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.

Vectors and Rotations in 3-Dimensions: Vector Algebra for the C++ Programmer

DTIC Science & Technology

2016-12-01

Proving Ground, MD 21005-5068 This report describes 2 C++ classes: a Vector class for performing vector algebra in 3-dimensional space ( 3D ) and a Rotation...class for performing rotations of vectors in 3D . Each class is self-contained in a single header file (Vector.h and Rotation.h) so that a C...vector, rotation, 3D , quaternion, C++ tools, rotation sequence, Euler angles, yaw, pitch, roll, orientation 98 Richard Saucier 410-278-6721Unclassified

Axial vector Z‧ and anomaly cancellation

NASA Astrophysics Data System (ADS)

Ismail, Ahmed; Keung, Wai-Yee; Tsao, Kuo-Hsing; Unwin, James

2017-05-01

Whilst the prospect of new Z‧ gauge bosons with only axial couplings to the Standard Model (SM) fermions is widely discussed, examples of anomaly-free renormalisable models are lacking in the literature. We look to remedy this by constructing several motivated examples. Specifically, we consider axial vectors which couple universally to all SM fermions, as well as those which are generation-specific, leptophilic, and leptophobic. Anomaly cancellation typically requires the presence of new coloured and charged chiral fermions, and we argue that in a large class of models masses of these new states are expected to be comparable to that of the axial vector. Finally, an axial vector mediator could provide a portal between SM and hidden sector states, and we also consider the possibility that the axial vector couples to dark matter. If the dark matter relic density is set due to freeze-out via the axial vector, this strongly constrains the parameter space.

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

PubMed

Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

2007-05-01

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Complex equiangular tight frames

NASA Astrophysics Data System (ADS)

Tropp, Joel A.

2005-08-01

A complex equiangular tight frame (ETF) is a tight frame consisting of N unit vectors in Cd whose absolute inner products are identical. One may view complex ETFs as a natural geometric generalization of an orthonormal basis. Numerical evidence suggests that these objects do not arise for most pairs (d, N). The goal of this paper is to develop conditions on (d, N) under which complex ETFs can exist. In particular, this work concentrates on the class of harmonic ETFs, in which the components of the frame vectors are roots of unity. In this case, it is possible to leverage field theory to obtain stringent restrictions on the possible values for (d, N).

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

PubMed

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

2014-01-01

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

Recombinant modified vaccinia virus Ankara–simian immunodeficiency virus gag pol elicits cytotoxic T lymphocytes in rhesus monkeys detected by a major histocompatibility complex class I/peptide tetramer

PubMed Central

Seth, Aruna; Ourmanov, Ilnour; Kuroda, Marcelo J.; Schmitz, Jörn E.; Carroll, Miles W.; Wyatt, Linda S.; Moss, Bernard; Forman, Meryl A.; Hirsch, Vanessa M.; Letvin, Norman L.

1998-01-01

The utility of modified vaccinia virus Ankara (MVA) as a vector for eliciting AIDS virus-specific cytotoxic T lymphocytes (CTL) was explored in the simian immunodeficiency virus (SIV)/rhesus monkey model. After two intramuscular immunizations with recombinant MVA-SIVSM gag pol, the monkeys developed a Gag epitope-specific CTL response readily detected in peripheral blood lymphocytes by using a functional killing assay. Moreover, those immunizations also elicited a population of CD8+ T lymphocytes in the peripheral blood that bound a specific major histocompatibility complex class I/peptide tetramer. These Gag epitope-specific CD8+ T lymphocytes also were demonstrated by using both functional and tetramer-binding assays in lymph nodes of the immunized monkeys. These observations suggest that MVA may prove a useful vector for an HIV-1 vaccine. They also suggest that tetramer staining may be a useful technology for monitoring CTL generation in vaccine trials in nonhuman primates and in humans. PMID:9707609

Some Correlation Functions in Matrix Product Ground States of One-Dimensional Two-State Chains

NASA Astrophysics Data System (ADS)

Shariati, Ahmad; Aghamohammadi, Amir; Fatollahi, Amir H.; Khorrami, Mohammad

2014-04-01

Consider one-dimensional chains with nearest neighbour interactions, for which to each site correspond two independent states (say up and down), and the ground state is a matrix product state. It has been shown [23] that for such systems, the ground states are linear combinations of specific vectors which are essentially direct products of specific numbers of ups and downs, symmetrized in a generalized manner. By a generalized manner, it is meant that the coefficient corresponding to the interchange of states of two sites, in not necessarily plus one or minus one, but a phase which depends on the Hamiltonian and the position of the two sites. Such vectors are characterized by a phase χ, the N-th power of which is one (where N is the number of sites), and an integer. Corresponding to χ, there is another integer M which is the smallest positive integer that χM is one. Two classes of correlation functions for such systems (basically correlation functions for such vectors) are calculated. The first class consists of correlation functions of tensor products of one-site diagonal observables; the second class consists of correlation functions of tensor products of less than M one-site observables (but not necessarily diagonal).

A new class of N=2 topological amplitudes

NASA Astrophysics Data System (ADS)

Antoniadis, I.; Hohenegger, S.; Narain, K. S.; Sokatchev, E.

2009-12-01

We describe a new class of N=2 topological amplitudes that compute a particular class of BPS terms in the low energy effective supergravity action. Specifically they compute the coupling F(( where F, λ and ϕ are gauge field strengths, gaugino and holomorphic vector multiplet scalars. The novel feature of these terms is that they depend both on the vector and hypermultiplet moduli. The BPS nature of these terms implies that they satisfy a holomorphicity condition with respect to vector moduli and a harmonicity condition as well as a second order differential equation with respect to hypermultiplet moduli. We study these conditions explicitly in heterotic string theory and show that they are indeed satisfied up to anomalous boundary terms in the world-sheet moduli space. We also analyze the boundary terms in the holomorphicity and harmonicity equations at a generic point in the vector and hyper moduli space. In particular we show that the obstruction to the holomorphicity arises from the one loop threshold correction to the gauge couplings and we argue that this is due to the contribution of non-holomorphic couplings to the connected graphs via elimination of the auxiliary fields.

Sparse Modeling of Human Actions from Motion Imagery

DTIC Science & Technology

2011-09-02

is here developed. Spatio-temporal features that char- acterize local changes in the image are rst extracted. This is followed by the learning of a...video comes from the optimal sparse linear com- bination of the learned basis vectors (action primitives) representing the actions. A low...computational cost deep-layer model learning the inter- class correlations of the data is added for increasing discriminative power. In spite of its simplicity

Wick Product for Commutation Relations Connected with Yang-Baxter Operators and New Constructions of Factors

NASA Astrophysics Data System (ADS)

Krsolarlak, Ilona

We analyze a certain class of von Neumann algebras generated by selfadjoint elements , for satisfying the general commutation relations: Such algebras can be continuously embedded into some closure of the set of finite linear combinations of vectors , where is an orthonormal basis of a Hilbert space . The operator which represents the vector is denoted by and called the ``Wick product'' of the operators . We describe explicitly the form of this product. Also, we estimate the operator norm of for . Finally we apply these two results and prove that under the assumption all the von Neumann algebras considered are II1 factors.

[Cloning and gene expression in lactic acid bacteria].

PubMed

Bondarenko, V M; Beliavskaia, V A

2000-01-01

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

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

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

Arrighi, Bill

2016-03-03

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

Characteristic classes of gauge systems

NASA Astrophysics Data System (ADS)

Lyakhovich, S. L.; Sharapov, A. A.

2004-12-01

We define and study invariants which can be uniformly constructed for any gauge system. By a gauge system we understand an (anti-)Poisson supermanifold provided with an odd Hamiltonian self-commuting vector field called a homological vector field. This definition encompasses all the cases usually included into the notion of a gauge theory in physics as well as some other similar (but different) structures like Lie or Courant algebroids. For Lagrangian gauge theories or Hamiltonian first class constrained systems, the homological vector field is identified with the classical BRST transformation operator. We define characteristic classes of a gauge system as universal cohomology classes of the homological vector field, which are uniformly constructed in terms of this vector field itself. Not striving to exhaustively classify all the characteristic classes in this work, we compute those invariants which are built up in terms of the first derivatives of the homological vector field. We also consider the cohomological operations in the space of all the characteristic classes. In particular, we show that the (anti-)Poisson bracket becomes trivial when applied to the space of all the characteristic classes, instead the latter space can be endowed with another Lie bracket operation. Making use of this Lie bracket one can generate new characteristic classes involving higher derivatives of the homological vector field. The simplest characteristic classes are illustrated by the examples relating them to anomalies in the traditional BV or BFV-BRST theory and to characteristic classes of (singular) foliations.

Review of insecticide resistance and behavioral avoidance of vectors of human diseases in Thailand

PubMed Central

2013-01-01

Physiological resistance and behavioral responses of mosquito vectors to insecticides are critical aspects of the chemical-based disease control equation. The complex interaction between lethal, sub-lethal and excitation/repellent ('excito-repellent’) properties of chemicals is typically overlooked in vector management and control programs. The development of “physiological” resistance, metabolic and/or target site modifications, to insecticides has been well documented in many insect groups and disease vectors around the world. In Thailand, resistance in many mosquito populations has developed to all three classes of insecticidal active ingredients currently used for vector control with a majority being synthetic-derived pyrethroids. Evidence of low-grade insecticide resistance requires immediate countermeasures to mitigate further intensification and spread of the genetic mechanisms responsible for resistance. This can take the form of rotation of a different class of chemical, addition of a synergist, mixtures of chemicals or concurrent mosaic application of different classes of chemicals. From the gathered evidence, the distribution and degree of physiological resistance has been restricted in specific areas of Thailand in spite of long-term use of chemicals to control insect pests and disease vectors throughout the country. Most surprisingly, there have been no reported cases of pyrethroid resistance in anopheline populations in the country from 2000 to 2011. The precise reasons for this are unclear but we assume that behavioral avoidance to insecticides may play a significant role in reducing the selection pressure and thus occurrence and spread of insecticide resistance. The review herein provides information regarding the status of physiological resistance and behavioral avoidance of the primary mosquito vectors of human diseases to insecticides in Thailand from 2000 to 2011. PMID:24294938

Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

PubMed

van der Ster, Björn J P; Bennis, Frank C; Delhaas, Tammo; Westerhof, Berend E; Stok, Wim J; van Lieshout, Johannes J

2017-01-01

Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock. Method: In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of -50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included : volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. Model performance was tested by quantifying the predictions with three methods : sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates. Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73-0.98 and class 2: 0.56-0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived pulse height. The highest combination for class 2 was found for the model with the volumetric features (0.72 and 0.91). Conclusion: The most sensitive models for the detection of advanced CBV reduction comprised data that describe features from volumetric parameters and from cerebral blood flow velocity hemodynamics. In a validated model of hemorrhage in humans these parameters provide the best indication of the progression of central hypovolemia.

Collinear and vector interaction of light waves in nonlinear optical crystals KTiOPO4("KTP"), Ba2NaNb5O15 ("banana")

NASA Astrophysics Data System (ADS)

Deinekina, N. A.; Korosteleva, I. A.; Kravchenko, O. V.; Faleev, D. S.

2016-11-01

Esents the research results of biaxial crystals with mm2 symmetry class. These crystals were used for determining regularities of nonlinear conversion of broadband optical emission on the basis of collinear and vector light waves interactions of different nature. The quantities of the basis nonlinear optical characteristics of "KTP" (KTiOPO4) and "banana" (Ba2NaNb5O15) crystals were calculated in case of synchronous conversion of broadband emission from the area of 0.8 - 2.8 micron to the visible spectrum of 0.4 - 0.7 micron. The nonlinear optical characteristics of "KTP" crystals are defined by their geometrical structure, the mode of interaction of light waves, and the infra-red spectrum width, that was experimentally confirmed on "KTP" crystal. The quality characteristics β were calculated for the "KTP" crystal. For "banana" crystal the angle of phase synchronism θc changes insignificantly when the observation plane is changed. It can be explained by the fact that the biaxiality of crystal is not strongly expressed, because of the basis refraction indices the conditions nz<=ny≈nx are performed.

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

PubMed

Dropulic, Boro

2005-07-01

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

Finite Geometries in Quantum Theory:. from Galois (fields) to Hjelmslev (rings)

NASA Astrophysics Data System (ADS)

Saniga, Metod; Planat, Michel

Geometries over Galois fields (and related finite combinatorial structures/algebras) have recently been recognized to play an ever-increasing role in quantum theory, especially when addressing properties of mutually unbiased bases (MUBs). The purpose of this contribution is to show that completely new vistas open up if we consider a generalized class of finite (projective) geometries, viz. those defined over Galois rings and/or other finite Hjelmslev rings. The case is illustrated by demonstrating that the basic combinatorial properties of a complete set of MUBs of a q-dimensional Hilbert space { H}q, q = pr with p being a prime and r a positive integer, are qualitatively mimicked by the configuration of points lying on a proper conic in a projective Hjelmslev plane defined over a Galois ring of characteristic p2 and rank r. The q vectors of a basis of { H}q correspond to the q points of a (so-called) neighbour class and the q + 1 MUBs answer to the total number of (pairwise disjoint) neighbour classes on the conic. Although this remarkable analogy is still established at the level of cardinalities only, we currently work on constructing an explicit mapping by associating a MUB to each neighbour class of the points of the conic and a state vector of this MUB to a particular point of the class. Further research in this direction may prove to be of great relevance for many areas of quantum information theory, in particular for quantum information processing.

Stable orthogonal local discriminant embedding for linear dimensionality reduction.

PubMed

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

2013-07-01

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

Teleporting an unknown quantum state with unit fidelity and unit probability via a non-maximally entangled channel and an auxiliary system

NASA Astrophysics Data System (ADS)

Rashvand, Taghi

2016-11-01

We present a new scheme for quantum teleportation that one can teleport an unknown state via a non-maximally entangled channel with certainly, using an auxiliary system. In this scheme depending on the state of the auxiliary system, one can find a class of orthogonal vectors set as a basis which by performing von Neumann measurement in each element of this class Alice can teleport an unknown state with unit fidelity and unit probability. A comparison of our scheme with some previous schemes is given and we will see that our scheme has advantages that the others do not.

Preventing vaccinia virus class-I epitopes presentation by HSV-ICP47 enhances the immunogenicity of a TAP-independent cancer vaccine epitope.

PubMed

Raafat, Nermin; Sadowski-Cron, Charlotte; Mengus, Chantal; Heberer, Michael; Spagnoli, Giulio C; Zajac, Paul

2012-09-01

Herpes simplex virus protein ICP47, encoded by US12 gene, strongly downregulates major histocompatibility complex (MHC) class-I antigen restricted presentation by blocking transporter associated with antigen processing (TAP) protein. To decrease viral vector antigenic immunodominance and MHC class-I driven clearance, we engineered recombinant vaccinia viruses (rVV) expressing ICP47 alone (rVV-US12) or together with endoplasmic reticulum (ER)-targeted Melan-A/MART-1(27-35) model tumor epitope (rVV-MUS12). In this study, we show that antigen presenting cells (APC), infected with rVV-US12, display a decreased ability to present TAP dependent MHC class-I restricted viral antigens to CD8+ T-cells. While HLA class-I cell surface expression is strongly downregulated, other important immune related molecules such as CD80, CD44 and, most importantly, MHC class-II are unaffected. Characterization of rVV-MUS12 infected cells demonstrates that over-expression of a TAP-independent peptide, partially compensates for ICP47 induced surface MHC class-I downregulation (30% vs. 70% respectively). Most importantly, in conditions where clearance of infected APC by virus-specific CTL represents a limiting factor, a significant enhancement of CTL responses to the tumor epitope can be detected in cultures stimulated with rVV-MUS12, as compared to those stimulated by rVV-MART alone. Such reagents could become of high relevance in multiple boost protocols required for cancer immunotherapy, to limit vector-specific responsiveness. Copyright © 2011 UICC.

Field theory of hyperfluid

NASA Astrophysics Data System (ADS)

Ariki, Taketo

2018-02-01

A hyperfluid model is constructed on the basis of its action entirely free from external constraints, regarding the hyperfluid as a self-consistent classical field. Intrinsic hypermomentum is no longer a supplemental variable given by external constraints, but arises purely from the diffeomorphism covariance of dynamical field. The field-theoretic approach allows natural classification of a hyperfluid on the basis of its symmetry group and corresponding homogeneous space; scalar, spinor, vector, and tensor fluids are introduced as simple examples. Apart from phenomenological constraints, the theory predicts the hypermomentum exchange of fluid via field-theoretic interactions of various classes; fluid–fluid interactions, minimal and non-minimal SU(n) -gauge couplings, and coupling with metric-affine gravity are all successfully formulated within the classical regime.

17 CFR 50.4 - Classes of swaps required to be cleared.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Fixed-to-floating swap class Currency U.S. dollar (USD) Euro (EUR) Sterling (GBP) Yen (JPY). Floating.... Conditional Notional Amounts No No No No. Specification Basis swap class Currency U.S. dollar (USD) Euro (EUR... agreement class Currency U.S. dollar (USD) Euro (EUR) Sterling (GBP) Yen (JPY). Floating Rate Indexes LIBOR...

[Progress in application of targeting viral vector regulated by microRNA in gene therapy: a review].

PubMed

Zhang, Guohai; Wang, Qizhao; Zhang, Jinghong; Xu, Ruian

2010-06-01

A safe and effective targeting viral vector is the key factor for successful clinical gene therapy. microRNA, a class of small, single-stranded endogenous RNAs, act as post-transcriptional regulators of gene expression. The discovery of these kind regulatory elements provides a new approach to regulate gene expression more accurately. In this review, we elucidated the principle of microRNA in regulation of targeting viral vector. The applications of microRNA in the fields of elimination contamination from replication competent virus, reduction of transgene-specific immunity, promotion of cancer-targeted gene therapy and development of live attenuated vaccines were also discussed.

Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Gatos, I.; Tsantis, S.; Karamesini, M.; Skouroliakou, A.; Kagadis, G.

2015-09-01

Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive examination.

A new range-free localisation in wireless sensor networks using support vector machine

NASA Astrophysics Data System (ADS)

Wang, Zengfeng; Zhang, Hao; Lu, Tingting; Sun, Yujuan; Liu, Xing

2018-02-01

Location information of sensor nodes is of vital importance for most applications in wireless sensor networks (WSNs). This paper proposes a new range-free localisation algorithm using support vector machine (SVM) and polar coordinate system (PCS), LSVM-PCS. In LSVM-PCS, two sets of classes are first constructed based on sensor nodes' polar coordinates. Using the boundaries of the defined classes, the operation region of WSN field is partitioned into a finite number of polar grids. Each sensor node can be localised into one of the polar grids by executing two localisation algorithms that are developed on the basis of SVM classification. The centre of the resident polar grid is then estimated as the location of the sensor node. In addition, a two-hop mass-spring optimisation (THMSO) is also proposed to further improve the localisation accuracy of LSVM-PCS. In THMSO, both neighbourhood information and non-neighbourhood information are used to refine the sensor node location. The results obtained verify that the proposed algorithm provides a significant improvement over existing localisation methods.

Ex vivo tetramer staining and cell surface phenotyping for early activation markers CD38 and HLA-DR to enumerate and characterize malaria antigen-specific CD8+ T-cells induced in human volunteers immunized with a Plasmodium falciparum adenovirus-vectored malaria vaccine expressing AMA1.

PubMed

Schwenk, Robert; Banania, Glenna; Epstein, Judy; Kim, Yohan; Peters, Bjoern; Belmonte, Maria; Ganeshan, Harini; Huang, Jun; Reyes, Sharina; Stryhn, Anette; Ockenhouse, Christian F; Buus, Soren; Richie, Thomas L; Sedegah, Martha

2013-10-29

Malaria is responsible for up to a 600,000 deaths per year; conveying an urgent need for the development of a malaria vaccine. Studies with whole sporozoite vaccines in mice and non-human primates have shown that sporozoite-induced CD8+ T cells targeting liver stage antigens can mediate sterile protection. There is a need for a direct method to identify and phenotype malaria vaccine-induced CD8+ T cells in humans. Fluorochrome-labelled tetramers consisting of appropriate MHC class I molecules in complex with predicted binding peptides derived from Plasmodium falciparum AMA-1 were used to label ex vivo AMA-1 epitope specific CD8+ T cells from research subjects responding strongly to immunization with the NMRC-M3V-Ad-PfCA (adenovirus-vectored) malaria vaccine. The identification of these CD8+ T cells on the basis of their expression of early activation markers was also investigated. Analyses by flow cytometry demonstrated that two of the six tetramers tested: TLDEMRHFY: HLA-A*01:01 and NEVVVKEEY: HLA-B*18:01, labelled tetramer-specific CD8+ T cells from two HLA-A*01:01 volunteers and one HLA-B*18:01 volunteer, respectively. By contrast, post-immune CD8+ T cells from all six of the immunized volunteers exhibited enhanced expression of the CD38 and HLA-DRhi early activation markers. For the three volunteers with positive tetramer staining, the early activation phenotype positive cells included essentially all of the tetramer positive, malaria epitope- specific CD8+ T cells suggesting that the early activation phenotype could identify all malaria vaccine-induced CD8+ T cells without prior knowledge of their exact epitope specificity. The results demonstrated that class I tetramers can identify ex vivo malaria vaccine antigen-specific CD8+ T cells and could therefore be used to determine their frequency, cell surface phenotype and transcription factor usage. The results also demonstrated that vaccine antigen-specific CD8+ T cells could be identified by activation markers without prior knowledge of their antigen-specificity, using a subunit vaccine for proof-of-concept. Whether, whole parasite or adjuvanted protein vaccines will also induce {CD38 and HLA-DRhi}+ CD8+ T cell populations reflective of the antigen-specific response will the subject of future investigations.

Practical utilization of recombinant AAV vector reference standards: focus on vector genomes titration by free ITR qPCR.

PubMed

D'Costa, Susan; Blouin, Veronique; Broucque, Frederic; Penaud-Budloo, Magalie; François, Achille; Perez, Irene C; Le Bec, Christine; Moullier, Philippe; Snyder, Richard O; Ayuso, Eduard

2016-01-01

Clinical trials using recombinant adeno-associated virus (rAAV) vectors have demonstrated efficacy and a good safety profile. Although the field is advancing quickly, vector analytics and harmonization of dosage units are still a limitation for commercialization. AAV reference standard materials (RSMs) can help ensure product safety by controlling the consistency of assays used to characterize rAAV stocks. The most widely utilized unit of vector dosing is based on the encapsidated vector genome. Quantitative polymerase chain reaction (qPCR) is now the most common method to titer vector genomes (vg); however, significant inter- and intralaboratory variations have been documented using this technique. Here, RSMs and rAAV stocks were titered on the basis of an inverted terminal repeats (ITRs) sequence-specific qPCR and we found an artificial increase in vg titers using a widely utilized approach. The PCR error was introduced by using single-cut linearized plasmid as the standard curve. This bias was eliminated using plasmid standards linearized just outside the ITR region on each end to facilitate the melting of the palindromic ITR sequences during PCR. This new "Free-ITR" qPCR delivers vg titers that are consistent with titers obtained with transgene-specific qPCR and could be used to normalize in-house product-specific AAV vector standards and controls to the rAAV RSMs. The free-ITR method, including well-characterized controls, will help to calibrate doses to compare preclinical and clinical data in the field.

Object recognition of real targets using modelled SAR images

NASA Astrophysics Data System (ADS)

Zherdev, D. A.

2017-12-01

In this work the problem of recognition is studied using SAR images. The algorithm of recognition is based on the computation of conjugation indices with vectors of class. The support subspaces for each class are constructed by exception of the most and the less correlated vectors in a class. In the study we examine the ability of a significant feature vector size reduce that leads to recognition time decrease. The images of targets form the feature vectors that are transformed using pre-trained convolutional neural network (CNN).

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

PubMed Central

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

2017-01-01

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

Teleparallelism as a universal connection on null hypersurfaces in general relativity

NASA Technical Reports Server (NTRS)

Mazur, P. O.; Sokolowski, L. M.

1986-01-01

It is shown that a close relationship between the inner geometry of a null hypersurface N3 and the Newman-Penrose (NP) (1962, 1963) spin coefficient formalism exists. Projecting the null complex NP tetrad onto N3, two triads of basis vectors in N3 are obtained. The inner geometry of N3 is based on the assumption that these vectors are parallelly transported along the surface; this gives rise to the teleparallel connection as a metric nonsymmetric affine connection. The gauge freedom for the choice of the basis triads is given by the isotropy subgroup of the local Lorentz group leaving invariant the direction of the null generators of N3, and teleparallelism is determined by the equivalence class of the basis triads with respect to the global gauge group. Nine of the twelve NP coefficients are identified as the triad components of the torsion and the second fundamental form of N3. The resulting generalized Gauss-Codazzi equations are identical to nine of the NP equations, i.e., to the half of the Ricci identities. This result gives a geometrical meaning to the entire formalism. Finally a general proof of Penrose's theorem that the shear of the null generators of N3 is the only initial null datum for a gravitational field on N3 is presented.

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.

A hierarchical preconditioner for the electric field integral equation on unstructured meshes based on primal and dual Haar bases

NASA Astrophysics Data System (ADS)

Adrian, S. B.; Andriulli, F. P.; Eibert, T. F.

2017-02-01

A new hierarchical basis preconditioner for the electric field integral equation (EFIE) operator is introduced. In contrast to existing hierarchical basis preconditioners, it works on arbitrary meshes and preconditions both the vector and the scalar potential within the EFIE operator. This is obtained by taking into account that the vector and the scalar potential discretized with loop-star basis functions are related to the hypersingular and the single layer operator (i.e., the well known integral operators from acoustics). For the single layer operator discretized with piecewise constant functions, a hierarchical preconditioner can easily be constructed. Thus the strategy we propose in this work for preconditioning the EFIE is the transformation of the scalar and the vector potential into operators equivalent to the single layer operator and to its inverse. More specifically, when the scalar potential is discretized with star functions as source and testing functions, the resulting matrix is a single layer operator discretized with piecewise constant functions and multiplied left and right with two additional graph Laplacian matrices. By inverting these graph Laplacian matrices, the discretized single layer operator is obtained, which can be preconditioned with the hierarchical basis. Dually, when the vector potential is discretized with loop functions, the resulting matrix can be interpreted as a hypersingular operator discretized with piecewise linear functions. By leveraging on a scalar Calderón identity, we can interpret this operator as spectrally equivalent to the inverse single layer operator. Then we use a linear-in-complexity, closed-form inverse of the dual hierarchical basis to precondition the hypersingular operator. The numerical results show the effectiveness of the proposed preconditioner and the practical impact of theoretical developments in real case scenarios.

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

NASA Astrophysics Data System (ADS)

Perov, N. I.

1985-02-01

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

Which coordinate system for modelling path integration?

PubMed

Vickerstaff, Robert J; Cheung, Allen

2010-03-21

Path integration is a navigation strategy widely observed in nature where an animal maintains a running estimate, called the home vector, of its location during an excursion. Evidence suggests it is both ancient and ubiquitous in nature, and has been studied for over a century. In that time, canonical and neural network models have flourished, based on a wide range of assumptions, justifications and supporting data. Despite the importance of the phenomenon, consensus and unifying principles appear lacking. A fundamental issue is the neural representation of space needed for biological path integration. This paper presents a scheme to classify path integration systems on the basis of the way the home vector records and updates the spatial relationship between the animal and its home location. Four extended classes of coordinate systems are used to unify and review both canonical and neural network models of path integration, from the arthropod and mammalian literature. This scheme demonstrates analytical equivalence between models which may otherwise appear unrelated, and distinguishes between models which may superficially appear similar. A thorough analysis is carried out of the equational forms of important facets of path integration including updating, steering, searching and systematic errors, using each of the four coordinate systems. The type of available directional cue, namely allothetic or idiothetic, is also considered. It is shown that on balance, the class of home vectors which includes the geocentric Cartesian coordinate system, appears to be the most robust for biological systems. A key conclusion is that deducing computational structure from behavioural data alone will be difficult or impossible, at least in the absence of an analysis of random errors. Consequently it is likely that further theoretical insights into path integration will require an in-depth study of the effect of noise on the four classes of home vectors. Copyright 2009 Elsevier Ltd. All rights reserved.

A real-time TaqMan polymerase chain reaction for the identification of Culex vectors of West Nile and Saint Louis encephalitis viruses in North America.

PubMed

Sanogo, Yibayiri O; Kim, Chang-Hyun; Lampman, Richard; Novak, Robert J

2007-07-01

In North America, West Nile and St. Louis encephalitis viruses have been detected in a wide range of vector species, but the majority of isolations continue to be from pools of mixed mosquitoes in the Culex subgenus Culex. Unfortunately, the morphologic identification of these important disease vectors is often difficult, particularly in regions of sympatry. We developed a sensitive real-time TaqMan polymerase chain reaction assay that allows reliable identification of Culex mosquitoes including Culex pipiens pipiens, Cx. p. quinquefasciatus, Cx. restuans, Cx. salinarius, Cx. nigripalpus, and Cx. tarsalis. Primers and fluorogenic probes specific to each species were designed based on sequences of the acetylcholinesterase gene (Ace2). Both immature and adult mosquitoes were successfully identified as individuals and as mixed species pools. This identification technique provides the basis for a rapid, sensitive, and high-throughput method for expounding the species-specific contribution of vectors to various phases of arbovirus transmission.

Algorithm for detection the QRS complexes based on support vector machine

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Thyra Abstract Interface Package

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

Bartlett, Roscoe A.

2005-09-01

Thrya primarily defines a set of abstract C++ class interfaces needed for the development of abstract numerical atgorithms (ANAs) such as iterative linear solvers, transient solvers all the way up to optimization. At the foundation of these interfaces are abstract C++ classes for vectors, vector spaces, linear operators and multi-vectors. Also included in the Thyra package is C++ code for creating concrete vector, vector space, linear operator, and multi-vector subclasses as well as other utilities to aid in the development of ANAs. Currently, very general and efficient concrete subclass implementations exist for serial and SPMD in-core vectors and multi-vectors. Codemore » also currently exists for testing objects and providing composite objects such as product vectors.« less

Benchmark of Machine Learning Methods for Classification of a SENTINEL-2 Image

NASA Astrophysics Data System (ADS)

Pirotti, F.; Sunar, F.; Piragnolo, M.

2016-06-01

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performance.

17 CFR 50.4 - Classes of swaps required to be cleared.

Code of Federal Regulations, 2013 CFR

2013-04-01

... class Currency U.S. dollar (USD) Euro (EUR) Sterling (GBP) Yen (JPY). Floating Rate Indexes LIBOR... Amounts No No No No. Specification Basis swap class Currency U.S. dollar (USD) Euro (EUR) Sterling (GBP... Currency U.S. dollar (USD) Euro (EUR) Sterling (GBP) Yen (JPY). Floating Rate Indexes LIBOR EURIBOR LIBOR...

Grassmannian Kaluza-Klein theory

NASA Astrophysics Data System (ADS)

Ellicott, P.; Toms, D. J.

1989-07-01

An effort is made to analyze the general structure of Grassmanian Kaluza-Klein theory for a wider class of theories than those considered by Ross (1981) by removing the restrictions he imposed on the commutation relations of basis vectors in the bundle. The coordinates for the extra dimensions are taken to be anticommuting. An attempt is also made to show how this approach relates to the work of Delbourgo et al. (1988), and in particular to see whether or not the metric ansatz in their work is consistent with the higher-dimensional field equations.

A Prototype SSVEP Based Real Time BCI Gaming System

PubMed Central

Martišius, Ignas

2016-01-01

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

A Prototype SSVEP Based Real Time BCI Gaming System.

PubMed

Martišius, Ignas; Damaševičius, Robertas

2016-01-01

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

Generation of monoclonal antibodies to vertebrate albumins for analysis of arthropod blood meals.

PubMed

Schwab, Lori Kae; Nardi, James B; Holly, Theresa; Wang, Liping; Frye, Janie; Novak, Robert J

2011-06-01

An immunoassay using monoclonal antibodies (MAbs) that are specific for different vertebrate taxa (from class to species) has been developed that simplifies and facilitates analysis of vertebrate blood meals from arthropod vectors. The MAbs have been prepared against the single protein albumin, the most abundant protein in vertebrate sera. A panel of these antibodies has been generated against albumins from 33 species of vertebrates, representing four classes, 15 orders, and 25 families. Immunoreactivity of albumin in mosquito blood meals can be detected as late as 48 h after feeding. Immunoassays with MAbs can be carried out in the field as well as the laboratory. Used in conjunction with nucleic acid assays or used alone with an appropriate assortment of antibodies, the assay is simple, sensitive, and unambiguous. © 2011 The Society for Vector Ecology.

Expanding specificity of class 1 restricted CD8+ T cells for viral epitopes following multiple inoculations of swine with a human adenivorus vectored foot-and-mouth disease virus (FMDV) vaccine

USDA-ARS?s Scientific Manuscript database

The immune response to the highly acute foot-and-mouth disease virus (FMDV) is routinely reported as a measure of serum antibody. However, a critical effector function of immune responses combating viral infection of mammals is the cytotoxic T lymphocyte (CTL) response, mediated by virus specific ...

A Study on Aircraft Engine Control Systems for Integrated Flight and Propulsion Control

NASA Astrophysics Data System (ADS)

Yamane, Hideaki; Matsunaga, Yasushi; Kusakawa, Takeshi; Yasui, Hisako

The Integrated Flight and Propulsion Control (IFPC) for a highly maneuverable aircraft and a fighter-class engine with pitch/yaw thrust vectoring is described. Of the two IFPC functions the aircraft maneuver control utilizes the thrust vectoring based on aerodynamic control surfaces/thrust vectoring control allocation specified by the Integrated Control Unit (ICU) of a FADEC (Full Authority Digital Electronic Control) system. On the other hand in the Performance Seeking Control (PSC) the ICU identifies engine's various characteristic changes, optimizes manipulated variables and finally adjusts engine control parameters in cooperation with the Engine Control Unit (ECU). It is shown by hardware-in-the-loop simulation that the thrust vectoring can enhance aircraft maneuverability/agility and that the PSC can improve engine performance parameters such as SFC (specific fuel consumption), thrust and gas temperature.

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

PubMed

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

2015-07-01

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

Developmental neurogenetics of sexual dimorphism in Aedes aegypti

PubMed Central

Duman-Scheel, Molly; Syed, Zainulabeuddin

2015-01-01

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

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Chattopadhyay, Subhagata; Yu, Wenwei; Ang, Peng Chuan Alvin

2011-06-01

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

The generalized formula for angular velocity vector of the moving coordinate system

NASA Astrophysics Data System (ADS)

Ermolin, Vladislav S.; Vlasova, Tatyana V.

2018-05-01

There are various ways for introducing the concept of the instantaneous angular velocity vector. In this paper we propose a method based on introducing of this concept by construction of the solution for the system of kinematic equations. These equations connect the function vectors defining the motion of the basis, and their derivatives. Necessary and sufficient conditions for the existence and uniqueness of the solution of this system are established. The instantaneous angular velocity vector is a solution of the algebraic system of equations. It is built explicitly. The derived formulas for the angular velocity vector generalize the earlier results, both for a basis of an affine oblique coordinate system and for an orthonormal basis.

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Minimally doubled fermions at one loop

NASA Astrophysics Data System (ADS)

Capitani, Stefano; Weber, Johannes; Wittig, Hartmut

2009-10-01

Minimally doubled fermions have been proposed as a cost-effective realization of chiral symmetry at non-zero lattice spacing. Using lattice perturbation theory at one loop, we study their renormalization properties. Specifically, we investigate the consequences of the breaking of hyper-cubic symmetry, which is a typical feature of this class of fermionic discretizations. Our results for the quark self-energy indicate that the four-momentum undergoes a renormalization which is linearly divergent. We also compute renormalization factors for quark bilinears, construct the conserved vector and axial-vector currents and verify that at one loop the renormalization factors of the latter are equal to one.

Insights into multimodal imaging classification of ADHD

PubMed Central

Colby, John B.; Rudie, Jeffrey D.; Brown, Jesse A.; Douglas, Pamela K.; Cohen, Mark S.; Shehzad, Zarrar

2012-01-01

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD. PMID:22912605

Surface representations of two- and three-dimensional fluid flow topology

NASA Technical Reports Server (NTRS)

Helman, James L.; Hesselink, Lambertus

1990-01-01

We discuss our work using critical point analysis to generate representations of the vector field topology of numerical flow data sets. Critical points are located and characterized in a two-dimensional domain, which may be either a two-dimensional flow field or the tangential velocity field near a three-dimensional body. Tangent curves are then integrated out along the principal directions of certain classes of critical points. The points and curves are linked to form a skeleton representing the two-dimensional vector field topology. When generated from the tangential velocity field near a body in a three-dimensional flow, the skeleton includes the critical points and curves which provide a basis for analyzing the three-dimensional structure of the flow separation. The points along the separation curves in the skeleton are used to start tangent curve integrations to generate surfaces representing the topology of the associated flow separations.

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier.

PubMed

Huang, Nantian; Chen, Huaijin; Cai, Guowei; Fang, Lihua; Wang, Yuqiang

2016-11-10

Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF₆ HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods.

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

PubMed Central

Huang, Nantian; Chen, Huaijin; Cai, Guowei; Fang, Lihua; Wang, Yuqiang

2016-01-01

Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF6 HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods. PMID:27834902

Structure of an Insecticide Sequestering Carboxylesterase from the Disease Vector Culex quinquefasciatus: What Makes an Enzyme a Good Insecticide Sponge?

PubMed

Hopkins, Davis H; Fraser, Nicholas J; Mabbitt, Peter D; Carr, Paul D; Oakeshott, John G; Jackson, Colin J

2017-10-17

Carboxylesterase (CBE)-mediated metabolic resistance to organophosphate and carbamate insecticides is a major problem for the control of insect disease vectors, such as the mosquito. The most common mechanism involves overexpression of CBEs that bind to the insecticide with high affinity, thereby sequestering them before they can interact with their target. However, the absence of any structure for an insecticide-sequestering CBE limits our understanding of the molecular basis for this process. We present the first structure of a CBE involved in sequestration, Cqestβ2 1 , from the mosquito disease vector Culex quinquefasciatus. Lysine methylation was used to obtain the crystal structure of Cqestβ2 1 , which adopts a canonical α/β-hydrolase fold that has high similarity to the target of organophosphate and carbamate insecticides, acetylcholinesterase. Sequence similarity networks of the insect carboxyl/cholinesterase family demonstrate that CBEs associated with metabolic insecticide resistance across many species share a level of similarity that distinguishes them from a variety of other classes. This is further emphasized by the structural similarities and differences in the binding pocket and active site residues of Cqestβ2 1 and other insect carboxyl/cholinesterases. Stopped-flow and steady-state inhibition studies support a major role for Cqestβ2 1 in organophosphate resistance and a minor role in carbamate resistance. Comparison with another isoform associated with insecticide resistance, Cqestβ1, showed both enzymes have similar affinity to insecticides, despite 16 amino acid differences between the two proteins. This provides a molecular understanding of pesticide sequestration by insect CBEs and could facilitate the design of CBE-specific inhibitors to circumvent this resistance mechanism in the future.

A Simple Colorimetric Assay for Specific Detection of Glutathione-S Transferase Activity Associated with DDT Resistance in Mosquitoes

PubMed Central

Rajatileka, Shavanti; Steven, Andrew; Hemingway, Janet; Ranson, Hilary; Paine, Mark; Vontas, John

2010-01-01

Background Insecticide-based methods represent the most effective means of blocking the transmission of vector borne diseases. However, insecticide resistance poses a serious threat and there is a need for tools, such as diagnostic tests for resistance detection, that will improve the sustainability of control interventions. The development of such tools for metabolism-based resistance in mosquito vectors lags behind those for target site resistance mutations. Methodology/Principal Findings We have developed and validated a simple colorimetric assay for the detection of Epsilon class Glutathione transferases (GST)-based DDT resistance in mosquito species, such as Aedes aegypti, the major vector of dengue and yellow fever worldwide. The colorimetric assay is based on the specific alkyl transferase activity of Epsilon GSTs for the haloalkene substrate iodoethane, which produces a dark blue colour highly correlated with AaGSTE2-2-overexpression in individual mosquitoes. The colour can be measured visually and spectrophotometrically. Conclusions/Significance The novel assay is substantially more sensitive compared to the gold standard CDNB assay and allows the discrimination of moderate resistance phenotypes. We anticipate that it will have direct application in routine vector monitoring as a resistance indicator and possibly an important impact on disease vector control. PMID:20824165

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

PubMed

Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, María P

2015-01-01

The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network's modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.

Soft Computing Application in Fault Detection of Induction Motor

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

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

2010-10-26

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

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.

Modified Vaccinia Virus Ankara-Infected Dendritic Cells Present CD4+ T-Cell Epitopes by Endogenous Major Histocompatibility Complex Class II Presentation Pathways

PubMed Central

Thiele, Frank; Tao, Sha; Zhang, Yi; Muschaweckh, Andreas; Zollmann, Tina; Protzer, Ulrike; Abele, Rubert

2014-01-01

ABSTRACT CD4+ T lymphocytes play a central role in the immune system and mediate their function after recognition of their respective antigens presented on major histocompatibility complex II (MHCII) molecules on antigen-presenting cells (APCs). Conventionally, phagocytosed antigens are loaded on MHCII for stimulation of CD4+ T cells. Certain epitopes, however, can be processed directly from intracellular antigens and are presented on MHCII (endogenous MHCII presentation). Here we characterized the MHCII antigen presentation pathways that are possibly involved in the immune response upon vaccination with modified vaccinia virus Ankara (MVA), a promising live viral vaccine vector. We established CD4+ T-cell lines specific for MVA-derived epitopes as tools for in vitro analysis of MHCII antigen processing and presentation in MVA-infected APCs. We provide evidence that infected APCs are able to directly transfer endogenous viral proteins into the MHCII pathway to efficiently activate CD4+ T cells. By using knockout mice and chemical inhibitory compounds, we further elucidated the molecular basis, showing that among the various subcellular pathways investigated, proteasomes and autophagy are key players in the endogenous MHCII presentation during MVA infection. Interestingly, although proteasomal processing plays an important role, neither TAP nor LAMP-2 was found to be involved in the peptide transport. Defining the molecular mechanism of MHCII presentation during MVA infection provides a basis for improving MVA-based vaccination strategies by aiming for enhanced CD4+ T-cell activation by directing antigens into the responsible pathways. IMPORTANCE This work contributes significantly to our understanding of the immunogenic properties of pathogens by deciphering antigen processing pathways contributing to efficient activation of antigen-specific CD4+ T cells. We identified autophagosome formation, proteasomal activity, and lysosomal integrity as being crucial for endogenous CD4+ T-cell activation. Since poxvirus vectors such as MVA are already used in clinical trials as recombinant vaccines, the data provide important information for the future design of optimized poxviral vaccines for the study of advanced immunotherapy options. PMID:25520512

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…

Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports.

PubMed

Qiu, John X; Yoon, Hong-Jun; Fearn, Paul A; Tourassi, Georgia D

2018-01-01

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.

Development of a sugar-binding residue prediction system from protein sequences using support vector machine.

PubMed

Banno, Masaki; Komiyama, Yusuke; Cao, Wei; Oku, Yuya; Ueki, Kokoro; Sumikoshi, Kazuya; Nakamura, Shugo; Terada, Tohru; Shimizu, Kentaro

2017-02-01

Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (https://doi.org/10.5281/zenodo.61513). Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Discrimination of malignant lymphomas and leukemia using Radon transform based-higher order spectra

NASA Astrophysics Data System (ADS)

Luo, Yi; Celenk, Mehmet; Bejai, Prashanth

2006-03-01

A new algorithm that can be used to automatically recognize and classify malignant lymphomas and leukemia is proposed in this paper. The algorithm utilizes the morphological watersheds to obtain boundaries of cells from cell images and isolate them from the surrounding background. The areas of cells are extracted from cell images after background subtraction. The Radon transform and higher-order spectra (HOS) analysis are utilized as an image processing tool to generate class feature vectors of different type cells and to extract testing cells' feature vectors. The testing cells' feature vectors are then compared with the known class feature vectors for a possible match by computing the Euclidean distances. The cell in question is classified as belonging to one of the existing cell classes in the least Euclidean distance sense.

Direct Lymph Node Vaccination of Lentivector/Prostate-Specific Antigen is Safe and Generates Tissue-Specific Responses in Rhesus Macaques.

PubMed

Au, Bryan C; Lee, Chyan-Jang; Lopez-Perez, Orlay; Foltz, Warren; Felizardo, Tania C; Wang, James C M; Huang, Ju; Fan, Xin; Madden, Melissa; Goldstein, Alyssa; Jaffray, David A; Moloo, Badru; McCart, J Andrea; Medin, Jeffrey A

2016-02-19

Anti-cancer immunotherapy is emerging from a nadir and demonstrating tangible benefits to patients. A variety of approaches are now employed. We are invoking antigen (Ag)-specific responses through direct injections of recombinant lentivectors (LVs) that encode sequences for tumor-associated antigens into multiple lymph nodes to optimize immune presentation/stimulation. Here we first demonstrate the effectiveness and antigen-specificity of this approach in mice challenged with prostate-specific antigen (PSA)-expressing tumor cells. Next we tested the safety and efficacy of this approach in two cohorts of rhesus macaques as a prelude to a clinical trial application. Our vector encodes the cDNA for rhesus macaque PSA and a rhesus macaque cell surface marker to facilitate vector titering and tracking. We utilized two independent injection schemas demarcated by the timing of LV administration. In both cohorts we observed marked tissue-specific responses as measured by clinical evaluations and magnetic resonance imaging of the prostate gland. Tissue-specific responses were sustained for up to six months-the end-point of the study. Control animals immunized against an irrelevant Ag were unaffected. We did not observe vector spread in test or control animals or perturbations of systemic immune parameters. This approach thus offers an "off-the-shelf" anti-cancer vaccine that could be made at large scale and injected into patients-even on an out-patient basis.

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

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

Use of LANDSAT imagery for wildlife habitat mapping in northeast and east central Alaska

NASA Technical Reports Server (NTRS)

Lent, P. C. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Two scenes were analyzed by applying an iterative cluster analysis to a 2% random data sample and then using the resulting clusters as a training set basis for maximum likelihood classification. Twenty-six and twenty-seven categorical classes, respectively resulted from this process. The majority of classes in each case were quite specific vegetation types; each of these types has specific value as moose habitat.

Automated diagnosis of epilepsy using CWT, HOS and texture parameters.

PubMed

Acharya, U Rajendra; Yanti, Ratna; Zheng, Jia Wei; Krishnan, M Muthu Rama; Tan, Jen Hong; Martis, Roshan Joy; Lim, Choo Min

2013-06-01

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

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

NASA Astrophysics Data System (ADS)

Kalanov, Temur Z.

2014-03-01

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

Diversity of breeding habitats of anophelines (Diptera: Culicidae) in Ramgarh district, Jharkhand, India.

PubMed

Pandey, Siddharth; Das, M K; Dhiman, Ramesh C

2016-01-01

The Ramgarh district of Jharkhand state, India is highly malarious owing to abundance of different malaria vector species, namely Anopheles culicifacies, An. fluviatilis and An. annularis. In spite of high prevalence of malaria vectors in Ramgarh, their larval ecology and climatic conditions affecting malaria dynamics have never been studied. Therefore, the objective of this study was to identify the diversity of potential breeding habitats and breeding preferences of anopheline vectors in the Ramgarh district. Anopheles immature collection was carried out at potential aquatic habitats in Ramgarh and Gola sites using the standard dipper on fortnightly basis from August 2012 to July 2013. The immatures were reared till adult emergence and further identified using standard keys. Temperature of outdoor and water bodies was recorded through temperature data loggers, and rainfall through standard rain gauges installed at each site. A total of 6495 immature specimens representing 17 Anopheles species including three malaria vectors, viz. An. culicifacies, An. fluviatilis and An. annularis were collected from 11 types of breeding habitats. The highly preferred breeding habitats of vector anophelines were river bed pools, rivulets, wells, ponds, river margins, ditches and irrigation channels. Larval abundance of vector species showed site-specific variation with temperature and rainfall patterns throughout the year. The Shannon-Weiner diversity index ranged from 0.19 to 1.94 at Ramgarh site and 0.16 to 1.76 at Gola site. The study revealed that malaria vector species have been adapted to breed in a wide range of water bodies. The regular monitoring of such specific vector breeding sites under changing ecological and environmental conditions will be useful in guiding larval control operations selectively for effective vector/ malaria control.

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

Classification of Stellar Spectra with Fuzzy Minimum Within-Class Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhong-bao, Liu; Wen-ai, Song; Jing, Zhang; Wen-juan, Zhao

2017-06-01

Classification is one of the important tasks in astronomy, especially in spectra analysis. Support Vector Machine (SVM) is a typical classification method, which is widely used in spectra classification. Although it performs well in practice, its classification accuracies can not be greatly improved because of two limitations. One is it does not take the distribution of the classes into consideration. The other is it is sensitive to noise. In order to solve the above problems, inspired by the maximization of the Fisher's Discriminant Analysis (FDA) and the SVM separability constraints, fuzzy minimum within-class support vector machine (FMWSVM) is proposed in this paper. In FMWSVM, the distribution of the classes is reflected by the within-class scatter in FDA and the fuzzy membership function is introduced to decrease the influence of the noise. The comparative experiments with SVM on the SDSS datasets verify the effectiveness of the proposed classifier FMWSVM.

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

ERIC Educational Resources Information Center

Biomedical Interdisciplinary Curriculum Project, Berkeley, CA.

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

Relaxation of the single-slip condition in strain-gradient plasticity

PubMed Central

Anguige, Keith; Dondl, Patrick W.

2014-01-01

We consider the variational formulation of both geometrically linear and geometrically nonlinear elasto-plasticity subject to a class of hard single-slip conditions. Such side conditions typically render the associated boundary-value problems non-convex. We show that, for a large class of non-smooth plastic distortions, a given single-slip condition (specification of Burgers vectors) can be relaxed by introducing a microstructure through a two-stage process of mollification and lamination. The relaxed model can be thought of as an aid to simulating macroscopic plastic behaviour without the need to resolve arbitrarily fine spatial scales. PMID:25197243

Relaxation of the single-slip condition in strain-gradient plasticity.

PubMed

Anguige, Keith; Dondl, Patrick W

2014-09-08

We consider the variational formulation of both geometrically linear and geometrically nonlinear elasto-plasticity subject to a class of hard single-slip conditions. Such side conditions typically render the associated boundary-value problems non-convex. We show that, for a large class of non-smooth plastic distortions, a given single-slip condition (specification of Burgers vectors) can be relaxed by introducing a microstructure through a two-stage process of mollification and lamination. The relaxed model can be thought of as an aid to simulating macroscopic plastic behaviour without the need to resolve arbitrarily fine spatial scales.

Specific Mutation of a Gammaherpesvirus-Expressed Antigen in Response to CD8 T Cell Selection In Vivo

PubMed Central

Loh, Joy; Popkin, Daniel L.; Droit, Lindsay; Braaten, Douglas C.; Zhao, Guoyan; Zhang, Xin; Vachharajani, Punit; Myers, Nancy; Hansen, Ted H.

2012-01-01

Herpesviruses are thought to be highly genetically stable, and their use as vaccine vectors has been proposed. However, studies of the human gammaherpesvirus, Epstein-Barr virus, have found viral isolates containing mutations in HLA class I-restricted epitopes. Using murine gammaherpesvirus 68 expressing ovalbumin (OVA), we examined the stability of a gammaherpesvirus antigenic locus under strong CD8 T cell selection in vivo. OVA-specific CD8 T cells selected viral isolates containing mutations in the OVA locus but minimal alterations in other genomic regions. Thus, a CD8 T cell response to a gammaherpesvirus-expressed antigen that is not essential for replication or pathogenesis can result in selective mutation of that antigen in vivo. This finding may have relevance for the use of herpesvirus vectors for chronic antigen expression in vivo. PMID:22171269

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.

An information measure for class discrimination. [in remote sensing of crop observation

NASA Technical Reports Server (NTRS)

Shen, S. S.; Badhwar, G. D.

1986-01-01

This article describes a separability measure for class discrimination. This measure is based on the Fisher information measure for estimating the mixing proportion of two classes. The Fisher information measure not only provides a means to assess quantitatively the information content in the features for separating classes, but also gives the lower bound for the variance of any unbiased estimate of the mixing proportion based on observations of the features. Unlike most commonly used separability measures, this measure is not dependent on the form of the probability distribution of the features and does not imply a specific estimation procedure. This is important because the probability distribution function that describes the data for a given class does not have simple analytic forms, such as a Gaussian. Results of applying this measure to compare the information content provided by three Landsat-derived feature vectors for the purpose of separating small grains from other crops are presented.

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.

Structure of the mouse sex peptide pheromone ESP1 reveals a molecular basis for specific binding to the class C G-protein-coupled vomeronasal receptor.

PubMed

Yoshinaga, Sosuke; Sato, Toru; Hirakane, Makoto; Esaki, Kaori; Hamaguchi, Takashi; Haga-Yamanaka, Sachiko; Tsunoda, Mai; Kimoto, Hiroko; Shimada, Ichio; Touhara, Kazushige; Terasawa, Hiroaki

2013-05-31

Exocrine gland-secreting peptide 1 (ESP1) is a sex pheromone that is released in male mouse tear fluids and enhances female sexual receptive behavior. ESP1 is selectively recognized by a specific class C G-protein-coupled receptor (GPCR), V2Rp5, among the hundreds of receptors expressed in vomeronasal sensory neurons (VSNs). The specific sensing mechanism of the mammalian peptide pheromone by the class C GPCR remains to be elucidated. Here we identified the minimal functional region needed to retain VSN-stimulating activity in ESP1 and determined its three-dimensional structure, which adopts a helical fold stabilized by an intramolecular disulfide bridge with extensive charged patches. We then identified the amino acids involved in the activation of VSNs by a structure-based mutational analysis, revealing that the highly charged surface is crucial for the ESP1 activity. We also demonstrated that ESP1 specifically bound to an extracellular region of V2Rp5 by an in vitro pulldown assay. Based on homology modeling of V2Rp5 using the structure of the metabotropic glutamate receptor, we constructed a docking model of the ESP1-V2Rp5 complex in which the binding interface exhibited good electrostatic complementarity. These experimental results, supported by the molecular docking simulations, reveal that charge-charge interactions determine the specificity of ESP1 binding to V2Rp5 in the large extracellular region characteristic of class C GPCRs. The present study provides insights into the structural basis for the narrowly tuned sensing of mammalian peptide pheromones by class C GPCRs.

Interpretations and pitfalls in modelling vector-transmitted infections.

PubMed

Amaku, M; Azevedo, F; Burattini, M N; Coutinho, F A B; Lopez, L F; Massad, E

2015-07-01

In this paper we propose a debate on the role of mathematical models in evaluating control strategies for vector-borne infections. Mathematical models must have their complexity adjusted to their goals, and we have basically two classes of models. At one extreme we have models that are intended to check if our intuition about why a certain phenomenon occurs is correct. At the other extreme, we have models whose goals are to predict future outcomes. These models are necessarily very complex. There are models in between these classes. Here we examine two models, one of each class and study the possible pitfalls that may be incurred. We begin by showing how to simplify the description of a complicated model for a vector-borne infection. Next, we examine one example found in a recent paper that illustrates the dangers of basing control strategies on models without considering their limitations. The model in this paper is of the second class. Following this, we review an interesting paper (a model of the first class) that contains some biological assumptions that are inappropriate for dengue but may apply to other vector-borne infections. In conclusion, we list some misgivings about modelling presented in this paper for debate.

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

PubMed

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

2017-09-01

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

In situ pneumococcal vaccine production and delivery through a hybrid biological-biomaterial vector

PubMed Central

Li, Yi; Beitelshees, Marie; Fang, Lei; Hill, Andrew; Ahmadi, Mahmoud Kamal; Chen, Mingfu; Davidson, Bruce A.; Knight, Paul; Smith, Randall J.; Andreadis, Stelios T.; Hakansson, Anders P.; Jones, Charles H.; Pfeifer, Blaine A.

2016-01-01

The type and potency of an immune response provoked during vaccination will determine ultimate success in disease prevention. The basis for this response will be the design and implementation of antigen presentation to the immune system. Whereas direct antigen administration will elicit some form of immunological response, a more sophisticated approach would couple the antigen of interest to a vector capable of broad delivery formats and designed for heightened response. New antigens associated with pneumococcal disease virulence were used to test the delivery and adjuvant capabilities of a hybrid biological-biomaterial vector consisting of a bacterial core electrostatically coated with a cationic polymer. The hybrid design provides (i) passive and active targeting of antigen-presenting cells, (ii) natural and multicomponent adjuvant properties, (iii) dual intracellular delivery mechanisms, and (iv) a simple formulation mechanism. In addition, the hybrid format enables device-specific, or in situ, antigen production and consolidation via localization within the bacterial component of the vector. This capability eliminates the need for dedicated antigen production and purification before vaccination efforts while leveraging the aforementioned features of the overall delivery device. We present the first disease-specific utilization of the vector toward pneumococcal disease highlighted by improved immune responses and protective capabilities when tested against traditional vaccine formulations and a range of clinically relevant Streptococcus pneumoniae strains. More broadly, the results point to similar levels of success with other diseases that would benefit from the production, delivery, and efficacy capabilities offered by the hybrid vector. PMID:27419235

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

PubMed

Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

2014-01-01

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

An operational satellite scatterometer for wind vector measurements over the ocean

NASA Technical Reports Server (NTRS)

Grantham, W. L.; Bracalente, E. M.; Jones, W. L.; Schrader, J. H.; Schroeder, L. C.; Mitchell, J. L.

1975-01-01

Performance requirements and design characteristics of a microwave scatterometer wind sensor for measuring surface winds over the oceans on a global basis are described. Scatterometer specifications are developed from user requirements of wind vector measurement range and accuracy, swath width, resolution cell size and measurement grid spacing. A detailed analysis is performed for a baseline fan-beam scatterometer design, and its performance capabilities for meeting the SeaSat-A user requirements. Various modes of operation are discussed which will allow the resolution of questions concerning the effects of sea state on the scatterometer wind sensing ability and to verify design boundaries of the instrument.

Intracellular trafficking of hybrid gene delivery vectors.

PubMed

Keswani, Rahul K; Lazebnik, Mihael; Pack, Daniel W

2015-06-10

Viral and non-viral gene delivery vectors are in development for human gene therapy, but both exhibit disadvantages such as inadequate efficiency, lack of cell-specific targeting or safety concerns. We have recently reported the design of hybrid delivery vectors combining retrovirus-like particles with synthetic polymers or lipids that are efficient, provide sustained gene expression and are more stable compared to native retroviruses. To guide further development of this promising class of gene delivery vectors, we have investigated their mechanisms of intracellular trafficking. Moloney murine leukemia virus-like particles (M-VLPs) were complexed with chitosan (Chi) or liposomes (Lip) comprising DOTAP, DOPE and cholesterol to form the hybrid vectors (Chi/M-VLPs and Lip/M-VLPs, respectively). Transfection efficiency and cellular internalization of the vectors were quantified in the presence of a panel of inhibitors of various endocytic pathways. Intracellular transport and trafficking kinetics of the hybrid vectors were dependent on the synthetic component and used a combination of clathrin- and caveolar-dependent endocytosis and macropinocytosis. Chi/M-VLPs were slower to transfect compared to Lip/M-VLPs due to the delayed detachment of the synthetic component. The synthetic component of hybrid gene delivery vectors plays a significant role in their cellular interactions and processing and is a key parameter for the design of more efficient gene delivery vehicles. Copyright © 2015 Elsevier B.V. All rights reserved.

Epidemiological Implications of Host Biodiversity and Vector Biology: Key Insights from Simple Models.

PubMed

Dobson, Andrew D M; Auld, Stuart K J R

2016-04-01

Models used to investigate the relationship between biodiversity change and vector-borne disease risk often do not explicitly include the vector; they instead rely on a frequency-dependent transmission function to represent vector dynamics. However, differences between classes of vector (e.g., ticks and insects) can cause discrepancies in epidemiological responses to environmental change. Using a pair of disease models (mosquito- and tick-borne), we simulated substitutive and additive biodiversity change (where noncompetent hosts replaced or were added to competent hosts, respectively), while considering different relationships between vector and host densities. We found important differences between classes of vector, including an increased likelihood of amplified disease risk under additive biodiversity change in mosquito models, driven by higher vector biting rates. We also draw attention to more general phenomena, such as a negative relationship between initial infection prevalence in vectors and likelihood of dilution, and the potential for a rise in density of infected vectors to occur simultaneously with a decline in proportion of infected hosts. This has important implications; the density of infected vectors is the most valid metric for primarily zoonotic infections, while the proportion of infected hosts is more relevant for infections where humans are a primary host.

Predicting complications of percutaneous coronary intervention using a novel support vector method.

PubMed

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases). The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Predicting complications of percutaneous coronary intervention using a novel support vector method

PubMed Central

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

Objective To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Materials and methods Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. Results The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer–Lemeshow χ2 value (seven cases) and the mean cross-entropy error (eight cases). Conclusions The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains. PMID:23599229

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

DOE PAGES

Qiu, John; Yoon, Hong-Jun; Fearn, Paul A.; ...

2017-05-03

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations asmore » the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.« less

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

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

Qiu, John; Yoon, Hong-Jun; Fearn, Paul A.

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations asmore » the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.« less

Effect of nitrogen fertilizer on growth, form, and wood quality of eastern cottonwood

Treesearch

D.S. DeBell; E.H. Mallonee; L.T. Alford

1975-01-01

A 9-year-old cottonwood plantation near Fitler, Mississippi was fertilized with ammonium nitrate (150 and 300 lbs N/A) in May 1970. Fertilizer increased diameter (b.h.) growth of dominant, codominant, and intermediate crown classes by 109, 174 and 482 percent, respectively. Form class of fertilized trees also increased. On a whole-stem basis, specific gravity declined...

Mapping raised bogs with an iterative one-class classification approach

NASA Astrophysics Data System (ADS)

Mack, Benjamin; Roscher, Ribana; Stenzel, Stefanie; Feilhauer, Hannes; Schmidtlein, Sebastian; Waske, Björn

2016-10-01

Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative OCC approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative OCC outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier.

Diversification of C. elegans Motor Neuron Identity via Selective Effector Gene Repression.

PubMed

Kerk, Sze Yen; Kratsios, Paschalis; Hart, Michael; Mourao, Romulo; Hobert, Oliver

2017-01-04

A common organizational feature of nervous systems is the existence of groups of neurons that share common traits but can be divided into individual subtypes based on anatomical or molecular features. We elucidate the mechanistic basis of neuronal diversification processes in the context of C.elegans ventral cord motor neurons that share common traits that are directly activated by the terminal selector UNC-3. Diversification of motor neurons into different classes, each characterized by unique patterns of effector gene expression, is controlled by distinct combinations of phylogenetically conserved, class-specific transcriptional repressors. These repressors are continuously required in postmitotic neurons to prevent UNC-3, which is active in all neuron classes, from activating class-specific effector genes in specific motor neuron subsets via discrete cis-regulatory elements. The strategy of antagonizing the activity of broadly acting terminal selectors of neuron identity in a subtype-specific fashion may constitute a general principle of neuron subtype diversification. Copyright © 2017 Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Tahaei, Marzieh S.; Reader, Andrew J.

2016-09-01

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

Horse cDNA clones encoding two MHC class I genes

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

Barbis, D.P.; Maher, J.K.; Stanek, J.

1994-12-31

Two full-length clones encoding MHC class I genes were isolated by screening a horse cDNA library, using a probe encoding in human HLA-A2.2Y allele. The library was made in the pcDNA1 vector (Invitrogen, San Diego, CA), using mRNA from peripheral blood lymphocytes obtained from a Thoroughbred stallion (No. 0834) homozygous for a common horse MHC haplotype (ELA-A2, -B2, -D2; Antczak et al. 1984; Donaldson et al. 1988). The clones were sequenced, using SP6 and T7 universal primers and horse-specific oligonucleotides designed to extend previously determined sequences.

Stable solutions of inflation driven by vector fields

NASA Astrophysics Data System (ADS)

Emami, Razieh; Mukohyama, Shinji; Namba, Ryo; Zhang, Ying-li

2017-03-01

Many models of inflation driven by vector fields alone have been known to be plagued by pathological behaviors, namely ghost and/or gradient instabilities. In this work, we seek a new class of vector-driven inflationary models that evade all of the mentioned instabilities. We build our analysis on the Generalized Proca Theory with an extension to three vector fields to realize isotropic expansion. We obtain the conditions required for quasi de-Sitter solutions to be an attractor analogous to the standard slow-roll one and those for their stability at the level of linearized perturbations. Identifying the remedy to the existing unstable models, we provide a simple example and explicitly show its stability. This significantly broadens our knowledge on vector inflationary scenarios, reviving potential phenomenological interests for this class of models.

Cosmology for quadratic gravity in generalized Weyl geometry

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

Jiménez, Jose Beltrán; Heisenberg, Lavinia; Koivisto, Tomi S.

A class of vector-tensor theories arises naturally in the framework of quadratic gravity in spacetimes with linear vector distortion. Requiring the absence of ghosts for the vector field imposes an interesting condition on the allowed connections with vector distortion: the resulting one-parameter family of connections generalises the usual Weyl geometry with polar torsion. The cosmology of this class of theories is studied, focusing on isotropic solutions wherein the vector field is dominated by the temporal component. De Sitter attractors are found and inhomogeneous perturbations around such backgrounds are analysed. In particular, further constraints on the models are imposed by excludingmore » pathologies in the scalar, vector and tensor fluctuations. Various exact background solutions are presented, describing a constant and an evolving dark energy, a bounce and a self-tuning de Sitter phase. However, the latter two scenarios are not viable under a closer scrutiny.« less

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

PubMed

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

2008-09-01

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

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Uniqueness of thermodynamic projector and kinetic basis of molecular individualism

NASA Astrophysics Data System (ADS)

Gorban, Alexander N.; Karlin, Iliya V.

2004-05-01

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

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis.

PubMed

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

Sex and Class Differences in Parent-Child Interaction: A Test of Kohn's Hypothesis. Scientific Paper No. 4181.

ERIC Educational Resources Information Center

Gecas, Viktor; Nye, F. Ivan

This paper examines sex and class differences in the style and circumstances of parental discipline of the child. Specifically, we have focused on Melvin Kohn's suggestive hypothesis that white collar parents stress the development of internal standards of conduct in their children and thus are more likely to discipline the child on the basis of…

Knowledge Space: A Conceptual Basis for the Organization of Knowledge

ERIC Educational Resources Information Center

Meincke, Peter P. M.; Atherton, Pauline

1976-01-01

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

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.

AAV capsid CD8+ T-cell epitopes are highly conserved across AAV serotypes

PubMed Central

Hui, Daniel J; Edmonson, Shyrie C; Podsakoff, Gregory M; Pien, Gary C; Ivanciu, Lacramioara; Camire, Rodney M; Ertl, Hildegund; Mingozzi, Federico; High, Katherine A; Basner-Tschakarjan, Etiena

2015-01-01

Adeno-associated virus (AAV) has become one of the most promising vectors in gene transfer in the last 10 years with successful translation to clinical trials in humans and even market approval for a first gene therapy product in Europe. Administration to humans, however, revealed that adaptive immune responses against the vector capsid can present an obstacle to sustained transgene expression due to the activation and expansion of capsid-specific T cells. The limited number of peripheral blood mononuclear cells (PBMCs) obtained from samples within clinical trials allows for little more than monitoring of T-cell responses. We were able to identify immunodominant major histocompatibility complex (MHC) class I epitopes for common human leukocyte antigen (HLA) types by using spleens isolated from subjects undergoing splenectomy for non-malignant indications as a source of large numbers of lymphocytes and restimulating them with single AAV capsid peptides in vitro. Further experiments confirmed that these epitopes are naturally processed and functionally relevant. The design of more effective and less immunogenic AAV vectors, and precise immune monitoring of vector-infused subjects, are facilitated by these findings. PMID:26445723

AAV capsid CD8+ T-cell epitopes are highly conserved across AAV serotypes.

PubMed

Hui, Daniel J; Edmonson, Shyrie C; Podsakoff, Gregory M; Pien, Gary C; Ivanciu, Lacramioara; Camire, Rodney M; Ertl, Hildegund; Mingozzi, Federico; High, Katherine A; Basner-Tschakarjan, Etiena

2015-01-01

Adeno-associated virus (AAV) has become one of the most promising vectors in gene transfer in the last 10 years with successful translation to clinical trials in humans and even market approval for a first gene therapy product in Europe. Administration to humans, however, revealed that adaptive immune responses against the vector capsid can present an obstacle to sustained transgene expression due to the activation and expansion of capsid-specific T cells. The limited number of peripheral blood mononuclear cells (PBMCs) obtained from samples within clinical trials allows for little more than monitoring of T-cell responses. We were able to identify immunodominant major histocompatibility complex (MHC) class I epitopes for common human leukocyte antigen (HLA) types by using spleens isolated from subjects undergoing splenectomy for non-malignant indications as a source of large numbers of lymphocytes and restimulating them with single AAV capsid peptides in vitro. Further experiments confirmed that these epitopes are naturally processed and functionally relevant. The design of more effective and less immunogenic AAV vectors, and precise immune monitoring of vector-infused subjects, are facilitated by these findings.

Detection of receptor-specific murine leukemia virus binding to cells by immunofluorescence analysis.

PubMed Central

Kadan, M J; Sturm, S; Anderson, W F; Eglitis, M A

1992-01-01

Four classes of murine leukemia virus (MuLV) which display distinct cellular tropisms and bind to different retrovirus receptors to initiate virus infection have been described. In the present study, we describe a rapid, sensitive immunofluorescence assay useful for characterizing the initial binding of MuLV to cells. By using the rat monoclonal antibody 83A25 (L. H. Evans, R. P. Morrison, F. G. Malik, J. Portis, and W. J. Britt, J. Virol. 64:6176-6183, 1990), which recognizes an epitope of the envelope gp70 molecule common to the different classes of MuLV, it is possible to analyse the binding of ecotropic, amphotropic, or xenotropic MuLV by using only a single combination of primary and secondary antibodies. The MuLV binding detected by this assay is envelope receptor specific and matches the susceptibility to infection determined for cells from a variety of species. The binding of amphotropic MuLV to NIH 3T3 cells was shown to be rapid, saturable, and temperature dependent. Chinese hamster ovary (CHO-K1) cells normally lack the ability to bind ecotropic virus and are not infectible by ecotropic vectors. Expression of the cloned ecotropic retrovirus receptor gene (Rec) in CHO-K1 cells confers high levels of ecotropic virus-specific binding and confers susceptibility to infection. Characterization of MuLV binding to primary cells may provide insight into the infectibility of cells by retroviruses and aid in the selection of appropriate vectors for gene transfer experiments. PMID:1312632

Quantum chaos for nonstandard symmetry classes in the Feingold-Peres model of coupled tops

NASA Astrophysics Data System (ADS)

Fan, Yiyun; Gnutzmann, Sven; Liang, Yuqi

2017-12-01

We consider two coupled quantum tops with angular momentum vectors L and M . The coupling Hamiltonian defines the Feingold-Peres model, which is a known paradigm of quantum chaos. We show that this model has a nonstandard symmetry with respect to the Altland-Zirnbauer tenfold symmetry classification of quantum systems, which extends the well-known threefold way of Wigner and Dyson (referred to as "standard" symmetry classes here). We identify the nonstandard symmetry classes BD I0 (chiral orthogonal class with no zero modes), BD I1 (chiral orthogonal class with one zero mode), and C I (antichiral orthogonal class) as well as the standard symmetry class A I (orthogonal class). We numerically analyze the specific spectral quantum signatures of chaos related to the nonstandard symmetries. In the microscopic density of states and in the distribution of the lowest positive energy eigenvalue, we show that the Feingold-Peres model follows the predictions of the Gaussian ensembles of random-matrix theory in the appropriate symmetry class if the corresponding classical dynamics is chaotic. In a crossover to mixed and near-integrable classical dynamics, we show that these signatures disappear or strongly change.

Quantum chaos for nonstandard symmetry classes in the Feingold-Peres model of coupled tops.

PubMed

Fan, Yiyun; Gnutzmann, Sven; Liang, Yuqi

2017-12-01

We consider two coupled quantum tops with angular momentum vectors L and M. The coupling Hamiltonian defines the Feingold-Peres model, which is a known paradigm of quantum chaos. We show that this model has a nonstandard symmetry with respect to the Altland-Zirnbauer tenfold symmetry classification of quantum systems, which extends the well-known threefold way of Wigner and Dyson (referred to as "standard" symmetry classes here). We identify the nonstandard symmetry classes BDI_{0} (chiral orthogonal class with no zero modes), BDI_{1} (chiral orthogonal class with one zero mode), and CI (antichiral orthogonal class) as well as the standard symmetry class AI (orthogonal class). We numerically analyze the specific spectral quantum signatures of chaos related to the nonstandard symmetries. In the microscopic density of states and in the distribution of the lowest positive energy eigenvalue, we show that the Feingold-Peres model follows the predictions of the Gaussian ensembles of random-matrix theory in the appropriate symmetry class if the corresponding classical dynamics is chaotic. In a crossover to mixed and near-integrable classical dynamics, we show that these signatures disappear or strongly change.

Scalar and vector perturbations in a universe with discrete and continuous matter sources

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

Eingorn, Maxim; Kiefer, Claus; Zhuk, Alexander, E-mail: maxim.eingorn@gmail.com, E-mail: kiefer@thp.uni-koeln.de, E-mail: ai.zhuk2@gmail.com

We study a universe filled with dust-like matter in the form of discrete inhomogeneities (e.g., galaxies and their groups and clusters) and two sets of perfect fluids with linear and nonlinear equations of state, respectively. The background spacetime geometry is defined by the FLRW metric. In the weak gravitational field limit, we develop the first-order scalar and vector cosmological perturbation theory. Our approach works at all cosmological scales (i.e. sub-horizon and super-horizon ones) and incorporates linear and nonlinear effects with respect to energy density fluctuations. We demonstrate that the scalar perturbation (i.e. the gravitational potential) as well as the vectormore » perturbation can be split into individual contributions from each matter source. Each of these contributions satisfies its own equation. The velocity-independent parts of the individual gravitational potentials are characterized by a finite time-dependent Yukawa interaction range being the same for each individual contribution. We also obtain the exact form of the gravitational potential and vector perturbation related to the discrete matter sources. The self-consistency of our approach is thoroughly checked. The derived equations can form the theoretical basis for numerical simulations for a wide class of cosmological models.« less

nanos-Driven expression of piggyBac transposase induces mobilization of a synthetic autonomous transposon in the malaria vector mosquito, Anopheles stephensi.

PubMed

Macias, Vanessa M; Jimenez, Alyssa J; Burini-Kojin, Bianca; Pledger, David; Jasinskiene, Nijole; Phong, Celine Hien; Chu, Karen; Fazekas, Aniko; Martin, Kelcie; Marinotti, Osvaldo; James, Anthony A

2017-08-01

Transposons are a class of selfish DNA elements that can mobilize within a genome. If mobilization is accompanied by an increase in copy number (replicative transposition), the transposon may sweep through a population until it is fixed in all of its interbreeding members. This introgression has been proposed as the basis for drive systems to move genes with desirable phenotypes into target species. One such application would be to use them to move a gene conferring resistance to malaria parasites throughout a population of vector mosquitos. We assessed the feasibility of using the piggyBac transposon as a gene-drive mechanism to distribute anti-malarial transgenes in populations of the malaria vector, Anopheles stephensi. We designed synthetic gene constructs that express the piggyBac transposase in the female germline using the control DNA of the An. stephensi nanos orthologous gene linked to marker genes to monitor inheritance. Two remobilization events were observed with a frequency of one every 23 generations, a rate far below what would be useful to drive anti-pathogen transgenes into wild mosquito populations. We discuss the possibility of optimizing this system and the impetus to do so. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Helicity is the only integral invariant of volume-preserving transformations

PubMed Central

Enciso, Alberto; Peralta-Salas, Daniel; de Lizaur, Francisco Torres

2016-01-01

We prove that any regular integral invariant of volume-preserving transformations is equivalent to the helicity. Specifically, given a functional ℐ defined on exact divergence-free vector fields of class C1 on a compact 3-manifold that is associated with a well-behaved integral kernel, we prove that ℐ is invariant under arbitrary volume-preserving diffeomorphisms if and only if it is a function of the helicity. PMID:26864201

Evaluation of Commercial Agrochemicals as New Tools for Malaria Vector Control.

PubMed

Hoppé, Mark; Hueter, Ottmar F; Bywater, Andy; Wege, Philip; Maienfisch, Peter

2016-10-01

Malaria is a vector-borne and life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. The vector control insecticide market represents a small fraction of the crop protection market and is estimated to be valued at up to $500 million at the active ingredient level. Insecticide resistance towards the current WHOPES-approved products urgently requires the development of new tools to protect communities against the transmission of malaria. The evaluation of commercial products for malaria vector control is a viable and cost effective strategy to identify new malaria vector control products. Several examples of such spin-offs from crop protection insecticides are already evidencing the success of this strategy, namely pirimiphos-methyl for indoor residual sprays and spinosad, diflubenzuron, novaluron, and pyriproxifen for mosquito larvae control, a supplementary technology for control of malaria vectors. In our study the adulticidal activities of 81 insecticides representing 23 insecticidal modes of action classes, 34 fungicides from 6 fungicidal mode of action classes and 15 herbicides from 2 herbicidal modes of action classes were tested in a newly developed screening system. WHOPES approved insecticides for malaria vector control consistently caused 80-100% mortality of adult Anopheles stephensi at application rates between 0.2 and 20 mg active ingradient (AI) litre -1 . Chlorfenapyr, fipronil, carbosulfan and endosulfan showed the expected good activity. Four new insecticides and three fungicides with promising activity against adult mosquitoes were identified, namely the insecticides acetamiprid, thiamethoxam, thiocyclam and metaflumizone and the fungicides diflumetorin, picoxystrobin, and fluazinam. Some of these compounds certainly deserve to be further evaluated for malaria vector control. This is the first report describing good activity of commercial fungicides against malaria vectors.

Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson's correlation.

PubMed

Graña, M; Termenon, M; Savio, A; Gonzalez-Pinto, A; Echeveste, J; Pérez, J M; Besga, A

2011-09-20

The aim of this paper is to obtain discriminant features from two scalar measures of Diffusion Tensor Imaging (DTI) data, Fractional Anisotropy (FA) and Mean Diffusivity (MD), and to train and test classifiers able to discriminate Alzheimer's Disease (AD) patients from controls on the basis of features extracted from the FA or MD volumes. In this study, support vector machine (SVM) classifier was trained and tested on FA and MD data. Feature selection is done computing the Pearson's correlation between FA or MD values at voxel site across subjects and the indicative variable specifying the subject class. Voxel sites with high absolute correlation are selected for feature extraction. Results are obtained over an on-going study in Hospital de Santiago Apostol collecting anatomical T1-weighted MRI volumes and DTI data from healthy control subjects and AD patients. FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in several cross-validation studies, supporting the usefulness of DTI-derived features as an image-marker for AD and to the feasibility of building Computer Aided Diagnosis systems for AD based on them. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

An optimal control strategies using vaccination and fogging in dengue fever transmission model

NASA Astrophysics Data System (ADS)

Fitria, Irma; Winarni, Pancahayani, Sigit; Subchan

2017-08-01

This paper discussed regarding a model and an optimal control problem of dengue fever transmission. We classified the model as human and vector (mosquito) population classes. For the human population, there are three subclasses, such as susceptible, infected, and resistant classes. Then, for the vector population, we divided it into wiggler, susceptible, and infected vector classes. Thus, the model consists of six dynamic equations. To minimize the number of dengue fever cases, we designed two optimal control variables in the model, the giving of fogging and vaccination. The objective function of this optimal control problem is to minimize the number of infected human population, the number of vector, and the cost of the controlling efforts. By giving the fogging optimally, the number of vector can be minimized. In this case, we considered the giving of vaccination as a control variable because it is one of the efforts that are being developed to reduce the spreading of dengue fever. We used Pontryagin Minimum Principle to solve the optimal control problem. Furthermore, the numerical simulation results are given to show the effect of the optimal control strategies in order to minimize the epidemic of dengue fever.

LFSPMC: Linear feature selection program using the probability of misclassification

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.; Marion, B. P.

1975-01-01

The computational procedure and associated computer program for a linear feature selection technique are presented. The technique assumes that: a finite number, m, of classes exists; each class is described by an n-dimensional multivariate normal density function of its measurement vectors; the mean vector and covariance matrix for each density function are known (or can be estimated); and the a priori probability for each class is known. The technique produces a single linear combination of the original measurements which minimizes the one-dimensional probability of misclassification defined by the transformed densities.

A person-centered analysis of posttraumatic stress disorder symptoms following a natural disaster: predictors of latent class membership.

PubMed

Rosellini, Anthony J; Coffey, Scott F; Tracy, Melissa; Galea, Sandro

2014-01-01

The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Person-Centered Analysis of Posttraumatic Stress Disorder Symptoms Following a Natural Disaster: Predictors of Latent Class Membership

PubMed Central

Rosellini, Anthony J.; Coffey, Scott F.; Tracy, Melissa; Galea, Sandro

2014-01-01

The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs. PMID:24334161

A Case Study on the Impact of Homogenous Small Class Instruction as an Academic Intervention for At-Risk Students

ERIC Educational Resources Information Center

Mays, Jessica E.

2015-01-01

This case study examined the impact of a site-specific intervention for at-risk students in a small rural elementary school in the foothills of North Carolina. The research site uses a small homogenous class setting as a basis for accelerating academic growth for students who are considered at-risk in literacy based on the state-required literacy…

Certified Reduced Basis Model Characterization: a Frequentistic Uncertainty Framework

DTIC Science & Technology

2011-01-11

14) It then follows that the Legendre coefficient random vector, (Z [0], Z [1], . . . , Z [I])(ω), is (I+1)– variate normally distributed with mean (δ...I. Note each two-sided inequality represents two constraints. 3. PDE-Based Statistical Inference We now proceed to the parametrized partial...appearance of defects or geometric variations relative to an initial baseline, or perhaps manufacturing departures from nominal specifications; if our

A Search for Vector Magnetic Field Variations Associated with the M-Class Flares of 1991 June 10 in AR 6659

NASA Technical Reports Server (NTRS)

Hagyard, Mona J.; Stark, B. A.; Venkatakrishnan, P.

1998-01-01

A careful analysis of a 6-hour time sequence of vector magnetograms of AR 6659, observed on 1991 June 10 with the MSFC vector magnetograph, has revealed only minor changes in the vector magnetic field azimuths in the vicinity of two M-class flares, and the association of these changes with the flares is not unambiguous. In this paper we present our analysis of the data which includes comparison of vector magnetograms prior to and during the flares, calculation of distributions of the rms variation of the azimuth at each pixel in the field of view of the active region, and examination of the variation with time of the azimuths at every pixel covered by the main flare emissions as observed with the H-alpha telescope coaligned with the vector magnetograph. We also present results of an analysis of evolutionary changes in the azimuth over the field of view of the active region.

A class of high resolution explicit and implicit shock-capturing methods

NASA Technical Reports Server (NTRS)

Yee, H. C.

1989-01-01

An attempt is made to give a unified and generalized formulation of a class of high resolution, explicit and implicit shock capturing methods, and to illustrate their versatility in various steady and unsteady complex shock wave computations. Included is a systematic review of the basic design principle of the various related numerical methods. Special emphasis is on the construction of the basis nonlinear, spatially second and third order schemes for nonlinear scalar hyperbolic conservation laws and the methods of extending these nonlinear scalar schemes to nonlinear systems via the approximate Riemann solvers and the flux vector splitting approaches. Generalization of these methods to efficiently include equilibrium real gases and large systems of nonequilibrium flows are discussed. Some issues concerning the applicability of these methods that were designed for homogeneous hyperbolic conservation laws to problems containing stiff source terms and shock waves are also included. The performance of some of these schemes is illustrated by numerical examples for 1-, 2- and 3-dimensional gas dynamics problems.

Test functions for three-dimensional control-volume mixed finite-element methods on irregular grids

USGS Publications Warehouse

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

2000-01-01

Numerical methods based on unstructured grids, with irregular cells, usually require discrete shape functions to approximate the distribution of quantities across cells. For control-volume mixed finite-element methods, vector shape functions are used to approximate the distribution of velocities across cells and vector test functions are used to minimize the error associated with the numerical approximation scheme. For a logically cubic mesh, the lowest-order shape functions are chosen in a natural way to conserve intercell fluxes that vary linearly in logical space. Vector test functions, while somewhat restricted by the mapping into the logical reference cube, admit a wider class of possibilities. Ideally, an error minimization procedure to select the test function from an acceptable class of candidates would be the best procedure. Lacking such a procedure, we first investigate the effect of possible test functions on the pressure distribution over the control volume; specifically, we look for test functions that allow for the elimination of intermediate pressures on cell faces. From these results, we select three forms for the test function for use in a control-volume mixed method code and subject them to an error analysis for different forms of grid irregularity; errors are reported in terms of the discrete L2 norm of the velocity error. Of these three forms, one appears to produce optimal results for most forms of grid irregularity.

Sparse Solutions for Single Class SVMs: A Bi-Criterion Approach

NASA Technical Reports Server (NTRS)

Das, Santanu; Oza, Nikunj C.

2011-01-01

In this paper we propose an innovative learning algorithm - a variation of One-class nu Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent. We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class nu SVMs while reducing both training time and test time by several factors.

Primer-optimized results and trends for circular phasing and other circle-to-circle impulsive coplanar rendezvous

NASA Astrophysics Data System (ADS)

Sandrik, Suzannah

Optimal solutions to the impulsive circular phasing problem, a special class of orbital maneuver in which impulsive thrusts shift a vehicle's orbital position by a specified angle, are found using primer vector theory. The complexities of optimal circular phasing are identified and illustrated using specifically designed Matlab software tools. Information from these new visualizations is applied to explain discrepancies in locally optimal solutions found by previous researchers. Two non-phasing circle-to-circle impulsive rendezvous problems are also examined to show the applicability of the tools developed here to a broader class of problems and to show how optimizing these rendezvous problems differs from the circular phasing case.

Horizontal vectorization of electron repulsion integrals.

PubMed

Pritchard, Benjamin P; Chow, Edmond

2016-10-30

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

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

NASA Technical Reports Server (NTRS)

Freund, Roland W.; Nachtigal, Noeel M.

1992-01-01

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

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

Restriction to gene flow is associated with changes in the molecular basis of pyrethroid resistance in the malaria vector Anopheles funestus

PubMed Central

Barnes, Kayla G.; Irving, Helen; Chiumia, Martin; Mzilahowa, Themba; Coleman, Michael; Hemingway, Janet; Wondji, Charles S.

2017-01-01

Resistance to pyrethroids, the sole insecticide class recommended for treating bed nets, threatens the control of major malaria vectors, including Anopheles funestus. Effective management of resistance requires an understanding of the dynamics and mechanisms driving resistance. Here, using genome-wide transcription and genetic diversity analyses, we show that a shift in the molecular basis of pyrethroid resistance in southern African populations of this species is associated with a restricted gene flow. Across the most highly endemic and densely populated regions in Malawi, An. funestus is resistant to pyrethroids, carbamates, and organochlorides. Genome-wide microarray-based transcription analysis identified overexpression of cytochrome P450 genes as the main mechanism driving this resistance. The most up-regulated genes include cytochrome P450s (CYP) CYP6P9a, CYP6P9b and CYP6M7. However, a significant shift in the overexpression profile of these genes was detected across a south/north transect, with CYP6P9a and CYP6P9b more highly overexpressed in the southern resistance front and CYP6M7 predominant in the northern front. A genome-wide genetic structure analysis of southern African populations of An. funestus from Zambia, Malawi, and Mozambique revealed a restriction of gene flow between populations, in line with the geographical variation observed in the transcriptomic analysis. Genetic polymorphism analysis of the three key resistance genes, CYP6P9a, CYP6P9b, and CYP6M7, support barriers to gene flow that are shaping the underlying molecular basis of pyrethroid resistance across southern Africa. This barrier to gene flow is likely to impact the design and implementation of resistance management strategies in the region. PMID:28003461

Ultra-low background DNA cloning system.

PubMed

Goto, Kenta; Nagano, Yukio

2013-01-01

Yeast-based in vivo cloning is useful for cloning DNA fragments into plasmid vectors and is based on the ability of yeast to recombine the DNA fragments by homologous recombination. Although this method is efficient, it produces some by-products. We have developed an "ultra-low background DNA cloning system" on the basis of yeast-based in vivo cloning, by almost completely eliminating the generation of by-products and applying the method to commonly used Escherichia coli vectors, particularly those lacking yeast replication origins and carrying an ampicillin resistance gene (Amp(r)). First, we constructed a conversion cassette containing the DNA sequences in the following order: an Amp(r) 5' UTR (untranslated region) and coding region, an autonomous replication sequence and a centromere sequence from yeast, a TRP1 yeast selectable marker, and an Amp(r) 3' UTR. This cassette allowed conversion of the Amp(r)-containing vector into the yeast/E. coli shuttle vector through use of the Amp(r) sequence by homologous recombination. Furthermore, simultaneous transformation of the desired DNA fragment into yeast allowed cloning of this DNA fragment into the same vector. We rescued the plasmid vectors from all yeast transformants, and by-products containing the E. coli replication origin disappeared. Next, the rescued vectors were transformed into E. coli and the by-products containing the yeast replication origin disappeared. Thus, our method used yeast- and E. coli-specific "origins of replication" to eliminate the generation of by-products. Finally, we successfully cloned the DNA fragment into the vector with almost 100% efficiency.

Differential spatial activity patterns of acupuncture by a machine learning based analysis

NASA Astrophysics Data System (ADS)

You, Youbo; Bai, Lijun; Xue, Ting; Zhong, Chongguang; Liu, Zhenyu; Tian, Jie

2011-03-01

Acupoint specificity, lying at the core of the Traditional Chinese Medicine, underlies the theoretical basis of acupuncture application. However, recent studies have reported that acupuncture stimulation at nonacupoint and acupoint can both evoke similar signal intensity decreases in multiple regions. And these regions were spatially overlapped. We used a machine learning based Support Vector Machine (SVM) approach to elucidate the specific neural response pattern induced by acupuncture stimulation. Group analysis demonstrated that stimulation at two different acupoints (belong to the same nerve segment but different meridians) could elicit distinct neural response patterns. Our findings may provide evidence for acupoint specificity.

Practical Implementation of Multiple Model Adaptive Estimation Using Neyman-Pearson Based Hypothesis Testing and Spectral Estimation Tools

DTIC Science & Technology

1996-09-01

Generalized Likelihood Ratio (GLR) and voting techniques. The third class consisted of multiple hypothesis filter detectors, specifically the MMAE. The...vector version, versus a tensor if we use the matrix version of the power spectral density estimate. Using this notation, we will derive an...as MATLAB , have an intrinsic sample covariance computation available, which makes this method quite easy to implement. In practice, the mean for the

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

Emami, Razieh; Mukohyama, Shinji; Namba, Ryo

Many models of inflation driven by vector fields alone have been known to be plagued by pathological behaviors, namely ghost and/or gradient instabilities. In this work, we seek a new class of vector-driven inflationary models that evade all of the mentioned instabilities. We build our analysis on the Generalized Proca Theory with an extension to three vector fields to realize isotropic expansion. We obtain the conditions required for quasi de-Sitter solutions to be an attractor analogous to the standard slow-roll one and those for their stability at the level of linearized perturbations. Identifying the remedy to the existing unstable models,more » we provide a simple example and explicitly show its stability. This significantly broadens our knowledge on vector inflationary scenarios, reviving potential phenomenological interests for this class of models.« less

Computer-Assisted Transgenesis of Caenorhabditis elegans for Deep Phenotyping

PubMed Central

Gilleland, Cody L.; Falls, Adam T.; Noraky, James; Heiman, Maxwell G.; Yanik, Mehmet F.

2015-01-01

A major goal in the study of human diseases is to assign functions to genes or genetic variants. The model organism Caenorhabditis elegans provides a powerful tool because homologs of many human genes are identifiable, and large collections of genetic vectors and mutant strains are available. However, the delivery of such vector libraries into mutant strains remains a long-standing experimental bottleneck for phenotypic analysis. Here, we present a computer-assisted microinjection platform to streamline the production of transgenic C. elegans with multiple vectors for deep phenotyping. Briefly, animals are immobilized in a temperature-sensitive hydrogel using a standard multiwell platform. Microinjections are then performed under control of an automated microscope using precision robotics driven by customized computer vision algorithms. We demonstrate utility by phenotyping the morphology of 12 neuronal classes in six mutant backgrounds using combinations of neuron-type-specific fluorescent reporters. This technology can industrialize the assignment of in vivo gene function by enabling large-scale transgenic engineering. PMID:26163188

Study of Equatorial Ionospheric irregularities and Mapping of Electron Density Profiles and Ionograms

DTIC Science & Technology

2012-03-09

equation is a product of a complex basis vector in Jackson and a linear combination of plane wave functions. We convert both the amplitudes and the...wave function arguments from complex scalars to complex vectors . This conversion allows us to separate the electric field vector and the imaginary...magnetic field vector , because exponentials of imaginary scalars convert vectors to imaginary vectors and vice versa, while ex- ponentials of imaginary

[Fast discrimination of edible vegetable oil based on Raman spectroscopy].

PubMed

Zhou, Xiu-Jun; Dai, Lian-Kui; Li, Sheng

2012-07-01

A novel method to fast discriminate edible vegetable oils by Raman spectroscopy is presented. The training set is composed of different edible vegetable oils with known classes. Based on their original Raman spectra, baseline correction and normalization were applied to obtain standard spectra. Two characteristic peaks describing the unsaturated degree of vegetable oil were selected as feature vectors; then the centers of all classes were calculated. For an edible vegetable oil with unknown class, the same pretreatment and feature extraction methods were used. The Euclidian distances between the feature vector of the unknown sample and the center of each class were calculated, and the class of the unknown sample was finally determined by the minimum distance. For 43 edible vegetable oil samples from seven different classes, experimental results show that the clustering effect of each class was more obvious and the class distance was much larger with the new feature extraction method compared with PCA. The above classification model can be applied to discriminate unknown edible vegetable oils rapidly and accurately.

Spiking Neural P Systems With Rules on Synapses Working in Maximum Spiking Strategy.

PubMed

Tao Song; Linqiang Pan

2015-06-01

Spiking neural P systems (called SN P systems for short) are a class of parallel and distributed neural-like computation models inspired by the way the neurons process information and communicate with each other by means of impulses or spikes. In this work, we introduce a new variant of SN P systems, called SN P systems with rules on synapses working in maximum spiking strategy, and investigate the computation power of the systems as both number and vector generators. Specifically, we prove that i) if no limit is imposed on the number of spikes in any neuron during any computation, such systems can generate the sets of Turing computable natural numbers and the sets of vectors of positive integers computed by k-output register machine; ii) if an upper bound is imposed on the number of spikes in each neuron during any computation, such systems can characterize semi-linear sets of natural numbers as number generating devices; as vector generating devices, such systems can only characterize the family of sets of vectors computed by sequential monotonic counter machine, which is strictly included in family of semi-linear sets of vectors. This gives a positive answer to the problem formulated in Song et al., Theor. Comput. Sci., vol. 529, pp. 82-95, 2014.

Calculating vibrational spectra with sum of product basis functions without storing full-dimensional vectors or matrices.

PubMed

Leclerc, Arnaud; Carrington, Tucker

2014-05-07

We propose an iterative method for computing vibrational spectra that significantly reduces the memory cost of calculations. It uses a direct product primitive basis, but does not require storing vectors with as many components as there are product basis functions. Wavefunctions are represented in a basis each of whose functions is a sum of products (SOP) and the factorizable structure of the Hamiltonian is exploited. If the factors of the SOP basis functions are properly chosen, wavefunctions are linear combinations of a small number of SOP basis functions. The SOP basis functions are generated using a shifted block power method. The factors are refined with a rank reduction algorithm to cap the number of terms in a SOP basis function. The ideas are tested on a 20-D model Hamiltonian and a realistic CH3CN (12 dimensional) potential. For the 20-D problem, to use a standard direct product iterative approach one would need to store vectors with about 10(20) components and would hence require about 8 × 10(11) GB. With the approach of this paper only 1 GB of memory is necessary. Results for CH3CN agree well with those of a previous calculation on the same potential.

Application of three controls optimally in a vector-borne disease - a mathematical study

NASA Astrophysics Data System (ADS)

Kar, T. K.; Jana, Soovoojeet

2013-10-01

We have proposed and analyzed a vector-borne disease model with three types of controls for the eradication of the disease. Four different classes for the human population namely susceptible, infected, recovered and vaccinated and two different classes for the vector populations namely susceptible and infected are considered. In the first part of our analysis the disease dynamics are described for fixed controls and some inferences have been drawn regarding the spread of the disease. Next the optimal control problem is formulated and solved considering control parameters as time dependent. Different possible combination of controls are used and their effectiveness are compared by numerical simulation.

Jacobian projection reduced-order models for dynamic systems with contact nonlinearities

NASA Astrophysics Data System (ADS)

Gastaldi, Chiara; Zucca, Stefano; Epureanu, Bogdan I.

2018-02-01

In structural dynamics, the prediction of the response of systems with localized nonlinearities, such as friction dampers, is of particular interest. This task becomes especially cumbersome when high-resolution finite element models are used. While state-of-the-art techniques such as Craig-Bampton component mode synthesis are employed to generate reduced order models, the interface (nonlinear) degrees of freedom must still be solved in-full. For this reason, a new generation of specialized techniques capable of reducing linear and nonlinear degrees of freedom alike is emerging. This paper proposes a new technique that exploits spatial correlations in the dynamics to compute a reduction basis. The basis is composed of a set of vectors obtained using the Jacobian of partial derivatives of the contact forces with respect to nodal displacements. These basis vectors correspond to specifically chosen boundary conditions at the contacts over one cycle of vibration. The technique is shown to be effective in the reduction of several models studied using multiple harmonics with a coupled static solution. In addition, this paper addresses another challenge common to all reduction techniques: it presents and validates a novel a posteriori error estimate capable of evaluating the quality of the reduced-order solution without involving a comparison with the full-order solution.

Major Histocompatibility Complex in Human - HLA System: Biological Role and Impact for Practical Medicine.

PubMed

Alexeev, Leonid P.

1999-10-01

Interactions of HLA constitute the key basis for development of the whole number of pathologies, starting from oncological and infectious diseases, and ending with autoimmune disorders and allergies. The most demonstrable example is oncopathology. The fact is that HLA class I (namely, its non-polymorphic determinants) have recently been shown to be the main target for so called natural (or non-specific) killer cells (NK). Naturally, the profound decrease of class I histocompatibility antigens on the surface of pathologically changed cells, impairing cellular interaction between NK and target cells, "takes them out" from the control of NK. As a result, the body looses one of the most important protective functions. Quite another type of impairment of HLA role in cellular interaction may be the basis of autoimmune diseases. The most successful results were obtained in studies of insulin dependent diabetes. One of the main pathogenic factors was shown to be marked elevation (aberrant expression) of HLA on islet cells (insulin producers). This, in its turn, is the consequence of dysfunction and activation of genes, responsible for "assembly and transport" of HLA class II. The problem about role of HLA in cell interactions in allergy is rather novel, but poor studied trend, however some obtained results are encouraging. The point is that the unique feature in expression of class II histocompatibility antigens, specific for allergy, was revealed for recent years. Expression of class II histocompatibility antigens is appeared to be sharply increased on B lymphocytes of allergic patients.

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.

New fuzzy support vector machine for the class imbalance problem in medical datasets classification.

PubMed

Gu, Xiaoqing; Ni, Tongguang; Wang, Hongyuan

2014-01-01

In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.

Turtle Functions Downstream of Cut in Differentially Regulating Class Specific Dendrite Morphogenesis in Drosophila

PubMed Central

Sulkowski, Mikolaj J.; Iyer, Srividya Chandramouli; Kurosawa, Mathieu S.; Iyer, Eswar Prasad R.; Cox, Daniel N.

2011-01-01

Background Dendritic morphology largely determines patterns of synaptic connectivity and electrochemical properties of a neuron. Neurons display a myriad diversity of dendritic geometries which serve as a basis for functional classification. Several types of molecules have recently been identified which regulate dendrite morphology by acting at the levels of transcriptional regulation, direct interactions with the cytoskeleton and organelles, and cell surface interactions. Although there has been substantial progress in understanding the molecular mechanisms of dendrite morphogenesis, the specification of class-specific dendritic arbors remains largely unexplained. Furthermore, the presence of numerous regulators suggests that they must work in concert. However, presently, few genetic pathways regulating dendrite development have been defined. Methodology/Principal Findings The Drosophila gene turtle belongs to an evolutionarily conserved class of immunoglobulin superfamily members found in the nervous systems of diverse organisms. We demonstrate that Turtle is differentially expressed in Drosophila da neurons. Moreover, MARCM analyses reveal Turtle acts cell autonomously to exert class specific effects on dendritic growth and/or branching in da neuron subclasses. Using transgenic overexpression of different Turtle isoforms, we find context-dependent, isoform-specific effects on mediating dendritic branching in class II, III and IV da neurons. Finally, we demonstrate via chromatin immunoprecipitation, qPCR, and immunohistochemistry analyses that Turtle expression is positively regulated by the Cut homeodomain transcription factor and via genetic interaction studies that Turtle is downstream effector of Cut-mediated regulation of da neuron dendrite morphology. Conclusions/Significance Our findings reveal that Turtle proteins differentially regulate the acquisition of class-specific dendrite morphologies. In addition, we have established a transcriptional regulatory interaction between Cut and Turtle, representing a novel pathway for mediating class specific dendrite development. PMID:21811639

Visualizing vector field topology in fluid flows

NASA Technical Reports Server (NTRS)

Helman, James L.; Hesselink, Lambertus

1991-01-01

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

Minimal supergravity models of inflation

NASA Astrophysics Data System (ADS)

Ferrara, Sergio; Kallosh, Renata; Linde, Andrei; Porrati, Massimo

2013-10-01

We present a superconformal master action for a class of supergravity models with one arbitrary function defining the Jordan frame. It leads to a gauge-invariant action for a real vector multiplet, which upon gauge fixing describes a massive vector multiplet, or to a dual formulation with a linear multiplet and a massive tensor field. In both cases the models have one real scalar, the inflaton, naturally suited for single-field inflation. Vectors and tensors required by supersymmetry to complement a single real scalar do not acquire vacuum expectation values during inflation, so there is no need to stabilize the extra scalars that are always present in the theories with chiral matter multiplets. The new class of models can describe any inflaton potential that vanishes at its minimum and grows monotonically away from the minimum. In this class of supergravity models, one can fit any desirable choice of inflationary parameters ns and r.

Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

PubMed Central

Mourão-Miranda, Janaina; Hardoon, David R.; Hahn, Tim; Marquand, Andre F.; Williams, Steve C.R.; Shawe-Taylor, John; Brammer, Michael

2011-01-01

Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers. PMID:21723950

Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans

PubMed Central

Vontas, John; Martins, Ademir J.; Ng, Lee Ching; Koou, Sin Ying; Dusfour, Isabelle; Raghavendra, Kamaraju; Pinto, João; Corbel, Vincent; David, Jean-Philippe; Weetman, David

2017-01-01

Both Aedes aegytpi and Ae. albopictus are major vectors of 5 important arboviruses (namely chikungunya virus, dengue virus, Rift Valley fever virus, yellow fever virus, and Zika virus), making these mosquitoes an important factor in the worldwide burden of infectious disease. Vector control using insecticides coupled with larval source reduction is critical to control the transmission of these viruses to humans but is threatened by the emergence of insecticide resistance. Here, we review the available evidence for the geographical distribution of insecticide resistance in these 2 major vectors worldwide and map the data collated for the 4 main classes of neurotoxic insecticide (carbamates, organochlorines, organophosphates, and pyrethroids). Emerging resistance to all 4 of these insecticide classes has been detected in the Americas, Africa, and Asia. Target-site mutations and increased insecticide detoxification have both been linked to resistance in Ae. aegypti and Ae. albopictus but more work is required to further elucidate metabolic mechanisms and develop robust diagnostic assays. Geographical distributions are provided for the mechanisms that have been shown to be important to date. Estimating insecticide resistance in unsampled locations is hampered by a lack of standardisation in the diagnostic tools used and by a lack of data in a number of regions for both resistance phenotypes and genotypes. The need for increased sampling using standard methods is critical to tackle the issue of emerging insecticide resistance threatening human health. Specifically, diagnostic doses and well-characterised susceptible strains are needed for the full range of insecticides used to control Ae. aegypti and Ae. albopictus to standardise measurement of the resistant phenotype, and calibrated diagnostic assays are needed for the major mechanisms of resistance. PMID:28727779

Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans.

PubMed

Moyes, Catherine L; Vontas, John; Martins, Ademir J; Ng, Lee Ching; Koou, Sin Ying; Dusfour, Isabelle; Raghavendra, Kamaraju; Pinto, João; Corbel, Vincent; David, Jean-Philippe; Weetman, David

2017-07-01

Both Aedes aegytpi and Ae. albopictus are major vectors of 5 important arboviruses (namely chikungunya virus, dengue virus, Rift Valley fever virus, yellow fever virus, and Zika virus), making these mosquitoes an important factor in the worldwide burden of infectious disease. Vector control using insecticides coupled with larval source reduction is critical to control the transmission of these viruses to humans but is threatened by the emergence of insecticide resistance. Here, we review the available evidence for the geographical distribution of insecticide resistance in these 2 major vectors worldwide and map the data collated for the 4 main classes of neurotoxic insecticide (carbamates, organochlorines, organophosphates, and pyrethroids). Emerging resistance to all 4 of these insecticide classes has been detected in the Americas, Africa, and Asia. Target-site mutations and increased insecticide detoxification have both been linked to resistance in Ae. aegypti and Ae. albopictus but more work is required to further elucidate metabolic mechanisms and develop robust diagnostic assays. Geographical distributions are provided for the mechanisms that have been shown to be important to date. Estimating insecticide resistance in unsampled locations is hampered by a lack of standardisation in the diagnostic tools used and by a lack of data in a number of regions for both resistance phenotypes and genotypes. The need for increased sampling using standard methods is critical to tackle the issue of emerging insecticide resistance threatening human health. Specifically, diagnostic doses and well-characterised susceptible strains are needed for the full range of insecticides used to control Ae. aegypti and Ae. albopictus to standardise measurement of the resistant phenotype, and calibrated diagnostic assays are needed for the major mechanisms of resistance.

[Analysis of commercial specifications and grades of wild and cultivated Gentianae Macrophyllae Radix based on multi-indicative constituents].

PubMed

Yang, Yan-Mei; Lin, Li; Lu, You-Yuan; Ma, Xiao-Hui; Jin, Ling; Zhu, Tian-Tian

2016-03-01

The study is aimed to analyze the commercial specifications and grades of wild and cultivated Gentianae Macrophllae Radix based on multi-indicative constituents. The seven kinds of main chemical components containing in Gentianae Macrophyllae Radix were determined by UPLC, and then the quality levels of chemical component of Gentianae Macrophyllae Radix were clustered and classified by modern statistical methods (canonical correspondence analysis, Fisher discriminant analysis and so on). The quality indices were selected and their correlations were analyzed. Lastly, comprehensively quantitative grade division for quality under different commodity-specifications and different grades of same commodity-specifications of wild and planting were divided. The results provide a basis for a reasonable division of specification and grade of the commodity of Gentianae Macrophyllae Radix. The range of quality evaluation of main index components (gentiopicrin, loganin acid and swertiamarin) was proposed, and the Herbal Quality Index (HQI) was introduced. The rank discriminant function was established based on the quality by Fisher discriminant analysis. According to the analysis, the quality of wild and cultivated Luobojiao, one of the commercial specification of Gentianae Macrophyllae Radix was the best, Mahuajiao, the other commercial specification, was average , Xiaoqinjiao was inferior. Among grades, the quality of first-class cultivated Luobojiao was the worst, of second class secondary, and the third class the best; The quality of the first-class of wild Luobojiao was secondary, and the second-class the best; The quality of the second-class of Mahuajiao was secondary, and the first-class was the best; the quality of first-class Xiaoqinjiao was secondary, and the second-class was the better one between the two grades, but not obvious significantly. The method provides a new idea and method for evaluation of comprehensively quantitative on the quality of Gentianae Macrophyllae Radix. Copyright© by the Chinese Pharmaceutical Association.

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

PubMed Central

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. Objective The aims were to describe how to: (i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii) automatically identify the features that best distinguish the groups. Methods The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described – simple or complex; presentation order – which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders. Results and Conclusion A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods. PMID:29213908

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

NASA Astrophysics Data System (ADS)

Heyouni, M.; Sadok, H.

2008-04-01

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

A structural basis for antigen presentation by the MHC class Ib molecule, Qa-1b.

PubMed

Zeng, Li; Sullivan, Lucy C; Vivian, Julian P; Walpole, Nicholas G; Harpur, Christopher M; Rossjohn, Jamie; Clements, Craig S; Brooks, Andrew G

2012-01-01

The primary function of the monomorphic MHC class Ib molecule Qa-1(b) is to present peptides derived from the leader sequences of other MHC class I molecules for recognition by the CD94-NKG2 receptors expressed by NK and T cells. Whereas the mode of peptide presentation by its ortholog HLA-E, and subsequent recognition by CD94-NKG2A, is known, the molecular basis of Qa-1(b) function is unclear. We have assessed the interaction between Qa-1(b) and CD94-NKG2A and shown that they interact with an affinity of 17 μM. Furthermore, we have determined the structure of Qa-1(b) bound to the leader sequence peptide, Qdm (AMAPRTLLL), to a resolution of 1.9 Å and compared it with that of HLA-E. The crystal structure provided a basis for understanding the restricted peptide repertoire of Qa-1(b). Whereas the Qa-1(b-AMAPRTLLL) complex was similar to that of HLA-E, significant sequence and structural differences were observed between the respective Ag-binding clefts. However, the conformation of the Qdm peptide bound by Qa-1(b) was very similar to that of peptide bound to HLA-E. Although a number of conserved innate receptors can recognize heterologous ligands from other species, the structural differences between Qa-1(b) and HLA-E manifested in CD94-NKG2A ligand recognition being species specific despite similarities in peptide sequence and conformation. Collectively, our data illustrate the structural homology between Qa-1(b) and HLA-E and provide a structural basis for understanding peptide repertoire selection and the specificity of the interaction of Qa-1(b) with CD94-NKG2 receptors.

Immune Response to Recombinant Adenovirus in Humans: Capsid Components from Viral Input Are Targets for Vector-Specific Cytotoxic T Lymphocytes

PubMed Central

Molinier-Frenkel, Valérie; Gahery-Segard, Hanne; Mehtali, Majid; Le Boulaire, Christophe; Ribault, Sébastien; Boulanger, Pierre; Tursz, Thomas; Guillet, Jean-Gérard; Farace, Françoise

2000-01-01

We previously demonstrated that a single injection of 109 PFU of recombinant adenovirus into patients induces strong vector-specific immune responses (H. Gahéry-Ségard, V. Molinier-Frenkel, C. Le Boulaire, P. Saulnier, P. Opolon, R. Lengagne, E. Gautier, A. Le Cesne, L. Zitvogel, A. Venet, C. Schatz, M. Courtney, T. Le Chevalier, T. Tursz, J.-G. Guillet, and F. Farace, J. Clin. Investig. 100:2218–2226, 1997). In the present study we analyzed the mechanism of vector recognition by cytotoxic T lymphocytes (CTL). CD8+ CTL lines were derived from two patients and maintained in long-term cultures. Target cell infections with E1-deleted and E1-plus E2-deleted adenoviruses, as well as transcription-blocking experiments with actinomycin D, revealed that host T-cell recognition did not require viral gene transcription. Target cells treated with brefeldin A were not lysed, indicating that viral input protein-derived peptides are associated with HLA class I molecules. Using recombinant capsid component-loaded targets, we observed that the three major proteins could be recognized. These results raise the question of the use of multideleted adenoviruses for gene therapy in the quest to diminish antivector CTL responses. PMID:10906225

Cloning of murine RNA polymerase I-specific TAF factors: conserved interactions between the subunits of the species-specific transcription initiation factor TIF-IB/SL1.

PubMed

Heix, J; Zomerdijk, J C; Ravanpay, A; Tjian, R; Grummt, I

1997-03-04

Promoter selectivity for all three classes of eukaryotic RNA polymerases is brought about by multimeric protein complexes containing TATA box binding protein (TBP) and specific TBP-associated factors (TAFs). Unlike class II- and III-specific TBP-TAF complexes, the corresponding murine and human class I-specific transcription initiation factor TIF-IB/SL1 exhibits a pronounced selectivity for its homologous promoter. As a first step toward understanding the molecular basis of species-specific promoter recognition, we cloned the cDNAs encoding the three mouse pol I-specific TBP-associated factors (TAFIs) and compared the amino acid sequences of the murine TAFIs with their human counterparts. The four subunits from either species can form stable chimeric complexes that contain stoichiometric amounts of TBP and TAFIs, demonstrating that differences in the primary structure of human and mouse TAFIs do not dramatically alter the network of protein-protein contacts responsible for assembly of the multimeric complex. Thus, primate vs. rodent promoter selectivity mediated by the TBP-TAFI complex is likely to be the result of cumulative subtle differences between individual subunits that lead to species-specific properties of RNA polymerase I transcription.

The modality-specific organization of grammatical categories: evidence from impaired spoken and written sentence production.

PubMed

Rapp, B; Caramazza, A

1997-02-01

We describe the case of a brain-damaged individual whose speech is characterized by difficulty with practically all words except for elements of the closed class vocabulary. In contrast, his written sentence production exhibits a complementary impairment involving the omission of closed class vocabulary items and the relative sparing of nouns. On the basis of these differences we argue: (1) that grammatical categories constitute an organizing parameter of representation and/or processing for each of the independent, modality-specific lexicons, and (2) that these observations contribute to the growing evidence that access to the orthographic and phonological forms of words can occur independently.

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

Tenney, Rebeca M.; Bell, Christie L.; Wilson, James M., E-mail: wilsonjm@mail.med.upenn.edu

Adeno-associated virus serotype 8 (AAV8) is a promising vector for liver-directed gene therapy. Although efficient uncoating of viral capsids has been implicated in AAV8's robust liver transduction, much about the biology of AAV8 hepatotropism remains unclear. Our study investigated the structural basis of AAV8 liver transduction efficiency by constructing chimeric vector capsids containing sequences derived from AAV8 and AAV2 – a highly homologous yet poorly hepatotropic serotype. Engineered vectors containing capsid variable regions (VR) VII and IX from AAV8 in an AAV2 backbone mediated near AAV8-like transduction in mouse liver, with higher numbers of chimeric genomes detected in whole livermore » cells and isolated nuclei. Interestingly, chimeric capsids within liver nuclei also uncoated similarly to AAV8 by 6 weeks after administration, in contrast with AAV2, of which a significantly smaller proportion were uncoated. This study links specific AAV capsid regions to the transduction ability of a clinically relevant AAV serotype. - Highlights: • We construct chimeric vectors to identify determinants of AAV8 liver transduction. • An AAV2-based vector with 17 AAV8 residues exhibited high liver transduction in mice. • This vector also surpassed AAV2 in cell entry, nuclear entry and onset of expression. • Most chimeric vector particles were uncoated at 6 weeks, like AAV8 and unlike AAV2. • Chimera retained heparin binding and was antigenically distinct from AAV2 and AAV8.« less

In vitro evaluation of a mammary gland specific expression vector encoding recombinant human lysozyme for development of transgenic dairy goat embryos.

PubMed

Gui, Tao; Zhang, Meiling; Chen, Jianwen; Zhang, Yuanliang; Zhou, Naru; Zhang, Yu; Tao, Jia; Sui, Liucai; Li, Yunsheng; Liu, Ya; Zhang, Xiaorong; Zhang, Yunhai

2012-08-01

A vector expressing human lysozyme (pBC1-hLYZ-GFP-Neo) was evaluated for gene and protein expression following liposome-mediated transformation of C-127 mouse mammary cancer cells. Cultures of G418-resistant clones were harvested 24-72 h after induction with prolactin, insulin and hydrocortisone. Target gene expression was analyzed by RT-PCR and Western blot and recombinant human lysozyme (rhLYZ) bacteriostatic activity was also evaluated. The hLYZ gene was correctly transcribed and translated in C-127 cells and hLYZ inhibited gram-positive bacterial growth, indicating the potential of this expression vector for development of a mammary gland bioreactor in goats. Guanzhong dairy goat skin fibroblasts transfected with pBC1-hLYZ-GFP-Neo were used to construct a goat embryo transgenically expressing rhLYZ by somatic nuclear transplantation with a blastocyst rate of 9.0 ± 2.8 %. These data establish the basis for cultivation of mastitis-resistant hLYZ transgenic goats.

Energy expenditure estimation during daily military routine with body-fixed sensors.

PubMed

Wyss, Thomas; Mäder, Urs

2011-05-01

The purpose of this study was to develop and validate an algorithm for estimating energy expenditure during the daily military routine on the basis of data collected using body-fixed sensors. First, 8 volunteers completed isolated physical activities according to an established protocol, and the resulting data were used to develop activity-class-specific multiple linear regressions for physical activity energy expenditure on the basis of hip acceleration, heart rate, and body mass as independent variables. Second, the validity of these linear regressions was tested during the daily military routine using indirect calorimetry (n = 12). Volunteers' mean estimated energy expenditure did not significantly differ from the energy expenditure measured with indirect calorimetry (p = 0.898, 95% confidence interval = -1.97 to 1.75 kJ/min). We conclude that the developed activity-class-specific multiple linear regressions applied to the acceleration and heart rate data allow estimation of energy expenditure in 1-minute intervals during daily military routine, with accuracy equal to indirect calorimetry.

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

PubMed Central

2014-01-01

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

Unique Normal Form and the Associated Coefficients for a Class of Three-Dimensional Nilpotent Vector Fields

NASA Astrophysics Data System (ADS)

Li, Jing; Kou, Liying; Wang, Duo; Zhang, Wei

2017-12-01

In this paper, we mainly focus on the unique normal form for a class of three-dimensional vector fields via the method of transformation with parameters. A general explicit recursive formula is derived to compute the higher order normal form and the associated coefficients, which can be achieved easily by symbolic calculations. To illustrate the efficiency of the approach, a comparison of our result with others is also presented.

78 FR 34556 - Establishment of Class E Airspace; Tobe, CO

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-10

... facilitate vectoring of Instrument Flight Rules (IFR) aircraft under control of Denver and Albuquerque Air... Albuquerque ARTCC by vectoring aircraft from en route airspace to terminal areas. This action is necessary for...

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.

Equivalent Vectors

ERIC Educational Resources Information Center

Levine, Robert

2004-01-01

The cross-product is a mathematical operation that is performed between two 3-dimensional vectors. The result is a vector that is orthogonal or perpendicular to both of them. Learning about this for the first time while taking Calculus-III, the class was taught that if AxB = AxC, it does not necessarily follow that B = C. This seemed baffling. The…

Symmetry of semi-reduced lattices.

PubMed

Stróż, Kazimierz

2015-05-01

The main result of this work is extension of the famous characterization of Bravais lattices according to their metrical, algebraic and geometric properties onto a wide class of primitive lattices (including Buerger-reduced, nearly Buerger-reduced and a substantial part of Delaunay-reduced) related to low-restricted semi-reduced descriptions (s.r.d.'s). While the `geometric' operations in Bravais lattices map the basis vectors into themselves, the `arithmetic' operators in s.r.d. transform the basis vectors into cell vectors (basis vectors, face or space diagonals) and are represented by matrices from the set {\\bb V} of all 960 matrices with the determinant ±1 and elements {0, ±1} of the matrix powers. A lattice is in s.r.d. if the moduli of off-diagonal elements in both the metric tensors M and M(-1) are smaller than corresponding diagonal elements sharing the same column or row. Such lattices are split into 379 s.r.d. types relative to the arithmetic holohedries. Metrical criteria for each type do not need to be explicitly given but may be modelled as linear derivatives {\\bb M}(p,q,r), where {\\bb M} denotes the set of 39 highest-symmetry metric tensors, and p,q,r describe changes of appropriate interplanar distances. A sole filtering of {\\bb V} according to an experimental s.r.d. metric and subsequent geometric interpretation of the filtered matrices lead to mathematically stable and rich information on the Bravais-lattice symmetry and deviations from the exact symmetry. The emphasis on the crystallographic features of lattices was obtained by shifting the focus (i) from analysis of a lattice metric to analysis of symmetry matrices [Himes & Mighell (1987). Acta Cryst. A43, 375-384], (ii) from the isometric approach and invariant subspaces to the orthogonality concept {some ideas in Le Page [J. Appl. Cryst. (1982), 15, 255-259]} and splitting indices [Stróż (2011). Acta Cryst. A67, 421-429] and (iii) from fixed cell transformations to transformations derivable via geometric information (Himes & Mighell, 1987; Le Page, 1982). It is illustrated that corresponding arithmetic and geometric holohedries share space distribution of symmetry elements. Moreover, completeness of the s.r.d. types reveals their combinatorial structure and simplifies the crystallographic description of structural phase transitions, especially those observed with the use of powder diffraction. The research proves that there are excellent theoretical and practical reasons for looking at crystal lattice symmetry from an entirely new and surprising point of view - the combinatorial set {\\bb V} of matrices, their semi-reduced lattice context and their geometric properties.

Practical auxiliary basis implementation of Rung 3.5 functionals

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

Janesko, Benjamin G., E-mail: b.janesko@tcu.edu; Scalmani, Giovanni; Frisch, Michael J.

2014-07-21

Approximate exchange-correlation functionals for Kohn-Sham density functional theory often benefit from incorporating exact exchange. Exact exchange is constructed from the noninteracting reference system's nonlocal one-particle density matrix γ(r{sup -vector},r{sup -vector}′). Rung 3.5 functionals attempt to balance the strengths and limitations of exact exchange using a new ingredient, a projection of γ(r{sup -vector},r{sup -vector} ′) onto a semilocal model density matrix γ{sub SL}(ρ(r{sup -vector}),∇ρ(r{sup -vector}),r{sup -vector}−r{sup -vector} ′). γ{sub SL} depends on the electron density ρ(r{sup -vector}) at reference point r{sup -vector}, and is closely related to semilocal model exchange holes. We present a practical implementation of Rung 3.5 functionals, expandingmore » the r{sup -vector}−r{sup -vector} ′ dependence of γ{sub SL} in an auxiliary basis set. Energies and energy derivatives are obtained from 3D numerical integration as in standard semilocal functionals. We also present numerical tests of a range of properties, including molecular thermochemistry and kinetics, geometries and vibrational frequencies, and bandgaps and excitation energies. Rung 3.5 functionals typically provide accuracy intermediate between semilocal and hybrid approximations. Nonlocal potential contributions from γ{sub SL} yield interesting successes and failures for band structures and excitation energies. The results enable and motivate continued exploration of Rung 3.5 functional forms.« less

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

Reduced Order Podolsky Model

NASA Astrophysics Data System (ADS)

Thibes, Ronaldo

2017-02-01

We perform the canonical and path integral quantizations of a lower-order derivatives model describing Podolsky's generalized electrodynamics. The physical content of the model shows an auxiliary massive vector field coupled to the usual electromagnetic field. The equivalence with Podolsky's original model is studied at classical and quantum levels. Concerning the dynamical time evolution, we obtain a theory with two first-class and two second-class constraints in phase space. We calculate explicitly the corresponding Dirac brackets involving both vector fields. We use the Senjanovic procedure to implement the second-class constraints and the Batalin-Fradkin-Vilkovisky path integral quantization scheme to deal with the symmetries generated by the first-class constraints. The physical interpretation of the results turns out to be simpler due to the reduced derivatives order permeating the equations of motion, Dirac brackets and effective action.

Fast angular synchronization for phase retrieval via incomplete information

NASA Astrophysics Data System (ADS)

Viswanathan, Aditya; Iwen, Mark

2015-08-01

We consider the problem of recovering the phase of an unknown vector, x ∈ ℂd, given (normalized) phase difference measurements of the form xjxk*/|xjxk*|, j,k ∈ {1,...,d}, and where xj* denotes the complex conjugate of xj. This problem is sometimes referred to as the angular synchronization problem. This paper analyzes a linear-time-in-d eigenvector-based angular synchronization algorithm and studies its theoretical and numerical performance when applied to a particular class of highly incomplete and possibly noisy phase difference measurements. Theoretical results are provided for perfect (noiseless) measurements, while numerical simulations demonstrate the robustness of the method to measurement noise. Finally, we show that this angular synchronization problem and the specific form of incomplete phase difference measurements considered arise in the phase retrieval problem - where we recover an unknown complex vector from phaseless (or magnitude) measurements.

A Peptide Filtering Relation Quantifies MHC Class I Peptide Optimization

PubMed Central

Goldstein, Leonard D.; Howarth, Mark; Cardelli, Luca; Emmott, Stephen; Elliott, Tim; Werner, Joern M.

2011-01-01

Major Histocompatibility Complex (MHC) class I molecules enable cytotoxic T lymphocytes to destroy virus-infected or cancerous cells, thereby preventing disease progression. MHC class I molecules provide a snapshot of the contents of a cell by binding to protein fragments arising from intracellular protein turnover and presenting these fragments at the cell surface. Competing fragments (peptides) are selected for cell-surface presentation on the basis of their ability to form a stable complex with MHC class I, by a process known as peptide optimization. A better understanding of the optimization process is important for our understanding of immunodominance, the predominance of some T lymphocyte specificities over others, which can determine the efficacy of an immune response, the danger of immune evasion, and the success of vaccination strategies. In this paper we present a dynamical systems model of peptide optimization by MHC class I. We incorporate the chaperone molecule tapasin, which has been shown to enhance peptide optimization to different extents for different MHC class I alleles. Using a combination of published and novel experimental data to parameterize the model, we arrive at a relation of peptide filtering, which quantifies peptide optimization as a function of peptide supply and peptide unbinding rates. From this relation, we find that tapasin enhances peptide unbinding to improve peptide optimization without significantly delaying the transit of MHC to the cell surface, and differences in peptide optimization across MHC class I alleles can be explained by allele-specific differences in peptide binding. Importantly, our filtering relation may be used to dynamically predict the cell surface abundance of any number of competing peptides by MHC class I alleles, providing a quantitative basis to investigate viral infection or disease at the cellular level. We exemplify this by simulating optimization of the distribution of peptides derived from Human Immunodeficiency Virus Gag-Pol polyprotein. PMID:22022238

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.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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

PubMed Central

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

2013-01-01

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

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.

26 CFR 521.103 - Scope of the convention.

Code of Federal Regulations, 2010 CFR

2010-04-01

... UNDER TAX CONVENTIONS DENMARK General Income Tax Taxation of Nonresident Aliens Who Are Residents of... convention, to be accomplished on a reciprocal basis, are to avoid double taxation upon major items of income... looking to the avoidance of double taxation and fiscal evasion. (b) The specific classes of income from...

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.

Incremental classification learning for anomaly detection in medical images

NASA Astrophysics Data System (ADS)

Giritharan, Balathasan; Yuan, Xiaohui; Liu, Jianguo

2009-02-01

Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

AAVrh.10-mediated expression of an anti-cocaine antibody mediates persistent passive immunization that suppresses cocaine-induced behavior.

PubMed

Rosenberg, Jonathan B; Hicks, Martin J; De, Bishnu P; Pagovich, Odelya; Frenk, Esther; Janda, Kim D; Wee, Sunmee; Koob, George F; Hackett, Neil R; Kaminsky, Stephen M; Worgall, Stefan; Tignor, Nicole; Mezey, Jason G; Crystal, Ronald G

2012-05-01

Cocaine addiction is a major problem affecting all societal and economic classes for which there is no effective therapy. We hypothesized an effective anti-cocaine vaccine could be developed by using an adeno-associated virus (AAV) gene transfer vector as the delivery vehicle to persistently express an anti-cocaine monoclonal antibody in vivo, which would sequester cocaine in the blood, preventing access to cognate receptors in the brain. To accomplish this, we constructed AAVrh.10antiCoc.Mab, an AAVrh.10 gene transfer vector expressing the heavy and light chains of the high affinity anti-cocaine monoclonal antibody GNC92H2. Intravenous administration of AAVrh.10antiCoc.Mab to mice mediated high, persistent serum levels of high-affinity, cocaine-specific antibodies that sequestered intravenously administered cocaine in the blood. With repeated intravenous cocaine challenge, naive mice exhibited hyperactivity, while the AAVrh.10antiCoc.Mab-vaccinated mice were completely resistant to the cocaine. These observations demonstrate a novel strategy for cocaine addiction by requiring only a single administration of an AAV vector mediating persistent, systemic anti-cocaine passive immunity.

Analysis of programming properties and the row-column generation method for 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, WeiDa

2013-12-01

This paper deals with fast methods for training a 1-norm support vector machine (SVM). First, we define a specific class of linear programming with many sparse constraints, i.e., row-column sparse constraint linear programming (RCSC-LP). In nature, the 1-norm SVM is a sort of RCSC-LP. In order to construct subproblems for RCSC-LP and solve them, a family of row-column generation (RCG) methods is introduced. RCG methods belong to a category of decomposition techniques, and perform row and column generations in a parallel fashion. Specially, for the 1-norm SVM, the maximum size of subproblems of RCG is identical with the number of Support Vectors (SVs). We also introduce a semi-deleting rule for RCG methods and prove the convergence of RCG methods when using the semi-deleting rule. Experimental results on toy data and real-world datasets illustrate that it is efficient to use RCG to train the 1-norm SVM, especially in the case of small SVs. Copyright © 2013 Elsevier Ltd. All rights reserved.

Two-component vector solitons in defocusing Kerr-type media with spatially modulated nonlinearity

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

Zhong, Wei-Ping, E-mail: zhongwp6@126.com; Texas A and M University at Qatar, P.O. Box 23874 Doha; Belić, Milivoj

2014-12-15

We present a class of exact solutions to the coupled (2+1)-dimensional nonlinear Schrödinger equation with spatially modulated nonlinearity and a special external potential, which describe the evolution of two-component vector solitons in defocusing Kerr-type media. We find a robust soliton solution, constructed with the help of Whittaker functions. For specific choices of the topological charge, the radial mode number and the modulation depth, the solitons may exist in various forms, such as the half-moon, necklace-ring, and sawtooth vortex-ring patterns. Our results show that the profile of such solitons can be effectively controlled by the topological charge, the radial mode number,more » and the modulation depth. - Highlights: • Two-component vector soliton clusters in defocusing Kerr-type media are reported. • These soliton clusters are constructed with the help of Whittaker functions. • The half-moon, necklace-ring and vortex-ring patterns are found. • The profile of these solitons can be effectively controlled by three soliton parameters.« less

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

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

DTIC Science & Technology

2010-11-01

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

Approximate techniques of structural reanalysis

NASA Technical Reports Server (NTRS)

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

1974-01-01

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

Multiple Ordinal Regression by Maximizing the Sum of Margins

PubMed Central

Hamsici, Onur C.; Martinez, Aleix M.

2016-01-01

Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a Support Vector Machine algorithm. Specifically, the goal is to learn a set of classifiers with common direction vectors and different biases correctly separating the ordered classes. Current algorithms are either required to solve a quadratic optimization problem, which is computationally expensive, or are based on maximizing the minimum margin (i.e., a fixed margin strategy) between a set of hyperplanes, which biases the solution to the closest margin. Another drawback of these strategies is that they are limited to order the classes using a single ranking variable (e.g., perceived length). In this paper, we define a multiple ordinal regression algorithm based on maximizing the sum of the margins between every consecutive class with respect to one or more rankings (e.g., perceived length and weight). We provide derivations of an efficient, easy-to-implement iterative solution using a Sequential Minimal Optimization procedure. We demonstrate the accuracy of our solutions in several datasets. In addition, we provide a key application of our algorithms in estimating human subjects’ ordinal classification of attribute associations to object categories. We show that these ordinal associations perform better than the binary one typically employed in the literature. PMID:26529784

Summary and critique of the new NIH guidelines for recombinant DNA research.

PubMed

Szybalski, W

1979-03-01

New NIH Guidelines for research involving recombinant DNA (R-DNA) molecules were issued on December 15, 1978. These are composed of four main parts, the first defining R-DNA and specifying prohibitions and exemptions, the second describing physical and biological containment, the third assigning the containment levels for many R-DNA experiments, and the fourth detailing the roles and responsibilities of the investigator, research institutions and NIH. Although the new Guidelines reduce restrictions, principally on those R-DNA experiments that use Escherichia coli K-12 host-vector systems, and exempt from the Guidelines several classes of experiments on prokaryotes that naturally exchange their DNA, most of their provisions are unjustified by the present assessment of the absence of any practical risks; many totally innocuous experiments are unnecessarily restricted and even virtually prohibited mainly because no host-vector systems were officially certified. The term Guidelines is a misnomer since they are mandatory regulations, even without any statutory basis. They impose large but unnecessary bureaucratic burdens on scientists, research institutions, research committees and NIH, and represent unwarranted censorship of basic research, which is antithetical to the creativity of human thought, thus posing serious dangers to the traditional freedom of inquiry.

A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals.

PubMed

Gupta, Anubha; Singh, Pushpendra; Karlekar, Mandar

2018-05-01

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.

40 CFR 503.15 - Operational standards-pathogens and vector attraction reduction.

Code of Federal Regulations, 2014 CFR

2014-07-01

... met when bulk sewage sludge is applied to a lawn or a home garden. (3) The Class A pathogen... home garden. (3) One of the vector attraction reduction requirements in § 503.33 (b)(1) through (b)(8...

40 CFR 503.15 - Operational standards-pathogens and vector attraction reduction.

Code of Federal Regulations, 2013 CFR

2013-07-01

... met when bulk sewage sludge is applied to a lawn or a home garden. (3) The Class A pathogen... home garden. (3) One of the vector attraction reduction requirements in § 503.33 (b)(1) through (b)(8...

40 CFR 503.15 - Operational standards-pathogens and vector attraction reduction.

Code of Federal Regulations, 2012 CFR

2012-07-01

... met when bulk sewage sludge is applied to a lawn or a home garden. (3) The Class A pathogen... home garden. (3) One of the vector attraction reduction requirements in § 503.33 (b)(1) through (b)(8...

Priming-boosting vaccination with recombinant Mycobacterium bovis bacillus Calmette-Guérin and a nonreplicating vaccinia virus recombinant leads to long-lasting and effective immunity.

PubMed

Ami, Yasushi; Izumi, Yasuyuki; Matsuo, Kazuhiro; Someya, Kenji; Kanekiyo, Masaru; Horibata, Shigeo; Yoshino, Naoto; Sakai, Koji; Shinohara, Katsuaki; Matsumoto, Sohkichi; Yamada, Takeshi; Yamazaki, Shudo; Yamamoto, Naoki; Honda, Mitsuo

2005-10-01

Virus-specific T-cell responses can limit immunodeficiency virus type 1 (HIV-1) transmission and prevent disease progression and so could serve as the basis for an affordable, safe, and effective vaccine in humans. To assess their potential for a vaccine, we used Mycobacterium bovis bacillus Calmette-Guérin (BCG)-Tokyo and a replication-deficient vaccinia virus strain (DIs) as vectors to express full-length gag from simian immunodeficiency viruses (SIVs) (rBCG-SIVgag and rDIsSIVgag). Cynomolgus macaques were vaccinated with either rBCG-SIVgag dermally as a single modality or in combination with rDIsSIVgag intravenously. When cynomologus macaques were primed with rBCG-SIVgag and then boosted with rDIsSIVgag, high levels of gamma interferon (IFN-gamma) spot-forming cells specific for SIV Gag were induced. This combination regimen elicited effective protective immunity against mucosal challenge with pathogenic simian-human immunodeficiency virus for the 1 year the macaques were under observation. Antigen-specific intracellular IFN-gamma activity was similarly induced in each of the macaques with the priming-boosting regimen. Other groups receiving the opposite combination or the single-modality vaccines were not effectively protected. These results suggest that a recombinant M. bovis BCG-based vector may have potential as an HIV/AIDS vaccine when administered in combination with a replication-deficient vaccinia virus DIs vector in a priming-boosting strategy.

Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

NASA Astrophysics Data System (ADS)

Yu, Lean; Wang, Shouyang; Lai, K. K.

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

Cloning of murine RNA polymerase I-specific TAF factors: Conserved interactions between the subunits of the species-specific transcription initiation factor TIF-IB/SL1

PubMed Central

Heix, Jutta; Zomerdijk, Joost C. B. M.; Ravanpay, Ali; Tjian, Robert; Grummt, Ingrid

1997-01-01

Promoter selectivity for all three classes of eukaryotic RNA polymerases is brought about by multimeric protein complexes containing TATA box binding protein (TBP) and specific TBP-associated factors (TAFs). Unlike class II- and III-specific TBP–TAF complexes, the corresponding murine and human class I-specific transcription initiation factor TIF-IB/SL1 exhibits a pronounced selectivity for its homologous promoter. As a first step toward understanding the molecular basis of species-specific promoter recognition, we cloned the cDNAs encoding the three mouse pol I-specific TBP-associated factors (TAFIs) and compared the amino acid sequences of the murine TAFIs with their human counterparts. The four subunits from either species can form stable chimeric complexes that contain stoichiometric amounts of TBP and TAFIs, demonstrating that differences in the primary structure of human and mouse TAFIs do not dramatically alter the network of protein–protein contacts responsible for assembly of the multimeric complex. Thus, primate vs. rodent promoter selectivity mediated by the TBP–TAFI complex is likely to be the result of cumulative subtle differences between individual subunits that lead to species-specific properties of RNA polymerase I transcription. PMID:9050847

78 FR 78298 - Proposed Establishment of Class E Airspace; Phoenix, AZ

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-26

...-0956; Airspace Docket No. 13-AWP-17] Proposed Establishment of Class E Airspace; Phoenix, AZ AGENCY... rulemaking (NPRM). SUMMARY: This action proposes to establish Class E airspace at the Phoenix VHF Omni-Directional Radio Range Tactical Air Navigation Aid (VORTAC), Phoenix, AZ, to facilitate vectoring of...

78 FR 45478 - Proposed Establishment of Class E Airspace; Salmon, ID

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-29

...-0531; Airspace Docket No. 13-ANM-20] Proposed Establishment of Class E Airspace; Salmon, ID AGENCY... action proposes to establish Class E airspace at the Salmon VHF Omni-Directional Radio Range/Distance Measuring Equipment (VOR/DME) navigation aid, Salmon, ID, to facilitate vectoring of Instrument Flight Rules...

Fradkin-Bacry-Ruegg-Souriau perihelion vector for Gorringe-Leach equations

NASA Astrophysics Data System (ADS)

Grandati, Yves; Bérard, Alain; Mohrbach, Hervé

2010-02-01

We show that every generalized Gorringe-Leach equation admits an associated Fradkin-Bacry-Ruegg-Souriau’s vector which, in general, is only a piecewise conserved quantity. In the case of dualizable generalized Gorringe-Leach equations, which include the case of conservative motions in central power law potentials, the image sets of the FBRS vectors for dual classes are dual images of each other.

32 CFR 1642.3 - Basis for classification in Class 3-A.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 6 2011-07-01 2011-07-01 false Basis for classification in Class 3-A. 1642.3... CLASSIFICATION OF REGISTRANTS DEFERRED BECAUSE OF HARDSHIP TO DEPENDENTS § 1642.3 Basis for classification in... registrant for classification in Class 3-A, the board will first determine whether the registrant's wife...

Electroweak phase transition in the {mu}{nu}SSM

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

Chung, Daniel J. H.; School of Physics, Korea Institute for Advanced Study, 207-43, Cheongnyangni2-dong, Dongdaemun-gu, Seoul 130-722; Long, Andrew J.

2010-06-15

An extension of the minimal supersymmetric standard model called the {mu}{nu}SSM does not allow a conventional thermal leptogenesis scenario because of the low scale seesaw that it utilizes. Hence, we investigate the possibility of electroweak baryogenesis. Specifically, we identify a parameter region for which the electroweak phase transition is sufficiently strongly first order to realize electroweak baryogenesis. In addition to transitions that are similar to those in the next-to-minimal supersymmetric standard model, we find a novel class of phase transitions in which there is a rotation in the singlet vector space.

Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.

PubMed

Elhaj, Fatin A; Salim, Naomie; Harris, Arief R; Swee, Tan Tian; Ahmed, Taqwa

2016-04-01

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support vector machine and radial basis function method. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A constrained joint source/channel coder design and vector quantization of nonstationary sources

NASA Technical Reports Server (NTRS)

Sayood, Khalid; Chen, Y. C.; Nori, S.; Araj, A.

1993-01-01

The emergence of broadband ISDN as the network for the future brings with it the promise of integration of all proposed services in a flexible environment. In order to achieve this flexibility, asynchronous transfer mode (ATM) has been proposed as the transfer technique. During this period a study was conducted on the bridging of network transmission performance and video coding. The successful transmission of variable bit rate video over ATM networks relies on the interaction between the video coding algorithm and the ATM networks. Two aspects of networks that determine the efficiency of video transmission are the resource allocation algorithm and the congestion control algorithm. These are explained in this report. Vector quantization (VQ) is one of the more popular compression techniques to appear in the last twenty years. Numerous compression techniques, which incorporate VQ, have been proposed. While the LBG VQ provides excellent compression, there are also several drawbacks to the use of the LBG quantizers including search complexity and memory requirements, and a mismatch between the codebook and the inputs. The latter mainly stems from the fact that the VQ is generally designed for a specific rate and a specific class of inputs. In this work, an adaptive technique is proposed for vector quantization of images and video sequences. This technique is an extension of the recursively indexed scalar quantization (RISQ) algorithm.

CYP6 P450 enzymes and ACE-1 duplication produce extreme and multiple insecticide resistance in the malaria mosquito Anopheles gambiae.

PubMed

Edi, Constant V; Djogbénou, Luc; Jenkins, Adam M; Regna, Kimberly; Muskavitch, Marc A T; Poupardin, Rodolphe; Jones, Christopher M; Essandoh, John; Kétoh, Guillaume K; Paine, Mark J I; Koudou, Benjamin G; Donnelly, Martin J; Ranson, Hilary; Weetman, David

2014-03-01

Malaria control relies heavily on pyrethroid insecticides, to which susceptibility is declining in Anopheles mosquitoes. To combat pyrethroid resistance, application of alternative insecticides is advocated for indoor residual spraying (IRS), and carbamates are increasingly important. Emergence of a very strong carbamate resistance phenotype in Anopheles gambiae from Tiassalé, Côte d'Ivoire, West Africa, is therefore a potentially major operational challenge, particularly because these malaria vectors now exhibit resistance to multiple insecticide classes. We investigated the genetic basis of resistance to the most commonly-applied carbamate, bendiocarb, in An. gambiae from Tiassalé. Geographically-replicated whole genome microarray experiments identified elevated P450 enzyme expression as associated with bendiocarb resistance, most notably genes from the CYP6 subfamily. P450s were further implicated in resistance phenotypes by induction of significantly elevated mortality to bendiocarb by the synergist piperonyl butoxide (PBO), which also enhanced the action of pyrethroids and an organophosphate. CYP6P3 and especially CYP6M2 produced bendiocarb resistance via transgenic expression in Drosophila in addition to pyrethroid resistance for both genes, and DDT resistance for CYP6M2 expression. CYP6M2 can thus cause resistance to three distinct classes of insecticide although the biochemical mechanism for carbamates is unclear because, in contrast to CYP6P3, recombinant CYP6M2 did not metabolise bendiocarb in vitro. Strongly bendiocarb resistant mosquitoes also displayed elevated expression of the acetylcholinesterase ACE-1 gene, arising at least in part from gene duplication, which confers a survival advantage to carriers of additional copies of resistant ACE-1 G119S alleles. Our results are alarming for vector-based malaria control. Extreme carbamate resistance in Tiassalé An. gambiae results from coupling of over-expressed target site allelic variants with heightened CYP6 P450 expression, which also provides resistance across contrasting insecticides. Mosquito populations displaying such a diverse basis of extreme and cross-resistance are likely to be unresponsive to standard insecticide resistance management practices.

A proposal for the molecular basis of μ and δ opiate receptor differentiation based on modeling of two types of cyclic enkephalins and a narcotic alkaloid

NASA Astrophysics Data System (ADS)

Michel, André; Villeneuve, Gérald; DiMaio, John

1991-12-01

The molecular basis underlying the divergent receptor selectivity of two cyclic opioid peptides Tyr-c[ N δ- d-Orn2-Gly-Phe-Leu-] (c-ORN) and [ d-Pen2, l-Cys5]-enkephalinamide (c-PEN) was investigated using a molecular modeling approach. Ring closure and conformational searching procedures were used to determine low-energy cyclic backbone conformers. Following reinsertion of amino acid side chains, the narcotic alkaloid 7α-[(1R)-1-methyl-1-hydroxy-3-phenylpropyl]-6,14-endoethenotetrahydro oripavine (PEO) was used as a flexible template for bimolecular superpositions with each of the determined peptide ring conformers using the coplanarity and cocentricity of the phenolic rings as the minimum constraint. A vector space of PEO, accounting for all possible orientations for the C21-aromatic ring of PEO served as a geometrical locus for the aromatic ring of the Phe4 residue in the opioid peptides. Although a vast number of polypeptide conformations satisfied the criteria of the opiate pharmacophore, they could be grouped into three classes differing in magnitude and sign of the torsional angle values of the tyrosyl side chain. Only class III conformers for both c-ORN and c-PEN, having tyramine dihedral angles χ1 =-150° ± 30° and χ2=-155° ± 20°, had significant structural and conformational properties that were mutually compatible while respecting the PEO vector space. Comparison of these properties in the context of the divergent receptor selectivity of the studied opioid peptides suggests that the increased distortion of the peptide backbone in the closure region of c-PEN together with the pendant β,β-dimethyl group, combine to generate a steric volume which is absent in c-ORN and that may be incompatible with a restrictive topography of the μ receptor. The nature and stereo-chemistry of substituents adjacent to the closure region of the peptides could also modulate receptor selection by interacting with a charged (δ) or neutral (μ) subsite.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

PubMed

Ghanat Bari, Mehrab; Ung, Choong Yong; Zhang, Cheng; Zhu, Shizhen; Li, Hu

2017-08-01

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 10 8 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

Stability properties of a general class of nonlinear dynamical systems

NASA Astrophysics Data System (ADS)

Gléria, I. M.; Figueiredo, A.; Rocha Filho, T. M.

2001-05-01

We establish sufficient conditions for the boundedness of the trajectories and the stability of the fixed points in a class of general nonlinear systems, the so-called quasi-polynomial vector fields, with the help of a natural embedding of such systems in a family of generalized Lotka-Volterra (LV) equations. A purely algebraic procedure is developed to determine such conditions. We apply our method to obtain new results for LV systems, by a reparametrization in time variable, and to study general nonlinear vector fields, originally far from the LV format.

Antigen presentation by MART-1 adenovirus-transduced interleukin-10-polarized human monocyte-derived dendritic cells

PubMed Central

Mehrotra, Shikhar; Chhabra, Arvind; Chakraborty, Abolokita; Chattopadhyay, Subhasis; Slowik, Mark; Stevens, Robert; Zengou, Ryan; Mathias, Clinton; Butterfield, Lisa H; Dorsky, David I; Economou, James S; Mukherji, Bijay; Chakraborty, Nitya G

2004-01-01

Dendritic cells (DC) play critical roles in generating an immune response and in inducing tolerance. Diverse microenvironmental factors can ‘polarize’ DC toward an immunogenic or non-immunogenic phenotype. Among the various microenvironmental factors, interleukin-10 (IL-10) exhibits a potent immunosuppressive effect on antigen-presenting cells (APC). Here, we show that monocyte-derived DC generated in the presence of IL-10 exhibit a profound down-regulation of many genes that are associated with immune activation and show that the IL-10-grown DC are poor stimulators of CD8+ T cells in a strictly autologous and major histocompatibility complex (MHC) class I-restricted melanoma antigen recognized by T cells (MART-1) epitope presentation system. However, these IL-10-grown DC can efficiently activate the epitope-specific CD8+ T cells when they are made to present the epitope following transduction with an adenoviral vector expressing the MART-1 antigen. In addition, we show that the MART-1 protein colocalizes with the MHC class I protein, equally well, in the iDC and in the DC cultured in presence of IL-10 when both DC types are infected with the viral vector. We also show that the vector transduced DC present the MART-127–35 epitope for a sustained period compared to the peptide pulsed DC. These data suggest that although DCs generated in the presence of IL-10 tend to be non-immunogenic, they are capable of processing and presenting an antigen when the antigen is synthesized within the DC. PMID:15554925

Support vector machine as a binary classifier for automated object detection in remotely sensed data

NASA Astrophysics Data System (ADS)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Multi-stage Vector-Borne Zoonoses Models: A Global Analysis.

PubMed

Bichara, Derdei; Iggidr, Abderrahman; Smith, Laura

2018-04-25

A class of models that describes the interactions between multiple host species and an arthropod vector is formulated and its dynamics investigated. A host-vector disease model where the host's infection is structured into n stages is formulated and a complete global dynamics analysis is provided. The basic reproduction number acts as a sharp threshold, that is, the disease-free equilibrium is globally asymptotically stable (GAS) whenever [Formula: see text] and that a unique interior endemic equilibrium exists and is GAS if [Formula: see text]. We proceed to extend this model with m host species, capturing a class of zoonoses where the cross-species bridge is an arthropod vector. The basic reproduction number of the multi-host-vector, [Formula: see text], is derived and shown to be the sum of basic reproduction numbers of the model when each host is isolated with an arthropod vector. It is shown that the disease will persist in all hosts as long as it persists in one host. Moreover, the overall basic reproduction number increases with respect to the host and that bringing the basic reproduction number of each isolated host below unity in each host is not sufficient to eradicate the disease in all hosts. This is a type of "amplification effect," that is, for the considered vector-borne zoonoses, the increase in host diversity increases the basic reproduction number and therefore the disease burden.

Equiangular tight frames and unistochastic matrices

NASA Astrophysics Data System (ADS)

Goyeneche, Dardo; Turek, Ondřej

2017-06-01

We demonstrate that a complex equiangular tight frame composed of N vectors in dimension d, denoted ETF (d, N), exists if and only if a certain bistochastic matrix, univocally determined by N and d, belongs to a special class of unistochastic matrices. This connection allows us to find new complex ETFs in infinitely many dimensions and to derive a method to introduce non-trivial free parameters in ETFs. We present an explicit six-parametric family of complex ETF(6,16), which defines a family of symmetric POVMs. Minimal and maximal possible average entanglement of the vectors within this qubit-qutrit family are described. Furthermore, we propose an efficient numerical procedure to compute the unitary matrix underlying a unistochastic matrix, which we apply to find all existing classes of complex ETFs containing up to 20 vectors.

Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform.

PubMed

Rajagopal, Rekha; Ranganathan, Vidhyapriya

2018-06-05

Automation in cardiac arrhythmia classification helps medical professionals make accurate decisions about the patient's health. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. The design phase of the classification model comprises the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, and arrhythmia classification using a collaborative decision from the K nearest neighbor classifier (KNN) and a support vector machine (SVM). The proposed model is able to classify 5 arrhythmia classes as per the ANSI/AAMI EC57: 1998 classification standard. Level 1 of the proposed model involves classification using the KNN and the classifier is trained with examples from all classes. Level 2 involves classification using an SVM and is trained specifically to classify overlapped classes. The final classification of a test heartbeat pertaining to a particular class is done using the proposed KNN/SVM hybrid model. The experimental results demonstrated that the average sensitivity of the proposed model was 92.56%, the average specificity 99.35%, the average positive predictive value 98.13%, the average F-score 94.5%, and the average accuracy 99.78%. The results obtained using the proposed model were compared with the results of discriminant, tree, and KNN classifiers. The proposed model is able to achieve a high classification accuracy.

32 CFR 1639.3 - Basis for classification in Class 2-D.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Basis for classification in Class 2-D. 1639.3... CLASSIFICATION OF REGISTRANTS PREPARING FOR THE MINISTRY § 1639.3 Basis for classification in Class 2-D. (a) In... maintained for qualification for the deferment. (b) The registrant's classification shall be determined on...

32 CFR 1639.3 - Basis for classification in Class 2-D.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 6 2011-07-01 2011-07-01 false Basis for classification in Class 2-D. 1639.3... CLASSIFICATION OF REGISTRANTS PREPARING FOR THE MINISTRY § 1639.3 Basis for classification in Class 2-D. (a) In... maintained for qualification for the deferment. (b) The registrant's classification shall be determined on...

Service Learning and Community Engagement for English Classes

ERIC Educational Resources Information Center

McLeod, Aïda Koçi

2017-01-01

Service learning--sometimes known as community engagement--is a well-documented pedagogical approach with a long history, a strong theoretical basis, a specific ethos, and many passionate advocates. Yet it is conspicuously underused as a teaching method in the worldwide field of English language teaching. In this article, I argue that English…

Estimation of proportions in mixed pixels through their region characterization

NASA Technical Reports Server (NTRS)

Chittineni, C. B. (Principal Investigator)

1981-01-01

A region of mixed pixels can be characterized through the probability density function of proportions of classes in the pixels. Using information from the spectral vectors of a given set of pixels from the mixed pixel region, expressions are developed for obtaining the maximum likelihood estimates of the parameters of probability density functions of proportions. The proportions of classes in the mixed pixels can then be estimated. If the mixed pixels contain objects of two classes, the computation can be reduced by transforming the spectral vectors using a transformation matrix that simultaneously diagonalizes the covariance matrices of the two classes. If the proportions of the classes of a set of mixed pixels from the region are given, then expressions are developed for obtaining the estmates of the parameters of the probability density function of the proportions of mixed pixels. Development of these expressions is based on the criterion of the minimum sum of squares of errors. Experimental results from the processing of remotely sensed agricultural multispectral imagery data are presented.

Quantum and electromagnetic propagation with the conjugate symmetric Lanczos method.

PubMed

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

2008-02-14

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

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

PubMed Central

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

2007-01-01

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

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

78 FR 18268 - Proposed Establishment of Class E Airspace; Blue Mesa, CO

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-26

...-0193; Airspace Docket No. 13-ANM-9] Proposed Establishment of Class E Airspace; Blue Mesa, CO AGENCY... action proposes to establish Class E airspace at the Blue Mesa VHF Omni-Directional Radio Range/Distance Measuring Equipment (VOR/DME), Blue Mesa, CO to facilitate vectoring of Instrument Flight Rules (IFR...

78 FR 45474 - Proposed Establishment of Class E Airspace; Cut Bank, MT

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-29

...-0532; Airspace Docket No. 13-ANM-21] Proposed Establishment of Class E Airspace; Cut Bank, MT AGENCY... action proposes to establish Class E airspace at the Cut Bank VHF Omni-Directional Radio Range Tactical Air Navigational Aid (VORTAC) navigation aid, Cut Bank, MT, to facilitate vectoring of Instrument...

Detection of segments with fetal QRS complex from abdominal maternal ECG recordings using support vector machine

NASA Astrophysics Data System (ADS)

Delgado, Juan A.; Altuve, Miguel; Nabhan Homsi, Masun

2015-12-01

This paper introduces a robust method based on the Support Vector Machine (SVM) algorithm to detect the presence of Fetal QRS (fQRS) complexes in electrocardiogram (ECG) recordings provided by the PhysioNet/CinC challenge 2013. ECG signals are first segmented into contiguous frames of 250 ms duration and then labeled in six classes. Fetal segments are tagged according to the position of fQRS complex within each one. Next, segment features extraction and dimensionality reduction are obtained by applying principal component analysis on Haar-wavelet transform. After that, two sub-datasets are generated to separate representative segments from atypical ones. Imbalanced class problem is dealt by applying sampling without replacement on each sub-dataset. Finally, two SVMs are trained and cross-validated using the two balanced sub-datasets separately. Experimental results show that the proposed approach achieves high performance rates in fetal heartbeats detection that reach up to 90.95% of accuracy, 92.16% of sensitivity, 88.51% of specificity, 94.13% of positive predictive value and 84.96% of negative predictive value. A comparative study is also carried out to show the performance of other two machine learning algorithms for fQRS complex estimation, which are K-nearest neighborhood and Bayesian network.

Computing energy levels of CH4, CHD3, CH3D, and CH3F with a direct product basis and coordinates based on the methyl subsystem.

PubMed

Zhao, Zhiqiang; Chen, Jun; Zhang, Zhaojun; Zhang, Dong H; Wang, Xiao-Gang; Carrington, Tucker; Gatti, Fabien

2018-02-21

Quantum mechanical calculations of ro-vibrational energies of CH 4 , CHD 3 , CH 3 D, and CH 3 F were made with two different numerical approaches. Both use polyspherical coordinates. The computed energy levels agree, confirming the accuracy of the methods. In the first approach, for all the molecules, the coordinates are defined using three Radau vectors for the CH 3 subsystem and a Jacobi vector between the remaining atom and the centre of mass of CH 3 . Euler angles specifying the orientation of a frame attached to CH 3 with respect to a frame attached to the Jacobi vector are used as vibrational coordinates. A direct product potential-optimized discrete variable vibrational basis is used to build a Hamiltonian matrix. Ro-vibrational energies are computed using a re-started Arnoldi eigensolver. In the second approach, the coordinates are the spherical coordinates associated with four Radau vectors or three Radau vectors and a Jacobi vector, and the frame is an Eckart frame. Vibrational basis functions are products of contracted stretch and bend functions, and eigenvalues are computed with the Lanczos algorithm. For CH 4 , CHD 3 , and CH 3 D, we report the first J > 0 energy levels computed on the Wang-Carrington potential energy surface [X.-G. Wang and T. Carrington, J. Chem. Phys. 141(15), 154106 (2014)]. For CH 3 F, the potential energy surface of Zhao et al. [J. Chem. Phys. 144, 204302 (2016)] was used. All the results are in good agreement with experimental data.

Computing energy levels of CH4, CHD3, CH3D, and CH3F with a direct product basis and coordinates based on the methyl subsystem

NASA Astrophysics Data System (ADS)

Zhao, Zhiqiang; Chen, Jun; Zhang, Zhaojun; Zhang, Dong H.; Wang, Xiao-Gang; Carrington, Tucker; Gatti, Fabien

2018-02-01

Quantum mechanical calculations of ro-vibrational energies of CH4, CHD3, CH3D, and CH3F were made with two different numerical approaches. Both use polyspherical coordinates. The computed energy levels agree, confirming the accuracy of the methods. In the first approach, for all the molecules, the coordinates are defined using three Radau vectors for the CH3 subsystem and a Jacobi vector between the remaining atom and the centre of mass of CH3. Euler angles specifying the orientation of a frame attached to CH3 with respect to a frame attached to the Jacobi vector are used as vibrational coordinates. A direct product potential-optimized discrete variable vibrational basis is used to build a Hamiltonian matrix. Ro-vibrational energies are computed using a re-started Arnoldi eigensolver. In the second approach, the coordinates are the spherical coordinates associated with four Radau vectors or three Radau vectors and a Jacobi vector, and the frame is an Eckart frame. Vibrational basis functions are products of contracted stretch and bend functions, and eigenvalues are computed with the Lanczos algorithm. For CH4, CHD3, and CH3D, we report the first J > 0 energy levels computed on the Wang-Carrington potential energy surface [X.-G. Wang and T. Carrington, J. Chem. Phys. 141(15), 154106 (2014)]. For CH3F, the potential energy surface of Zhao et al. [J. Chem. Phys. 144, 204302 (2016)] was used. All the results are in good agreement with experimental data.

A force vector and surface orientation sensor for intelligent grasping

NASA Technical Reports Server (NTRS)

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

1991-01-01

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

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

DTIC Science & Technology

2007-07-19

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

Distinct susceptibility of HIV vaccine vector-induced CD4 T cells to HIV infection

PubMed Central

Niu, Qingli; Hou, Wei; Churchyard, Gavin; Nitayaphan, Sorachai; Pitisuthithum, Punnee; Rerks-Ngarm, Supachai; Franchini, Genoveffa

2018-01-01

The concerns raised from adenovirus 5 (Ad5)-based HIV vaccine clinical trials, where excess HIV infections were observed in some vaccine recipients, have highlighted the importance of understanding host responses to vaccine vectors and the HIV susceptibility of vector-specific CD4 T cells in HIV vaccination. Our recent study reported that human Ad5-specific CD4 T cells induced by Ad5 vaccination (RV156A trial) are susceptible to HIV. Here we further investigated the HIV susceptibility of vector-specific CD4 T cells induced by ALVAC, a canarypox viral vector tested in the Thai trial RV144, as compared to Ad5 vector-specific CD4 T cells in the HVTN204 trial. We showed that while Ad5 vector-specific CD4 T cells were readily susceptible to HIV, ALVAC-specific CD4 T cells in RV144 PBMC were substantially less susceptible to both R5 and X4 HIV in vitro. The lower HIV susceptibility of ALVAC-specific CD4 T cells was associated with the reduced surface expression of HIV entry co-receptors CCR5 and CXCR4 on these cells. Phenotypic analyses identified that ALVAC-specific CD4 T cells displayed a strong Th1 phenotype, producing higher levels of IFN-γ and CCL4 (MIP-1β) but little IL-17. Of interest, ALVAC and Ad5 vectors induced distinct profiles of vector-specific CD8 vs. CD4 T-cell proliferative responses in PBMC, with ALVAC preferentially inducing CD8 T-cell proliferation, while Ad5 vector induced CD4 T-cell proliferation. Depletion of ALVAC-, but not Ad5-, induced CD8 T cells in PBMC led to a modest increase in HIV infection of vector-specific CD4 T cells, suggesting a role of ALVAC-specific CD8 T cells in protecting ALVAC-specific CD4 T cells from HIV. Taken together, our data provide strong evidence for distinct HIV susceptibility of CD4 T cells induced by different vaccine vectors and highlight the importance of better evaluating anti-vector responses in HIV vaccination. PMID:29474461

Self-organizing map (SOM) of space acceleration measurement system (SAMS) data.

PubMed

Sinha, A; Smith, A D

1999-01-01

In this paper, space acceleration measurement system (SAMS) data have been classified using self-organizing map (SOM) networks without any supervision; i.e., no a priori knowledge is assumed regarding input patterns belonging to a certain class. Input patterns are created on the basis of power spectral densities of SAMS data. Results for SAMS data from STS-50 and STS-57 missions are presented. Following issues are discussed in details: impact of number of neurons, global ordering of SOM weight vectors, effectiveness of a SOM in data classification, and effects of shifting time windows in the generation of input patterns. The concept of 'cascade of SOM networks' is also developed and tested. It has been found that a SOM network can successfully classify SAMS data obtained during STS-50 and STS-57 missions.

Self-organizing map (SOM) of space acceleration measurement system (SAMS) data

NASA Technical Reports Server (NTRS)

Sinha, A.; Smith, A. D.

1999-01-01

In this paper, space acceleration measurement system (SAMS) data have been classified using self-organizing map (SOM) networks without any supervision; i.e., no a priori knowledge is assumed regarding input patterns belonging to a certain class. Input patterns are created on the basis of power spectral densities of SAMS data. Results for SAMS data from STS-50 and STS-57 missions are presented. Following issues are discussed in details: impact of number of neurons, global ordering of SOM weight vectors, effectiveness of a SOM in data classification, and effects of shifting time windows in the generation of input patterns. The concept of 'cascade of SOM networks' is also developed and tested. It has been found that a SOM network can successfully classify SAMS data obtained during STS-50 and STS-57 missions.

Permanent, lowered HLA class I expression using lentivirus vectors with shRNA constructs: Averting cytotoxicity by alloreactive T lymphocytes.

PubMed

Haga, K; Lemp, N A; Logg, C R; Nagashima, J; Faure-Kumar, E; Gomez, G G; Kruse, C A; Mendez, R; Stripecke, R; Kasahara, N; Kasahara, N A; Cicciarelli, J C

2006-12-01

Transplantation of many tissues requires histocompatibility matching of human leukocyte antigens (HLA) to prevent graft rejection, to reduce the level of immunosuppression needed to maintain graft survival, and to minimize the risk of graft-versus-host disease, particularly in the case of bone marrow transplantation. However, recent advances in fields of gene delivery and genetic regulation technologies have opened the possibility of engineering grafts that display reduced levels of HLA expression. Suppression of HLA expression could help to overcome the limitations imposed by extensive HLA polymorphisms that restrict the availability of suitable donors, necessitate the maintenance of large donor registries, and complicate the logistics of procuring and delivering matched tissues and organs to the recipient. Accordingly, we investigated whether knockdown of HLA by RNA interference (RNAi), a ubiquitous regulatory system that can efficiently and selectively inhibit the expression of specific gene products, would enable allogeneic cells to evade immune recognition. For efficient and stable delivery of short hairpin-type RNAi constructs (shRNA), we employed lentivirus-based gene transfer vectors, which provide a delivery system that can achieve integration into genomic DNA, thereby permanently modifying transduced graft cells. Our results show that lentivirus-mediated delivery of shRNA targeting pan-Class I and allele-specific HLA can achieve efficient and dose-dependent reduction in surface expression of HLA in human cells, associated with enhanced resistance to alloreactive T lymphocyte-mediated cytotoxicity, while avoiding MHC-non-restricted killing. We hypothesize that RNAi-induced silencing of HLA expression has the potential to create histocompatibility-enhanced, and, eventually, perhaps "universally" compatible cellular grafts.

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.

Legendre submanifolds in contact manifolds as attractors and geometric nonequilibrium thermodynamics

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

Goto, Shin-itiro, E-mail: sgoto@ims.ac.jp

It has been proposed that equilibrium thermodynamics is described on Legendre submanifolds in contact geometry. It is shown in this paper that Legendre submanifolds embedded in a contact manifold can be expressed as attractors in phase space for a certain class of contact Hamiltonian vector fields. By giving a physical interpretation that points outside the Legendre submanifold can represent nonequilibrium states of thermodynamic variables, in addition to that points of a given Legendre submanifold can represent equilibrium states of the variables, this class of contact Hamiltonian vector fields is physically interpreted as a class of relaxation processes, in which thermodynamicmore » variables achieve an equilibrium state from a nonequilibrium state through a time evolution, a typical nonequilibrium phenomenon. Geometric properties of such vector fields on contact manifolds are characterized after introducing a metric tensor field on a contact manifold. It is also shown that a contact manifold and a strictly convex function induce a lower dimensional dually flat space used in information geometry where a geometrization of equilibrium statistical mechanics is constructed. Legendre duality on contact manifolds is explicitly stated throughout.« less

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

USGS Publications Warehouse

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

2006-01-01

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

Construction and Evaluation of Novel Rhesus Monkey Adenovirus Vaccine Vectors

DOE PAGES

Abbink, Peter; Maxfield, Lori F.; Ng'ang'a, David; ...

2014-11-19

Adenovirus vectors are widely used as vaccine candidates for a variety of pathogens, including HIV-1. To date, human and chimpanzee adenoviruses have been explored in detail as vaccine vectors. Furthermore, the phylogeny of human and chimpanzee adenoviruses is overlapping, and preexisting humoral and cellular immunity to both are exhibited in human populations worldwide. More distantly related adenoviruses may therefore offer advantages as vaccine vectors. We describe the primary isolation and vectorization of three novel adenoviruses from rhesus monkeys. The seroprevalence of these novel rhesus monkey adenovirus vectors was extremely low in sub-Saharan Africa human populations, and these vectors proved tomore » have immunogenicity comparable to that of human and chimpanzee adenovirus vaccine vectors in mice. These rhesus monkey adenoviruses phylogenetically clustered with the poorly described adenovirus species G and robustly stimulated innate immune responses. These novel adenoviruses represent a new class of candidate vaccine vectors.« less

Construction and Evaluation of Novel Rhesus Monkey Adenovirus Vaccine Vectors

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

Abbink, Peter; Maxfield, Lori F.; Ng'ang'a, David

Adenovirus vectors are widely used as vaccine candidates for a variety of pathogens, including HIV-1. To date, human and chimpanzee adenoviruses have been explored in detail as vaccine vectors. Furthermore, the phylogeny of human and chimpanzee adenoviruses is overlapping, and preexisting humoral and cellular immunity to both are exhibited in human populations worldwide. More distantly related adenoviruses may therefore offer advantages as vaccine vectors. We describe the primary isolation and vectorization of three novel adenoviruses from rhesus monkeys. The seroprevalence of these novel rhesus monkey adenovirus vectors was extremely low in sub-Saharan Africa human populations, and these vectors proved tomore » have immunogenicity comparable to that of human and chimpanzee adenovirus vaccine vectors in mice. These rhesus monkey adenoviruses phylogenetically clustered with the poorly described adenovirus species G and robustly stimulated innate immune responses. These novel adenoviruses represent a new class of candidate vaccine vectors.« less

Consumer Expectations of Capacity Constrains and Their Effect on the Demand for Multi-Class Air Travel

NASA Technical Reports Server (NTRS)

Battersby, Bryn D.

2003-01-01

This paper argues that a consumer's decision on ticket class takes into account the expected likelihood of obtaining a seat in a particular class which, in turn, partially depends on an optimum "transaction cost". Taking into account the preferences of the consumer and the information that the consumer is endowed with, the consumer will select a ticket that includes its own optimal transaction cost. This motivates the inclusion of the capacity constraint as a proxy independent variable for these consumer expectations This then forms the basis of a model of air-travel demand with specific reference to Australia. A censored likelihood function allowing for correlation in the disturbance term across k classes is introduced. The correlation in the disturbances arises as a result of the interdependence of the capacity constraints in k different ticket classes on each flight.

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

PubMed Central

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

2015-01-01

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

On a Class of Hairy Square Barriers and Gamow Vectors

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

Fernandez-Garcia, N.

The second order Darboux-Gamow transformation is applied to deform square one dimensional barriers in non-relativistic quantum mechanics. The initial and the new 'hairy' potentials have the same transmission probabilities (for the appropriate parameters). In general, new Gamow vectors are constructed as Darboux deformations of the initial ones.

Representation of magnetic fields in space

NASA Technical Reports Server (NTRS)

Stern, D. P.

1975-01-01

Several methods by which a magnetic field in space can be represented are reviewed with particular attention to problems of the observed geomagnetic field. Time dependence is assumed to be negligible, and five main classes of representation are described by vector potential, scalar potential, orthogonal vectors, Euler potentials, and expanded magnetic field.

CRITICAL EVALUATION OF THE EFFECTIVENESS OF SEWAGE SLUDGE DISINFECTION AND VECTOR ATTRACTION REDUCTION PROCESSES

EPA Science Inventory

What is the current state of management practices for biosolids production and application, and how can those be made more effective? How effective are Class B disinfection and vector attraction processes, and public access and harvesting restrictions at reducing the public's exp...

A T Matrix Method Based upon Scalar Basis Functions

NASA Technical Reports Server (NTRS)

Mackowski, D.W.; Kahnert, F. M.; Mishchenko, Michael I.

2013-01-01

A surface integral formulation is developed for the T matrix of a homogenous and isotropic particle of arbitrary shape, which employs scalar basis functions represented by the translation matrix elements of the vector spherical wave functions. The formulation begins with the volume integral equation for scattering by the particle, which is transformed so that the vector and dyadic components in the equation are replaced with associated dipole and multipole level scalar harmonic wave functions. The approach leads to a volume integral formulation for the T matrix, which can be extended, by use of Green's identities, to the surface integral formulation. The result is shown to be equivalent to the traditional surface integral formulas based on the VSWF basis.

Application of information-retrieval methods to the classification of physical data

NASA Technical Reports Server (NTRS)

Mamotko, Z. N.; Khorolskaya, S. K.; Shatrovskiy, L. I.

1975-01-01

Scientific data received from satellites are characterized as a multi-dimensional time series, whose terms are vector functions of a vector of measurement conditions. Information retrieval methods are used to construct lower dimensional samples on the basis of the condition vector, in order to obtain these data and to construct partial relations. The methods are applied to the joint Soviet-French Arkad project.

The Effects of City Streets on an Urban Disease Vector

PubMed Central

Barbu, Corentin M.; Hong, Andrew; Manne, Jennifer M.; Small, Dylan S.; Quintanilla Calderón, Javier E.; Sethuraman, Karthik; Quispe-Machaca, Víctor; Ancca-Juárez, Jenny; Cornejo del Carpio, Juan G.; Málaga Chavez, Fernando S.; Náquira, César; Levy, Michael Z.

2013-01-01

With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran's spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru. Our Moran's decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p<0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies. PMID:23341756

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

NASA Astrophysics Data System (ADS)

Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines

PubMed Central

Jenssen, Robert; Kloft, Marius; Zien, Alexander; Sonnenburg, Sören; Müller, Klaus-Robert

2012-01-01

We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. This enables us to implement computationally efficient solvers based on sequential minimal and chunking optimization. As a further contribution, the primal problem formulation is developed in terms of regularized risk minimization and the hinge loss, revealing the score function to be used in the actual classification of test patterns. We investigate Scatter SVM properties related to generalization ability, computational efficiency, sparsity and sensitivity maps, and report promising results. PMID:23118845

77 FR 74905 - Self-Regulatory Organizations; Miami International Securities Exchange, LLC; Notice of Filing and...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-18

... Customer. The Marketing Fee is charged only in a Market Maker's assigned classes because it is in these... provided by the MIAX. Marketing Fee MIAX will assess a Marketing Fee to all MIAX Market Makers for..., on a monthly basis, disburse collected Marketing Fees to specific Electronic Exchange Members in...

Do High-Ability Students Disidentify with Science? A Descriptive Study of U.S. Ninth Graders in 2009

ERIC Educational Resources Information Center

Andersen, Lori; Chen, Jason A.

2016-01-01

The present study describes science expectancy-value motivation classes within a nationally representative sample of students who were U.S. ninth graders in 2009. An expectancy-value model was the basis for science-specific profile indicators (self-efficacy, attainment value, utility value, interest-enjoyment value). Using exploratory latent class…

Structural basis of cargo recognitions for class V myosins

PubMed Central

Wei, Zhiyi; Liu, Xiaotian; Yu, Cong; Zhang, Mingjie

2013-01-01

Class V myosins (MyoV), the most studied unconventional myosins, recognize numerous cargos mainly via the motor’s globular tail domain (GTD). Little is known regarding how MyoV-GTD recognizes such a diverse array of cargos specifically. Here, we solved the crystal structures of MyoVa-GTD in its apo-form and in complex with two distinct cargos, melanophilin and Rab interacting lysosomal protein-like 2. The apo-MyoVa-GTD structure indicates that most mutations found in patients with Griscelli syndrome, microvillus inclusion disease, or cancers or in “dilute” rodents likely impair the folding of GTD. The MyoVa-GTD/cargo complex structure reveals two distinct cargo-binding surfaces, one primarily via charge–charge interaction and the other mainly via hydrophobic interactions. Structural and biochemical analysis reveal the specific cargo-binding specificities of various isoforms of mammalian MyoV as well as very different cargo recognition mechanisms of MyoV between yeast and higher eukaryotes. The MyoVa-GTD structures resolved here provide a framework for future functional studies of vertebrate class V myosins. PMID:23798443

HLA Engineering of Human Pluripotent Stem Cells

PubMed Central

Riolobos, Laura; Hirata, Roli K; Turtle, Cameron J; Wang, Pei-Rong; Gornalusse, German G; Zavajlevski, Maja; Riddell, Stanley R; Russell, David W

2013-01-01

The clinical use of human pluripotent stem cells and their derivatives is limited by the rejection of transplanted cells due to differences in their human leukocyte antigen (HLA) genes. This has led to the proposed use of histocompatible, patient-specific stem cells; however, the preparation of many different stem cell lines for clinical use is a daunting task. Here, we develop two distinct genetic engineering approaches that address this problem. First, we use a combination of gene targeting and mitotic recombination to derive HLA-homozygous embryonic stem cell (ESC) subclones from an HLA-heterozygous parental line. A small bank of HLA-homozygous stem cells with common haplotypes would match a significant proportion of the population. Second, we derive HLA class I–negative cells by targeted disruption of both alleles of the Beta-2 Microglobulin (B2M) gene in ESCs. Mixed leukocyte reactions and peptide-specific HLA-restricted CD8+ T cell responses were reduced in class I–negative cells that had undergone differentiation in embryoid bodies. These B2M−/− ESCs could act as universal donor cells in applications where the transplanted cells do not express HLA class II genes. Both approaches used adeno-associated virus (AAV) vectors for efficient gene targeting in the absence of potentially genotoxic nucleases, and produced pluripotent, transgene-free cell lines. PMID:23629003

HLA engineering of human pluripotent stem cells.

PubMed

Riolobos, Laura; Hirata, Roli K; Turtle, Cameron J; Wang, Pei-Rong; Gornalusse, German G; Zavajlevski, Maja; Riddell, Stanley R; Russell, David W

2013-06-01

The clinical use of human pluripotent stem cells and their derivatives is limited by the rejection of transplanted cells due to differences in their human leukocyte antigen (HLA) genes. This has led to the proposed use of histocompatible, patient-specific stem cells; however, the preparation of many different stem cell lines for clinical use is a daunting task. Here, we develop two distinct genetic engineering approaches that address this problem. First, we use a combination of gene targeting and mitotic recombination to derive HLA-homozygous embryonic stem cell (ESC) subclones from an HLA-heterozygous parental line. A small bank of HLA-homozygous stem cells with common haplotypes would match a significant proportion of the population. Second, we derive HLA class I-negative cells by targeted disruption of both alleles of the Beta-2 Microglobulin (B2M) gene in ESCs. Mixed leukocyte reactions and peptide-specific HLA-restricted CD8(+) T cell responses were reduced in class I-negative cells that had undergone differentiation in embryoid bodies. These B2M(-/-) ESCs could act as universal donor cells in applications where the transplanted cells do not express HLA class II genes. Both approaches used adeno-associated virus (AAV) vectors for efficient gene targeting in the absence of potentially genotoxic nucleases, and produced pluripotent, transgene-free cell lines.

Mannosylated poly(beta-amino esters) for targeted antigen presenting cell immune modulation

PubMed Central

Jones, Charles H.; Chen, Mingfu; Ravikrishnan, Anitha; Reddinger, Ryan; Zhang, Guojian; Hakansson, Anders P.; Pfeifer, Blaine A.

2014-01-01

Given the rise of antibiotic resistance and other difficult-to-treat diseases, genetic vaccination is a promising preventative approach that can be tailored and scaled according to the vector chosen for gene delivery. However, most vectors currently utilized rely on ubiquitous delivery mechanisms that ineffectively target important immune effectors such as antigen presenting cells (APCs). As such, APC targeting allows the option for tuning the direction (humoral vs cell-mediated) and strength of the resulting immune responses. In this work, we present the development and assessment of a library of mannosylated poly(beta-amino esters) (PBAEs) that represent a new class of easily synthesized APC-targeting cationic polymers. Polymeric characterization and assessment methodologies were designed to provide a more realistic physiochemical profile prior to in vivo evaluation. Gene delivery assessment in vitro showed significant improvement upon PBAE mannosylation and suggested that mannose-mediated uptake and processing influence the magnitude of gene delivery. Furthermore, mannosylated PBAEs demonstrated a strong, efficient, and safe in vivo humoral immune response without use of adjuvants when compared to genetic and protein control antigens. In summary, the gene delivery effectiveness provided by mannosylated PBAE vectors offers specificity and potency in directing APC activation and subsequent immune responses. PMID:25453962

AAVrh.10-Mediated Expression of an Anti-Cocaine Antibody Mediates Persistent Passive Immunization That Suppresses Cocaine-Induced Behavior

PubMed Central

Rosenberg, Jonathan B.; Hicks, Martin J.; De, Bishnu P.; Pagovich, Odelya; Frenk, Esther; Janda, Kim D.; Wee, Sunmee; Koob, George F.; Hackett, Neil R.; Kaminsky, Stephen M.; Worgall, Stefan; Tignor, Nicole; Mezey, Jason G.

2012-01-01

Abstract Cocaine addiction is a major problem affecting all societal and economic classes for which there is no effective therapy. We hypothesized an effective anti-cocaine vaccine could be developed by using an adeno-associated virus (AAV) gene transfer vector as the delivery vehicle to persistently express an anti-cocaine monoclonal antibody in vivo, which would sequester cocaine in the blood, preventing access to cognate receptors in the brain. To accomplish this, we constructed AAVrh.10antiCoc.Mab, an AAVrh.10 gene transfer vector expressing the heavy and light chains of the high affinity anti-cocaine monoclonal antibody GNC92H2. Intravenous administration of AAVrh.10antiCoc.Mab to mice mediated high, persistent serum levels of high-affinity, cocaine-specific antibodies that sequestered intravenously administered cocaine in the blood. With repeated intravenous cocaine challenge, naive mice exhibited hyperactivity, while the AAVrh.10antiCoc.Mab-vaccinated mice were completely resistant to the cocaine. These observations demonstrate a novel strategy for cocaine addiction by requiring only a single administration of an AAV vector mediating persistent, systemic anti-cocaine passive immunity. PMID:22486244

Using monomer vibrational wavefunctions to compute numerically exact (12D) rovibrational levels of water dimer

NASA Astrophysics Data System (ADS)

Wang, Xiao-Gang; Carrington, Tucker

2018-02-01

We compute numerically exact rovibrational levels of water dimer, with 12 vibrational coordinates, on the accurate CCpol-8sf ab initio flexible monomer potential energy surface [C. Leforestier et al., J. Chem. Phys. 137, 014305 (2012)]. It does not have a sum-of-products or multimode form and therefore quadrature in some form must be used. To do the calculation, it is necessary to use an efficient basis set and to develop computational tools, for evaluating the matrix-vector products required to calculate the spectrum, that obviate the need to store the potential on a 12D quadrature grid. The basis functions we use are products of monomer vibrational wavefunctions and standard rigid-monomer basis functions (which involve products of three Wigner functions). Potential matrix-vector products are evaluated using the F matrix idea previously used to compute rovibrational levels of 5-atom and 6-atom molecules. When the coupling between inter- and intra-monomer coordinates is weak, this crude adiabatic type basis is efficient (only a few monomer vibrational wavefunctions are necessary), although the calculation of matrix elements is straightforward. It is much easier to use than an adiabatic basis. The product structure of the basis is compatible with the product structure of the kinetic energy operator and this facilitates computation of matrix-vector products. Compared with the results obtained using a [6 + 6]D adiabatic approach, we find good agreement for the inter-molecular levels and larger differences for the intra-molecular water bend levels.

Using monomer vibrational wavefunctions to compute numerically exact (12D) rovibrational levels of water dimer.

PubMed

Wang, Xiao-Gang; Carrington, Tucker

2018-02-21

We compute numerically exact rovibrational levels of water dimer, with 12 vibrational coordinates, on the accurate CCpol-8sf ab initio flexible monomer potential energy surface [C. Leforestier et al., J. Chem. Phys. 137, 014305 (2012)]. It does not have a sum-of-products or multimode form and therefore quadrature in some form must be used. To do the calculation, it is necessary to use an efficient basis set and to develop computational tools, for evaluating the matrix-vector products required to calculate the spectrum, that obviate the need to store the potential on a 12D quadrature grid. The basis functions we use are products of monomer vibrational wavefunctions and standard rigid-monomer basis functions (which involve products of three Wigner functions). Potential matrix-vector products are evaluated using the F matrix idea previously used to compute rovibrational levels of 5-atom and 6-atom molecules. When the coupling between inter- and intra-monomer coordinates is weak, this crude adiabatic type basis is efficient (only a few monomer vibrational wavefunctions are necessary), although the calculation of matrix elements is straightforward. It is much easier to use than an adiabatic basis. The product structure of the basis is compatible with the product structure of the kinetic energy operator and this facilitates computation of matrix-vector products. Compared with the results obtained using a [6 + 6]D adiabatic approach, we find good agreement for the inter-molecular levels and larger differences for the intra-molecular water bend levels.

Propensity, Probability, and Quantum Theory

NASA Astrophysics Data System (ADS)

Ballentine, Leslie E.

2016-08-01

Quantum mechanics and probability theory share one peculiarity. Both have well established mathematical formalisms, yet both are subject to controversy about the meaning and interpretation of their basic concepts. Since probability plays a fundamental role in QM, the conceptual problems of one theory can affect the other. We first classify the interpretations of probability into three major classes: (a) inferential probability, (b) ensemble probability, and (c) propensity. Class (a) is the basis of inductive logic; (b) deals with the frequencies of events in repeatable experiments; (c) describes a form of causality that is weaker than determinism. An important, but neglected, paper by P. Humphreys demonstrated that propensity must differ mathematically, as well as conceptually, from probability, but he did not develop a theory of propensity. Such a theory is developed in this paper. Propensity theory shares many, but not all, of the axioms of probability theory. As a consequence, propensity supports the Law of Large Numbers from probability theory, but does not support Bayes theorem. Although there are particular problems within QM to which any of the classes of probability may be applied, it is argued that the intrinsic quantum probabilities (calculated from a state vector or density matrix) are most naturally interpreted as quantum propensities. This does not alter the familiar statistical interpretation of QM. But the interpretation of quantum states as representing knowledge is untenable. Examples show that a density matrix fails to represent knowledge.

Substitution of blood coagulation factor X-binding to Ad5 by position-specific PEGylation: Preventing vector clearance and preserving infectivity.

PubMed

Krutzke, L; Prill, J M; Engler, T; Schmidt, C Q; Xu, Z; Byrnes, A P; Simmet, T; Kreppel, F

2016-08-10

The biodistribution of adenovirus type 5 (Ad5) vector particles is heavily influenced by interaction of the particles with plasma proteins, including coagulation factor X (FX), which binds specifically to the major Ad5 capsid protein hexon. FX mediates hepatocyte transduction by intravenously-injected Ad5 vectors and shields vector particles from neutralization by natural antibodies and complement. In mice, mutant Ad5 vectors that are ablated for FX-binding become detargeted from hepatocytes, which is desirable for certain applications, but unfortunately such FX-nonbinding vectors also become sensitive to neutralization by mouse plasma proteins. To improve the properties of Ad5 vectors for systemic delivery, we developed a strategy to replace the natural FX shield by a site-specific chemical polyethylene glycol shield. Coupling of polyethylene glycol to a specific site in hexon hypervariable region 1 yielded vector particles that were protected from neutralization by natural antibodies and complement although they were unable to bind FX. These vector particles evaded macrophages in vitro and showed significantly improved pharmacokinetics and hepatocyte transduction in vivo. Thus, site-specific shielding of Ad5 vectors with polyethylene glycol rendered vectors FX-independent and greatly improved their properties for systemic gene therapy. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Classification of skin cancer images using local binary pattern and SVM classifier

NASA Astrophysics Data System (ADS)

Adjed, Faouzi; Faye, Ibrahima; Ababsa, Fakhreddine; Gardezi, Syed Jamal; Dass, Sarat Chandra

2016-11-01

In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability to discriminate the melanoma and non-melanoma skin tissues. Support vector machine (SVM) is applied on the feature matrix for classification into two skin image classes (malignant and benign). The method achieves good classification accuracy of 76.1% with sensitivity of 75.6% and specificity of 76.7%.

Remote Sensing the Patterns of Vector-borne Disease in El Nino and non-El Nino Years

NASA Technical Reports Server (NTRS)

Wood, B. L.; Chang, J.; Lobitz, B.; Beck, L.; DAntoni, Hector (Technical Monitor)

1997-01-01

The relationship between El Nino and non-El Nino and the patterns of vector-borne disease can be viewed at a variety of spatial and temporal scales. At one extreme are long term predictions of changing precipitation and temperature patterns at continental and global scales. At the opposite extreme are the local or site specific ecological changes associated with the long term events. In order to understand and address the human health consequences of El Nino events, especially the patterns of vector-borne diseases, it is necessary to combine both scales of observation. At a local or regional scale the patterns of vector-borne diseases are determined by temperature, precipitation, and habitat availability. These factors, as well as disease incidence can be altered by El Nino events. Remote sensing data such as that acquired by the NOAA AVHRR and Landsat TM sensors can be used to characterize and monitor changing ecological conditions and therefore predict vector-borne disease patterns. The authors present the results of preliminary work on the analysis of historical AVHRR and TM data acquired during El Nino and nonfatal Nino years to characterize ecological conditions in Peru on a monthly basis. This information will then be combined with disease data to determine the relationship between changes in ecological conditions and disease incidence. Our goal is to produce a sequence of remotely sensed images which can be used to show the ecological and disease patterns associated with long term El Nino events and predictions.

Dendrimer D5 is a vector for peptide transport to brain cells.

PubMed

Sarantseva, S V; Bolshakova, O I; Timoshenko, S I; Kolobov, A A; Schwarzman, A L

2011-02-01

Dendrimers are a new class of nonviral vectors for gene or drug transport. Dendrimer capacity to penetrate through the blood-brain barrier remaines little studied. Biotinylated polylysine dendrimer D5, similarly to human growth hormone biotinylated fragment covalently bound to D5 dendrimer, penetrates through the blood-brain barrier and accumulates in Drosophila brain after injection into the abdomen. Hence, D5 dendrimer can serve as a vector for peptide transport to brain cells.

HMM for hyperspectral spectrum representation and classification with endmember entropy vectors

NASA Astrophysics Data System (ADS)

Arabi, Samir Y. W.; Fernandes, David; Pizarro, Marco A.

2015-10-01

The Hyperspectral images due to its good spectral resolution are extensively used for classification, but its high number of bands requires a higher bandwidth in the transmission data, a higher data storage capability and a higher computational capability in processing systems. This work presents a new methodology for hyperspectral data classification that can work with a reduced number of spectral bands and achieve good results, comparable with processing methods that require all hyperspectral bands. The proposed method for hyperspectral spectra classification is based on the Hidden Markov Model (HMM) associated to each Endmember (EM) of a scene and the conditional probabilities of each EM belongs to each other EM. The EM conditional probability is transformed in EM vector entropy and those vectors are used as reference vectors for the classes in the scene. The conditional probability of a spectrum that will be classified is also transformed in a spectrum entropy vector, which is classified in a given class by the minimum ED (Euclidian Distance) among it and the EM entropy vectors. The methodology was tested with good results using AVIRIS spectra of a scene with 13 EM considering the full 209 bands and the reduced spectral bands of 128, 64 and 32. For the test area its show that can be used only 32 spectral bands instead of the original 209 bands, without significant loss in the classification process.

Internal amino acid state modulates yeast taste neurons to support protein homeostasis in Drosophila

PubMed Central

Itskov, Pavel M; Baltazar, Célia; Moreira, José-Maria

2018-01-01

To optimize fitness, animals must dynamically match food choices to their current needs. For drosophilids, yeast fulfills most dietary protein and micronutrient requirements. While several yeast metabolites activate known gustatory receptor neurons (GRNs) in Drosophila melanogaster, the chemosensory channels mediating yeast feeding remain unknown. Here we identify a class of proboscis GRNs required for yeast intake. Within this class, taste peg GRNs are specifically required to sustain yeast feeding. Sensillar GRNs, however, mediate feeding initiation. Furthermore, the response of yeast GRNs, but not sweet GRNs, is enhanced following deprivation from amino acids, providing a potential basis for protein-specific appetite. Although nutritional and reproductive states synergistically increase yeast appetite, reproductive state acts independently of nutritional state, modulating processing downstream of GRNs. Together, these results suggest that different internal states act at distinct levels of a dedicated gustatory circuit to elicit nutrient-specific appetites towards a complex, ecologically relevant protein source. PMID:29393045

Teaching Vectors Through an Interactive Game Based Laboratory

NASA Astrophysics Data System (ADS)

O'Brien, James; Sirokman, Gergely

2014-03-01

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

Impact of childhood asthma on growth trajectories in early adolescence: Findings from the Childhood Asthma Prevention Study (CAPS).

PubMed

Movin, Maria; Garden, Frances L; Protudjer, Jennifer L P; Ullemar, Vilhelmina; Svensdotter, Frida; Andersson, David; Kruse, Andreas; Cowell, Chris T; Toelle, Brett G; Marks, Guy B; Almqvist, Catarina

2017-04-01

Understanding the associations between childhood asthma and growth in early adolescence by accounting for the heterogeneity of growth during puberty has been largely unexplored. The objective was to identify sex-specific classes of growth trajectories during early adolescence, using a method which takes the heterogeneity of growth into account and to evaluate the association between childhood asthma and different classes of growth trajectories in adolescence. Our longitudinal study included participants with a family history of asthma born during 1997-1999 in Sydney, Australia. Hence, all participants were at high risk for asthma. Asthma status was ascertained at 8 years of age using data from questionnaires and lung function tests. Growth trajectories between 11 and 14 years of age were classified using a latent basis growth mixture model. Multinomial regression analyses were used to evaluate the association between asthma and the categorized classes of growth trajectories. In total, 316 participants (51.6% boys), representing 51.3% of the entire cohort, were included. Sex-specific classes of growth trajectories were defined. Among boys, asthma was not associated with the classes of growth trajectories. Girls with asthma were more likely than girls without asthma to belong to a class with later growth (OR: 3.79, 95% CI: 1.33, 10.84). Excluding participants using inhaled corticosteroids or adjusting for confounders did not significantly change the results for either sex. We identified sex-specific heterogeneous classes of growth using growth mixture modelling. Associations between childhood asthma and different classes of growth trajectories were found for girls only. © 2016 Asian Pacific Society of Respirology.

The cytochrome P450 CYP6P4 is responsible for the high pyrethroid resistance in knockdown resistance-free Anopheles arabiensis.

PubMed

Ibrahim, Sulaiman S; Riveron, Jacob M; Stott, Robert; Irving, Helen; Wondji, Charles S

2016-01-01

Pyrethroid insecticides are the front line vector control tools used in bed nets to reduce malaria transmission and its burden. However, resistance in major vectors such as Anopheles arabiensis is posing a serious challenge to the success of malaria control. Herein, we elucidated the molecular and biochemical basis of pyrethroid resistance in a knockdown resistance-free Anopheles arabiensis population from Chad, Central Africa. Using heterologous expression of P450s in Escherichia coli coupled with metabolism assays we established that the over-expressed P450 CYP6P4, located in the major pyrethroid resistance (rp1) quantitative trait locus (QTL), is responsible for resistance to Type I and Type II pyrethroid insecticides, with the exception of deltamethrin, in correlation with field resistance profile. However, CYP6P4 exhibited no metabolic activity towards non-pyrethroid insecticides, including DDT, bendiocarb, propoxur and malathion. Combining fluorescent probes inhibition assays with molecular docking simulation, we established that CYP6P4 can bind deltamethrin but cannot metabolise it. This is possibly due to steric hindrance because of the large vdW radius of bromine atoms of the dihalovinyl group of deltamethrin which docks into the heme catalytic centre. The establishment of CYP6P4 as a partial pyrethroid resistance gene explained the observed field resistance to permethrin, and its inability to metabolise deltamethrin probably explained the high mortality from deltamethrin exposure in the field populations of this Sudano-Sahelian An. arabiensis. These findings describe the heterogeneity in resistance towards insecticides, even from the same class, highlighting the need to thoroughly understand the molecular basis of resistance before implementing resistance management/control tools. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

The cytochrome P450 CYP6P4 is responsible for the high pyrethroid resistance in knockdown resistance-free Anopheles arabiensis

PubMed Central

Ibrahim, Sulaiman S.; Riveron, Jacob M.; Stott, Robert; Irving, Helen; Wondji, Charles S.

2016-01-01

Pyrethroid insecticides are the front line vector control tools used in bed nets to reduce malaria transmission and its burden. However, resistance in major vectors such as Anopheles arabiensis is posing a serious challenge to the success of malaria control. Herein, we elucidated the molecular and biochemical basis of pyrethroid resistance in a knockdown resistance-free Anopheles arabiensis population from Chad, Central Africa. Using heterologous expression of P450s in Escherichia coli coupled with metabolism assays we established that the over-expressed P450 CYP6P4, located in the major pyrethroid resistance (rp1) quantitative trait locus (QTL), is responsible for resistance to Type I and Type II pyrethroid insecticides, with the exception of deltamethrin, in correlation with field resistance profile. However, CYP6P4 exhibited no metabolic activity towards non-pyrethroid insecticides, including DDT, bendiocarb, propoxur and malathion. Combining fluorescent probes inhibition assays with molecular docking simulation, we established that CYP6P4 can bind deltamethrin but cannot metabolise it. This is possibly due to steric hindrance because of the large vdW radius of bromine atoms of the dihalovinyl group of deltamethrin which docks into the heme catalytic centre. The establishment of CYP6P4 as a partial pyrethroid resistance gene explained the observed field resistance to permethrin, and its inability to metabolise deltamethrin probably explained the high mortality from deltamethrin exposure in the field populations of this Sudano-Sahelian An. arabiensis. These findings describe the heterogeneity in resistance towards insecticides, even from the same class, highlighting the need to thoroughly understand the molecular basis of resistance before implementing resistance management/control tools. PMID:26548743

Game-theoretic approach to joint transmitter adaptation and power control in wireless systems.

PubMed

Popescu, Dimitrie C; Rawat, Danda B; Popescu, Otilia; Saquib, Mohamad

2010-06-01

Game theory has emerged as a new mathematical tool in the analysis and design of wireless communication systems, being particularly useful in studying the interactions among adaptive transmitters that attempt to achieve specific objectives without cooperation. In this paper, we present a game-theoretic approach to the problem of joint transmitter adaptation and power control in wireless systems, where users' transmissions are subject to quality-of-service requirements specified in terms of target signal-to-interference-plus-noise ratios (SINRs) and nonideal vector channels between transmitters and receivers are explicitly considered. Our approach is based on application of separable games, which are a specific class of noncooperative games where the players' cost is a separable function of their strategic choices. We formally state a joint codeword and power adaptation game, which is separable, and we study its properties in terms of its subgames, namely, the codeword adaptation subgame and the power adaptation subgame. We investigate the necessary conditions for an optimal Nash equilibrium and show that this corresponds to an ensemble of user codewords and powers, which maximizes the sum capacity of the corresponding multiaccess vector channel model, and for which the specified target SINRs are achieved with minimum transmitted power.

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei; Zhou, Peicheng; Guo, Lei

2014-12-01

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Method for indexing and retrieving manufacturing-specific digital imagery based on image content

DOEpatents

Ferrell, Regina K.; Karnowski, Thomas P.; Tobin, Jr., Kenneth W.

2004-06-15

A method for indexing and retrieving manufacturing-specific digital images based on image content comprises three steps. First, at least one feature vector can be extracted from a manufacturing-specific digital image stored in an image database. In particular, each extracted feature vector corresponds to a particular characteristic of the manufacturing-specific digital image, for instance, a digital image modality and overall characteristic, a substrate/background characteristic, and an anomaly/defect characteristic. Notably, the extracting step includes generating a defect mask using a detection process. Second, using an unsupervised clustering method, each extracted feature vector can be indexed in a hierarchical search tree. Third, a manufacturing-specific digital image associated with a feature vector stored in the hierarchicial search tree can be retrieved, wherein the manufacturing-specific digital image has image content comparably related to the image content of the query image. More particularly, can include two data reductions, the first performed based upon a query vector extracted from a query image. Subsequently, a user can select relevant images resulting from the first data reduction. From the selection, a prototype vector can be calculated, from which a second-level data reduction can be performed. The second-level data reduction can result in a subset of feature vectors comparable to the prototype vector, and further comparable to the query vector. An additional fourth step can include managing the hierarchical search tree by substituting a vector average for several redundant feature vectors encapsulated by nodes in the hierarchical search tree.

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

PubMed Central

Gould, Ian C.; Shepherd, Alana M.; Laurens, Kristin R.; Cairns, Murray J.; Carr, Vaughan J.; Green, Melissa J.

2014-01-01

Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features. PMID:25379435

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

NASA Astrophysics Data System (ADS)

Watanabe, Tatsuhito; Katsura, Seiichiro

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

A MODELING APPROACH FOR ESTIMATING WATERSHED IMPERVIOUS SURFACE AREA FROM NATIONAL LAND COVER DATA 92

EPA Science Inventory

We used National Land Cover Data 92 (NLCD92), vector impervious surface data, and raster GIS overlay methods to derive impervious surface coefficients per NLCD92 class in portions of the Nfid-Atlantic physiographic region. The methods involve a vector to raster conversion of the ...

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.

Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers.

PubMed

Yu, Hualong; Hong, Shufang; Yang, Xibei; Ni, Jun; Dan, Yuanyuan; Qin, Bin

2013-01-01

DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results. Some studies have considered this problem, yet they merely focus on binary-class problem. In this paper, we dealt with multiclass imbalanced classification problem, as encountered in cancer DNA microarray, by using ensemble learning. We utilized one-against-all coding strategy to transform multiclass to multiple binary classes, each of them carrying out feature subspace, which is an evolving version of random subspace that generates multiple diverse training subsets. Next, we introduced one of two different correction technologies, namely, decision threshold adjustment or random undersampling, into each training subset to alleviate the damage of class imbalance. Specifically, support vector machine was used as base classifier, and a novel voting rule called counter voting was presented for making a final decision. Experimental results on eight skewed multiclass cancer microarray datasets indicate that unlike many traditional classification approaches, our methods are insensitive to class imbalance.

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

PubMed

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

2009-06-15

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

Brucella lipopolysaccharide reinforced Salmonella delivering Brucella immunogens protects mice against virulent challenge.

PubMed

Lalsiamthara, Jonathan; Lee, John Hwa

2017-06-01

Intracellular pathogen Salmonella exhibits natural infection broadly analogous to Brucella, this phenomenon makes Salmonella a pragmatic choice for an anti-Brucella vaccine delivery platform. In this study we developed and formulated a combination of four attenuated Salmonella Typhimurium live vector strains delivering heterologous Brucella antigens (rBs), namely lumazine synthase, proline racemase subunit A, lipoprotein outer membrane protein-19, and Cu-Zn superoxide dismutase. With an aim to develop a cross-protecting vaccine, Brucella pan-species conserved rBs were selected. The present study compared the efficacy of smooth and rough variants of Salmonella delivery vector and also evaluated the inclusion of purified Brucella lipopolysaccharide (LPS) in the formulation. Immunization of SPF-BALB/c mice with the vaccine combinations significantly (P≤0.05) reduced splenic wild-type Brucella abortus 544 colonization as compared to non-immunized mice as well as Salmonella only immunized mice. Increased induction of Brucella specific-IgG, sIgA production, and antigen-specific splenocyte proliferative responses were observed in the mice immunized with the formulations as compared to naïve or vector only immunized mice. Modulatory effects of rB and LPS on production of interleukin (IL)-4, IL-12, and interferon-γ were detected in splenocytes of mice immunized with the formulation. Rough Salmonella variant in combination with LPS could further enhance the efficacy of the delivery when applied intraperitoneally. Taken together, it is compelling that Brucella LPS-augmented Salmonella vector delivering immunogenic Brucella proteins may be more suitable than the current non-ideal live Brucella abortus vaccine. The vaccine system also provides a basis for the development of cross-protecting vaccine capable of preventing multispecies brucellosis. Copyright © 2017 Elsevier B.V. All rights reserved.

Discontinuity Detection in the Shield Metal Arc Welding Process

PubMed Central

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-01-01

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries. PMID:28489045

Discontinuity Detection in the Shield Metal Arc Welding Process.

PubMed

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-05-10

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors-a microphone and piezoelectric-that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system's high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.

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

DTIC Science & Technology

1998-09-01

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

General approach to reversing ketol-acid reductoisomerase cofactor dependence from NADPH to NADH

DOE PAGES

Brinkmann-Chen, Sabine; Flock, Tilman; Cahn, Jackson K. B.; ...

2013-06-17

To date, efforts to switch the cofactor specificity of oxidoreductases from nicotinamide adenine dinucleotide phosphate (NADPH) to nicotinamide adenine dinucleotide (NADH) have been made on a case-by-case basis with varying degrees of success. Here we present a straightforward recipe for altering the cofactor specificity of a class of NADPH-dependent oxidoreductases, the ketol-acid reductoisomerases (KARIs). Combining previous results for an engineered NADH-dependent variant of Escherichia coli KARI with available KARI crystal structures and a comprehensive KARI-sequence alignment, we identified key cofactor specificity determinants and used this information to construct five KARIs with reversed cofactor preference. Additional directed evolution generated two enzymesmore » having NADH-dependent catalytic efficiencies that are greater than the wild-type enzymes with NADPH. As a result, high-resolution structures of a wild-type/variant pair reveal the molecular basis of the cofactor switch.« less

A novel role of HLA class I in the pathology of medulloblastoma.

PubMed

Smith, Courtney; Santi, Mariarita; Rajan, Bhargavi; Rushing, Elisabeth J; Choi, Mi Rim; Rood, Brian R; Cornelison, Robert; MacDonald, Tobey J; Vukmanovic, Stanislav

2009-07-12

MHC class I expression by cancer cells enables specific antigen recognition by the immune system and protection of the host. However, in some cancer types MHC class I expression is associated with an unfavorable outcome. We explored the basis of MHC class I association with unfavorable prognostic marker expression in the case of medulloblastoma. We investigated expression of four essential components of MHC class I (heavy chain, beta2m, TAP1 and TAP2) in 10 medulloblastoma mRNA samples, a tissue microarray containing 139 medulloblastoma tissues and 3 medulloblastoma cell lines. Further, in medulloblastoma cell lines we evaluated the effects of HLA class I engagement on activation of ERK1/2 and migration in vitro. The majority of specimens displayed undetectable or low levels of the heavy chains. Medulloblastomas expressing high levels of HLA class I displayed significantly higher levels of anaplasia and c-myc expression, markers of poor prognosis. Binding of beta2m or a specific antibody to open forms of HLA class I promoted phosphorylation of ERK1/2 in medulloblastoma cell line with high levels, but not in the cell line with low levels of HLA heavy chain. This treatment also promoted ERK1/2 activation dependent migration of medulloblastoma cells. MHC class I expression in medulloblastoma is associated with anaplasia and c-myc expression, markers of poor prognosis. Peptide- and/or beta2m-free forms of MHC class I may contribute to a more malignant phenotype of medulloblastoma by modulating activation of signaling molecules such as ERK1/2 that stimulates cell mobility.

A novel role of HLA class I in the pathology of medulloblastoma

PubMed Central

Smith, Courtney; Santi, Mariarita; Rajan, Bhargavi; Rushing, Elisabeth J; Choi, Mi Rim; Rood, Brian R; Cornelison, Robert; MacDonald, Tobey J; Vukmanovic, Stanislav

2009-01-01

Background MHC class I expression by cancer cells enables specific antigen recognition by the immune system and protection of the host. However, in some cancer types MHC class I expression is associated with an unfavorable outcome. We explored the basis of MHC class I association with unfavorable prognostic marker expression in the case of medulloblastoma. Methods We investigated expression of four essential components of MHC class I (heavy chain, β2m, TAP1 and TAP2) in 10 medulloblastoma mRNA samples, a tissue microarray containing 139 medulloblastoma tissues and 3 medulloblastoma cell lines. Further, in medulloblastoma cell lines we evaluated the effects of HLA class I engagement on activation of ERK1/2 and migration in vitro. Results The majority of specimens displayed undetectable or low levels of the heavy chains. Medulloblastomas expressing high levels of HLA class I displayed significantly higher levels of anaplasia and c-myc expression, markers of poor prognosis. Binding of β2m or a specific antibody to open forms of HLA class I promoted phosphorylation of ERK1/2 in medulloblastoma cell line with high levels, but not in the cell line with low levels of HLA heavy chain. This treatment also promoted ERK1/2 activation dependent migration of medulloblastoma cells. Conclusion MHC class I expression in medulloblastoma is associated with anaplasia and c-myc expression, markers of poor prognosis. Peptide- and/or β2m-free forms of MHC class I may contribute to a more malignant phenotype of medulloblastoma by modulating activation of signaling molecules such as ERK1/2 that stimulates cell mobility. PMID:19594892

Classification of Alzheimer's disease patients with hippocampal shape wrapper-based feature selection and support vector machine

NASA Astrophysics Data System (ADS)

Young, Jonathan; Ridgway, Gerard; Leung, Kelvin; Ourselin, Sebastien

2012-02-01

It is well known that hippocampal atrophy is a marker of the onset of Alzheimer's disease (AD) and as a result hippocampal volumetry has been used in a number of studies to provide early diagnosis of AD and predict conversion of mild cognitive impairment patients to AD. However, rates of atrophy are not uniform across the hippocampus making shape analysis a potentially more accurate biomarker. This study studies the hippocampi from 226 healthy controls, 148 AD patients and 330 MCI patients obtained from T1 weighted structural MRI images from the ADNI database. The hippocampi are anatomically segmented using the MAPS multi-atlas segmentation method, and the resulting binary images are then processed with SPHARM software to decompose their shapes as a weighted sum of spherical harmonic basis functions. The resulting parameterizations are then used as feature vectors in Support Vector Machine (SVM) classification. A wrapper based feature selection method was used as this considers the utility of features in discriminating classes in combination, fully exploiting the multivariate nature of the data and optimizing the selected set of features for the type of classifier that is used. The leave-one-out cross validated accuracy obtained on training data is 88.6% for classifying AD vs controls and 74% for classifying MCI-converters vs MCI-stable with very compact feature sets, showing that this is a highly promising method. There is currently a considerable fall in accuracy on unseen data indicating that the feature selection is sensitive to the data used, however feature ensemble methods may overcome this.

Structural Basis for Specific Inhibition of tRNA Synthetase by an ATP Competitive Inhibitor.

PubMed

Fang, Pengfei; Han, Hongyan; Wang, Jing; Chen, Kaige; Chen, Xin; Guo, Min

2015-06-18

Pharmaceutical inhibitors of aminoacyl-tRNA synthetases demand high species and family specificity. The antimalarial ATP-mimetic cladosporin selectively inhibits Plasmodium falciparum LysRS (PfLysRS). How the binding to a universal ATP site achieves the specificity is unknown. Here we report three crystal structures of cladosporin with human LysRS, PfLysRS, and a Pf-like human LysRS mutant. In all three structures, cladosporin occupies the class defining ATP-binding pocket, replacing the adenosine portion of ATP. Three residues holding the methyltetrahydropyran moiety of cladosporin are critical for the specificity of cladosporin against LysRS over other class II tRNA synthetase families. The species-exclusive inhibition of PfLysRS is linked to a structural divergence beyond the active site that mounts a lysine-specific stabilizing response to binding cladosporin. These analyses reveal that inherent divergence of tRNA synthetase structural assembly may allow for highly specific inhibition even through the otherwise universal substrate binding pocket and highlight the potential for structure-driven drug development. Copyright © 2015 Elsevier Ltd. All rights reserved.

In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine

PubMed Central

Lu, Peng; Chen, Jianxin; Zhao, Huihui; Gao, Yibo; Luo, Liangtao; Zuo, Xiaohan; Shi, Qi; Yang, Yiping; Yi, Jianqiang; Wang, Wei

2012-01-01

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM. PMID:22567030

Coherent states for the relativistic harmonic oscillator

NASA Technical Reports Server (NTRS)

Aldaya, Victor; Guerrero, J.

1995-01-01

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

Useful Web Sites for International Business Communication Education: New Information Sources for an Expanding Field

ERIC Educational Resources Information Center

Mayfield, Jacqueline; Mayfield, Milton; Kohl, John

2005-01-01

The World Wide Web presents many opportunities for improving the instructional quality of international business communication related classes by providing access to a large variety of information sources. These sources can be used as supplements to traditional texts, as the basis for specific program assignments, or even as the main focus of a…

Isolation and characterization of a cDNA clone coding for a glutathione S-transferase class delta enzyme from the biting midge Culicoides variipennis sonorensis Wirth and Jones.

PubMed

Abdallah, M A; Pollenz, R S; Droog, F N; Nunamaker, R A; Tabachnick, W J; Murphy, K E

2000-12-01

Culicoides variipennis sonorensis is the primary vector of bluetongue viruses in North America. Glutathione S-transferases (GSTs) are enzymes that catalyze nucleophilic substitutions, converting reactive lipophilic molecules into soluble conjugates. Increased GST activity is associated with development of insecticide resistance. Described here is the isolation of the first cDNA encoding a C. variipennis GST. The clone consists of 720 translated bases encoding a protein with a M(r) of approximately 24,800 composed of 219 amino acids. The deduced amino acid sequence is similar (64%-74%) to class Delta (previously named Theta) GSTs from the dipteran genera Musca, Drosophila, Lucilia and Anopheles. The cDNA was subcloned into pET-11b, expressed in Epicurian coli BL21 (DE3) and has a specific activity of approximately 28,000 units/mg for the substrate 1-chloro-2,4-dinitrobenzene.

The Effect of Three-Dimensional Freestream Disturbances on the Supersonic Flow Past a Wedge

NASA Technical Reports Server (NTRS)

Duck, Peter W.; Lasseigne, D. Glenn; Hussaini, M. Y.

1997-01-01

The interaction between a shock wave (attached to a wedge) and small amplitude, three-dimensional disturbances of a uniform, supersonic, freestream flow are investigated. The paper extends the two-dimensional study of Duck et al, through the use of vector potentials, which render the problem tractable by the same techniques as in the two-dimensional case, in particular by expansion of the solution by means of a Fourier-Bessel series, in appropriately chosen coordinates. Results are presented for specific classes of freestream disturbances, and the study shows conclusively that the shock is stable to all classes of disturbances (i.e. time periodic perturbations to the shock do not grow downstream), provided the flow downstream of the shock is supersonic (loosely corresponding to the weak shock solution). This is shown from our numerical results and also by asymptotic analysis of the Fourier-Bessel series, valid far downstream of the shock.

Interframe vector wavelet coding technique

NASA Astrophysics Data System (ADS)

Wus, John P.; Li, Weiping

1997-01-01

Wavelet coding is often used to divide an image into multi- resolution wavelet coefficients which are quantized and coded. By 'vectorizing' scalar wavelet coding and combining this with vector quantization (VQ), vector wavelet coding (VWC) can be implemented. Using a finite number of states, finite-state vector quantization (FSVQ) takes advantage of the similarity between frames by incorporating memory into the video coding system. Lattice VQ eliminates the potential mismatch that could occur using pre-trained VQ codebooks. It also eliminates the need for codebook storage in the VQ process, thereby creating a more robust coding system. Therefore, by using the VWC coding method in conjunction with the FSVQ system and lattice VQ, the formulation of a high quality very low bit rate coding systems is proposed. A coding system using a simple FSVQ system where the current state is determined by the previous channel symbol only is developed. To achieve a higher degree of compression, a tree-like FSVQ system is implemented. The groupings are done in this tree-like structure from the lower subbands to the higher subbands in order to exploit the nature of subband analysis in terms of the parent-child relationship. Class A and Class B video sequences from the MPEG-IV testing evaluations are used in the evaluation of this coding method.

RNA interference mediated in human primary cells via recombinant baculoviral vectors.

PubMed

Nicholson, Linda J; Philippe, Marie; Paine, Alan J; Mann, Derek A; Dolphin, Colin T

2005-04-01

The success of RNA interference (RNAi) in mammalian cells, mediated by siRNAs or shRNA-generating plasmids, is dependent, to an extent, upon transfection efficiency. This is a particular problem with primary cells, which are often difficult to transfect using cationic lipid vehicles. Effective RNAi in primary cells is thus best achieved with viral vectors, and retro-, adeno-, and lentivirus RNAi systems have been described. However, the use of such human viral vectors is inherently problematic, e.g., Class 2 status and requirement of secondary helper functions. Although insect cells are their natural host, baculoviruses also transduce a range of vertebrate cell lines and primary cells with high efficiency. The inability of baculoviral vectors to replicate in mammalian cells, their Class 1 status, and the simplicity of their construction make baculovirus an attractive alternative gene delivery vector. We have developed a baculoviral-based RNAi system designed to express shRNAs and GFP from U6 and CMV promoters, respectively. Transduction of Saos2, HepG2, Huh7, and primary human hepatic stellate cells with a baculoviral construct expressing shRNAs targeting lamin A/C resulted in effective knockdown of the corresponding mRNA and protein. Development of this baculoviral-based system provides an additional shRNA delivery option for RNAi-based investigations in mammalian cells.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Discovery of Rigidified α,β-Unsaturated Imines as New Resistance-breaking Insecticides for Malaria Vector Control.

PubMed

Arlt, Alexander; Böhnke, Niels; Horstmann, Sebastian; Vermeer, Arnoldus W P; Werner, Stefan; Velten, Robert

2016-10-01

During our continuous search for new resistance-breaking insecticides applicable to malaria vector control, a new class of α,β-unsaturated imines was identified by applying the principle of conformational rigidification as a powerful tool for compound optimisation. Herein we describe the successful synthesis of these compounds and their biological test results. Our lead compound 16 from this insecticidal class outperforms market standards, notably for the control of mosquito strains that exhibit either metabolic or target-site resistance to these established insecticides. In our model system for insecticide-treated mosquito nets the compound reveals long-lasting efficacy for up to several months.

Application of fast Fourier transforms to the direct solution of a class of two-dimensional separable elliptic equations on the sphere

NASA Technical Reports Server (NTRS)

Moorthi, Shrinivas; Higgins, R. W.

1993-01-01

An efficient, direct, second-order solver for the discrete solution of a class of two-dimensional separable elliptic equations on the sphere (which generally arise in implicit and semi-implicit atmospheric models) is presented. The method involves a Fourier transformation in longitude and a direct solution of the resulting coupled second-order finite-difference equations in latitude. The solver is made efficient by vectorizing over longitudinal wave-number and by using a vectorized fast Fourier transform routine. It is evaluated using a prescribed solution method and compared with a multigrid solver and the standard direct solver from FISHPAK.

JRSP of three-particle state via three tripartite GHZ class in quantum noisy channels

NASA Astrophysics Data System (ADS)

Falaye, Babatunde James; Sun, Guo-Hua; Camacho-Nieto, Oscar; Dong, Shi-Hai

2016-10-01

We present a scheme for joint remote state preparation (JRSP) of three-particle state via three tripartite Greenberger-Horne-Zeilinger (GHZ) entangled states as the quantum channel linking the parties. We use eight-qubit mutually orthogonal basis vector as measurement point of departure. The likelihood of success for this scheme has been found to be 1/8. However, by putting some special cases into consideration, the chances can be ameliorated to 1/4 and 1. The effects of amplitude-damping noise, phase-damping noise and depolarizing noise on this scheme have been scrutinized and the analytical derivations of fidelities for the quantum noisy channels have been presented. We found that for 0.55≤η≤1, the states conveyed through depolarizing channel lose more information than phase-damping channel while the information loss through amplitude damping channel is most minimal.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging

PubMed Central

Gholami, Behnood; Tannenbaum, Allen R.

2011-01-01

Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent “pure” facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners. PMID:20172803

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

DOE PAGES

Carlberg, Kevin T.

2014-11-05

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

Hawaii 50 m Wind Power Class

Science.gov Websites

Power Class Geospatial_Data_Presentation_Form: vector digital data Other_Citation_Details: The wind weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants. Description: Abstract: Annual average

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

PubMed

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

2018-01-01

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

Extended vector-tensor theories

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

Kimura, Rampei; Naruko, Atsushi; Yoshida, Daisuke, E-mail: rampei@th.phys.titech.ac.jp, E-mail: naruko@th.phys.titech.ac.jp, E-mail: yoshida@th.phys.titech.ac.jp

Recently, several extensions of massive vector theory in curved space-time have been proposed in many literatures. In this paper, we consider the most general vector-tensor theories that contain up to two derivatives with respect to metric and vector field. By imposing a degeneracy condition of the Lagrangian in the context of ADM decomposition of space-time to eliminate an unwanted mode, we construct a new class of massive vector theories where five degrees of freedom can propagate, corresponding to three for massive vector modes and two for massless tensor modes. We find that the generalized Proca and the beyond generalized Procamore » theories up to the quartic Lagrangian, which should be included in this formulation, are degenerate theories even in curved space-time. Finally, introducing new metric and vector field transformations, we investigate the properties of thus obtained theories under such transformations.« less

AUTOCLASS III - AUTOMATIC CLASS DISCOVERY FROM DATA

NASA Technical Reports Server (NTRS)

Cheeseman, P. C.

1994-01-01

The program AUTOCLASS III, Automatic Class Discovery from Data, uses Bayesian probability theory to provide a simple and extensible approach to problems such as classification and general mixture separation. Its theoretical basis is free from ad hoc quantities, and in particular free of any measures which alter the data to suit the needs of the program. As a result, the elementary classification model used lends itself easily to extensions. The standard approach to classification in much of artificial intelligence and statistical pattern recognition research involves partitioning of the data into separate subsets, known as classes. AUTOCLASS III uses the Bayesian approach in which classes are described by probability distributions over the attributes of the objects, specified by a model function and its parameters. The calculation of the probability of each object's membership in each class provides a more intuitive classification than absolute partitioning techniques. AUTOCLASS III is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or omitted. The user specifies a class probability distribution function by associating attribute sets with supplied likelihood function terms. AUTOCLASS then searches in the space of class numbers and parameters for the maximally probable combination. It returns the set of class probability function parameters, and the class membership probabilities for each data instance. AUTOCLASS III is written in Common Lisp, and is designed to be platform independent. This program has been successfully run on Symbolics and Explorer Lisp machines. It has been successfully used with the following implementations of Common LISP on the Sun: Franz Allegro CL, Lucid Common Lisp, and Austin Kyoto Common Lisp and similar UNIX platforms; under the Lucid Common Lisp implementations on VAX/VMS v5.4, VAX/Ultrix v4.1, and MIPS/Ultrix v4, rev. 179; and on the Macintosh personal computer. The minimum Macintosh required is the IIci. This program will not run under CMU Common Lisp or VAX/VMS DEC Common Lisp. A minimum of 8Mb of RAM is required for Macintosh platforms and 16Mb for workstations. The standard distribution medium for this program is a .25 inch streaming magnetic tape cartridge in UNIX tar format. It is also available on a 3.5 inch diskette in UNIX tar format and a 3.5 inch diskette in Macintosh format. An electronic copy of the documentation is included on the distribution medium. AUTOCLASS was developed between March 1988 and March 1992. It was initially released in May 1991. Sun is a trademark of Sun Microsystems, Inc. UNIX is a registered trademark of AT&T Bell Laboratories. DEC, VAX, VMS, and ULTRIX are trademarks of Digital Equipment Corporation. Macintosh is a trademark of Apple Computer, Inc. Allegro CL is a registered trademark of Franz, Inc.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Prediction of future uniform milk prices in Florida federal milk marketing order 6 from milk futures markets.

PubMed

De Vries, A; Feleke, S

2008-12-01

This study assessed the accuracy of 3 methods that predict the uniform milk price in Federal Milk Marketing Order 6 (Florida). Predictions were made for 1 to 12 mo into the future. Data were from January 2003 to May 2007. The CURRENT method assumed that future uniform milk prices were equal to the last announced uniform milk price. The F+BASIS and F+UTIL methods were based on the milk futures markets because the futures prices reflect the market's expectation of the class III and class IV cash prices that are announced monthly by USDA. The F+BASIS method added an exponentially weighted moving average of the difference between the class III cash price and the historical uniform milk price (also known as basis) to the class III futures price. The F+UTIL method used the class III and class IV futures prices, the most recently announced butter price, and historical utilizations to predict the skim milk prices, butterfat prices, and utilizations in all 4 classes. Predictions of future utilizations were made with a Holt-Winters smoothing method. Federal Milk Marketing Order 6 had high class I utilization (85 +/- 4.8%). Mean and standard deviation of the class III and class IV cash prices were $13.39 +/- 2.40/cwt (1 cwt = 45.36 kg) and $12.06 +/- 1.80/cwt, respectively. The actual uniform price in Tampa, Florida, was $16.62 +/- 2.16/cwt. The basis was $3.23 +/- 1.23/cwt. The F+BASIS and F+UTIL predictions were generally too low during the period considered because the class III cash prices were greater than the corresponding class III futures prices. For the 1- to 6-mo-ahead predictions, the root of the mean squared prediction errors from the F+BASIS method were $1.12, $1.20, $1.55, $1.91, $2.16, and $2.34/cwt, respectively. The root of the mean squared prediction errors ranged from $2.50 to $2.73/cwt for predictions up to 12 mo ahead. Results from the F+UTIL method were similar. The accuracies of the F+BASIS and F+UTIL methods for all 12 fore-cast horizons were not significantly different. Application of the modified Mariano-Diebold tests showed that no method included all the information contained in the other methods. In conclusion, both F+BASIS and F+UTIL methods tended to more accurately predict the future uniform milk prices than the CURRENT method, but prediction errors could be substantial even a few months into the future. The majority of the prediction error was caused by the inefficiency of the futures markets to predict the class III cash prices.

Comparisons and Selections of Features and Classifiers for Short Text Classification

NASA Astrophysics Data System (ADS)

Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

2017-10-01

Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

A dityrosine network mediated by dual oxidase and peroxidase influences the persistence of Lyme disease pathogens within the vector.

PubMed

Yang, Xiuli; Smith, Alexis A; Williams, Mark S; Pal, Utpal

2014-05-02

Ixodes scapularis ticks transmit a wide array of human and animal pathogens including Borrelia burgdorferi; however, how tick immune components influence the persistence of invading pathogens remains unknown. As originally demonstrated in Caenorhabditis elegans and later in Anopheles gambiae, we show here that an acellular gut barrier, resulting from the tyrosine cross-linking of the extracellular matrix, also exists in I. scapularis ticks. This dityrosine network (DTN) is dependent upon a dual oxidase (Duox), which is a member of the NADPH oxidase family. The Ixodes genome encodes for a single Duox and at least 16 potential peroxidase proteins, one of which, annotated as ISCW017368, together with Duox has been found to be indispensible for DTN formation. This barrier influences pathogen survival in the gut, as an impaired DTN in Doux knockdown or in specific peroxidase knockdown ticks, results in reduced levels of B. burgdorferi persistence within ticks. Absence of a complete DTN formation in knockdown ticks leads to the activation of specific tick innate immune pathway genes that potentially resulted in the reduction of spirochete levels. Together, these results highlighted the evolution of the DTN in a diverse set of arthropod vectors, including ticks, and its role in protecting invading pathogens like B. burgdorferi. Further understanding of the molecular basis of tick innate immune responses, vector-pathogen interaction, and their contributions in microbial persistence may help the development of new targets for disrupting the pathogen life cycle.

Boosting specificity of MEG artifact removal by weighted support vector machine.

PubMed

Duan, Fang; Phothisonothai, Montri; Kikuchi, Mitsuru; Yoshimura, Yuko; Minabe, Yoshio; Watanabe, Kastumi; Aihara, Kazuyuki

2013-01-01

An automatic artifact removal method of magnetoencephalogram (MEG) was presented in this paper. The method proposed is based on independent components analysis (ICA) and support vector machine (SVM). However, different from the previous studies, in this paper we consider two factors which would influence the performance. First, the imbalance factor of independent components (ICs) of MEG is handled by weighted SVM. Second, instead of simply setting a fixed weight to each class, a re-weighting scheme is used for the preservation of useful MEG ICs. Experimental results on manually marked MEG dataset showed that the method proposed could correctly distinguish the artifacts from the MEG ICs. Meanwhile, 99.72% ± 0.67 of MEG ICs were preserved. The classification accuracy was 97.91% ± 1.39. In addition, it was found that this method was not sensitive to individual differences. The cross validation (leave-one-subject-out) results showed an averaged accuracy of 97.41% ± 2.14.

Bayes estimation on parameters of the single-class classifier. [for remotely sensed crop data

NASA Technical Reports Server (NTRS)

Lin, G. C.; Minter, T. C.

1976-01-01

Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.

Recent advances in dendrimer-based nanovectors for tumor-targeted drug and gene delivery

PubMed Central

Kesharwani, Prashant; Iyer, Arun K.

2015-01-01

Advances in the application of nanotechnology in medicine have given rise to multifunctional smart nanocarriers that can be engineered with tunable physicochemical characteristics to deliver one or more therapeutic agent(s) safely and selectively to cancer cells, including intracellular organelle-specific targeting. Dendrimers having properties resembling biomolecules, with well-defined 3D nanopolymeric architectures, are emerging as a highly attractive class of drug and gene delivery vector. The presence of numerous peripheral functional groups on hyperbranched dendrimers affords efficient conjugation of targeting ligands and biomarkers that can recognize and bind to receptors overexpressed on cancer cells for tumor-cell-specific delivery. The present review compiles the recent advances in dendrimer-mediated drug and gene delivery to tumors by passive and active targeting principles with illustrative examples. PMID:25555748

On Finsler spacetimes with a timelike Killing vector field

NASA Astrophysics Data System (ADS)

Caponio, Erasmo; Stancarone, Giuseppe

2018-04-01

We study Finsler spacetimes and Killing vector fields taking care of the fact that the generalised metric tensor associated to the Lorentz–Finsler function L is in general well defined only on a subset of the slit tangent bundle. We then introduce a new class of Finsler spacetimes endowed with a timelike Killing vector field that we call stationary splitting Finsler spacetimes. We characterize when a Finsler spacetime with a timelike Killing vector field is locally a stationary splitting. Finally, we show that the causal structure of a stationary splitting is the same of one of two Finslerian static spacetimes naturally associated to the stationary splitting.

Classification of jet fuel properties by near-infrared spectroscopy using fuzzy rule-building expert systems and support vector machines.

PubMed

Xu, Zhanfeng; Bunker, Christopher E; Harrington, Peter de B

2010-11-01

Monitoring the changes of jet fuel physical properties is important because fuel used in high-performance aircraft must meet rigorous specifications. Near-infrared (NIR) spectroscopy is a fast method to characterize fuels. Because of the complexity of NIR spectral data, chemometric techniques are used to extract relevant information from spectral data to accurately classify physical properties of complex fuel samples. In this work, discrimination of fuel types and classification of flash point, freezing point, boiling point (10%, v/v), boiling point (50%, v/v), and boiling point (90%, v/v) of jet fuels (JP-5, JP-8, Jet A, and Jet A1) were investigated. Each physical property was divided into three classes, low, medium, and high ranges, using two evaluations with different class boundary definitions. The class boundaries function as the threshold to alarm when the fuel properties change. Optimal partial least squares discriminant analysis (oPLS-DA), fuzzy rule-building expert system (FuRES), and support vector machines (SVM) were used to build the calibration models between the NIR spectra and classes of physical property of jet fuels. OPLS-DA, FuRES, and SVM were compared with respect to prediction accuracy. The validation of the calibration model was conducted by applying bootstrap Latin partition (BLP), which gives a measure of precision. Prediction accuracy of 97 ± 2% of the flash point, 94 ± 2% of freezing point, 99 ± 1% of the boiling point (10%, v/v), 98 ± 2% of the boiling point (50%, v/v), and 96 ± 1% of the boiling point (90%, v/v) were obtained by FuRES in one boundaries definition. Both FuRES and SVM obtained statistically better prediction accuracy over those obtained by oPLS-DA. The results indicate that combined with chemometric classifiers NIR spectroscopy could be a fast method to monitor the changes of jet fuel physical properties.

Cellular and molecular mechanisms of HIV-1 integration targeting.

PubMed

Engelman, Alan N; Singh, Parmit K

2018-07-01

Integration is central to HIV-1 replication and helps mold the reservoir of cells that persists in AIDS patients. HIV-1 interacts with specific cellular factors to target integration to interior regions of transcriptionally active genes within gene-dense regions of chromatin. The viral capsid interacts with several proteins that are additionally implicated in virus nuclear import, including cleavage and polyadenylation specificity factor 6, to suppress integration into heterochromatin. The viral integrase protein interacts with transcriptional co-activator lens epithelium-derived growth factor p75 to principally position integration within gene bodies. The integrase additionally senses target DNA distortion and nucleotide sequence to help fine-tune the specific phosphodiester bonds that are cleaved at integration sites. Research into virus-host interactions that underlie HIV-1 integration targeting has aided the development of a novel class of integrase inhibitors and may help to improve the safety of viral-based gene therapy vectors.

Feasibility of use of fatty acid and triacylglycerol profiles for the authentication of commercial labelling in Iberian dry-cured sausages.

PubMed

Horcada, Alberto; Fernández-Cabanás, Víctor M; Polvillo, Oliva; Botella, Baltasar; Cubiles, M Dolores; Pino, Rafael; Narváez-Rivas, Mónica; León-Camacho, Manuel; Acuña, Rafael Rodríguez

2013-12-15

In the present study, fatty acid and triacylglycerol profiles were used to evaluate the possibility of authenticating Iberian dry-cured sausages according to their label specifications. 42 Commercial brand 'chorizo' and 39 commercial brand 'salchichón' sausages from Iberian pigs were purchased. 36 Samples were labelled Bellota and 45 bore the generic Ibérico label. In the market, Bellota is considered to be a better class than the generic Ibérico since products with the Bellota label are manufactured with high quality fat obtained from extensively reared pigs fed on acorns and pasture. Analyses of fatty acids and triacylglycerols were carried out by gas chromatography and a flame ion detector. A CP-SIL 88 column (highly substituted cyanopropyl phase; 50 m × 0.25 mm i.d., 0.2 µm film thickness) (Varian, Palo Alto, USA) was used for fatty acid analysis and a fused silica capillary DB-17HT column (50% phenyl-50% methylpolysiloxane; 30 m × 0.25 mm i.d., 0.15 µm film thickness) was used for triacylglycerols. Twelve fatty acids and 16 triacylglycerols were identified. Various discriminant models (linear quadratic discriminant analyses, logistic regression and support vector machines) were trained to predict the sample class (Bellota or Ibérico). These models included fatty acids and triacylglycerols separately and combined fatty acid and triacylglycerol profiles. The number of correctly classified samples according to discriminant analyses can be considered low (lower than 65%). The greatest discriminant rate was obtained when triacylglycerol profiles were included in the model, whilst using a combination of fatty acid and triacylglycerol profiles did not improve the rate of correct assignation. The values that represent the reliability of prediction of the samples according to the label specification were higher for the Ibérico class than for the Bellota class. In fact, quadratic and Support Vector Machine discriminate analyses were not able to assign the Bellota class (0%) when combined fatty acids and triacylglycerols were included in the model. The use of fatty acid and triacylglycerol profiles to discriminate Iberian dry-cured sausages in the market according to their labelling information is unclear. In order to ensure the genuineness of Iberian dry-cured sausages in the market, identification of fatty acid and triacylglycerol profiles should be combined with the application of quality standard traceability techniques. © 2013 Published by Elsevier B.V.

A Gold Standards Approach to Training Instructors to Evaluate Crew Performance

NASA Technical Reports Server (NTRS)

Baker, David P.; Dismukes, R. Key

2003-01-01

The Advanced Qualification Program requires that airlines evaluate crew performance in Line Oriented Simulation. For this evaluation to be meaningful, instructors must observe relevant crew behaviors and evaluate those behaviors consistently and accurately against standards established by the airline. The airline industry has largely settled on an approach in which instructors evaluate crew performance on a series of event sets, using standardized grade sheets on which behaviors specific to event set are listed. Typically, new instructors are given a class in which they learn to use the grade sheets and practice evaluating crew performance observed on videotapes. These classes emphasize reliability, providing detailed instruction and practice in scoring so that all instructors within a given class will give similar scores to similar performance. This approach has value but also has important limitations; (1) ratings within one class of new instructors may differ from those of other classes; (2) ratings may not be driven primarily by the specific behaviors on which the company wanted the crews to be scored; and (3) ratings may not be calibrated to company standards for level of performance skill required. In this paper we provide a method to extend the existing method of training instructors to address these three limitations. We call this method the "gold standards" approach because it uses ratings from the company's most experienced instructors as the basis for training rater accuracy. This approach ties the training to the specific behaviors on which the experienced instructors based their ratings.

78 FR 65556 - Establishment of Class E Airspace; Cut Bank, MT

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-01

...-0532; Airspace Docket No. 13-ANM-21] Establishment of Class E Airspace; Cut Bank, MT AGENCY: Federal... at the Cut Bank VHF Omni-Directional Radio Range Tactical Air Navigational Aid (VORTAC) navigation aid, Cut Bank, MT, to facilitate vectoring of Instrument Flight Rules (IFR) aircraft under control of...

78 FR 78299 - Proposed Establishment of Class E Airspace; Truth or Consequences, NM

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-26

...-0995; Airspace Docket No. 13-ASW-30] Proposed Establishment of Class E Airspace; Truth or Consequences... Truth or Consequences VHF Omni-Directional Radio Range Tactical Air Navigation Aid (VORTAC), Truth or Consequences, NM, to facilitate vectoring of Instrument Flight Rules (IFR) aircraft under control of...

78 FR 65555 - Establishment of Class E Airspace; Salmon, ID

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-01

...-0531; Airspace Docket No. 13-ANM-20] Establishment of Class E Airspace; Salmon, ID AGENCY: Federal... at the Salmon VHF Omni-Directional Radio Range/Distance Measuring Equipment (VOR/DME) navigation aid, Salmon, ID, to facilitate vectoring of Instrument Flight Rules (IFR) aircraft under control of Salt Lake...

"Analytical" vector-functions I

NASA Astrophysics Data System (ADS)

Todorov, Vladimir Todorov

2017-12-01

In this note we try to give a new (or different) approach to the investigation of analytical vector functions. More precisely a notion of a power xn; n ∈ ℕ+ of a vector x ∈ ℝ3 is introduced which allows to define an "analytical" function f : ℝ3 → ℝ3. Let furthermore f (ξ )= ∑n =0 ∞ anξn be an analytical function of the real variable ξ. Here we replace the power ξn of the number ξ with the power of a vector x ∈ ℝ3 to obtain a vector "power series" f (x )= ∑n =0 ∞ anxn . We research some properties of the vector series as well as some applications of this idea. Note that an "analytical" vector function does not depend of any basis, which may be used in research into some problems in physics.

Revisiting the Procedures for the Vector Data Quality Assurance in Practice

NASA Astrophysics Data System (ADS)

Erdoğan, M.; Torun, A.; Boyacı, D.

2012-07-01

Immense use of topographical data in spatial data visualization, business GIS (Geographic Information Systems) solutions and applications, mobile and location-based services forced the topo-data providers to create standard, up-to-date and complete data sets in a sustainable frame. Data quality has been studied and researched for more than two decades. There have been un-countable numbers of references on its semantics, its conceptual logical and representations and many applications on spatial databases and GIS. However, there is a gap between research and practice in the sense of spatial data quality which increases the costs and decreases the efficiency of data production. Spatial data quality is well-known by academia and industry but usually in different context. The research on spatial data quality stated several issues having practical use such as descriptive information, metadata, fulfillment of spatial relationships among data, integrity measures, geometric constraints etc. The industry and data producers realize them in three stages; pre-, co- and post data capturing. The pre-data capturing stage covers semantic modelling, data definition, cataloguing, modelling, data dictionary and schema creation processes. The co-data capturing stage covers general rules of spatial relationships, data and model specific rules such as topologic and model building relationships, geometric threshold, data extraction guidelines, object-object, object-belonging class, object-non-belonging class, class-class relationships to be taken into account during data capturing. And post-data capturing stage covers specified QC (quality check) benchmarks and checking compliance to general and specific rules. The vector data quality criteria are different from the views of producers and users. But these criteria are generally driven by the needs, expectations and feedbacks of the users. This paper presents a practical method which closes the gap between theory and practice. Development of spatial data quality concepts into developments and application requires existence of conceptual, logical and most importantly physical existence of data model, rules and knowledge of realization in a form of geo-spatial data. The applicable metrics and thresholds are determined on this concrete base. This study discusses application of geo-spatial data quality issues and QA (quality assurance) and QC procedures in the topographic data production. Firstly we introduce MGCP (Multinational Geospatial Co-production Program) data profile of NATO (North Atlantic Treaty Organization) DFDD (DGIWG Feature Data Dictionary), the requirements of data owner, the view of data producers for both data capturing and QC and finally QA to fulfil user needs. Then, our practical and new approach which divides the quality into three phases is introduced. Finally, implementation of our approach to accomplish metrics, measures and thresholds of quality definitions is discussed. In this paper, especially geometry and semantics quality and quality control procedures that can be performed by the producers are discussed. Some applicable best-practices that we experienced on techniques of quality control, defining regulations that define the objectives and data production procedures are given in the final remarks. These quality control procedures should include the visual checks over the source data, captured vector data and printouts, some automatic checks that can be performed by software and some semi-automatic checks by the interaction with quality control personnel. Finally, these quality control procedures should ensure the geometric, semantic, attribution and metadata quality of vector data.

CONSTRUCTION OF SCALAR AND VECTOR FINITE ELEMENT FAMILIES ON POLYGONAL AND POLYHEDRAL MESHES

PubMed Central

GILLETTE, ANDREW; RAND, ALEXANDER; BAJAJ, CHANDRAJIT

2016-01-01

We combine theoretical results from polytope domain meshing, generalized barycentric coordinates, and finite element exterior calculus to construct scalar- and vector-valued basis functions for conforming finite element methods on generic convex polytope meshes in dimensions 2 and 3. Our construction recovers well-known bases for the lowest order Nédélec, Raviart-Thomas, and Brezzi-Douglas-Marini elements on simplicial meshes and generalizes the notion of Whitney forms to non-simplicial convex polygons and polyhedra. We show that our basis functions lie in the correct function space with regards to global continuity and that they reproduce the requisite polynomial differential forms described by finite element exterior calculus. We present a method to count the number of basis functions required to ensure these two key properties. PMID:28077939

CONSTRUCTION OF SCALAR AND VECTOR FINITE ELEMENT FAMILIES ON POLYGONAL AND POLYHEDRAL MESHES.

PubMed

Gillette, Andrew; Rand, Alexander; Bajaj, Chandrajit

2016-10-01

We combine theoretical results from polytope domain meshing, generalized barycentric coordinates, and finite element exterior calculus to construct scalar- and vector-valued basis functions for conforming finite element methods on generic convex polytope meshes in dimensions 2 and 3. Our construction recovers well-known bases for the lowest order Nédélec, Raviart-Thomas, and Brezzi-Douglas-Marini elements on simplicial meshes and generalizes the notion of Whitney forms to non-simplicial convex polygons and polyhedra. We show that our basis functions lie in the correct function space with regards to global continuity and that they reproduce the requisite polynomial differential forms described by finite element exterior calculus. We present a method to count the number of basis functions required to ensure these two key properties.

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

PubMed

Ugorcáková, J; Bukovská, G; Timko, J

2000-01-01

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

Manifolds for pose tracking from monocular video

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

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.

The magnetic evolution of AR 6555 which lead to two impulsive, readily compact, X-type flares

NASA Technical Reports Server (NTRS)

Ambastha, A.; Fontenla, J. M.; Kalman, B.; Csepura, GY.

1995-01-01

We study the evolution of the vector magnetic field and the sunspot motions observed in AR 6555 during 23-26 Mar. 1991. This region displays two locations of large magnetic shear that were also sites of flare activity. The first location produced two large (X-class) flares during the period covered by our observations. The second location had larger magnetic shear than the first, but produced only small (M- and C-class) flares during our observations. We study the evolution of the photospheric magnetic field in relation to the large flares in the first location. These flares occurred around the same included polarity, and have very similar characteristics (soft X-ray light curves, energies, etc.). However, the whole active region has changed substantially in the period between them. We found several characteristics of the region that appear related to the occurrence of these flares. (1) The flares occurred near regions of large magnetic 'shear,' but not at the locations of maximum shear or maximum field. (2) Potential field extrapolations of the observed field suggest that the topology changed, prior to the first of the two flares, in such a way that a null appeared in the coarse magnetic field. (3) This null was located close to both X-class flares, and remained in that location for a few days while the two flares were observed. (4) The flaring region has a pattern of vector field and sunspot motions in which material is 'squeezed' along the polarity inversion line. This pattern is very different from that usually associated with shearing arcades, but it is similar to that suggested previously by Fontenla and Davis. The vertical electric currents, inferred from the transverse field, are consistent with this pattern. (5) A major reconfiguration of the longitudinal field and the vertical electric currents occurred just prior to the first of the two flares. Both changes imply substantial variations of the magnetic structure of the region. On the basis of the available data we suggest that these changes made the flaring possible, and we develop a scenario that can explain the origin of the magnetic free energy that was released in these flares.

The Magnetic Evolution of AR 6555 which led to Two Impulsive, Relatively Compact, X-Type Flares

NASA Technical Reports Server (NTRS)

Fontenla, J. M.; Ambastha, A.; Kalman, B.; Csepura, Gy.

1995-01-01

We study the evolution of the vector magnetic field and the sunspot motions observed in AR 6555 during 1991 March 23-26. This region displays two locations of large magnetic shear that were also sites of flare activity. The first location produced two large (X-class) flares during the period covered by our observations. The second location had larger magnetic shear than the first but produced only small (M- and C-class) flares during our observations. We study the evolution of the photospheric magnetic field in relation to the large flares in the first location. These flares occurred around the same included polarity and have very similar characteristics (soft X-ray light curves, energies, etc,). However, the whole active region has changed substantially in the period between them. We found several characteristics of the region that appear related to the occurrence of these flares: (1) The flares occurred near regions of large magnetic 'shear' but not at the locations of maximum shear or maximum field. (2) Potential field extrapolations of the observed field suggest that the topology changed, prior to the first of the two flares, in such a way that a null appeared in the coarse magnetic field. (3) This null was located close to both X-class flares and remained in that location for a few days while the two flares were observed. (4) The flaring region has a pattern of vector field and sunspot motions in which material is 'squeezed' along the polarity inversion line. This pattern is very different from that usually associated with shearing arcades, but it is similar to that suggested previously by Fontenia and Davis. The vertical electric currents, inferred from the transverse field, are consistent with this pattern. (5) A major reconfiguration of the longitudinal field and the vertical electric currents occurred just prior to the first of the two flares. Both changes imply substantial variations of the magnetic structure of the region. On the basis of the available data we suggest that these changes made the flaring possible, and we develop a scenario that can explain the origin of the magnetic free-energy that was released in these flares.

On synchronisation of a class of complex chaotic systems with complex unknown parameters via integral sliding mode control

NASA Astrophysics Data System (ADS)

Tirandaz, Hamed; Karami-Mollaee, Ali

2018-06-01

Chaotic systems demonstrate complex behaviour in their state variables and their parameters, which generate some challenges and consequences. This paper presents a new synchronisation scheme based on integral sliding mode control (ISMC) method on a class of complex chaotic systems with complex unknown parameters. Synchronisation between corresponding states of a class of complex chaotic systems and also convergence of the errors of the system parameters to zero point are studied. The designed feedback control vector and complex unknown parameter vector are analytically achieved based on the Lyapunov stability theory. Moreover, the effectiveness of the proposed methodology is verified by synchronisation of the Chen complex system and the Lorenz complex systems as the leader and the follower chaotic systems, respectively. In conclusion, some numerical simulations related to the synchronisation methodology is given to illustrate the effectiveness of the theoretical discussions.

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

NASA Astrophysics Data System (ADS)

Kotaru, Appala Raju; Joshi, Ramesh C.

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

7 CFR 810.2203 - Basis of determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... GRAIN United States Standards for Wheat Principles Governing the Application of Standards § 810.2203..., wheat of other classes, contrasting classes, and subclasses is made on the basis of the grain when free...

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

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

Chang, X; Yang, D

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was usedmore » to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.« less

Dopamine receptor antagonists as new mode-of-action insecticide leads for control of Aedes and Culex mosquito vectors.

PubMed

Nuss, Andrew B; Ejendal, Karin F K; Doyle, Trevor B; Meyer, Jason M; Lang, Emma G; Watts, Val J; Hill, Catherine A

2015-03-01

New mode-of-action insecticides are sought to provide continued control of pesticide resistant arthropod vectors of neglected tropical diseases (NTDs). We previously identified antagonists of the AaDOP2 D1-like dopamine receptor (DAR) from the yellow fever mosquito, Aedes aegypti, with toxicity to Ae. aegypti larvae as leads for novel insecticides. To extend DAR-based insecticide discovery, we evaluated the molecular and pharmacological characteristics of an orthologous DAR target, CqDOP2, from Culex quinquefasciatus, the vector of lymphatic filariasis and West Nile virus. CqDOP2 has 94.7% amino acid identity to AaDOP2 and 28.3% identity to the human D1-like DAR, hD1. CqDOP2 and AaDOP2 exhibited similar pharmacological responses to biogenic amines and DAR antagonists in cell-based assays. The antagonists amitriptyline, amperozide, asenapine, chlorpromazine and doxepin were between 35 to 227-fold more selective at inhibiting the response of CqDOP2 and AaDOP2 in comparison to hD1. Antagonists were toxic to both C. quinquefasciatus and Ae. aegypti larvae, with LC50 values ranging from 41 to 208 μM 72 h post-exposure. Orthologous DOP2 receptors identified from the African malaria mosquito, Anopheles gambiae, the sand fly, Phlebotomus papatasi and the tsetse fly, Glossina morsitans, had high sequence similarity to CqDOP2 and AaDOP2. DAR antagonists represent a putative new insecticide class with activity against C. quinquefasciatus and Ae. aegypti, the two most important mosquito vectors of NTDs. There has been limited change in the sequence and pharmacological properties of the DOP2 DARs of these species since divergence of the tribes Culicini and Aedini. We identified antagonists selective for mosquito versus human DARs and observed a correlation between DAR pharmacology and the in vivo larval toxicity of antagonists. These data demonstrate that sequence similarity can be predictive of target potential. On this basis, we propose expanded insecticide discovery around orthologous DOP2 targets from additional dipteran vectors.

Bethe vectors for XXX-spin chain

NASA Astrophysics Data System (ADS)

Burdík, Čestmír; Fuksa, Jan; Isaev, Alexei

2014-11-01

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

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

NASA Astrophysics Data System (ADS)

Ohkubo, Yusuke

2018-05-01

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

StruLocPred: structure-based protein subcellular localisation prediction using multi-class support vector machine.

PubMed

Zhou, Wengang; Dickerson, Julie A

2012-01-01

Knowledge of protein subcellular locations can help decipher a protein's biological function. This work proposes new features: sequence-based: Hybrid Amino Acid Pair (HAAP) and two structure-based: Secondary Structural Element Composition (SSEC) and solvent accessibility state frequency. A multi-class Support Vector Machine is developed to predict the locations. Testing on two established data sets yields better prediction accuracies than the best available systems. Comparisons with existing methods show comparable results to ESLPred2. When StruLocPred is applied to the entire Arabidopsis proteome, over 77% of proteins with known locations match the prediction results. An implementation of this system is at http://wgzhou.ece. iastate.edu/StruLocPred/.

Polarization ellipse and Stokes parameters in geometric algebra.

PubMed

Santos, Adler G; Sugon, Quirino M; McNamara, Daniel J

2012-01-01

In this paper, we use geometric algebra to describe the polarization ellipse and Stokes parameters. We show that a solution to Maxwell's equation is a product of a complex basis vector in Jackson and a linear combination of plane wave functions. We convert both the amplitudes and the wave function arguments from complex scalars to complex vectors. This conversion allows us to separate the electric field vector and the imaginary magnetic field vector, because exponentials of imaginary scalars convert vectors to imaginary vectors and vice versa, while exponentials of imaginary vectors only rotate the vector or imaginary vector they are multiplied to. We convert this expression for polarized light into two other representations: the Cartesian representation and the rotated ellipse representation. We compute the conversion relations among the representation parameters and their corresponding Stokes parameters. And finally, we propose a set of geometric relations between the electric and magnetic fields that satisfy an equation similar to the Poincaré sphere equation.

Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

PubMed

Wu, Dongrui; Lance, Brent J; Parsons, Thomas D

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

Expression mapping using a retroviral vector for CD8+ T cell epitopes: definition of a Mycobacterium tuberculosis peptide presented by H2-Dd.

PubMed

Aoshi, Taiki; Suzuki, Mina; Uchijima, Masato; Nagata, Toshi; Koide, Yukio

2005-03-01

Identification of CD8+ T cell epitopes is important because detection of specific CD8+ T cells after infection or immunization requires prior knowledge of epitope specificity. Furthermore, identification of CD8+ T cell epitopes permits the development of specific preventive and therapeutic approaches to both infections and tumors. Thus far, CD8+ T cell epitopes have been identified either using an overlapping peptide library covering an entire protein, or using algorithms designed to identify likely peptides that bind to major histocompatibility complex (MHC) class I molecules. The synthesis of overlapping peptides can be prohibitively expensive, and the algorithm programs used to predict CD8+ T cell epitopes are not always accurate. Here we describe a retroviral expression system that specifically allows longer polypeptides and shorter peptides to be expressed in the cytoplasm, and thereby to be processed onto class I MHC molecules. T cells from mice that were immunized with a DNA vaccine encoding MPT-51 were probed against MHC-compatible cell lines retrovirally transduced with overlapping gene fragments encoding 120-140 amino acids of the MPT-51 molecule. After further testing of shorter peptide sequences, we identified a CD8+ T cell epitope using cell lines expressing a relatively small number of algorithm-predicted candidate epitopes. We found that one of the requirements for cell surface display of the 20-mer peptide was the need for cotranslational ubiquitination. The restriction molecule was identified as Dd following transduction with MHC class I genes followed by transduction with the oligonucleotide encoding the epitope. The retroviral expression system described here is cost-effective, particularly if the target molecule is large, and could be adapted to identifying T cell epitopes recognized in infectious disease and against tumor cell antigens.

Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

PubMed Central

Wu, Dongrui; Lance, Brent J.; Parsons, Thomas D.

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing. PMID:23437188

Self-focusing therapeutic gene delivery with intelligent gene vector swarms: intra-swarm signalling through receptor transgene expression in targeted cells.

PubMed

Tolmachov, Oleg E

2015-01-01

Gene delivery in vivo that is tightly focused on the intended target cells is essential to maximize the benefits of gene therapy and to reduce unwanted side-effects. Cell surface markers are immediately available for probing by therapeutic gene vectors and are often used to direct gene transfer with these vectors to specific target cell populations. However, it is not unusual for the choice of available extra-cellular markers to be too scarce to provide a reliable definition of the desired therapeutically relevant set of target cells. Therefore, interrogation of intra-cellular determinants of cell-specificity, such as tissue-specific transcription factors, can be vital in order to provide detailed cell-guiding information to gene vector particles. An important improvement in cell-specific gene delivery can be achieved through auto-buildup in vector homing efficiency using intelligent 'self-focusing' of swarms of vector particles on target cells. Vector self-focusing was previously suggested to rely on the release of diffusible chemo-attractants after a successful target-specific hit by 'scout' vector particles. I hypothesize that intelligent self-focusing behaviour of swarms of cell-targeted therapeutic gene vectors can be accomplished without the employment of difficult-to-use diffusible chemo-attractants, instead relying on the intra-swarm signalling through cells expressing a non-diffusible extra-cellular receptor for the gene vectors. In the proposed model, cell-guiding information is gathered by the 'scout' gene vector particles, which: (1) attach to a variety of cells via a weakly binding (low affinity) receptor; (2) successfully facilitate gene transfer into these cells; (3) query intra-cellular determinants of cell-specificity with their transgene expression control elements and (4) direct the cell-specific biosynthesis of a vector-encoded strongly binding (high affinity) cell-surface receptor. Free members of the vector swarm loaded with therapeutic cargo are then attracted to and internalized into the intended target cells via the expressed cognate strongly binding extra-cellular receptor, causing escalation of gene transfer into these cells and increasing the copy number of the therapeutic gene expression modules. Such self-focusing swarms of gene vectors can be either homogeneous, with 'scout' and 'therapeutic' members of the swarm being structurally identical, or, alternatively, heterogeneous (split), with 'scout' and 'therapeutic' members of the swarm being structurally specialized. It is hoped that the proposed self-focusing cell-targeted gene vector swarms with receptor-mediated intra-swarm signalling could be particularly effective in 'top-up' gene delivery scenarios, achieving high-level and sustained expression of therapeutic transgenes that are prone to shut-down through degradation and silencing. Crucially, in contrast to low-precision 'general location' vector guidance by diffusible chemo-attractants, ear-marking non-diffusible receptors can provide high-accuracy targeting of therapeutic vector particles to the specific cell, which has undergone a 'successful cell-specific hit' by a 'scout' vector particle. Opportunities for cell targeting could be expanded, since in the proposed model of self-focusing it could be possible to probe a broad selection of intra-cellular determinants of cell-specificity and not just to rely exclusively on extra-cellular markers of cell-specificity. By employing such self-focusing gene vectors for the improvement of cell-targeted delivery of therapeutic genes, e.g., in cancer therapy or gene addition therapy of recessive genetic diseases, it could be possible to broaden a leeway for the reduction of the vector load and, consequently, to minimize undesired vector cytotoxicity, immune reactions, and the risk of inadvertent genetic modification of germline cells in genetic treatment in vivo. Copyright © 2014 Elsevier B.V. All rights reserved.

Culicoides Species Communities Associated with Wild Ruminant Ecosystems in Spain: Tracking the Way to Determine Potential Bridge Vectors for Arboviruses.

PubMed

Talavera, Sandra; Muñoz-Muñoz, Francesc; Durán, Mauricio; Verdún, Marta; Soler-Membrives, Anna; Oleaga, Álvaro; Arenas, Antonio; Ruiz-Fons, Francisco; Estrada, Rosa; Pagès, Nitu

2015-01-01

The genus Culicoides Latreille 1809 is a well-known vector for protozoa, filarial worms and, above all, numerous viruses. The Bluetongue virus (BTV) and the recently emerged Schmallenberg virus (SBV) are responsible for important infectious, non-contagious, insect-borne viral diseases found in domestic ruminants and transmitted by Culicoides spp. Both of these diseases have been detected in wild ruminants, but their role as reservoirs during the vector-free season still remains relatively unknown. In fact, we tend to ignore the possibility of wild ruminants acting as a source of disease (BTV, SBV) and permitting its reintroduction to domestic ruminants during the following vector season. In this context, a knowledge of the composition of the Culicoides species communities that inhabit areas where there are wild ruminants is of major importance as the presence of a vector species is a prerequisite for disease transmission. In this study, samplings were conducted in areas inhabited by different wild ruminant species; samples were taken in both 2009 and 2010, on a monthly basis, during the peak season for midge activity (in summer and autumn). A total of 102,693 specimens of 40 different species of the genus Culicoides were trapped; these included major BTV and SBV vector species. The most abundant vector species were C. imicola and species of the Obsoletus group, which represented 15% and 11% of total numbers of specimens, respectively. At the local scale, the presence of major BTV and SBV vector species in areas with wild ruminants coincided with that of the nearest sentinel farms included in the Spanish Bluetongue Entomological Surveillance Programme, although their relative abundance varied. The data suggest that such species do not exhibit strong host specificity towards either domestic or wild ruminants and that they could consequently play a prominent role as bridge vectors for different pathogens between both types of ruminants. This finding would support the hypothesis that wild ruminants could act as reservoirs for such pathogens, and subsequently be involved in the reintroduction of disease to livestock on neighbouring farms.

Recent Advances in Non-viral Vectors for Gene Delivery

PubMed Central

Guo, Xia; Huang, Leaf

2011-01-01

CONSPECTUS Non-viral vectors, typically based on cationic lipids or polymers, are preferred due to safety concerns with viral vectors. So far, non-viral vectors can proficiently transfect cells in culture, but obtaining efficient nanomedicines is far from evident. To overcome the hurdles associated with non-viral vectors is significant for improving delivery efficiency and therapeutic effect of nucleic acid. The drawbacks include the strong interaction of cationic delivery vehicles with blood components, uptake by the reticuloendothelial system (RES), toxicity, targeting ability of the carriers to the cells of interest, and so on. PEGylation is the predominant method used to reduce the binding of plasma proteins with non-viral vectors and minimize the clearance by RES after intravenous administration. The nanoparticles that are not rapidly cleared from the circulation accumulate in the tumors due to the enhanced permeability and retention effect, and the targeting ligands attached to the distal end of the PEGylated components allow binding to the receptors on the target cell surface. Neutral or anionic liposomes have been also developed for systemic delivery of nucleic acids in experimental animal model. Designing and synthesizing novel cationic lipids and polymers, and binding nucleic acid with peptides, targeting ligands, polymers, or environmentally sensitive moieties also attract many attentions for resolving the problems encountered by non-viral vectors. The application of inorganic nanoparticles in nucleic acid delivery is an emerging field, too. Recently, different classes of non-viral vectors appear to be converging and the features of different classes of non-viral vectors could be combined in one strategy. More hurdles associated with efficient nucleic acid delivery therefore might be expected to be overcome. In this account, we will focus on these novel non-viral vectors, which are classified into multifunctional hybrid nucleic acid vectors, novel membrane/core nanoparticles for nucleic acid delivery and ultrasound-responsive nucleic acid vectors. The systemic delivery studies are highlighted. Finally, we bring forward the prospect for nucleic acid delivery. We think a better understandings of the fate of the nanoparticles inside the cell and of the interactions between the parts of hybrid particles will lead to a delivery system suitable for clinical use. We also underscore the value of sustained release of nucleic acid and presume making vectors targeted to cells with sustained release in vivo should be an interesting research challenge. PMID:21870813

Phylogenetic and experimental characterization of an acyl-ACP thioesterase family reveals significant diversity in enzymatic specificity and activity.

PubMed

Jing, Fuyuan; Cantu, David C; Tvaruzkova, Jarmila; Chipman, Jay P; Nikolau, Basil J; Yandeau-Nelson, Marna D; Reilly, Peter J

2011-08-10

Acyl-acyl carrier protein thioesterases (acyl-ACP TEs) catalyze the hydrolysis of the thioester bond that links the acyl chain to the sulfhydryl group of the phosphopantetheine prosthetic group of ACP. This reaction terminates acyl chain elongation of fatty acid biosynthesis, and in plant seeds it is the biochemical determinant of the fatty acid compositions of storage lipids. To explore acyl-ACP TE diversity and to identify novel acyl ACP-TEs, 31 acyl-ACP TEs from wide-ranging phylogenetic sources were characterized to ascertain their in vivo activities and substrate specificities. These acyl-ACP TEs were chosen by two different approaches: 1) 24 TEs were selected from public databases on the basis of phylogenetic analysis and fatty acid profile knowledge of their source organisms; and 2) seven TEs were molecularly cloned from oil palm (Elaeis guineensis), coconut (Cocos nucifera) and Cuphea viscosissima, organisms that produce medium-chain and short-chain fatty acids in their seeds. The in vivo substrate specificities of the acyl-ACP TEs were determined in E. coli. Based on their specificities, these enzymes were clustered into three classes: 1) Class I acyl-ACP TEs act primarily on 14- and 16-carbon acyl-ACP substrates; 2) Class II acyl-ACP TEs have broad substrate specificities, with major activities toward 8- and 14-carbon acyl-ACP substrates; and 3) Class III acyl-ACP TEs act predominantly on 8-carbon acyl-ACPs. Several novel acyl-ACP TEs act on short-chain and unsaturated acyl-ACP or 3-ketoacyl-ACP substrates, indicating the diversity of enzymatic specificity in this enzyme family. These acyl-ACP TEs can potentially be used to diversify the fatty acid biosynthesis pathway to produce novel fatty acids.

Phylogenetic and experimental characterization of an acyl-ACP thioesterase family reveals significant diversity in enzymatic specificity and activity

PubMed Central

2011-01-01

Background Acyl-acyl carrier protein thioesterases (acyl-ACP TEs) catalyze the hydrolysis of the thioester bond that links the acyl chain to the sulfhydryl group of the phosphopantetheine prosthetic group of ACP. This reaction terminates acyl chain elongation of fatty acid biosynthesis, and in plant seeds it is the biochemical determinant of the fatty acid compositions of storage lipids. Results To explore acyl-ACP TE diversity and to identify novel acyl ACP-TEs, 31 acyl-ACP TEs from wide-ranging phylogenetic sources were characterized to ascertain their in vivo activities and substrate specificities. These acyl-ACP TEs were chosen by two different approaches: 1) 24 TEs were selected from public databases on the basis of phylogenetic analysis and fatty acid profile knowledge of their source organisms; and 2) seven TEs were molecularly cloned from oil palm (Elaeis guineensis), coconut (Cocos nucifera) and Cuphea viscosissima, organisms that produce medium-chain and short-chain fatty acids in their seeds. The in vivo substrate specificities of the acyl-ACP TEs were determined in E. coli. Based on their specificities, these enzymes were clustered into three classes: 1) Class I acyl-ACP TEs act primarily on 14- and 16-carbon acyl-ACP substrates; 2) Class II acyl-ACP TEs have broad substrate specificities, with major activities toward 8- and 14-carbon acyl-ACP substrates; and 3) Class III acyl-ACP TEs act predominantly on 8-carbon acyl-ACPs. Several novel acyl-ACP TEs act on short-chain and unsaturated acyl-ACP or 3-ketoacyl-ACP substrates, indicating the diversity of enzymatic specificity in this enzyme family. Conclusion These acyl-ACP TEs can potentially be used to diversify the fatty acid biosynthesis pathway to produce novel fatty acids. PMID:21831316

Human action classification using procrustes shape theory

NASA Astrophysics Data System (ADS)

Cho, Wanhyun; Kim, Sangkyoon; Park, Soonyoung; Lee, Myungeun

2015-02-01

In this paper, we propose new method that can classify a human action using Procrustes shape theory. First, we extract a pre-shape configuration vector of landmarks from each frame of an image sequence representing an arbitrary human action, and then we have derived the Procrustes fit vector for pre-shape configuration vector. Second, we extract a set of pre-shape vectors from tanning sample stored at database, and we compute a Procrustes mean shape vector for these preshape vectors. Third, we extract a sequence of the pre-shape vectors from input video, and we project this sequence of pre-shape vectors on the tangent space with respect to the pole taking as a sequence of mean shape vectors corresponding with a target video. And we calculate the Procrustes distance between two sequences of the projection pre-shape vectors on the tangent space and the mean shape vectors. Finally, we classify the input video into the human action class with minimum Procrustes distance. We assess a performance of the proposed method using one public dataset, namely Weizmann human action dataset. Experimental results reveal that the proposed method performs very good on this dataset.

[Sendai virus vector: vector development and its application to health care and biotechnology].

PubMed

Iida, Akihiro

2007-06-01

Sendai virus (SeV) is an enveloped virus with a nonsegmented negative-strand RNA genome and a member of the paramyxovirus family. We have developed SeV vector which has shown a high efficiently of gene transfer and expression of foreign genes to a wide range of dividing and non-dividing mammalian cells and tissues. One of the characteristics of the vector is that the genome is located exclusively in the cytoplasm of infected cells and does not go through a DNA phase; thus there is no concern about unwanted integration of foreign sequences into chromosomal DNA. Therefore, this new class of "cytoplasmic RNA vector", an RNA vector with cytoplasmic expression, is expected to be a safer and more efficient viral vector than existing vectors for application to human therapy in various fields including gene therapy and vaccination. In this review, I describe development of Sendai virus vector, its application in the field of biotechnology and clinical application aiming to treat for a large number of diseases including cancer, cardiovascular disease, infectious diseases and neurologic disorders.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary support vector machines. Building from the success of statistical EVT based recognition methods such as PI-SVM and W-SVM on the open set problem, we present a new general supervised learning algorithm for multi-class classification and multi-class open set recognition called the Extreme Value Local Basis (EVLB). The design of this algorithm is motivated by the observation that extrema from known negative class distributions are the closest negative points to any positive sample during training, and thus should be used to define the parameters of a probabilistic decision model. In the EVLB, the kernel distribution for each positive training sample is estimated via an EVT distribution fit over the distances to the separating hyperplane between positive training sample and closest negative samples, with a subset of the overall positive training data retained to form a probabilistic decision boundary. Using this subset as a frame of reference, the probability of a sample at test time decreases as it moves away from the positive class. Possessing this property, the EVLB is well-suited to open set recognition problems where samples from unknown or novel classes are encountered at test. Our experimental evaluation shows that the EVLB provides a substantial improvement in scalability compared to standard radial basis function kernel machines, as well as P I-SVM and W-SVM, with improved accuracy in many cases. We evaluate our algorithm on open set variations of the standard visual learning benchmarks, as well as with an open subset of classes from Caltech 256 and ImageNet. Our experiments show that PI-SVM, WSVM and EVLB provide significant advances over the previous state-of-the-art solutions for the same tasks.

New QCD sum rules based on canonical commutation relations

NASA Astrophysics Data System (ADS)

Hayata, Tomoya

2012-04-01

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

Construction and Evaluation of Novel Rhesus Monkey Adenovirus Vaccine Vectors

PubMed Central

Abbink, Peter; Maxfield, Lori F.; Ng'ang'a, David; Borducchi, Erica N.; Iampietro, M. Justin; Bricault, Christine A.; Teigler, Jeffrey E.; Blackmore, Stephen; Parenteau, Lily; Wagh, Kshitij; Handley, Scott A.; Zhao, Guoyan; Virgin, Herbert W.; Korber, Bette

2014-01-01

ABSTRACT Adenovirus vectors are widely used as vaccine candidates for a variety of pathogens, including HIV-1. To date, human and chimpanzee adenoviruses have been explored in detail as vaccine vectors. The phylogeny of human and chimpanzee adenoviruses is overlapping, and preexisting humoral and cellular immunity to both are exhibited in human populations worldwide. More distantly related adenoviruses may therefore offer advantages as vaccine vectors. Here we describe the primary isolation and vectorization of three novel adenoviruses from rhesus monkeys. The seroprevalence of these novel rhesus monkey adenovirus vectors was extremely low in sub-Saharan Africa human populations, and these vectors proved to have immunogenicity comparable to that of human and chimpanzee adenovirus vaccine vectors in mice. These rhesus monkey adenoviruses phylogenetically clustered with the poorly described adenovirus species G and robustly stimulated innate immune responses. These novel adenoviruses represent a new class of candidate vaccine vectors. IMPORTANCE Although there have been substantial efforts in the development of vaccine vectors from human and chimpanzee adenoviruses, far less is known about rhesus monkey adenoviruses. In this report, we describe the isolation and vectorization of three novel rhesus monkey adenoviruses. These vectors exhibit virologic and immunologic characteristics that make them attractive as potential candidate vaccine vectors for both HIV-1 and other pathogens. PMID:25410856

Construction and evaluation of novel rhesus monkey adenovirus vaccine vectors.

PubMed

Abbink, Peter; Maxfield, Lori F; Ng'ang'a, David; Borducchi, Erica N; Iampietro, M Justin; Bricault, Christine A; Teigler, Jeffrey E; Blackmore, Stephen; Parenteau, Lily; Wagh, Kshitij; Handley, Scott A; Zhao, Guoyan; Virgin, Herbert W; Korber, Bette; Barouch, Dan H

2015-02-01

Adenovirus vectors are widely used as vaccine candidates for a variety of pathogens, including HIV-1. To date, human and chimpanzee adenoviruses have been explored in detail as vaccine vectors. The phylogeny of human and chimpanzee adenoviruses is overlapping, and preexisting humoral and cellular immunity to both are exhibited in human populations worldwide. More distantly related adenoviruses may therefore offer advantages as vaccine vectors. Here we describe the primary isolation and vectorization of three novel adenoviruses from rhesus monkeys. The seroprevalence of these novel rhesus monkey adenovirus vectors was extremely low in sub-Saharan Africa human populations, and these vectors proved to have immunogenicity comparable to that of human and chimpanzee adenovirus vaccine vectors in mice. These rhesus monkey adenoviruses phylogenetically clustered with the poorly described adenovirus species G and robustly stimulated innate immune responses. These novel adenoviruses represent a new class of candidate vaccine vectors. Although there have been substantial efforts in the development of vaccine vectors from human and chimpanzee adenoviruses, far less is known about rhesus monkey adenoviruses. In this report, we describe the isolation and vectorization of three novel rhesus monkey adenoviruses. These vectors exhibit virologic and immunologic characteristics that make them attractive as potential candidate vaccine vectors for both HIV-1 and other pathogens. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Distinct Conformations of Ly49 Natural Killer Cell Receptors Mediate MHC Class I Recognition in Trans and Cis

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

Back, J.; Malchiodi, E; Cho, S

2009-01-01

Certain cell-surface receptors engage ligands expressed on juxtaposed cells and ligands on the same cell. The structural basis for trans versus cis binding is not known. Here, we showed that Ly49 natural killer (NK) cell receptors bound two MHC class I (MHC-I) molecules in trans when the two ligand-binding domains were backfolded onto the long stalk region. In contrast, dissociation of the ligand-binding domains from the stalk and their reorientation relative to the NK cell membrane allowed monovalent binding of MHC-I in cis. The distinct conformations (backfolded and extended) define the structural basis for cis-trans binding by Ly49 receptors andmore » explain the divergent functional consequences of cis versus trans interactions. Further analyses identified specific stalk segments that were not required for MHC-I binding in trans but were essential for inhibitory receptor function. These data identify multiple distinct roles of stalk regions for receptor function.« less

Controllability in nonlinear systems

NASA Technical Reports Server (NTRS)

Hirschorn, R. M.

1975-01-01

An explicit expression for the reachable set is obtained for a class of nonlinear systems. This class is described by a chain condition on the Lie algebra of vector fields associated with each nonlinear system. These ideas are used to obtain a generalization of a controllability result for linear systems in the case where multiplicative controls are present.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

NASA Astrophysics Data System (ADS)

Mustapha, S.; Braytee, A.; Ye, L.

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

Automated thematic mapping and change detection of ERTS-A images. [farmlands, cities, and mountain identification in Utah, Washington, Arizona, and California

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1974-01-01

The author has identified the following significant results. A diffraction pattern analysis of MSS images led to the development of spatial signatures for farm land, urban areas and mountains. Four spatial features are employed to describe the spatial characteristics of image cells in the digital data. Three spectral features are combined with the spatial features to form a seven dimensional vector describing each cell. Then, the classification of the feature vectors is accomplished by using the maximum likelihood criterion. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month, but vary substantially between seasons. Three ERTS-1 images from the Phoenix, Arizona area were processed, and recognition rates between 85% and 100% were obtained for the terrain classes of desert, farms, mountains, and urban areas. To eliminate the need for training data, a new clustering algorithm has been developed. Seven ERTS-1 images from four test sites have been processed through the clustering algorithm, and high recognition rates have been achieved for all terrain classes.

Method of Menu Selection by Gaze Movement Using AC EOG Signals

NASA Astrophysics Data System (ADS)

Kanoh, Shin'ichiro; Futami, Ryoko; Yoshinobu, Tatsuo; Hoshimiya, Nozomu

A method to detect the direction and the distance of voluntary eye gaze movement from EOG (electrooculogram) signals was proposed and tested. In this method, AC-amplified vertical and horizontal transient EOG signals were classified into 8-class directions and 2-class distances of voluntary eye gaze movements. A horizontal and a vertical EOGs during eye gaze movement at each sampling time were treated as a two-dimensional vector, and the center of gravity of the sample vectors whose norms were more than 80% of the maximum norm was used as a feature vector to be classified. By the classification using the k-nearest neighbor algorithm, it was shown that the averaged correct detection rates on each subject were 98.9%, 98.7%, 94.4%, respectively. This method can avoid strict EOG-based eye tracking which requires DC amplification of very small signal. It would be useful to develop robust human interfacing systems based on menu selection for severely paralyzed patients.

A Genealogy of Convex Solids Via Local and Global Bifurcations of Gradient Vector Fields

NASA Astrophysics Data System (ADS)

Domokos, Gábor; Holmes, Philip; Lángi, Zsolt

2016-12-01

Three-dimensional convex bodies can be classified in terms of the number and stability types of critical points on which they can balance at rest on a horizontal plane. For typical bodies, these are non-degenerate maxima, minima, and saddle points, the numbers of which provide a primary classification. Secondary and tertiary classifications use graphs to describe orbits connecting these critical points in the gradient vector field associated with each body. In previous work, it was shown that these classifications are complete in that no class is empty. Here, we construct 1- and 2-parameter families of convex bodies connecting members of adjacent primary and secondary classes and show that transitions between them can be realized by codimension 1 saddle-node and saddle-saddle (heteroclinic) bifurcations in the gradient vector fields. Our results indicate that all combinatorially possible transitions can be realized in physical shape evolution processes, e.g., by abrasion of sedimentary particles.

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

Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires.

PubMed

Cinelli, Mattia; Sun, Yuxin; Best, Katharine; Heather, James M; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-04-01

Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone. The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases. The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund's Adjuvant. The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893 . The Decombinator package is available at github.com/innate2adaptive/Decombinator . The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html . b.chain@ucl.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

Comparing writing style feature-based classification methods for estimating user reputations in social media.

PubMed

Suh, Jong Hwan

2016-01-01

In recent years, the anonymous nature of the Internet has made it difficult to detect manipulated user reputations in social media, as well as to ensure the qualities of users and their posts. To deal with this, this study designs and examines an automatic approach that adopts writing style features to estimate user reputations in social media. Under varying ways of defining Good and Bad classes of user reputations based on the collected data, it evaluates the classification performance of the state-of-art methods: four writing style features, i.e. lexical, syntactic, structural, and content-specific, and eight classification techniques, i.e. four base learners-C4.5, Neural Network (NN), Support Vector Machine (SVM), and Naïve Bayes (NB)-and four Random Subspace (RS) ensemble methods based on the four base learners. When South Korea's Web forum, Daum Agora, was selected as a test bed, the experimental results show that the configuration of the full feature set containing content-specific features and RS-SVM combining RS and SVM gives the best accuracy for classification if the test bed poster reputations are segmented strictly into Good and Bad classes by portfolio approach. Pairwise t tests on accuracy confirm two expectations coming from the literature reviews: first, the feature set adding content-specific features outperform the others; second, ensemble learning methods are more viable than base learners. Moreover, among the four ways on defining the classes of user reputations, i.e. like, dislike, sum, and portfolio, the results show that the portfolio approach gives the highest accuracy.

Structure of adeno-associated virus-2 in complex with neutralizing monoclonal antibody A20

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

McCraw, Dustin M.; O'Donnell, Jason K.; Taylor, Kenneth A.

2012-09-15

The use of adeno-associated virus (AAV) as a gene therapy vector is limited by the host neutralizing immune response. The cryo-electron microscopy (EM) structure at 8.5 A resolution is determined for a complex of AAV-2 with the Fab' fragment of monoclonal antibody (MAb) A20, the most extensively characterized AAV MAb. The binding footprint is determined through fitting the cryo-EM reconstruction with a homology model following sequencing of the variable domain, and provides a structural basis for integrating diverse prior epitope mappings. The footprint extends from the previously implicated plateau to the side of the spike, and into the conserved canyon,more » covering a larger area than anticipated. Comparison with structures of binding and non-binding serotypes indicates that recognition depends on a combination of subtle serotype-specific features. Separation of the neutralizing epitope from the heparan sulfate cell attachment site encourages attempts to develop immune-resistant vectors that can still bind to target cells.« less

Full-reference quality assessment of stereoscopic images by learning binocular receptive field properties.

PubMed

Shao, Feng; Li, Kemeng; Lin, Weisi; Jiang, Gangyi; Yu, Mei; Dai, Qionghai

2015-10-01

Quality assessment of 3D images encounters more challenges than its 2D counterparts. Directly applying 2D image quality metrics is not the solution. In this paper, we propose a new full-reference quality assessment for stereoscopic images by learning binocular receptive field properties to be more in line with human visual perception. To be more specific, in the training phase, we learn a multiscale dictionary from the training database, so that the latent structure of images can be represented as a set of basis vectors. In the quality estimation phase, we compute sparse feature similarity index based on the estimated sparse coefficient vectors by considering their phase difference and amplitude difference, and compute global luminance similarity index by considering luminance changes. The final quality score is obtained by incorporating binocular combination based on sparse energy and sparse complexity. Experimental results on five public 3D image quality assessment databases demonstrate that in comparison with the most related existing methods, the devised algorithm achieves high consistency with subjective assessment.

Assessing the use of multiple sources in student essays.

PubMed

Hastings, Peter; Hughes, Simon; Magliano, Joseph P; Goldman, Susan R; Lawless, Kimberly

2012-09-01

The present study explored different approaches for automatically scoring student essays that were written on the basis of multiple texts. Specifically, these approaches were developed to classify whether or not important elements of the texts were present in the essays. The first was a simple pattern-matching approach called "multi-word" that allowed for flexible matching of words and phrases in the sentences. The second technique was latent semantic analysis (LSA), which was used to compare student sentences to original source sentences using its high-dimensional vector-based representation. Finally, the third was a machine-learning technique, support vector machines, which learned a classification scheme from the corpus. The results of the study suggested that the LSA-based system was superior for detecting the presence of explicit content from the texts, but the multi-word pattern-matching approach was better for detecting inferences outside or across texts. These results suggest that the best approach for analyzing essays of this nature should draw upon multiple natural language processing approaches.

Breast Cancer Detection with Reduced Feature Set.

PubMed

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

2015-01-01

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

Position Extrema in Keplerian Relative Motion: A Gröbner Basis Approach

NASA Astrophysics Data System (ADS)

Allgeier, Shawn E.; Fitz-Coy, Norman G.; Erwin, R. Scott

2012-12-01

This paper analyzes the relative motion between two spacecraft in orbit. Specifically, the paper provides bounds for relative spacecraft position-based measures which impact spacecraft formation-flight mission design and analysis. Previous efforts have provided bounds for the separation distance between two spacecraft. This paper presents a methodology for bounding the local vertical, horizontal, and cross track components of the relative position vector in a spacecraft centered, rotating reference frame. Three metrics are derived and a methodology for bounding them is presented. The solution of the extremal equations for the metrics is formulated as an affine variety and obtained using a Gröbner basis reduction. No approximations are utilized and the only assumption is that the two spacecraft are in bound Keplerian orbits. Numerical examples are included to demonstrate the efficacy of the method. The metrics have utility to the mission designer of formation flight architectures, with relevance to Earth observation constellations.

Some fundamentals regarding kinematics and generalized forces for multibody dynamics

NASA Technical Reports Server (NTRS)

Hodges, Dewey H.

1990-01-01

In order to illustrate the various forms in which generalized forces can arise from diverse subsystem analyses in multibody dynamics, intrinsic dynamical equations for the rotational dynamics of a rigid body are derived from Hamilton's principle. Two types of generalized forces are derived: (1) those associated with the virtual rotation vector in some orthogonal basis, and (2) those associated with varying generalized coordinates. As one physical or kinematical result (such as a frequency or a specific direction cosine) cannot rely on this selection, a 'blind' coupling of two models in which generalized forces are calculated in different ways would be wrong. Both types should use the same rotational coordinates and should denote the virtual rotation on a similar basis according to method 1, or in terms of common rotational coordinates and their diversifications as in method 2. Alternatively, the generalized forces and coordinates of one model may be transformed to those of the other.

Molecular basis for allosteric specificity regulation in class Ia ribonucleotide reductase from Escherichia coli

PubMed Central

Zimanyi, Christina M; Chen, Percival Yang-Ting; Kang, Gyunghoon; Funk, Michael A; Drennan, Catherine L

2016-01-01

Ribonucleotide reductase (RNR) converts ribonucleotides to deoxyribonucleotides, a reaction that is essential for DNA biosynthesis and repair. This enzyme is responsible for reducing all four ribonucleotide substrates, with specificity regulated by the binding of an effector to a distal allosteric site. In all characterized RNRs, the binding of effector dATP alters the active site to select for pyrimidines over purines, whereas effectors dGTP and TTP select for substrates ADP and GDP, respectively. Here, we have determined structures of Escherichia coli class Ia RNR with all four substrate/specificity effector-pairs bound (CDP/dATP, UDP/dATP, ADP/dGTP, GDP/TTP) that reveal the conformational rearrangements responsible for this remarkable allostery. These structures delineate how RNR ‘reads’ the base of each effector and communicates substrate preference to the active site by forming differential hydrogen bonds, thereby maintaining the proper balance of deoxynucleotides in the cell. DOI: http://dx.doi.org/10.7554/eLife.07141.001 PMID:26754917

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2015-01-01

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

Determination of human DNA polymerase utilization for the repair of a model ionizing radiation-induced DNA strand break lesion in a defined vector substrate

NASA Technical Reports Server (NTRS)

Winters, T. A.; Russell, P. S.; Kohli, M.; Dar, M. E.; Neumann, R. D.; Jorgensen, T. J.

1999-01-01

Human DNA polymerase and DNA ligase utilization for the repair of a major class of ionizing radiation-induced DNA lesion [DNA single-strand breaks containing 3'-phosphoglycolate (3'-PG)] was examined using a novel, chemically defined vector substrate containing a single, site-specific 3'-PG single-strand break lesion. In addition, the major human AP endonuclease, HAP1 (also known as APE1, APEX, Ref-1), was tested to determine if it was involved in initiating repair of 3'-PG-containing single-strand break lesions. DNA polymerase beta was found to be the primary polymerase responsible for nucleotide incorporation at the lesion site following excision of the 3'-PG blocking group. However, DNA polymerase delta/straightepsilon was also capable of nucleotide incorporation at the lesion site following 3'-PG excision. In addition, repair reactions catalyzed by DNA polymerase beta were found to be most effective in the presence of DNA ligase III, while those catalyzed by DNA polymerase delta/straightepsilon appeared to be more effective in the presence of DNA ligase I. Also, it was demonstrated that the repair initiating 3'-PG excision reaction was not dependent upon HAP1 activity, as judged by inhibition of HAP1 with neutralizing HAP1-specific polyclonal antibody.

Gauge-invariant formalism of cosmological weak lensing

NASA Astrophysics Data System (ADS)

Yoo, Jaiyul; Grimm, Nastassia; Mitsou, Ermis; Amara, Adam; Refregier, Alexandre

2018-04-01

We present the gauge-invariant formalism of cosmological weak lensing, accounting for all the relativistic effects due to the scalar, vector, and tensor perturbations at the linear order. While the light propagation is fully described by the geodesic equation, the relation of the photon wavevector to the physical quantities requires the specification of the frames, where they are defined. By constructing the local tetrad bases at the observer and the source positions, we clarify the relation of the weak lensing observables such as the convergence, the shear, and the rotation to the physical size and shape defined in the source rest-frame and the observed angle and redshift measured in the observer rest-frame. Compared to the standard lensing formalism, additional relativistic effects contribute to all the lensing observables. We explicitly verify the gauge-invariance of the lensing observables and compare our results to previous work. In particular, we demonstrate that even in the presence of the vector and tensor perturbations, the physical rotation of the lensing observables vanishes at the linear order, while the tetrad basis rotates along the light propagation compared to a FRW coordinate. Though the latter is often used as a probe of primordial gravitational waves, the rotation of the tetrad basis is indeed not a physical observable. We further clarify its relation to the E-B decomposition in weak lensing. Our formalism provides a transparent and comprehensive perspective of cosmological weak lensing.

Monthly evaporation forecasting using artificial neural networks and support vector machines

NASA Astrophysics Data System (ADS)

Tezel, Gulay; Buyukyildiz, Meral

2016-04-01

Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population.

PubMed

Niño-Sandoval, Tania Camila; Guevara Perez, Sonia V; González, Fabio A; Jaque, Robinson Andrés; Infante-Contreras, Clementina

2016-04-01

The mandibular bone is an important part of the forensic facial reconstruction and it has the possibility of getting lost in skeletonized remains; for this reason, it is necessary to facilitate the identification process simulating the mandibular position only through craniomaxillary measures, for this task, different modeling techniques have been performed, but they only contemplate a straight facial profile that belong to skeletal pattern Class I, but the 24.5% corresponding to the Colombian skeletal patterns Class II and III are not taking into account, besides, craniofacial measures do not follow a parametric trend or a normal distribution. The aim of this study was to employ an automatic non-parametric method as the Support Vector Machines to classify skeletal patterns through craniomaxillary variables, in order to simulate the natural mandibular position on a contemporary Colombian sample. Lateral cephalograms (229) of Colombian young adults of both sexes were collected. Landmark coordinates protocols were used to create craniomaxillary variables. A Support Vector Machine with a linear kernel classifier model was trained on a subset of the available data and evaluated over the remaining samples. The weights of the model were used to select the 10 best variables for classification accuracy. An accuracy of 74.51% was obtained, defined by Pr-A-N, N-Pr-A, A-N-Pr, A-Te-Pr, A-Pr-Rhi, Rhi-A-Pr, Pr-A-Te, Te-Pr-A, Zm-A-Pr and PNS-A-Pr angles. The Class Precision and the Class Recall showed a correct distinction of the Class II from the Class III and vice versa. Support Vector Machines created an important model of classification of skeletal patterns using craniomaxillary variables that are not commonly used in the literature and could be applicable to the 24.5% of the contemporary Colombian sample. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Rate determination from vector observations

NASA Technical Reports Server (NTRS)

Weiss, Jerold L.

1993-01-01

Vector observations are a common class of attitude data provided by a wide variety of attitude sensors. Attitude determination from vector observations is a well-understood process and numerous algorithms such as the TRIAD algorithm exist. These algorithms require measurement of the line of site (LOS) vector to reference objects and knowledge of the LOS directions in some predetermined reference frame. Once attitude is determined, it is a simple matter to synthesize vehicle rate using some form of lead-lag filter, and then, use it for vehicle stabilization. Many situations arise, however, in which rate knowledge is required but knowledge of the nominal LOS directions are not available. This paper presents two methods for determining spacecraft angular rates from vector observations without a priori knowledge of the vector directions. The first approach uses an extended Kalman filter with a spacecraft dynamic model and a kinematic model representing the motion of the observed LOS vectors. The second approach uses a 'differential' TRIAD algorithm to compute the incremental direction cosine matrix, from which vehicle rate is then derived.

Geometry of generalized depolarizing channels

NASA Astrophysics Data System (ADS)

Burrell, Christian K.

2009-10-01

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

Geometry of generalized depolarizing channels

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

Burrell, Christian K.

2009-10-15

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

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

PubMed Central

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

2013-01-01

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

The FKMM-invariant in low dimension

NASA Astrophysics Data System (ADS)

De Nittis, Giuseppe; Gomi, Kiyonori

2018-05-01

In this paper, we investigate the problem of the cohomological classification of "Quaternionic" vector bundles in low dimension (d≤slant 3). We show that there exists a characteristic class κ , called the FKMM-invariant, which takes value in the relative equivariant Borel cohomology and completely classifies "Quaternionic" vector bundles in low dimension. The main subject of the paper concerns a discussion about the surjectivity of κ.

The Major Antigenic Membrane Protein of “Candidatus Phytoplasma asteris” Selectively Interacts with ATP Synthase and Actin of Leafhopper Vectors

PubMed Central

Galetto, Luciana; Bosco, Domenico; Balestrini, Raffaella; Genre, Andrea; Fletcher, Jacqueline; Marzachì, Cristina

2011-01-01

Phytoplasmas, uncultivable phloem-limited phytopathogenic wall-less bacteria, represent a major threat to agriculture worldwide. They are transmitted in a persistent, propagative manner by phloem-sucking Hemipteran insects. Phytoplasma membrane proteins are in direct contact with hosts and are presumably involved in determining vector specificity. Such a role has been proposed for phytoplasma transmembrane proteins encoded by circular extrachromosomal elements, at least one of which is a plasmid. Little is known about the interactions between major phytoplasma antigenic membrane protein (Amp) and insect vector proteins. The aims of our work were to identify vector proteins interacting with Amp and to investigate their role in transmission specificity. In controlled transmission experiments, four Hemipteran species were identified as vectors of “Candidatus Phytoplasma asteris”, the chrysanthemum yellows phytoplasmas (CYP) strain, and three others as non-vectors. Interactions between a labelled (recombinant) CYP Amp and insect proteins were analysed by far Western blots and affinity chromatography. Amp interacted specifically with a few proteins from vector species only. Among Amp-binding vector proteins, actin and both the α and β subunits of ATP synthase were identified by mass spectrometry and Western blots. Immunofluorescence confocal microscopy and Western blots of plasma membrane and mitochondrial fractions confirmed the localisation of ATP synthase, generally known as a mitochondrial protein, in plasma membranes of midgut and salivary gland cells in the vector Euscelidius variegatus. The vector-specific interaction between phytoplasma Amp and insect ATP synthase is demonstrated for the first time, and this work also supports the hypothesis that host actin is involved in the internalization and intracellular motility of phytoplasmas within their vectors. Phytoplasma Amp is hypothesized to play a crucial role in insect transmission specificity. PMID:21799902

Vector Potential, Electromagnetic Induction and "Physical Meaning"

ERIC Educational Resources Information Center

Giuliani, G.

2010-01-01

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

A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose

PubMed Central

Rahman, Mohammad Mizanur; Suksompong, Prapun; Toochinda, Pisanu; Taparugssanagorn, Attaphongse

2017-01-01

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms. PMID:28895910

A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose.

PubMed

Rahman, Mohammad Mizanur; Charoenlarpnopparut, Chalie; Suksompong, Prapun; Toochinda, Pisanu; Taparugssanagorn, Attaphongse

2017-09-12

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k -nearest neighbor ( k -NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms.

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

PubMed

Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

2017-09-12

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

An object-oriented approach to nested data parallelism

NASA Technical Reports Server (NTRS)

Sheffler, Thomas J.; Chatterjee, Siddhartha

1994-01-01

This paper describes an implementation technique for integrating nested data parallelism into an object-oriented language. Data-parallel programming employs sets of data called 'collections' and expresses parallelism as operations performed over the elements of a collection. When the elements of a collection are also collections, then there is the possibility for 'nested data parallelism.' Few current programming languages support nested data parallelism however. In an object-oriented framework, a collection is a single object. Its type defines the parallel operations that may be applied to it. Our goal is to design and build an object-oriented data-parallel programming environment supporting nested data parallelism. Our initial approach is built upon three fundamental additions to C++. We add new parallel base types by implementing them as classes, and add a new parallel collection type called a 'vector' that is implemented as a template. Only one new language feature is introduced: the 'foreach' construct, which is the basis for exploiting elementwise parallelism over collections. The strength of the method lies in the compilation strategy, which translates nested data-parallel C++ into ordinary C++. Extracting the potential parallelism in nested 'foreach' constructs is called 'flattening' nested parallelism. We show how to flatten 'foreach' constructs using a simple program transformation. Our prototype system produces vector code which has been successfully run on workstations, a CM-2, and a CM-5.

Bioelectrical impedance vector distribution in the first year of life.

PubMed

Savino, Francesco; Grasso, Giulia; Cresi, Francesco; Oggero, Roberto; Silvestro, Leandra

2003-06-01

We assessed the bioelectrical impedance vector distribution in a sample of healthy infants in the first year of life, which is not available in literature. The study was conducted as a cross-sectional study in 153 healthy Caucasian infants (90 male and 63 female) younger than 1 y, born at full term, adequate for gestational age, free from chronic diseases or growth problems, and not feverish. Z scores for weight, length, cranial circumference, and body mass index for the study population were within the range of +/-1.5 standard deviations according to the Euro-Growth Study references. Concurrent anthropometrics (weight, length, and cranial circumference), body mass index, and bioelectrical impedance (resistance and reactance) measurements were made by the same operator. Whole-body (hand to foot) tetrapolar measurements were performed with a single-frequency (50 kHz), phase-sensitive impedance analyzer. The study population was subdivided into three classes of age for statistical analysis: 0 to 3.99 mo, 4 to 7.99 mo, and 8 to 11.99 mo. Using the bivariate normal distribution of resistance and reactance components standardized by the infant's length, the bivariate 95% confidence limits for the mean impedance vector separated by sex and age groups were calculated and plotted. Further, the bivariate 95%, 75%, and 50% tolerance intervals for individual vector measurements in the first year of life were plotted. Resistance and reactance values often fluctuated during the first year of life, particularly as raw measurements (without normalization by subject's length). However, 95% confidence ellipses of mean vectors from the three age groups overlapped each other, as did confidence ellipses by sex for each age class, indicating no significant vector migration during the first year of life. We obtained an estimate of mean impedance vector in a sample of healthy infants in the first year of life and calculated the bivariate values for an individual vector (95%, 75%, and 50% tolerance ellipses).

[Analysis of commercial grades of Schisandrae Sphenantherae Fructus based on schisantherin].

PubMed

Wang, Zhen-Heng; Jin, Ling; Ma, Yi; Cui, Zhi-Jia; Li, Qian; Huang, De-Dong

2017-10-01

Schisandrae Sphenantherae Fructus, from different producing areas, were collected and divided into three grades. The moisture content and total ash were determined on the basis of the pharmacopoeia method, and schisantherin was determined by UPLC. The study is aimed to find the commercial specifications and grades of Schisandrae Sphenantherae Fructus based on schisantherin content. The results showed that the content of water, total ash and schisantherin of Schisandrae Sphenantherae Fructus from different producing areas qualified. There was no significant difference between different grades of schisandrin content, but the second-class were the highest, first-class and third-class were lower. It means that schisandrin content is not positive correlation to commercial grade. The study will be helpful to the production, management and clinical practice of Schisandrae Sphenantherae Fructus. Copyright© by the Chinese Pharmaceutical Association.

Locating potential biosignatures on Europa from surface geology observations.

PubMed

Figueredo, Patricio H; Greeley, Ronald; Neuer, Susanne; Irwin, Louis; Schulze-Makuch, Dirk

2003-01-01

We evaluated the astrobiological potential of the major classes of geologic units on Europa with respect to possible biosignatures preservation on the basis of surface geology observations. These observations are independent of any formational model and therefore provide an objective, though preliminary, evaluation. The assessment criteria include high mobility of material, surface concentration of non-ice components, relative youth, textural roughness, and environmental stability. Our review determined that, as feature classes, low-albedo smooth plains, smooth bands, and chaos hold the highest potential, primarily because of their relative young age, the emplacement of low-viscosity material, and indications of material exchange with the subsurface. Some lineaments and impact craters may be promising sites for closer study despite the comparatively lower astrobiological potential of their classes. This assessment will be expanded by multidisciplinary examination of the potential for habitability of specific features.

Time since death and decay rate constants of Norway spruce and European larch deadwood in subalpine forests determined using dendrochronology and radiocarbon dating

NASA Astrophysics Data System (ADS)

Petrillo, M.; Cherubini, P.; Fravolini, G.; Ascher, J.; Schärer, M.; Synal, H.-A.; Bertoldi, D.; Camin, F.; Larcher, R.; Egli, M.

2015-09-01

Due to the large size and highly heterogeneous spatial distribution of deadwood, the time scales involved in the coarse woody debris (CWD) decay of Picea abies (L.) Karst. and Larix decidua Mill. in Alpine forests have been poorly investigated and are largely unknown. We investigated the CWD decay dynamics in an Alpine valley in Italy using the five-decay class system commonly employed for forest surveys, based on a macromorphological and visual assessment. For the decay classes 1 to 3, most of the dendrochronological samples were cross-dated to assess the time that had elapsed since tree death, but for decay classes 4 and 5 (poorly preserved tree rings) and some others not having enough tree rings, radiocarbon dating was used. In addition, density, cellulose and lignin data were measured for the dated CWD. The decay rate constants for spruce and larch were estimated on the basis of the density loss using a single negative exponential model. In the decay classes 1 to 3, the ages of the CWD were similar varying between 1 and 54 years for spruce and 3 and 40 years for larch with no significant differences between the classes; classes 1-3 are therefore not indicative for deadwood age. We found, however, distinct tree species-specific differences in decay classes 4 and 5, with larch CWD reaching an average age of 210 years in class 5 and spruce only 77 years. The mean CWD rate constants were 0.012 to 0.018 yr-1 for spruce and 0.005 to 0.012 yr-1 for larch. Cellulose and lignin time trends half-lives (using a multiple-exponential model) could be derived on the basis of the ages of the CWD. The half-lives for cellulose were 21 yr for spruce and 50 yr for larch. The half-life of lignin is considerably higher and may be more than 100 years in larch CWD.

Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping.

PubMed

Mukherjee, Sumanta; Warwicker, Jim; Chandra, Nagasuma

2015-07-01

T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.

Multipole Vector Anomalies in the First-Year WMAP Data: A Cut-Sky Analysis

NASA Astrophysics Data System (ADS)

Bielewicz, P.; Eriksen, H. K.; Banday, A. J.; Górski, K. M.; Lilje, P. B.

2005-12-01

We apply the recently defined multipole vector framework to the frequency-specific first-year WMAP sky maps, estimating the low-l multipole coefficients from the high-latitude sky by means of a power equalization filter. While most previous analyses of this type have considered only heavily processed (and foreground-contaminated) full-sky maps, the present approach allows for greater control of residual foregrounds and therefore potentially also for cosmologically important conclusions. The low-l spherical harmonic coefficients and corresponding multipole vectors are tabulated for easy reference. Using this formalism, we reassess a set of earlier claims of both cosmological and noncosmological low-l correlations on the basis of multipole vectors. First, we show that the apparent l=3 and 8 correlation claimed by Copi and coworkers is present only in the heavily processed map produced by Tegmark and coworkers and must therefore be considered an artifact of that map. Second, the well-known quadrupole-octopole correlation is confirmed at the 99% significance level and shown to be robust with respect to frequency and sky cut. Previous claims are thus supported by our analysis. Finally, the low-l alignment with respect to the ecliptic claimed by Schwarz and coworkers is nominally confirmed in this analysis, but also shown to be very dependent on severe a posteriori choices. Indeed, we show that given the peculiar quadrupole-octopole arrangement, finding such a strong alignment with the ecliptic is not unusual.

Dynamic ocean provinces: a multi-sensor approach to global marine ecophysiology

NASA Astrophysics Data System (ADS)

Dowell, M.; Campbell, J.; Moore, T.

The concept of oceanic provinces or domains has existed for well over a century. Such systems, whether real or only conceptual, provide a useful framework for understanding the mechanisms controlling biological, physical and chemical processes and their interactions. Criteria have been established for defining provinces based on physical forcings, availability of light and nutrients, complexity of the marine food web, and other factors. In general, such classification systems reflect the heterogeneous nature of the ocean environment, and the effort of scientists to comprehend the whole system by understanding its various homogeneous components. If provinces are defined strictly on the basis of geospatial or temporal criteria (e.g., latitude zones, bathymetry, or season), the resulting maps exhibit discontinuities that are uncharacteristic of the ocean. While this may be useful for many purposes, it is unsatisfactory in that it does not capture the dynamic nature of fluid boundaries in the ocean. Boundaries fixed in time and space do not allow us to observe interannual or longer-term variability (e.g., regime shifts) that may result from climate change. The current study illustrates the potential of using fuzzy logic as a means of classifying the ocean into objectively defined provinces using properties measurable from satellite sensors (MODIS and SeaWiFS). This approach accommodates the dynamic variability of provinces which can be updated as each image is processed. We adopt this classification as the basis for parameterizing specific algorithms for each of the classes. Once the class specific algorithms have been applied, retrievals are then recomposed into a single blended product based on the "weighted" fuzzy memberships. This will be demonstrated through animations of multi-year time- series of monthly composites of the individual classes or provinces. The provinces themselves are identified on the basis of global fields of chlorophyll, sea surface temperature and PAR which will also be subsequently used to parameterize primary production (PP) algorithms. Two applications of the proposed dynamic classification are presented. The first applies different peer-reviewed PP algorithms to the different classes and objectively evaluates their performance to select the algorithm which performs best, and then merges results into a single primary production product. A second application illustrates the variability of P I parameters in each province and- analyzes province specific variability in the quantum yield of photosynthesis. Finally results illustrating how this approach is implemented in estimating global oceanic primary production are presented.

Immune Protection of Nonhuman Primates against Ebola Virus with Single Low-Dose Adenovirus Vectors Encoding Modified GPs

PubMed Central

Geisbert, Joan B; Shedlock, Devon J; Xu, Ling; Lamoreaux, Laurie; Custers, Jerome H. H. V; Popernack, Paul M; Yang, Zhi-Yong; Pau, Maria G; Roederer, Mario; Koup, Richard A; Goudsmit, Jaap; Jahrling, Peter B; Nabel, Gary J

2006-01-01

Background Ebola virus causes a hemorrhagic fever syndrome that is associated with high mortality in humans. In the absence of effective therapies for Ebola virus infection, the development of a vaccine becomes an important strategy to contain outbreaks. Immunization with DNA and/or replication-defective adenoviral vectors (rAd) encoding the Ebola glycoprotein (GP) and nucleoprotein (NP) has been previously shown to confer specific protective immunity in nonhuman primates. GP can exert cytopathic effects on transfected cells in vitro, and multiple GP forms have been identified in nature, raising the question of which would be optimal for a human vaccine. Methods and Findings To address this question, we have explored the efficacy of mutant GPs from multiple Ebola virus strains with reduced in vitro cytopathicity and analyzed their protective effects in the primate challenge model, with or without NP. Deletion of the GP transmembrane domain eliminated in vitro cytopathicity but reduced its protective efficacy by at least one order of magnitude. In contrast, a point mutation was identified that abolished this cytopathicity but retained immunogenicity and conferred immune protection in the absence of NP. The minimal effective rAd dose was established at 1010 particles, two logs lower than that used previously. Conclusions Expression of specific GPs alone vectored by rAd are sufficient to confer protection against lethal challenge in a relevant nonhuman primate model. Elimination of NP from the vaccine and dose reductions to 1010 rAd particles do not diminish protection and simplify the vaccine, providing the basis for selection of a human vaccine candidate. PMID:16683867

Nonlinear Structured Growth Mixture Models in Mplus and OpenMx

PubMed Central

Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne

2014-01-01

Growth mixture models (GMMs; Muthén & Muthén, 2000; Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this paper, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the Mplus and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study-Kindergarten Cohort to illustrate their use. PMID:25419006

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

Content based image retrieval using local binary pattern operator and data mining techniques.

PubMed

Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

2015-01-01

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

LBP and SIFT based facial expression recognition

NASA Astrophysics Data System (ADS)

Sumer, Omer; Gunes, Ece O.

2015-02-01

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Deformations of vector-scalar models

NASA Astrophysics Data System (ADS)

Barnich, Glenn; Boulanger, Nicolas; Henneaux, Marc; Julia, Bernard; Lekeu, Victor; Ranjbar, Arash

2018-02-01

Abelian vector fields non-minimally coupled to uncharged scalar fields arise in many contexts. We investigate here through algebraic methods their consistent deformations ("gaugings"), i.e., the deformations that preserve the number (but not necessarily the form or the algebra) of the gauge symmetries. Infinitesimal consistent deformations are given by the BRST cohomology classes at ghost number zero. We parametrize explicitly these classes in terms of various types of global symmetries and corresponding Noether currents through the characteristic cohomology related to antifields and equations of motion. The analysis applies to all ghost numbers and not just ghost number zero. We also provide a systematic discussion of the linear and quadratic constraints on these parameters that follow from higher-order consistency. Our work is relevant to the gaugings of extended supergravities.

Antigen Presentation by Individually Transferred HLA Class I Genes in HLA-A, HLA-B, HLA-C Null Human Cell Line Generated Using the Multiplex CRISPR-Cas9 System.

PubMed

Hong, Cheol-Hwa; Sohn, Hyun-Jung; Lee, Hyun-Joo; Cho, Hyun-Il; Kim, Tai-Gyu

Human leukocyte antigens (HLAs) are essential immune molecules that affect transplantation and adoptive immunotherapy. When hematopoietic stem cells or organs are transplanted with HLA-mismatched recipients, graft-versus-host disease or graft rejection can be induced by allogeneic immune responses. The function of each HLA allele has been studied using HLA-deficient cells generated from mutant cell lines or by RNA interference, zinc finger nuclease, and the CRISPR/Cas9 system. To improve HLA gene editing, we attempted to generate an HLA class I null cell line using the multiplex CRISPR/Cas9 system by targeting exons 2 and 3 of HLA-A, HLA-B, and HLA-C genes simultaneously. Multiplex HLA editing could induce the complete elimination of HLA class I genes by bi-allelic gene disruption on target sites which was defined by flow cytometry and target-specific polymerase chain reaction. Furthermore, artificial antigen-presenting cells were generated by transfer of a single HLA class I allele and co-stimulatory molecules into this novel HLA class I null cell line. Artificial antigen-presenting cells showed HLA-restricted antigen presentation following antigen processing and were successfully used for the efficient generation of tumor antigen-specific cytotoxic T cells in vitro. The efficient editing of HLA genes may provide a basis for universal cellular therapies and transplantation.

Global biogeography of human infectious diseases.

PubMed

Murray, Kris A; Preston, Nicholas; Allen, Toph; Zambrana-Torrelio, Carlos; Hosseini, Parviez R; Daszak, Peter

2015-10-13

The distributions of most infectious agents causing disease in humans are poorly resolved or unknown. However, poorly known and unknown agents contribute to the global burden of disease and will underlie many future disease risks. Existing patterns of infectious disease co-occurrence could thus play a critical role in resolving or anticipating current and future disease threats. We analyzed the global occurrence patterns of 187 human infectious diseases across 225 countries and seven epidemiological classes (human-specific, zoonotic, vector-borne, non-vector-borne, bacterial, viral, and parasitic) to show that human infectious diseases exhibit distinct spatial grouping patterns at a global scale. We demonstrate, using outbreaks of Ebola virus as a test case, that this spatial structuring provides an untapped source of prior information that could be used to tighten the focus of a range of health-related research and management activities at early stages or in data-poor settings, including disease surveillance, outbreak responses, or optimizing pathogen discovery. In examining the correlates of these spatial patterns, among a range of geographic, epidemiological, environmental, and social factors, mammalian biodiversity was the strongest predictor of infectious disease co-occurrence overall and for six of the seven disease classes examined, giving rise to a striking congruence between global pathogeographic and "Wallacean" zoogeographic patterns. This clear biogeographic signal suggests that infectious disease assemblages remain fundamentally constrained in their distributions by ecological barriers to dispersal or establishment, despite the homogenizing forces of globalization. Pathogeography thus provides an overarching context in which other factors promoting infectious disease emergence and spread are set.

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

A Feature Mining Based Approach for the Classification of Text Documents into Disjoint Classes.

ERIC Educational Resources Information Center

Nieto Sanchez, Salvador; Triantaphyllou, Evangelos; Kraft, Donald

2002-01-01

Proposes a new approach for classifying text documents into two disjoint classes. Highlights include a brief overview of document clustering; a data mining approach called the One Clause at a Time (OCAT) algorithm which is based on mathematical logic; vector space model (VSM); and comparing the OCAT to the VSM. (Author/LRW)

78 FR 58158 - Establishment of Class E Airspace; Wasatch, UT

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-23

...., long. 111[deg]07'28'' W.; to Lat. 39[deg]03'55'' N., long. 110[deg]37'49'' W.; to Lat. 38[deg]28'51'' N... Aviation Administration (FAA), DOT. ACTION: Final rule. SUMMARY: This action establishes Class E airspace..., Wasatch, UT, to facilitate vectoring of Instrument Flight Rules (IFR) aircraft under control of Salt Lake...

WORKING WITH ALKALINE MATERIALS TO ACHIEVE A CLASS B, CLASS A, AND/OR A BIOSOLIDS THAT DOES NOT ATTRACT VECTORS

EPA Science Inventory

This workshop presentation begins with a discussion of the use of lime and other alkaline materials from the very earliest times to the present for killing bacteria, viruses and parasites and for controlling odors in wastewaters and sludge. It answers the question "How did EPA ar...

Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.

PubMed

Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E

2010-09-17

Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Studies on deflection area vectors of QRS and T and ventricular gradient in right ventricular hypertrophy.

PubMed

Kawaguchi, Y

1985-04-01

QRS deflection area vector (Aqrs), T deflection area vector (At) and ventricular gradient (G) in right ventricular hypertrophy were studied in 53 subjects divided on the basis of cardiac catheterization data into four subgroups; normal controls, mild MS group, right ventricular pressure overload group and right ventricular volume overload group. Aqrs, At and G of the four subgroups were calculated using a microcomputer and compared. Aqrs in right ventricular pressure overload group and volume overload group was shifted to the right and slightly anteriorly from that in normal control group. At in right ventricular pressure overload group and volume overload group was shifted slightly upwards and significantly posteriorly from that in the normal control and mild MS groups. G in right ventricular pressure overload group and volume overload group was shifted to the right and significantly posteriorly from that in normal control and mild MS groups. Using multivariative analysis, we developed criteria for diagnosing right ventricular hypertrophy with At: 0.059At(Z) - 0.0145 [At] - 0.2608 less than or equal to 0. Application of this criteria achieved 82.4% (28 of 34) sensitivity in the patients with right ventricular hypertrophy and 90.9% (10 of 11) specificity in the normal control subjects.

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

PubMed Central

Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M.; Ramírez, Javier; Yuan, Ti-Fei; Zhang, Yudong

2016-01-01

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss. PMID:27807415

Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification.

PubMed

Dai, Baisheng; Wu, Xiangqian; Bu, Wei

2016-01-01

Retinal microaneurysms (MAs) are the earliest clinically observable lesions of diabetic retinopathy. Reliable automated MAs detection is thus critical for early diagnosis of diabetic retinopathy. This paper proposes a novel method for the automated MAs detection in color fundus images based on gradient vector analysis and class imbalance classification, which is composed of two stages, i.e. candidate MAs extraction and classification. In the first stage, a candidate MAs extraction algorithm is devised by analyzing the gradient field of the image, in which a multi-scale log condition number map is computed based on the gradient vectors for vessel removal, and then the candidate MAs are localized according to the second order directional derivatives computed in different directions. Due to the complexity of fundus image, besides a small number of true MAs, there are also a large amount of non-MAs in the extracted candidates. Classifying the true MAs and the non-MAs is an extremely class imbalanced classification problem. Therefore, in the second stage, several types of features including geometry, contrast, intensity, edge, texture, region descriptors and other features are extracted from the candidate MAs and a class imbalance classifier, i.e., RUSBoost, is trained for the MAs classification. With the Retinopathy Online Challenge (ROC) criterion, the proposed method achieves an average sensitivity of 0.433 at 1/8, 1/4, 1/2, 1, 2, 4 and 8 false positives per image on the ROC database, which is comparable with the state-of-the-art approaches, and 0.321 on the DiaRetDB1 V2.1 database, which outperforms the state-of-the-art approaches.

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning.

PubMed

Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M; Ramírez, Javier; Yuan, Ti-Fei; Zhang, Yudong

2016-01-01

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

Spinning particles in vacuum spacetimes of different curvature types

NASA Astrophysics Data System (ADS)

Semerák, O.; Šrámek, M.

2015-09-01

We consider the motion of spinning test particles with nonzero rest mass in the "pole-dipole" approximation, as described by the Mathisson-Papapetrou-Dixon (MPD) equations, and examine its properties in dependence on the spin supplementary condition added to close the system. In order to better understand the spin-curvature interaction, the MPD equation of motion is decomposed in the orthonormal tetrad whose time vector is given by the four-velocity Vμ chosen to fix the spin condition (the "reference observer") and the first spatial vector by the corresponding spin sμ; such projections do not contain the Weyl scalars Ψ0 and Ψ4 obtained in the associated Newman-Penrose (NP) null tetrad. One natural option of how to choose the remaining two spatial basis vectors is shown to follow "intrinsically" whenever Vμ has been chosen; it is realizable if the particle's four-velocity and four-momentum are not parallel. In order to see how the problem depends on the algebraic type of curvature, one first identifies the first vector of the NP tetrad kμ with the highest-multiplicity principal null direction of the Weyl tensor, and then sets Vμ so that kμ belong to the spin-bivector eigenplane. In spacetimes of any algebraic type but III, it is known to be possible to rotate the tetrads so as to become "transverse," namely so that Ψ1 and Ψ3 vanish. If the spin-bivector eigenplane could be made to coincide with the real-vector plane of any of such transverse frames, the spinning particle motion would consequently be fully determined by Ψ2 and the cosmological constant; however, this can be managed in exceptional cases only. Besides focusing on specific Petrov types, we derive several sets of useful relations that are valid generally and check whether/how the exercise simplifies for some specific types of motion. The particular option of having four-velocity parallel to four-momentum is advocated, and a natural resolution of nonuniqueness of the corresponding reference observer Vμ is suggested.

MIRO and IRbase: IT Tools for the Epidemiological Monitoring of Insecticide Resistance in Mosquito Disease Vectors

PubMed Central

Dialynas, Emmanuel; Topalis, Pantelis; Vontas, John; Louis, Christos

2009-01-01

Background Monitoring of insect vector populations with respect to their susceptibility to one or more insecticides is a crucial element of the strategies used for the control of arthropod-borne diseases. This management task can nowadays be achieved more efficiently when assisted by IT (Information Technology) tools, ranging from modern integrated databases to GIS (Geographic Information System). Here we describe an application ontology that we developed de novo, and a specially designed database that, based on this ontology, can be used for the purpose of controlling mosquitoes and, thus, the diseases that they transmit. Methodology/Principal Findings The ontology, named MIRO for Mosquito Insecticide Resistance Ontology, developed using the OBO-Edit software, describes all pertinent aspects of insecticide resistance, including specific methodology and mode of action. MIRO, then, forms the basis for the design and development of a dedicated database, IRbase, constructed using open source software, which can be used to retrieve data on mosquito populations in a temporally and spatially separate way, as well as to map the output using a Google Earth interface. The dependency of the database on the MIRO allows for a rational and efficient hierarchical search possibility. Conclusions/Significance The fact that the MIRO complies with the rules set forward by the OBO (Open Biomedical Ontologies) Foundry introduces cross-referencing with other biomedical ontologies and, thus, both MIRO and IRbase are suitable as parts of future comprehensive surveillance tools and decision support systems that will be used for the control of vector-borne diseases. MIRO is downloadable from and IRbase is accessible at VectorBase, the NIAID-sponsored open access database for arthropod vectors of disease. PMID:19547750

The Coordinate Orthogonality Check (corthog)

NASA Astrophysics Data System (ADS)

Avitabile, P.; Pechinsky, F.

1998-05-01

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

Reversible vector ratchets for skyrmion systems

NASA Astrophysics Data System (ADS)

Ma, X.; Reichhardt, C. J. Olson; Reichhardt, C.

2017-03-01

We show that ac driven skyrmions interacting with an asymmetric substrate provide a realization of a class of ratchet system which we call a vector ratchet that arises due to the effect of the Magnus term on the skyrmion dynamics. In a vector ratchet, the dc motion induced by the ac drive can be described as a vector that can be rotated clockwise or counterclockwise relative to the substrate asymmetry direction. Up to a full 360∘ rotation is possible for varied ac amplitudes or skyrmion densities. In contrast to overdamped systems, in which ratchet motion is always parallel to the substrate asymmetry direction, vector ratchets allow the ratchet motion to be in any direction relative to the substrate asymmetry. It is also possible to obtain a reversal in the direction of rotation of the vector ratchet, permitting the creation of a reversible vector ratchet. We examine vector ratchets for ac drives applied parallel or perpendicular to the substrate asymmetry direction, and show that reverse ratchet motion can be produced by collective effects. No reversals occur for an isolated skyrmion on an asymmetric substrate. Since a vector ratchet can produce motion in any direction, it could represent a method for controlling skyrmion motion for spintronic applications.

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data

NASA Astrophysics Data System (ADS)

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-01

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data.

PubMed

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-21

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Eye movement analysis for activity recognition using electrooculography.

PubMed

Bulling, Andreas; Ward, Jamie A; Gellersen, Hans; Tröster, Gerhard

2011-04-01

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

PubMed

Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

Plasmid-mediated resistance to protein biosynthesis inhibitors in staphylococci.

PubMed

Schwarz, Stefan; Fessler, Andrea T; Hauschild, Tomasz; Kehrenberg, Corinna; Kadlec, Kristina

2011-12-01

Protein biosynthesis inhibitors (PBIs) represent powerful antimicrobial agents for the control of bacterial infections. In staphylococci, numerous resistance genes are known to be involved in resistance to PBIs, most of which mediate resistance to a specific class/subclass of PBIs, though a few genes do confer a multidrug resistance phenotype-up to five classes/subclasses of PBIs. Plasmids play a key role in the dissemination of PBI resistance among staphylococci, as they primarily carry plasmid-borne PBI resistance genes; however, plasmids also can be vectors for transposon-borne PBI resistance genes. Small plasmids that carry single PBI resistance genes are widespread among staphylococci of human and animal origin. Various mechanisms exist by which they can recombine, form cointegrates, or integrate into chromosomal DNA or larger plasmids. We provide an overview of the current knowledge of plasmid-mediated PBI resistance in staphylococci, with particular reference to the currently known PBI resistance genes, their association with mobile genetic elements, and the recombination/integration processes that control their mobility. © 2011 New York Academy of Sciences.

Quantity, Revisited: An Object-Oriented Reusable Class

NASA Technical Reports Server (NTRS)

Funston, Monica Gayle; Gerstle, Walter; Panthaki, Malcolm

1998-01-01

"Quantity", a prototype implementation of an object-oriented class, was developed for two reasons: to help engineers and scientists manipulate the many types of quantities encountered during routine analysis, and to create a reusable software component to for large domain-specific applications. From being used as a stand-alone application to being incorporated into an existing computational mechanics toolkit, "Quantity" appears to be a useful and powerful object. "Quantity" has been designed to maintain the full engineering meaning of values with respect to units and coordinate systems. A value is a scalar, vector, tensor, or matrix, each of which is composed of Value Components, each of which may be an integer, floating point number, fuzzy number, etc., and its associated physical unit. Operations such as coordinate transformation and arithmetic operations are handled by member functions of "Quantity". The prototype has successfully tested such characteristics as maintaining a numeric value, an associated unit, and an annotation. In this paper we further explore the design of "Quantity", with particular attention to coordinate systems.

Hybrid Nanomaterial Complexes for Advanced Phage-guided Gene Delivery

PubMed Central

Yata, Teerapong; Lee, Koon-Yang; Dharakul, Tararaj; Songsivilai, Sirirurg; Bismarck, Alexander; Mintz, Paul J; Hajitou, Amin

2014-01-01

Developing nanomaterials that are effective, safe, and selective for gene transfer applications is challenging. Bacteriophages (phage), viruses that infect bacteria only, have shown promise for targeted gene transfer applications. Unfortunately, limited progress has been achieved in improving their potential to overcome mammalian cellular barriers. We hypothesized that chemical modification of the bacteriophage capsid could be applied to improve targeted gene delivery by phage vectors into mammalian cells. Here, we introduce a novel hybrid system consisting of two classes of nanomaterial systems, cationic polymers and M13 bacteriophage virus particles genetically engineered to display a tumor-targeting ligand and carry a transgene cassette. We demonstrate that the phage complex with cationic polymers generates positively charged phage and large aggregates that show enhanced cell surface attachment, buffering capacity, and improved transgene expression while retaining cell type specificity. Moreover, phage/polymer complexes carrying a therapeutic gene achieve greater cancer cell killing than phage alone. This new class of hybrid nanomaterial platform can advance targeted gene delivery applications by bacteriophage. PMID:25118171

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Self-entanglement of long linear DNA vectors using transient non-B-DNA attachment points: a new concept for improvement of non-viral therapeutic gene delivery.

PubMed

Tolmachov, Oleg E

2012-05-01

The cell-specific and long-term expression of therapeutic transgenes often requires a full array of native gene control elements including distal enhancers, regulatory introns and chromatin organisation sequences. The delivery of such extended gene expression modules to human cells can be accomplished with non-viral high-molecular-weight DNA vectors, in particular with several classes of linear DNA vectors. All high-molecular-weight DNA vectors are susceptible to damage by shear stress, and while for some of the vectors the harmful impact of shear stress can be minimised through the transformation of the vectors to compact topological configurations by supercoiling and/or knotting, linear DNA vectors with terminal loops or covalently attached terminal proteins cannot be self-compacted in this way. In this case, the only available self-compacting option is self-entangling, which can be defined as the folding of single DNA molecules into a configuration with mutual restriction of molecular motion by the individual segments of bent DNA. A negatively charged phosphate backbone makes DNA self-repulsive, so it is reasonable to assume that a certain number of 'sticky points' dispersed within DNA could facilitate the entangling by bringing DNA segments into proximity and by interfering with the DNA slipping away from the entanglement. I propose that the spontaneous entanglement of vector DNA can be enhanced by the interlacing of the DNA with sites capable of mutual transient attachment through the formation of non-B-DNA forms, such as interacting cruciform structures, inter-segment triplexes, slipped-strand DNA, left-handed duplexes (Z-forms) or G-quadruplexes. It is expected that the non-B-DNA based entanglement of the linear DNA vectors would consist of the initial transient and co-operative non-B-DNA mediated binding events followed by tight self-ensnarement of the vector DNA. Once in the nucleoplasm of the target human cells, the DNA can be disentangled by type II topoisomerases. The technology for such self-entanglement can be an avenue for the improvement of gene delivery with high-molecular-weight naked DNA using therapeutically important methods associated with considerable shear stress. Priority applications include in vivo muscle electroporation and sonoporation for Duchenne muscular dystrophy patients, aerosol inhalation to reach the target lung cells of cystic fibrosis patients and bio-ballistic delivery to skin melanomas with the vector DNA adsorbed on gold or tungsten projectiles. Copyright © 2012 Elsevier Ltd. All rights reserved.

Receptor-mediated gene transfer vectors: progress towards genetic pharmaceuticals.

PubMed

Molas, M; Gómez-Valadés, A G; Vidal-Alabró, A; Miguel-Turu, M; Bermudez, J; Bartrons, R; Perales, J C

2003-10-01

Although specific delivery to tissues and unique cell types in vivo has been demonstrated for many non-viral vectors, current methods are still inadequate for human applications, mainly because of limitations on their efficiencies. All the steps required for an efficient receptor-mediated gene transfer process may in principle be exploited to enhance targeted gene delivery. These steps are: DNA/vector binding, internalization, subcellular trafficking, vesicular escape, nuclear import, and unpacking either for transcription or other functions (i.e., antisense, RNA interference, etc.). The large variety of vector designs that are currently available, usually aimed at improving the efficiency of these steps, has complicated the evaluation of data obtained from specific derivatives of such vectors. The importance of the structure of the final vector and the consequences of design decisions at specific steps on the overall efficiency of the vector will be discussed in detail. We emphasize in this review that stability in serum and thus, proper bioavailability of vectors to their specific receptors may be the single greatest limiting factor on the overall gene transfer efficiency in vivo. We discuss current approaches to overcome the intrinsic instability of synthetic vectors in the blood. In this regard, a summary of the structural features of the vectors obtained from current protocols will be presented and their functional characteristics evaluated. Dissecting information on molecular conjugates obtained by such methodologies, when carefully evaluated, should provide important guidelines for the creation of effective, targeted and safe DNA therapeutics.

The role of population inertia in predicting the outcome of stage-structured biological invasions.

PubMed

Guiver, Chris; Dreiwi, Hanan; Filannino, Donna-Maria; Hodgson, Dave; Lloyd, Stephanie; Townley, Stuart

2015-07-01

Deterministic dynamic models for coupled resident and invader populations are considered with the purpose of finding quantities that are effective at predicting when the invasive population will become established asymptotically. A key feature of the models considered is the stage-structure, meaning that the populations are described by vectors of discrete developmental stage- or age-classes. The vector structure permits exotic transient behaviour-phenomena not encountered in scalar models. Analysis using a linear Lyapunov function demonstrates that for the class of population models considered, a large so-called population inertia is indicative of successful invasion. Population inertia is an indicator of transient growth or decline. Furthermore, for the class of models considered, we find that the so-called invasion exponent, an existing index used in models for invasion, is not always a reliable comparative indicator of successful invasion. We highlight these findings through numerical examples and a biological interpretation of why this might be the case is discussed. Copyright © 2015. Published by Elsevier Inc.

Support Vector Data Descriptions and k-Means Clustering: One Class?

PubMed

Gornitz, Nico; Lima, Luiz Alberto; Muller, Klaus-Robert; Kloft, Marius; Nakajima, Shinichi

2017-09-27

We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and k-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to k-means. In particular, our approach leads to a new interpretation of k-means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD.

The Automation System Censor Speech for the Indonesian Rude Swear Words Based on Support Vector Machine and Pitch Analysis

NASA Astrophysics Data System (ADS)

Endah, S. N.; Nugraheni, D. M. K.; Adhy, S.; Sutikno

2017-04-01

According to Law No. 32 of 2002 and the Indonesian Broadcasting Commission Regulation No. 02/P/KPI/12/2009 & No. 03/P/KPI/12/2009, stated that broadcast programs should not scold with harsh words, not harass, insult or demean minorities and marginalized groups. However, there are no suitable tools to censor those words automatically. Therefore, researches to develop a system of intelligent software to censor the words automatically are needed. To conduct censor, the system must be able to recognize the words in question. This research proposes the classification of speech divide into two classes using Support Vector Machine (SVM), first class is set of rude words and the second class is set of properly words. The speech pitch values as an input in SVM, it used for the development of the system for the Indonesian rude swear word. The results of the experiment show that SVM is good for this system.

Perl-speaks-NONMEM (PsN)--a Perl module for NONMEM related programming.

PubMed

Lindbom, Lars; Ribbing, Jakob; Jonsson, E Niclas

2004-08-01

The NONMEM program is the most widely used nonlinear regression software in population pharmacokinetic/pharmacodynamic (PK/PD) analyses. In this article we describe a programming library, Perl-speaks-NONMEM (PsN), intended for programmers that aim at using the computational capability of NONMEM in external applications. The library is object oriented and written in the programming language Perl. The classes of the library are built around NONMEM's data, model and output files. The specification of the NONMEM model is easily set or changed through the model and data file classes while the output from a model fit is accessed through the output file class. The classes have methods that help the programmer perform common repetitive tasks, e.g. summarising the output from a NONMEM run, setting the initial estimates of a model based on a previous run or truncating values over a certain threshold in the data file. PsN creates a basis for the development of high-level software using NONMEM as the regression tool.

Pilot study of facial soft tissue thickness differences among three skeletal classes in Japanese females.

PubMed

Utsuno, Hajime; Kageyama, Toru; Uchida, Keiichi; Yoshino, Mineo; Oohigashi, Shina; Miyazawa, Hiroo; Inoue, Katsuhiro

2010-02-25

Facial reconstruction is a technique used in forensic anthropology to estimate the appearance of the antemortem face from unknown human skeletal remains. This requires accurate skull assessment (for variables such as age, sex, and race) and soft tissue thickness data. However, the skull can provide only limited information, and further data are needed to reconstruct the face. The authors herein obtained further information from the skull in order to reconstruct the face more accurately. Skulls can be classified into three facial types on the basis of orthodontic skeletal classes (namely, straight facial profile, type I, convex facial profile, type II, and concave facial profile, type III). This concept was applied to facial tissue measurement and soft tissue depth was compared in each skeletal class in a Japanese female population. Differences of soft tissue depth between skeletal classes were observed, and this information may enable more accurate reconstruction than sex-specific depth alone. 2009 Elsevier Ireland Ltd. All rights reserved.

Subtyping depression by clinical features: the Australasian database.

PubMed

Parker, G; Roy, K; Hadzi-Pavlovic, D; Mitchell, P; Wilhelm, K; Menkes, D B; Snowdon, J; Loo, C; Schweitzer, I

2000-01-01

To distinguish psychotic, melancholic and a residual non-melancholic class on the basis of clinical features alone. Previous studies at our Mood Disorders Unit (MDU) favour a hierarchical model, with the classes able to be distinguished by two specific clinical features, but any such intramural study risks rater bias and requires external replication. This replication study involved 27 Australasian psychiatrist raters, thus extending the sample and raters beyond the MDU facility. They collected clinical feature data using a standardized assessment with precoded rating options. A psychotic depression (PD) class was derived by respecting DSM-IV decision rules while a cluster analysis distinguished melancholic (MEL) and non-melancholic classes. The MELs were distinguished virtually entirely by the presence of significant psychomotor disturbance (PMD), as rated by the observationally based CORE measure, with over-representation on only three of an extensive set of 'endogeneity symptoms'. In comparison to PMD, endogeneity symptoms appear to be poor indicators of 'melancholic' type, confounding typology with severity. Results again support the hierarchical model.

Fuzzy Classification of Ocean Color Satellite Data for Bio-optical Algorithm Constituent Retrievals

NASA Technical Reports Server (NTRS)

Campbell, Janet W.

1998-01-01

The ocean has been traditionally viewed as a 2 class system. Morel and Prieur (1977) classified ocean water according to the dominant absorbent particle suspended in the water column. Case 1 is described as having a high concentration of phytoplankton (and detritus) relative to other particles. Conversely, case 2 is described as having inorganic particles such as suspended sediments in high concentrations. Little work has gone into the problem of mixing bio-optical models for these different water types. An approach is put forth here to blend bio-optical algorithms based on a fuzzy classification scheme. This scheme involves two procedures. First, a clustering procedure identifies classes and builds class statistics from in-situ optical measurements. Next, a classification procedure assigns satellite pixels partial memberships to these classes based on their ocean color reflectance signature. These membership assignments can be used as the basis for a weighting retrievals from class-specific bio-optical algorithms. This technique is demonstrated with in-situ optical measurements and an image from the SeaWiFS ocean color satellite.

Poly(ethylene glycol) analogs grafted with low molecular weight poly(ethylene imine) as non-viral gene vectors.

PubMed

Zhang, Zhenfang; Yang, Cuihong; Duan, Yajun; Wang, Yanming; Liu, Jianfeng; Wang, Lianyong; Kong, Deling

2010-07-01

A novel class of non-viral gene vectors consisting of low molecular weight poly(ethylene imine) (PEI) (molecular weight 800 Da) grafted onto degradable linear poly(ethylene glycol) (PEG) analogs was synthesized. First, a Michael addition reaction between poly(ethylene glycol) diacrylates (PEGDA) (molecular weight 258 Da) and d,l-dithiothreitol (DTT) was carried out to generate a linear polymer (PEG-DTT) having a terminal thiol, methacrylate and pendant hydroxyl functional groups. Five PEG-DTT analogs were synthesized by varying the molar ratio of diacrylates to thiols from 1.2:1 to 1:1.2. Then PEI (800 Da) was grafted onto the main chain of the PEG-DTTs using 1,1'-carbonyldiimidazole as the linker. The above reaction gave rise to a new class of non-viral gene vectors, (PEG-DTT)-g-PEI copolymers, which can effectively complex DNA to form nanoparticles. The molecular weights and structures of the copolymers were characterized by gel permeation chromatography, (1)H nuclear magnetic resonance and Fourier transform infrared spectroscopy. The size of the nanoparticles was<200 nm and the surface charge of the nanoparticles, expressed as the zeta potential, was between+20 and+40 mV. Cytotoxicity assays showed that the copolymers exhibited much lower cytotoxicities than high molecular weight PEI (25 kDa). Transfection was performed in cultured HeLa, HepG2, MCF-7 and COS-7 cells. The copolymers showed higher transfection efficiencies than PEI (25 kDa) tested in four cell lines. The presence of serum (up to 30%) had no inhibitory effect on the transfection efficiency. These results indicate that this new class of non-viral gene vectors may be a promising gene carrier that is worth further investigation. Copyright 2010 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Molecular analysis of vector genome structures after liver transduction by conventional and self-complementary adeno-associated viral serotype vectors in murine and nonhuman primate models.

PubMed

Sun, Xun; Lu, You; Bish, Lawrence T; Calcedo, Roberto; Wilson, James M; Gao, Guangping

2010-06-01

Vectors based on several new adeno-associated viral (AAV) serotypes demonstrated strong hepatocyte tropism and transduction efficiency in both small- and large-animal models for liver-directed gene transfer. Efficiency of liver transduction by AAV vectors can be further improved in both murine and nonhuman primate (NHP) animals when the vector genomes are packaged in a self-complementary (sc) format. In an attempt to understand potential molecular mechanism(s) responsible for enhanced transduction efficiency of the sc vector in liver, we performed extensive molecular studies of genome structures of conventional single-stranded (ss) and sc AAV vectors from liver after AAV gene transfer in both mice and NHPs. These included treatment with exonucleases with specific substrate preferences, single-cutter restriction enzyme digestion and polarity-specific hybridization-based vector genome mapping, and bacteriophage phi29 DNA polymerase-mediated and double-stranded circular template-specific rescue of persisted circular genomes. In mouse liver, vector genomes of both genome formats seemed to persist primarily as episomal circular forms, but sc vectors converted into circular forms more rapidly and efficiently. However, the overall differences in vector genome abundance and structure in the liver between ss and sc vectors could not account for the remarkable differences in transduction. Molecular structures of persistent genomes of both ss and sc vectors were significantly more heterogeneous in macaque liver, with noticeable structural rearrangements that warrant further characterizations.

Molecular Analysis of Vector Genome Structures After Liver Transduction by Conventional and Self-Complementary Adeno-Associated Viral Serotype Vectors in Murine and Nonhuman Primate Models

PubMed Central

Sun, Xun; Lu, You; Bish, Lawrence T.; Calcedo, Roberto; Wilson, James M.

2010-01-01

Abstract Vectors based on several new adeno-associated viral (AAV) serotypes demonstrated strong hepatocyte tropism and transduction efficiency in both small- and large-animal models for liver-directed gene transfer. Efficiency of liver transduction by AAV vectors can be further improved in both murine and nonhuman primate (NHP) animals when the vector genomes are packaged in a self-complementary (sc) format. In an attempt to understand potential molecular mechanism(s) responsible for enhanced transduction efficiency of the sc vector in liver, we performed extensive molecular studies of genome structures of conventional single-stranded (ss) and sc AAV vectors from liver after AAV gene transfer in both mice and NHPs. These included treatment with exonucleases with specific substrate preferences, single-cutter restriction enzyme digestion and polarity-specific hybridization-based vector genome mapping, and bacteriophage ϕ29 DNA polymerase-mediated and double-stranded circular template-specific rescue of persisted circular genomes. In mouse liver, vector genomes of both genome formats seemed to persist primarily as episomal circular forms, but sc vectors converted into circular forms more rapidly and efficiently. However, the overall differences in vector genome abundance and structure in the liver between ss and sc vectors could not account for the remarkable differences in transduction. Molecular structures of persistent genomes of both ss and sc vectors were significantly more heterogeneous in macaque liver, with noticeable structural rearrangements that warrant further characterizations. PMID:20113166

Baryon number and lepton universality violation in leptoquark and diquark models

NASA Astrophysics Data System (ADS)

Assad, Nima; Fornal, Bartosz; Grinstein, Benjamín

2018-02-01

We perform a systematic study of models involving leptoquarks and diquarks with masses well below the grand unification scale and demonstrate that a large class of them is excluded due to rapid proton decay. After singling out the few phenomenologically viable color triplet and sextet scenarios, we show that there exist only two leptoquark models which do not suffer from tree-level proton decay and which have the potential for explaining the recently discovered anomalies in B meson decays. Both of those models, however, contain dimension five operators contributing to proton decay and require a new symmetry forbidding them to emerge at a higher scale. This has a particularly nice realization for the model with the vector leptoquark (3 , 1) 2 / 3, which points to a specific extension of the Standard Model, namely the Pati-Salam unification model, where this leptoquark naturally arises as the new gauge boson. We explore this possibility in light of recent B physics measurements. Finally, we analyze also a vector diquark model, discussing its LHC phenomenology and showing that it has nontrivial predictions for neutron-antineutron oscillation experiments.

On efficient randomized algorithms for finding the PageRank vector

NASA Astrophysics Data System (ADS)

Gasnikov, A. V.; Dmitriev, D. Yu.

2015-03-01

Two randomized methods are considered for finding the PageRank vector; in other words, the solution of the system p T = p T P with a stochastic n × n matrix P, where n ˜ 107-109, is sought (in the class of probability distributions) with accuracy ɛ: ɛ ≫ n -1. Thus, the possibility of brute-force multiplication of P by the column is ruled out in the case of dense objects. The first method is based on the idea of Markov chain Monte Carlo algorithms. This approach is efficient when the iterative process p {/t+1 T} = p {/t T} P quickly reaches a steady state. Additionally, it takes into account another specific feature of P, namely, the nonzero off-diagonal elements of P are equal in rows (this property is used to organize a random walk over the graph with the matrix P). Based on modern concentration-of-measure inequalities, new bounds for the running time of this method are presented that take into account the specific features of P. In the second method, the search for a ranking vector is reduced to finding the equilibrium in the antagonistic matrix game where S n (1) is a unit simplex in ℝ n and I is the identity matrix. The arising problem is solved by applying a slightly modified Grigoriadis-Khachiyan algorithm (1995). This technique, like the Nazin-Polyak method (2009), is a randomized version of Nemirovski's mirror descent method. The difference is that randomization in the Grigoriadis-Khachiyan algorithm is used when the gradient is projected onto the simplex rather than when the stochastic gradient is computed. For sparse matrices P, the method proposed yields noticeably better results.

Identification and validation of a gene causing cross-resistance between insecticide classes in Anopheles gambiae from Ghana.

PubMed

Mitchell, Sara N; Stevenson, Bradley J; Müller, Pie; Wilding, Craig S; Egyir-Yawson, Alexander; Field, Stuart G; Hemingway, Janet; Paine, Mark J I; Ranson, Hilary; Donnelly, Martin James

2012-04-17

In the last decade there have been marked reductions in malaria incidence in sub-Saharan Africa. Sustaining these reductions will rely upon insecticides to control the mosquito malaria vectors. We report that in the primary African malaria vector, Anopheles gambiae sensu stricto, a single enzyme, CYP6M2, confers resistance to two classes of insecticide. This is unique evidence in a disease vector of cross-resistance associated with a single metabolic gene that simultaneously reduces the efficacy of two of the four classes of insecticide routinely used for malaria control. The gene-expression profile of a highly DDT-resistant population of A. gambiae s.s. from Ghana was characterized using a unique whole-genome microarray. A number of genes were significantly overexpressed compared with two susceptible West African colonies, including genes from metabolic families previously linked to insecticide resistance. One of the most significantly overexpressed probe groups (false-discovery rate-adjusted P < 0.0001) belonged to the cytochrome P450 gene CYP6M2. This gene is associated with pyrethroid resistance in wild A. gambiae s.s. populations) and can metabolize both type I and type II pyrethroids in recombinant protein assays. Using in vitro assays we show that recombinant CYP6M2 is also capable of metabolizing the organochlorine insecticide DDT in the presence of solubilizing factor sodium cholate.

Identification and validation of a gene causing cross-resistance between insecticide classes in Anopheles gambiae from Ghana

PubMed Central

Mitchell, Sara N.; Stevenson, Bradley J.; Müller, Pie; Wilding, Craig S.; Egyir-Yawson, Alexander; Field, Stuart G.; Hemingway, Janet; Paine, Mark J. I.; Ranson, Hilary; Donnelly, Martin James

2012-01-01

In the last decade there have been marked reductions in malaria incidence in sub-Saharan Africa. Sustaining these reductions will rely upon insecticides to control the mosquito malaria vectors. We report that in the primary African malaria vector, Anopheles gambiae sensu stricto, a single enzyme, CYP6M2, confers resistance to two classes of insecticide. This is unique evidence in a disease vector of cross-resistance associated with a single metabolic gene that simultaneously reduces the efficacy of two of the four classes of insecticide routinely used for malaria control. The gene-expression profile of a highly DDT-resistant population of A. gambiae s.s. from Ghana was characterized using a unique whole-genome microarray. A number of genes were significantly overexpressed compared with two susceptible West African colonies, including genes from metabolic families previously linked to insecticide resistance. One of the most significantly overexpressed probe groups (false-discovery rate-adjusted P < 0.0001) belonged to the cytochrome P450 gene CYP6M2. This gene is associated with pyrethroid resistance in wild A. gambiae s.s. populations) and can metabolize both type I and type II pyrethroids in recombinant protein assays. Using in vitro assays we show that recombinant CYP6M2 is also capable of metabolizing the organochlorine insecticide DDT in the presence of solubilizing factor sodium cholate. PMID:22460795

Lyme disease bacterium does not affect attraction to rodent odour in the tick vector.

PubMed

Berret, Jérémy; Voordouw, Maarten Jeroen

2015-04-28

Vector-borne pathogens experience a conflict of interest when the arthropod vector chooses a vertebrate host that is incompetent for pathogen transmission. The qualitative manipulation hypothesis suggests that vector-borne pathogens can resolve this conflict in their favour by manipulating the host choice behaviour of the arthropod vector. European Lyme disease is a model system for studying this conflict because Ixodes ricinus is a generalist tick species that vectors Borrelia pathogens that are specialized on different classes of vertebrate hosts. Avian specialists like B. garinii cannot survive in rodent reservoir hosts and vice versa for rodent specialists like B. afzelii. The present study tested whether Borrelia genospecies influenced the attraction of field-collected I. ricinus nymphs to rodent odours. Nymphs were significantly attracted to questing perches that had been scented with mouse odours. However, there was no difference in questing behaviour between nymphs infected with rodent- versus bird-specialized Borrelia genospecies. Our study suggests that the tick, and not the pathogen, controls the early stages of host choice behaviour.

78 FR 34554 - Establishment of Class E Airspace; Blue Mesa, CO

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-10

...This action establishes Class E airspace at Blue Mesa VHF Omni-Directional Radio Range/Distance Measuring Equipment (VOR/DME), Blue Mesa, CO, to facilitate vectoring of Instrument Flight Rules (IFR) aircraft under control of Denver and Albuquerque Air Route Traffic Control Centers (ARTCCs). This improves the safety and management of IFR operations within the National Airspace System.

Structural and Functional Analysis of the Human HDAC4 Catalytic Domain Reveals a Regulatory Structural Zinc-binding Domain*S⃞

PubMed Central

Bottomley, Matthew J.; Lo Surdo, Paola; Di Giovine, Paolo; Cirillo, Agostino; Scarpelli, Rita; Ferrigno, Federica; Jones, Philip; Neddermann, Petra; De Francesco, Raffaele; Steinkühler, Christian; Gallinari, Paola; Carfí, Andrea

2008-01-01

Histone deacetylases (HDACs) regulate chromatin status and gene expression, and their inhibition is of significant therapeutic interest. To date, no biological substrate for class IIa HDACs has been identified, and only low activity on acetylated lysines has been demonstrated. Here, we describe inhibitor-bound and inhibitor-free structures of the histone deacetylase-4 catalytic domain (HDAC4cd) and of an HDAC4cd active site mutant with enhanced enzymatic activity toward acetylated lysines. The structures presented, coupled with activity data, provide the molecular basis for the intrinsically low enzymatic activity of class IIa HDACs toward acetylated lysines and reveal active site features that may guide the design of class-specific inhibitors. In addition, these structures reveal a conformationally flexible structural zinc-binding domain conserved in all class IIa enzymes. Importantly, either the mutation of residues coordinating the structural zinc ion or the binding of a class IIa selective inhibitor prevented the association of HDAC4 with the N-CoR·HDAC3 repressor complex. Together, these data suggest a key role of the structural zinc-binding domain in the regulation of class IIa HDAC functions. PMID:18614528

Structural and functional analysis of the human HDAC4 catalytic domain reveals a regulatory structural zinc-binding domain.

PubMed

Bottomley, Matthew J; Lo Surdo, Paola; Di Giovine, Paolo; Cirillo, Agostino; Scarpelli, Rita; Ferrigno, Federica; Jones, Philip; Neddermann, Petra; De Francesco, Raffaele; Steinkühler, Christian; Gallinari, Paola; Carfí, Andrea

2008-09-26

Histone deacetylases (HDACs) regulate chromatin status and gene expression, and their inhibition is of significant therapeutic interest. To date, no biological substrate for class IIa HDACs has been identified, and only low activity on acetylated lysines has been demonstrated. Here, we describe inhibitor-bound and inhibitor-free structures of the histone deacetylase-4 catalytic domain (HDAC4cd) and of an HDAC4cd active site mutant with enhanced enzymatic activity toward acetylated lysines. The structures presented, coupled with activity data, provide the molecular basis for the intrinsically low enzymatic activity of class IIa HDACs toward acetylated lysines and reveal active site features that may guide the design of class-specific inhibitors. In addition, these structures reveal a conformationally flexible structural zinc-binding domain conserved in all class IIa enzymes. Importantly, either the mutation of residues coordinating the structural zinc ion or the binding of a class IIa selective inhibitor prevented the association of HDAC4 with the N-CoR.HDAC3 repressor complex. Together, these data suggest a key role of the structural zinc-binding domain in the regulation of class IIa HDAC functions.

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.

Tensor Sparse Coding for Positive Definite Matrices.

PubMed

Sivalingam, Ravishankar; Boley, Daniel; Morellas, Vassilios; Papanikolopoulos, Nikos

2013-08-02

In recent years, there has been extensive research on sparse representation of vector-valued signals. In the matrix case, the data points are merely vectorized and treated as vectors thereafter (for e.g., image patches). However, this approach cannot be used for all matrices, as it may destroy the inherent structure of the data. Symmetric positive definite (SPD) matrices constitute one such class of signals, where their implicit structure of positive eigenvalues is lost upon vectorization. This paper proposes a novel sparse coding technique for positive definite matrices, which respects the structure of the Riemannian manifold and preserves the positivity of their eigenvalues, without resorting to vectorization. Synthetic and real-world computer vision experiments with region covariance descriptors demonstrate the need for and the applicability of the new sparse coding model. This work serves to bridge the gap between the sparse modeling paradigm and the space of positive definite matrices.

Tensor sparse coding for positive definite matrices.

PubMed

Sivalingam, Ravishankar; Boley, Daniel; Morellas, Vassilios; Papanikolopoulos, Nikolaos

2014-03-01

In recent years, there has been extensive research on sparse representation of vector-valued signals. In the matrix case, the data points are merely vectorized and treated as vectors thereafter (for example, image patches). However, this approach cannot be used for all matrices, as it may destroy the inherent structure of the data. Symmetric positive definite (SPD) matrices constitute one such class of signals, where their implicit structure of positive eigenvalues is lost upon vectorization. This paper proposes a novel sparse coding technique for positive definite matrices, which respects the structure of the Riemannian manifold and preserves the positivity of their eigenvalues, without resorting to vectorization. Synthetic and real-world computer vision experiments with region covariance descriptors demonstrate the need for and the applicability of the new sparse coding model. This work serves to bridge the gap between the sparse modeling paradigm and the space of positive definite matrices.

Support Vector Machines for Multitemporal and Multisensor Change Detection in a Mining Area

NASA Astrophysics Data System (ADS)

Hecheltjen, Antje; Waske, Bjorn; Thonfeld, Frank; Braun, Matthias; Menz, Gunter

2010-12-01

Long-term change detection often implies the challenge of incorporating multitemporal data from different sensors. Most of the conventional change detection algorithms are designed for bi-temporal datasets from the same sensors detecting only the existence of changes. The labeling of change areas remains a difficult task. To overcome such drawbacks, much attention has been given lately to algorithms arising from machine learning, such as Support Vector Machines (SVMs). While SVMs have been applied successfully for land cover classifications, the exploitation of this approach for change detection is still in its infancy. Few studies have already proven the applicability of SVMs for bi- and multitemporal change detection using data from one sensor only. In this paper we demonstrate the application of SVM for multitemporal and -sensor change detection. Our study site covers lignite open pit mining areas in the German state North Rhine-Westphalia. The dataset consists of bi-temporal Landsat data and multi-temporal ERS SAR data covering two time slots (2001 and 2009). The SVM is conducted using the IDL program imageSVM. Change is deduced from one time slot to the next resulting in two change maps. In contrast to change detection, which is based on post-classification comparison, change detection is seen here as a specific classification problem. Thus, changes are directly classified from a layer-stack of the two years. To reduce the number of change classes, we created a change mask using the magnitude of Change Vector Analysis (CVA). Training data were selected for different change classes (e.g. forest to mining or mining to agriculture) as well as for the no-change classes (e.g. agriculture). Subsequently, they were divided in two independent sets for training the SVMs and accuracy assessment, respectively. Our study shows the applicability of SVMs to classify changes via SVMs. The proposed method yielded a change map of reclaimed and active mines. The use of ERS SAR data, however, did not add to the accuracy compared to Landsat data only. A great advantage compared to other change detection approaches are the labeled change maps, which are a direct output of the methodology. Our approach also overcomes the drawback of post-classification comparison, namely the propagation of classification inaccuracies.

A VLF-based technique in applications to digital control of nonlinear hybrid multirate systems

NASA Astrophysics Data System (ADS)

Vassilyev, Stanislav; Ulyanov, Sergey; Maksimkin, Nikolay

2017-01-01

In this paper, a technique for rigorous analysis and design of nonlinear multirate digital control systems on the basis of the reduction method and sublinear vector Lyapunov functions is proposed. The control system model under consideration incorporates continuous-time dynamics of the plant and discrete-time dynamics of the controller and takes into account uncertainties of the plant, bounded disturbances, nonlinear characteristics of sensors and actuators. We consider a class of multirate systems where the control update rate is slower than the measurement sampling rates and periodic non-uniform sampling is admitted. The proposed technique does not use the preliminary discretization of the system, and, hence, allows one to eliminate the errors associated with the discretization and improve the accuracy of analysis. The technique is applied to synthesis of digital controller for a flexible spacecraft in the fine stabilization mode and decentralized controller for a formation of autonomous underwater vehicles. Simulation results are provided to validate the good performance of the designed controllers.

Calibration Errors in Interferometric Radio Polarimetry

NASA Astrophysics Data System (ADS)

Hales, Christopher A.

2017-08-01

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

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

NASA Astrophysics Data System (ADS)

Vanhamäki, H.; Amm, O.; Viljanen, A.

2006-10-01

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

Camouflage target reconnaissance based on hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Hua, Wenshen; Guo, Tong; Liu, Xun

2015-08-01

Efficient camouflaged target reconnaissance technology makes great influence on modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. Hyperspectral target detection and classification technology are utilized to achieve single class and multi-class camouflaged targets reconnaissance respectively. Constrained energy minimization (CEM), a widely used algorithm in hyperspectral target detection, is employed to achieve one class camouflage target reconnaissance. Then, support vector machine (SVM), a classification method, is proposed to achieve multi-class camouflage target reconnaissance. Experiments have been conducted to demonstrate the efficiency of the proposed method.

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

PubMed

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

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

PubMed

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

2011-04-04

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

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

PubMed Central

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

2011-01-01

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

Object Classification With Joint Projection and Low-Rank Dictionary Learning.

PubMed

Foroughi, Homa; Ray, Nilanjan; Hong Zhang

2018-02-01

For an object classification system, the most critical obstacles toward real-world applications are often caused by large intra-class variability, arising from different lightings, occlusion, and corruption, in limited sample sets. Most methods in the literature would fail when the training samples are heavily occluded, corrupted or have significant illumination or viewpoint variations. Besides, most of the existing methods and especially deep learning-based methods, need large training sets to achieve a satisfactory recognition performance. Although using the pre-trained network on a generic large-scale data set and fine-tune it to the small-sized target data set is a widely used technique, this would not help when the content of base and target data sets are very different. To address these issues simultaneously, we propose a joint projection and low-rank dictionary learning method using dual graph constraints. Specifically, a structured class-specific dictionary is learned in the low-dimensional space, and the discrimination is further improved by imposing a graph constraint on the coding coefficients, that maximizes the intra-class compactness and inter-class separability. We enforce structural incoherence and low-rank constraints on sub-dictionaries to reduce the redundancy among them, and also make them robust to variations and outliers. To preserve the intrinsic structure of data, we introduce a supervised neighborhood graph into the framework to make the proposed method robust to small-sized and high-dimensional data sets. Experimental results on several benchmark data sets verify the superior performance of our method for object classification of small-sized data sets, which include a considerable amount of different kinds of variation, and may have high-dimensional feature vectors.

The underlying pathway structure of biochemical reaction networks

PubMed Central

Schilling, Christophe H.; Palsson, Bernhard O.

1998-01-01

Bioinformatics is yielding extensive, and in some cases complete, genetic and biochemical information about individual cell types and cellular processes, providing the composition of living cells and the molecular structure of its components. These components together perform integrated cellular functions that now need to be analyzed. In particular, the functional definition of biochemical pathways and their role in the context of the whole cell is lacking. In this study, we show how the mass balance constraints that govern the function of biochemical reaction networks lead to the translation of this problem into the realm of linear algebra. The functional capabilities of biochemical reaction networks, and thus the choices that cells can make, are reflected in the null space of their stoichiometric matrix. The null space is spanned by a finite number of basis vectors. We present an algorithm for the synthesis of a set of basis vectors for spanning the null space of the stoichiometric matrix, in which these basis vectors represent the underlying biochemical pathways that are fundamental to the corresponding biochemical reaction network. In other words, all possible flux distributions achievable by a defined set of biochemical reactions are represented by a linear combination of these basis pathways. These basis pathways thus represent the underlying pathway structure of the defined biochemical reaction network. This development is significant from a fundamental and conceptual standpoint because it yields a holistic definition of biochemical pathways in contrast to definitions that have arisen from the historical development of our knowledge about biochemical processes. Additionally, this new conceptual framework will be important in defining, characterizing, and studying biochemical pathways from the rapidly growing information on cellular function. PMID:9539712

Role of the major antigenic membrane protein in phytoplasma transmission by two insect vector species.

PubMed

Rashidi, Mahnaz; Galetto, Luciana; Bosco, Domenico; Bulgarelli, Andrea; Vallino, Marta; Veratti, Flavio; Marzachì, Cristina

2015-09-30

Phytoplasmas are bacterial plant pathogens (class Mollicutes), transmitted by phloem feeding leafhoppers, planthoppers and psyllids in a persistent/propagative manner. Transmission of phytoplasmas is under the control of behavioral, environmental and geographical factors, but molecular interactions between membrane proteins of phytoplasma and vectors may also be involved. The aim of the work was to provide experimental evidence that in vivo interaction between phytoplasma antigenic membrane protein (Amp) and vector proteins has a role in the transmission process. In doing so, we also investigated the topology of the interaction at the gut epithelium and at the salivary glands, the two barriers encountered by the phytoplasma during vector colonization. Experiments were performed on the 'Candidatus Phytoplasma asteris' chrysanthemum yellows strain (CYP), and the two leafhopper vectors Macrosteles quadripunctulatus Kirschbaum and Euscelidius variegatus Kirschbaum. To specifically address the interaction of CYP Amp at the gut epithelium barrier, insects were artificially fed with media containing either the recombinant phytoplasma protein Amp, or the antibody (A416) or both, and transmission, acquisition and inoculation efficiencies were measured. An abdominal microinjection protocol was employed to specifically address the interaction of CYP Amp at the salivary gland barrier. Phytoplasma suspension was added with Amp or A416 or both, injected into healthy E. variegatus adults and then infection and inoculation efficiencies were measured. An internalization assay was developed, consisting of dissected salivary glands from healthy E. variegatus exposed to phytoplasma suspension alone or together with A416 antibody. The organs were then either observed in confocal microscopy or subjected to DNA extraction and phytoplasma quantification by qPCR, to visualize and quantify possible differences among treatments in localization/presence/number of CYP cells. Artificial feeding and abdominal microinjection protocols were developed to address the two barriers separately. The in vivo interactions between Amp of 'Candidatus Phytoplasma asteris' Chrysanthemum yellows strain (CYP) and vector proteins were studied by evaluating their effects on phytoplasma transmission by Euscelidius variegatus and Macrosteles quadripunctulatus leafhoppers. An internalization assay was developed, consisting of dissected salivary glands from healthy E. variegatus exposed to phytoplasma suspension alone or together with anti-Amp antibody. To visualize possible differences among treatments in localization/presence of CYP cells, the organs were observed in confocal microscopy. Pre-feeding of E. variegatus and M. quadripunctulatus on anti-Amp antibody resulted in a significant decrease of acquisition efficiencies in both species. Inoculation efficiency of microinjected E. variegatus with CYP suspension and anti-Amp antibody was significantly reduced compared to that of the control with phytoplasma suspension only. The possibility that this was due to reduced infection efficiency or antibody-mediated inhibition of phytoplasma multiplication was ruled out. These results provided the first indirect proof of the role of Amp in the transmission process. Protocols were developed to assess the in vivo role of the phytoplasma native major antigenic membrane protein in two phases of the vector transmission process: movement through the midgut epithelium and colonization of the salivary glands. These methods will be useful also to characterize other phytoplasma-vector combinations. Results indicated for the first time that native CYP Amp is involved in vivo in specific crossing of the gut epithelium and salivary gland colonization during early phases of vector infection.

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.

Targeted delivery of non-viral vectors to cartilage in vivo using a chondrocyte-homing peptide identified by phage display.

PubMed

Pi, Yanbin; Zhang, Xin; Shi, Junjun; Zhu, Jinxian; Chen, Wenqing; Zhang, Chenguang; Gao, Weiwei; Zhou, Chunyan; Ao, Yingfang

2011-09-01

Gene therapy is a promising method for osteoarthritis and cartilage injury. However, specifically delivering target genes into chondrocytes is a great challenge because of their non-vascularity and the dense extracellular matrix of cartilage. In our study, we identified a chondrocyte-affinity peptide (CAP, DWRVIIPPRPSA) by phage display technology. Subsequent analysis suggests that the peptide can efficiently interact specifically with chondrocytes without any species specificity. Polyethylenimine (PEI) was covalently modified with CAP to construct a non-viral vector for cartilage-targeted therapy. To investigate the cartilage-targeting property of the CAP-modified vector, FITC-labeled CAP conjugated PEI/DNA particles were injected into rabbit knee joints, and visualized under confocal microscope. Higher concentrations of CAP-modified vector were detected in the cartilage and specifically taken up by chondrocytes compared with a randomly scrambled peptide (SP)-modified vector. To evaluate cartilage-targeting transfection efficiency, the GFP and luciferase genes were delivered into knee joints using CAP- and SP-modified PEI. Cartilage transfections mediated by CAP-modified PEI were much more efficient and specific than those by SP-modified PEI. This result suggests that CAP-modified PEI could be used as a specific cartilage-targeting vector for cartilage disorders. Copyright © 2011 Elsevier Ltd. All rights reserved.

Low-rate image coding using vector quantization

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

Makur, A.

1990-01-01

This thesis deals with the development and analysis of a computationally simple vector quantization image compression system for coding monochrome images at low bit rate. Vector quantization has been known to be an effective compression scheme when a low bit rate is desirable, but the intensive computation required in a vector quantization encoder has been a handicap in using it for low rate image coding. The present work shows that, without substantially increasing the coder complexity, it is indeed possible to achieve acceptable picture quality while attaining a high compression ratio. Several modifications to the conventional vector quantization coder aremore » proposed in the thesis. These modifications are shown to offer better subjective quality when compared to the basic coder. Distributed blocks are used instead of spatial blocks to construct the input vectors. A class of input-dependent weighted distortion functions is used to incorporate psychovisual characteristics in the distortion measure. Computationally simple filtering techniques are applied to further improve the decoded image quality. Finally, unique designs of the vector quantization coder using electronic neural networks are described, so that the coding delay is reduced considerably.« less

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

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

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

Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2016-12-01

In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior construction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In this paper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.

A novel strategy for the identification of antigens that are recognised by bovine MHC class I restricted cytotoxic T cells in a protozoan infection using reverse vaccinology.

PubMed

Graham, Simon P; Honda, Yoshikazu; Pellé, Roger; Mwangi, Duncan M; Glew, E Jane; de Villiers, Etienne P; Shah, Trushar; Bishop, Richard; van der Bruggen, Pierre; Nene, Vishvanath; Taracha, Evans L N

2007-02-09

Immunity against the bovine protozoan parasite Theileria parva has previously been shown to be mediated through lysis of parasite-infected cells by MHC class I restricted CD8+ cytotoxic T lymphocytes. It is hypothesized that identification of CTL target schizont antigens will aid the development of a sub-unit vaccine. We exploited the availability of the complete genome sequence data and bioinformatics tools to identify genes encoding secreted or membrane anchored proteins that may be processed and presented by the MHC class I molecules of infected cells to CTL. Of the 986 predicted open reading frames (ORFs) encoded by chromosome 1 of the T. parva genome, 55 were selected based on the presence of a signal peptide and/or a transmembrane helix domain. Thirty six selected ORFs were successfully cloned into a eukaryotic expression vector, transiently transfected into immortalized bovine skin fibroblasts and screened in vitro using T. parva-specific CTL. Recognition of gene products by CTL was assessed using an IFN-gamma ELISpot assay. A 525 base pair ORF encoding a 174 amino acid protein, designated Tp2, was identified by T. parva-specific CTL from 4 animals. These CTL recognized and lysed Tp2 transfected skin fibroblasts and recognized 4 distinct epitopes. Significantly, Tp2 specific CD8+ T cell responses were observed during the protective immune response against sporozoite challenge. The identification of an antigen containing multiple CTL epitopes and its apparent immunodominance during a protective anti-parasite response makes Tp2 an attractive candidate for evaluation of its vaccine potential.

Enhanced Vaccine-Induced CD8+ T Cell Responses to Malaria Antigen ME-TRAP by Fusion to MHC Class II Invariant Chain

PubMed Central

Spencer, Alexandra J.; Cottingham, Matthew G.; Jenks, Jennifer A.; Longley, Rhea J.; Capone, Stefania; Colloca, Stefano; Folgori, Antonella; Cortese, Riccardo; Nicosia, Alfredo; Bregu, Migena; Hill, Adrian V. S.

2014-01-01

The orthodox role of the invariant chain (CD74; Ii) is in antigen presentation to CD4+ T cells, but enhanced CD8+ T cells responses have been reported after vaccination with vectored viral vaccines encoding a fusion of Ii to the antigen of interest. In this study we assessed whether fusion of the malarial antigen, ME-TRAP, to Ii could increase the vaccine-induced CD8+ T cell response. Following single or heterologous prime-boost vaccination of mice with a recombinant chimpanzee adenovirus vector, ChAd63, or recombinant modified vaccinia virus Ankara (MVA), higher frequencies of antigen-specific CD4+ and CD8+ T cells were observed, with the largest increases observed following a ChAd63-MVA heterologous prime-boost regimen. Studies in non-human primates confirmed the ability of Ii-fusion to augment the T cell response, where a 4-fold increase was maintained up to 11 weeks after the MVA boost. Of the numerous different approaches explored to increase vectored vaccine induced immunogenicity over the years, fusion to the invariant chain showed a consistent enhancement in CD8+ T cell responses across different animal species and may therefore find application in the development of vaccines against human malaria and other diseases where high levels of cell-mediated immunity are required. PMID:24945248

A new model for CD8+ T cell memory inflation based upon a recombinant adenoviral vector1

PubMed Central

Bolinger, Beatrice; Sims, Stuart; O’Hara, Geraldine; de Lara, Catherine; Tchilian, Elma; Firner, Sonja; Engeler, Daniel; Ludewig, Burkhard; Klenerman, Paul

2013-01-01

CD8+ T cell memory inflation, first described in murine cytomegalovirus (MCMV) infection, is characterized by the accumulation of high-frequency, functional antigen-specific CD8+ T cell pools with an effector-memory phenotype and enrichment in peripheral organs. Although persistence of antigen is considered essential, the rules underpinning memory inflation are still unclear. The MCMV model is, however, complicated by the virus’s low-level persistence, and stochastic reactivation. We developed a new model of memory inflation based upon a βgal-recombinant adenovirus vector (Ad-LacZ). After i.v. administration in C57BL/6 mice we observe marked memory inflation in the βgal96 epitope, while a second epitope, βgal497, undergoes classical memory formation. The inflationary T cell responses show kinetics, distribution, phenotype and functions similar to those seen in MCMV and are reproduced using alternative routes of administration. Memory inflation in this model is dependent on MHC Class II. As in MCMV, only the inflating epitope showed immunoproteasome-independence. These data define a new model for memory inflation, which is fully replication-independent, internally controlled and reproduces the key immunologic features of the CD8+ T cell response. This model provides insight into the mechanisms responsible for memory inflation, and since it is based on a vaccine vector, also is relevant to novel T cell-inducing vaccines in humans. PMID:23509359

System for Automated Calibration of Vector Modulators

NASA Technical Reports Server (NTRS)

Lux, James; Boas, Amy; Li, Samuel

2009-01-01

Vector modulators are used to impose baseband modulation on RF signals, but non-ideal behavior limits the overall performance. The non-ideal behavior of the vector modulator is compensated using data collected with the use of an automated test system driven by a LabVIEW program that systematically applies thousands of control-signal values to the device under test and collects RF measurement data. The technology innovation automates several steps in the process. First, an automated test system, using computer controlled digital-to-analog converters (DACs) and a computer-controlled vector network analyzer (VNA) systematically can apply different I and Q signals (which represent the complex number by which the RF signal is multiplied) to the vector modulator under test (VMUT), while measuring the RF performance specifically, gain and phase. The automated test system uses the LabVIEW software to control the test equipment, collect the data, and write it to a file. The input to the Lab - VIEW program is either user-input for systematic variation, or is provided in a file containing specific test values that should be fed to the VMUT. The output file contains both the control signals and the measured data. The second step is to post-process the file to determine the correction functions as needed. The result of the entire process is a tabular representation, which allows translation of a desired I/Q value to the required analog control signals to produce a particular RF behavior. In some applications, corrected performance is needed only for a limited range. If the vector modulator is being used as a phase shifter, there is only a need to correct I and Q values that represent points on a circle, not the entire plane. This innovation has been used to calibrate 2-GHz MMIC (monolithic microwave integrated circuit) vector modulators in the High EIRP Cluster Array project (EIRP is high effective isotropic radiated power). These calibrations were then used to create correction tables to allow the commanding of the phase shift in each of four channels used as a phased array for beam steering of a Ka-band (32-GHz) signal. The system also was the basis of a breadboard electronic beam steering system. In this breadboard, the goal was not to make systematic measurements of the properties of a vector modulator, but to drive the breadboard with a series of test patterns varying in phase and amplitude. This is essentially the same calibration process, but with the difference that the data collection process is oriented toward collecting breadboard performance, rather than the measurement of output from a network analyzer.

Genotoxicity of retroviral hematopoietic stem cell gene therapy

PubMed Central

Trobridge, Grant D

2012-01-01

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

Research of facial feature extraction based on MMC

NASA Astrophysics Data System (ADS)

Xue, Donglin; Zhao, Jiufen; Tang, Qinhong; Shi, Shaokun

2017-07-01

Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.

User's guide to PANCOR: A panel method program for interference assessment in slotted-wall wind tunnels

NASA Technical Reports Server (NTRS)

Kemp, William B., Jr.

1990-01-01

Guidelines are presented for use of the computer program PANCOR to assess the interference due to tunnel walls and model support in a slotted wind tunnel test section at subsonic speeds. Input data requirements are described in detail and program output and general program usage are described. The program is written for effective automatic vectorization on a CDC CYBER 200 class vector processing system.

On Traveling Waves in Lattices: The Case of Riccati Lattices

NASA Astrophysics Data System (ADS)

Dimitrova, Zlatinka

2012-09-01

The method of simplest equation is applied for analysis of a class of lattices described by differential-difference equations that admit traveling-wave solutions constructed on the basis of the solution of the Riccati equation. We denote such lattices as Riccati lattices. We search for Riccati lattices within two classes of lattices: generalized Lotka-Volterra lattices and generalized Holling lattices. We show that from the class of generalized Lotka-Volterra lattices only the Wadati lattice belongs to the class of Riccati lattices. Opposite to this many lattices from the Holling class are Riccati lattices. We construct exact traveling wave solutions on the basis of the solution of Riccati equation for three members of the class of generalized Holling lattices.

Metagenomic Virome Analysis of Culex Mosquitoes from Kenya and China

PubMed Central

Atoni, Evans; Wang, Yujuan; Karungu, Samuel; Waruhiu, Cecilia; Zohaib, Ali; Obanda, Vincent; Agwanda, Bernard; Mutua, Morris; Xia, Han; Yuan, Zhiming

2018-01-01

Many blood-feeding arthropods are known vectors of viruses that are a source of unprecedented global health concern. Mosquitoes are an integral part of these arthropod vectors. Advancements in next-generation sequencing and bioinformatics has expanded our knowledge on the richness of viruses harbored by arthropods. In the present study, we applied a metagenomic approach to determine the intercontinental virome diversity of Culex quinquefasciatus and Culex tritaeniorhynchus in Kwale, Kenya and provinces of Hubei and Yunnan in China. Our results showed that viromes from the three locations were strikingly diverse and comprised 30 virus families specific to vertebrates, invertebrates, plants, and protozoa as well as unclassified group of viruses. Though sampled at different times, both Kwale and Hubei mosquito viromes were dominated by vertebrate viruses, in contrast to the Yunnan mosquito virome, which was dominated by insect-specific viruses. However, each virome was unique in terms of virus proportions partly influenced by type of ingested meals (blood, nectar, plant sap, environment substrates). The dominant vertebrate virus family in the Kwale virome was Papillomaviridae (57%) while in Hubei it was Herpesviridae (30%) and the Yunnan virome was dominated by an unclassified viruses group (27%). Given that insect-specific viruses occur naturally in their hosts, they should be the basis for defining the viromes. Hence, the dominant insect-specific viruses in Kwale, Hubei, and Yunnan were Baculoviridae, Nimaviridae and Iflaviridae, respectively. Our study is preliminary but contributes to growing and much needed knowledge, as mosquito viromes could be manipulated to prevent and control pathogenic arboviruses. PMID:29329230

Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.

PubMed

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

Image Classification of Human Carcinoma Cells Using Complex Wavelet-Based Covariance Descriptors

PubMed Central

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A. Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-WT) coefficients and several morphological attributes are computed. Directionally selective DT-WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html. PMID:23341908

Habitat use and tolerance levels of macroinvertebrates concerning hydraulic stress in hydropeaking rivers - A case study at the Ziller River in Austria.

PubMed

Leitner, P; Hauer, C; Graf, W

2017-01-01

Artificial flow fluctuations due to the operation of hydropower plants, frequently described as hydropeaking, result in a constant decrease of biomass of specific macrozoobenthos (MZB) taxa. For the presented case study, we assessed three reaches in the Ziller River catchment. At each sampling reach we performed the Multi-Habitat-Sampling (MHS) method with a Water Framework Directive (WFD) compliant AQEM/MHS net according to the Austrian guideline. Additionally, a hydraulic-specific sampling was conducted with a modified Box (Surber) sampler. As a basis for predictive habitat modelling of the MZB fauna, we measured abiotic parameters like mean (v 40 ) and bottom-near (v bottom ) flow rate or water depth respectively, for each box sample. In addition, the choriotope type, representing grain size classes, was determined. One of the main results is, that the national status assessment was not capable to reflect the impact of pulse release at the investigated river stretches on the basis of status classes. Moreover, we figured out that 1) habitats of stagnophilic macroinvertebrate taxa are minimized in channelized stretches affected by hydropeaking, leading to heavy quantitative losses for populations, becoming apparent in significant decreases in total individual numbers and biomass for many taxa. 2) The minor respond of the ecological status class in affected stretches by applying the WFD compliant national assessment method for macroinvertebrates owes to the tolerance of rheobiont or rheophilic taxa commonly classified as indicators for good conditions regarding saprobity or degradation score. 3) A development of a stressor-specific sampling design is required as the MHS method largely ignores vulnerable habitats. 4) The habitat suitability of selected species provides efficient expertise for impact assessment and mitigation measure design in terms of predictive habitat modelling. Copyright © 2016 Elsevier B.V. All rights reserved.

An intertwined method for making low-rank, sum-of-product basis functions that makes it possible to compute vibrational spectra of molecules with more than 10 atoms

PubMed Central

Thomas, Phillip S.

2017-01-01

We propose a method for solving the vibrational Schrödinger equation with which one can compute spectra for molecules with more than ten atoms. It uses sum-of-product (SOP) basis functions stored in a canonical polyadic tensor format and generated by evaluating matrix-vector products. By doing a sequence of partial optimizations, in each of which the factors in a SOP basis function for a single coordinate are optimized, the rank of the basis functions is reduced as matrix-vector products are computed. This is better than using an alternating least squares method to reduce the rank, as is done in the reduced-rank block power method. Partial optimization is better because it speeds up the calculation by about an order of magnitude and allows one to significantly reduce the memory cost. We demonstrate the effectiveness of the new method by computing vibrational spectra of two molecules, ethylene oxide (C2H4O) and cyclopentadiene (C5H6), with 7 and 11 atoms, respectively. PMID:28571348

An intertwined method for making low-rank, sum-of-product basis functions that makes it possible to compute vibrational spectra of molecules with more than 10 atoms.

PubMed

Thomas, Phillip S; Carrington, Tucker

2017-05-28

We propose a method for solving the vibrational Schrödinger equation with which one can compute spectra for molecules with more than ten atoms. It uses sum-of-product (SOP) basis functions stored in a canonical polyadic tensor format and generated by evaluating matrix-vector products. By doing a sequence of partial optimizations, in each of which the factors in a SOP basis function for a single coordinate are optimized, the rank of the basis functions is reduced as matrix-vector products are computed. This is better than using an alternating least squares method to reduce the rank, as is done in the reduced-rank block power method. Partial optimization is better because it speeds up the calculation by about an order of magnitude and allows one to significantly reduce the memory cost. We demonstrate the effectiveness of the new method by computing vibrational spectra of two molecules, ethylene oxide (C 2 H 4 O) and cyclopentadiene (C 5 H 6 ), with 7 and 11 atoms, respectively.

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

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

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

2015-06-15

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

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Learning Styles in the Classroom: Educational Benefit or Planning Exercise?

ERIC Educational Resources Information Center

Allcock, Sarah J.; Hulme, Julie A.

2010-01-01

Differentiation of teaching is encouraged to accommodate student diversity. This study investigated whether using learning styles as a basis for differentiation improved A-level student performance, compared to differentiation on the basis of academic ability. Matched classes of A-level psychology students participated. In one class, learning…

Balancing aggregation and smoothing errors in inverse models

DOE PAGES

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

2015-06-30

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

Balancing aggregation and smoothing errors in inverse models

NASA Astrophysics Data System (ADS)

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

2015-01-01

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

Balancing aggregation and smoothing errors in inverse models

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

Disabled infectious single cycle-herpes simplex virus (DISC-HSV) as a vector for immunogene therapy of cancer.

PubMed

Rees, Robert C; McArdle, Stephanie; Mian, Shahid; Li, Geng; Ahmad, Murrium; Parkinson, Richard; Ali, Selman A

2002-02-01

Disabled infectious single cycle-herpes simplex viruses (DISC-HSV) have been shown to be safe for use in humans and may be considered efficacious as vectors for immunogene therapy in cancer. Preclinical studies show that DISC-HSV is an efficient delivery system for cytokine genes and antigens. DISC-HSV infects a high proportion of cells, resulting in rapid gene expression for at least 72 h. The DISC-HSV-mGM-CSF vector, when inoculated into tumors, induces tumor regression in a high percentage of animals, concomitant with establishing a cytotoxic T-cell response, which is MHC class I restricted and directed against peptides of known tumor antigens. The inherent properties of DISC-HSV makes it a suitable vector for consideration in human immunogene therapy trials.

Diffeomorphism invariance and black hole entropy

NASA Astrophysics Data System (ADS)

Huang, Chao-Guang; Guo, Han-Ying; Wu, Xiaoning

2003-11-01

The Noether-charge and the Hamiltonian realizations for the diff(M) algebra in diffeomorphism-invariant gravitational theories without a cosmological constant in any dimension are studied in a covariant formalism. We analyze how the Hamiltonian functionals form the diff(M) algebra under the Poisson brackets and show how the Noether charges with respect to the diffeomorphism generated by the vector fields and their variations in n-dimensional general relativity form this algebra. The asymptotic behaviors of vector fields generating diffeomorphism of the manifold with boundaries are discussed. It is shown that the “central extension” for a large class of vector fields is always zero on the Killing horizon. We also check whether choosing the vector fields near the horizon may pick up the Virasoro algebra. The conclusion is unfortunately negative in any dimension.

Configurations of a two-tiered amplified gene expression system in adenoviral vectors designed to improve the specificity of in vivo prostate cancer imaging

PubMed Central

Sato, M; Figueiredo, ML; Burton, JB; Johnson, M; Chen, M; Powell, R; Gambhir, SS; Carey, M; Wu, L

2009-01-01

Effective treatment for recurrent, disseminated prostate cancer is notably limited. We have developed adenoviral vectors with a prostate-specific two-step transcriptional amplification (TSTA) system that would express therapeutic genes at a robust level to target metastatic disease. The TSTA system employs the prostate-specific antigen (PSA) promoter/enhancer to drive a potent synthetic activator, which in turn activates the expression of the therapeutic gene. In this study, we explored different configurations of this bipartite system and discovered that physical separation of the two TSTA components into E1 and E3 regions of adenovirus was able to enhance androgen regulation and cell-discriminatory expression. The TSTA vectors that express imaging reporter genes were assessed by noninvasive imaging technologies in animal models. The improved selectivity of the E1E3 configured vector was reflected in silenced ectopic expression in the lung. Significantly, the enhanced specificity of the E1E3 vector enabled the detection of lung metastasis of prostate cancer. An E1E3 TSTA vector that expresses the herpes simplex virus thymidine kinase gene can effectively direct positron emission tomography (PET) imaging of the tumor. The prostate-targeted gene delivery vectors with robust and cell-specific expression capability will advance the development of safe and effective imaging guided therapy for recurrent metastatic stages of prostate cancer. PMID:18305574