversion algorithm is confronted to measurements extracted from the 3D Fresnel database.

A new Method for the Estimation of Initial Condition Uncertainty Structures in Mesoscale Models

NASA Astrophysics Data System (ADS)

Keller, J. D.; Bach, L.; Hense, A.

2012-12-01

The estimation of fast growing error modes of a system is a key interest of ensemble data assimilation when assessing uncertainty in initial conditions. Over the last two decades three methods (and variations of these methods) have evolved for global numerical weather prediction models: ensemble Kalman filter, singular vectors and breeding of growing modes (or now ensemble transform). While the former incorporates a priori model error information and observation error estimates to determine ensemble initial conditions, the latter two techniques directly address the error structures associated with Lyapunov vectors. However, in global models these structures are mainly associated with transient global wave patterns. When assessing initial condition uncertainty in mesoscale limited area models, several problems regarding the aforementioned techniques arise: (a) additional sources of uncertainty on the smaller scales contribute to the error and (b) error structures from the global scale may quickly move through the model domain (depending on the size of the domain). To address the latter problem, perturbation structures from global models are often included in the mesoscale predictions as perturbed boundary conditions. However, the initial perturbations (when used) are often generated with a variant of an ensemble Kalman filter which does not necessarily focus on the large scale error patterns. In the framework of the European regional reanalysis project of the Hans-Ertel-Center for Weather Research we use a mesoscale model with an implemented nudging data assimilation scheme which does not support ensemble data assimilation at all. In preparation of an ensemble-based regional reanalysis and for the estimation of three-dimensional atmospheric covariance structures, we implemented a new method for the assessment of fast growing error modes for mesoscale limited area models. The so-called self-breeding is development based on the breeding of growing modes technique. Initial perturbations are integrated forward for a short time period and then rescaled and added to the initial state again. Iterating this rapid breeding cycle provides estimates for the initial uncertainty structure (or local Lyapunov vectors) given a specific norm. To avoid that all ensemble perturbations converge towards the leading local Lyapunov vector we apply an ensemble transform variant to orthogonalize the perturbations in the sub-space spanned by the ensemble. By choosing different kind of norms to measure perturbation growth, this technique allows for estimating uncertainty patterns targeted at specific sources of errors (e.g. convection, turbulence). With case study experiments we show applications of the self-breeding method for different sources of uncertainty and different horizontal scales.

Identifying saltcedar with hyperspectral data and support vector machines

USDA-ARS?s Scientific Manuscript database

Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

A FRET-Based Method for Probing the Conformational Behavior of an Intrinsically Disordered Repeat Domain from Bordetella pertussis Adenylate Cyclase

DTIC Science & Technology

2009-10-22

ELEMENT NUMBER 6. AUTHOR( S ) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES...MONITORING AGENCY NAME( S ) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM( S ) 11. SPONSOR/MONITOR’S REPORT NUMBER( S ) 12. DISTRIBUTION/AVAILABILITY STATEMENT...fusion protein C*-BR( S )-Y* expression vector pET/C*-CyaA1488-1680-Y*, nonfluorescent CFP expression vector pET/CFP*, and the maltose binding protein-RTX

Applying spectral unmixing and support vector machine to airborne hyperspectral imagery for detecting giant reed

USDA-ARS?s Scientific Manuscript database

This study evaluated linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and support vector machine (SVM) techniques for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern ...

Support vector machines classifiers of physical activities in preschoolers

USDA-ARS?s Scientific Manuscript database

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier

USDA-ARS?s Scientific Manuscript database

This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and an optimized support-vector-machine (SVM) classification scheme to characterize and classify wrinkle appearance of fabric. Fabric images were decomposed with the wavelet transform (WT), and five parame...

Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

EPA Science Inventory

Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database

PubMed Central

Butkiewicz, Mariusz; Lowe, Edward W.; Mueller, Ralf; Mendenhall, Jeffrey L.; Teixeira, Pedro L.; Weaver, C. David; Meiler, Jens

2013-01-01

With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed. PMID:23299552

Automatic detection of atrial fibrillation in cardiac vibration signals.

PubMed

Brueser, C; Diesel, J; Zink, M D H; Winter, S; Schauerte, P; Leonhardt, S

2013-01-01

We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs.

PubMed

Balamurugan, P; Shevade, Shirish; Sundararajan, S

2015-10-01

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion

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

Skraba, Primoz; Rosen, Paul; Wang, Bei

Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with amore » guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. Here, we apply our method to synthetic and simulation datasets to demonstrate its effectiveness.« less

Metabolic biotinylation of lentiviral pseudotypes for scalable paramagnetic microparticle-dependent manipulation.

PubMed

Nesbeth, Darren; Williams, Sharon L; Chan, Lucas; Brain, Tony; Slater, Nigel K H; Farzaneh, Farzin; Darling, David

2006-04-01

Nonviral, host-derived proteins on lentiviral vector surfaces can have a profound effect on the vector's biology as they can both promote infection and provide resistance to complement inactivation. We have exploited this to engineer a specific posttranslational modification of a "nonenvelope," virally associated protein. The bacterial biotin ligase (BirA) and a modified human DeltaLNGFR have been introduced into HEK293T cells and their protein products directed to the lumen of the endoplasmic reticulum. The BirA then couples biotin to an acceptor peptide that has been fused to the DeltaLNGFR. This results in the covalent linkage of biotin to the extracellular domain of the DeltaLNGFR expressed on the cell surface. Lentiviral vectors from these cells are metabolically labeled with biotin in the presence of free biotin. These biotinylated lentiviral vectors have a high affinity for streptavidin paramagnetic particles and, once captured, are easily manipulated in vitro. This is illustrated by the concentration of lentiviral vectors pseudotyped with either the VSV-G or an amphotropic envelope in excess of 4500-fold. This new cell line has the potential for widespread application to envelope pseudotypes compatible with lentiviral vector production.

Native and engineered tropism of vectors derived from a rare species D adenovirus serotype 43.

PubMed

Belousova, Natalya; Mikheeva, Galina; Xiong, Chiyi; Stagg, Loren J; Gagea, Mihai; Fox, Patricia S; Bassett, Roland L; Ladbury, John E; Braun, Michael B; Stehle, Thilo; Li, Chun; Krasnykh, Victor

2016-08-16

Unique molecular properties of species D adenoviruses (Ads)-the most diverse yet underexplored group of Ads-have been used to develop improved gene vectors. The low seroprevalence in humans of adenovirus serotype 43 (Ad43), an otherwise unstudied species D Ad, identified this rare serotype as an attractive new human gene therapy vector platform. Thus, in this study we wished to assess biological properties of Ad43 essential to its vectorization. We found that (1) Ad43 virions do not bind blood coagulation factor X and cause low random transduction upon vascular delivery; (2) they clear host tissues more quickly than do traditionally used Ad5 vectors; (3) Ad43 uses CD46 as primary receptor; (4) Ad43 can use integrins as alternative primary receptors. As the first step toward vectorization of Ad43, we demonstrated that the primary receptor specificity of the Ad43 fiber can be altered to achieve infection via Her2, an established oncotarget. Whereas this modification required use of the Ad5 fiber shaft, the presence of this domain in chimeric virions did not make them susceptible for neutralization by anti-Ad5 antibodies.

Native and engineered tropism of vectors derived from a rare species D adenovirus serotype 43

PubMed Central

Belousova, Natalya; Mikheeva, Galina; Xiong, Chiyi; Stagg, Loren J.; Gagea, Mihai; Fox, Patricia S.; Bassett, Roland L.; Ladbury, John E.; Braun, Michael B.; Stehle, Thilo; Li, Chun; Krasnykh, Victor

2016-01-01

Unique molecular properties of species D adenoviruses (Ads)—the most diverse yet underexplored group of Ads—have been used to develop improved gene vectors. The low seroprevalence in humans of adenovirus serotype 43 (Ad43), an otherwise unstudied species D Ad, identified this rare serotype as an attractive new human gene therapy vector platform. Thus, in this study we wished to assess biological properties of Ad43 essential to its vectorization. We found that (1) Ad43 virions do not bind blood coagulation factor X and cause low random transduction upon vascular delivery; (2) they clear host tissues more quickly than do traditionally used Ad5 vectors; (3) Ad43 uses CD46 as primary receptor; (4) Ad43 can use integrins as alternative primary receptors. As the first step toward vectorization of Ad43, we demonstrated that the primary receptor specificity of the Ad43 fiber can be altered to achieve infection via Her2, an established oncotarget. Whereas this modification required use of the Ad5 fiber shaft, the presence of this domain in chimeric virions did not make them susceptible for neutralization by anti-Ad5 antibodies. PMID:27462785

Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion.

PubMed

Skraba, Primoz; Rosen, Paul; Wang, Bei; Chen, Guoning; Bhatia, Harsh; Pascucci, Valerio

2016-02-29

Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with a guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. We apply our method to synthetic and simulation datasets to demonstrate its effectiveness.

Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion

DOE PAGES

Skraba, Primoz; Rosen, Paul; Wang, Bei; ...

2016-02-29

Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with amore » guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. Here, we apply our method to synthetic and simulation datasets to demonstrate its effectiveness.« less

An integrated vector system for cellular studies of phage display-derived peptides.

PubMed

Voss, Stephan D; DeGrand, Alec M; Romeo, Giulio R; Cantley, Lewis C; Frangioni, John V

2002-09-15

Peptide phage display is a method by which large numbers of diverse peptides can be screened for binding to a target of interest. Even when successful, the rate-limiting step is usually validation of peptide bioactivity using living cells. In this paper, we describe an integrated system of vectors that expedites both the screening and the characterization processes. Library construction and screening is performed using an optimized type 3 phage display vector, mJ(1), which is shown to accept peptide libraries of at least 23 amino acids in length. Peptide coding sequences are shuttled from mJ(1) into one of three families of mammalian expression vectors for cell physiological studies. The vector pAL(1) expresses phage display-derived peptides as Gal4 DNA binding domain fusion proteins for transcriptional activation studies. The vectors pG(1), pG(1)N, and pG(1)C express phage display-derived peptides as green fluorescent protein fusions targeted to the entire cell, nucleus, or cytoplasm, respectively. The vector pAP(1) expresses phage display-derived peptides as fusions to secreted placental alkaline phosphatase. Such enzyme fusions can be used as highly sensitive affinity reagents for high-throughput assays and for cloning of peptide-binding cell surface receptors. Taken together, this system of vectors should facilitate the development of phage display-derived peptides into useful biomolecules.

A Mapping of the Electron Localization Function for Earth Materials

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

Gibbs, Gerald V.; Cox, David F.; Ross, Nancy

2005-06-01

The electron localization function, ELF, generated for a number of geometry-optimized earth materials, provides a graphical representation of the spatial localization of the probability electron density distribution as embodied in domains ascribed to localized bond and lone pair electrons. The lone pair domains, displayed by the silica polymorphs quartz, coesite and cristobalite, are typically banana-shaped and oriented perpendicular to the plane of the SiOSi angle at ~0.60 Å from the O atom on the reflex side of the angle. With decreasing angle, the domains increase in magnitude, indicating an increase in the nucleophilic character of the O atom, rendering itmore » more susceptible to potential electrophilic attack. The Laplacian isosurface maps of the experimental and theoretical electron density distribution for coesite substantiates the increase in the size of the domain with decreasing angle. Bond pair domains are displayed along each of the SiO bond vectors as discrete concave hemispherically-shaped domains at ~0.70 Å from the O atom. For more closed-shell ionic bonded interactions, the bond and lone pair domains are often coalesced, resulting in concave hemispherical toroidal-shaped domains with local maxima centered along the bond vectors. As the shared covalent character of the bonded interactions increases, the bond and lone pair domains are better developed as discrete domains. ELF isosurface maps generated for the earth materials tremolite, diopside, talc and dickite display banana-shaped lone pair domains associated with the bridging O atoms of SiOSi angles and concave hemispherical toroidal bond pair domains associated with the nonbridging ones. The lone pair domains in dickite and talc provide a basis for understanding the bonded interactions between the adjacent neutral layers. Maps were also generated for beryl, cordierite, quartz, low albite, forsterite, wadeite, åkermanite, pectolite, periclase, hurlbutite, thortveitite and vanthoffite. Strategies are reviewed for finding potential H docking sites in the silica polymorphs and related materials. As observed in an earlier study, the ELF is capable of generating bond and lone pair domains that are similar in number and arrangement to those provided by Laplacian and deformation electron density distributions. The formation of the bond and lone pair domains in the silica polymorphs and the progressive decrease in the SiO length as the value of the electron density at the bond critical point increases indicates that the SiO bonded interaction has a substantial component of covalent character.« less

Nonlinear (time domain) and linearized (time and frequency domain) solutions to the compressible Euler equations in conservation law form

NASA Technical Reports Server (NTRS)

Sreenivas, Kidambi; Whitfield, David L.

1995-01-01

Two linearized solvers (time and frequency domain) based on a high resolution numerical scheme are presented. The basic approach is to linearize the flux vector by expressing it as a sum of a mean and a perturbation. This allows the governing equations to be maintained in conservation law form. A key difference between the time and frequency domain computations is that the frequency domain computations require only one grid block irrespective of the interblade phase angle for which the flow is being computed. As a result of this and due to the fact that the governing equations for this case are steady, frequency domain computations are substantially faster than the corresponding time domain computations. The linearized equations are used to compute flows in turbomachinery blade rows (cascades) arising due to blade vibrations. Numerical solutions are compared to linear theory (where available) and to numerical solutions of the nonlinear Euler equations.

Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis

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

Liu, Eva Sau Fan; Department of Health Technology and Informatics, The Hong Kong Polytechnic University; Wu, Vincent Wing Cheung

Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, followingmore » the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality.« less

PlasmoGEM, a database supporting a community resource for large-scale experimental genetics in malaria parasites.

PubMed

Schwach, Frank; Bushell, Ellen; Gomes, Ana Rita; Anar, Burcu; Girling, Gareth; Herd, Colin; Rayner, Julian C; Billker, Oliver

2015-01-01

The Plasmodium Genetic Modification (PlasmoGEM) database (http://plasmogem.sanger.ac.uk) provides access to a resource of modular, versatile and adaptable vectors for genome modification of Plasmodium spp. parasites. PlasmoGEM currently consists of >2000 plasmids designed to modify the genome of Plasmodium berghei, a malaria parasite of rodents, which can be requested by non-profit research organisations free of charge. PlasmoGEM vectors are designed with long homology arms for efficient genome integration and carry gene specific barcodes to identify individual mutants. They can be used for a wide array of applications, including protein localisation, gene interaction studies and high-throughput genetic screens. The vector production pipeline is supported by a custom software suite that automates both the vector design process and quality control by full-length sequencing of the finished vectors. The PlasmoGEM web interface allows users to search a database of finished knock-out and gene tagging vectors, view details of their designs, download vector sequence in different formats and view available quality control data as well as suggested genotyping strategies. We also make gDNA library clones and intermediate vectors available for researchers to produce vectors for themselves. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Selection vector filter framework

NASA Astrophysics Data System (ADS)

Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.

2003-10-01

We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.

General n-dimensional quadrature transform and its application to interferogram demodulation.

PubMed

Servin, Manuel; Quiroga, Juan Antonio; Marroquin, Jose Luis

2003-05-01

Quadrature operators are useful for obtaining the modulating phase phi in interferometry and temporal signals in electrical communications. In carrier-frequency interferometry and electrical communications, one uses the Hilbert transform to obtain the quadrature of the signal. In these cases the Hilbert transform gives the desired quadrature because the modulating phase is monotonically increasing. We propose an n-dimensional quadrature operator that transforms cos(phi) into -sin(phi) regardless of the frequency spectrum of the signal. With the quadrature of the phase-modulated signal, one can easily calculate the value of phi over all the domain of interest. Our quadrature operator is composed of two n-dimensional vector fields: One is related to the gradient of the image normalized with respect to local frequency magnitude, and the other is related to the sign of the local frequency of the signal. The inner product of these two vector fields gives us the desired quadrature signal. This quadrature operator is derived in the image space by use of differential vector calculus and in the frequency domain by use of a n-dimensional generalization of the Hilbert transform. A robust numerical algorithm is given to find the modulating phase of two-dimensional single-image closed-fringe interferograms by use of the ideas put forward.

Effect of DNA sequence of Fab fragment on yield characteristics and cell growth of E. coli.

PubMed

Kulmala, Antti; Huovinen, Tuomas; Lamminmäki, Urpo

2017-06-19

Codon usage is one of the factors influencing recombinant protein expression. We were interested in the codon usage of an antibody Fab fragment gene exhibiting extreme toxicity in the E. coli host. The toxic synthetic human Fab gene contained domains optimized by the "one amino acid-one codon" method. We redesigned five segments of the Fab gene with a "codon harmonization" method described by Angov et al. and studied the effects of these changes on cell viability, Fab yield and display on filamentous phage using different vectors and bacterial strains. The harmonization considerably reduced toxicity, increased Fab expression from negligible levels to 10 mg/l, and restored the display on phage. Testing the impact of the individual redesigned segments revealed that the most significant effects were conferred by changes in the constant domain of the light chain. For some of the Fab gene variants, we also observed striking differences in protein yields when cloned from a chloramphenicol resistant vector into an identical vector, except with ampicillin resistance. In conclusion, our results show that the expression of a heterodimeric secretory protein can be improved by harmonizing selected DNA segments by synonymous codons and reveal additional complexity involved in heterologous protein expression.

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

PubMed Central

2014-01-01

Background It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusion SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:24776231

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.

PubMed

Cao, Renzhi; Wang, Zheng; Wang, Yiheng; Cheng, Jianlin

2014-04-28

It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

The NRL relocatable ocean/acoustic ensemble forecast system

NASA Astrophysics Data System (ADS)

Rowley, C.; Martin, P.; Cummings, J.; Jacobs, G.; Coelho, E.; Bishop, C.; Hong, X.; Peggion, G.; Fabre, J.

2009-04-01

A globally relocatable regional ocean nowcast/forecast system has been developed to support rapid implementation of new regional forecast domains. The system is in operational use at the Naval Oceanographic Office for a growing number of regional and coastal implementations. The new system is the basis for an ocean acoustic ensemble forecast and adaptive sampling capability. We present an overview of the forecast system and the ocean ensemble and adaptive sampling methods. The forecast system consists of core ocean data analysis and forecast modules, software for domain configuration, surface and boundary condition forcing processing, and job control, and global databases for ocean climatology, bathymetry, tides, and river locations and transports. The analysis component is the Navy Coupled Ocean Data Assimilation (NCODA) system, a 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. The forecast component is the Navy Coastal Ocean Model (NCOM). The system supports one-way nesting and multiple assimilation methods. The ensemble system uses the ensemble transform technique with error variance estimates from the NCODA analysis to represent initial condition error. Perturbed surface forcing or an atmospheric ensemble is used to represent errors in surface forcing. The ensemble transform Kalman filter is used to assess the impact of adaptive observations on future analysis and forecast uncertainty for both ocean and acoustic properties.

Nucleon structure from 2+1-flavor domain-wall QCD

NASA Astrophysics Data System (ADS)

Ohta, Shigemi

2018-03-01

Nucleon-structure calculations of isovector vector-and axialvector-current form factors, transversity and scalar charge, and quark momentum and helicity fractions are reported from two recent 2+1-flavor dynamical domain-wall fermions lattice-QCD ensembles generated jointly by the RIKEN-BNL-Columbia and UKQCD Collaborations with Iwasaki × dislocation-suppressing-determinatn-ratio gauge action at inverse lattice spacing of 1.378(7) GeV and pion mass values of 249.4(3) and 172.3(3) MeV.

Eddy current modeling in linear and nonlinear multifilamentary composite materials

NASA Astrophysics Data System (ADS)

Menana, Hocine; Farhat, Mohamad; Hinaje, Melika; Berger, Kevin; Douine, Bruno; Lévêque, Jean

2018-04-01

In this work, a numerical model is developed for a rapid computation of eddy currents in composite materials, adaptable for both carbon fiber reinforced polymers (CFRPs) for NDT applications and multifilamentary high temperature superconductive (HTS) tapes for AC loss evaluation. The proposed model is based on an integro-differential formulation in terms of the electric vector potential in the frequency domain. The high anisotropy and the nonlinearity of the considered materials are easily handled in the frequency domain.

ML 3.0 smoothed aggregation user's guide.

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

Sala, Marzio; Hu, Jonathan Joseph; Tuminaro, Raymond Stephen

2004-05-01

ML is a multigrid preconditioning package intended to solve linear systems of equations Az = b where A is a user supplied n x n sparse matrix, b is a user supplied vector of length n and x is a vector of length n to be computed. ML should be used on large sparse linear systems arising from partial differential equation (PDE) discretizations. While technically any linear system can be considered, ML should be used on linear systems that correspond to things that work well with multigrid methods (e.g. elliptic PDEs). ML can be used as a stand-alone package ormore » to generate preconditioners for a traditional iterative solver package (e.g. Krylov methods). We have supplied support for working with the AZTEC 2.1 and AZTECOO iterative package [15]. However, other solvers can be used by supplying a few functions. This document describes one specific algebraic multigrid approach: smoothed aggregation. This approach is used within several specialized multigrid methods: one for the eddy current formulation for Maxwell's equations, and a multilevel and domain decomposition method for symmetric and non-symmetric systems of equations (like elliptic equations, or compressible and incompressible fluid dynamics problems). Other methods exist within ML but are not described in this document. Examples are given illustrating the problem definition and exercising multigrid options.« less

ML 3.1 smoothed aggregation user's guide.

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

Sala, Marzio; Hu, Jonathan Joseph; Tuminaro, Raymond Stephen

2004-10-01

ML is a multigrid preconditioning package intended to solve linear systems of equations Ax = b where A is a user supplied n x n sparse matrix, b is a user supplied vector of length n and x is a vector of length n to be computed. ML should be used on large sparse linear systems arising from partial differential equation (PDE) discretizations. While technically any linear system can be considered, ML should be used on linear systems that correspond to things that work well with multigrid methods (e.g. elliptic PDEs). ML can be used as a stand-alone package ormore » to generate preconditioners for a traditional iterative solver package (e.g. Krylov methods). We have supplied support for working with the Aztec 2.1 and AztecOO iterative package [16]. However, other solvers can be used by supplying a few functions. This document describes one specific algebraic multigrid approach: smoothed aggregation. This approach is used within several specialized multigrid methods: one for the eddy current formulation for Maxwell's equations, and a multilevel and domain decomposition method for symmetric and nonsymmetric systems of equations (like elliptic equations, or compressible and incompressible fluid dynamics problems). Other methods exist within ML but are not described in this document. Examples are given illustrating the problem definition and exercising multigrid options.« less

Vector solitons with polarization instability and locked polarization in a fiber laser

NASA Astrophysics Data System (ADS)

Tang, Dingkang; Zhang, Jian-Guo; Liu, Yuanshan

2012-07-01

We investigate the characteristics of vector solitons with and without locked phase velocities of orthogonal polarization components in a specially-designed laser cavity which is formed by a bidirectional fiber loop together with a semiconductor saturable absorber mirror. The characteristics of the two states are compared in the temporal and spectrum domain, respectively. Both of the two states exhibit the characteristic of mode locking while the two orthogonal polarization components are not resolved. However, for the vector soliton with unlocked phase velocities, identical intensity varies after passing through a polarization beam splitter (PBS) outside the laser cavity. Contrary to the polarization rotation locked vector soliton, the intensity does not change periodically. For the polarization-locked vector soliton (PLVS), the identical pulse intensity is still obtained after passing through the PBS and can be observed on the oscilloscope screen after photodetection. A coupler instead of a circulator is integrated in the laser cavity and strong interaction on the polarization resolved spectra of the PLVS is observed. By comparing the two states, we conclude that interaction between the two orthogonal components contributes to the locked phase velocities.

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.

Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases

PubMed Central

Barrios, José Miguel; Verstraeten, Willem W.; Maes, Piet; Aerts, Jean-Marie; Farifteh, Jamshid; Coppin, Pol

2012-01-01

The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the physical habitat of pathogens’ vectors and/or hosts, and urban areas, thus humans. This study implements the concept behind gravity models in the spatial spread of two vector-borne diseases, nephropathia epidemica and Lyme borreliosis, based on current knowledge on the transmission mechanism of these diseases. Two sources of information on vegetated systems were tested: the CORINE land cover map and MODIS NDVI. The size of vegetated areas near urban centers and a local indicator of occupation-related exposure were found significant predictors of disease risk. Both the land cover map and the space-borne dataset were suited yet not equivalent input sources to locate and measure vegetated areas of importance for disease spread. The overall results point at the compatibility of the gravity model concept and the spatial spread of vector-borne diseases. PMID:23202882

Using the gravity model to estimate the spatial spread of vector-borne diseases.

PubMed

Barrios, José Miguel; Verstraeten, Willem W; Maes, Piet; Aerts, Jean-Marie; Farifteh, Jamshid; Coppin, Pol

2012-11-30

The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the physical habitat of pathogens’ vectors and/or hosts, and urban areas, thus humans. This study implements the concept behind gravity models in the spatial spread of two vector-borne diseases, nephropathia epidemica and Lyme borreliosis, based on current knowledge on the transmission mechanism of these diseases. Two sources of information on vegetated systems were tested: the CORINE land cover map and MODIS NDVI. The size of vegetated areas near urban centers and a local indicator of occupation-related exposure were found significant predictors of disease risk. Both the land cover map and the space-borne dataset were suited yet not equivalent input sources to locate and measure vegetated areas of importance for disease spread. The overall results point at the compatibility of the gravity model concept and the spatial spread of vector-borne diseases.

Rapid Assembly of Customized TALENs into Multiple Delivery Systems

PubMed Central

Zhang, Zhengxing; Zhang, Siliang; Huang, Xin; Orwig, Kyle E.; Sheng, Yi

2013-01-01

Transcriptional activator-like effector nucleases (TALENs) have become a powerful tool for genome editing. Here we present an efficient TALEN assembly approach in which TALENs are assembled by direct Golden Gate ligation into Gateway® Entry vectors from a repeat variable di-residue (RVD) plasmid array. We constructed TALEN pairs targeted to mouse Ddx3 subfamily genes, and demonstrated that our modified TALEN assembly approach efficiently generates accurate TALEN moieties that effectively introduce mutations into target genes. We generated “user friendly” TALEN Entry vectors containing TALEN expression cassettes with fluorescent reporter genes that can be efficiently transferred via Gateway (LR) recombination into different delivery systems. We demonstrated that the TALEN Entry vectors can be easily transferred to an adenoviral delivery system to expand application to cells that are difficult to transfect. Since TALENs work in pairs, we also generated a TALEN Entry vector set that combines a TALEN pair into one PiggyBac transposon-based destination vector. The approach described here can also be modified for construction of TALE transcriptional activators, repressors or other functional domains. PMID:24244669

Production of lentiviral vectors

PubMed Central

Merten, Otto-Wilhelm; Hebben, Matthias; Bovolenta, Chiara

2016-01-01

Lentiviral vectors (LV) have seen considerably increase in use as gene therapy vectors for the treatment of acquired and inherited diseases. This review presents the state of the art of the production of these vectors with particular emphasis on their large-scale production for clinical purposes. In contrast to oncoretroviral vectors, which are produced using stable producer cell lines, clinical-grade LV are in most of the cases produced by transient transfection of 293 or 293T cells grown in cell factories. However, more recent developments, also, tend to use hollow fiber reactor, suspension culture processes, and the implementation of stable producer cell lines. As is customary for the biotech industry, rather sophisticated downstream processing protocols have been established to remove any undesirable process-derived contaminant, such as plasmid or host cell DNA or host cell proteins. This review compares published large-scale production and purification processes of LV and presents their process performances. Furthermore, developments in the domain of stable cell lines and their way to the use of production vehicles of clinical material will be presented. PMID:27110581

1-norm support vector novelty detection and its sparseness.

PubMed

Zhang, Li; Zhou, WeiDa

2013-12-01

This paper proposes a 1-norm support vector novelty detection (SVND) method and discusses its sparseness. 1-norm SVND is formulated as a linear programming problem and uses two techniques for inducing sparseness, or the 1-norm regularization and the hinge loss function. We also find two upper bounds on the sparseness of 1-norm SVND, or exact support vector (ESV) and kernel Gram matrix rank bounds. The ESV bound indicates that 1-norm SVND has a sparser representation model than SVND. The kernel Gram matrix rank bound can loosely estimate the sparseness of 1-norm SVND. Experimental results show that 1-norm SVND is feasible and effective. Copyright © 2013 Elsevier Ltd. All rights reserved.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

PubMed Central

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. PMID:29275361

Enhanced line integral convolution with flow feature detection

DOT National Transportation Integrated Search

1995-01-01

Prepared ca. 1995. The Line Integral Convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overall flow patterns in a 2D domain [Cabral & Leedom '93]. The method produces a flow texture imag...

Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates

USDA-ARS?s Scientific Manuscript database

Methods based on sequence data analysis facilitate the tracking of disease outbreaks, allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are used postfactum after an outbreak has happened. Here, we show that support vector machine a...

Support vector machine incremental learning triggered by wrongly predicted samples

NASA Astrophysics Data System (ADS)

Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

2018-05-01

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

NASA Astrophysics Data System (ADS)

Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

2010-01-01

In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

Quantum Support Vector Machine for Big Data Classification

NASA Astrophysics Data System (ADS)

Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

2014-09-01

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

Peptide-matrix-mediated gene transfer of an oxygen-insensitive hypoxia-inducible factor-1alpha variant for local induction of angiogenesis.

PubMed

Trentin, Diana; Hall, Heike; Wechsler, Sandra; Hubbell, Jeffrey A

2006-02-21

Hypoxia-inducible factor (HIF) constitutes a target in therapeutic angiogenesis. HIF-1alpha functions as a sensor of hypoxia and induces expression of vascular endothelial growth factor (VEGF), which then induces angiogenesis. To explore the potential of HIF-1alpha gene therapy in stimulating wound healing, we delivered a gene encoding a stabilized form of HIF-1alpha, lacking the oxygen-sensitive degradation domain, namely HIF-1alpha deltaODD, by using a previously characterized peptide-based gene delivery vector in fibrin as a surgical matrix. The peptide vector consisted of multiple domains: (i) A cysteine-flanked lysine hexamer provided DNA interactions that were stable extracellularly but destabilized intracellularly after reduction of the formed disulfide bonds. This DNA-binding domain was fused to either (ii) a fibrin-binding peptide for entrapment within the matrix or (iii) a nuclear localization sequence for efficient nuclear targeting. The HIF-1alpha deltaODD gene was expressed and translocated to the nucleus under normoxic conditions, leading to up-regulation of vascular endothelial growth factor (VEGF)-A165 mRNA and protein levels in vitro. When the peptide-DNA nanoparticles entrapped in fibrin matrices were applied to full-thickness dermal wounds in the mouse (10 microg per wound in 30 microl of fibrin), angiogenesis was increased comparably strongly to that induced by VEGF-A165 protein (1.25 microg per wound in 30 microl of fibrin). However, the maturity of the vessels induced by HIF-1alpha deltaODD was significantly higher than that induced by VEGF-A165 protein, as shown by stabilization of the neovessels with smooth muscle. Nonviral, local administration of this potent angiogenesis-inducing gene by using this peptide vector represents a powerful approach in tissue engineering and therapeutic angiogenesis.

Packaging of HCV-RNA into lentiviral vector

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

Caval, Vincent; Piver, Eric; Service de Biochimie et Biologie Moleculaire, CHRU de Tours

2011-11-04

Highlights: Black-Right-Pointing-Pointer Description of HCV-RNA Core-D1 interactions. Black-Right-Pointing-Pointer In vivo evaluation of the packaging of HCV genome. Black-Right-Pointing-Pointer Determination of the role of the three basic sub-domains of D1. Black-Right-Pointing-Pointer Heterologous system involving HIV-1 vector particles to mobilise HCV genome. Black-Right-Pointing-Pointer Full length mobilisation of HCV genome and HCV-receptor-independent entry. -- Abstract: The advent of infectious molecular clones of Hepatitis C virus (HCV) has unlocked the understanding of HCV life cycle. However, packaging of the genomic RNA, which is crucial to generate infectious viral particles, remains poorly understood. Molecular interactions of the domain 1 (D1) of HCV Core protein andmore » HCV RNA have been described in vitro. Since compaction of genetic information within HCV genome has hampered conventional mutational approach to study packaging in vivo, we developed a novel heterologous system to evaluate the interactions between HCV RNA and Core D1. For this, we took advantage of the recruitment of Vpr fusion-proteins into HIV-1 particles. By fusing HCV Core D1 to Vpr we were able to package and transfer a HCV subgenomic replicon into a HIV-1 based lentiviral vector. We next examined how deletion mutants of basic sub-domains of Core D1 influenced HCV RNA recruitment. The results emphasized the crucial role of the first and third basic regions of D1 in packaging. Interestingly, the system described here allowed us to mobilise full-length JFH1 genome in CD81 defective cells, which are normally refractory to HCV infection. This finding paves the way to an evaluation of the replication capability of HCV in various cell types.« less

Subband directional vector quantization in radiological image compression

NASA Astrophysics Data System (ADS)

Akrout, Nabil M.; Diab, Chaouki; Prost, Remy; Goutte, Robert; Amiel, Michel

1992-05-01

The aim of this paper is to propose a new scheme for image compression. The method is very efficient for images which have directional edges such as the tree-like structure of the coronary vessels in digital angiograms. This method involves two steps. First, the original image is decomposed at different resolution levels using a pyramidal subband decomposition scheme. For decomposition/reconstruction of the image, free of aliasing and boundary errors, we use an ideal band-pass filter bank implemented in the Discrete Cosine Transform domain (DCT). Second, the high-frequency subbands are vector quantized using a multiresolution codebook with vertical and horizontal codewords which take into account the edge orientation of each subband. The proposed method reduces the blocking effect encountered at low bit rates in conventional vector quantization.

A support vector machine approach for classification of welding defects from ultrasonic signals

NASA Astrophysics Data System (ADS)

Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

2014-07-01

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

PubMed

Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

2012-09-01

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Using Data Mining for Wine Quality Assessment

NASA Astrophysics Data System (ADS)

Cortez, Paulo; Teixeira, Juliana; Cerdeira, António; Almeida, Fernando; Matos, Telmo; Reis, José

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.

Work, Family and Community Support as Predictors of Work-Family Conflict: A Study of Low-Income Workers

ERIC Educational Resources Information Center

Griggs, Tracy Lambert; Casper, Wendy J.; Eby, Lillian T.

2013-01-01

This study examines relationships between support from work, family and community domains with time- and strain-based work-family conflict in a sample of low-income workers. Results reveal significant within-domain and cross-domain relationships between support from all three life domains with work--family conflict. With respect to family support,…

Rabies virus glycoprotein as a carrier for anthrax protective antigen

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

Smith, Mary Ellen; Koser, Martin; Xiao Sa

2006-09-30

Live viral vectors expressing foreign antigens have shown great promise as vaccines against viral diseases. However, safety concerns remain a major problem regarding the use of even highly attenuated viral vectors. Using the rabies virus (RV) envelope protein as a carrier molecule, we show here that inactivated RV particles can be utilized to present Bacillus anthracis protective antigen (PA) domain-4 in the viral membrane. In addition to the RV glycoprotein (G) transmembrane and cytoplasmic domains, a portion of the RV G ectodomain was required to express the chimeric RV G anthrax PA on the cell surface. The novel antigen wasmore » also efficiently incorporated into RV virions. Mice immunized with the inactivated recombinant RV virions exhibited seroconversion against both RV G and anthrax PA, and a second inoculation greatly increased these responses. These data demonstrate that a viral envelope protein can carry a bacterial protein and that a viral carrier can display whole polypeptides compared to the limited epitope presentation of previous viral systems.« less

Domain Specificity between Peer Support and Self-Concept

ERIC Educational Resources Information Center

Leung, Kim Chau; Marsh, Herbert W.; Craven, Rhonda G.; Yeung, Alexander S.; Abduljabbar, Adel S.

2013-01-01

Peer support interventions have mostly neglected the domain specificity of intervention effects. In two studies, the present investigation examined the domain specificity of peer support interventions targeting specific domains of self-concept. In Study 1, participants ("n" = 50) who had received an academically oriented peer support…

Bayesian data assimilation provides rapid decision support for vector-borne diseases.

PubMed

Jewell, Chris P; Brown, Richard G

2015-07-06

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Non-overlapped P- and S-wave Poynting vectors and their solution by the grid method

NASA Astrophysics Data System (ADS)

Lu, Yongming; Liu, Qiancheng

2018-06-01

The Poynting vector represents the local directional energy flux density of seismic waves in geophysics. It is widely used in elastic reverse time migration to analyze source illumination, suppress low-wavenumber noise, correct for image polarity and extract angle-domain common-image gathers. However, the P- and S-waves are mixed together during wavefield propagation so that the P and S energy fluxes are not clean everywhere, especially at the overlapped points. In this paper, we use a modified elastic-wave equation in which the P and S vector wavefields are naturally separated. Then, we develop an efficient method to evaluate the separable P and S Poynting vectors, respectively, based on the view that the group velocity and phase velocity have the same direction in isotropic elastic media. We furthermore formulate our method using an unstructured mesh-based modeling method named the grid method. Finally, we verify our method using two numerical examples.

Design and experimental verification for optical module of optical vector-matrix multiplier.

PubMed

Zhu, Weiwei; Zhang, Lei; Lu, Yangyang; Zhou, Ping; Yang, Lin

2013-06-20

Optical computing is a new method to implement signal processing functions. The multiplication between a vector and a matrix is an important arithmetic algorithm in the signal processing domain. The optical vector-matrix multiplier (OVMM) is an optoelectronic system to carry out this operation, which consists of an electronic module and an optical module. In this paper, we propose an optical module for OVMM. To eliminate the cross talk and make full use of the optical elements, an elaborately designed structure that involves spherical lenses and cylindrical lenses is utilized in this optical system. The optical design software package ZEMAX is used to optimize the parameters and simulate the whole system. Finally, experimental data is obtained through experiments to evaluate the overall performance of the system. The results of both simulation and experiment indicate that the system constructed can implement the multiplication between a matrix with dimensions of 16 by 16 and a vector with a dimension of 16 successfully.

Characteristics of Minimally Oversized Adeno-Associated Virus Vectors Encoding Human Factor VIII Generated Using Producer Cell Lines and Triple Transfection.

PubMed

Nambiar, Bindu; Cornell Sookdeo, Cathleen; Berthelette, Patricia; Jackson, Robert; Piraino, Susan; Burnham, Brenda; Nass, Shelley; Souza, David; O'Riordan, Catherine R; Vincent, Karen A; Cheng, Seng H; Armentano, Donna; Kyostio-Moore, Sirkka

2017-02-01

Several ongoing clinical studies are evaluating recombinant adeno-associated virus (rAAV) vectors as gene delivery vehicles for a variety of diseases. However, the production of vectors with genomes >4.7 kb is challenging, with vector preparations frequently containing truncated genomes. To determine whether the generation of oversized rAAVs can be improved using a producer cell-line (PCL) process, HeLaS3-cell lines harboring either a 5.1 or 5.4 kb rAAV vector genome encoding codon-optimized cDNA for human B-domain deleted Factor VIII (FVIII) were isolated. High-producing "masterwells" (MWs), defined as producing >50,000 vg/cell, were identified for each oversized vector. These MWs provided stable vector production for >20 passages. The quality and potency of the AAVrh8R/FVIII-5.1 and AAVrh8R/FVIII-5.4 vectors generated by the PCL method were then compared to those prepared via transient transfection (TXN). Southern and dot blot analyses demonstrated that both production methods resulted in packaging of heterogeneously sized genomes. However, the PCL-derived rAAV vector preparations contained some genomes >4.7 kb, whereas the majority of genomes generated by the TXN method were ≤4.7 kb. The PCL process reduced packaging of non-vector DNA for both the AAVrh8R/FVIII-5.1 and the AAVrh8R/FVIII-5.4 kb vector preparations. Furthermore, more DNA-containing viral particles were obtained for the AAVrh8R/FVIII-5.1 vector. In a mouse model of hemophilia A, animals administered a PCL-derived rAAV vector exhibited twofold higher plasma FVIII activity and increased levels of vector genomes in the liver than mice treated with vector produced via TXN did. Hence, the quality of oversized vectors prepared using the PCL method is greater than that of vectors generated using the TXN process, and importantly this improvement translates to enhanced performance in vivo.

Support Vector Machines: Relevance Feedback and Information Retrieval.

ERIC Educational Resources Information Center

Drucker, Harris; Shahrary, Behzad; Gibbon, David C.

2002-01-01

Compares support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred. Includes nine tables. (Contains 24…

Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression

Treesearch

Jeffrey T. Walton

2008-01-01

Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (...

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

PubMed

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

nu-Anomica: A Fast Support Vector Based Novelty Detection Technique

NASA Technical Reports Server (NTRS)

Das, Santanu; Bhaduri, Kanishka; Oza, Nikunj C.; Srivastava, Ashok N.

2009-01-01

In this paper we propose nu-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In -Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5 - 20 times.

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity.

PubMed

Öztoprak, Hüseyin; Toycan, Mehmet; Alp, Yaşar Kemal; Arıkan, Orhan; Doğutepe, Elvin; Karakaş, Sirel

2017-12-01

Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted with many problems. A novel classification approach that discriminates ADHD and nonADHD groups over the time-frequency domain features of event-related potential (ERP) recordings that are taken during Stroop task is presented. Time-Frequency Hermite-Atomizer (TFHA) technique is used for the extraction of high resolution time-frequency domain features that are highly localized in time-frequency domain. Based on an extensive investigation, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain the best discriminating features. When the best three features were used, the classification accuracy for the training dataset reached 98%, and the use of five features further improved the accuracy to 99.5%. The accuracy was 100% for the testing dataset. Based on extensive experiments, the delta band emerged as the most contributing frequency band and statistical parameters emerged as the most contributing feature group. The classification performance of this study suggests that TFHA can be employed as an auxiliary component of the diagnostic and prognostic procedures for ADHD. The features obtained in this study can potentially contribute to the neuroelectrical understanding and clinical diagnosis of ADHD. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

No-Reference Video Quality Assessment Based on Statistical Analysis in 3D-DCT Domain.

PubMed

Li, Xuelong; Guo, Qun; Lu, Xiaoqiang

2016-05-13

It is an important task to design models for universal no-reference video quality assessment (NR-VQA) in multiple video processing and computer vision applications. However, most existing NR-VQA metrics are designed for specific distortion types which are not often aware in practical applications. A further deficiency is that the spatial and temporal information of videos is hardly considered simultaneously. In this paper, we propose a new NR-VQA metric based on the spatiotemporal natural video statistics (NVS) in 3D discrete cosine transform (3D-DCT) domain. In the proposed method, a set of features are firstly extracted based on the statistical analysis of 3D-DCT coefficients to characterize the spatiotemporal statistics of videos in different views. These features are used to predict the perceived video quality via the efficient linear support vector regression (SVR) model afterwards. The contributions of this paper are: 1) we explore the spatiotemporal statistics of videos in 3DDCT domain which has the inherent spatiotemporal encoding advantage over other widely used 2D transformations; 2) we extract a small set of simple but effective statistical features for video visual quality prediction; 3) the proposed method is universal for multiple types of distortions and robust to different databases. The proposed method is tested on four widely used video databases. Extensive experimental results demonstrate that the proposed method is competitive with the state-of-art NR-VQA metrics and the top-performing FR-VQA and RR-VQA metrics.

Techniques utilized in the simulated altitude testing of a 2D-CD vectoring and reversing nozzle

NASA Technical Reports Server (NTRS)

Block, H. Bruce; Bryant, Lively; Dicus, John H.; Moore, Allan S.; Burns, Maureen E.; Solomon, Robert F.; Sheer, Irving

1988-01-01

Simulated altitude testing of a two-dimensional, convergent-divergent, thrust vectoring and reversing exhaust nozzle was accomplished. An important objective of this test was to develop test hardware and techniques to properly operate a vectoring and reversing nozzle within the confines of an altitude test facility. This report presents detailed information on the major test support systems utilized, the operational performance of the systems and the problems encountered, and test equipment improvements recommended for future tests. The most challenging support systems included the multi-axis thrust measurement system, vectored and reverse exhaust gas collection systems, and infrared temperature measurement systems used to evaluate and monitor the nozzle. The feasibility of testing a vectoring and reversing nozzle of this type in an altitude chamber was successfully demonstrated. Supporting systems performed as required. During reverser operation, engine exhaust gases were successfully captured and turned downstream. However, a small amount of exhaust gas spilled out the collector ducts' inlet openings when the reverser was opened more than 60 percent. The spillage did not affect engine or nozzle performance. The three infrared systems which viewed the nozzle through the exhaust collection system worked remarkably well considering the harsh environment.

An extension of the finite cell method using boolean operations

NASA Astrophysics Data System (ADS)

Abedian, Alireza; Düster, Alexander

2017-05-01

In the finite cell method, the fictitious domain approach is combined with high-order finite elements. The geometry of the problem is taken into account by integrating the finite cell formulation over the physical domain to obtain the corresponding stiffness matrix and load vector. In this contribution, an extension of the FCM is presented wherein both the physical and fictitious domain of an element are simultaneously evaluated during the integration. In the proposed extension of the finite cell method, the contribution of the stiffness matrix over the fictitious domain is subtracted from the cell, resulting in the desired stiffness matrix which reflects the contribution of the physical domain only. This method results in an exponential rate of convergence for porous domain problems with a smooth solution and accurate integration. In addition, it reduces the computational cost, especially when applying adaptive integration schemes based on the quadtree/octree. Based on 2D and 3D problems of linear elastostatics, numerical examples serve to demonstrate the efficiency and accuracy of the proposed method.

Determination of domain wall chirality using in situ Lorentz transmission electron microscopy

DOE PAGES

Chess, Jordan J.; Montoya, Sergio A.; Fullerton, Eric E.; ...

2017-02-23

Controlling domain wall chirality is increasingly seen in non-centrosymmetric materials. Mapping chiral magnetic domains requires knowledge about all the vector components of the magnetization, which poses a problem for conventional Lorentz transmission electron microscopy (LTEM) that is only sensitive to magnetic fields perpendicular to the electron beams direction of travel. The standard approach in LTEM for determining the third component of the magnetization is to tilt the sample to some angle and record a second image. Furthermore, this presents a problem for any domain structures that are stabilized by an applied external magnetic field (e.g. skyrmions), because the standard LTEMmore » setup does not allow independent control of the angle of an applied magnetic field, and sample tilt angle. Here we show that applying a modified transport of intensity equation analysis to LTEM images collected during an applied field sweep, we can determine the domain wall chirality of labyrinth domains in a perpendicularly magnetized material, avoiding the need to tilt the sample.« less

Determination of domain wall chirality using in situ Lorentz transmission electron microscopy

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

Chess, Jordan J.; Montoya, Sergio A.; Fullerton, Eric E.

Controlling domain wall chirality is increasingly seen in non-centrosymmetric materials. Mapping chiral magnetic domains requires knowledge about all the vector components of the magnetization, which poses a problem for conventional Lorentz transmission electron microscopy (LTEM) that is only sensitive to magnetic fields perpendicular to the electron beams direction of travel. The standard approach in LTEM for determining the third component of the magnetization is to tilt the sample to some angle and record a second image. Furthermore, this presents a problem for any domain structures that are stabilized by an applied external magnetic field (e.g. skyrmions), because the standard LTEMmore » setup does not allow independent control of the angle of an applied magnetic field, and sample tilt angle. Here we show that applying a modified transport of intensity equation analysis to LTEM images collected during an applied field sweep, we can determine the domain wall chirality of labyrinth domains in a perpendicularly magnetized material, avoiding the need to tilt the sample.« less

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

Bayesian data assimilation provides rapid decision support for vector-borne diseases

PubMed Central

Jewell, Chris P.; Brown, Richard G.

2015-01-01

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

Generation of single domain antibody fragments derived from camelids and generation of manifold constructs.

PubMed

Vincke, Cécile; Gutiérrez, Carlos; Wernery, Ulrich; Devoogdt, Nick; Hassanzadeh-Ghassabeh, Gholamreza; Muyldermans, Serge

2012-01-01

Immunizing a camelid (camels and llamas) with soluble, properly folded proteins raises an affinity-matured immune response in the unique camelid heavy-chain only antibodies (HCAbs). The peripheral blood lymphocytes of the immunized animal are used to clone the antigen-binding antibody fragment from the HCAbs in a phage display vector. A representative aliquot of the library of these antigen-binding fragments is used to retrieve single domain antigen-specific binders by successive rounds of panning. These single domain antibody fragments are cloned in tandem to generate manifold constructs (bivalent, biparatopic or bispecific constructs) to increase their functional affinity, to increase specificity, or to connect two independent antigen molecules.

Field-induced metastability of the modulation wave vector in a magnetic soliton lattice

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

Zhu, M.; Peng, J.; Hong, T.

We present magnetic-field-induced metastability of the magnetic soliton lattice in a bilayer ruthenate Ca 3(Ru 1–xFe x) 2O 7(x=0.05) through single-crystal neutron diffraction study. We show that the incommensurability of the modulation wave vector at zero field strongly depends on the history of magnetic field at low temperature, and that the equilibrium ground state can be achieved by warming above a characteristic temperature T g~37K. Lastly, we suggest that such metastability might be associated with the domain wall pinning by the magnetic Fe dopants.

Vorticity vector-potential method based on time-dependent curvilinear coordinates for two-dimensional rotating flows in closed configurations

NASA Astrophysics Data System (ADS)

Fu, Yuan; Zhang, Da-peng; Xie, Xi-lin

2018-04-01

In this study, a vorticity vector-potential method for two-dimensional viscous incompressible rotating driven flows is developed in the time-dependent curvilinear coordinates. The method is applicable in both inertial and non-inertial frames of reference with the advantage of a fixed and regular calculation domain. The numerical method is applied to triangle and curved triangle configurations in constant and varying rotational angular velocity cases respectively. The evolutions of flow field are studied. The geostrophic effect, unsteady effect and curvature effect on the evolutions are discussed.

Field-induced metastability of the modulation wave vector in a magnetic soliton lattice

DOE PAGES

Zhu, M.; Peng, J.; Hong, T.; ...

2017-04-19

We present magnetic-field-induced metastability of the magnetic soliton lattice in a bilayer ruthenate Ca 3(Ru 1–xFe x) 2O 7(x=0.05) through single-crystal neutron diffraction study. We show that the incommensurability of the modulation wave vector at zero field strongly depends on the history of magnetic field at low temperature, and that the equilibrium ground state can be achieved by warming above a characteristic temperature T g~37K. Lastly, we suggest that such metastability might be associated with the domain wall pinning by the magnetic Fe dopants.

Generalizations of Tikhonov's regularized method of least squares to non-Euclidean vector norms

NASA Astrophysics Data System (ADS)

Volkov, V. V.; Erokhin, V. I.; Kakaev, V. V.; Onufrei, A. Yu.

2017-09-01

Tikhonov's regularized method of least squares and its generalizations to non-Euclidean norms, including polyhedral, are considered. The regularized method of least squares is reduced to mathematical programming problems obtained by "instrumental" generalizations of the Tikhonov lemma on the minimal (in a certain norm) solution of a system of linear algebraic equations with respect to an unknown matrix. Further studies are needed for problems concerning the development of methods and algorithms for solving reduced mathematical programming problems in which the objective functions and admissible domains are constructed using polyhedral vector norms.

Vorticity vector-potential method based on time-dependent curvilinear coordinates for two-dimensional rotating flows in closed configurations

NASA Astrophysics Data System (ADS)

Fu, Yuan; Zhang, Da-peng; Xie, Xi-lin

2018-03-01

In this study, a vorticity vector-potential method for two-dimensional viscous incompressible rotating driven flows is developed in the time-dependent curvilinear coordinates. The method is applicable in both inertial and non-inertial frames of reference with the advantage of a fixed and regular calculation domain. The numerical method is applied to triangle and curved triangle configurations in constant and varying rotational angular velocity cases respectively. The evolutions of flow field are studied. The geostrophic effect, unsteady effect and curvature effect on the evolutions are discussed.

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.

Toward a hybrid brain-computer interface based on repetitive visual stimuli with missing events.

PubMed

Wu, Yingying; Li, Man; Wang, Jing

2016-07-26

Steady-state visually evoked potentials (SSVEPs) can be elicited by repetitive stimuli and extracted in the frequency domain with satisfied performance. However, the temporal information of such stimulus is often ignored. In this study, we utilized repetitive visual stimuli with missing events to present a novel hybrid BCI paradigm based on SSVEP and omitted stimulus potential (OSP). Four discs flickering from black to white with missing flickers served as visual stimulators to simultaneously elicit subject's SSVEPs and OSPs. Key parameters in the new paradigm, including flicker frequency, optimal electrodes, missing flicker duration and intervals of missing events were qualitatively discussed with offline data. Two omitted flicker patterns including missing black/white disc were proposed and compared. Averaging times were optimized with Information Transfer Rate (ITR) in online experiments, where SSVEPs and OSPs were identified using Canonical Correlation Analysis in the frequency domain and Support Vector Machine (SVM)-Bayes fusion in the time domain, respectively. The online accuracy and ITR (mean ± standard deviation) over nine healthy subjects were 79.29 ± 18.14 % and 19.45 ± 11.99 bits/min with missing black disc pattern, and 86.82 ± 12.91 % and 24.06 ± 10.95 bits/min with missing white disc pattern, respectively. The proposed BCI paradigm, for the first time, demonstrated that SSVEPs and OSPs can be simultaneously elicited in single visual stimulus pattern and recognized in real-time with satisfied performance. Besides the frequency features such as SSVEP elicited by repetitive stimuli, we found a new feature (OSP) in the time domain to design a novel hybrid BCI paradigm by adding missing events in repetitive stimuli.

Visualization of Morse connection graphs for topologically rich 2D vector fields.

PubMed

Szymczak, Andrzej; Sipeki, Levente

2013-12-01

Recent advances in vector field topologymake it possible to compute its multi-scale graph representations for autonomous 2D vector fields in a robust and efficient manner. One of these representations is a Morse Connection Graph (MCG), a directed graph whose nodes correspond to Morse sets, generalizing stationary points and periodic trajectories, and arcs - to trajectories connecting them. While being useful for simple vector fields, the MCG can be hard to comprehend for topologically rich vector fields, containing a large number of features. This paper describes a visual representation of the MCG, inspired by previous work on graph visualization. Our approach aims to preserve the spatial relationships between the MCG arcs and nodes and highlight the coherent behavior of connecting trajectories. Using simulations of ocean flow, we show that it can provide useful information on the flow structure. This paper focuses specifically on MCGs computed for piecewise constant (PC) vector fields. In particular, we describe extensions of the PC framework that make it more flexible and better suited for analysis of data on complex shaped domains with a boundary. We also describe a topology simplification scheme that makes our MCG visualizations less ambiguous. Despite the focus on the PC framework, our approach could also be applied to graph representations or topological skeletons computed using different methods.

Bayesian Analogy with Relational Transformations

ERIC Educational Resources Information Center

Lu, Hongjing; Chen, Dawn; Holyoak, Keith J.

2012-01-01

How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy…

Role of MUC4-NIDO domain in the MUC4-mediated metastasis of pancreatic cancer cells.

PubMed

Senapati, S; Gnanapragassam, V S; Moniaux, N; Momi, N; Batra, S K

2012-07-12

MUC4 is a large transmembrane type I glycoprotein that is overexpressed in pancreatic cancer (PC) and has been shown to be associated with its progression and metastasis. However, the exact cellular and molecular mechanism(s) through which MUC4 promotes metastasis of PC cells has been sparsely studied. Here we showed that the nidogen-like (NIDO) domain of MUC4, which is similar to the G1-domain present in the nidogen or entactin (an extracellular matrix protein), contributes to the protein-protein interaction property of MUC4. By this interaction, MUC4 promotes breaching of basement membrane (BM) integrity, and spreading of cancer cells. These observations are corroborated with the data from our study using an engineered MUC4 protein without the NIDO domain, which was ectopically expressed in the MiaPaCa PC cells, lacking endogenous MUC4 and nidogen protein. The in vitro studies demonstrated an enhanced invasiveness of MiaPaCa cells expressing MUC4 (MiaPaCa-MUC4) compared with vector-transfected cells (MiaPaCa-Vec; P=0.003) or cells expressing MUC4 without the NIDO domain (MiaPaCa-MUC4-NIDO(Δ); P=0.03). However, the absence of NIDO-domain has no significant role on cell growth and motility (P=0.93). In the in vivo studies, all the mice orthotopically implanted with MiPaCa-MUC4 cells developed metastasis to the liver as compared with MiaPaCa-Vec or the MiaPaCa-MUC4-NIDO(Δ) group, hence, supporting our in vitro observations. Additionally, a reduced binding (P=0.0004) of MiaPaCa-MUC4-NIDO(Δ) cells to the fibulin-2 coated plates compared with MiaPaCa-MUC4 cells indicated a possible interaction between the MUC4-NIDO domain and fibulin-2, a nidogen-interacting protein. Furthermore, in PC tissue samples, MUC4 colocalized with the fibulin-2 present in the BM. Altogether, our findings demonstrate that the MUC4-NIDO domain significantly contributes to the MUC4-mediated metastasis of PC cells. This may be partly due to the interaction between the MUC4-NIDO domain and fibulin-2.

Structural vibration-based damage classification of delaminated smart composite laminates

NASA Astrophysics Data System (ADS)

Khan, Asif; Kim, Heung Soo; Sohn, Jung Woo

2018-03-01

Separation along the interfaces of layers (delamination) is a principal mode of failure in laminated composites and its detection is of prime importance for structural integrity of composite materials. In this work, structural vibration response is employed to detect and classify delaminations in piezo-bonded laminated composites. Improved layerwise theory and finite element method are adopted to develop the electromechanically coupled governing equation of a smart composite laminate with and without delaminations. Transient responses of the healthy and damaged structures are obtained through a surface bonded piezoelectric sensor by solving the governing equation in the time domain. Wavelet packet transform (WPT) and linear discriminant analysis (LDA) are employed to extract discriminative features from the structural vibration response of the healthy and delaminated structures. Dendrogram-based support vector machine (DSVM) is used to classify the discriminative features. The confusion matrix of the classification algorithm provided physically consistent results.

Generating Performance Models for Irregular Applications

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

Friese, Ryan D.; Tallent, Nathan R.; Vishnu, Abhinav

2017-05-30

Many applications have irregular behavior --- non-uniform input data, input-dependent solvers, irregular memory accesses, unbiased branches --- that cannot be captured using today's automated performance modeling techniques. We describe new hierarchical critical path analyses for the \\Palm model generation tool. To create a model's structure, we capture tasks along representative MPI critical paths. We create a histogram of critical tasks with parameterized task arguments and instance counts. To model each task, we identify hot instruction-level sub-paths and model each sub-path based on data flow, instruction scheduling, and data locality. We describe application models that generate accurate predictions for strong scalingmore » when varying CPU speed, cache speed, memory speed, and architecture. We present results for the Sweep3D neutron transport benchmark; Page Rank on multiple graphs; Support Vector Machine with pruning; and PFLOTRAN's reactive flow/transport solver with domain-induced load imbalance.« less

SUPAR: Smartphone as a ubiquitous physical activity recognizer for u-healthcare services.

PubMed

Fahim, Muhammad; Lee, Sungyoung; Yoon, Yongik

2014-01-01

Current generation smartphone can be seen as one of the most ubiquitous device for physical activity recognition. In this paper we proposed a physical activity recognizer to provide u-healthcare services in a cost effective manner by utilizing cloud computing infrastructure. Our model is comprised on embedded triaxial accelerometer of the smartphone to sense the body movements and a cloud server to store and process the sensory data for numerous kind of services. We compute the time and frequency domain features over the raw signals and evaluate different machine learning algorithms to identify an accurate activity recognition model for four kinds of physical activities (i.e., walking, running, cycling and hopping). During our experiments we found Support Vector Machine (SVM) algorithm outperforms for the aforementioned physical activities as compared to its counterparts. Furthermore, we also explain how smartphone application and cloud server communicate with each other.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals.

PubMed

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R; Guerra-Hernandez, Erick I; Almanza-Ojeda, Dora L; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J; Ibarra-Manzano, Mario A

2016-03-05

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

PubMed Central

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R.; Guerra-Hernandez, Erick I.; Almanza-Ojeda, Dora L.; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J.; Ibarra-Manzano, Mario A.

2016-01-01

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states. PMID:26959029

Machine Learning Toolkit for Extreme Scale

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

2014-03-31

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are consideredmore » in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets« less

Defect Inspection of Flip Chip Solder Bumps Using an Ultrasonic Transducer

PubMed Central

Su, Lei; Shi, Tielin; Xu, Zhensong; Lu, Xiangning; Liao, Guanglan

2013-01-01

Surface mount technology has spurred a rapid decrease in the size of electronic packages, where solder bump inspection of surface mount packages is crucial in the electronics manufacturing industry. In this study we demonstrate the feasibility of using a 230 MHz ultrasonic transducer for nondestructive flip chip testing. The reflected time domain signal was captured when the transducer scanning the flip chip, and the image of the flip chip was generated by scanning acoustic microscopy. Normalized cross-correlation was used to locate the center of solder bumps for segmenting the flip chip image. Then five features were extracted from the signals and images. The support vector machine was adopted to process the five features for classification and recognition. The results show the feasibility of this approach with high recognition rate, proving that defect inspection of flip chip solder bumps using the ultrasonic transducer has high potential in microelectronics packaging.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

PubMed

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

HHsvm: fast and accurate classification of profile–profile matches identified by HHsearch

PubMed Central

Dlakić, Mensur

2009-01-01

Motivation: Recently developed profile–profile methods rival structural comparisons in their ability to detect homology between distantly related proteins. Despite this tremendous progress, many genuine relationships between protein families cannot be recognized as comparisons of their profiles result in scores that are statistically insignificant. Results: Using known evolutionary relationships among protein superfamilies in SCOP database, support vector machines were trained on four sets of discriminatory features derived from the output of HHsearch. Upon validation, it was shown that the automatic classification of all profile–profile matches was superior to fixed threshold-based annotation in terms of sensitivity and specificity. The effectiveness of this approach was demonstrated by annotating several domains of unknown function from the Pfam database. Availability: Programs and scripts implementing the methods described in this manuscript are freely available from http://hhsvm.dlakiclab.org/. Contact: mdlakic@montana.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19773335

Discrimination of transgenic soybean seeds by terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Wei; Liu, Changhong; Chen, Feng; Yang, Jianbo; Zheng, Lei

2016-10-01

Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of glyphosate-resistant and conventional soybean seeds and their hybrid descendants was examined by terahertz time-domain spectroscopy system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative and standard normal variate (SNV) transformation pre-treatments were applied to classify soybean seeds based on genotype. Results demonstrated clear differences among glyphosate-resistant, hybrid descendants and conventional non-transformed soybean seeds could easily be visualized with an excellent classification (accuracy was 88.33% in validation set) using the LS-SVM and the spectra with SNV pre-treatment. The results indicated that THz spectroscopy techniques together with chemometrics would be a promising technique to distinguish transgenic soybean seeds from non-transformed seeds with high efficiency and without any major sample preparation.

D meson semileptonic form factors in Nf = 3 QCD with Möbius domain-wall quarks

NASA Astrophysics Data System (ADS)

Kaneko, Takashi; Colquhoun, Brian; Fukaya, Hidenori; Hashimoto, Shoji

2018-03-01

e present our calculation of D → π and D → K semileptonic form factors in Nf = 2 + 1 lattice QCD. We simulate three lattice cutoffs a-1 ≃ 2.5, 3.6 and 4.5 GeV with pion masses as low as 230 MeV. The Möbius domain-wall action is employed for both light and charm quarks. We present our results for the vector and scalar form factors and discuss their dependence on the lattice spacing, light quark masses and momentum transfer.

Frequency domain, waveform inversion of laboratory crosswell radar data

USGS Publications Warehouse

Ellefsen, Karl J.; Mazzella, Aldo T.; Horton, Robert J.; McKenna, Jason R.

2010-01-01

A new waveform inversion for crosswell radar is formulated in the frequency-domain for a 2.5D model. The inversion simulates radar waves using the vector Helmholtz equation for electromagnetic waves. The objective function is minimized using a backpropagation method suitable for a 2.5D model. The inversion is tested by processing crosswell radar data collected in a laboratory tank. The estimated model is consistent with the known electromagnetic properties of the tank. The formulation for the 2.5D model can be extended to inversions of acoustic and elastic data.

Application of Modern Control Design Methodologies to a Multi-Segmented Deformable Mirror System

DTIC Science & Technology

1991-05-23

state matrices, and the state equations are X= Ax + Bu (2.3) y = Cm + Du (2.4) The only dynamics modeled are associated with the six segment phasing...relationship between the L 2 and H2 spaces, the vector H2 norm can be found from the application of Parseval’s Theorem to Equation 3.1, yielding V112...of this minimization problem can be found using Riccati equations {1]. ’With a slight abuse of notation, time domain functions and frequency domain

Simultaneous Range/Velocity Detection with an Ultra-Wideband Random Noise Radar Through Fully Digital Cross-Correlation in the Time Domain

DTIC Science & Technology

2011-03-24

equates to N < 2.237×108 samples . For the hardware used in this paper where B ≈ 370MHz [29], the limit on the correlation time becomes T < 0.30 s. This...When implementing the digital correlator in the time domain, each discrete sample of the reference signal, sref , must be interpolated based on the...through digital correlation comes from the number of samples in the measurement vector, L. The computational re- quirements grow with L. As Figure 2.9

Csk Homologous Kinase, a Potential Regulator of CXCR4-Medicated Breast Cancer Cell Metastasis

DTIC Science & Technology

2011-08-01

is a non-receptor tyrosine kinase and a second member of the Csk family. Like Csk, CHK has Src homology 2 ( SH2 ) and SH3 domains and lacks the...MSCV-retroviral vectors encoding either wild-type CHK or kinase -dead CHK or wild type SH2 domain or SH2 -R147A or SH2 -G129A. All these constructs were... Kinase , a Potential Regulator of CXCR4-Medicated Breast Cancer Cell Metastasis Byeong-Chel Lee The University of Pittsburgh Pittsburgh, PA 15213

Display of adenoregulin with a novel Pichia pastoris cell surface display system.

PubMed

Ren, Ren; Jiang, Zhengbing; Liu, Meiyun; Tao, Xinyi; Ma, Yushu; Wei, Dongzhi

2007-02-01

Two Pichia pastoris cell surface display vectors were constructed. The vectors consisted of the flocculation functional domain of Flo1p with its own secretion signal sequence or the alpha-factor secretion signal sequence, a polyhistidine (6xHis) tag for detection, an enterokinase recognition site, and the insertion sites for target proteins. Adenoregulin (ADR) is a 33-amino-acid antimicrobial peptide isolated from Phyllomedusa bicolor skin. The ADR was expressed and displayed on the Pichia pastoris KM71 cell surface with the system reported. The displayed recombinant ADR fusion protein was detected by fluorescence microscopy and confocal laser scanning microscopy (CLSM). The antimicrobial activity of the recombinant adenoregulin was detected after proteolytic cleavage of the fusion protein on cell surface. The validity of the Pichia pastoris cell surface display vectors was proved by the displayed ADR.

Expression, Purification and Characterization of Ricin vectors used for exogenous antigen delivery into the MHC Class I presentation pathway

PubMed Central

Marsden, Catherine J.; Lord, J. Michael; Roberts, Lynne M.

2003-01-01

Disarmed versions of the cytotoxin ricin can deliver fused peptides into target cells leading to MHC class I-restricted antigen presentation [Smith et al. J Immunol 2002; 169:99-107]. The ricin delivery vector must contain an attenuated catalytic domain to prevent target cell death, and the fused peptide epitope must remain intact for delivery and functional loading to MHC class I molecules. Expression in E. coli and purification by cation exchange chromatography of the fusion protein is described. Before used for delivery, the activity of the vector must be characterized in vitro, via an N-glycosidase assay, and in vivo, by a cytotoxicity assay. The presence of an intact epitope must be confirmed using mass spectrometry by comparing the actual mass with the predicted mass. PMID:12734560

Domain-Invariant Partial-Least-Squares Regression.

PubMed

Nikzad-Langerodi, Ramin; Zellinger, Werner; Lughofer, Edwin; Saminger-Platz, Susanne

2018-05-11

Multivariate calibration models often fail to extrapolate beyond the calibration samples because of changes associated with the instrumental response, environmental condition, or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical devices, while generic methods for calibration-model adaptation are largely missing. To fill this gap, we here introduce domain-invariant partial-least-squares (di-PLS) regression, which extends ordinary PLS by a domain regularizer in order to align the source and target distributions in the latent-variable space. We show that a domain-invariant weight vector can be derived in closed form, which allows the integration of (partially) labeled data from the source and target domains as well as entirely unlabeled data from the latter. We test our approach on a simulated data set where the aim is to desensitize a source calibration model to an unknown interfering agent in the target domain (i.e., unsupervised model adaptation). In addition, we demonstrate unsupervised, semisupervised, and supervised model adaptation by di-PLS on two real-world near-infrared (NIR) spectroscopic data sets.

Estimation of Teacher Practices Based on Text Transcripts of Teacher Speech Using a Support Vector Machine Algorithm

ERIC Educational Resources Information Center

Araya, Roberto; Plana, Francisco; Dartnell, Pablo; Soto-Andrade, Jorge; Luci, Gina; Salinas, Elena; Araya, Marylen

2012-01-01

Teacher practice is normally assessed by observers who watch classes or videos of classes. Here, we analyse an alternative strategy that uses text transcripts and a support vector machine classifier. For each one of the 710 videos of mathematics classes from the 2005 Chilean National Teacher Assessment Programme, a single 4-minute slice was…

Using latent semantic analysis and the predication algorithm to improve extraction of meanings from a diagnostic corpus.

PubMed

Jorge-Botana, Guillermo; Olmos, Ricardo; León, José Antonio

2009-11-01

There is currently a widespread interest in indexing and extracting taxonomic information from large text collections. An example is the automatic categorization of informally written medical or psychological diagnoses, followed by the extraction of epidemiological information or even terms and structures needed to formulate guiding questions as an heuristic tool for helping doctors. Vector space models have been successfully used to this end (Lee, Cimino, Zhu, Sable, Shanker, Ely & Yu, 2006; Pakhomov, Buntrock & Chute, 2006). In this study we use a computational model known as Latent Semantic Analysis (LSA) on a diagnostic corpus with the aim of retrieving definitions (in the form of lists of semantic neighbors) of common structures it contains (e.g. "storm phobia", "dog phobia") or less common structures that might be formed by logical combinations of categories and diagnostic symptoms (e.g. "gun personality" or "germ personality"). In the quest to bring definitions into line with the meaning of structures and make them in some way representative, various problems commonly arise while recovering content using vector space models. We propose some approaches which bypass these problems, such as Kintsch's (2001) predication algorithm and some corrections to the way lists of neighbors are obtained, which have already been tested on semantic spaces in a non-specific domain (Jorge-Botana, León, Olmos & Hassan-Montero, under review). The results support the idea that the predication algorithm may also be useful for extracting more precise meanings of certain structures from scientific corpora, and that the introduction of some corrections based on vector length may increases its efficiency on non-representative terms.

Expected energy-based restricted Boltzmann machine for classification.

PubMed

Elfwing, S; Uchibe, E; Doya, K

2015-04-01

In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach to provide a self-contained RBM method for classification, inspired by free-energy based function approximation (FE-RBM), originally proposed for reinforcement learning. For classification, the FE-RBM method computes the output for an input vector and a class vector by the negative free energy of an RBM. Learning is achieved by stochastic gradient-descent using a mean-squared error training objective. In an earlier study, we demonstrated that the performance and the robustness of FE-RBM function approximation can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that the learning performance of RBM function approximation can be further improved by computing the output by the negative expected energy (EE-RBM), instead of the negative free energy. To create a deep learning architecture, we stack several RBMs on top of each other. We also connect the class nodes to all hidden layers to try to improve the performance even further. We validate the classification performance of EE-RBM using the MNIST data set and the NORB data set, achieving competitive performance compared with other classifiers such as standard neural networks, deep belief networks, classification RBMs, and support vector machines. The purpose of using the NORB data set is to demonstrate that EE-RBM with binary input nodes can achieve high performance in the continuous input domain. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Soft and hard classification by reproducing kernel Hilbert space methods.

PubMed

Wahba, Grace

2002-12-24

Reproducing kernel Hilbert space (RKHS) methods provide a unified context for solving a wide variety of statistical modelling and function estimation problems. We consider two such problems: We are given a training set [yi, ti, i = 1, em leader, n], where yi is the response for the ith subject, and ti is a vector of attributes for this subject. The value of y(i) is a label that indicates which category it came from. For the first problem, we wish to build a model from the training set that assigns to each t in an attribute domain of interest an estimate of the probability pj(t) that a (future) subject with attribute vector t is in category j. The second problem is in some sense less ambitious; it is to build a model that assigns to each t a label, which classifies a future subject with that t into one of the categories or possibly "none of the above." The approach to the first of these two problems discussed here is a special case of what is known as penalized likelihood estimation. The approach to the second problem is known as the support vector machine. We also note some alternate but closely related approaches to the second problem. These approaches are all obtained as solutions to optimization problems in RKHS. Many other problems, in particular the solution of ill-posed inverse problems, can be obtained as solutions to optimization problems in RKHS and are mentioned in passing. We caution the reader that although a large literature exists in all of these topics, in this inaugural article we are selectively highlighting work of the author, former students, and other collaborators.

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis.

PubMed

Liu, Bin; Wang, Xiaolong; Lin, Lei; Dong, Qiwen; Wang, Xuan

2008-12-01

Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences. In this paper, a novel building block of proteins called Top-n-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-n-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-n-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-n-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-n-grams and LSA gives significantly better results compared to related methods. The method based on Top-n-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-n-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

PubMed

Zhou, Hang; Yang, Yang; Shen, Hong-Bin

2017-03-15

Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models. In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell. www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/. hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

PubMed Central

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality. PMID:23049544

Medical image compression based on vector quantization with variable block sizes in wavelet domain.

PubMed

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

Characterization of a 3rd Generation Lentiviral Vector Pseudotyped With Nipah Virus Envelope Proteins For Endothelial Cell Transduction

PubMed Central

Witting, Scott R.; Vallanda, Priya; Gamble, Aisha L.

2013-01-01

Lentiviruses are becoming progressively more popular as gene therapy vectors due to their ability to integrate into quiescent cells and recent clinical trial successes. Directing these vectors to specific cell types and limiting off-target transduction in vivo remains a challenge. Replacing the viral envelope proteins responsible for cellular binding, or pseudotyping, remains a common method to improve lentiviral targeting. Here, we describe the development of a high titer, 3rd generation lentiviral vector pseudotyped with Nipah virus fusion protein (NiV-F) and attachment protein (NiV-G). Critical to high titers was truncation of the cytoplasmic domains of both NiV-F and NiV-G. As known targets of wild-type Nipah virus, primary endothelial cells are shown to be effectively transduced by the Nipah pseudotype. In contrast, human CD34+ hematopoietic progenitors were not significantly transduced. Additionally, the Nipah pseudotype has increased stability in human serum compared to VSV pseudotyped lentivirus. These findings suggest that the use of Nipah virus envelope proteins in 3rd generation lentiviral vectors would be a valuable tool for gene delivery targeted to endothelial cells. PMID:23698741

Characterization of a third generation lentiviral vector pseudotyped with Nipah virus envelope proteins for endothelial cell transduction.

PubMed

Witting, S R; Vallanda, P; Gamble, A L

2013-10-01

Lentiviruses are becoming progressively more popular as gene therapy vectors due to their ability to integrate into quiescent cells and recent clinical trial successes. Directing these vectors to specific cell types and limiting off-target transduction in vivo remains a challenge. Replacing the viral envelope proteins responsible for cellular binding, or pseudotyping, remains a common method to improve lentiviral targeting. Here, we describe the development of a high titer, third generation lentiviral vector pseudotyped with Nipah virus fusion protein (NiV-F) and attachment protein (NiV-G). Critical to high titers was truncation of the cytoplasmic domains of both NiV-F and NiV-G. As known targets of wild-type Nipah virus, primary endothelial cells are shown to be effectively transduced by the Nipah pseudotype. In contrast, human CD34+ hematopoietic progenitors were not significantly transduced. Additionally, the Nipah pseudotype has increased stability in human serum compared with vesicular stomatitis virus pseudotyped lentivirus. These findings suggest that the use of Nipah virus envelope proteins in third generation lentiviral vectors would be a valuable tool for gene delivery targeted to endothelial cells.

Attenuated Human Parainfluenza Virus Type 1 Expressing the Respiratory Syncytial Virus (RSV) Fusion (F) Glycoprotein from an Added Gene: Effects of Prefusion Stabilization and Packaging of RSV F

PubMed Central

Liu, Xiang; Liang, Bo; Ngwuta, Joan; Liu, Xueqiao; Surman, Sonja; Lingemann, Matthias; Kwong, Peter D.; Graham, Barney S.; Collins, Peter L.

2017-01-01

ABSTRACT Human respiratory syncytial virus (RSV) is the most prevalent worldwide cause of severe respiratory tract infection in infants and young children. Human parainfluenza virus type 1 (HPIV1) also causes severe pediatric respiratory illness, especially croup. Both viruses lack vaccines. Here, we describe the preclinical development of a bivalent RSV/HPIV1 vaccine based on a recombinant HPIV1 vector, attenuated by a stabilized mutation, that expresses RSV F protein modified for increased stability in the prefusion (pre-F) conformation by previously described disulfide bond (DS) and hydrophobic cavity-filling (Cav1) mutations. RSV F was expressed from the first or second gene position as the full-length protein or as a chimeric protein with its transmembrane and cytoplasmic tail (TMCT) domains substituted with those of HPIV1 F in an effort to direct packaging in the vector particles. All constructs were recovered by reverse genetics. The TMCT versions of RSV F were packaged in the rHPIV1 particles much more efficiently than their full-length counterparts. In hamsters, the presence of the RSV F gene, and in particular the TMCT versions, was attenuating and resulted in reduced immunogenicity. However, the vector expressing full-length RSV F from the pre-N position was immunogenic for RSV and HPIV1. It conferred complement-independent high-quality RSV-neutralizing antibodies at titers similar to those of wild-type RSV and provided protection against RSV challenge. The vectors exhibited stable RSV F expression in vitro and in vivo. In conclusion, an attenuated rHPIV1 vector expressing a pre-F-stabilized form of RSV F demonstrated promising immunogenicity and should be further developed as an intranasal pediatric vaccine. IMPORTANCE RSV and HPIV1 are major viral causes of acute pediatric respiratory illness for which no vaccines or suitable antiviral drugs are available. The RSV F glycoprotein is the major RSV neutralization antigen. We used a rHPIV1 vector, bearing a stabilized attenuating mutation, to express the RSV F glycoprotein bearing amino acid substitutions that increase its stability in the pre-F form, the most immunogenic form that elicits highly functional virus-neutralizing antibodies. RSV F was expressed from the pre-N or N-P gene position of the rHPIV1 vector as a full-length protein or as a chimeric form with its TMCT domain derived from HPIV1 F. TMCT modification greatly increased packaging of RSV F into the vector particles but also increased vector attenuation in vivo, resulting in reduced immunogenicity. In contrast, full-length RSV F expressed from the pre-N position was immunogenic, eliciting complement-independent RSV-neutralizing antibodies and providing protection against RSV challenge. PMID:28835504

tESA: a distributional measure for calculating semantic relatedness.

PubMed

Rybinski, Maciej; Aldana-Montes, José Francisco

2016-12-28

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in the ever growing domain of biomedical informatics. The problem of most state-of-the-art methods for calculating semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this paper we present tESA, an extension to a well known Explicit Semantic Relatedness (ESA) method. In our extension we use two separate sets of vectors, corresponding to different sections of the articles from the underlying corpus of documents, as opposed to the original method, which only uses a single vector space. We present an evaluation of Life Sciences domain-focused applicability of both tESA and domain-adapted Explicit Semantic Analysis. The methods are tested against a set of standard benchmarks established for the evaluation of biomedical semantic relatedness quality. Our experiments show that the propsed method achieves results comparable with or superior to the current state-of-the-art methods. Additionally, a comparative discussion of the results obtained with tESA and ESA is presented, together with a study of the adaptability of the methods to different corpora and their performance with different input parameters. Our findings suggest that combined use of the semantics from different sections (i.e. extending the original ESA methodology with the use of title vectors) of the documents of scientific corpora may be used to enhance the performance of a distributional semantic relatedness measures, which can be observed in the largest reference datasets. We also present the impact of the proposed extension on the size of distributional representations.

Fast support vector data descriptions for novelty detection.

PubMed

Liu, Yi-Hung; Liu, Yan-Chen; Chen, Yen-Jen

2010-08-01

Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors. For applications where fast real-time response is needed, how to speed up the decision function is crucial. This paper aims at dealing with the issue of reducing the testing time complexity of SVDD. A method called fast SVDD (F-SVDD) is proposed. Unlike the traditional methods which all try to compress a kernel expansion into one with fewer terms, the proposed F-SVDD directly finds the preimage of a feature vector, and then uses a simple relationship between this feature vector and the SVDD sphere center to re-express the center with a single vector. The decision function of F-SVDD contains only one kernel term, and thus the decision boundary of F-SVDD is only spherical in the original space. Hence, the run-time complexity of the F-SVDD decision function is no longer linear in the support vectors, but is a constant, no matter how large the training set size is. In this paper, we also propose a novel direct preimage-finding method, which is noniterative and involves no free parameters. The unique preimage can be obtained in real time by the proposed direct method without taking trial-and-error. For demonstration, several real-world data sets and a large-scale data set, the extended MIT face data set, are used in experiments. In addition, a practical industry example regarding liquid crystal display micro-defect inspection is also used to compare the applicability of SVDD and our proposed F-SVDD when faced with mass data input. The results are very encouraging.

Support Vector Machine-Based Endmember Extraction

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

Filippi, Anthony M; Archibald, Richard K

Introduced in this paper is the utilization of Support Vector Machines (SVMs) to automatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality, and hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this Support Vector Machine-Based Endmembermore » Extraction (SVM-BEE) algorithm has the capability of autonomously determining endmembers from multiple clusters with computational speed and accuracy, while maintaining a robust tolerance to noise.« less

The C-terminus of the Polerovirus P5 readthrough domain limits virus infection to the phloem

USDA-ARS?s Scientific Manuscript database

Unlike most plant viruses, poleroviruses are restricted to vascular phloem tissues, from which they are transmitted by their aphid vectors. Phloem limitation has been attributed to the absence of virus proteins facilitating movement or counteracting plant defense. The polerovirus capsid is composed ...

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review.

PubMed

Gambhir, Shalini; Malik, Sanjay Kumar; Kumar, Yugal

2016-12-01

In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy.

Constraining primordial vector mode from B-mode polarization

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

Saga, Shohei; Ichiki, Kiyotomo; Shiraishi, Maresuke, E-mail: saga.shohei@nagoya-u.jp, E-mail: maresuke.shiraishi@pd.infn.it, E-mail: ichiki@a.phys.nagoya-u.ac.jp

The B-mode polarization spectrum of the Cosmic Microwave Background (CMB) may be the smoking gun of not only the primordial tensor mode but also of the primordial vector mode. If there exist nonzero vector-mode metric perturbations in the early Universe, they are known to be supported by anisotropic stress fluctuations of free-streaming particles such as neutrinos, and to create characteristic signatures on both the CMB temperature, E-mode, and B-mode polarization anisotropies. We place constraints on the properties of the primordial vector mode characterized by the vector-to-scalar ratio r{sub v} and the spectral index n{sub v} of the vector-shear power spectrum,more » from the Planck and BICEP2 B-mode data. We find that, for scale-invariant initial spectra, the ΛCDM model including the vector mode fits the data better than the model including the tensor mode. The difference in χ{sup 2} between the vector and tensor models is Δχ{sup 2} = 3.294, because, on large scales the vector mode generates smaller temperature fluctuations than the tensor mode, which is preferred for the data. In contrast, the tensor mode can fit the data set equally well if we allow a significantly blue-tilted spectrum. We find that the best-fitting tensor mode has a large blue tilt and leads to an indistinct reionization bump on larger angular scales. The slightly red-tilted vector mode supported by the current data set can also create O(10{sup -22})-Gauss magnetic fields at cosmological recombination. Our constraints should motivate research that considers models of the early Universe that involve the vector mode.« less

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

PubMed

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu-Au porphyry deposit in the central Iran

NASA Astrophysics Data System (ADS)

Fatehi, Moslem; Asadi, Hooshang H.

2017-04-01

In this study, the application of a transductive support vector machine (TSVM), an innovative semi-supervised learning algorithm, has been proposed for mapping the potential drill targets at a detailed exploration stage. The semi-supervised learning method is a hybrid of supervised and unsupervised learning approach that simultaneously uses both training and non-training data to design a classifier. By using the TSVM algorithm, exploration layers at the Dalli porphyry Cu-Au deposit in the central Iran were integrated to locate the boundary of the Cu-Au mineralization for further drilling. By applying this algorithm on the non-training (unlabeled) and limited training (labeled) Dalli exploration data, the study area was classified in two domains of Cu-Au ore and waste. Then, the results were validated by the earlier block models created, using the available borehole and trench data. In addition to TSVM, the support vector machine (SVM) algorithm was also implemented on the study area for comparison. Thirty percent of the labeled exploration data was used to evaluate the performance of these two algorithms. The results revealed 87 percent correct recognition accuracy for the TSVM algorithm and 82 percent for the SVM algorithm. The deepest inclined borehole, recently drilled in the western part of the Dalli deposit, indicated that the boundary of Cu-Au mineralization, as identified by the TSVM algorithm, was only 15 m off from the actual boundary intersected by this borehole. According to the results of the TSVM algorithm, six new boreholes were suggested for further drilling at the Dalli deposit. This study showed that the TSVM algorithm could be a useful tool for enhancing the mineralization zones and consequently, ensuring a more accurate drill hole planning.

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

PubMed

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

PubMed

Paiva, Joana S; Cardoso, João; Pereira, Tânia

2018-01-01

The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

MotionExplorer: exploratory search in human motion capture data based on hierarchical aggregation.

PubMed

Bernard, Jürgen; Wilhelm, Nils; Krüger, Björn; May, Thorsten; Schreck, Tobias; Kohlhammer, Jörn

2013-12-01

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

PubMed

Zhang, Yi; Li, Peiyang; Zhu, Xuyang; Su, Steven W; Guo, Qing; Xu, Peng; Yao, Dezhong

2017-01-01

The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which outperforms other feature models.

Implications of Domain-General "Psychological Support Skills" for Transfer of Skill and Acquisition of Expertise

ERIC Educational Resources Information Center

Eccles, David W.; Feltovich, Paul J.

2008-01-01

The article proposes that individuals who acquire certain psychological support skills may experience accelerated learning and enhanced performance in many domains. In support of this proposal, we present evidence that these skills enhance learning and performance, that they are domain-general in that they can be applied in a variety of domains,…

A Web application for the management of clinical workflow in image-guided and adaptive proton therapy for prostate cancer treatments.

PubMed

Yeung, Daniel; Boes, Peter; Ho, Meng Wei; Li, Zuofeng

2015-05-08

Image-guided radiotherapy (IGRT), based on radiopaque markers placed in the prostate gland, was used for proton therapy of prostate patients. Orthogonal X-rays and the IBA Digital Image Positioning System (DIPS) were used for setup correction prior to treatment and were repeated after treatment delivery. Following a rationale for margin estimates similar to that of van Herk,(1) the daily post-treatment DIPS data were analyzed to determine if an adaptive radiotherapy plan was necessary. A Web application using ASP.NET MVC5, Entity Framework, and an SQL database was designed to automate this process. The designed features included state-of-the-art Web technologies, a domain model closely matching the workflow, a database-supporting concurrency and data mining, access to the DIPS database, secured user access and roles management, and graphing and analysis tools. The Model-View-Controller (MVC) paradigm allowed clean domain logic, unit testing, and extensibility. Client-side technologies, such as jQuery, jQuery Plug-ins, and Ajax, were adopted to achieve a rich user environment and fast response. Data models included patients, staff, treatment fields and records, correction vectors, DIPS images, and association logics. Data entry, analysis, workflow logics, and notifications were implemented. The system effectively modeled the clinical workflow and IGRT process.

Skipping the real world: Classification of PolSAR images without explicit feature extraction

NASA Astrophysics Data System (ADS)

Hänsch, Ronny; Hellwich, Olaf

2018-06-01

The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset).

A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects

PubMed Central

Ng, Selina S. Y.; Tse, Peter W.; Tsui, Kwok L.

2014-01-01

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets. PMID:24419162

Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

PubMed Central

Bang, Jae Won; Choi, Jong-Suk; Park, Kang Ryoung

2013-01-01

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods. PMID:23669713

A one-versus-all class binarization strategy for bearing diagnostics of concurrent defects.

PubMed

Ng, Selina S Y; Tse, Peter W; Tsui, Kwok L

2014-01-13

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets.

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

Progressive Classification Using Support Vector Machines

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Kocurek, Michael

2009-01-01

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Right hemisphere grey matter structure and language outcomes in chronic left hemisphere stroke

PubMed Central

Xing, Shihui; Lacey, Elizabeth H.; Skipper-Kallal, Laura M.; Jiang, Xiong; Harris-Love, Michelle L.; Zeng, Jinsheng

2016-01-01

The neural mechanisms underlying recovery of language after left hemisphere stroke remain elusive. Although older evidence suggested that right hemisphere language homologues compensate for damage in left hemisphere language areas, the current prevailing theory suggests that right hemisphere engagement is ineffective or even maladaptive. Using a novel combination of support vector regression-based lesion-symptom mapping and voxel-based morphometry, we aimed to determine whether local grey matter volume in the right hemisphere independently contributes to aphasia outcomes after chronic left hemisphere stroke. Thirty-two left hemisphere stroke survivors with aphasia underwent language assessment with the Western Aphasia Battery-Revised and tests of other cognitive domains. High-resolution T1-weighted images were obtained in aphasia patients and 30 demographically matched healthy controls. Support vector regression-based multivariate lesion-symptom mapping was used to identify critical language areas in the left hemisphere and then to quantify each stroke survivor’s lesion burden in these areas. After controlling for these direct effects of the stroke on language, voxel-based morphometry was then used to determine whether local grey matter volumes in the right hemisphere explained additional variance in language outcomes. In brain areas in which grey matter volumes related to language outcomes, we then compared grey matter volumes in patients and healthy controls to assess post-stroke plasticity. Lesion–symptom mapping showed that specific left hemisphere regions related to different language abilities. After controlling for lesion burden in these areas, lesion size, and demographic factors, grey matter volumes in parts of the right temporoparietal cortex positively related to spontaneous speech, naming, and repetition scores. Examining whether domain general cognitive functions might explain these relationships, partial correlations demonstrated that grey matter volumes in these clusters related to verbal working memory capacity, but not other cognitive functions. Further, grey matter volumes in these areas were greater in stroke survivors than healthy control subjects. To confirm this result, 10 chronic left hemisphere stroke survivors with no history of aphasia were identified. Grey matter volumes in right temporoparietal clusters were greater in stroke survivors with aphasia compared to those without history of aphasia. These findings suggest that the grey matter structure of right hemisphere posterior dorsal stream language homologues independently contributes to language production abilities in chronic left hemisphere stroke, and that these areas may undergo hypertrophy after a stroke causing aphasia. PMID:26521078

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.

MosquitoMap and the Mal-area calculator: new web tools to relate mosquito species distribution with vector borne disease

PubMed Central

2010-01-01

Background Mosquitoes are important vectors of diseases but, in spite of various mosquito faunistic surveys globally, there is a need for a spatial online database of mosquito collection data and distribution summaries. Such a resource could provide entomologists with the results of previous mosquito surveys, and vector disease control workers, preventative medicine practitioners, and health planners with information relating mosquito distribution to vector-borne disease risk. Results A web application called MosquitoMap was constructed comprising mosquito collection point data stored in an ArcGIS 9.3 Server/SQL geodatabase that includes administrative area and vector species x country lookup tables. In addition to the layer containing mosquito collection points, other map layers were made available including environmental, and vector and pathogen/disease distribution layers. An application within MosquitoMap called the Mal-area calculator (MAC) was constructed to quantify the area of overlap, for any area of interest, of vector, human, and disease distribution models. Data standards for mosquito records were developed for MosquitoMap. Conclusion MosquitoMap is a public domain web resource that maps and compares georeferenced mosquito collection points to other spatial information, in a geographical information system setting. The MAC quantifies the Mal-area, i.e. the area where it is theoretically possible for vector-borne disease transmission to occur, thus providing a useful decision tool where other disease information is limited. The Mal-area approach emphasizes the independent but cumulative contribution to disease risk of the vector species predicted present. MosquitoMap adds value to, and makes accessible, the results of past collecting efforts, as well as providing a template for other arthropod spatial databases. PMID:20167090

MosquitoMap and the Mal-area calculator: new web tools to relate mosquito species distribution with vector borne disease.

PubMed

Foley, Desmond H; Wilkerson, Richard C; Birney, Ian; Harrison, Stanley; Christensen, Jamie; Rueda, Leopoldo M

2010-02-18

Mosquitoes are important vectors of diseases but, in spite of various mosquito faunistic surveys globally, there is a need for a spatial online database of mosquito collection data and distribution summaries. Such a resource could provide entomologists with the results of previous mosquito surveys, and vector disease control workers, preventative medicine practitioners, and health planners with information relating mosquito distribution to vector-borne disease risk. A web application called MosquitoMap was constructed comprising mosquito collection point data stored in an ArcGIS 9.3 Server/SQL geodatabase that includes administrative area and vector species x country lookup tables. In addition to the layer containing mosquito collection points, other map layers were made available including environmental, and vector and pathogen/disease distribution layers. An application within MosquitoMap called the Mal-area calculator (MAC) was constructed to quantify the area of overlap, for any area of interest, of vector, human, and disease distribution models. Data standards for mosquito records were developed for MosquitoMap. MosquitoMap is a public domain web resource that maps and compares georeferenced mosquito collection points to other spatial information, in a geographical information system setting. The MAC quantifies the Mal-area, i.e. the area where it is theoretically possible for vector-borne disease transmission to occur, thus providing a useful decision tool where other disease information is limited. The Mal-area approach emphasizes the independent but cumulative contribution to disease risk of the vector species predicted present. MosquitoMap adds value to, and makes accessible, the results of past collecting efforts, as well as providing a template for other arthropod spatial databases.

Supporting multiple domains in a single reuse repository

NASA Technical Reports Server (NTRS)

Eichmann, David A.

1992-01-01

Domain analysis typically results in the construction of a domain-specific repository. Such a repository imposes artificial boundaries on the sharing of similar assets between related domains. A lattice-based approach to repository modeling can preserve a reuser's domain specific view of the repository, while avoiding replication of commonly used assets and supporting a more general perspective on domain interrelationships.

A recombinant novirhabdovirus presenting at the surface the E Glycoprotein from West Nile Virus (WNV) is immunogenic and provides partial protection against lethal WNV challenge in BALB/c mice.

PubMed

Nzonza, Angella; Lecollinet, Sylvie; Chat, Sophie; Lowenski, Steeve; Mérour, Emilie; Biacchesi, Stéphane; Brémont, Michel

2014-01-01

West Nile Virus (WNV) is a zoonotic mosquito-transmitted flavivirus that can infect and cause disease in mammals including humans. Our study aimed at developing a WNV vectored vaccine based on a fish Novirhabdovirus, the Viral Hemorrhagic Septicemia virus (VHSV). VHSV replicates at temperatures lower than 20°C and is naturally inactivated at higher temperatures. A reverse genetics system has recently been developed in our laboratory for VHSV allowing the addition of genes in the viral genome and the recovery of the respective recombinant viruses (rVHSV). In this study, we have generated rVHSV vectors bearing the complete WNV envelope gene (EWNV) (rVHSV-EWNV) or fragments encoding E subdomains (either domain III alone or domain III fused to domain II) (rVHSV-DIIIWNV and rVHSV-DII-DIIIWNV, respectively) in the VHSV genome between the N and P cistrons. With the objective to enhance the targeting of the EWNV protein or EWNV-derived domains to the surface of VHSV virions, Novirhadovirus G-derived signal peptide and transmembrane domain (SPG and TMG) were fused to EWNV at its amino and carboxy termini, respectively. By Western-blot analysis, electron microscopy observations or inoculation experiments in mice, we demonstrated that both the EWNV and the DIIIWNV could be expressed at the viral surface of rVHSV upon addition of SPG. Every constructs expressing EWNV fused to SPG protected 40 to 50% of BALB/cJ mice against WNV lethal challenge and specifically rVHSV-SPGEWNV induced a neutralizing antibody response that correlated with protection. Surprisingly, rVHSV expressing EWNV-derived domain III or II and III were unable to protect mice against WNV challenge, although these domains were highly incorporated in the virion and expressed at the viral surface. In this study we demonstrated that a heterologous glycoprotein and non membrane-anchored protein, can be efficiently expressed at the surface of rVHSV making this approach attractive to develop new vaccines against various pathogens.

Habitat Suitability Model for the Distribution of Ixodes scapularis (Acari: Ixodidae) in Minnesota.

PubMed

Johnson, T L; Bjork, J K H; Neitzel, D F; Dorr, F M; Schiffman, E K; Eisen, R J

2016-05-01

Ixodes scapularis Say, the black-legged tick, is the primary vector in the eastern United States of several pathogens causing human diseases including Lyme disease, anaplasmosis, and babesiosis. Over the past two decades, I. scapularis-borne diseases have increased in incidence as well as geographic distribution. Lyme disease exists in two major foci in the United States, one encompassing northeastern states and the other in the Upper Midwest. Minnesota represents a state with an appreciable increase in counties reporting I. scapularis-borne illnesses, suggesting geographic expansion of vector populations in recent years. Recent tick distribution records support this assumption. Here, we used those records to create a fine resolution, subcounty-level distribution model for I. scapularis using variable response curves in addition to tests of variable importance. The model identified 19% of Minnesota as potentially suitable for establishment of the tick and indicated with high accuracy (AUC = 0.863) that the distribution is driven by land cover type, summer precipitation, maximum summer temperatures, and annual temperature variation. We provide updated records of established populations near the northwestern species range limit and present a model that increases our understanding of the potential distribution of I. scapularis in Minnesota. Published by Oxford University Press on behalf of Entomological Society of America 2016. This work is written by US Government employees and is in the public domain in the US.

The crystal structures of two salivary cystatins from the tick Ixodes scapularis and the effect of these inhibitors on the establishment of Borrelia burgdorferi infection in a murine model

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

Kotsyfakis, Michalis; Horka, Helena; Salat, Jiri

2010-11-17

We have previously demonstrated that two salivary cysteine protease inhibitors from the Borrelia burgdorferi (Lyme disease) vector Ixodes scapularis - namely sialostatins L and L2 - play an important role in tick biology, as demonstrated by the fact that silencing of both sialostatins in tandem results in severe feeding defects. Here we show that sialostatin L2 - but not sialostatin L - facilitates the growth of B. burgdorferi in murine skin. To examine the structural basis underlying these differential effects of the two sialostatins, we have determined the crystal structures of both sialostatin L and L2. This is the firstmore » structural analysis of cystatins from an invertebrate source. Sialostatin L2 crystallizes as a monomer with an 'unusual' conformation of the N-terminus, while sialostatin L crystallizes as a domain-swapped dimer with an N-terminal conformation similar to other cystatins. Deletion of the 'unusual' N-terminal five residues of sialostatin L2 results in marked changes in its selectivity, suggesting that this region is a particularly important determinant of the biochemical activity of sialostatin L2. Collectively, our results reveal the structure of two tick salivary components that facilitate vector blood feeding and that one of them also supports pathogen transmission to the vertebrate host.« less

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C.

PubMed

Stoean, Ruxandra; Stoean, Catalin; Lupsor, Monica; Stefanescu, Horia; Badea, Radu

2011-01-01

Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance. The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis). Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the evolutionary method is viewed in comparison to the traditional one. The multifaceted discrimination into all five degrees of fibrosis and the slightly less difficult common separation into solely three related stages are both investigated. The resulting performance proves the superiority over the standard support vector classification and the attained formula is helpful in providing an immediate calculation of the liver stage for new cases, while establishing the presence/absence and comprehending the weight of each medical factor with respect to a certain fibrosis level. The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage. Perhaps most importantly, it significantly surpasses the classical support vector machines, which are both widely used and technically sound. All these therefore confirm the promise of the new methodology towards a dependable support within the medical decision-making. Copyright © 2010 Elsevier B.V. All rights reserved.

Interpreting linear support vector machine models with heat map molecule coloring

PubMed Central

2011-01-01

Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

Scalar and vector Keldysh models in the time domain

NASA Astrophysics Data System (ADS)

Kiselev, M. N.; Kikoin, K. A.

2009-04-01

The exactly solvable Keldysh model of disordered electron system in a random scattering field with extremely long correlation length is converted to the time-dependent model with extremely long relaxation. The dynamical problem is solved for the ensemble of two-level systems (TLS) with fluctuating well depths having the discrete Z 2 symmetry. It is shown also that the symmetric TLS with fluctuating barrier transparency may be described in terms of the vector Keldysh model with dime-dependent random planar rotations in xy plane having continuous SO(2) symmetry. Application of this model to description of dynamic fluctuations in quantum dots and optical lattices is discussed.

Flux-vector splitting algorithm for chain-rule conservation-law form

NASA Technical Reports Server (NTRS)

Shih, T. I.-P.; Nguyen, H. L.; Willis, E. A.; Steinthorsson, E.; Li, Z.

1991-01-01

A flux-vector splitting algorithm with Newton-Raphson iteration was developed for the 'full compressible' Navier-Stokes equations cast in chain-rule conservation-law form. The algorithm is intended for problems with deforming spatial domains and for problems whose governing equations cannot be cast in strong conservation-law form. The usefulness of the algorithm for such problems was demonstrated by applying it to analyze the unsteady, two- and three-dimensional flows inside one combustion chamber of a Wankel engine under nonfiring conditions. Solutions were obtained to examine the algorithm in terms of conservation error, robustness, and ability to handle complex flows on time-dependent grid systems.

LETTER TO THE EDITOR: Anti-self-dual Riemannian metrics without Killing vectors: can they be realized on K3?

NASA Astrophysics Data System (ADS)

Malykh, A. A.; Nutku, Y.; Sheftel, M. B.

2003-11-01

Explicit Riemannian metrics with Euclidean signature and anti-self-dual curvature that do not admit any Killing vectors are presented. The metric and the Riemann curvature scalars are homogeneous functions of degree zero in a single real potential and its derivatives. The solution for the potential is a sum of exponential functions which suggests that for the choice of a suitable domain of coordinates and parameters it can be the metric on a compact manifold. Then, by the theorem of Hitchin, it could be a class of metrics on K3, or on surfaces whose universal covering is K3.

Human adenovirus serotypes 3 and 5 bind to two different cellular receptors via the fiber head domain.

PubMed Central

Stevenson, S C; Rollence, M; White, B; Weaver, L; McClelland, A

1995-01-01

The adenovirus fiber protein is responsible for attachment of the virion to cell surface receptors. The identity of the cellular receptor which mediates binding is unknown, although there is evidence suggesting that two distinct adenovirus receptors interact with the group C (adenovirus type 5 [Ad5]) and the group B (Ad3) adenoviruses. In order to define the determinants of adenovirus receptor specificity, we have carried out a series of competition binding experiments using recombinant native fiber polypeptides from Ad5 and Ad3 and chimeric fiber proteins in which the head domains of Ad5 and Ad3 were exchanged. Specific binding of fiber to HeLa cell receptors was assessed with radiolabeled protein synthesized in vitro, and by competition analysis with baculovirus-expressed fiber protein. Fiber produced in vitro was found as both monomer and trimer, but only the assembled trimers had receptor binding activity. Competition data support the conclusion that Ad5 and Ad3 interact with different cellular receptors. The Ad5 receptor distribution on several cell lines was assessed with a fiber binding flow cytometric assay. HeLa cells were found to express high levels of receptor, while CHO and human diploid fibroblasts did not. A chimeric fiber containing the Ad5 fiber head domain blocked the binding of Ad5 fiber but not Ad3 fiber. Similarly, a chimeric fiber containing the Ad3 fiber head blocked the binding of labeled Ad3 fiber but not Ad5 fiber. In addition, the isolated Ad3 fiber head domain competed effectively with labeled Ad3 fiber for binding to HeLa cell receptors. These results demonstrate that the determinants of receptor binding are located in the head domain of the fiber and that the isolated head domain is capable of trimerization and binding to cellular receptors. Our results also show that it is possible to change the receptor specificity of the fiber protein by manipulation of sequences contained in the head domain. Modification or replacement of the fiber head domain with novel ligands may permit adenovirus vectors with new receptor specificities which could be useful for targeted gene delivery in vivo to be engineered. PMID:7707507

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.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Support vector machine for the diagnosis of malignant mesothelioma

NASA Astrophysics Data System (ADS)

Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

2018-04-01

Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

The optional selection of micro-motion feature based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Bo; Ren, Hongmei; Xiao, Zhi-he; Sheng, Jing

2017-11-01

Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).

Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain.

PubMed

Hou, Tingjun; Zhang, Wei; Case, David A; Wang, Wei

2008-02-29

Many important protein-protein interactions are mediated by peptide recognition modular domains, such as the Src homology 3 (SH3), SH2, PDZ, and WW domains. Characterizing the interaction interface of domain-peptide complexes and predicting binding specificity for modular domains are critical for deciphering protein-protein interaction networks. Here, we propose the use of an energetic decomposition analysis to characterize domain-peptide interactions and the molecular interaction energy components (MIECs), including van der Waals, electrostatic, and desolvation energy between residue pairs on the binding interface. We show a proof-of-concept study on the amphiphysin-1 SH3 domain interacting with its peptide ligands. The structures of the human amphiphysin-1 SH3 domain complexed with 884 peptides were first modeled using virtual mutagenesis and optimized by molecular mechanics (MM) minimization. Next, the MIECs between domain and peptide residues were computed using the MM/generalized Born decomposition analysis. We conducted two types of statistical analyses on the MIECs to demonstrate their usefulness for predicting binding affinities of peptides and for classifying peptides into binder and non-binder categories. First, combining partial least squares analysis and genetic algorithm, we fitted linear regression models between the MIECs and the peptide binding affinities on the training data set. These models were then used to predict binding affinities for peptides in the test data set; the predicted values have a correlation coefficient of 0.81 and an unsigned mean error of 0.39 compared with the experimentally measured ones. The partial least squares-genetic algorithm analysis on the MIECs revealed the critical interactions for the binding specificity of the amphiphysin-1 SH3 domain. Next, a support vector machine (SVM) was employed to build classification models based on the MIECs of peptides in the training set. A rigorous training-validation procedure was used to assess the performances of different kernel functions in SVM and different combinations of the MIECs. The best SVM classifier gave satisfactory predictions for the test set, indicated by average prediction accuracy rates of 78% and 91% for the binding and non-binding peptides, respectively. We also showed that the performance of our approach on both binding affinity prediction and binder/non-binder classification was superior to the performances of the conventional MM/Poisson-Boltzmann solvent-accessible surface area and MM/generalized Born solvent-accessible surface area calculations. Our study demonstrates that the analysis of the MIECs between peptides and the SH3 domain can successfully characterize the binding interface, and it provides a framework to derive integrated prediction models for different domain-peptide systems.

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis

PubMed Central

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-01-01

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. PMID:26403370

Digital TV processing system

NASA Technical Reports Server (NTRS)

1975-01-01

Two digital video data compression systems directly applicable to the Space Shuttle TV Communication System were described: (1) For the uplink, a low rate monochrome data compressor is used. The compression is achieved by using a motion detection technique in the Hadamard domain. To transform the variable source rate into a fixed rate, an adaptive rate buffer is provided. (2) For the downlink, a color data compressor is considered. The compression is achieved first by intra-color transformation of the original signal vector, into a vector which has lower information entropy. Then two-dimensional data compression techniques are applied to the Hadamard transformed components of this last vector. Mathematical models and data reliability analyses were also provided for the above video data compression techniques transmitted over a channel encoded Gaussian channel. It was shown that substantial gains can be achieved by the combination of video source and channel coding.

Efficient Power Network Analysis with Modeling of Inductive Effects

NASA Astrophysics Data System (ADS)

Zeng, Shan; Yu, Wenjian; Hong, Xianlong; Cheng, Chung-Kuan

In this paper, an efficient method is proposed to accurately analyze large-scale power/ground (P/G) networks, where inductive parasitics are modeled with the partial reluctance. The method is based on frequency-domain circuit analysis and the technique of vector fitting [14], and obtains the time-domain voltage response at given P/G nodes. The frequency-domain circuit equation including partial reluctances is derived, and then solved with the GMRES algorithm with rescaling, preconditioning and recycling techniques. With the merit of sparsified reluctance matrix and iterative solving techniques for the frequency-domain circuit equations, the proposed method is able to handle large-scale P/G networks with complete inductive modeling. Numerical results show that the proposed method is orders of magnitude faster than HSPICE, several times faster than INDUCTWISE [4], and capable of handling the inductive P/G structures with more than 100, 000 wire segments.

Crossover in growth laws for phase-separating binary fluids: molecular dynamics simulations.

PubMed

Ahmad, Shaista; Das, Subir K; Puri, Sanjay

2012-03-01

Pattern and dynamics during phase separation in a symmetrical binary (A+B) Lennard-Jones fluid are studied via molecular dynamics simulations after quenching homogeneously mixed critical (50:50) systems to temperatures below the critical one. The morphology of the domains, rich in A or B particles, is observed to be bicontinuous. The early-time growth of the average domain size is found to be consistent with the Lifshitz-Slyozov law for diffusive domain coarsening. After a characteristic time, dependent on the temperature, we find a clear crossover to an extended viscous hydrodynamic regime where the domains grow linearly with time. Pattern formation in the present system is compared with that in solid binary mixtures, as a function of temperature. Important results for the finite-size and temperature effects on the small-wave-vector behavior of the scattering function are also presented.

Resolution enhancement of low-quality videos using a high-resolution frame

NASA Astrophysics Data System (ADS)

Pham, Tuan Q.; van Vliet, Lucas J.; Schutte, Klamer

2006-01-01

This paper proposes an example-based Super-Resolution (SR) algorithm of compressed videos in the Discrete Cosine Transform (DCT) domain. Input to the system is a Low-Resolution (LR) compressed video together with a High-Resolution (HR) still image of similar content. Using a training set of corresponding LR-HR pairs of image patches from the HR still image, high-frequency details are transferred from the HR source to the LR video. The DCT-domain algorithm is much faster than example-based SR in spatial domain 6 because of a reduction in search dimensionality, which is a direct result of the compact and uncorrelated DCT representation. Fast searching techniques like tree-structure vector quantization 16 and coherence search1 are also key to the improved efficiency. Preliminary results on MJPEG sequence show promising result of the DCT-domain SR synthesis approach.

[Construction and expression of a eukaryotic expression vector containing human CR2-Fc fusion protein].

PubMed

Li, Xinxin; Wu, Zhihao; Zhang, Chuanfu; Jia, Leili; Song, Hongbin; Xu, Yuanyong

2014-01-01

To construct a eukaryotic expression vector containing human complement receptor 2 (CR2)-Fc and express the CR2-Fc fusion protein in Chinese hamster ovary (CHO) cells. The extracellular domain of human CR2 and IgG1 Fc were respectively amplified, ligated and inserted into the eukaryotic expression vector PCI-neo. After verified by restriction enzyme digestion and sequencing, the recombinant plasmid was transfected into CHO K1 cells. The ones with stable expression of the fusion protein were obtained by means of G418 selection. The expression of the CR2-Fc fusion protein was detected and confirmed by SDS-PAGE and Western blotting. Restriction enzyme digestion and sequencing demonstrated that the recombinant plasmid was valid. SDS-PAGE showed that relative molecular mass (Mr;) of the purified product was consistent with the expected value. Western blotting further proved the single band at the same position. We constructed the eukaryotic expression vector of CR2-Fc/PCI-neo successfully. The obtained fusion protein was active and can be used for the further study of the role in HIV control.

Evaluation of Cache-based Superscalar and Cacheless Vector Architectures for Scientific Computations

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Carter, Jonathan; Shalf, John; Skinner, David; Ethier, Stephane; Biswas, Rupak; Djomehri, Jahed; VanderWijngaart, Rob

2003-01-01

The growing gap between sustained and peak performance for scientific applications has become a well-known problem in high performance computing. The recent development of parallel vector systems offers the potential to bridge this gap for a significant number of computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX6 vector processor and the cache-based IBM Power3/4 superscalar architectures across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines a full spectrum of low-level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks using some simple optimizations. Finally, we evaluate the perfor- mance of several numerical codes from key scientific computing domains. Overall results demonstrate that the SX6 achieves high performance on a large fraction of our application suite and in many cases significantly outperforms the RISC-based architectures. However, certain classes of applications are not easily amenable to vectorization and would likely require extensive reengineering of both algorithm and implementation to utilize the SX6 effectively.

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.

On the sparseness of 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, Weida

2010-04-01

There is some empirical evidence available showing that 1-norm Support Vector Machines (1-norm SVMs) have good sparseness; however, both how good sparseness 1-norm SVMs can reach and whether they have a sparser representation than that of standard SVMs are not clear. In this paper we take into account the sparseness of 1-norm SVMs. Two upper bounds on the number of nonzero coefficients in the decision function of 1-norm SVMs are presented. First, the number of nonzero coefficients in 1-norm SVMs is at most equal to the number of only the exact support vectors lying on the +1 and -1 discriminating surfaces, while that in standard SVMs is equal to the number of support vectors, which implies that 1-norm SVMs have better sparseness than that of standard SVMs. Second, the number of nonzero coefficients is at most equal to the rank of the sample matrix. A brief review of the geometry of linear programming and the primal steepest edge pricing simplex method are given, which allows us to provide the proof of the two upper bounds and evaluate their tightness by experiments. Experimental results on toy data sets and the UCI data sets illustrate our analysis. Copyright 2009 Elsevier Ltd. All rights reserved.

Weighted K-means support vector machine for cancer prediction.

PubMed

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

Investigation of Performance Improvements Including Application of Inlet Guide Vanes to a Cross-flow Fan

DTIC Science & Technology

2009-09-01

25 Figure 17. IGV Cut Out from Fluid Domain...Figure 22. Installed IGVS as Viewed from the CFF Inlet.................................................30 Figure 23. Schematic of Turbine Test Rig (TTR...44 Figure 28. Close In View of Velocity Vector Plot Near IGVS for 6IGV Model..............45 Figure 29

Expression of Anaplasma marginale ankyrin repeat-containing proteins during infection of the mammalian host and tick vector

USDA-ARS?s Scientific Manuscript database

Using searches of the NCBI conserved domain database and SMART genomic architecture analysis, we identified three ankyrin repeat-containing genes in Anaplasma marginale: AM705, AM926 and AM638. Recombinant protein was used to immunize mice and generate fusion hybridomas secreting protein-specific mo...

Evidence from Fold Vergence for Coaxial Ductile Flow within the Otago Subduction Wedge, South Island, New Zealand

NASA Astrophysics Data System (ADS)

Rahl, J. M.; Brandon, M. T.

2001-12-01

There has been a long-standing debate about the tectonic significance of the highly folded Otago schist, which is exposed in the Otago uplift on the South Island of New Zealand. This uplift marks the forearc high of a long-lived subduction wedge that flanked the Mesozoic Gondwana margin. The exhumed metamorphic rocks record temperatures up to ~450 C and depths up to ~25 km. The dominant foliation is generally gently dipping, with attitudes that follow the form of the uplift. Mesoscale folds are common, and some regional-scale fold are recognized as well. Previous workers have argued that deformation within the Otago Schist resulted from trenchward shearing above a subducting oceanic plate, but there has been little evidence for systematic fold vergence across the uplift to support this idea. We describe here a new method for analysing the average vergence of a pervasively folded unit, with the goal to test the degree of non-coaxiality associated with ductile deformation within the Otago wedge. Our analysis exploits a new database, compiled by the New Zealand IGNS, which summarizes thousands of structural measurements for the Otago Schist. Vergence is defined in the usual manner as the asymmetry of the fold relative to its axial plane. This asymmetry is attributed to shear-induced rotation at the scale of the fold. Non-coaxial shear can be locally induced, especially in strongly layered schist sequences. We want to know if the fold vergence is systematically developed at the regional scale. Each fold is represented by a vector parallel to the fold axis, with a direction defined by the right-handed rotation implied by the vergence. We argue that the net vergence is well approximated by the Fisher vector mean of the vergence vectors. In addition, we consider the spatial distribution of the vergence vectors across the uplift, with a specific focus on the structural boundary between the Caples and Torlesse, two accretionary units that make up the wedge. We find that there is no systematic vergence at the regional scale or localized along the Torlesse/Caples boundary. We do find local domains at the 10 km scale with a consistent vergence direction, but at large scales, the vergence pattern becomes averaged out. These result support the idea that the Otago schist formed by vertical shortening within a regional-scale coaxial flow field, perhaps driven by underplating beneath the uplift. Furthermore, the lack of a regional-scale vergence implies very weak coupling across the subduction zone.

Image/text automatic indexing and retrieval system using context vector approach

NASA Astrophysics Data System (ADS)

Qing, Kent P.; Caid, William R.; Ren, Clara Z.; McCabe, Patrick

1995-11-01

Thousands of documents and images are generated daily both on and off line on the information superhighway and other media. Storage technology has improved rapidly to handle these data but indexing this information is becoming very costly. HNC Software Inc. has developed a technology for automatic indexing and retrieval of free text and images. This technique is demonstrated and is based on the concept of `context vectors' which encode a succinct representation of the associated text and features of sub-image. In this paper, we will describe the Automated Librarian System which was designed for free text indexing and the Image Content Addressable Retrieval System (ICARS) which extends the technique from the text domain into the image domain. Both systems have the ability to automatically assign indices for a new document and/or image based on the content similarities in the database. ICARS also has the capability to retrieve images based on similarity of content using index terms, text description, and user-generated images as a query without performing segmentation or object recognition.

Geographic and ecological distribution of the dengue and chikungunya virus vectors Aedes aegypti and Aedes albopictus in three major Cameroonian towns.

PubMed

Kamgang, B; Happi, J Y; Boisier, P; Njiokou, F; Hervé, J-P; Simard, F; Paupy, C

2010-06-01

Aedes albopictus (Diptera: Culicidae) was first reported in Central Africa in 2000, together with the indigenous mosquito species Aedes aegypti (Diptera: Culicidae). Because Ae. albopictus can also transmit arboviruses, its introduction is a public health concern. We undertook a comparative study in three Cameroonian towns (Sahelian domain: Garoua; equatorial domain: Douala and Yaoundé) in order to document infestation by the two species and their ecological preferences. High and variable levels of pre-imaginal Ae. aegypti and Ae. albopictus infestation were detected. Only Ae. aegypti was encountered in Garoua, whereas both species were found in Douala and Yaoundé, albeit with significant differences in their relative prevalence. Peridomestic water containers were the most strongly colonized and productive larval habitats for both species. No major differences in types of larval habitat were found, but Ae. albopictus preferentially bred in containers containing plant debris or surrounded by vegetation, whereas Ae. aegypti tended to breed in containers located in environments with a high density of buildings. These findings may have important implications for vector control strategies.

A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

PubMed

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

Determination of bulk domain structure and magnetization processes in bcc ferromagnetic alloys: Analysis of magnetostriction in F e83G a17

NASA Astrophysics Data System (ADS)

He, Yangkun; Coey, J. M. D.; Schaefer, Rudolf; Jiang, Chengbao

2018-01-01

The ground state of macroscopic samples of magnetically ordered materials is a domain state because of magnetostatic energy or entropy, yet we have limited experimental means for imaging the bulk domain structure and the magnetization process directly. The common methods available reveal the domains at the surface or in electron- or x-ray transparent lamellae, not those in the bulk. The magnetization curve just reflects the vector sum of the moments of all the domains in the sample, but magnetostriction curves are more informative. They are strongly influenced by the domain structure in the unmagnetized state and its evolution during the magnetization process in an applied field. Here we report a method of determining the bulk domain structure in a cubic magnetostrictive material by combining magneto-optic Kerr microscopy with magnetostriction and magnetization measurements on single crystals as a function of applied field. We analyze the magnetostriction of F e83G a17 crystals in terms of a domain structure that is greatly influenced by sample shape and heat treatment. Saturation magnetostriction measurements are used to determine the fraction of domains orientated along the three 〈100 〉 axes in the initial state. Domain wall motion and rotation process have characteristic signatures in the magnetostriction curves, including those associated with the Δ E effect and domain rotation through a 〈110 〉 auxetic direction.

Examining the Types of Social Support and the Actual Sources of Support in Older Chinese and Korean Immigrants

ERIC Educational Resources Information Center

Wong, Sabrina T.; Yoo, Grace J.; Stewart, Anita L.

2005-01-01

This study explored social support domains and actual sources of support for older Chinese and Korean immigrants and compared them to the traditional domains based on mainly White, middle class populations. Fifty-two older Cantonese and Korean speaking immigrants participated in one of eight focus groups. We identified four similar domains:…

An iterative solver for the 3D Helmholtz equation

NASA Astrophysics Data System (ADS)

Belonosov, Mikhail; Dmitriev, Maxim; Kostin, Victor; Neklyudov, Dmitry; Tcheverda, Vladimir

2017-09-01

We develop a frequency-domain iterative solver for numerical simulation of acoustic waves in 3D heterogeneous media. It is based on the application of a unique preconditioner to the Helmholtz equation that ensures convergence for Krylov subspace iteration methods. Effective inversion of the preconditioner involves the Fast Fourier Transform (FFT) and numerical solution of a series of boundary value problems for ordinary differential equations. Matrix-by-vector multiplication for iterative inversion of the preconditioned matrix involves inversion of the preconditioner and pointwise multiplication of grid functions. Our solver has been verified by benchmarking against exact solutions and a time-domain solver.

Mesophilic and hyperthermophilic adenylate kinases differ in their tolerance to random fragmentation.

PubMed

Segall-Shapiro, Thomas H; Nguyen, Peter Q; Dos Santos, Edgardo D; Subedi, Saurav; Judd, Justin; Suh, Junghae; Silberg, Jonathan J

2011-02-11

The extent to which thermostability influences the location of protein fragmentation sites that allow retention of function is not known. To evaluate this, we used a novel transposase-based approach to create libraries of vectors that express structurally-related fragments of Bacillus subtilis adenylate kinase (BsAK) and Thermotoga neapolitana adenylate kinase (TnAK) with identical modifications at their termini, and we selected for variants in each library that complement the growth of Escherichia coli with a temperature-sensitive adenylate kinase (AK). Mutants created using the hyperthermophilic TnAK were found to support growth with a higher frequency (44%) than those generated from the mesophilic BsAK (6%), and selected TnAK mutants complemented E. coli growth more strongly than homologous BsAK variants. Sequencing of functional clones from each library also identified a greater dispersion of fragmentation sites within TnAK. Nondisruptive fission sites were observed within the AMP binding and core domains of both AK homologs. However, only TnAK contained sites within the lid domain, which undergoes dynamic fluctuations that are critical for catalysis. These findings implicate the flexible lid domain as having an increased sensitivity to fission events at physiological temperatures. In addition, they provide evidence that comparisons of nondisruptive fission sites in homologous proteins could be useful for finding dynamic regions whose conformational fluctuations are important for function, and they show that the discovery of protein fragments that cooperatively function in mesophiles can be aided by the use of thermophilic enzymes as starting points for protein design. Copyright © 2010 Elsevier Ltd. All rights reserved.

Applying a general triclinic transpression model to highly partitioned brittle-ductile shear zones: A case study from the Torcal de Antequera massif, external Betics, southern Spain

NASA Astrophysics Data System (ADS)

Díaz-Azpiroz, M.; Barcos, L.; Balanyá, J. C.; Fernández, C.; Expósito, I.; Czeck, D. M.

2014-11-01

Oblique convergence and subsequent transpression kinematics can be considered as the general situation in most convergent and strike-slip tectonic boundaries. To better understand such settings, progressively more complex kinematic models have been proposed, which need to be tested against natural shear zones using standardized procedures that minimise subjectivism. In this work, a protocol to test a general triclinic transpression model is applied to the Torcal de Antequera massif (TAM), an essentially brittle shear zone. Our results, given as kinematic parameters of the transpressive flow (transpression obliquity, ϕ; extrusion obliquity, υ; and kinematic vorticity number, Wk), suggest that the bulk triclinic transpressive flow imposed on the TAM was partitioned into two different flow fields, following a general partitioning type. As such, one flow field produced narrow structural domains located at the limits of the TAM, where mainly dextral strike-slip simple-shear-dominated transpression took place (Outer domains, ODs). In contrast, the remaining part of the bulk flow produced pure-shear-dominated dextral triclinic transpression at the inner part of the TAM (Inner domain, ID). A graphical method relating internal (ϕ, Wk) to far-field (dip of the shear zone boundary, δ; angle of oblique convergence, α) transpression parameters is proposed to obtain the theoretical horizontal velocity vector (V→), which in the case of the TAM, ranges between 099 and 118. These results support the applicability of kinematic models of triclinic transpression to brittle-ductile shear zones and the potential utility of the proposed protocol.

Discovering body site and severity modifiers in clinical texts

PubMed Central

Dligach, Dmitriy; Bethard, Steven; Becker, Lee; Miller, Timothy; Savova, Guergana K

2014-01-01

Objective To research computational methods for discovering body site and severity modifiers in clinical texts. Methods We cast the task of discovering body site and severity modifiers as a relation extraction problem in the context of a supervised machine learning framework. We utilize rich linguistic features to represent the pairs of relation arguments and delegate the decision about the nature of the relationship between them to a support vector machine model. We evaluate our models using two corpora that annotate body site and severity modifiers. We also compare the model performance to a number of rule-based baselines. We conduct cross-domain portability experiments. In addition, we carry out feature ablation experiments to determine the contribution of various feature groups. Finally, we perform error analysis and report the sources of errors. Results The performance of our method for discovering body site modifiers achieves F1 of 0.740–0.908 and our method for discovering severity modifiers achieves F1 of 0.905–0.929. Discussion Results indicate that both methods perform well on both in-domain and out-domain data, approaching the performance of human annotators. The most salient features are token and named entity features, although syntactic dependency features also contribute to the overall performance. The dominant sources of errors are infrequent patterns in the data and inability of the system to discern deeper semantic structures. Conclusions We investigated computational methods for discovering body site and severity modifiers in clinical texts. Our best system is released open source as part of the clinical Text Analysis and Knowledge Extraction System (cTAKES). PMID:24091648

Discovering body site and severity modifiers in clinical texts.

PubMed

Dligach, Dmitriy; Bethard, Steven; Becker, Lee; Miller, Timothy; Savova, Guergana K

2014-01-01

To research computational methods for discovering body site and severity modifiers in clinical texts. We cast the task of discovering body site and severity modifiers as a relation extraction problem in the context of a supervised machine learning framework. We utilize rich linguistic features to represent the pairs of relation arguments and delegate the decision about the nature of the relationship between them to a support vector machine model. We evaluate our models using two corpora that annotate body site and severity modifiers. We also compare the model performance to a number of rule-based baselines. We conduct cross-domain portability experiments. In addition, we carry out feature ablation experiments to determine the contribution of various feature groups. Finally, we perform error analysis and report the sources of errors. The performance of our method for discovering body site modifiers achieves F1 of 0.740-0.908 and our method for discovering severity modifiers achieves F1 of 0.905-0.929. Results indicate that both methods perform well on both in-domain and out-domain data, approaching the performance of human annotators. The most salient features are token and named entity features, although syntactic dependency features also contribute to the overall performance. The dominant sources of errors are infrequent patterns in the data and inability of the system to discern deeper semantic structures. We investigated computational methods for discovering body site and severity modifiers in clinical texts. Our best system is released open source as part of the clinical Text Analysis and Knowledge Extraction System (cTAKES).

T-ray relevant frequencies for osteosarcoma classification

NASA Astrophysics Data System (ADS)

Withayachumnankul, W.; Ferguson, B.; Rainsford, T.; Findlay, D.; Mickan, S. P.; Abbott, D.

2006-01-01

We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

Recombinase-Mediated Cassette Exchange Using Adenoviral Vectors.

PubMed

Kolb, Andreas F; Knowles, Christopher; Pultinevicius, Patrikas; Harbottle, Jennifer A; Petrie, Linda; Robinson, Claire; Sorrell, David A

2017-01-01

Site-specific recombinases are important tools for the modification of mammalian genomes. In conjunction with viral vectors, they can be utilized to mediate site-specific gene insertions in animals and in cell lines which are difficult to transfect. Here we describe a method for the generation and analysis of an adenovirus vector supporting a recombinase-mediated cassette exchange reaction and discuss the advantages and limitations of this approach.

A domains-based taxonomy of supported accommodation for people with severe and persistent mental illness.

PubMed

Siskind, Dan; Harris, Meredith; Pirkis, Jane; Whiteford, Harvey

2013-06-01

A lack of definitional clarity in supported accommodation and the absence of a widely accepted system for classifying supported accommodation models creates barriers to service planning and evaluation. We undertook a systematic review of existing supported accommodation classification systems. Using a structured system for qualitative data analysis, we reviewed the stratification features in these classification systems, identified the key elements of supported accommodation and arranged them into domains and dimensions to create a new taxonomy. The existing classification systems were mapped onto the new taxonomy to verify the domains and dimensions. Existing classification systems used either a service-level characteristic or programmatic approach. We proposed a taxonomy based around four domains: duration of tenure; patient characteristics; housing characteristics; and service characteristics. All of the domains in the taxonomy were drawn from the existing classification structures; however, none of the existing classification structures covered all of the domains in the taxonomy. Existing classification systems are regionally based, limited in scope and lack flexibility. A domains-based taxonomy can allow more accurate description of supported accommodation services, aid in identifying the service elements likely to improve outcomes for specific patient populations, and assist in service planning.

Functional Pathways of Social Support for Mental Health in Work and Family Domains Among Chinese Scientific and Technological Professionals.

PubMed

Gan, Yiqun; Gan, Tingting; Chen, Zhiyan; Miao, Miao; Zhang, Kan

2015-10-01

This study investigated the role of social support in the complex pattern of associations among stressors, work-family interferences and depression in the domains of work and family. A questionnaire was administered to a nationwide sample of 11,419 Chinese science and technology professionals. Several structural equation models were specified to determine whether social support functioned as a predictor or a mediator. Using Mplus 5.0, we compared the moderation model, the independence model, the antecedent model and the mediation model. The results revealed that the relationship between work-family interference and social support was domain specific. The independence model fit the data best in the work domain. Both the moderation model and the antecedent model fit the family domain data equally well. The current study was conducted to answer the need for comprehensive investigations of cultural uniqueness in the antecedents of work-family interference. The domain specificity, i.e. the multiple channels of the functions of support in the family domain and not in the work domain, ensures that this study is unique and culturally specific. Copyright © 2014 John Wiley & Sons, Ltd.

Assessment of tropism and effectiveness of new primate-derived hybrid recombinant AAV serotypes in the mouse and primate retina.

PubMed

Charbel Issa, Peter; De Silva, Samantha R; Lipinski, Daniel M; Singh, Mandeep S; Mouravlev, Alexandre; You, Qisheng; Barnard, Alun R; Hankins, Mark W; During, Matthew J; Maclaren, Robert E

2013-01-01

Adeno-associated viral vectors (AAV) have been shown to be safe in the treatment of retinal degenerations in clinical trials. Thus, improving the efficiency of viral gene delivery has become increasingly important to increase the success of clinical trials. In this study, structural domains of different rAAV serotypes isolated from primate brain were combined to create novel hybrid recombinant AAV serotypes, rAAV2/rec2 and rAAV2/rec3. The efficacy of these novel serotypes were assessed in wild type mice and in two models of retinal degeneration (the Abca4(-/-) mouse which is a model for Stargardt disease and in the Pde6b(rd1/rd1) mouse) in vivo, in primate tissue ex-vivo, and in the human-derived SH-SY5Y cell line, using an identical AAV2 expression cassette. We show that these novel hybrid serotypes can transduce retinal tissue in mice and primates efficiently, although no more than AAV2/2 and rAAV2/5 serotypes. Transduction efficiency appeared lower in the Abca4(-/-) mouse compared to wild type with all vectors tested, suggesting an effect of specific retinal diseases on the efficiency of gene delivery. Shuffling of AAV capsid domains may have clinical applications for patients who develop T-cell immune responses following AAV gene therapy, as specific peptide antigen sequences could be substituted using this technique prior to vector re-treatments.

Retargeting of adenovirus vectors through genetic fusion of a single-chain or single-domain antibody to capsid protein IX.

PubMed

Poulin, Kathy L; Lanthier, Robert M; Smith, Adam C; Christou, Carin; Risco Quiroz, Milagros; Powell, Karen L; O'Meara, Ryan W; Kothary, Rashmi; Lorimer, Ian A; Parks, Robin J

2010-10-01

Adenovirus (Ad) vectors are the most commonly used system for gene therapy applications, due in part to their ability to infect a wide array of cell types and tissues. However, many therapies would benefit from the ability to target the Ad vector only to specific cells, such as tumor cells for cancer gene therapy. In this study, we investigated the utility of capsid protein IX (pIX) as a platform for the presentation of single-chain variable-fragment antibodies (scFv) and single-domain antibodies (sdAb) for virus retargeting. We show that scFv can be displayed on the capsid through genetic fusion to native pIX but that these molecules fail to retarget the virus, due to improper folding of the scFv. Redirecting expression of the fusion protein to the endoplasmic reticulum (ER) results in correct folding of the scFv and allows it to recognize its epitope; however, ER-targeted pIX-scFv was incorporated into the Ad capsid at a very low level which was not sufficient to retarget virus infection. In contrast, a pIX-sdAb construct was efficiently incorporated into the Ad capsid and enhanced virus infection of cells expressing the targeted receptor. Taken together, our data indicate that pIX is an effective platform for presentation of large targeting polypeptides on the surface of the virus capsid, but the nature of the ligand can significantly affect its association with virions.