WorldWideScience

Sample records for type-specific sparse labeling

  1. Genetically-directed, cell type-specific sparse labeling for the analysis of neuronal morphology.

    Directory of Open Access Journals (Sweden)

    Thomas Rotolo

    Full Text Available In mammals, genetically-directed cell labeling technologies have not yet been applied to the morphologic analysis of neurons with very large and complex arbors, an application that requires extremely sparse labeling and that is only rendered practical by limiting the labeled population to one or a few predetermined neuronal subtypes.In the present study we have addressed this application by using CreER technology to non-invasively label very small numbers of neurons so that their morphologies can be fully visualized. Four lines of IRES-CreER knock-in mice were constructed to permit labeling selectively in cholinergic or catecholaminergic neurons [choline acetyltransferase (ChAT-IRES-CreER or tyrosine hydroxylase (TH-IRES-CreER], predominantly in projection neurons [neurofilament light chain (NFL-IRES-CreER], or broadly in neurons and some glia [vesicle-associated membrane protein2 (VAMP2-IRES-CreER]. When crossed to the Z/AP reporter and exposed to 4-hydroxytamoxifen in the early postnatal period, the number of neurons expressing the human placental alkaline phosphatase reporter can be reproducibly lowered to fewer than 50 per brain. Sparse Cre-mediated recombination in ChAT-IRES-CreER;Z/AP mice shows the full axonal and dendritic arbors of individual forebrain cholinergic neurons, the first time that the complete morphologies of these very large neurons have been revealed in any species.Sparse genetically-directed, cell type-specific neuronal labeling with IRES-creER lines should prove useful for studying a wide variety of questions in neuronal development and disease.

  2. Direct methods and residue type specific isotope labeling in NMR structure determination and model-driven sequential assignment

    International Nuclear Information System (INIS)

    Schedlbauer, Andreas; Auer, Renate; Ledolter, Karin; Tollinger, Martin; Kloiber, Karin; Lichtenecker, Roman; Ruedisser, Simon; Hommel, Ulrich; Schmid, Walther; Konrat, Robert; Kontaxis, Georg

    2008-01-01

    Direct methods in NMR based structure determination start from an unassigned ensemble of unconnected gaseous hydrogen atoms. Under favorable conditions they can produce low resolution structures of proteins. Usually a prohibitively large number of NOEs is required, to solve a protein structure ab-initio, but even with a much smaller set of distance restraints low resolution models can be obtained which resemble a protein fold. One problem is that at such low resolution and in the absence of a force field it is impossible to distinguish the correct protein fold from its mirror image. In a hybrid approach these ambiguous models have the potential to aid in the process of sequential backbone chemical shift assignment when 13 C β and 13 C' shifts are not available for sensitivity reasons. Regardless of the overall fold they enhance the information content of the NOE spectra. These, combined with residue specific labeling and minimal triple-resonance data using 13 C α connectivity can provide almost complete sequential assignment. Strategies for residue type specific labeling with customized isotope labeling patterns are of great advantage in this context. Furthermore, this approach is to some extent error-tolerant with respect to data incompleteness, limited precision of the peak picking, and structural errors caused by misassignment of NOEs

  3. Binning sequences using very sparse labels within a metagenome

    Directory of Open Access Journals (Sweden)

    Halgamuge Saman K

    2008-04-01

    Full Text Available Abstract Background In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels to assign other reads based on their compositional similarity. Results The proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM, and called Seeded GSOM (S-GSOM. We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments sampled from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds. The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, k-mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all k-mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the ≥ 10 reads datasets and comparable in the ≥ 8 kb benchmark tests. Conclusion In the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of

  4. Porosity estimation by semi-supervised learning with sparsely available labeled samples

    Science.gov (United States)

    Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi

    2017-09-01

    This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.

  5. Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization.

    Science.gov (United States)

    Liao, Shu; Gao, Yaozong; Shi, Yinghuan; Yousuf, Ambereen; Karademir, Ibrahim; Oto, Aytekin; Shen, Dinggang

    2013-01-01

    Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.

  6. Optimization of amino acid type-specific 13C and 15N labeling for the backbone assignment of membrane proteins by solution- and solid-state NMR with the UPLABEL algorithm

    International Nuclear Information System (INIS)

    Hefke, Frederik; Bagaria, Anurag; Reckel, Sina; Ullrich, Sandra Johanna; Dötsch, Volker; Glaubitz, Clemens; Güntert, Peter

    2011-01-01

    We present a computational method for finding optimal labeling patterns for the backbone assignment of membrane proteins and other large proteins that cannot be assigned by conventional strategies. Following the approach of Kainosho and Tsuji (Biochemistry 21:6273–6279 (1982)), types of amino acids are labeled with 13 C or/and 15 N such that cross peaks between 13 CO(i – 1) and 15 NH(i) result only for pairs of sequentially adjacent amino acids of which the first is labeled with 13 C and the second with 15 N. In this way, unambiguous sequence-specific assignments can be obtained for unique pairs of amino acids that occur exactly once in the sequence of the protein. To be practical, it is crucial to limit the number of differently labeled protein samples that have to be prepared while obtaining an optimal extent of labeled unique amino acid pairs. Our computer algorithm UPLABEL for optimal unique pair labeling, implemented in the program CYANA and in a standalone program, and also available through a web portal, uses combinatorial optimization to find for a given amino acid sequence labeling patterns that maximize the number of unique pair assignments with a minimal number of differently labeled protein samples. Various auxiliary conditions, including labeled amino acid availability and price, previously known partial assignments, and sequence regions of particular interest can be taken into account when determining optimal amino acid type-specific labeling patterns. The method is illustrated for the assignment of the human G-protein coupled receptor bradykinin B2 (B 2 R) and applied as a starting point for the backbone assignment of the membrane protein proteorhodopsin.

  7. Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

    OpenAIRE

    Tong, Tong; Wolz, Robin; Coupe, Pierrick; Hajnal, Joseph V.; Rueckert, Daniel

    2013-01-01

    International audience; We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labe...

  8. Sparse "1"3C labelling for solid-state NMR studies of P. pastoris expressed eukaryotic seven-transmembrane proteins

    International Nuclear Information System (INIS)

    Liu, Jing; Liu, Chang; Fan, Ying; Munro, Rachel A.; Ladizhansky, Vladimir; Brown, Leonid S.; Wang, Shenlin

    2016-01-01

    We demonstrate a novel sparse "1"3C labelling approach for methylotrophic yeast P. pastoris expression system, towards solid-state NMR studies of eukaryotic membrane proteins. The labelling scheme was achieved by co-utilizing natural abundance methanol and specifically "1"3C labelled glycerol as carbon sources in the expression medium. This strategy improves the spectral resolution by 1.5 fold, displays site-specific labelling patterns, and has advantages for collecting long-range distance restraints for structure determination of large eukaryotic membrane proteins by solid-state NMR.

  9. Application of amino acid type-specific 1H- and 14N-labeling in a 2H-, 15N-labeled background to a 47 kDa homodimer: Potential for NMR structure determination of large proteins

    International Nuclear Information System (INIS)

    Kelly, Mark J.S.; Krieger, Cornelia; Ball, Linda J.; Yu Yihua; Richter, Gerald; Schmieder, Peter; Bacher, Adelbert; Oschkinat, Hartmut

    1999-01-01

    NMR investigations of larger macromolecules (>20 kDa) are severely hindered by rapid 1H and 13C transverse relaxation. Replacement of non-exchangeable protons with deuterium removes many efficient 1H-1H and 1H-13C relaxation pathways. The main disadvantage of deuteration is that many of the protons which would normally be the source of NOE-based distance restraints are removed. We report the development of a novel labeling strategy which is based on specific protonation and 14N-labeling of the residues phenylalanine, tyrosine, threonine, isoleucine and valine in a fully deuterated, 15N-labeled background. This allows the application of heteronuclear half-filters, 15N-editing and 1H-TOCSY experiments to select for particular magnetization transfer pathways. Results from investigations of a 47 kDa dimeric protein labeled in this way demonstrated that the method provides useful information for the structure determination of large proteins

  10. NMR characterization of HtpG, the E. coli Hsp90, using sparse labeling with 13C-methyl alanine.

    Science.gov (United States)

    Pederson, Kari; Chalmers, Gordon R; Gao, Qi; Elnatan, Daniel; Ramelot, Theresa A; Ma, Li-Chung; Montelione, Gaetano T; Kennedy, Michael A; Agard, David A; Prestegard, James H

    2017-07-01

    A strategy for acquiring structural information from sparsely isotopically labeled large proteins is illustrated with an application to the E. coli heat-shock protein, HtpG (high temperature protein G), a 145 kDa dimer. It uses 13 C-alanine methyl labeling in a perdeuterated background to take advantage of the sensitivity and resolution of Methyl-TROSY spectra, as well as the backbone-centered structural information from 1 H- 13 C residual dipolar couplings (RDCs) of alanine methyl groups. In all, 40 of the 47 expected crosspeaks were resolved and 36 gave RDC data. Assignments of crosspeaks were partially achieved by transferring assignments from those made on individual domains using triple resonance methods. However, these were incomplete and in many cases the transfer was ambiguous. A genetic algorithm search for consistency between predictions based on domain structures and measurements for chemical shifts and RDCs allowed 60% of the 40 resolved crosspeaks to be assigned with confidence. Chemical shift changes of these crosspeaks on adding an ATP analog to the apo-protein are shown to be consistent with structural changes expected on comparing previous crystal structures for apo- and complex- structures. RDCs collected on the assigned alanine methyl peaks are used to generate a new solution model for the apo-protein structure.

  11. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan; Gao, Xin

    2014-01-01

    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

  12. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-07-06

    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

  13. Supervised Transfer Sparse Coding

    KAUST Repository

    Al-Shedivat, Maruan

    2014-07-27

    A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

  14. Discriminative sparse coding on multi-manifolds

    KAUST Repository

    Wang, J.J.-Y.; Bensmail, H.; Yao, N.; Gao, Xin

    2013-01-01

    Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

  15. Discriminative sparse coding on multi-manifolds

    KAUST Repository

    Wang, J.J.-Y.

    2013-09-26

    Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

  16. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

    2014-01-01

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

  17. Cell Type-Specific Contributions to the TSC Neuropathology

    Science.gov (United States)

    2017-08-01

    AWARD NUMBER: W81XWH-16-1-0415 TITLE: Cell Type-Specific Contributions to the TSC Neuropathology PRINCIPAL INVESTIGATOR: Gabriella D’Arcangelo...AND SUBTITLE Cell Type-Specific Contributions to the TSC Neuropathology 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-16-1-0415 5c. PROGRAM...how heterozygous and homozygous Tsc2 mutations affect the development of mutant excitatory neurons as well as other surrounding brain cells , in vivo

  18. Structural Sparse Tracking

    KAUST Repository

    Zhang, Tianzhu

    2015-06-01

    Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.

  19. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-04-17

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

  20. Structural Sparse Tracking

    KAUST Repository

    Zhang, Tianzhu; Yang, Ming-Hsuan; Ahuja, Narendra; Ghanem, Bernard; Yan, Shuicheng; Xu, Changsheng; Liu, Si

    2015-01-01

    candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs

  1. SparseM: A Sparse Matrix Package for R *

    Directory of Open Access Journals (Sweden)

    Roger Koenker

    2003-02-01

    Full Text Available SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.

  2. Sparse distributed memory overview

    Science.gov (United States)

    Raugh, Mike

    1990-01-01

    The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

  3. Efficient convolutional sparse coding

    Science.gov (United States)

    Wohlberg, Brendt

    2017-06-20

    Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

  4. Sparse approximation with bases

    CERN Document Server

    2015-01-01

    This book systematically presents recent fundamental results on greedy approximation with respect to bases. Motivated by numerous applications, the last decade has seen great successes in studying nonlinear sparse approximation. Recent findings have established that greedy-type algorithms are suitable methods of nonlinear approximation in both sparse approximation with respect to bases and sparse approximation with respect to redundant systems. These insights, combined with some previous fundamental results, form the basis for constructing the theory of greedy approximation. Taking into account the theoretical and practical demand for this kind of theory, the book systematically elaborates a theoretical framework for greedy approximation and its applications.  The book addresses the needs of researchers working in numerical mathematics, harmonic analysis, and functional analysis. It quickly takes the reader from classical results to the latest frontier, but is written at the level of a graduate course and do...

  5. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed

    2018-04-08

    Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

  6. Discrete Sparse Coding.

    Science.gov (United States)

    Exarchakis, Georgios; Lücke, Jörg

    2017-11-01

    Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

  7. Sparse inpainting and isotropy

    Energy Technology Data Exchange (ETDEWEB)

    Feeney, Stephen M.; McEwen, Jason D.; Peiris, Hiranya V. [Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT (United Kingdom); Marinucci, Domenico; Cammarota, Valentina [Department of Mathematics, University of Rome Tor Vergata, via della Ricerca Scientifica 1, Roma, 00133 (Italy); Wandelt, Benjamin D., E-mail: s.feeney@imperial.ac.uk, E-mail: marinucc@axp.mat.uniroma2.it, E-mail: jason.mcewen@ucl.ac.uk, E-mail: h.peiris@ucl.ac.uk, E-mail: wandelt@iap.fr, E-mail: cammarot@axp.mat.uniroma2.it [Kavli Institute for Theoretical Physics, Kohn Hall, University of California, 552 University Road, Santa Barbara, CA, 93106 (United States)

    2014-01-01

    Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.

  8. Sparse matrix test collections

    Energy Technology Data Exchange (ETDEWEB)

    Duff, I.

    1996-12-31

    This workshop will discuss plans for coordinating and developing sets of test matrices for the comparison and testing of sparse linear algebra software. We will talk of plans for the next release (Release 2) of the Harwell-Boeing Collection and recent work on improving the accessibility of this Collection and others through the World Wide Web. There will only be three talks of about 15 to 20 minutes followed by a discussion from the floor.

  9. Neuronal survival in the brain: neuron type-specific mechanisms

    DEFF Research Database (Denmark)

    Pfisterer, Ulrich Gottfried; Khodosevich, Konstantin

    2017-01-01

    Neurogenic regions of mammalian brain produce many more neurons that will eventually survive and reach a mature stage. Developmental cell death affects both embryonically produced immature neurons and those immature neurons that are generated in regions of adult neurogenesis. Removal of substantial...... numbers of neurons that are not yet completely integrated into the local circuits helps to ensure that maturation and homeostatic function of neuronal networks in the brain proceed correctly. External signals from brain microenvironment together with intrinsic signaling pathways determine whether...... for survival in a certain brain region. This review focuses on how immature neurons survive during normal and impaired brain development, both in the embryonic/neonatal brain and in brain regions associated with adult neurogenesis, and emphasizes neuron type-specific mechanisms that help to survive for various...

  10. Cell-type-specific expression of NFIX in the developing and adult cerebellum.

    Science.gov (United States)

    Fraser, James; Essebier, Alexandra; Gronostajski, Richard M; Boden, Mikael; Wainwright, Brandon J; Harvey, Tracey J; Piper, Michael

    2017-07-01

    Transcription factors from the nuclear factor one (NFI) family have been shown to play a central role in regulating neural progenitor cell differentiation within the embryonic and post-natal brain. NFIA and NFIB, for instance, promote the differentiation and functional maturation of granule neurons within the cerebellum. Mice lacking Nfix exhibit delays in the development of neuronal and glial lineages within the cerebellum, but the cell-type-specific expression of this transcription factor remains undefined. Here, we examined the expression of NFIX, together with various cell-type-specific markers, within the developing and adult cerebellum using both chromogenic immunohistochemistry and co-immunofluorescence labelling and confocal microscopy. In embryos, NFIX was expressed by progenitor cells within the rhombic lip and ventricular zone. After birth, progenitor cells within the external granule layer, as well as migrating and mature granule neurons, expressed NFIX. Within the adult cerebellum, NFIX displayed a broad expression profile, and was evident within granule cells, Bergmann glia, and interneurons, but not within Purkinje neurons. Furthermore, transcriptomic profiling of cerebellar granule neuron progenitor cells showed that multiple splice variants of Nfix are expressed within this germinal zone of the post-natal brain. Collectively, these data suggest that NFIX plays a role in regulating progenitor cell biology within the embryonic and post-natal cerebellum, as well as an ongoing role within multiple neuronal and glial populations within the adult cerebellum.

  11. Compressed sensing & sparse filtering

    CERN Document Server

    Carmi, Avishy Y; Godsill, Simon J

    2013-01-01

    This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related app

  12. Cell-Type-Specific Splicing of Piezo2 Regulates Mechanotransduction

    Directory of Open Access Journals (Sweden)

    Marcin Szczot

    2017-12-01

    Full Text Available Summary: Piezo2 is a mechanically activated ion channel required for touch discrimination, vibration detection, and proprioception. Here, we discovered that Piezo2 is extensively spliced, producing different Piezo2 isoforms with distinct properties. Sensory neurons from both mice and humans express a large repertoire of Piezo2 variants, whereas non-neuronal tissues express predominantly a single isoform. Notably, even within sensory ganglia, we demonstrate the splicing of Piezo2 to be cell type specific. Biophysical characterization revealed substantial differences in ion permeability, sensitivity to calcium modulation, and inactivation kinetics among Piezo2 splice variants. Together, our results describe, at the molecular level, a potential mechanism by which transduction is tuned, permitting the detection of a variety of mechanosensory stimuli. : Szczot et al. find that the mechanoreceptor Piezo2 is extensively alternatively spliced, generating multiple distinct isoforms. Their findings indicate that these splice products have specific tissue and cell type expression patterns and exhibit differences in receptor properties. Keywords: Piezo, touch, sensation, ion-channel, splicing

  13. Cardiac Glycoside Glucoevatromonoside Induces Cancer Type-Specific Cell Death

    Directory of Open Access Journals (Sweden)

    Naira F. Z. Schneider

    2018-03-01

    Full Text Available Cardiac glycosides (CGs are natural compounds used traditionally to treat congestive heart diseases. Recent investigations repositioned CGs as potential anticancer agents. To discover novel cytotoxic CG scaffolds, we selected the cardenolide glucoevatromonoside (GEV out of 46 CGs for its low nanomolar anti-lung cancer activity. GEV presented reduced toxicity toward non-cancerous cell types (lung MRC-5 and PBMC and high-affinity binding to the Na+/K+-ATPase α subunit, assessed by computational docking. GEV-induced cell death was caspase-independent, as investigated by a multiparametric approach, and culminates in severe morphological alterations in A549 cells, monitored by transmission electron microscopy, live cell imaging and flow cytometry. This non-canonical cell death was not preceded or accompanied by exacerbation of autophagy. In the presence of GEV, markers of autophagic flux (e.g. LC3I-II conversion were impacted, even in presence of bafilomycin A1. Cell death induction remained unaffected by calpain, cathepsin, parthanatos, or necroptosis inhibitors. Interestingly, GEV triggered caspase-dependent apoptosis in U937 acute myeloid leukemia cells, witnessing cancer-type specific cell death induction. Differential cell cycle modulation by this CG led to a G2/M arrest, cyclin B1 and p53 downregulation in A549, but not in U937 cells. We further extended the anti-cancer potential of GEV to 3D cell culture using clonogenic and spheroid formation assays and validated our findings in vivo by zebrafish xenografts. Altogether, GEV shows an interesting anticancer profile with the ability to exert cytotoxic effects via induction of different cell death modalities.

  14. Sparse distributed memory

    Science.gov (United States)

    Denning, Peter J.

    1989-01-01

    Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.

  15. Parallel Sparse Matrix - Vector Product

    DEFF Research Database (Denmark)

    Alexandersen, Joe; Lazarov, Boyan Stefanov; Dammann, Bernd

    This technical report contains a case study of a sparse matrix-vector product routine, implemented for parallel execution on a compute cluster with both pure MPI and hybrid MPI-OpenMP solutions. C++ classes for sparse data types were developed and the report shows how these class can be used...

  16. Sparse decompositions in 'incoherent' dictionaries

    DEFF Research Database (Denmark)

    Gribonval, R.; Nielsen, Morten

    2003-01-01

    a unique sparse representation in such a dictionary. In particular, it is proved that the result of Donoho and Huo, concerning the replacement of a combinatorial optimization problem with a linear programming problem when searching for sparse representations, has an analog for dictionaries that may...

  17. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-12-01

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

  18. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-04-11

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.

  19. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup; Swanson, Robin; Heide, Felix; Wetzstein, Gordon; Heidrich, Wolfgang

    2017-01-01

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

  20. Turbulent flows over sparse canopies

    Science.gov (United States)

    Sharma, Akshath; García-Mayoral, Ricardo

    2018-04-01

    Turbulent flows over sparse and dense canopies exerting a similar drag force on the flow are investigated using Direct Numerical Simulations. The dense canopies are modelled using a homogeneous drag force, while for the sparse canopy, the geometry of the canopy elements is represented. It is found that on using the friction velocity based on the local shear at each height, the streamwise velocity fluctuations and the Reynolds stress within the sparse canopy are similar to those from a comparable smooth-wall case. In addition, when scaled with the local friction velocity, the intensity of the off-wall peak in the streamwise vorticity for sparse canopies also recovers a value similar to a smooth-wall. This indicates that the sparse canopy does not significantly disturb the near-wall turbulence cycle, but causes its rescaling to an intensity consistent with a lower friction velocity within the canopy. In comparison, the dense canopy is found to have a higher damping effect on the turbulent fluctuations. For the case of the sparse canopy, a peak in the spectral energy density of the wall-normal velocity, and Reynolds stress is observed, which may indicate the formation of Kelvin-Helmholtz-like instabilities. It is also found that a sparse canopy is better modelled by a homogeneous drag applied on the mean flow alone, and not the turbulent fluctuations.

  1. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

    2015-01-01

    predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths

  2. In Defense of Sparse Tracking: Circulant Sparse Tracker

    KAUST Repository

    Zhang, Tianzhu; Bibi, Adel Aamer; Ghanem, Bernard

    2016-01-01

    Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

  3. In Defense of Sparse Tracking: Circulant Sparse Tracker

    KAUST Repository

    Zhang, Tianzhu

    2016-12-13

    Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

  4. Language Recognition via Sparse Coding

    Science.gov (United States)

    2016-09-08

    explanation is that sparse coding can achieve a near-optimal approximation of much complicated nonlinear relationship through local and piecewise linear...training examples, where x(i) ∈ RN is the ith example in the batch. Optionally, X can be normalized and whitened before sparse coding for better result...normalized input vectors are then ZCA- whitened [20]. Em- pirically, we choose ZCA- whitening over PCA- whitening , and there is no dimensionality reduction

  5. Shearlets and Optimally Sparse Approximations

    DEFF Research Database (Denmark)

    Kutyniok, Gitta; Lemvig, Jakob; Lim, Wang-Q

    2012-01-01

    Multivariate functions are typically governed by anisotropic features such as edges in images or shock fronts in solutions of transport-dominated equations. One major goal both for the purpose of compression as well as for an efficient analysis is the provision of optimally sparse approximations...... optimally sparse approximations of this model class in 2D as well as 3D. Even more, in contrast to all other directional representation systems, a theory for compactly supported shearlet frames was derived which moreover also satisfy this optimality benchmark. This chapter shall serve as an introduction...... to and a survey about sparse approximations of cartoon-like images by band-limited and also compactly supported shearlet frames as well as a reference for the state-of-the-art of this research field....

  6. Sparse Representations of Hyperspectral Images

    KAUST Repository

    Swanson, Robin J.

    2015-01-01

    Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

  7. Sparse Representations of Hyperspectral Images

    KAUST Repository

    Swanson, Robin J.

    2015-11-23

    Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

  8. Image understanding using sparse representations

    CERN Document Server

    Thiagarajan, Jayaraman J; Turaga, Pavan; Spanias, Andreas

    2014-01-01

    Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blin

  9. Joint Sparse Recovery With Semisupervised MUSIC

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-05-01

    Discrete multiple signal classification (MUSIC) with its low computational cost and mild condition requirement becomes a significant noniterative algorithm for joint sparse recovery (JSR). However, it fails in rank defective problem caused by coherent or limited amount of multiple measurement vectors (MMVs). In this letter, we provide a novel sight to address this problem by interpreting JSR as a binary classification problem with respect to atoms. Meanwhile, MUSIC essentially constructs a supervised classifier based on the labeled MMVs so that its performance will heavily depend on the quality and quantity of these training samples. From this viewpoint, we develop a semisupervised MUSIC (SS-MUSIC) in the spirit of machine learning, which declares that the insufficient supervised information in the training samples can be compensated from those unlabeled atoms. Instead of constructing a classifier in a fully supervised manner, we iteratively refine a semisupervised classifier by exploiting the labeled MMVs and some reliable unlabeled atoms simultaneously. Through this way, the required conditions and iterations can be greatly relaxed and reduced. Numerical experimental results demonstrate that SS-MUSIC can achieve much better recovery performances than other MUSIC extended algorithms as well as some typical greedy algorithms for JSR in terms of iterations and recovery probability.

  10. Sparse PCA with Oracle Property.

    Science.gov (United States)

    Gu, Quanquan; Wang, Zhaoran; Liu, Han

    In this paper, we study the estimation of the k -dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank- k , and attains a [Formula: see text] statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets.

  11. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin

    2015-04-03

    © 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

  12. Pairwise Constraint-Guided Sparse Learning for Feature Selection.

    Science.gov (United States)

    Liu, Mingxia; Zhang, Daoqiang

    2016-01-01

    Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.

  13. Sparse Matrices in Frame Theory

    DEFF Research Database (Denmark)

    Lemvig, Jakob; Krahmer, Felix; Kutyniok, Gitta

    2014-01-01

    Frame theory is closely intertwined with signal processing through a canon of methodologies for the analysis of signals using (redundant) linear measurements. The canonical dual frame associated with a frame provides a means for reconstruction by a least squares approach, but other dual frames...... yield alternative reconstruction procedures. The novel paradigm of sparsity has recently entered the area of frame theory in various ways. Of those different sparsity perspectives, we will focus on the situations where frames and (not necessarily canonical) dual frames can be written as sparse matrices...

  14. Diffusion Indexes with Sparse Loadings

    DEFF Research Database (Denmark)

    Kristensen, Johannes Tang

    The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the problem by using the LASSO as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model which is better suited for forecasting compared...... it to be an important alternative to PC....

  15. Sparse Linear Identifiable Multivariate Modeling

    DEFF Research Database (Denmark)

    Henao, Ricardo; Winther, Ole

    2011-01-01

    and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...

  16. Programming for Sparse Minimax Optimization

    DEFF Research Database (Denmark)

    Jonasson, K.; Madsen, Kaj

    1994-01-01

    We present an algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration, a linear minimax problem is solved for a basic step. If necessary, this is followed...... by the determination of a minimum norm corrective step based on a first-order Taylor approximation. No Hessian information needs to be stored. Global convergence is proved. This new method has been extensively tested and compared with other methods, including two well known codes for nonlinear programming...

  17. Dynamic Representations of Sparse Graphs

    DEFF Research Database (Denmark)

    Brodal, Gerth Stølting; Fagerberg, Rolf

    1999-01-01

    We present a linear space data structure for maintaining graphs with bounded arboricity—a large class of sparse graphs containing e.g. planar graphs and graphs of bounded treewidth—under edge insertions, edge deletions, and adjacency queries. The data structure supports adjacency queries in worst...... case O(c) time, and edge insertions and edge deletions in amortized O(1) and O(c+log n) time, respectively, where n is the number of nodes in the graph, and c is the bound on the arboricity....

  18. Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara; Ghodsi, Ali; Clemmensen, Line H.

    2017-01-01

    Principal component analysis (PCA) is one of the main unsupervised pre-processing methods for dimension reduction. When the training labels are available, it is worth using a supervised PCA strategy. In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA...

  19. Learning a Nonnegative Sparse Graph for Linear Regression.

    Science.gov (United States)

    Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

    2015-09-01

    Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.

  20. Classification of mislabelled microarrays using robust sparse logistic regression.

    Science.gov (United States)

    Bootkrajang, Jakramate; Kabán, Ata

    2013-04-01

    Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters. In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy. The code is available from http://cs.bham.ac.uk/∼jxb008. Supplementary data are available at Bioinformatics online.

  1. Multi-label Learning with Missing Labels Using Mixed Dependency Graphs

    KAUST Repository

    Wu, Baoyuan

    2018-04-06

    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e., some of their labels are missing). The key point to handle missing labels is propagating the label information from the provided labels to missing labels, through a dependency graph that each label of each instance is treated as a node. We build this graph by utilizing different types of label dependencies. Specifically, the instance-level similarity is served as undirected edges to connect the label nodes across different instances and the semantic label hierarchy is used as directed edges to connect different classes. This base graph is referred to as the mixed dependency graph, as it includes both undirected and directed edges. Furthermore, we present another two types of label dependencies to connect the label nodes across different classes. One is the class co-occurrence, which is also encoded as undirected edges. Combining with the above base graph, we obtain a new mixed graph, called mixed graph with co-occurrence (MG-CO). The other is the sparse and low rank decomposition of the whole label matrix, to embed high-order dependencies over all labels. Combining with the base graph, the new mixed graph is called as MG-SL (mixed graph with sparse and low rank decomposition). Based on MG-CO and MG-SL, we further propose two convex transductive formulations of the MLML problem, denoted as MLMG-CO and MLMG-SL respectively. In both formulations, the instance-level similarity is embedded through a quadratic smoothness term, while the semantic label hierarchy is used as a linear constraint. In MLMG-CO, the class co-occurrence is also formulated as a quadratic smoothness term, while the sparse and low rank decomposition is incorporated into MLMG-SL, through two additional matrices (one is assumed as sparse, and the other is assumed as low

  2. Real-time PCR for type-specific identification of herpes simplex in clinical samples: evaluation of type-specific results in the context of CNS diseases.

    Science.gov (United States)

    Meylan, Sylvain; Robert, Daniel; Estrade, Christine; Grimbuehler, Valérie; Péter, Olivier; Meylan, Pascal R; Sahli, Roland

    2008-02-01

    HSV-1 and HSV-2 cause CNS infections of dissimilar clinico-pathological characteristics with prognostic and therapeutic implications. To validate a type-specific real-time PCR that uses MGB/LNA Taqman probes and to review the virologico-clinical data of 25 eligible patients with non-neonatal CNS infections. This real-time PCR was evaluated against conventional PCR (26 CSF and 20 quality controls), and LightCycler assay (51 mucocutaneous, 8 CSF and 32 quality controls) and culture/immunofluorescence (75 mucocutaneous) to assess typing with independent methods. Taqman real-time PCR detected 240 HSV genomes per ml CSF, a level appropriate for the management of patients, and provided unambiguous typing for the 104 positive (62 HSV-1 and 42 HSV-2) out the 160 independent clinical samples tested. HSV type diagnosed by Taqman real-time PCR predicted final diagnosis (meningitis versus encephalitis/meningoencephalitis, p<0.001) in 24/25 patients at time of presentation, in contrast to clinical evaluation. Our real-time PCR, as a sensitive and specific means for type-specific HSV diagnosis, provided rapid prognostic information for patient management.

  3. Food Labels

    Science.gov (United States)

    ... Staying Safe Videos for Educators Search English Español Food Labels KidsHealth / For Teens / Food Labels What's in ... to have at least 95% organic ingredients. Making Food Labels Work for You The first step in ...

  4. Bayesian Inference Methods for Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand

    2013-01-01

    This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

  5. Robust visual tracking via multiscale deep sparse networks

    Science.gov (United States)

    Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

    2017-04-01

    In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

  6. Image fusion using sparse overcomplete feature dictionaries

    Science.gov (United States)

    Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

    2015-10-06

    Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

  7. Sparse Image Reconstruction in Computed Tomography

    DEFF Research Database (Denmark)

    Jørgensen, Jakob Sauer

    In recent years, increased focus on the potentially harmful effects of x-ray computed tomography (CT) scans, such as radiation-induced cancer, has motivated research on new low-dose imaging techniques. Sparse image reconstruction methods, as studied for instance in the field of compressed sensing...... applications. This thesis takes a systematic approach toward establishing quantitative understanding of conditions for sparse reconstruction to work well in CT. A general framework for analyzing sparse reconstruction methods in CT is introduced and two sets of computational tools are proposed: 1...... contributions to a general set of computational characterization tools. Thus, the thesis contributions help advance sparse reconstruction methods toward routine use in...

  8. When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

    KAUST Repository

    Wang, Jim Jing-Yan; Cui, Xuefeng; Yu, Ge; Guo, Lili; Gao, Xin

    2017-01-01

    Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays

  9. Neural Network for Sparse Reconstruction

    Directory of Open Access Journals (Sweden)

    Qingfa Li

    2014-01-01

    Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.

  10. Diffusion Indexes With Sparse Loadings

    DEFF Research Database (Denmark)

    Kristensen, Johannes Tang

    2017-01-01

    The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the problem by using the least absolute shrinkage and selection operator (LASSO) as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model...... in forecasting accuracy and thus find it to be an important alternative to PC. Supplementary materials for this article are available online....

  11. Sparse and stable Markowitz portfolios.

    Science.gov (United States)

    Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace

    2009-07-28

    We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.

  12. SPARSE FARADAY ROTATION MEASURE SYNTHESIS

    International Nuclear Information System (INIS)

    Andrecut, M.; Stil, J. M.; Taylor, A. R.

    2012-01-01

    Faraday rotation measure synthesis is a method for analyzing multichannel polarized radio emissions, and it has emerged as an important tool in the study of Galactic and extragalactic magnetic fields. The method requires the recovery of the Faraday dispersion function from measurements restricted to limited wavelength ranges, which is an ill-conditioned deconvolution problem. Here, we discuss a recovery method that assumes a sparse approximation of the Faraday dispersion function in an overcomplete dictionary of functions. We discuss the general case when both thin and thick components are included in the model, and we present the implementation of a greedy deconvolution algorithm. We illustrate the method with several numerical simulations that emphasize the effect of the covered range and sampling resolution in the Faraday depth space, and the effect of noise on the observed data.

  13. Morphological changes in different populations of bladder afferent neurons detected by herpes simplex virus (HSV) vectors with cell-type-specific promoters in mice with spinal cord injury.

    Science.gov (United States)

    Shimizu, Nobutaka; Doyal, Mark F; Goins, William F; Kadekawa, Katsumi; Wada, Naoki; Kanai, Anthony J; de Groat, William C; Hirayama, Akihide; Uemura, Hirotsugu; Glorioso, Joseph C; Yoshimura, Naoki

    2017-11-19

    Functional and morphological changes in C-fiber bladder afferent pathways are reportedly involved in neurogenic detrusor overactivity (NDO) after spinal cord injury (SCI). This study examined the morphological changes in different populations of bladder afferent neurons after SCI using replication-defective herpes simplex virus (HSV) vectors encoding the mCherry reporter driven by neuronal cell-type-specific promoters. Spinal intact (SI) and SCI mice were injected into the bladder wall with HSV mCherry vectors driven by the cytomegalovirus (CMV) promoter, CGRP promoter, TRPV1 promoter or neurofilament 200 (NF200) promoter. Two weeks after vector inoculation into the bladder wall, L1 and L6 dorsal root ganglia (DRG) were removed bilaterally for immunofluorescent staining using anti-mCherry antibody. The number of CMV promoter vector-labeled neurons was not altered after SCI. The number of CGRP and TRPV1 promoter vector-labeled neurons was significantly increased whereas the number of NF200 vector-labeled neurons was decreased in L6 DRG after SCI. The median size of CGRP promoter-labeled C-fiber neurons was increased from 247.0 in SI mice to 271.3μm 2 in SCI mice whereas the median cell size of TRPV1 promoter vector-labeled neurons was decreased from 245.2 in SI mice to 216.5μm 2 in SCI mice. CGRP and TRPV1 mRNA levels of laser-captured bladder afferent neurons labeled with Fast Blue were significantly increased in SCI mice compared to SI mice. Thus, using a novel HSV vector-mediated neuronal labeling technique, we found that SCI induces expansion of the CGRP- and TRPV1-expressing C-fiber cell population, which could contribute to C-fiber afferent hyperexcitability and NDO after SCI. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  14. Numerical solution of large sparse linear systems

    International Nuclear Information System (INIS)

    Meurant, Gerard; Golub, Gene.

    1982-02-01

    This note is based on one of the lectures given at the 1980 CEA-EDF-INRIA Numerical Analysis Summer School whose aim is the study of large sparse linear systems. The main topics are solving least squares problems by orthogonal transformation, fast Poisson solvers and solution of sparse linear system by iterative methods with a special emphasis on preconditioned conjuguate gradient method [fr

  15. Sparse seismic imaging using variable projection

    NARCIS (Netherlands)

    Aravkin, Aleksandr Y.; Tu, Ning; van Leeuwen, Tristan

    2013-01-01

    We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using

  16. A quantitative comparison of cell-type-specific microarray gene expression profiling methods in the mouse brain.

    Directory of Open Access Journals (Sweden)

    Benjamin W Okaty

    Full Text Available Expression profiling of restricted neural populations using microarrays can facilitate neuronal classification and provide insight into the molecular bases of cellular phenotypes. Due to the formidable heterogeneity of intermixed cell types that make up the brain, isolating cell types prior to microarray processing poses steep technical challenges that have been met in various ways. These methodological differences have the potential to distort cell-type-specific gene expression profiles insofar as they may insufficiently filter out contaminating mRNAs or induce aberrant cellular responses not normally present in vivo. Thus we have compared the repeatability, susceptibility to contamination from off-target cell-types, and evidence for stress-responsive gene expression of five different purification methods--Laser Capture Microdissection (LCM, Translating Ribosome Affinity Purification (TRAP, Immunopanning (PAN, Fluorescence Activated Cell Sorting (FACS, and manual sorting of fluorescently labeled cells (Manual. We found that all methods obtained comparably high levels of repeatability, however, data from LCM and TRAP showed significantly higher levels of contamination than the other methods. While PAN samples showed higher activation of apoptosis-related, stress-related and immediate early genes, samples from FACS and Manual studies, which also require dissociated cells, did not. Given that TRAP targets actively translated mRNAs, whereas other methods target all transcribed mRNAs, observed differences may also reflect translational regulation.

  17. Modification of surface/neuron interfaces for neural cell-type specific responses: a review

    International Nuclear Information System (INIS)

    Chen, Cen; Kong, Xiangdong; Lee, In-Seop

    2016-01-01

    Surface/neuron interfaces have played an important role in neural repair including neural prostheses and tissue engineered scaffolds. This comprehensive literature review covers recent studies on the modification of surface/neuron interfaces. These interfaces are identified in cases both where the surfaces of substrates or scaffolds were in direct contact with cells and where the surfaces were modified to facilitate cell adhesion and controlling cell-type specific responses. Different sources of cells for neural repair are described, such as pheochromocytoma neuronal-like cell, neural stem cell (NSC), embryonic stem cell (ESC), mesenchymal stem cell (MSC) and induced pluripotent stem cell (iPS). Commonly modified methods are discussed including patterned surfaces at micro- or nano-scale, surface modification with conducting coatings, and functionalized surfaces with immobilized bioactive molecules. These approaches to control cell-type specific responses have enormous potential implications in neural repair. (paper)

  18. Bridging the Gap: Towards a Cell-Type Specific Understanding of Neural Circuits Underlying Fear Behaviors

    Science.gov (United States)

    McCullough, KM; Morrison, FG; Ressler, KJ

    2016-01-01

    Fear and anxiety-related disorders are remarkably common and debilitating, and are often characterized by dysregulated fear responses. Rodent models of fear learning and memory have taken great strides towards elucidating the specific neuronal circuitries underlying the learning of fear responses. The present review addresses recent research utilizing optogenetic approaches to parse circuitries underlying fear behaviors. It also highlights the powerful advances made when optogenetic techniques are utilized in a genetically defined, cell-type specific, manner. The application of next-generation genetic and sequencing approaches in a cell-type specific context will be essential for a mechanistic understanding of the neural circuitry underlying fear behavior and for the rational design of targeted, circuit specific, pharmacologic interventions for the treatment and prevention of fear-related disorders. PMID:27470092

  19. Prevalence of type-specific HPV among female university students from northern Brazil

    OpenAIRE

    Vieira, Rodrigo Covre; Monteiro, Jeniffer do Socorro Valente; Manso, Est?fane Primo; dos Santos, Maria Renata Mendon?a; Tsutsumi, Mihoko Yamamoto; Ishikawa, Edna Aoba Yassui; Ferrari, Stephen Francis; Lima, Karla Val?ria Batista; de Sousa, Ma?sa Silva

    2015-01-01

    Background Human papillomavirus (HPV) infection is associated with cervical cancer, the most frequent cancer in women from northern Brazil. Assessment of the short-term impact of HPV vaccination depends on the availability of data on the prevalence of type-specific HPV in young women in the pre-immunization period, although these data are currently unavailable for the study region. The aim of this study was to estimate the distribution of all mucosal HPV genotypes, including low- and high-ris...

  20. Micro solid-phase radioimmunoassay for detection of herpesvirus type-specific antibody: specificity and sensitivity

    Energy Technology Data Exchange (ETDEWEB)

    Adler-Storthz, K.; Matson, D.O.; Adam, E.; Dreesman, G.R. (Baylor Univ., Houston, TX (USA). Coll. of Medicine)

    1983-02-01

    The specificity and sensitivity of a micro solid-phase radioimmunoassay (micro-SPRIA) that detects type-specific IgG antibody to herpes simplex virus types 1 and 2 (HSV1 and HSV2) were evaluated. Glycoproteins VP123 (molecular weight, 123,000) of HSV1 and VP119 (molecular weight, 119,000) of HSV2 were found to display the greatest degree of antigenic type-specificity of several HSV antigens tested with the micro-SPRIA technique. When testing a group of sera, negative for anti-HSV antibodies by microneutralization, in the micro-SPRIA, a range of negative reactivities was noted, suggesting that cut-points should be determined for each antigen preparation. The micro-SPRIA detected appropriate antibody activity in patients with recurrent infection and a marked agreement was noted in comparison to detection of anti-HSV antibodies measured with the microneutralization test. The type-specificity of the micro-SPRIA was substantiated by the independence of test results using VP119 and VP123 antigens for a random group of positive sera. The assay is rapid, specific, and sensitive and allows the testing of multiple serum samples with a standardized set of reagents.

  1. Human muscle fibre type-specific regulation of AMPK and downstream targets by exercise

    DEFF Research Database (Denmark)

    Kristensen, Dorte Enggaard; Albers, Peter Hjorth; Prats, Clara

    2015-01-01

    are expressed in a fibre type-dependent manner and that fibre type-specific activation of AMPK and downstream targets is dependent on exercise intensity. Pools of type I and II fibres were prepared from biopsies of m. vastus lateralis from healthy men before and after two exercise trials; A) continuous cycling......AMP-activated protein kinase (AMPK) is a regulator of energy homeostasis during exercise. Studies suggest muscle fibre type-specific AMPK expression. However, fibre type-specific regulation of AMPK and downstream targets during exercise has not been proven. We hypothesized that AMPK subunits...... (CON) 30 min at 69 ± 1% VO2peak or B) interval cycling (INT) 30 min with 6 × 1.5 min high-intense bouts peaking at 95 ± 2% VO2peak . In type I vs. II fibres a higher β1 AMPK (+215%) and lower γ3 AMPK expression (-71%) was found. α1 , α2 , β2 and γ1 AMPK expression was similar between fibre types...

  2. Orthogonal sparse linear discriminant analysis

    Science.gov (United States)

    Liu, Zhonghua; Liu, Gang; Pu, Jiexin; Wang, Xiaohong; Wang, Haijun

    2018-03-01

    Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.

  3. Spectra of sparse random matrices

    International Nuclear Information System (INIS)

    Kuehn, Reimer

    2008-01-01

    We compute the spectral density for ensembles of sparse symmetric random matrices using replica. Our formulation of the replica-symmetric ansatz shares the symmetries of that suggested in a seminal paper by Rodgers and Bray (symmetry with respect to permutation of replica and rotation symmetry in the space of replica), but uses a different representation in terms of superpositions of Gaussians. It gives rise to a pair of integral equations which can be solved by a stochastic population-dynamics algorithm. Remarkably our representation allows us to identify pure-point contributions to the spectral density related to the existence of normalizable eigenstates. Our approach is not restricted to matrices defined on graphs with Poissonian degree distribution. Matrices defined on regular random graphs or on scale-free graphs, are easily handled. We also look at matrices with row constraints such as discrete graph Laplacians. Our approach naturally allows us to unfold the total density of states into contributions coming from vertices of different local coordinations and an example of such an unfolding is presented. Our results are well corroborated by numerical diagonalization studies of large finite random matrices

  4. Enhancing Scalability of Sparse Direct Methods

    International Nuclear Information System (INIS)

    Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia, Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan

    2007-01-01

    TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers

  5. Regression with Sparse Approximations of Data

    DEFF Research Database (Denmark)

    Noorzad, Pardis; Sturm, Bob L.

    2012-01-01

    We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

  6. Sparse adaptive filters for echo cancellation

    CERN Document Server

    Paleologu, Constantin

    2011-01-01

    Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati

  7. Technique detection software for Sparse Matrices

    Directory of Open Access Journals (Sweden)

    KHAN Muhammad Taimoor

    2009-12-01

    Full Text Available Sparse storage formats are techniques for storing and processing the sparse matrix data efficiently. The performance of these storage formats depend upon the distribution of non-zeros, within the matrix in different dimensions. In order to have better results we need a technique that suits best the organization of data in a particular matrix. So the decision of selecting a better technique is the main step towards improving the system's results otherwise the efficiency can be decreased. The purpose of this research is to help identify the best storage format in case of reduced storage size and high processing efficiency for a sparse matrix.

  8. Massive Asynchronous Parallelization of Sparse Matrix Factorizations

    Energy Technology Data Exchange (ETDEWEB)

    Chow, Edmond [Georgia Inst. of Technology, Atlanta, GA (United States)

    2018-01-08

    Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

  9. Fnip1 regulates skeletal muscle fiber type specification, fatigue resistance, and susceptibility to muscular dystrophy

    Science.gov (United States)

    Reyes, Nicholas L.; Banks, Glen B.; Tsang, Mark; Margineantu, Daciana; Gu, Haiwei; Djukovic, Danijel; Chan, Jacky; Torres, Michelle; Liggitt, H. Denny; Hirenallur-S, Dinesh K.; Hockenbery, David M.; Raftery, Daniel; Iritani, Brian M.

    2015-01-01

    Mammalian skeletal muscle is broadly characterized by the presence of two distinct categories of muscle fibers called type I “red” slow twitch and type II “white” fast twitch, which display marked differences in contraction strength, metabolic strategies, and susceptibility to fatigue. The relative representation of each fiber type can have major influences on susceptibility to obesity, diabetes, and muscular dystrophies. However, the molecular factors controlling fiber type specification remain incompletely defined. In this study, we describe the control of fiber type specification and susceptibility to metabolic disease by folliculin interacting protein-1 (Fnip1). Using Fnip1 null mice, we found that loss of Fnip1 increased the representation of type I fibers characterized by increased myoglobin, slow twitch markers [myosin heavy chain 7 (MyH7), succinate dehydrogenase, troponin I 1, troponin C1, troponin T1], capillary density, and mitochondria number. Cultured Fnip1-null muscle fibers had higher oxidative capacity, and isolated Fnip1-null skeletal muscles were more resistant to postcontraction fatigue relative to WT skeletal muscles. Biochemical analyses revealed increased activation of the metabolic sensor AMP kinase (AMPK), and increased expression of the AMPK-target and transcriptional coactivator PGC1α in Fnip1 null skeletal muscle. Genetic disruption of PGC1α rescued normal levels of type I fiber markers MyH7 and myoglobin in Fnip1-null mice. Remarkably, loss of Fnip1 profoundly mitigated muscle damage in a murine model of Duchenne muscular dystrophy. These results indicate that Fnip1 controls skeletal muscle fiber type specification and warrant further study to determine whether inhibition of Fnip1 has therapeutic potential in muscular dystrophy diseases. PMID:25548157

  10. Long-term in vitro, cell-type-specific genome-wide reprogramming of gene expression

    International Nuclear Information System (INIS)

    Hakelien, Anne-Mari; Gaustad, Kristine G.; Taranger, Christel K.; Skalhegg, Bjorn S.; Kuentziger, Thomas; Collas, Philippe

    2005-01-01

    We demonstrate a cell extract-based, genome-wide and heritable reprogramming of gene expression in vitro. Kidney epithelial 293T cells have previously been shown to take on T cell properties following a brief treatment with an extract of Jurkat T cells. We show here that 293T cells exposed for 1 h to a Jurkat cell extract undergo genome-wide, target cell-type-specific and long-lasting transcriptional changes. Microarray analyses indicate that on any given week after extract treatment, ∼2500 genes are upregulated >3-fold, of which ∼900 are also expressed in Jurkat cells. Concomitantly, ∼1500 genes are downregulated or repressed, of which ∼500 are also downregulated in Jurkat cells. Gene expression changes persist for over 30 passages (∼80 population doublings) in culture. Target cell-type specificity of these changes is shown by the lack of activation or repression of Jurkat-specific genes by extracts of 293T cells or carcinoma cells. Quantitative RT-PCR analysis confirms the long-term transcriptional activation of genes involved in key T cell functions. Additionally, growth of cells in suspended aggregates, expression of CD3 and CD28 T cell surface markers, and interleukin-2 secretion by 293T cells treated with extract of adult peripheral blood T cells illustrate a functional nuclear reprogramming. Therefore, target cell-type-specific and heritable changes in gene expression, and alterations in cell function, can be promoted by extracts derived from transformed cells as well as from adult primary cells

  11. Structure-based bayesian sparse reconstruction

    KAUST Repository

    Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

    2012-01-01

    Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical

  12. Biclustering via Sparse Singular Value Decomposition

    KAUST Repository

    Lee, Mihee

    2010-02-16

    Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.

  13. Tunable Sparse Network Coding for Multicast Networks

    DEFF Research Database (Denmark)

    Feizi, Soheil; Roetter, Daniel Enrique Lucani; Sørensen, Chres Wiant

    2014-01-01

    This paper shows the potential and key enabling mechanisms for tunable sparse network coding, a scheme in which the density of network coded packets varies during a transmission session. At the beginning of a transmission session, sparsely coded packets are transmitted, which benefits decoding...... complexity. At the end of a transmission, when receivers have accumulated degrees of freedom, coding density is increased. We propose a family of tunable sparse network codes (TSNCs) for multicast erasure networks with a controllable trade-off between completion time performance to decoding complexity...... a mechanism to perform efficient Gaussian elimination over sparse matrices going beyond belief propagation but maintaining low decoding complexity. Supporting simulation results are provided showing the trade-off between decoding complexity and completion time....

  14. SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2015-01-01

    minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two

  15. Learning sparse generative models of audiovisual signals

    OpenAIRE

    Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre

    2008-01-01

    This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

  16. Hyperspectral Unmixing with Robust Collaborative Sparse Regression

    Directory of Open Access Journals (Sweden)

    Chang Li

    2016-07-01

    Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

  17. Adolescent maturation of inhibitory inputs onto cingulate cortex neurons is cell-type specific and TrkB dependent

    Directory of Open Access Journals (Sweden)

    Angela eVandenberg

    2015-02-01

    Full Text Available The maturation of inhibitory circuits during adolescence may be tied to the onset of mental health disorders such as schizophrenia. Neurotrophin signaling likely plays a critical role in supporting inhibitory circuit development and is also implicated in psychiatric disease. Within the neocortex, subcircuits may mature at different times and show differential sensitivity to neurotrophin signaling. We measured miniature inhibitory and excitatory postsynaptic currents (mIPSC and mEPSCs in Layer 5 cell-types in the mouse anterior cingulate across the periadolescent period. We differentiated cell-types mainly by Thy1 YFP transgene expression and also retrobead injection labeling in the contralateral cingulate and ipsilateral pons. We found that YFP- neurons and commissural projecting neurons had lower frequency of mIPSCs than neighboring YFP+ neurons or pons projecting neurons in juvenile mice (P21-25. YFP- neurons and to a lesser extent commissural projecting neurons also showed a significant increase in mIPSC amplitude during the periadolescent period (P21-25 vs. P40-50, which was not seen in YFP+ neurons or pons projecting neurons. Systemic disruption of tyrosine kinase receptor B (TrkB signaling during P23-50 in TrkBF616A mice blocked developmental changes in mIPSC amplitude, without affecting miniature excitatory post synaptic currents (mEPSCs. Our data suggest that the maturation of inhibitory inputs onto layer 5 pyramidal neurons is cell-type specific. These data may inform our understanding of adolescent brain development across species and aid in identifying candidate subcircuits that may show greater vulnerability in mental illness.

  18. Common themes and cell type specific variations of higher order chromatin arrangements in the mouse

    Directory of Open Access Journals (Sweden)

    Cremer Thomas

    2005-12-01

    Full Text Available Abstract Background Similarities as well as differences in higher order chromatin arrangements of human cell types were previously reported. For an evolutionary comparison, we now studied the arrangements of chromosome territories and centromere regions in six mouse cell types (lymphocytes, embryonic stem cells, macrophages, fibroblasts, myoblasts and myotubes with fluorescence in situ hybridization and confocal laser scanning microscopy. Both species evolved pronounced differences in karyotypes after their last common ancestors lived about 87 million years ago and thus seem particularly suited to elucidate common and cell type specific themes of higher order chromatin arrangements in mammals. Results All mouse cell types showed non-random correlations of radial chromosome territory positions with gene density as well as with chromosome size. The distribution of chromosome territories and pericentromeric heterochromatin changed during differentiation, leading to distinct cell type specific distribution patterns. We exclude a strict dependence of these differences on nuclear shape. Positional differences in mouse cell nuclei were less pronounced compared to human cell nuclei in agreement with smaller differences in chromosome size and gene density. Notably, the position of chromosome territories relative to each other was very variable. Conclusion Chromosome territory arrangements according to chromosome size and gene density provide common, evolutionary conserved themes in both, human and mouse cell types. Our findings are incompatible with a previously reported model of parental genome separation.

  19. Layer- and Cell Type-Specific Modulation of Excitatory Neuronal Activity in the Neocortex

    Directory of Open Access Journals (Sweden)

    Gabriele Radnikow

    2018-01-01

    Full Text Available From an anatomical point of view the neocortex is subdivided into up to six layers depending on the cortical area. This subdivision has been described already by Meynert and Brodmann in the late 19/early 20. century and is mainly based on cytoarchitectonic features such as the size and location of the pyramidal cell bodies. Hence, cortical lamination is originally an anatomical concept based on the distribution of excitatory neuron. However, it has become apparent in recent years that apart from the layer-specific differences in morphological features, many functional properties of neurons are also dependent on cortical layer or cell type. Such functional differences include changes in neuronal excitability and synaptic activity by neuromodulatory transmitters. Many of these neuromodulators are released from axonal afferents from subcortical brain regions while others are released intrinsically. In this review we aim to describe layer- and cell-type specific differences in the effects of neuromodulator receptors in excitatory neurons in layers 2–6 of different cortical areas. We will focus on the neuromodulator systems using adenosine, acetylcholine, dopamine, and orexin/hypocretin as examples because these neuromodulator systems show important differences in receptor type and distribution, mode of release and functional mechanisms and effects. We try to summarize how layer- and cell type-specific neuromodulation may affect synaptic signaling in cortical microcircuits.

  20. Cell type-specific responses to salinity - the epidermal bladder cell transcriptome of Mesembryanthemum crystallinum.

    Science.gov (United States)

    Oh, Dong-Ha; Barkla, Bronwyn J; Vera-Estrella, Rosario; Pantoja, Omar; Lee, Sang-Yeol; Bohnert, Hans J; Dassanayake, Maheshi

    2015-08-01

    Mesembryanthemum crystallinum (ice plant) exhibits extreme tolerance to salt. Epidermal bladder cells (EBCs), developing on the surface of aerial tissues and specialized in sodium sequestration and other protective functions, are critical for the plant's stress adaptation. We present the first transcriptome analysis of EBCs isolated from intact plants, to investigate cell type-specific responses during plant salt adaptation. We developed a de novo assembled, nonredundant EBC reference transcriptome. Using RNAseq, we compared the expression patterns of the EBC-specific transcriptome between control and salt-treated plants. The EBC reference transcriptome consists of 37 341 transcript-contigs, of which 7% showed significantly different expression between salt-treated and control samples. We identified significant changes in ion transport, metabolism related to energy generation and osmolyte accumulation, stress signalling, and organelle functions, as well as a number of lineage-specific genes of unknown function, in response to salt treatment. The salinity-induced EBC transcriptome includes active transcript clusters, refuting the view of EBCs as passive storage compartments in the whole-plant stress response. EBC transcriptomes, differing from those of whole plants or leaf tissue, exemplify the importance of cell type-specific resolution in understanding stress adaptive mechanisms. No claim to original US government works. New Phytologist © 2015 New Phytologist Trust.

  1. Cell-type-specific role for nucleus accumbens neuroligin-2 in depression and stress susceptibility.

    Science.gov (United States)

    Heshmati, Mitra; Aleyasin, Hossein; Menard, Caroline; Christoffel, Daniel J; Flanigan, Meghan E; Pfau, Madeline L; Hodes, Georgia E; Lepack, Ashley E; Bicks, Lucy K; Takahashi, Aki; Chandra, Ramesh; Turecki, Gustavo; Lobo, Mary Kay; Maze, Ian; Golden, Sam A; Russo, Scott J

    2018-01-30

    Behavioral coping strategies are critical for active resilience to stress and depression; here we describe a role for neuroligin-2 (NLGN-2) in the nucleus accumbens (NAc). Neuroligins (NLGN) are a family of neuronal postsynaptic cell adhesion proteins that are constituents of the excitatory and inhibitory synapse. Importantly, NLGN-3 and NLGN-4 mutations are strongly implicated as candidates underlying the development of neuropsychiatric disorders with social disturbances such as autism, but the role of NLGN-2 in neuropsychiatric disease states is unclear. Here we show a reduction in NLGN-2 gene expression in the NAc of patients with major depressive disorder. Chronic social defeat stress in mice also decreases NLGN-2 selectively in dopamine D1-positive cells, but not dopamine D2-positive cells, within the NAc of stress-susceptible mice. Functional NLGN-2 knockdown produces bidirectional, cell-type-specific effects: knockdown in dopamine D1-positive cells promotes subordination and stress susceptibility, whereas knockdown in dopamine D2-positive cells mediates active defensive behavior. These findings establish a behavioral role for NAc NLGN-2 in stress and depression; provide a basis for targeted, cell-type specific therapy; and highlight the role of active behavioral coping mechanisms in stress susceptibility.

  2. Cell-type-specific gene delivery into neuronal cells in vitro and in vivo

    International Nuclear Information System (INIS)

    Parveen, Zahida; Mukhtar, Muhammad; Rafi, Mohammed; Wenger, David A.; Siddiqui, Khwaja M.; Siler, Catherine A.; Dietzschold, Bernhard; Pomerantz, Roger J.; Schnell, Matthias J.; Dornburg, Ralph

    2003-01-01

    The avian retroviruses reticuloendotheliosis virus strain A (REV-A) and spleen necrosis virus (SNV) are not naturally infectious in human cells. However, REV-A-derived viral vectors efficiently infect human cells when they are pseudotyped with envelope proteins displaying targeting ligands specific for human cell-surface receptors. Here we report that vectors containing the gag region of REV-A and pol of SNV can be pseudotyped with the envelope protein of vesicular stomatitis virus (VSV) and the glycoproteins of different rabies virus (RV) strains. Vectors pseudotyped with the envelope protein of the highly neurotropic RV strain CVS-N2c facilitated cell type-specific gene delivery into mouse and human neurons, but did not infect other human cell types. Moreover, when such vector particles were injected into the brain of newborn mice, only neuronal cells were infected in vivo. Cell-type-specific gene delivery into neurons may present quite specific gene therapy approaches for many degenerative diseases of the brain

  3. ERE environment- and cell type-specific transcriptional effects of estrogen in normal endometrial cells.

    Science.gov (United States)

    Lascombe, I; Sallot, M; Vuillermoz, C; Weisz, A; Adessi, G L; Jouvenot, M

    1998-04-30

    Our previous results have suggested a repression of E2 (17beta-estradiol) effect on the c-fos gene of cultured guinea-pig endometrial cells. To investigate this repression, the expression of three human c-fos gene recombinants, pFC1-BL (-2250/+41), pFC2-BL (-1400/+41) and pFC2E (-1300/-1050 and -230/+41), known to be E2-responsive in Hela cells, was studied in stromal (SC) and glandular epithelial cells (GEC). In both cellular types, pFC1-BL was not induced by E2, even in the presence of growth factors or co-transfected estrogen receptor. The pattern of pFC2-BL and pFC2E expression was strikingly different and depended on the cellular type: pFC2-BL and pFC2E induction was restricted to the glandular epithelial cells and did not occur in the SCs. We argue for a repression of E2 action which is dependent on the estrogen-responsive cis-acting element (ERE) environment and also cell type-specific involving DNA/protein and/or protein/protein interactions with cellular type-specific factors.

  4. A Modified Sparse Representation Method for Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Wei Wang

    2016-01-01

    Full Text Available In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit method is used to speed up the convergence of OMP (orthogonal matching pursuit. Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan’s JAFFE and CMU’s CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

  5. Synthesizing spatiotemporally sparse smartphone sensor data for bridge modal identification

    Science.gov (United States)

    Ozer, Ekin; Feng, Maria Q.

    2016-08-01

    Smartphones as vibration measurement instruments form a large-scale, citizen-induced, and mobile wireless sensor network (WSN) for system identification and structural health monitoring (SHM) applications. Crowdsourcing-based SHM is possible with a decentralized system granting citizens with operational responsibility and control. Yet, citizen initiatives introduce device mobility, drastically changing SHM results due to uncertainties in the time and the space domains. This paper proposes a modal identification strategy that fuses spatiotemporally sparse SHM data collected by smartphone-based WSNs. Multichannel data sampled with the time and the space independence is used to compose the modal identification parameters such as frequencies and mode shapes. Structural response time history can be gathered by smartphone accelerometers and converted into Fourier spectra by the processor units. Timestamp, data length, energy to power conversion address temporal variation, whereas spatial uncertainties are reduced by geolocation services or determining node identity via QR code labels. Then, parameters collected from each distributed network component can be extended to global behavior to deduce modal parameters without the need of a centralized and synchronous data acquisition system. The proposed method is tested on a pedestrian bridge and compared with a conventional reference monitoring system. The results show that the spatiotemporally sparse mobile WSN data can be used to infer modal parameters despite non-overlapping sensor operation schedule.

  6. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Yidong Tang

    2016-01-01

    Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

  7. Deep ensemble learning of sparse regression models for brain disease diagnosis.

    Science.gov (United States)

    Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

    2017-04-01

    Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Human papillomavirus type-specific prevalence in the cervical cancer screening population of Czech women.

    Directory of Open Access Journals (Sweden)

    Ruth Tachezy

    Full Text Available BACKGROUND: Infection with high-risk human papillomavirus (HPVtypes has been recognized as a causal factor for the development of cervical cancer and a number of other malignancies. Today, vaccines against HPV, highly effective in the prevention of persistent infection and precancerous lesions, are available for the routine clinical practice. OBJECTIVES: The data on the prevalence and type-specific HPV distribution in the population of each country are crucial for the surveillance of HPV type-specific prevalence at the onset of vaccination against HPV. METHODS: Women attending a preventive gynecological examination who had no history of abnormal cytological finding and/or surgery for cervical lesions were enrolled. All samples were tested for the presence of HPV by High-Risk Hybrid Capture 2 (HR HC2 and by a modified PCR-reverse line blot assay with broad spectrum primers (BS-RLB. RESULTS: Cervical smears of 1393 women were analyzed. In 6.5% of women, atypical cytological findings were detected. Altogether, 28.3% (394/1393 of women were positive for any HPV type by BS-RLB, 18.2% (254/1393 by HR HC2, and 22.3% (310/1393 by BS-RLB for HR HPV types. In women with atypical findings the prevalence for HR and any HPV types were significantly higher than in women with normal cytological findings. Overall, 36 different HPV types were detected, with HPV 16 being the most prevalent (4.8%. HPV positivity decreased with age; the highest prevalence was 31.5% in the age group 21-25 years. CONCLUSIONS: Our study subjects represent the real screening population. HPV prevalence in this population in the Czech Republic is higher than in other countries of Eastern Europe. Also the spectrum of the most prevalent HPV types differs from those reported by others but HPV 16 is, concordantly, the most prevalent type. Country-specific HPV type-specific prevalences provide baseline information which will enable to measure the impact of HPV vaccination in the future.

  9. Nutrition Labeling

    DEFF Research Database (Denmark)

    Grunert, Klaus G

    2013-01-01

    because consumers will avoid products that the label shows to be nutritionally deficient, but also because food producers will try to avoid marketing products that appear, according to the label, as nutritionally problematic, for example, because of a high content of saturated fat or salt. Nutrition......Nutrition labeling refers to the provision of information on a food product’s nutritional content on the package label. It can serve both public health and commercial purposes. From a public health perspective, the aim of nutrition labeling is to provide information that can enable consumers...... to make healthier choices when choosing food products. Nutrition labeling is thus closely linked to the notion of the informed consumer, that chooses products according to their aims, on the basis of the information at their disposal. Because many consumers are assumed to be interested in making healthy...

  10. Private Labels

    OpenAIRE

    Kolmačková, Zuzana

    2013-01-01

    This Bachelor Thesis titled Private labels deals with distribution strategy based on the introduction of private labels especially in retail chains. At the beginning it is focused on the general concept of private label offered by retailers, where is mentioned basic characteristics, history and structuring of distribution brands. Subsequently this thesis informs readers about the introduction of new special distribution brands, which focus primarily on the new consumption habits of customers....

  11. Multi-information fusion sparse coding with preserving local structure for hyperspectral image classification

    Science.gov (United States)

    Wei, Xiaohui; Zhu, Wen; Liao, Bo; Gu, Changlong; Li, Weibiao

    2017-10-01

    The key question of sparse coding (SC) is how to exploit the information that already exists to acquire the robust sparse representations (SRs) of distinguishing different objects for hyperspectral image (HSI) classification. We propose a multi-information fusion SC framework, which fuses the spectral, spatial, and label information in the same level, to solve the above question. In particular, pixels from disjointed spatial clusters, which are obtained by cutting the given HSI in space, are individually and sparsely encoded. Then, due to the importance of spatial structure, graph- and hypergraph-based regularizers are enforced to motivate the obtained representations smoothness and to preserve the local consistency for each spatial cluster. The latter simultaneously considers the spectrum, spatial, and label information of multiple pixels that have a great probability with the same label. Finally, a linear support vector machine is selected as the final classifier with the learned SRs as input. Experiments conducted on three frequently used real HSIs show that our methods can achieve satisfactory results compared with other state-of-the-art methods.

  12. Digital sorting of complex tissues for cell type-specific gene expression profiles.

    Science.gov (United States)

    Zhong, Yi; Wan, Ying-Wooi; Pang, Kaifang; Chow, Lionel M L; Liu, Zhandong

    2013-03-07

    Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles. Furthermore, these algorithms tend to report biased estimations. Here, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies. The results suggest that DSA is a specific and sensitivity algorithm in gene expression profile deconvolution and will be useful in studying individual cell types of complex tissues.

  13. Indirect micro-immunofluorescence test for detecting type-specific antibodies to herpes simplex virus.

    Science.gov (United States)

    Forsey, T; Darougar, S

    1980-02-01

    A rapid indirect micro-immunofluorescence test capable of detecting and differentiating type-specific antibodies to herpes simplex virus is described. The test proved highly sensitive and, in 80 patients with active herpes ocular infection, antibody was detected in 94%. No anti-herpes antibody was detected in a control group of 20 patients with adenovirus infections. Testing of animal sera prepared against herpes simplex virus types 1 and 2 and of human sera from cases of ocular and genital herpes infections showed that the test can differentiate antibodies to the infecting serotypes. Specimens of whole blood, taken by fingerprick, and eye secretions, both collected on cellulose sponges, could be tested by indirect micro-immunofluorescence. Anti-herpes IgG, IgM, and IgA can also be detected.

  14. Type-specific proactive interference in patients with semantic and phonological STM deficits.

    Science.gov (United States)

    Harris, Lara; Olson, Andrew; Humphreys, Glyn

    2014-01-01

    Prior neuropsychological evidence suggests that semantic and phonological components of short-term memory (STM) are functionally and neurologically distinct. The current paper examines proactive interference (PI) from semantic and phonological information in two STM-impaired patients, DS (semantic STM deficit) and AK (phonological STM deficit). In Experiment 1 probe recognition tasks with open and closed sets of stimuli were used. Phonological PI was assessed using nonword items, and semantic and phonological PI was assessed using words. In Experiment 2 phonological and semantic PI was elicited by an item recognition probe test with stimuli that bore phonological and semantic relations to the probes. The data suggested heightened phonological PI for the semantic STM patient, and exaggerated effects of semantic PI in the phonological STM case. The findings are consistent with an account of extremely rapid decay of activated type-specific representations in cases of severely impaired phonological and semantic STM.

  15. Neuronal type-specific gene expression profiling and laser-capture microdissection.

    Science.gov (United States)

    Pietersen, Charmaine Y; Lim, Maribel P; Macey, Laurel; Woo, Tsung-Ung W; Sonntag, Kai C

    2011-01-01

    The human brain is an exceptionally heterogeneous structure. In order to gain insight into the neurobiological basis of neural circuit disturbances in various neurologic or psychiatric diseases, it is often important to define the molecular cascades that are associated with these disturbances in a neuronal type-specific manner. This can be achieved by the use of laser microdissection, in combination with molecular techniques such as gene expression profiling. To identify neurons in human postmortem brain tissue, one can use the inherent properties of the neuron, such as pigmentation and morphology or its structural composition through immunohistochemistry (IHC). Here, we describe the isolation of homogeneous neuronal cells and high-quality RNA from human postmortem brain material using a combination of rapid IHC, Nissl staining, or simple morphology with Laser-Capture Microdissection (LCM) or Laser Microdissection (LMD).

  16. When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

    KAUST Repository

    Wang, Jim Jing-Yan

    2017-06-28

    Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

  17. Inference of type-specific HPV transmissibility, progression and clearance rates: a mathematical modelling approach.

    Directory of Open Access Journals (Sweden)

    Helen C Johnson

    Full Text Available Quantifying rates governing the clearance of Human Papillomavirus (HPV and its progression to clinical disease, together with viral transmissibility and the duration of naturally-acquired immunity, is essential in estimating the impact of vaccination programmes and screening or testing regimes. However, the complex natural history of HPV makes this difficult. We infer the viral transmissibility, rate of waning natural immunity and rates of progression and clearance of infection of 13 high-risk and 2 non-oncogenic HPV types, making use of a number of rich datasets from Sweden. Estimates of viral transmissibility, clearance of initial infection and waning immunity were derived in a Bayesian framework by fitting a susceptible-infectious-recovered-susceptible (SIRS transmission model to age- and type-specific HPV prevalence data from both a cross-sectional study and a randomised controlled trial (RCT of primary HPV screening. The models fitted well, but over-estimated the prevalence of four high-risk types with respect to the data. Three of these types (HPV-33, -35 and -58 are among the most closely related phylogenetically to the most prevalent HPV-16. The fourth (HPV-45 is the most closely related to HPV-18; the second most prevalent type. We suggest that this may be an indicator of cross-immunity. Rates of progression and clearance of clinical lesions were additionally estimated from longitudinal data gathered as part of the same RCT. Our estimates of progression and clearance rates are consistent with the findings of survival analysis studies and we extend the literature by estimating progression and clearance rates for non-16 and non-18 high-risk types. We anticipate that such type-specific estimates will be useful in the parameterisation of further models and in developing our understanding of HPV natural history.

  18. The female gametophyte: an emerging model for cell type-specific systems biology in plant development

    Directory of Open Access Journals (Sweden)

    Marc William Schmid

    2015-11-01

    Full Text Available Systems biology, a holistic approach describing a system emerging from the interactions of its molecular components, critically depends on accurate qualitative determination and quantitative measurements of these components. Development and improvement of large-scale profiling methods (omics now facilitates comprehensive measurements of many relevant molecules. For multicellular organisms, such as animals, fungi, algae, and plants, the complexity of the system is augmented by the presence of specialized cell types and organs, and a complex interplay within and between them. Cell type-specific analyses are therefore crucial for the understanding of developmental processes and environmental responses. This review first gives an overview of current methods used for large-scale profiling of specific cell types exemplified by recent advances in plant biology. The focus then lies on suitable model systems to study plant development and cell type specification. We introduce the female gametophyte of flowering plants as an ideal model to study fundamental developmental processes. Moreover, the female reproductive lineage is of importance for the emergence of evolutionary novelties such as an unequal parental contribution to the tissue nurturing the embryo or the clonal production of seeds by asexual reproduction (apomixis. Understanding these processes is not only interesting from a developmental or evolutionary perspective, but bears great potential for further crop improvement and the simplification of breeding efforts. We finally highlight novel methods, which are already available or which will likely soon facilitate large-scale profiling of the specific cell types of the female gametophyte in both model and non-model species. We conclude that it may take only few years until an evolutionary systems biology approach toward female gametogenesis may decipher some of its biologically most interesting and economically most valuable processes.

  19. PINK1 is required for timely cell-type specific mitochondrial clearance during Drosophila midgut metamorphosis.

    Science.gov (United States)

    Liu, Yan; Lin, Jingjing; Zhang, Minjie; Chen, Kai; Yang, Shengxi; Wang, Qun; Yang, Hongqin; Xie, Shusen; Zhou, Yongjian; Zhang, Xi; Chen, Fei; Yang, Yufeng

    2016-11-15

    Mitophagy is the selective degradation of mitochondria by autophagy, which is an important mitochondrial quality and quantity control process. During Drosophila metamorphosis, the degradation of midgut involves a large change in length and organization, which is mediated by autophagy. Here we noticed a cell-type specific mitochondrial clearance process that occurs in enterocytes (ECs), while most mitochondria remain in intestinal stem cells (ISCs) during metamorphosis. Although PINK1/PARKIN represent the canonical pathway for the elimination of impaired mitochondria in varied pathological conditions, their roles in developmental processes or normal physiological conditions have been less studied. To examine the potential contribution of PINK1 in developmental processes, we monitored the dynamic expression pattern of PINK1 in the midgut development by taking advantage of a newly CRISPR/Cas9 generated knock-in fly strain expressing PINK1-mCherry fusion protein that presumably recapitulates the endogenous expression pattern of PINK1. We disclosed a spatiotemporal correlation between the expression pattern of PINK1 and the mitochondrial clearance or persistence in ECs or ISCs respectively. By mosaic genetic analysis, we then demonstrated that PINK1 and PARKIN function epistatically to mediate the specific timely removal of mitochondria, and are involved in global autophagy in ECs during Drosophila midgut metamorphosis, with kinase-dead PINK1 exerting dominant negative effects. Taken together, our studies concluded that the PINK1/PARKIN is crucial for timely cell-type specific mitophagy under physiological conditions and demonstrated again that Drosophila midgut metamorphosis might serve as an elegant in vivo model to study autophagy. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Sparse Learning with Stochastic Composite Optimization.

    Science.gov (United States)

    Zhang, Weizhong; Zhang, Lijun; Jin, Zhongming; Jin, Rong; Cai, Deng; Li, Xuelong; Liang, Ronghua; He, Xiaofei

    2017-06-01

    In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√{log(1/δ)/T}) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT).

  1. In-place sparse suffix sorting

    DEFF Research Database (Denmark)

    Prezza, Nicola

    2018-01-01

    information regarding the lexicographical order of a size-b subset of all n text suffixes is often needed. Such information can be stored space-efficiently (in b words) in the sparse suffix array (SSA). The SSA and its relative sparse LCP array (SLCP) can be used as a space-efficient substitute of the sparse...... suffix tree. Very recently, Gawrychowski and Kociumaka [11] showed that the sparse suffix tree (and therefore SSA and SLCP) can be built in asymptotically optimal O(b) space with a Monte Carlo algorithm running in O(n) time. The main reason for using the SSA and SLCP arrays in place of the sparse suffix...... tree is, however, their reduced space of b words each. This leads naturally to the quest for in-place algorithms building these arrays. Franceschini and Muthukrishnan [8] showed that the full suffix array can be built in-place and in optimal running time. On the other hand, finding sub-quadratic in...

  2. Scalable group level probabilistic sparse factor analysis

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

    2017-01-01

    Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

  3. SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

    KAUST Repository

    Desmal, Abdulla

    2015-07-29

    A scheme for efficiently solving the nonlinear electromagnetic inverse scattering problem on sparse investigation domains is described. The proposed scheme reconstructs the (complex) dielectric permittivity of an investigation domain from fields measured away from the domain itself. Least-squares data misfit between the computed scattered fields, which are expressed as a nonlinear function of the permittivity, and the measured fields is constrained by the L0/L1-norm of the solution. The resulting minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two-dimensional problems, where the ``measured\\'\\' fields are synthetically generated or obtained from actual experiments. These numerical experiments demonstrate the accuracy, efficiency, and applicability of the proposed scheme in reconstructing sparse profiles with high permittivity values.

  4. Fast wavelet based sparse approximate inverse preconditioner

    Energy Technology Data Exchange (ETDEWEB)

    Wan, W.L. [Univ. of California, Los Angeles, CA (United States)

    1996-12-31

    Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.

  5. Sparse regularization for force identification using dictionaries

    Science.gov (United States)

    Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng

    2016-04-01

    The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.

  6. Sustainability Labeling

    NARCIS (Netherlands)

    Dam, van Y.K.

    2017-01-01

    Sustainability labeling originated from a need to protect the identity of alternative systems of food production and to increase market transparency. From the 1980s onwards sustainability labeling has changed into a policy instrument replacing direct government regulation of the food market, and a

  7. Analog system for computing sparse codes

    Science.gov (United States)

    Rozell, Christopher John; Johnson, Don Herrick; Baraniuk, Richard Gordon; Olshausen, Bruno A.; Ortman, Robert Lowell

    2010-08-24

    A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.

  8. Parallel transposition of sparse data structures

    DEFF Research Database (Denmark)

    Wang, Hao; Liu, Weifeng; Hou, Kaixi

    2016-01-01

    Many applications in computational sciences and social sciences exploit sparsity and connectivity of acquired data. Even though many parallel sparse primitives such as sparse matrix-vector (SpMV) multiplication have been extensively studied, some other important building blocks, e.g., parallel tr...... transposition in the latest vendor-supplied library on an Intel multicore CPU platform, and the MergeTrans approach achieves on average of 3.4-fold (up to 11.7-fold) speedup on an Intel Xeon Phi many-core processor....

  9. Structure-based bayesian sparse reconstruction

    KAUST Repository

    Quadeer, Ahmed Abdul

    2012-12-01

    Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.

  10. Binary Sparse Phase Retrieval via Simulated Annealing

    Directory of Open Access Journals (Sweden)

    Wei Peng

    2016-01-01

    Full Text Available This paper presents the Simulated Annealing Sparse PhAse Recovery (SASPAR algorithm for reconstructing sparse binary signals from their phaseless magnitudes of the Fourier transform. The greedy strategy version is also proposed for a comparison, which is a parameter-free algorithm. Sufficient numeric simulations indicate that our method is quite effective and suggest the binary model is robust. The SASPAR algorithm seems competitive to the existing methods for its efficiency and high recovery rate even with fewer Fourier measurements.

  11. Image Quality Assessment via Quality-aware Group Sparse Coding

    Directory of Open Access Journals (Sweden)

    Minglei Tong

    2014-12-01

    Full Text Available Image quality assessment has been attracting growing attention at an accelerated pace over the past decade, in the fields of image processing, vision and machine learning. In particular, general purpose blind image quality assessment is technically challenging and lots of state-of-the-art approaches have been developed to solve this problem, most under the supervised learning framework where the human scored samples are needed for training a regression model. In this paper, we propose an unsupervised learning approach that work without the human label. In the off-line stage, our method trains a dictionary covering different levels of image quality patch atoms across the training samples without knowing the human score, where each atom is associated with a quality score induced from the reference image; at the on-line stage, given each image patch, our method performs group sparse coding to encode the sample, such that the sample quality can be estimated from the few labeled atoms whose encoding coefficients are nonzero. Experimental results on the public dataset show the promising performance of our approach and future research direction is also discussed.

  12. The role of Sox6 in zebrafish muscle fiber type specification.

    Science.gov (United States)

    Jackson, Harriet E; Ono, Yosuke; Wang, Xingang; Elworthy, Stone; Cunliffe, Vincent T; Ingham, Philip W

    2015-01-01

    The transcription factor Sox6 has been implicated in regulating muscle fiber type-specific gene expression in mammals. In zebrafish, loss of function of the transcription factor Prdm1a results in a slow to fast-twitch fiber type transformation presaged by ectopic expression of sox6 in slow-twitch progenitors. Morpholino-mediated Sox6 knockdown can suppress this transformation but causes ectopic expression of only one of three slow-twitch specific genes assayed. Here, we use gain and loss of function analysis to analyse further the role of Sox6 in zebrafish muscle fiber type specification. The GAL4 binary misexpression system was used to express Sox6 ectopically in zebrafish embryos. Cis-regulatory elements were characterized using transgenic fish. Zinc finger nuclease mediated targeted mutagenesis was used to analyse the effects of loss of Sox6 function in embryonic, larval and adult zebrafish. Zebrafish transgenic for the GCaMP3 Calcium reporter were used to assay Ca2+ transients in wild-type and mutant muscle fibres. Ectopic Sox6 expression is sufficient to downregulate slow-twitch specific gene expression in zebrafish embryos. Cis-regulatory elements upstream of the slow myosin heavy chain 1 (smyhc1) and slow troponin c (tnnc1b) genes contain putative Sox6 binding sites required for repression of the former but not the latter. Embryos homozygous for sox6 null alleles expressed tnnc1b throughout the fast-twitch muscle whereas other slow-specific muscle genes, including smyhc1, were expressed ectopically in only a subset of fast-twitch fibers. Ca2+ transients in sox6 mutant fast-twitch fibers were intermediate in their speed and amplitude between those of wild-type slow- and fast-twitch fibers. sox6 homozygotes survived to adulthood and exhibited continued misexpression of tnnc1b as well as smaller slow-twitch fibers. They also exhibited a striking curvature of the spine. The Sox6 transcription factor is a key regulator of fast-twitch muscle fiber differentiation

  13. Cell type-specific termination of transcription by transposable element sequences.

    Science.gov (United States)

    Conley, Andrew B; Jordan, I King

    2012-09-30

    Transposable elements (TEs) encode sequences necessary for their own transposition, including signals required for the termination of transcription. TE sequences within the introns of human genes show an antisense orientation bias, which has been proposed to reflect selection against TE sequences in the sense orientation owing to their ability to terminate the transcription of host gene transcripts. While there is evidence in support of this model for some elements, the extent to which TE sequences actually terminate transcription of human gene across the genome remains an open question. Using high-throughput sequencing data, we have characterized over 9,000 distinct TE-derived sequences that provide transcription termination sites for 5,747 human genes across eight different cell types. Rarefaction curve analysis suggests that there may be twice as many TE-derived termination sites (TE-TTS) genome-wide among all human cell types. The local chromatin environment for these TE-TTS is similar to that seen for 3' UTR canonical TTS and distinct from the chromatin environment of other intragenic TE sequences. However, those TE-TTS located within the introns of human genes were found to be far more cell type-specific than the canonical TTS. TE-TTS were much more likely to be found in the sense orientation than other intragenic TE sequences of the same TE family and TE-TTS in the sense orientation terminate transcription more efficiently than those found in the antisense orientation. Alu sequences were found to provide a large number of relatively weak TTS, whereas LTR elements provided a smaller number of much stronger TTS. TE sequences provide numerous termination sites to human genes, and TE-derived TTS are particularly cell type-specific. Thus, TE sequences provide a powerful mechanism for the diversification of transcriptional profiles between cell types and among evolutionary lineages, since most TE-TTS are evolutionarily young. The extent of transcription

  14. Cell type-specific termination of transcription by transposable element sequences

    Directory of Open Access Journals (Sweden)

    Conley Andrew B

    2012-09-01

    Full Text Available Abstract Background Transposable elements (TEs encode sequences necessary for their own transposition, including signals required for the termination of transcription. TE sequences within the introns of human genes show an antisense orientation bias, which has been proposed to reflect selection against TE sequences in the sense orientation owing to their ability to terminate the transcription of host gene transcripts. While there is evidence in support of this model for some elements, the extent to which TE sequences actually terminate transcription of human gene across the genome remains an open question. Results Using high-throughput sequencing data, we have characterized over 9,000 distinct TE-derived sequences that provide transcription termination sites for 5,747 human genes across eight different cell types. Rarefaction curve analysis suggests that there may be twice as many TE-derived termination sites (TE-TTS genome-wide among all human cell types. The local chromatin environment for these TE-TTS is similar to that seen for 3′ UTR canonical TTS and distinct from the chromatin environment of other intragenic TE sequences. However, those TE-TTS located within the introns of human genes were found to be far more cell type-specific than the canonical TTS. TE-TTS were much more likely to be found in the sense orientation than other intragenic TE sequences of the same TE family and TE-TTS in the sense orientation terminate transcription more efficiently than those found in the antisense orientation. Alu sequences were found to provide a large number of relatively weak TTS, whereas LTR elements provided a smaller number of much stronger TTS. Conclusions TE sequences provide numerous termination sites to human genes, and TE-derived TTS are particularly cell type-specific. Thus, TE sequences provide a powerful mechanism for the diversification of transcriptional profiles between cell types and among evolutionary lineages, since most TE-TTS are

  15. Systematic and Cell Type-Specific Telomere Length Changes in Subsets of Lymphocytes

    Directory of Open Access Journals (Sweden)

    Jue Lin

    2016-01-01

    Full Text Available Telomeres, the protective DNA-protein complexes at the ends of linear chromosomes, are important for genome stability. Leukocyte or peripheral blood mononuclear cell (PBMC telomere length is a potential biomarker for human aging that integrates genetic, environmental, and lifestyle factors and is associated with mortality and risks for major diseases. However, only a limited number of studies have examined longitudinal changes of telomere length and few have reported data on sorted circulating immune cells. We examined the average telomere length (TL in CD4+, CD8+CD28+, and CD8+CD28− T cells, B cells, and PBMCs, cross-sectionally and longitudinally, in a cohort of premenopausal women. We report that TL changes over 18 months were correlated among these three T cell types within the same participant. Additionally, PBMC TL change was also correlated with those of all three T cell types, and B cells. The rate of shortening for B cells was significantly greater than for the three T cell types. CD8+CD28− cells, despite having the shortest TL, showed significantly more rapid attrition when compared to CD8+CD28+ T cells. These results suggest systematically coordinated, yet cell type-specific responses to factors and pathways contribute to telomere length regulation.

  16. Cell type-specific roles of Jak3 in IL-2-induced proliferative signal transduction

    International Nuclear Information System (INIS)

    Fujii, Hodaka

    2007-01-01

    Binding of interleukin-2 (IL-2) to its specific receptor induces activation of two members of Jak family protein tyrosine kinases, Jak1 and Jak3. An IL-2 receptor (IL-2R)-reconstituted NIH 3T3 fibroblast cell line proliferates in response to IL-2 only when hematopoietic lineage-specific Jak3 is ectopically expressed. However, the mechanism of Jak3-dependent proliferation in the fibroblast cell line is not known. Here, I showed that Jak3 expression is dispensable for IL-2-induced activation of Jak1 and Stat proteins and expression of nuclear proto-oncogenes in the IL-2R-reconstituted fibroblast cell line. Jak3 expression markedly enhanced these IL-2-induced signaling events. In contrast, Jak3 expression was essential for induction of cyclin genes involved in the G1-S transition. These data suggest a critical role of Jak3 in IL-2 signaling in the fibroblast cell line and may provide further insight into the cell type-specific mechanism of cytokine signaling

  17. Cell type-specific roles of Jak3 in IL-2-induced proliferative signal transduction

    Science.gov (United States)

    Fujii, Hodaka

    2007-01-01

    Binding of IL-2 to its specific receptor induces activation of two members of Jak family protein tyrosine kinases, Jak1 and Jak3. An IL-2R-reconstituted NIH 3T3 fibroblast cell line proliferates in response to IL-2 only when hematopoietic lineage-specific Jak3 is ectopically expressed. However, the mechanism of Jak3-dependent proliferation in the fibroblast cell line is not known. Here, I showed that Jak3 expression is dispensable for IL-2-induced activation of Jak1 and Stat proteins and expression of nuclear proto-oncogenes in the IL-2R-reconstituted fibroblast cell line. However, Jak3 expression markedly enhanced these IL-2-induced signaling events. In contrast, Jak3 expression was essential for induction of cyclin genes involved in the G1-S transition. These data suggest a critical role of Jak3 in IL-2 signaling in the fibroblast cell line and may provide further insight into the cell type-specific mechanism of cytokine signaling. PMID:17266928

  18. Focusing on neuronal cell-type specific mechanisms for brain circuit organization, function and dysfunction

    Institute of Scientific and Technical Information of China (English)

    Lu Li

    2017-01-01

    Mammalian brain circuits consist of dynamically interconnected neurons with characteristic morphology, physiology, connectivity and genetics which are often called neuronal cell types. Neuronal cell types have been considered as building blocks of brain circuits, but knowledge of how neuron types or subtypes connect to and interact with each other to perform neural computation is still lacking. Such mechanistic insights are critical not only to our understanding of normal brain functions, such as perception, motion and cognition, but also to brain disorders including Alzheimer's disease, Schizophrenia and epilepsy, to name a few. Thus it is necessary to carry out systematic and standardized studies on neuronal cell-type specific mechanisms for brain circuit organization and function, which will provide good opportunities to bridge basic and clinical research. Here based on recent technology advancements, we discuss the strategy to target and manipulate specific populations of neuronsin vivo to provide unique insights on how neuron types or subtypes behave, interact, and generate emergent properties in a fully connected brain network. Our approach is highlighted by combining transgenic animal models, targeted electrophysiology and imaging with robotics, thus complete and standardized mapping ofin vivo properties of genetically defined neuron populations can be achieved in transgenic mouse models, which will facilitate the development of novel therapeutic strategies for brain disorders.

  19. Cell type-specific characterization of nuclear DNA contents within complex tissues and organs

    Directory of Open Access Journals (Sweden)

    Lambert Georgina M

    2005-10-01

    Full Text Available Abstract Background Eukaryotic organisms are defined by the presence of a nucleus, which encloses the chromosomal DNA, and is characterized by its DNA content (C-value. Complex eukaryotic organisms contain organs and tissues that comprise interspersions of different cell types, within which polysomaty, endoreduplication, and cell cycle arrest is frequently observed. Little is known about the distribution of C-values across different cell types within these organs and tissues. Results We have developed, and describe here, a method to precisely define the C-value status within any specific cell type within complex organs and tissues of plants. We illustrate the application of this method to Arabidopsis thaliana, specifically focusing on the different cell types found within the root. Conclusion The method accurately and conveniently charts C-value within specific cell types, and provides novel insight into developmental processes. The method is, in principle, applicable to any transformable organism, including mammals, within which cell type specificity of regulation of endoreduplication, of polysomaty, and of cell cycle arrest is suspected.

  20. Cell type-specific translational repression of Cyclin B during meiosis in males.

    Science.gov (United States)

    Baker, Catherine Craig; Gim, Byung Soo; Fuller, Margaret T

    2015-10-01

    The unique cell cycle dynamics of meiosis are controlled by layers of regulation imposed on core mitotic cell cycle machinery components by the program of germ cell development. Although the mechanisms that regulate Cdk1/Cyclin B activity in meiosis in oocytes have been well studied, little is known about the trans-acting factors responsible for developmental control of these factors in male gametogenesis. During meiotic prophase in Drosophila males, transcript for the core cell cycle protein Cyclin B1 (CycB) is expressed in spermatocytes, but the protein does not accumulate in spermatocytes until just before the meiotic divisions. Here, we show that two interacting proteins, Rbp4 and Fest, expressed at the onset of spermatocyte differentiation under control of the developmental program of male gametogenesis, function to direct cell type- and stage-specific repression of translation of the core G2/M cell cycle component cycB during the specialized cell cycle of male meiosis. Binding of Fest to Rbp4 requires a 31-amino acid region within Rbp4. Rbp4 and Fest are required for translational repression of cycB in immature spermatocytes, with Rbp4 binding sequences in a cell type-specific shortened form of the cycB 3' UTR. Finally, we show that Fest is required for proper execution of meiosis I. © 2015. Published by The Company of Biologists Ltd.

  1. Cell type-specific suppression of mechanosensitive genes by audible sound stimulation.

    Science.gov (United States)

    Kumeta, Masahiro; Takahashi, Daiji; Takeyasu, Kunio; Yoshimura, Shige H

    2018-01-01

    Audible sound is a ubiquitous environmental factor in nature that transmits oscillatory compressional pressure through the substances. To investigate the property of the sound as a mechanical stimulus for cells, an experimental system was set up using 94.0 dB sound which transmits approximately 10 mPa pressure to the cultured cells. Based on research on mechanotransduction and ultrasound effects on cells, gene responses to the audible sound stimulation were analyzed by varying several sound parameters: frequency, wave form, composition, and exposure time. Real-time quantitative PCR analyses revealed a distinct suppressive effect for several mechanosensitive and ultrasound-sensitive genes that were triggered by sounds. The effect was clearly observed in a wave form- and pressure level-specific manner, rather than the frequency, and persisted for several hours. At least two mechanisms are likely to be involved in this sound response: transcriptional control and RNA degradation. ST2 stromal cells and C2C12 myoblasts exhibited a robust response, whereas NIH3T3 cells were partially and NB2a neuroblastoma cells were completely insensitive, suggesting a cell type-specific response to sound. These findings reveal a cell-level systematic response to audible sound and uncover novel relationships between life and sound.

  2. Human muscle fiber type-specific insulin signaling: Impact of obesity and type 2 diabetes

    DEFF Research Database (Denmark)

    Albers, Peter Hjorth; Pedersen, Andreas J T; Birk, Jesper Bratz

    2015-01-01

    Skeletal muscle is a heterogeneous tissue composed of different fiber types. Studies suggest that insulin-mediated glucose metabolism is different between muscle fiber types. We hypothesized that differences are due to fiber-type specific expression/regulation of insulin signaling elements and....../or metabolic enzymes. Pools of type I and II fibers were prepared from biopsies of the vastus lateralis muscles from lean, obese and type 2 diabetic subjects before and after a hyperinsulinemic-euglycemic clamp. Type I fibers compared to type II fibers have higher protein levels of the insulin receptor, GLUT4......, hexokinase II, glycogen synthase (GS), pyruvate dehydrogenase (PDH-E1α) and a lower protein content of Akt2, TBC1D4 and TBC1D1. In type I fibers compared to type II fibers, the phosphorylation-response to insulin was similar (TBC1D4, TBC1D1 and GS) or decreased (Akt and PDH-E1α). Phosphorylation...

  3. TACO: a general-purpose tool for predicting cell-type-specific transcription factor dimers.

    Science.gov (United States)

    Jankowski, Aleksander; Prabhakar, Shyam; Tiuryn, Jerzy

    2014-03-19

    Cooperative binding of transcription factor (TF) dimers to DNA is increasingly recognized as a major contributor to binding specificity. However, it is likely that the set of known TF dimers is highly incomplete, given that they were discovered using ad hoc approaches, or through computational analyses of limited datasets. Here, we present TACO (Transcription factor Association from Complex Overrepresentation), a general-purpose standalone software tool that takes as input any genome-wide set of regulatory elements and predicts cell-type-specific TF dimers based on enrichment of motif complexes. TACO is the first tool that can accommodate motif complexes composed of overlapping motifs, a characteristic feature of many known TF dimers. Our method comprehensively outperforms existing tools when benchmarked on a reference set of 29 known dimers. We demonstrate the utility and consistency of TACO by applying it to 152 DNase-seq datasets and 94 ChIP-seq datasets. Based on these results, we uncover a general principle governing the structure of TF-TF-DNA ternary complexes, namely that the flexibility of the complex is correlated with, and most likely a consequence of, inter-motif spacing.

  4. Protein conservation and variation suggest mechanisms of cell type-specific modulation of signaling pathways.

    Directory of Open Access Journals (Sweden)

    Martin H Schaefer

    2014-06-01

    Full Text Available Many proteins and signaling pathways are present in most cell types and tissues and yet perform specialized functions. To elucidate mechanisms by which these ubiquitous pathways are modulated, we overlaid information about cross-cell line protein abundance and variability, and evolutionary conservation onto functional pathway components and topological layers in the pathway hierarchy. We found that the input (receptors and the output (transcription factors layers evolve more rapidly than proteins in the intermediary transmission layer. In contrast, protein expression variability decreases from the input to the output layer. We observed that the differences in protein variability between the input and transmission layer can be attributed to both the network position and the tendency of variable proteins to physically interact with constitutively expressed proteins. Differences in protein expression variability and conservation are also accompanied by the tendency of conserved and constitutively expressed proteins to acquire somatic mutations, while germline mutations tend to occur in cell type-specific proteins. Thus, conserved core proteins in the transmission layer could perform a fundamental role in most cell types and are therefore less tolerant to germline mutations. In summary, we propose that the core signal transmission machinery is largely modulated by a variable input layer through physical protein interactions. We hypothesize that the bow-tie organization of cellular signaling on the level of protein abundance variability contributes to the specificity of the signal response in different cell types.

  5. Cell type-specific genetic and optogenetic tools reveal hippocampal CA2 circuits.

    Science.gov (United States)

    Kohara, Keigo; Pignatelli, Michele; Rivest, Alexander J; Jung, Hae-Yoon; Kitamura, Takashi; Suh, Junghyup; Frank, Dominic; Kajikawa, Koichiro; Mise, Nathan; Obata, Yuichi; Wickersham, Ian R; Tonegawa, Susumu

    2014-02-01

    The formation and recall of episodic memory requires precise information processing by the entorhinal-hippocampal network. For several decades, the trisynaptic circuit entorhinal cortex layer II (ECII)→dentate gyrus→CA3→CA1 and the monosynaptic circuit ECIII→CA1 have been considered the primary substrates of the network responsible for learning and memory. Circuits linked to another hippocampal region, CA2, have only recently come to light. Using highly cell type-specific transgenic mouse lines, optogenetics and patch-clamp recordings, we found that dentate gyrus cells, long believed to not project to CA2, send functional monosynaptic inputs to CA2 pyramidal cells through abundant longitudinal projections. CA2 innervated CA1 to complete an alternate trisynaptic circuit, but, unlike CA3, projected preferentially to the deep, rather than to the superficial, sublayer of CA1. Furthermore, contrary to existing knowledge, ECIII did not project to CA2. Our results allow a deeper understanding of the biology of learning and memory.

  6. Systematic review of type-specific pathophysiological symptoms of Sasang typology

    Directory of Open Access Journals (Sweden)

    Yoo Ri Han

    2016-06-01

    Full Text Available Previous studies on the Sasang typology have focused on the differential diagnosis of each Sasang type with type-specific pathophysiological symptoms (TSPS. The purpose of this study was to elucidate the latent physiological mechanism related to these clinical indicators. We searched six electronic databases for articles published from 1990 to 2015 using the Sasang typology-related keywords, and found and analyzed 35 such articles. The results were summarized into six TSPS categories: perspiration, temperature preference, sleep, defecation, urination, and susceptibility to stress. The Tae-Eum and So-Eum types showed contrasting features with TSPS, and the So-Yang type was in the middle. The Tae-Eum type has good digestive function, regular bowel movement and defecation, high sleep quality, and low susceptibility to stress and cold. The Tae-Eum type has relatively large volumes of sweat and feels fresh after sweating; however, the urine is highly concentrated. These clinical features might be related to the biopsychological traits of the Tae-Eum type, including a low trait anxiety level and high ponderal and body mass indices. This study used the autonomic reactivity hypothesis for explaining the pathophysiological predispositions in the Sasang typology. The Tae-Eum and So-Eum Sasang types have a low threshold in parasympathetic and sympathetic activation, respectively. This study provides a foundation for integrating traditional Korean personalized medicine and Western biomedicine.

  7. Subspace Based Blind Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki

    2012-01-01

    The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...

  8. Multilevel sparse functional principal component analysis.

    Science.gov (United States)

    Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

    2014-01-29

    We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

  9. Continuous speech recognition with sparse coding

    CSIR Research Space (South Africa)

    Smit, WJ

    2009-04-01

    Full Text Available generative model. The spike train is classified by making use of a spike train model and dynamic programming. It is computationally expensive to find a sparse code. We use an iterative subset selection algorithm with quadratic programming for this process...

  10. Multisnapshot Sparse Bayesian Learning for DOA

    DEFF Research Database (Denmark)

    Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki

    2016-01-01

    The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source...

  11. Better Size Estimation for Sparse Matrix Products

    DEFF Research Database (Denmark)

    Amossen, Rasmus Resen; Campagna, Andrea; Pagh, Rasmus

    2010-01-01

    We consider the problem of doing fast and reliable estimation of the number of non-zero entries in a sparse Boolean matrix product. Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1 ± ε approximation (with small probability of error) in expected t...

  12. Rotational image deblurring with sparse matrices

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Nagy, James G.; Tigkos, Konstantinos

    2014-01-01

    We describe iterative deblurring algorithms that can handle blur caused by a rotation along an arbitrary axis (including the common case of pure rotation). Our algorithms use a sparse-matrix representation of the blurring operation, which allows us to easily handle several different boundary...

  13. Feature based omnidirectional sparse visual path following

    OpenAIRE

    Goedemé, Toon; Tuytelaars, Tinne; Van Gool, Luc; Vanacker, Gerolf; Nuttin, Marnix

    2005-01-01

    Goedemé T., Tuytelaars T., Van Gool L., Vanacker G., Nuttin M., ''Feature based omnidirectional sparse visual path following'', Proceedings IEEE/RSJ international conference on intelligent robots and systems - IROS2005, pp. 1003-1008, August 2-6, 2005, Edmonton, Alberta, Canada.

  14. Comparison of sparse point distribution models

    DEFF Research Database (Denmark)

    Erbou, Søren Gylling Hemmingsen; Vester-Christensen, Martin; Larsen, Rasmus

    2010-01-01

    This paper compares several methods for obtaining sparse and compact point distribution models suited for data sets containing many variables. These are evaluated on a database consisting of 3D surfaces of a section of the pelvic bone obtained from CT scans of 33 porcine carcasses. The superior m...

  15. A sparse-grid isogeometric solver

    KAUST Repository

    Beck, Joakim

    2018-02-28

    Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

  16. A sparse version of IGA solvers

    KAUST Repository

    Beck, Joakim

    2017-07-30

    Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

  17. A sparse-grid isogeometric solver

    KAUST Repository

    Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

    2018-01-01

    Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

  18. A sparse version of IGA solvers

    KAUST Repository

    Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

    2017-01-01

    Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

  19. New methods for sampling sparse populations

    Science.gov (United States)

    Anna Ringvall

    2007-01-01

    To improve surveys of sparse objects, methods that use auxiliary information have been suggested. Guided transect sampling uses prior information, e.g., from aerial photographs, for the layout of survey strips. Instead of being laid out straight, the strips will wind between potentially more interesting areas. 3P sampling (probability proportional to prediction) uses...

  20. Pesticide Labels

    Science.gov (United States)

    Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

  1. Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

    Science.gov (United States)

    Gao, Zhi; Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Ramesh, Bharath; Zhai, Ruifang

    2018-05-06

    Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

  2. Labelling patients

    International Nuclear Information System (INIS)

    Strudwick, R.M.

    2016-01-01

    This article looks at how diagnostic radiographers label their patients. An ethnographic study of the workplace culture in one diagnostic imaging department was undertaken using participant observation for four months and semi-structured interviews with ten key informants. One of the key themes; the way in which radiographers label their patients, is explored in this article. It was found from the study that within the department studied the diagnostic radiographers labelled or categorised their patients based on the information that they had. This information is used to form judgements and these judgements were used to assist the radiographers in dealing with the many different people that they encountered in their work. This categorisation and labelling of the patient appears to assist the radiographer in their decision-making processes about the examination to be carried out and the patient they are to image. This is an important aspect of the role of the diagnostic radiographer. - Highlights: • I have studied the culture in one imaging department. • Radiographers label or categorise their patients. • These labels/categories are used to manage the patient. • This is an important aspect of the way in which radiographers work.

  3. Cell-type-specific roles for COX-2 in UVB-induced skin cancer

    Science.gov (United States)

    Herschman, Harvey

    2014-01-01

    In human tumors, and in mouse models, cyclooxygenase-2 (COX-2) levels are frequently correlated with tumor development/burden. In addition to intrinsic tumor cell expression, COX-2 is often present in fibroblasts, myofibroblasts and endothelial cells of the tumor microenvironment, and in infiltrating immune cells. Intrinsic cancer cell COX-2 expression is postulated as only one of many sources for prostanoids required for tumor promotion/progression. Although both COX-2 inhibition and global Cox-2 gene deletion ameliorate ultraviolet B (UVB)-induced SKH-1 mouse skin tumorigenesis, neither manipulation can elucidate the cell type(s) in which COX-2 expression is required for tumorigenesis; both eliminate COX-2 activity in all cells. To address this question, we created Cox-2 flox/flox mice, in which the Cox-2 gene can be eliminated in a cell-type-specific fashion by targeted Cre recombinase expression. Cox-2 deletion in skin epithelial cells of SKH-1 Cox-2 flox/flox;K14Cre + mice resulted, following UVB irradiation, in reduced skin hyperplasia and increased apoptosis. Targeted epithelial cell Cox-2 deletion also resulted in reduced tumor incidence, frequency, size and proliferation rate, altered tumor cell differentiation and reduced tumor vascularization. Moreover, Cox-2 flox/flox;K14Cre + papillomas did not progress to squamous cell carcinomas. In contrast, Cox-2 deletion in SKH-1 Cox-2 flox/flox; LysMCre + myeloid cells had no effect on UVB tumor induction. We conclude that (i) intrinsic epithelial COX-2 activity plays a major role in UVB-induced skin cancer, (ii) macrophage/myeloid COX-2 plays no role in UVB-induced skin cancer and (iii) either there may be another COX-2-dependent prostanoid source(s) that drives UVB skin tumor induction or there may exist a COX-2-independent pathway(s) to UVB-induced skin cancer. PMID:24469308

  4. Evolution of sexes from an ancestral mating-type specification pathway.

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    Sa Geng

    2014-07-01

    Full Text Available Male and female sexes have evolved repeatedly in eukaryotes but the origins of dimorphic sexes and their relationship to mating types in unicellular species are not understood. Volvocine algae include isogamous species such as Chlamydomonas reinhardtii, with two equal-sized mating types, and oogamous multicellular species such as Volvox carteri with sperm-producing males and egg-producing females. Theoretical work predicts genetic linkage of a gamete cell-size regulatory gene(s to an ancestral mating-type locus as a possible step in the evolution of dimorphic gametes, but this idea has not been tested. Here we show that, contrary to predictions, a single conserved mating locus (MT gene in volvocine algae-MID, which encodes a RWP-RK domain transcription factor-evolved from its ancestral role in C. reinhardtii as a mating-type specifier, to become a determinant of sperm and egg development in V. carteri. Transgenic female V. carteri expressing male MID produced functional sperm packets during sexual development. Transgenic male V. carteri with RNA interference (RNAi-mediated knockdowns of VcMID produced functional eggs, or self-fertile hermaphrodites. Post-transcriptional controls were found to regulate cell-type-limited expression and nuclear localization of VcMid protein that restricted its activity to nuclei of developing male germ cells and sperm. Crosses with sex-reversed strains uncoupled sex determination from sex chromosome identity and revealed gender-specific roles for male and female mating locus genes in sexual development, gamete fitness and reproductive success. Our data show genetic continuity between the mating-type specification and sex determination pathways of volvocine algae, and reveal evidence for gender-specific adaptations in the male and female mating locus haplotypes of Volvox. These findings will enable a deeper understanding of how a master regulator of mating-type determination in an ancestral unicellular species was

  5. Muscle-Type Specific Autophosphorylation of CaMKII Isoforms after Paced Contractions

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    Wouter Eilers

    2014-01-01

    Full Text Available We explored to what extent isoforms of the regulator of excitation-contraction and excitation-transcription coupling, calcium/calmodulin protein kinase II (CaMKII contribute to the specificity of myocellular calcium sensing between muscle types and whether concentration transients in its autophosphorylation can be simulated. CaMKII autophosphorylation at Thr287 was assessed in three muscle compartments of the rat after slow or fast motor unit-type stimulation and was compared against a computational model (CaMuZclE coupling myocellular calcium dynamics with CaMKII Thr287 phosphorylation. Qualitative differences existed between fast- (gastrocnemius medialis and slow-type muscle (soleus for the expression pattern of CaMKII isoforms. Phospho-Thr287 content of δA CaMKII, associated with nuclear functions, demonstrated a transient and compartment-specific increase after excitation, which contrasted to the delayed autophosphorylation of the sarcoplasmic reticulum-associated βM CaMKII. In soleus muscle, excitation-induced δA CaMKII autophosphorylation demonstrated frequency dependence (P = 0.02. In the glycolytic compartment of gastrocnemius medialis, CaMKII autophosphorylation after excitation was blunted. In silico assessment emphasized the importance of mitochondrial calcium buffer capacity for excitation-induced CaMKII autophosphorylation but did not predict its isoform specificity. The findings expose that CaMKII autophosphorylation with paced contractions is regulated in an isoform and muscle type-specific fashion and highlight properties emerging for phenotype-specific regulation of CaMKII.

  6. Muscle type-specific responses to NAD+ salvage biosynthesis promote muscle function in Caenorhabditis elegans.

    Science.gov (United States)

    Vrablik, Tracy L; Wang, Wenqing; Upadhyay, Awani; Hanna-Rose, Wendy

    2011-01-15

    Salvage biosynthesis of nicotinamide adenine dinucleotide (NAD(+)) from nicotinamide (NAM) lowers NAM levels and replenishes the critical molecule NAD(+) after it is hydrolyzed. This pathway is emerging as a regulator of multiple biological processes. Here we probe the contribution of the NAM-NAD(+) salvage pathway to muscle development and function using Caenorhabditis elegans. C. elegans males with mutations in the nicotinamidase pnc-1, which catalyzes the first step of this NAD(+) salvage pathway, cannot mate due to a spicule muscle defect. Multiple muscle types are impaired in the hermaphrodites, including body wall muscles, pharyngeal muscles and vulval muscles. An active NAD(+) salvage pathway is required for optimal function of each muscle cell type. However, we found surprising muscle-cell-type specificity in terms of both the timing and relative sensitivity to perturbation of NAD(+) production or NAM levels. Active NAD(+) biosynthesis during development is critical for function of the male spicule protractor muscles during adulthood, but these muscles can surprisingly do without salvage biosynthesis in adulthood under the conditions examined. The body wall muscles require ongoing NAD(+) salvage biosynthesis both during development and adulthood for maximum function. The vulval muscles do not function in the presence of elevated NAM concentrations, but NAM supplementation is only slightly deleterious to body wall muscles during development or upon acute application in adults. Thus, the pathway plays distinct roles in different tissues. As NAM-NAD(+) biosynthesis also impacts muscle differentiation in vertebrates, we propose that similar complexities may be found among vertebrate muscle cell types. Copyright © 2010 Elsevier Inc. All rights reserved.

  7. Cell-type specificity of ChIP-predicted transcription factor binding sites

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    Håndstad Tony

    2012-08-01

    Full Text Available Abstract Background Context-dependent transcription factor (TF binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq identifies genome-wide TF binding sites for one particular context—the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? Results We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. Conclusions Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data—ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure—we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.

  8. World Health Organization Guidelines for Containment of Poliovirus Following Type-Specific Polio Eradication - Worldwide, 2015.

    Science.gov (United States)

    Previsani, Nicoletta; Tangermann, Rudolph H; Tallis, Graham; Jafari, Hamid S

    2015-08-28

    In 1988, the World Health Assembly of the World Health Organization (WHO) resolved to eradicate polio worldwide. Among the three wild poliovirus (WPV) types (type 1, type 2, and type 3), WPV type 2 (WPV2) has been eliminated in the wild since 1999, and WPV type 3 (WPV3) has not been reported since 2012. In 2015, only Afghanistan and Pakistan have reported WPV transmission. On May 25, 2015, all WHO Member States endorsed World Health Assembly resolution 68.3 on full implementation of the Polio Eradication and Endgame Strategic Plan 2013-2018 (the Endgame Plan), and with it, the third Global Action Plan to minimize poliovirus facility-associated risk (GAPIII). All WHO Member States have committed to implementing appropriate containment of WPV2 in essential laboratory and vaccine production facilities* by the end of 2015 and of type 2 oral poliovirus vaccine (OPV2) within 3 months of global withdrawal of OPV2, which is planned for April 2016. This report summarizes critical steps for essential laboratory and vaccine production facilities that intend to retain materials confirmed to contain or potentially containing type-specific WPV, vaccine-derived poliovirus (VDPV), or OPV/Sabin viruses, and steps for nonessential facilities† that process specimens that contain or might contain polioviruses. National authorities will need to certify that the essential facilities they host meet the containment requirements described in GAPIII. After certification of WPV eradication, the use of all OPV will cease; final containment of all polioviruses after polio eradication and OPV cessation will minimize the risk for reintroduction of poliovirus into a polio-free world.

  9. Tissue-type-specific transcriptome analysis identifies developing xylem-specific promoters in poplar.

    Science.gov (United States)

    Ko, Jae-Heung; Kim, Hyun-Tae; Hwang, Ildoo; Han, Kyung-Hwan

    2012-06-01

    Plant biotechnology offers a means to create novel phenotypes. However, commercial application of biotechnology in crop improvement programmes is severely hindered by the lack of utility promoters (or freedom to operate the existing ones) that can drive gene expression in a tissue-specific or temporally controlled manner. Woody biomass is gaining popularity as a source of fermentable sugars for liquid fuel production. To improve the quantity and quality of woody biomass, developing xylem (DX)-specific modification of the feedstock is highly desirable. To develop utility promoters that can drive transgene expression in a DX-specific manner, we used the Affymetrix Poplar Genome Arrays to obtain tissue-type-specific transcriptomes from poplar stems. Subsequent bioinformatics analysis identified 37 transcripts that are specifically or strongly expressed in DX cells of poplar. After further confirmation of their DX-specific expression using semi-quantitative PCR, we selected four genes (DX5, DX8, DX11 and DX15) for in vivo confirmation of their tissue-specific expression in transgenic poplars. The promoter regions of the selected DX genes were isolated and fused to a β-glucuronidase (GUS)-reported gene in a binary vector. This construct was used to produce transgenic poplars via Agrobacterium-mediated transformation. The GUS expression patterns of the resulting transgenic plants showed that these promoters were active in the xylem cells at early seedling growth and had strongest expression in the developing xylem cells at later growth stages of poplar. We conclude that these DX promoters can be used as a utility promoter for DX-specific biomass engineering. © 2012 The Authors. Plant Biotechnology Journal © 2012 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd.

  10. Differentiation and fiber type-specific activity of a muscle creatine kinase intronic enhancer

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    Tai Phillip WL

    2011-07-01

    Full Text Available Abstract Background Hundreds of genes, including muscle creatine kinase (MCK, are differentially expressed in fast- and slow-twitch muscle fibers, but the fiber type-specific regulatory mechanisms are not well understood. Results Modulatory region 1 (MR1 is a 1-kb regulatory region within MCK intron 1 that is highly active in terminally differentiating skeletal myocytes in vitro. A MCK small intronic enhancer (MCK-SIE containing a paired E-box/myocyte enhancer factor 2 (MEF2 regulatory motif resides within MR1. The SIE's transcriptional activity equals that of the extensively characterized 206-bp MCK 5'-enhancer, but the MCK-SIE is flanked by regions that can repress its activity via the individual and combined effects of about 15 different but highly conserved 9- to 24-bp sequences. ChIP and ChIP-Seq analyses indicate that the SIE and the MCK 5'-enhancer are occupied by MyoD, myogenin and MEF2. Many other E-boxes located within or immediately adjacent to intron 1 are not occupied by MyoD or myogenin. Transgenic analysis of a 6.5-kb MCK genomic fragment containing the 5'-enhancer and proximal promoter plus the 3.2-kb intron 1, with and without MR1, indicates that MR1 is critical for MCK expression in slow- and intermediate-twitch muscle fibers (types I and IIa, respectively, but is not required for expression in fast-twitch muscle fibers (types IIb and IId. Conclusions In this study, we discovered that MR1 is critical for MCK expression in slow- and intermediate-twitch muscle fibers and that MR1's positive transcriptional activity depends on a paired E-box MEF2 site motif within a SIE. This is the first study to delineate the DNA controls for MCK expression in different skeletal muscle fiber types.

  11. Cell-Type Specific Development of the Hyperpolarization-Activated Current, Ih, in Prefrontal Cortical Neurons

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    Sha-Sha Yang

    2018-05-01

    Full Text Available H-current, also known as hyperpolarization-activated current (Ih, is an inward current generated by the hyperpolarization-activated cyclic nucleotide-gated (HCN cation channels. Ih plays an essential role in regulating neuronal properties, synaptic integration and plasticity, and synchronous activity in the brain. As these biological factors change across development, the brain undergoes varying levels of vulnerability to disorders like schizophrenia that disrupt prefrontal cortex (PFC-dependent function. However, developmental changes in Ih in PFC neurons remains untested. Here, we examine Ih in pyramidal neurons vs. gamma-aminobutyric acid (GABAergic parvalbumin-expressing (PV+ interneurons in developing mouse PFC. Our findings show that the amplitudes of Ih in these cell types are identical during the juvenile period but differ at later time points. In pyramidal neurons, Ih amplitude significantly increases from juvenile to adolescence and follows a similar trend into adulthood. In contrast, the amplitude of Ih in PV+ interneurons decreases from juvenile to adolescence, and does not change from adolescence to adulthood. Moreover, the kinetics of HCN channels in pyramidal neurons is significantly slower than in PV+ interneurons, with a gradual decrease in pyramidal neurons and a gradual increase in PV+ cells across development. Our study reveals distinct developmental trajectories of Ih in pyramidal neurons and PV+ interneurons. The cell-type specific alteration of Ih during the critical period from juvenile to adolescence reflects the contribution of Ih to the maturation of the PFC and PFC-dependent function. These findings are essential for a better understanding of normal PFC function, and for elucidating Ih’s crucial role in the pathophysiology of neurodevelopmental disorders.

  12. Epigenetic regulation of normal human mammary cell type-specific miRNAs

    Energy Technology Data Exchange (ETDEWEB)

    Vrba, Lukas [Univ. of Arizona, Tucson, AZ (United States). Arizona Cancer Center; Inst. of Plant Molecular Biology, Ceske Budejovice (Czech Republic). Biology Centre ASCR; Garbe, James C. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Center; Stampfer, Martha R. [Univ. of Arizona, Tucson, AZ (United States). Arizona Cancer Center; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Center; Futscher, Bernard W. [Univ. of Arizona, Tucson, AZ (United States). Arizona Cancer Center and Dept. of Pharmacology & Toxicology

    2011-08-26

    Epigenetic mechanisms are important regulators of cell type–specific genes, including miRNAs. In order to identify cell type-specific miRNAs regulated by epigenetic mechanisms, we undertook a global analysis of miRNA expression and epigenetic states in three isogenic pairs of human mammary epithelial cells (HMEC) and human mammary fibroblasts (HMF), which represent two differentiated cell types typically present within a given organ, each with a distinct phenotype and a distinct epigenotype. While miRNA expression and epigenetic states showed strong interindividual concordance within a given cell type, almost 10% of the expressed miRNA showed a cell type–specific pattern of expression that was linked to the epigenetic state of their promoter. The tissue-specific miRNA genes were epigenetically repressed in nonexpressing cells by DNA methylation (38%) and H3K27me3 (58%), with only a small set of miRNAs (21%) showing a dual epigenetic repression where both DNA methylation and H3K27me3 were present at their promoters, such as MIR10A and MIR10B. Individual miRNA clusters of closely related miRNA gene families can each display cell type–specific repression by the same or complementary epigenetic mechanisms, such as the MIR200 family, and MIR205, where fibroblasts repress MIR200C/141 by DNA methylation, MIR200A/200B/429 by H3K27me3, and MIR205 by both DNA methylation and H3K27me3. Since deregulation of many of the epigenetically regulated miRNAs that we identified have been linked to disease processes such as cancer, it is predicted that compromise of the epigenetic control mechanisms is important for this process. Overall, these results highlight the importance of epigenetic regulation in the control of normal cell type–specific miRNA expression.

  13. Cell-type specific roles for PTEN in establishing a functional retinal architecture.

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    Robert Cantrup

    Full Text Available The retina has a unique three-dimensional architecture, the precise organization of which allows for complete sampling of the visual field. Along the radial or apicobasal axis, retinal neurons and their dendritic and axonal arbors are segregated into layers, while perpendicular to this axis, in the tangential plane, four of the six neuronal types form patterned cellular arrays, or mosaics. Currently, the molecular cues that control retinal cell positioning are not well-understood, especially those that operate in the tangential plane. Here we investigated the role of the PTEN phosphatase in establishing a functional retinal architecture.In the developing retina, PTEN was localized preferentially to ganglion, amacrine and horizontal cells, whose somata are distributed in mosaic patterns in the tangential plane. Generation of a retina-specific Pten knock-out resulted in retinal ganglion, amacrine and horizontal cell hypertrophy, and expansion of the inner plexiform layer. The spacing of Pten mutant mosaic populations was also aberrant, as were the arborization and fasciculation patterns of their processes, displaying cell type-specific defects in the radial and tangential dimensions. Irregular oscillatory potentials were also observed in Pten mutant electroretinograms, indicative of asynchronous amacrine cell firing. Furthermore, while Pten mutant RGC axons targeted appropriate brain regions, optokinetic spatial acuity was reduced in Pten mutant animals. Finally, while some features of the Pten mutant retina appeared similar to those reported in Dscam-mutant mice, PTEN expression and activity were normal in the absence of Dscam.We conclude that Pten regulates somal positioning and neurite arborization patterns of a subset of retinal cells that form mosaics, likely functioning independently of Dscam, at least during the embryonic period. Our findings thus reveal an unexpected level of cellular specificity for the multi-purpose phosphatase, and

  14. A sparse electromagnetic imaging scheme using nonlinear landweber iterations

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2015-01-01

    Development and use of electromagnetic inverse scattering techniques for imagining sparse domains have been on the rise following the recent advancements in solving sparse optimization problems. Existing techniques rely on iteratively converting

  15. Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

    KAUST Repository

    Buse, Gerrit; Pflü ger, Dirk; Jacob, Riko

    2014-01-01

    In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated

  16. Improved Sparse Channel Estimation for Cooperative Communication Systems

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    Guan Gui

    2012-01-01

    Full Text Available Accurate channel state information (CSI is necessary at receiver for coherent detection in amplify-and-forward (AF cooperative communication systems. To estimate the channel, traditional methods, that is, least squares (LS and least absolute shrinkage and selection operator (LASSO, are based on assumptions of either dense channel or global sparse channel. However, LS-based linear method neglects the inherent sparse structure information while LASSO-based sparse channel method cannot take full advantage of the prior information. Based on the partial sparse assumption of the cooperative channel model, we propose an improved channel estimation method with partial sparse constraint. At first, by using sparse decomposition theory, channel estimation is formulated as a compressive sensing problem. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over global sparse channel estimation methods.

  17. Sparse reconstruction using distribution agnostic bayesian matching pursuit

    KAUST Repository

    Masood, Mudassir; Al-Naffouri, Tareq Y.

    2013-01-01

    A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics

  18. Lim homeobox genes in the Ctenophore Mnemiopsis leidyi: the evolution of neural cell type specification

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    Simmons David K

    2012-01-01

    Full Text Available Abstract Background Nervous systems are thought to be important to the evolutionary success and diversification of metazoans, yet little is known about the origin of simple nervous systems at the base of the animal tree. Recent data suggest that ctenophores, a group of macroscopic pelagic marine invertebrates, are the most ancient group of animals that possess a definitive nervous system consisting of a distributed nerve net and an apical statocyst. This study reports on details of the evolution of the neural cell type specifying transcription factor family of LIM homeobox containing genes (Lhx, which have highly conserved functions in neural specification in bilaterian animals. Results Using next generation sequencing, the first draft of the genome of the ctenophore Mnemiopsis leidyi has been generated. The Lhx genes in all animals are represented by seven subfamilies (Lhx1/5, Lhx3/4, Lmx, Islet, Lhx2/9, Lhx6/8, and LMO of which four were found to be represented in the ctenophore lineage (Lhx1/5, Lhx3/4, Lmx, and Islet. Interestingly, the ctenophore Lhx gene complement is more similar to the sponge complement (sponges do not possess neurons than to either the cnidarian-bilaterian or placozoan Lhx complements. Using whole mount in situ hybridization, the Lhx gene expression patterns were examined and found to be expressed around the blastopore and in cells that give rise to the apical organ and putative neural sensory cells. Conclusion This research gives us a first look at neural cell type specification in the ctenophore M. leidyi. Within M. leidyi, Lhx genes are expressed in overlapping domains within proposed neural cellular and sensory cell territories. These data suggest that Lhx genes likely played a conserved role in the patterning of sensory cells in the ancestor of sponges and ctenophores, and may provide a link to the expression of Lhx orthologs in sponge larval photoreceptive cells. Lhx genes were later co-opted into patterning more

  19. Sparse DOA estimation with polynomial rooting

    DEFF Research Database (Denmark)

    Xenaki, Angeliki; Gerstoft, Peter; Fernandez Grande, Efren

    2015-01-01

    Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve highresol......Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve...... highresolution imaging. Utilizing the dual optimal variables of the CS optimization problem, it is shown with Monte Carlo simulations that the DOAs are accurately reconstructed through polynomial rooting (Root-CS). Polynomial rooting is known to improve the resolution in several other DOA estimation methods...

  20. Sparse learning of stochastic dynamical equations

    Science.gov (United States)

    Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

    2018-06-01

    With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.

  1. Sparseness- and continuity-constrained seismic imaging

    Science.gov (United States)

    Herrmann, Felix J.

    2005-04-01

    Non-linear solution strategies to the least-squares seismic inverse-scattering problem with sparseness and continuity constraints are proposed. Our approach is designed to (i) deal with substantial amounts of additive noise (SNR formulating the solution of the seismic inverse problem in terms of an optimization problem. During the optimization, sparseness on the basis and continuity along the reflectors are imposed by jointly minimizing the l1- and anisotropic diffusion/total-variation norms on the coefficients and reflectivity, respectively. [Joint work with Peyman P. Moghaddam was carried out as part of the SINBAD project, with financial support secured through ITF (the Industry Technology Facilitator) from the following organizations: BG Group, BP, ExxonMobil, and SHELL. Additional funding came from the NSERC Discovery Grants 22R81254.

  2. A density functional for sparse matter

    DEFF Research Database (Denmark)

    Langreth, D.C.; Lundqvist, Bengt; Chakarova-Kack, S.D.

    2009-01-01

    forces in molecules, to adsorbed molecules, like benzene, naphthalene, phenol and adenine on graphite, alumina and metals, to polymer and carbon nanotube (CNT) crystals, and hydrogen storage in graphite and metal-organic frameworks (MOFs), and to the structure of DNA and of DNA with intercalators......Sparse matter is abundant and has both strong local bonds and weak nonbonding forces, in particular nonlocal van der Waals (vdW) forces between atoms separated by empty space. It encompasses a broad spectrum of systems, like soft matter, adsorption systems and biostructures. Density-functional...... theory (DFT), long since proven successful for dense matter, seems now to have come to a point, where useful extensions to sparse matter are available. In particular, a functional form, vdW-DF (Dion et al 2004 Phys. Rev. Lett. 92 246401; Thonhauser et al 2007 Phys. Rev. B 76 125112), has been proposed...

  3. Robust Fringe Projection Profilometry via Sparse Representation.

    Science.gov (United States)

    Budianto; Lun, Daniel P K

    2016-04-01

    In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment.

  4. Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest.

    Science.gov (United States)

    Wang, Xin; Lin, Peijie; Ho, Joshua W K

    2018-01-19

    It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs - a motif grammar - located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model. We present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar. Our bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific.

  5. Cell Type-Specific Manipulation with GFP-Dependent Cre Recombinase

    Science.gov (United States)

    Tang, Jonathan C Y; Rudolph, Stephanie; Dhande, Onkar S; Abraira, Victoria E; Choi, Seungwon; Lapan, Sylvain; Drew, Iain R; Drokhlyansky, Eugene; Huberman, Andrew D; Regehr, Wade G; Cepko, Constance L

    2016-01-01

    Summary There are many transgenic GFP reporter lines that allow visualization of specific populations of cells. Using such lines for functional studies requires a method that transforms GFP into a molecule that enables genetic manipulation. Here we report the creation of a method that exploits GFP for gene manipulation, Cre Recombinase Dependent on GFP (CRE-DOG), a split component system that uses GFP and its derivatives to directly induce Cre/loxP recombination. Using plasmid electroporation and AAV viral vectors, we delivered CRE-DOG to multiple GFP mouse lines, leading to effective recombination selectively in GFP-labeled cells. Further, CRE-DOG enabled optogenetic control of these neurons. Beyond providing a new set of tools for manipulation of gene expression selectively in GFP+ cells, we demonstrate that GFP can be used to reconstitute the activity of a protein not known to have a modular structure, suggesting that this strategy might be applicable to a wide range of proteins. PMID:26258682

  6. Solving Sparse Polynomial Optimization Problems with Chordal Structure Using the Sparse, Bounded-Degree Sum-of-Squares Hierarchy

    NARCIS (Netherlands)

    Marandi, Ahmadreza; de Klerk, Etienne; Dahl, Joachim

    The sparse bounded degree sum-of-squares (sparse-BSOS) hierarchy of Weisser, Lasserre and Toh [arXiv:1607.01151,2016] constructs a sequence of lower bounds for a sparse polynomial optimization problem. Under some assumptions, it is proven by the authors that the sequence converges to the optimal

  7. Multi-threaded Sparse Matrix Sparse Matrix Multiplication for Many-Core and GPU Architectures.

    Energy Technology Data Exchange (ETDEWEB)

    Deveci, Mehmet [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trott, Christian Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rajamanickam, Sivasankaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2018-01-01

    Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix- matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.

  8. Artifact detection in electrodermal activity using sparse recovery

    Science.gov (United States)

    Kelsey, Malia; Palumbo, Richard Vincent; Urbaneja, Alberto; Akcakaya, Murat; Huang, Jeannie; Kleckner, Ian R.; Barrett, Lisa Feldman; Quigley, Karen S.; Sejdic, Ervin; Goodwin, Matthew S.

    2017-05-01

    Electrodermal Activity (EDA) - a peripheral index of sympathetic nervous system activity - is a primary measure used in psychophysiology. EDA is widely accepted as an indicator of physiological arousal, and it has been shown to reveal when psychologically novel events occur. Traditionally, EDA data is collected in controlled laboratory experiments. However, recent developments in wireless biosensing have led to an increase in out-of-lab studies. This transition to ambulatory data collection has introduced challenges. In particular, artifacts such as wearer motion, changes in temperature, and electrical interference can be misidentified as true EDA responses. The inability to distinguish artifact from signal hinders analyses of ambulatory EDA data. Though manual procedures for identifying and removing EDA artifacts exist, they are time consuming - which is problematic for the types of longitudinal data sets represented in modern ambulatory studies. This manuscript presents a novel technique to automatically identify and remove artifacts in EDA data using curve fitting and sparse recovery methods. Our method was evaluated using labeled data to determine the accuracy of artifact identification. Procedures, results, conclusions, and future directions are presented.

  9. Noniterative MAP reconstruction using sparse matrix representations.

    Science.gov (United States)

    Cao, Guangzhi; Bouman, Charles A; Webb, Kevin J

    2009-09-01

    We present a method for noniterative maximum a posteriori (MAP) tomographic reconstruction which is based on the use of sparse matrix representations. Our approach is to precompute and store the inverse matrix required for MAP reconstruction. This approach has generally not been used in the past because the inverse matrix is typically large and fully populated (i.e., not sparse). In order to overcome this problem, we introduce two new ideas. The first idea is a novel theory for the lossy source coding of matrix transformations which we refer to as matrix source coding. This theory is based on a distortion metric that reflects the distortions produced in the final matrix-vector product, rather than the distortions in the coded matrix itself. The resulting algorithms are shown to require orthonormal transformations of both the measurement data and the matrix rows and columns before quantization and coding. The second idea is a method for efficiently storing and computing the required orthonormal transformations, which we call a sparse-matrix transform (SMT). The SMT is a generalization of the classical FFT in that it uses butterflies to compute an orthonormal transform; but unlike an FFT, the SMT uses the butterflies in an irregular pattern, and is numerically designed to best approximate the desired transforms. We demonstrate the potential of the noniterative MAP reconstruction with examples from optical tomography. The method requires offline computation to encode the inverse transform. However, once these offline computations are completed, the noniterative MAP algorithm is shown to reduce both storage and computation by well over two orders of magnitude, as compared to a linear iterative reconstruction methods.

  10. Galaxy redshift surveys with sparse sampling

    International Nuclear Information System (INIS)

    Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro; Jee, Inh; Jeong, Donghui; Blanc, Guillermo A.; Ciardullo, Robin; Gronwall, Caryl; Hagen, Alex; Schneider, Donald P.; Drory, Niv; Fabricius, Maximilian; Landriau, Martin; Finkelstein, Steven; Jogee, Shardha; Cooper, Erin Mentuch; Tuttle, Sarah; Gebhardt, Karl; Hill, Gary J.

    2013-01-01

    Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V survey ∼ 10Gpc 3 ) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V survey , we observe only a fraction of the volume. The distribution of observed regions should be chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V survey (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V survey (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys

  11. A view of Kanerva's sparse distributed memory

    Science.gov (United States)

    Denning, P. J.

    1986-01-01

    Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given.

  12. Wavelets for Sparse Representation of Music

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft; Harbo, Anders La-Cour

    2004-01-01

    We are interested in obtaining a sparse representation of music signals by means of a discrete wavelet transform (DWT). That means we want the energy in the representation to be concentrated in few DWT coefficients. It is well-known that the decay of the DWT coefficients is strongly related...... to the number of vanishing moments of the mother wavelet, and to the smoothness of the signal. In this paper we present the result of applying two classical families of wavelets to a series of musical signals. The purpose is to determine a general relation between the number of vanishing moments of the wavelet...

  13. Sparse dynamics for partial differential equations.

    Science.gov (United States)

    Schaeffer, Hayden; Caflisch, Russel; Hauck, Cory D; Osher, Stanley

    2013-04-23

    We investigate the approximate dynamics of several differential equations when the solutions are restricted to a sparse subset of a given basis. The restriction is enforced at every time step by simply applying soft thresholding to the coefficients of the basis approximation. By reducing or compressing the information needed to represent the solution at every step, only the essential dynamics are represented. In many cases, there are natural bases derived from the differential equations, which promote sparsity. We find that our method successfully reduces the dynamics of convection equations, diffusion equations, weak shocks, and vorticity equations with high-frequency source terms.

  14. Abnormal Event Detection Using Local Sparse Representation

    DEFF Research Database (Denmark)

    Ren, Huamin; Moeslund, Thomas B.

    2014-01-01

    We propose to detect abnormal events via a sparse subspace clustering algorithm. Unlike most existing approaches, which search for optimized normal bases and detect abnormality based on least square error or reconstruction error from the learned normal patterns, we propose an abnormality measurem...... is found that satisfies: the distance between its local space and the normal space is large. We evaluate our method on two public benchmark datasets: UCSD and Subway Entrance datasets. The comparison to the state-of-the-art methods validate our method's effectiveness....

  15. Partitioning sparse rectangular matrices for parallel processing

    Energy Technology Data Exchange (ETDEWEB)

    Kolda, T.G.

    1998-05-01

    The authors are interested in partitioning sparse rectangular matrices for parallel processing. The partitioning problem has been well-studied in the square symmetric case, but the rectangular problem has received very little attention. They will formalize the rectangular matrix partitioning problem and discuss several methods for solving it. They will extend the spectral partitioning method for symmetric matrices to the rectangular case and compare this method to three new methods -- the alternating partitioning method and two hybrid methods. The hybrid methods will be shown to be best.

  16. Functional fixedness in a technologically sparse culture.

    Science.gov (United States)

    German, Tim P; Barrett, H Clark

    2005-01-01

    Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory.

  17. Parallel preconditioning techniques for sparse CG solvers

    Energy Technology Data Exchange (ETDEWEB)

    Basermann, A.; Reichel, B.; Schelthoff, C. [Central Institute for Applied Mathematics, Juelich (Germany)

    1996-12-31

    Conjugate gradient (CG) methods to solve sparse systems of linear equations play an important role in numerical methods for solving discretized partial differential equations. The large size and the condition of many technical or physical applications in this area result in the need for efficient parallelization and preconditioning techniques of the CG method. In particular for very ill-conditioned matrices, sophisticated preconditioner are necessary to obtain both acceptable convergence and accuracy of CG. Here, we investigate variants of polynomial and incomplete Cholesky preconditioners that markedly reduce the iterations of the simply diagonally scaled CG and are shown to be well suited for massively parallel machines.

  18. Food labels

    DEFF Research Database (Denmark)

    Selsøe Sørensen, Henrik; Clement, Jesper; Gabrielsen, Gorm

    2012-01-01

    evidence for dividing consumers into two profiles: one relying on general food knowledge and another using knowledge related to signpost labels. In a combined eyetracking and questionnaire survey we analyse the influence of background knowledge and identify different patterns of visual attention......The food industry develops tasty and healthy food but fails to deliver the message to all consumers. The consumers’ background knowledge is essential for how they find and decode relevant elements in the cocktail of signs which fight for attention on food labels. In this exploratory study, we find...... for the two consumer profiles. This underlines the complexity in choosing and designing the ‘right’ elements for a food package that consumers actually look at and are able to make rational use of. In spite of any regulation of food information provided by authorities, consumers will still be confronted...

  19. Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2015-01-01

    We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis, and it is for...

  20. Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint

    Directory of Open Access Journals (Sweden)

    Zhi Gao

    2018-05-01

    Full Text Available Light detection and ranging (LiDAR sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs and unmanned aerial vehicles (UAVs to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

  1. Enzyme-Linked Immunosorbent Assay Using a Virus Type-Specific Peptide Based on a Subdomain of Envelope Protein Erns for Serologic Diagnosis of Pestivirus Infections in Swine

    Science.gov (United States)

    Langedijk, J. P. M.; Middel, W. G. J.; Meloen, R. H.; Kramps, J. A.; de Smit, J. A.

    2001-01-01

    Peptides deduced from the C-terminal end (residues 191 to 227) of pestivirus envelope protein Erns were used to develop enzyme-linked immunosorbent assays (ELISAs) to measure specifically antibodies against different types of pestiviruses. The choice of the peptide was based on the modular structure of the Erns protein, and the peptide was selected for its probable independent folding and good exposure, which would make it a good candidate for an antigenic peptide to be used in a diagnostic test. A solid-phase peptide ELISA which was cross-reactive for several types of pestivirus antibodies and which can be used for the general detection of pestivirus antibodies was developed. To identify type-specific pestivirus antibodies, a liquid-phase peptide ELISA, with a labeled, specific classical swine fever virus (CSFV) peptide and an unlabeled bovine viral diarrhea virus peptide to block cross-reactivity, was developed. Specificity and sensitivity of the liquid-phase peptide ELISA for CSFV were 98 and 100%, respectively. Because the peptide is a fragment of the Erns protein, it can be used to differentiate between infected and vaccinated animals when a vaccine based on the E2 protein, which is another pestivirus envelope protein, is used. PMID:11230402

  2. Interferometric interpolation of sparse marine data

    KAUST Repository

    Hanafy, Sherif M.

    2013-10-11

    We present the theory and numerical results for interferometrically interpolating 2D and 3D marine surface seismic profiles data. For the interpolation of seismic data we use the combination of a recorded Green\\'s function and a model-based Green\\'s function for a water-layer model. Synthetic (2D and 3D) and field (2D) results show that the seismic data with sparse receiver intervals can be accurately interpolated to smaller intervals using multiples in the data. An up- and downgoing separation of both recorded and model-based Green\\'s functions can help in minimizing artefacts in a virtual shot gather. If the up- and downgoing separation is not possible, noticeable artefacts will be generated in the virtual shot gather. As a partial remedy we iteratively use a non-stationary 1D multi-channel matching filter with the interpolated data. Results suggest that a sparse marine seismic survey can yield more information about reflectors if traces are interpolated by interferometry. Comparing our results to those of f-k interpolation shows that the synthetic example gives comparable results while the field example shows better interpolation quality for the interferometric method. © 2013 European Association of Geoscientists & Engineers.

  3. Balanced and sparse Tamo-Barg codes

    KAUST Repository

    Halbawi, Wael; Duursma, Iwan; Dau, Hoang; Hassibi, Babak

    2017-01-01

    We construct balanced and sparse generator matrices for Tamo and Barg's Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.

  4. Atmospheric inverse modeling via sparse reconstruction

    Science.gov (United States)

    Hase, Nils; Miller, Scot M.; Maaß, Peter; Notholt, Justus; Palm, Mathias; Warneke, Thorsten

    2017-10-01

    Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.

  5. Balanced and sparse Tamo-Barg codes

    KAUST Repository

    Halbawi, Wael

    2017-08-29

    We construct balanced and sparse generator matrices for Tamo and Barg\\'s Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.

  6. Manifold Adaptive Label Propagation for Face Clustering.

    Science.gov (United States)

    Pei, Xiaobing; Lyu, Zehua; Chen, Changqing; Chen, Chuanbo

    2015-08-01

    In this paper, a novel label propagation (LP) method is presented, called the manifold adaptive label propagation (MALP) method, which is to extend original LP by integrating sparse representation constraint into regularization framework of LP method. Similar to most LP, first of all, MALP also finds graph edges from given data and gives weights to the graph edges. Our goal is to find graph weights matrix adaptively. The key advantage of our approach is that MALP simultaneously finds graph weights matrix and predicts the label of unlabeled data. This paper also derives efficient algorithm to solve the proposed problem. Extensions of our MALP in kernel space and robust version are presented. The proposed method has been applied to the problem of semi-supervised face clustering using the well-known ORL, Yale, extended YaleB, and PIE datasets. Our experimental evaluations show the effectiveness of our method.

  7. Parallel sparse direct solver for integrated circuit simulation

    CERN Document Server

    Chen, Xiaoming; Yang, Huazhong

    2017-01-01

    This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques. · Introduces complicated algorithms of sparse linear solvers, using concise principles and simple examples, without complex theory or lengthy derivations; · Describes a parallel sparse direct solver that can be adopted to accelerate any SPICE-like integrated circuit simulato...

  8. Micro solid-phase radioimmunoassay for detection of herpesvirus type-specific antibody: parameters involved in standardization

    Energy Technology Data Exchange (ETDEWEB)

    Matson, D.O.; Adler-Storthz, K.; Adam, E.; Dreesman, G.R. (Baylor Univ., Houston, TX (USA). Coll. of Medicine)

    1983-02-01

    A micro solid-phase radioimmunoassay (micro-SPRIA) was developed to demonstrate type-specific antibodies to herpes simplex virus types 1 and 2 (HSV1 and HSV2). Glycoproteins from the 123,000 dalton region of HSV1 (VP123) and the 119,000 dalton region of HSV2 (VP119) were isolated on preparative polyacrylamide gels for use as antigens in the micro-SPRIA. Human sera selected from clinical samples by virological history and appropriate microneutralization data were used to standardize the micro-SPRIA. Optimization of the assay required the use of siliconized microtiter wells for adsorption of antigen. Maximized results were highly dependent on the concentrations of antigen, primary antibody, and secondary antibody as well as the diluents used for these principal test reagents. Incorporation of HSV glycoproteins of each respective type with the optimal condition established in this study facilitates the direct detection of type-specific antibody in human sera.

  9. A Sparse Approximate Inverse Preconditioner for Nonsymmetric Linear Systems

    Czech Academy of Sciences Publication Activity Database

    Benzi, M.; Tůma, Miroslav

    1998-01-01

    Roč. 19, č. 3 (1998), s. 968-994 ISSN 1064-8275 R&D Projects: GA ČR GA201/93/0067; GA AV ČR IAA230401 Keywords : large sparse systems * interative methods * preconditioning * approximate inverse * sparse linear systems * sparse matrices * incomplete factorizations * conjugate gradient -type methods Subject RIV: BA - General Mathematics Impact factor: 1.378, year: 1998

  10. A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

    Science.gov (United States)

    Chang, Lan; Zhang, Mengmeng; Li, Wei

    2017-10-01

    A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

  11. Dose-shaping using targeted sparse optimization

    Energy Technology Data Exchange (ETDEWEB)

    Sayre, George A.; Ruan, Dan [Department of Radiation Oncology, University of California - Los Angeles School of Medicine, 200 Medical Plaza, Los Angeles, California 90095 (United States)

    2013-07-15

    Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E{sub tot}{sup sparse}), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L{sub 1} norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E{sub tot

  12. Dose-shaping using targeted sparse optimization

    International Nuclear Information System (INIS)

    Sayre, George A.; Ruan, Dan

    2013-01-01

    Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E tot sparse ), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L 1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot sparse improves

  13. Dose-shaping using targeted sparse optimization.

    Science.gov (United States)

    Sayre, George A; Ruan, Dan

    2013-07-01

    Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method. In designing the energy minimization objective (E tot (sparse)), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot (sparse) improves tradeoff between

  14. Invariant TAD Boundaries Constrain Cell-Type-Specific Looping Interactions between Promoters and Distal Elements around the CFTR Locus.

    Science.gov (United States)

    Smith, Emily M; Lajoie, Bryan R; Jain, Gaurav; Dekker, Job

    2016-01-07

    Three-dimensional genome structure plays an important role in gene regulation. Globally, chromosomes are organized into active and inactive compartments while, at the gene level, looping interactions connect promoters to regulatory elements. Topologically associating domains (TADs), typically several hundred kilobases in size, form an intermediate level of organization. Major questions include how TADs are formed and how they are related to looping interactions between genes and regulatory elements. Here we performed a focused 5C analysis of a 2.8 Mb chromosome 7 region surrounding CFTR in a panel of cell types. We find that the same TAD boundaries are present in all cell types, indicating that TADs represent a universal chromosome architecture. Furthermore, we find that these TAD boundaries are present irrespective of the expression and looping of genes located between them. In contrast, looping interactions between promoters and regulatory elements are cell-type specific and occur mostly within TADs. This is exemplified by the CFTR promoter that in different cell types interacts with distinct sets of distal cell-type-specific regulatory elements that are all located within the same TAD. Finally, we find that long-range associations between loci located in different TADs are also detected, but these display much lower interaction frequencies than looping interactions within TADs. Interestingly, interactions between TADs are also highly cell-type-specific and often involve loci clustered around TAD boundaries. These data point to key roles of invariant TAD boundaries in constraining as well as mediating cell-type-specific long-range interactions and gene regulation. Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

  15. Herpes simplex virus latency-associated transcript sequence downstream of the promoter influences type-specific reactivation and viral neurotropism.

    Science.gov (United States)

    Bertke, Andrea S; Patel, Amita; Krause, Philip R

    2007-06-01

    Herpes simplex virus (HSV) establishes latency in sensory nerve ganglia during acute infection and may later periodically reactivate to cause recurrent disease. HSV type 1 (HSV-1) reactivates more efficiently than HSV-2 from trigeminal ganglia while HSV-2 reactivates more efficiently than HSV-1 from lumbosacral dorsal root ganglia (DRG) to cause recurrent orofacial and genital herpes, respectively. In a previous study, a chimeric HSV-2 that expressed the latency-associated transcript (LAT) from HSV-1 reactivated similarly to wild-type HSV-1, suggesting that the LAT influences the type-specific reactivation phenotype of HSV-2. To further define the LAT region essential for type-specific reactivation, we constructed additional chimeric HSV-2 viruses by replacing the HSV-2 LAT promoter (HSV2-LAT-P1) or 2.5 kb of the HSV-2 LAT sequence (HSV2-LAT-S1) with the corresponding regions from HSV-1. HSV2-LAT-S1 was impaired for reactivation in the guinea pig genital model, while its rescuant and HSV2-LAT-P1 reactivated with a wild-type HSV-2 phenotype. Moreover, recurrences of HSV-2-LAT-S1 were frequently fatal, in contrast to the relatively mild recurrences of the other viruses. During recurrences, HSV2-LAT-S1 DNA increased more in the sacral cord compared to its rescuant or HSV-2. Thus, the LAT sequence region, not the LAT promoter region, provides essential elements for type-specific reactivation of HSV-2 and also plays a role in viral neurotropism. HSV-1 DNA, as quantified by real-time PCR, was more abundant in the lumbar spinal cord, while HSV-2 DNA was more abundant in the sacral spinal cord, which may provide insights into the mechanism for type-specific reactivation and different patterns of central nervous system infection of HSV-1 and HSV-2.

  16. Data analysis in high-dimensional sparse spaces

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder

    classification techniques for high-dimensional problems are presented: Sparse discriminant analysis, sparse mixture discriminant analysis and orthogonality constrained support vector machines. The first two introduces sparseness to the well known linear and mixture discriminant analysis and thereby provide low...... are applied to classifications of fish species, ear canal impressions used in the hearing aid industry, microbiological fungi species, and various cancerous tissues and healthy tissues. In addition, novel applications of sparse regressions (also called the elastic net) to the medical, concrete, and food...

  17. Greedy vs. L1 convex optimization in sparse coding

    DEFF Research Database (Denmark)

    Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

    2015-01-01

    Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes. During this procedure, sparse codes which can be achieved...... solutions. Considering the property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classification application may not perform well in abnormality detection. Therefore, we compare the sparse codes and comprehensively evaluate...... their performance from various aspects to better understand their applicability, including computation time, reconstruction error, sparsity, detection...

  18. Label triangulation

    International Nuclear Information System (INIS)

    May, R.P.

    1983-01-01

    Label Triangulation (LT) with neutrons allows the investigation of the quaternary structure of biological multicomponent complexes under native conditions. Provided that the complex can be fully separated into and reconstituted from its single - protonated and deuterated - components, small angle neutron scattering (SANS) can give selective information on shapes and pair distances of these components. Following basic geometrical rules, the spatial arrangement of the components can be reconstructed from these data. LT has so far been successfully applied to the small and large ribosomal subunits and the transcriptase of E. coli. (author)

  19. Learning from Weak and Noisy Labels for Semantic Segmentation

    KAUST Repository

    Lu, Zhiwu

    2016-04-08

    A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these ‘free’ tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel L1-optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the L1-optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy.

  20. Learning from Weak and Noisy Labels for Semantic Segmentation

    KAUST Repository

    Lu, Zhiwu; Fu, Zhenyong; Xiang, Tao; Han, Peng; Wang, Liwei; Gao, Xin

    2016-01-01

    A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these ‘free’ tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel L1-optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the L1-optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy.

  1. Sparse Bayesian Learning for Nonstationary Data Sources

    Science.gov (United States)

    Fujimaki, Ryohei; Yairi, Takehisa; Machida, Kazuo

    This paper proposes an online Sparse Bayesian Learning (SBL) algorithm for modeling nonstationary data sources. Although most learning algorithms implicitly assume that a data source does not change over time (stationary), one in the real world usually does due to such various factors as dynamically changing environments, device degradation, sudden failures, etc (nonstationary). The proposed algorithm can be made useable for stationary online SBL by setting time decay parameters to zero, and as such it can be interpreted as a single unified framework for online SBL for use with stationary and nonstationary data sources. Tests both on four types of benchmark problems and on actual stock price data have shown it to perform well.

  2. Narrowband interference parameterization for sparse Bayesian recovery

    KAUST Repository

    Ali, Anum

    2015-09-11

    This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.

  3. Modern algorithms for large sparse eigenvalue problems

    International Nuclear Information System (INIS)

    Meyer, A.

    1987-01-01

    The volume is written for mathematicians interested in (numerical) linear algebra and in the solution of large sparse eigenvalue problems, as well as for specialists in engineering, who use the considered algorithms in the investigation of eigenoscillations of structures, in reactor physics, etc. Some variants of the algorithms based on the idea of a gradient-type direction of movement are presented and their convergence properties are discussed. From this, a general strategy for the direct use of preconditionings for the eigenvalue problem is derived. In this new approach the necessity of the solution of large linear systems is entirely avoided. Hence, these methods represent a new alternative to some other modern eigenvalue algorithms, as they show a slightly slower convergence on the one hand but essentially lower numerical and data processing problems on the other hand. A brief description and comparison of some well-known methods (i.e. simultaneous iteration, Lanczos algorithm) completes this volume. (author)

  4. Sparse random matrices: The eigenvalue spectrum revisited

    International Nuclear Information System (INIS)

    Semerjian, Guilhem; Cugliandolo, Leticia F.

    2003-08-01

    We revisit the derivation of the density of states of sparse random matrices. We derive a recursion relation that allows one to compute the spectrum of the matrix of incidence for finite trees that determines completely the low concentration limit. Using the iterative scheme introduced by Biroli and Monasson [J. Phys. A 32, L255 (1999)] we find an approximate expression for the density of states expected to hold exactly in the opposite limit of large but finite concentration. The combination of the two methods yields a very simple geometric interpretation of the tails of the spectrum. We test the analytic results with numerical simulations and we suggest an indirect numerical method to explore the tails of the spectrum. (author)

  5. ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.

    Science.gov (United States)

    Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

    High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓ r norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics.

  6. Miniature Laboratory for Detecting Sparse Biomolecules

    Science.gov (United States)

    Lin, Ying; Yu, Nan

    2005-01-01

    A miniature laboratory system has been proposed for use in the field to detect sparsely distributed biomolecules. By emphasizing concentration and sorting of specimens prior to detection, the underlying system concept would make it possible to attain high detection sensitivities without the need to develop ever more sensitive biosensors. The original purpose of the proposal is to aid the search for signs of life on a remote planet by enabling the detection of specimens as sparse as a few molecules or microbes in a large amount of soil, dust, rocks, water/ice, or other raw sample material. Some version of the system could prove useful on Earth for remote sensing of biological contamination, including agents of biological warfare. Processing in this system would begin with dissolution of the raw sample material in a sample-separation vessel. The solution in the vessel would contain floating microscopic magnetic beads coated with substances that could engage in chemical reactions with various target functional groups that are parts of target molecules. The chemical reactions would cause the targeted molecules to be captured on the surfaces of the beads. By use of a controlled magnetic field, the beads would be concentrated in a specified location in the vessel. Once the beads were thus concentrated, the rest of the solution would be discarded. This procedure would obviate the filtration steps and thereby also eliminate the filter-clogging difficulties of typical prior sample-concentration schemes. For ferrous dust/soil samples, the dissolution would be done first in a separate vessel before the solution is transferred to the microbead-containing vessel.

  7. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching.

    Science.gov (United States)

    Guo, Yanrong; Gao, Yaozong; Shen, Dinggang

    2016-04-01

    Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.

  8. A simple and reliable approach to docking protein-protein complexes from very sparse NOE-derived intermolecular distance restraints

    International Nuclear Information System (INIS)

    Tang, Chun; Clore, G. Marius

    2006-01-01

    A simple and reliable approach for docking protein-protein complexes from very sparse NOE-derived intermolecular distance restraints (as few as three from a single point) in combination with a novel representation for an attractive potential between mapped interaction surfaces is described. Unambiguous assignments of very sparse intermolecular NOEs are obtained using a reverse labeling strategy in which one the components is fully deuterated with the exception of selective protonation of the δ-methyl groups of isoleucine, while the other component is uniformly 13 C-labeled. This labeling strategy can be readily extended to selective protonation of Ala, Leu, Val or Met. The attractive potential is described by a 'reduced' radius of gyration potential applied specifically to a subset of interfacial residues (those with an accessible surface area ≥ 50% in the free proteins) that have been delineated by chemical shift perturbation. Docking is achieved by rigid body minimization on the basis of a target function comprising the sparse NOE distance restraints, a van der Waals repulsion potential and the 'reduced' radius of gyration potential. The method is demonstrated for two protein-protein complexes (EIN-HPr and IIA Glc -HPr) from the bacterial phosphotransferase system. In both cases, starting from 100 different random orientations of the X-ray structures of the free proteins, 100% convergence is achieved to a single cluster (with near identical atomic positions) with an overall backbone accuracy of ∼2 A. The approach described is not limited to NMR, since interfaces can also be mapped by alanine scanning mutagenesis, and sparse intermolecular distance restraints can be derived from double cycle mutagenesis, cross-linking combined with mass spectrometry, or fluorescence energy transfer

  9. A simple and reliable approach to docking protein-protein complexes from very sparse NOE-derived intermolecular distance restraints

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Chun; Clore, G. Marius [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States)], E-mail: mariusc@intra.niddk.nih.gov

    2006-09-15

    A simple and reliable approach for docking protein-protein complexes from very sparse NOE-derived intermolecular distance restraints (as few as three from a single point) in combination with a novel representation for an attractive potential between mapped interaction surfaces is described. Unambiguous assignments of very sparse intermolecular NOEs are obtained using a reverse labeling strategy in which one the components is fully deuterated with the exception of selective protonation of the {delta}-methyl groups of isoleucine, while the other component is uniformly {sup 13}C-labeled. This labeling strategy can be readily extended to selective protonation of Ala, Leu, Val or Met. The attractive potential is described by a 'reduced' radius of gyration potential applied specifically to a subset of interfacial residues (those with an accessible surface area {>=} 50% in the free proteins) that have been delineated by chemical shift perturbation. Docking is achieved by rigid body minimization on the basis of a target function comprising the sparse NOE distance restraints, a van der Waals repulsion potential and the 'reduced' radius of gyration potential. The method is demonstrated for two protein-protein complexes (EIN-HPr and IIA{sup Glc}-HPr) from the bacterial phosphotransferase system. In both cases, starting from 100 different random orientations of the X-ray structures of the free proteins, 100% convergence is achieved to a single cluster (with near identical atomic positions) with an overall backbone accuracy of {approx}2 A. The approach described is not limited to NMR, since interfaces can also be mapped by alanine scanning mutagenesis, and sparse intermolecular distance restraints can be derived from double cycle mutagenesis, cross-linking combined with mass spectrometry, or fluorescence energy transfer.

  10. Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

    Science.gov (United States)

    Moody, Daniela; Wohlberg, Brendt

    2018-01-02

    An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

  11. Sparse Source EEG Imaging with the Variational Garrote

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Stahlhut, Carsten; Hansen, Lars Kai

    2013-01-01

    EEG imaging, the estimation of the cortical source distribution from scalp electrode measurements, poses an extremely ill-posed inverse problem. Recent work by Delorme et al. (2012) supports the hypothesis that distributed source solutions are sparse. We show that direct search for sparse solutions...

  12. Support agnostic Bayesian matching pursuit for block sparse signals

    KAUST Repository

    Masood, Mudassir; Al-Naffouri, Tareq Y.

    2013-01-01

    priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal

  13. Local posterior concentration rate for multilevel sparse sequences

    NARCIS (Netherlands)

    Belitser, E.N.; Nurushev, N.

    2017-01-01

    We consider empirical Bayesian inference in the many normal means model in the situation when the high-dimensional mean vector is multilevel sparse, that is,most of the entries of the parameter vector are some fixed values. For instance, the traditional sparse signal is a particular case (with one

  14. Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

    Science.gov (United States)

    Khan, Zohaib; Shafait, Faisal; Mian, Ajmal

    2015-12-01

    A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.

  15. Confidence of model based shape reconstruction from sparse data

    DEFF Research Database (Denmark)

    Baka, N.; de Bruijne, Marleen; Reiber, J. H. C.

    2010-01-01

    Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks...

  16. Comparison of Methods for Sparse Representation of Musical Signals

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft; la Cour-Harbo, Anders

    2005-01-01

    by a number of sparseness measures and results are shown on the ℓ1 norm of the coefficients, using a dictionary containing a Dirac basis, a Discrete Cosine Transform, and a Wavelet Packet. Evaluated only on the sparseness Matching Pursuit is the best method, and it is also relatively fast....

  17. Robust Face Recognition Via Gabor Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    Hao Yu-Juan

    2016-01-01

    Full Text Available Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.

  18. Sparse Frequency Waveform Design for Radar-Embedded Communication

    Directory of Open Access Journals (Sweden)

    Chaoyun Mai

    2016-01-01

    Full Text Available According to the Tag application with function of covert communication, a method for sparse frequency waveform design based on radar-embedded communication is proposed. Firstly, sparse frequency waveforms are designed based on power spectral density fitting and quasi-Newton method. Secondly, the eigenvalue decomposition of the sparse frequency waveform sequence is used to get the dominant space. Finally the communication waveforms are designed through the projection of orthogonal pseudorandom vectors in the vertical subspace. Compared with the linear frequency modulation waveform, the sparse frequency waveform can further improve the bandwidth occupation of communication signals, thus achieving higher communication rate. A certain correlation exists between the reciprocally orthogonal communication signals samples and the sparse frequency waveform, which guarantees the low SER (signal error rate and LPI (low probability of intercept. The simulation results verify the effectiveness of this method.

  19. Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells.

    Directory of Open Access Journals (Sweden)

    Ruth Merkle

    2016-08-01

    Full Text Available Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC, is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO. However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid and H838 cells, we combined mathematical modeling with a method for feature selection, the L1 regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR. The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in

  20. Cell Type-Specific Chromatin Signatures Underline Regulatory DNA Elements in Human Induced Pluripotent Stem Cells and Somatic Cells.

    Science.gov (United States)

    Zhao, Ming-Tao; Shao, Ning-Yi; Hu, Shijun; Ma, Ning; Srinivasan, Rajini; Jahanbani, Fereshteh; Lee, Jaecheol; Zhang, Sophia L; Snyder, Michael P; Wu, Joseph C

    2017-11-10

    Regulatory DNA elements in the human genome play important roles in determining the transcriptional abundance and spatiotemporal gene expression during embryonic heart development and somatic cell reprogramming. It is not well known how chromatin marks in regulatory DNA elements are modulated to establish cell type-specific gene expression in the human heart. We aimed to decipher the cell type-specific epigenetic signatures in regulatory DNA elements and how they modulate heart-specific gene expression. We profiled genome-wide transcriptional activity and a variety of epigenetic marks in the regulatory DNA elements using massive RNA-seq (n=12) and ChIP-seq (chromatin immunoprecipitation combined with high-throughput sequencing; n=84) in human endothelial cells (CD31 + CD144 + ), cardiac progenitor cells (Sca-1 + ), fibroblasts (DDR2 + ), and their respective induced pluripotent stem cells. We uncovered 2 classes of regulatory DNA elements: class I was identified with ubiquitous enhancer (H3K4me1) and promoter (H3K4me3) marks in all cell types, whereas class II was enriched with H3K4me1 and H3K4me3 in a cell type-specific manner. Both class I and class II regulatory elements exhibited stimulatory roles in nearby gene expression in a given cell type. However, class I promoters displayed more dominant regulatory effects on transcriptional abundance regardless of distal enhancers. Transcription factor network analysis indicated that human induced pluripotent stem cells and somatic cells from the heart selected their preferential regulatory elements to maintain cell type-specific gene expression. In addition, we validated the function of these enhancer elements in transgenic mouse embryos and human cells and identified a few enhancers that could possibly regulate the cardiac-specific gene expression. Given that a large number of genetic variants associated with human diseases are located in regulatory DNA elements, our study provides valuable resources for deciphering

  1. Relaxations to Sparse Optimization Problems and Applications

    Science.gov (United States)

    Skau, Erik West

    Parsimony is a fundamental property that is applied to many characteristics in a variety of fields. Of particular interest are optimization problems that apply rank, dimensionality, or support in a parsimonious manner. In this thesis we study some optimization problems and their relaxations, and focus on properties and qualities of the solutions of these problems. The Gramian tensor decomposition problem attempts to decompose a symmetric tensor as a sum of rank one tensors.We approach the Gramian tensor decomposition problem with a relaxation to a semidefinite program. We study conditions which ensure that the solution of the relaxed semidefinite problem gives the minimal Gramian rank decomposition. Sparse representations with learned dictionaries are one of the leading image modeling techniques for image restoration. When learning these dictionaries from a set of training images, the sparsity parameter of the dictionary learning algorithm strongly influences the content of the dictionary atoms.We describe geometrically the content of trained dictionaries and how it changes with the sparsity parameter.We use statistical analysis to characterize how the different content is used in sparse representations. Finally, a method to control the structure of the dictionaries is demonstrated, allowing us to learn a dictionary which can later be tailored for specific applications. Variations of dictionary learning can be broadly applied to a variety of applications.We explore a pansharpening problem with a triple factorization variant of coupled dictionary learning. Another application of dictionary learning is computer vision. Computer vision relies heavily on object detection, which we explore with a hierarchical convolutional dictionary learning model. Data fusion of disparate modalities is a growing topic of interest.We do a case study to demonstrate the benefit of using social media data with satellite imagery to estimate hazard extents. In this case study analysis we

  2. Cell-type specific oxytocin gene expression from AAV delivered promoter deletion constructs into the rat supraoptic nucleus in vivo.

    Directory of Open Access Journals (Sweden)

    Raymond L Fields

    Full Text Available The magnocellular neurons (MCNs in the hypothalamus selectively express either oxytocin (OXT or vasopressin (AVP neuropeptide genes, a property that defines their phenotypes. Here we examine the molecular basis of this selectivity in the OXT MCNs by stereotaxic microinjections of adeno-associated virus (AAV vectors that contain various OXT gene promoter deletion constructs using EGFP as the reporter into the rat supraoptic nucleus (SON. Two weeks following injection of the AAVs, immunohistochemical assays of EGFP expression from these constructs were done to determine whether the EGFP reporter co-localizes with either the OXT- or AVP-immunoreactivity in the MCNs. The results show that the key elements in the OT gene promoter that regulate the cell-type specific expression the SON are located -216 to -100 bp upstream of the transcription start site. We hypothesize that within this 116 bp domain a repressor exists that inhibits expression specifically in AVP MCNs, thereby leading to the cell-type specific expression of the OXT gene only in the OXT MCNs.

  3. Prevalence of type-specific HPV infection by age and grade of cervical cytology: data from the ARTISTIC trial

    Science.gov (United States)

    Sargent, A; Bailey, A; Almonte, M; Turner, A; Thomson, C; Peto, J; Desai, M; Mather, J; Moss, S; Roberts, C; Kitchener, H C

    2008-01-01

    Human papillomavirus (HPV) infection causes cervical cancer and premalignant dysplasia. Type-specific HPV prevalence data provide a basis for assessing the impact of HPV vaccination programmes on cervical cytology. We report high-risk HPV (HR-HPV) type-specific prevalence data in relation to cervical cytology for 24 510 women (age range: 20–64; mean age 40.2 years) recruited into the ARTISTIC trial, which is being conducted within the routine NHS Cervical Screening Programme in Greater Manchester. The most common HR-HPV types were HPV16, 18, 31, 51 and 52, which accounted for 60% of all HR-HPV types detected. There was a marked decline in the prevalence of HR-HPV infection with age, but the proportion due to each HPV type did not vary greatly with age. Multiple infections were common below the age of 30 years but less so between age 30 and 64 years. Catch-up vaccination of this sexually active cohort would be expected to reduce the number of women with moderate or worse cytology by 45%, but the number with borderline or mild cytology would fall by only 7%, giving an overall reduction of 12% in the number of women with abnormal cytology and 27% in the number with any HR-HPV infection. In the absence of broader cross-protection, the large majority of low-grade and many high-grade abnormalities may still occur in sexually active vaccinated women. PMID:18392052

  4. Tissue- and Cell Type-Specific Expression of the Long Noncoding RNA Klhl14-AS in Mouse

    Directory of Open Access Journals (Sweden)

    Sara Carmela Credendino

    2017-01-01

    Full Text Available lncRNAs are acquiring increasing relevance as regulators in a wide spectrum of biological processes. The extreme heterogeneity in the mechanisms of action of these molecules, however, makes them very difficult to study, especially regarding their molecular function. A novel lncRNA has been recently identified as the most enriched transcript in mouse developing thyroid. Due to its genomic localization antisense to the protein-encoding Klhl14 gene, we named it Klhl14-AS. In this paper, we highlight that mouse Klhl14-AS produces at least five splicing variants, some of which have not been previously described. Klhl14-AS is expressed with a peculiar pattern, characterized by diverse relative abundance of its isoforms in different mouse tissues. We examine the whole expression level of Klhl14-AS in a panel of adult mouse tissues, showing that it is expressed in the thyroid, lung, kidney, testis, ovary, brain, and spleen, although at different levels. In situ hybridization analysis reveals that, in the context of each organ, Klhl14-AS shows a cell type-specific expression. Interestingly, databases report a similar expression profile for human Klhl14-AS. Our observations suggest that this lncRNA could play cell type-specific roles in several organs and pave the way for functional characterization of this gene in appropriate biological contexts.

  5. Sparse alignment for robust tensor learning.

    Science.gov (United States)

    Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

    2014-10-01

    Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

  6. Regression analysis of sparse asynchronous longitudinal data.

    Science.gov (United States)

    Cao, Hongyuan; Zeng, Donglin; Fine, Jason P

    2015-09-01

    We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

  7. Duplex scanning using sparse data sequences

    DEFF Research Database (Denmark)

    Møllenbach, S. K.; Jensen, Jørgen Arendt

    2008-01-01

    reconstruction of the missing samples possible. The periodic pattern has the length T = M + A samples, where M are for B-mode and A for velocity estimation. The missing samples can now be reconstructed using a filter bank. One filter bank reconstructs one missing sample, so the number of filter banks corresponds...... to M. The number of sub filters in every filter bank is the same as A. Every sub filter contains fractional delay (FD) filter and an interpolation function. Many different sequences can be selected to adapt the B-mode frame rate needed. The drawback of the method is that the maximum velocity detectable......, the fprf and the resolution are 15 MHz, 3.5 kHz, and 12 bit sample (8 kHz and 16 bit for the Carotid artery). The resulting data contains 8000 RF lines with 128 samples at a depth of 45 mm for the vein and 50 mm for Aorta. Sparse sequences are constructed from the full data sequences to have both...

  8. Transformer fault diagnosis using continuous sparse autoencoder.

    Science.gov (United States)

    Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou

    2016-01-01

    This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis.

  9. SLAP, Large Sparse Linear System Solution Package

    International Nuclear Information System (INIS)

    Greenbaum, A.

    1987-01-01

    1 - Description of program or function: SLAP is a set of routines for solving large sparse systems of linear equations. One need not store the entire matrix - only the nonzero elements and their row and column numbers. Any nonzero structure is acceptable, so the linear system solver need not be modified when the structure of the matrix changes. Auxiliary storage space is acquired and released within the routines themselves by use of the LRLTRAN POINTER statement. 2 - Method of solution: SLAP contains one direct solver, a band matrix factorization and solution routine, BAND, and several interactive solvers. The iterative routines are as follows: JACOBI, Jacobi iteration; GS, Gauss-Seidel Iteration; ILUIR, incomplete LU decomposition with iterative refinement; DSCG and ICCG, diagonal scaling and incomplete Cholesky decomposition with conjugate gradient iteration (for symmetric positive definite matrices only); DSCGN and ILUGGN, diagonal scaling and incomplete LU decomposition with conjugate gradient interaction on the normal equations; DSBCG and ILUBCG, diagonal scaling and incomplete LU decomposition with bi-conjugate gradient iteration; and DSOMN and ILUOMN, diagonal scaling and incomplete LU decomposition with ORTHOMIN iteration

  10. Understanding Food Labels

    Science.gov (United States)

    ... Healthy eating for girls Understanding food labels Understanding food labels There is lots of info on food ... need to avoid because of food allergies. Other food label terms top In addition to the Nutrition ...

  11. Object tracking by occlusion detection via structured sparse learning

    KAUST Repository

    Zhang, Tianzhu

    2013-06-01

    Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

  12. Manifold regularization for sparse unmixing of hyperspectral images.

    Science.gov (United States)

    Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

    2016-01-01

    Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

  13. Seismic classification through sparse filter dictionaries

    Energy Technology Data Exchange (ETDEWEB)

    Hickmann, Kyle Scott [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Srinivasan, Gowri [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-13

    We tackle a multi-label classi cation problem involving the relation between acoustic- pro le features and the measured seismogram. To isolate components of the seismo- grams unique to each class of acoustic pro le we build dictionaries of convolutional lters. The convolutional- lter dictionaries for the individual classes are then combined into a large dictionary for the entire seismogram set. A given seismogram is classi ed by computing its representation in the large dictionary and then comparing reconstruction accuracy with this representation using each of the sub-dictionaries. The sub-dictionary with the minimal reconstruction error identi es the seismogram class.

  14. An Atlas for Schistosoma mansoni Organs and Life-Cycle Stages Using Cell Type-Specific Markers and Confocal Microscopy

    Science.gov (United States)

    Cogswell, Alexis; Williams, David L.; Newmark, Phillip A.

    2011-01-01

    Schistosomiasis (bilharzia) is a tropical disease caused by trematode parasites (Schistosoma) that affects hundreds of millions of people in the developing world. Currently only a single drug (praziquantel) is available to treat this disease, highlighting the importance of developing new techniques to study Schistosoma. While molecular advances, including RNA interference and the availability of complete genome sequences for two Schistosoma species, will help to revolutionize studies of these animals, an array of tools for visualizing the consequences of experimental perturbations on tissue integrity and development needs to be made widely available. To this end, we screened a battery of commercially available stains, antibodies and fluorescently labeled lectins, many of which have not been described previously for analyzing schistosomes, for their ability to label various cell and tissue types in the cercarial stage of S. mansoni. This analysis uncovered more than 20 new markers that label most cercarial tissues, including the tegument, the musculature, the protonephridia, the secretory system and the nervous system. Using these markers we present a high-resolution visual depiction of cercarial anatomy. Examining the effectiveness of a subset of these markers in S. mansoni adults and miracidia, we demonstrate the value of these tools for labeling tissues in a variety of life-cycle stages. The methodologies described here will facilitate functional analyses aimed at understanding fundamental biological processes in these parasites. PMID:21408085

  15. A comprehensive study of sparse codes on abnormality detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

    2017-01-01

    Sparse representation has been applied successfully in abnor-mal event detection, in which the baseline is to learn a dic-tionary accompanied by sparse codes. While much empha-sis is put on discriminative dictionary construction, there areno comparative studies of sparse codes regarding abnormal-ity...... detection. We comprehensively study two types of sparsecodes solutions - greedy algorithms and convex L1-norm so-lutions - and their impact on abnormality detection perfor-mance. We also propose our framework of combining sparsecodes with different detection methods. Our comparative ex-periments are carried...

  16. Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays

    Science.gov (United States)

    Kwon, Daniel W.; Miller, David W.; Sedwick, Raymond J.

    2004-01-01

    Traditional methods of actuating spacecraft in sparse aperture arrays use propellant as a reaction mass. For formation flying systems, propellant becomes a critical consumable which can be quickly exhausted while maintaining relative orientation. Additional problems posed by propellant include optical contamination, plume impingement, thermal emission, and vibration excitation. For these missions where control of relative degrees of freedom is important, we consider using a system of electromagnets, in concert with reaction wheels, to replace the consumables. Electromagnetic Formation Flight sparse apertures, powered by solar energy, are designed differently from traditional propulsion systems, which are based on V. This paper investigates the design of sparse apertures both inside and outside the Earth's gravity field.

  17. Sparse Principal Component Analysis in Medical Shape Modeling

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

    2006-01-01

    Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

  18. Homeostasis or channelopathy? Acquired cell type-specific ion channel changes in temporal lobe epilepsy and their antiepileptic potential

    Science.gov (United States)

    Wolfart, Jakob; Laker, Debora

    2015-01-01

    Neurons continuously adapt the expression and functionality of their ion channels. For example, exposed to chronic excitotoxicity, neurons homeostatically downscale their intrinsic excitability. In contrast, the “acquired channelopathy” hypothesis suggests that proepileptic channel characteristics develop during epilepsy. We review cell type-specific channel alterations under different epileptic conditions and discuss the potential of channels that undergo homeostatic adaptations, as targets for antiepileptic drugs (AEDs). Most of the relevant studies have been performed on temporal lobe epilepsy (TLE), a widespread AED-refractory, focal epilepsy. The TLE patients, who undergo epilepsy surgery, frequently display hippocampal sclerosis (HS), which is associated with degeneration of cornu ammonis subfield 1 pyramidal cells (CA1 PCs). Although the resected human tissue offers insights, controlled data largely stem from animal models simulating different aspects of TLE and other epilepsies. Most of the cell type-specific information is available for CA1 PCs and dentate gyrus granule cells (DG GCs). Between these two cell types, a dichotomy can be observed: while DG GCs acquire properties decreasing the intrinsic excitability (in TLE models and patients with HS), CA1 PCs develop channel characteristics increasing intrinsic excitability (in TLE models without HS only). However, thorough examination of data on these and other cell types reveals the coexistence of protective and permissive intrinsic plasticity within neurons. These mechanisms appear differentially regulated, depending on the cell type and seizure condition. Interestingly, the same channel molecules that are upregulated in DG GCs during HS-related TLE, appear as promising targets for future AEDs and gene therapies. Hence, GCs provide an example of homeostatic ion channel adaptation which can serve as a primer when designing novel anti-epileptic strategies. PMID:26124723

  19. Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk.

    Science.gov (United States)

    Glynn, Robert J; Colditz, Graham A; Tamimi, Rulla M; Chen, Wendy Y; Hankinson, Susan E; Willett, Walter W; Rosner, Bernard

    2017-08-01

    A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status. We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses' Health Study during the period 1980-2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third. Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories. The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

  20. Cell-type-specific responses of RT4 neural cell lines to dibutyryl-cAMP: branch determination versus maturation

    International Nuclear Information System (INIS)

    Droms, K.; Sueoka, N.

    1987-01-01

    This report describes the induction of cell-type-specific maturation, by dibutyryl-cAMP and testololactone, of neuronal and glial properties in a family of cell lines derived from a rat peripheral neurotumor, RT4. This maturation allows further understanding of the process of determination because of the close lineage relationship between the cell types of the RT4 family. The RT4 family is characterized by the spontaneous conversion of one of the cell types, RT4-AC (stem-cell type), to any of three derivative cell types, RT4-B, RT4-D, or RT4-E, with a frequency of about 10(-5). The RT4-AC cells express some properties characteristic of both neuronal and glial cells. Of these neural properties expressed by RT4-AC cells, only the neuronal properties are expressed by the RT4-B and RT4-E cells, and only the glial properties are expressed by the RT4-D cells. This in vitro cell-type conversion of RT4-AC to three derivative cell types is a branch point for the coordinate regulation of several properties and seems to resemble determination in vivo. In our standard culture conditions, several other neuronal and glial properties are not expressed by these cell types. However, addition of dibutyryl-cAMP induces expression of additional properties, in a cell-type-specific manner: formation of long cellular processes in the RT4-B8 and RT4-E5 cell lines and expression of high-affinity uptake of gamma-aminobutyric acid, by a glial-cell-specific mechanism, in the RT4-D6-2 cell line. These new properties are maximally expressed 2-3 days after addition of dibutyryl-cAMP

  1. Human Papilloma Virus prevalence and type-specific relative contribution in invasive cervical cancer specimens from Italy

    Directory of Open Access Journals (Sweden)

    Lloveras Belén

    2010-06-01

    Full Text Available Abstract Background Cervical cancer represents an important global public health problem. It is the 2nd most common cancer among women worldwide. Human Papillomavirus (HPV infection is now well-established as a necessary cause of invasive cervical cancer (ICC development. Only a few studies on HPV prevalence and type-specific distribution in ICC have been conducted in Italy. Aim To describe the prevalence of HPV and the HPV type-specific distribution in ICC cases identified in Rome, Italy. Methods 140 paraffin embedded tissue blocks of primary ICC diagnosed between 2001 and 2006 were identified at the Regina Elena Cancer Institute in Rome (Italy. HPV was detected through amplification of HPV DNA using SPF-10 HPV broad-spectrum primers followed by DEIA and then genotyping by LiPA25 (version 1. Results 134 cases were considered suitable for HPV DNA detection after histological evaluation; and overall, 90.3% (121/134 HPV prevalence was detected. 111 cases had a single HPV type, 4 cases had an uncharacterized type (HPVX and 6 cases had multiple HPV infections. The five most common single HPV types among positive cases were: HPV16 (71/121; 58.7%, HPV18 (12/121; 9.9%, HPV31, HPV45 and HPV58 (5/121; 4.1% each. 2 (1.5% of the single infections and 2 (1.5% of the multiple infections contained low risk types. Statistically significant differences in the relative contribution of HPV18 were found when comparing squamous cell carcinomas with adenocarcinomas. Conclusions HPV16 and HPV18 accounted for almost 70% of all the HPV positive ICC cases. The study provides baseline information for further evaluation on the impact of recently introduced HPV vaccines in Italy.

  2. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer; Ghanem, Bernard

    2017-01-01

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images

  3. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2014-01-01

    with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm

  4. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun; Tan, Jieqing; Chen, Peng; Zhang, Jie; Helg, Lei

    2013-01-01

    as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL

  5. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm

  6. Robust visual tracking via multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates

  7. Sparse Machine Learning Methods for Understanding Large Text Corpora

    Data.gov (United States)

    National Aeronautics and Space Administration — Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational...

  8. Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

    KAUST Repository

    Sicat, Ronell Barrera; Kruger, Jens; Moller, Torsten; Hadwiger, Markus

    2014-01-01

    This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined

  9. Sparse Linear Solver for Power System Analysis Using FPGA

    National Research Council Canada - National Science Library

    Johnson, J. R; Nagvajara, P; Nwankpa, C

    2005-01-01

    .... Numerical solution to load flow equations are typically computed using Newton-Raphson iteration, and the most time consuming component of the computation is the solution of a sparse linear system...

  10. Support agnostic Bayesian matching pursuit for block sparse signals

    KAUST Repository

    Masood, Mudassir

    2013-05-01

    A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

  11. Detection of Pitting in Gears Using a Deep Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Yongzhi Qu

    2017-05-01

    Full Text Available In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

  12. Sparse logistic principal components analysis for binary data

    KAUST Repository

    Lee, Seokho; Huang, Jianhua Z.; Hu, Jianhua

    2010-01-01

    with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated

  13. Sparse reconstruction using distribution agnostic bayesian matching pursuit

    KAUST Repository

    Masood, Mudassir

    2013-11-01

    A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

  14. Occlusion detection via structured sparse learning for robust object tracking

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra

    2014-01-01

    occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our

  15. Object tracking by occlusion detection via structured sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra

    2013-01-01

    occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker

  16. Sparse Vector Distributions and Recovery from Compressed Sensing

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    It is well known that the performance of sparse vector recovery algorithms from compressive measurements can depend on the distribution underlying the non-zero elements of a sparse vector. However, the extent of these effects has yet to be explored, and formally presented. In this paper, I...... empirically investigate this dependence for seven distributions and fifteen recovery algorithms. The two morals of this work are: 1) any judgement of the recovery performance of one algorithm over that of another must be prefaced by the conditions for which this is observed to be true, including sparse vector...... distributions, and the criterion for exact recovery; and 2) a recovery algorithm must be selected carefully based on what distribution one expects to underlie the sensed sparse signal....

  17. Sparse encoding of automatic visual association in hippocampal networks

    DEFF Research Database (Denmark)

    Hulme, Oliver J; Skov, Martin; Chadwick, Martin J

    2014-01-01

    Intelligent action entails exploiting predictions about associations between elements of ones environment. The hippocampus and mediotemporal cortex are endowed with the network topology, physiology, and neurochemistry to automatically and sparsely code sensori-cognitive associations that can...

  18. Efficient collaborative sparse channel estimation in massive MIMO

    KAUST Repository

    Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.

    2015-01-01

    We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

  19. Fast convolutional sparse coding using matrix inversion lemma

    Czech Academy of Sciences Publication Activity Database

    Šorel, Michal; Šroubek, Filip

    2016-01-01

    Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf

  20. Discussion of CoSA: Clustering of Sparse Approximations

    Energy Technology Data Exchange (ETDEWEB)

    Armstrong, Derek Elswick [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-03-07

    The purpose of this talk is to discuss the possible applications of CoSA (Clustering of Sparse Approximations) to the exploitation of HSI (HyperSpectral Imagery) data. CoSA is presented by Moody et al. in the Journal of Applied Remote Sensing (“Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries”, Vol. 8, 2014) and is based on machine learning techniques.

  1. Efficient collaborative sparse channel estimation in massive MIMO

    KAUST Repository

    Masood, Mudassir

    2015-08-12

    We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

  2. A flexible framework for sparse simultaneous component based data integration

    Directory of Open Access Journals (Sweden)

    Van Deun Katrijn

    2011-11-01

    Full Text Available Abstract 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such

  3. A flexible framework for sparse simultaneous component based data integration.

    Science.gov (United States)

    Van Deun, Katrijn; Wilderjans, Tom F; van den Berg, Robert A; Antoniadis, Anestis; Van Mechelen, Iven

    2011-11-15

    High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform

  4. In-Storage Embedded Accelerator for Sparse Pattern Processing

    OpenAIRE

    Jun, Sang-Woo; Nguyen, Huy T.; Gadepally, Vijay N.; Arvind

    2016-01-01

    We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing, bioinformatics, subgraph matching, machine learning, and graph processing. One slice of our prototype accelerator is capable of handling up to 1TB of data, and experiments show that it can outperform C/C++ software solutions on a 16-core system at a fracti...

  5. Process Knowledge Discovery Using Sparse Principal Component Analysis

    DEFF Research Database (Denmark)

    Gao, Huihui; Gajjar, Shriram; Kulahci, Murat

    2016-01-01

    As the goals of ensuring process safety and energy efficiency become ever more challenging, engineers increasingly rely on data collected from such processes for informed decision making. During recent decades, extracting and interpreting valuable process information from large historical data sets...... SPCA approach that helps uncover the underlying process knowledge regarding variable relations. This approach systematically determines the optimal sparse loadings for each sparse PC while improving interpretability and minimizing information loss. The salient features of the proposed approach...

  6. Issues in Data Labelling

    NARCIS (Netherlands)

    Cowie, Roddy; Cox, Cate; Martin, Jeam-Claude; Batliner, Anton; Heylen, Dirk K.J.; Karpouzis, Kostas; Cowie, Roddy; Pelachaud, Catherine; Petta, Paolo

    2011-01-01

    Labelling emotion databases is not a purely technical matter. It is bound up with theoretical issues. Different issues affect labelling of emotional content, labelling of the signs that convey emotion, and labelling of the relevant context. Linked to these are representational issues, involving time

  7. Occlusion detection via structured sparse learning for robust object tracking

    KAUST Repository

    Zhang, Tianzhu

    2014-01-01

    Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.

  8. Exhaustive Search for Sparse Variable Selection in Linear Regression

    Science.gov (United States)

    Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

    2018-04-01

    We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

  9. Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

    Directory of Open Access Journals (Sweden)

    Xiangwei Xing

    2014-01-01

    Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

  10. Structure-aware Local Sparse Coding for Visual Tracking

    KAUST Repository

    Qi, Yuankai

    2018-01-24

    Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous stateof- the-art methods.

  11. Vector sparse representation of color image using quaternion matrix analysis.

    Science.gov (United States)

    Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong

    2015-04-01

    Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.

  12. Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla

    2016-03-01

    synthetically generated or actually measured scattered fields, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.

  13. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Chen, Ken Chung [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China); Shen, Steve G. F.; Yan, Jin [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Lee, Philip K. M.; Chow, Ben [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China); Liu, Nancy X. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China); Xia, James J. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)

    2014-04-15

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  14. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    International Nuclear Information System (INIS)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang; Chen, Ken Chung; Shen, Steve G. F.; Yan, Jin; Lee, Philip K. M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang

    2014-01-01

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  15. Mixed Map Labeling

    Directory of Open Access Journals (Sweden)

    Maarten Löffler

    2016-12-01

    Full Text Available Point feature map labeling is a geometric visualization problem, in which a set of input points must be labeled with a set of disjoint rectangles (the bounding boxes of the label texts. It is predominantly motivated by label placement in maps but it also has other visualization applications. Typically, labeling models either use internal labels, which must touch their feature point, or external (boundary labels, which are placed outside the input image and which are connected to their feature points by crossing-free leader lines. In this paper we study polynomial-time algorithms for maximizing the number of internal labels in a mixed labeling model that combines internal and external labels. The model requires that all leaders are parallel to a given orientation θ ∈ [0, 2π, the value of which influences the geometric properties and hence the running times of our algorithms.

  16. Quantification of localized vertebral deformities using a sparse wavelet-based shape model.

    Science.gov (United States)

    Zewail, R; Elsafi, A; Durdle, N

    2008-01-01

    Medical experts often examine hundreds of spine x-ray images to determine existence of various pathologies. Common pathologies of interest are anterior osteophites, disc space narrowing, and wedging. By careful inspection of the outline shapes of the vertebral bodies, experts are able to identify and assess vertebral abnormalities with respect to the pathology under investigation. In this paper, we present a novel method for quantification of vertebral deformation using a sparse shape model. Using wavelets and Independent component analysis (ICA), we construct a sparse shape model that benefits from the approximation power of wavelets and the capability of ICA to capture higher order statistics in wavelet space. The new model is able to capture localized pathology-related shape deformations, hence it allows for quantification of vertebral shape variations. We investigate the capability of the model to predict localized pathology related deformations. Next, using support-vector machines, we demonstrate the diagnostic capabilities of the method through the discrimination of anterior osteophites in lumbar vertebrae. Experiments were conducted using a set of 150 contours from digital x-ray images of lumbar spine. Each vertebra is labeled as normal or abnormal. Results reported in this work focus on anterior osteophites as the pathology of interest.

  17. Sparse multivariate measures of similarity between intra-modal neuroimaging datasets

    Directory of Open Access Journals (Sweden)

    Maria J. Rosa

    2015-10-01

    Full Text Available An increasing number of neuroimaging studies are now based on either combining more than one data modality (inter-modal or combining more than one measurement from the same modality (intra-modal. To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA. However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA, overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labelling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

  18. Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

    Directory of Open Access Journals (Sweden)

    Xinzheng Zhang

    2016-09-01

    Full Text Available Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with ℓ 1 -regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.

  19. Dual-specificity anti-sigma factor reinforces control of cell-type specific gene expression in Bacillus subtilis.

    Directory of Open Access Journals (Sweden)

    Mónica Serrano

    2015-04-01

    Full Text Available Gene expression during spore development in Bacillus subtilis is controlled by cell type-specific RNA polymerase sigma factors. σFand σE control early stages of development in the forespore and the mother cell, respectively. When, at an intermediate stage in development, the mother cell engulfs the forespore, σF is replaced by σG and σE is replaced by σK. The anti-sigma factor CsfB is produced under the control of σF and binds to and inhibits the auto-regulatory σG, but not σF. A position in region 2.1, occupied by an asparagine in σG and by a glutamate in οF, is sufficient for CsfB discrimination of the two sigmas, and allows it to delay the early to late switch in forespore gene expression. We now show that following engulfment completion, csfB is switched on in the mother cell under the control of σK and that CsfB binds to and inhibits σE but not σK, possibly to facilitate the switch from early to late gene expression. We show that a position in region 2.3 occupied by a conserved asparagine in σE and by a conserved glutamate in σK suffices for discrimination by CsfB. We also show that CsfB prevents activation of σG in the mother cell and the premature σG-dependent activation of σK. Thus, CsfB establishes negative feedback loops that curtail the activity of σE and prevent the ectopic activation of σG in the mother cell. The capacity of CsfB to directly block σE activity may also explain how CsfB plays a role as one of the several mechanisms that prevent σE activation in the forespore. Thus the capacity of CsfB to differentiate between the highly similar σF/σG and σE/σK pairs allows it to rinforce the cell-type specificity of these sigma factors and the transition from early to late development in B. subtilis, and possibly in all sporeformers that encode a CsfB orthologue.

  20. Sparse modeling of spatial environmental variables associated with asthma.

    Science.gov (United States)

    Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

    2015-02-01

    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

  1. Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold.

    Science.gov (United States)

    Glass, Edmund R; Dozmorov, Mikhail G

    2016-10-06

    The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from blood samples suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. Combined with cell counts, heterogeneous gene expression may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression, and a global cutoff to judge significance, such as False Discovery Rate (FDR). Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect linear regression. In this paper we quantify the parameter space affecting the performance of linear regression (sensitivity of cell type-specific differential expression detection) on a per-gene basis. We evaluated the effect of sample sizes, cell type-specific proportion variability, and mean squared error on sensitivity of cell type-specific differential expression detection using linear regression. Each parameter affected variability of cell type-specific expression estimates and, subsequently, the sensitivity of differential expression detection. We provide the R package, LRCDE, which performs linear regression-based cell type-specific differential expression (deconvolution) detection on a gene-by-gene basis. Accounting for variability around cell type-specific gene expression estimates, it computes per-gene t-statistics of differential detection, p-values, t-statistic-based sensitivity, group-specific mean squared error, and several gene-specific diagnostic metrics. The sensitivity of linear regression-based cell type-specific differential expression detection differed for each gene as a function of mean squared error, per group sample sizes, and variability of the proportions

  2. Clustered Regularly Interspaced Short Palindromic Repeats Are emm Type-Specific in Highly Prevalent Group A Streptococci.

    Science.gov (United States)

    Zheng, Po-Xing; Chan, Yuen-Chi; Chiou, Chien-Shun; Chiang-Ni, Chuan; Wang, Shu-Ying; Tsai, Pei-Jane; Chuang, Woei-Jer; Lin, Yee-Shin; Liu, Ching-Chuan; Wu, Jiunn-Jong

    2015-01-01

    Clustered regularly interspaced short palindromic repeats (CRISPR) are the bacterial adaptive immune system against foreign nucleic acids. Given the variable nature of CRISPR, it could be a good marker for molecular epidemiology. Group A streptococcus is one of the major human pathogens. It has two CRISPR loci, including CRISPR01 and CRISPR02. The aim of this study was to analyze the distribution of CRISPR-associated gene cassettes (cas) and CRISPR arrays in highly prevalent emm types. The cas cassette and CRISPR array in two CRISPR loci were analyzed in a total of 332 strains, including emm1, emm3, emm4, emm12, and emm28 strains. The CRISPR type was defined by the spacer content of each CRISPR array. All strains had at least one cas cassette or CRISPR array. More than 90% of the spacers were found in one emm type, specifically. Comparing the consistency between emm and CRISPR types by Simpson's index of diversity and the adjusted Wallace coefficient, CRISPR01 type was concordant to emm type, and CRISPR02 showed unidirectional congruence to emm type, suggesting that at least for the majority of isolates causing infection in high income countries, the emm type can be inferred from CRISPR analysis, which can further discriminate isolates sharing the same emm type.

  3. Npas4 regulates excitatory-inhibitory balance within neural circuits through cell-type-specific gene programs.

    Science.gov (United States)

    Spiegel, Ivo; Mardinly, Alan R; Gabel, Harrison W; Bazinet, Jeremy E; Couch, Cameron H; Tzeng, Christopher P; Harmin, David A; Greenberg, Michael E

    2014-05-22

    The nervous system adapts to experience by inducing a transcriptional program that controls important aspects of synaptic plasticity. Although the molecular mechanisms of experience-dependent plasticity are well characterized in excitatory neurons, the mechanisms that regulate this process in inhibitory neurons are only poorly understood. Here, we describe a transcriptional program that is induced by neuronal activity in inhibitory neurons. We find that, while neuronal activity induces expression of early-response transcription factors such as Npas4 in both excitatory and inhibitory neurons, Npas4 activates distinct programs of late-response genes in inhibitory and excitatory neurons. These late-response genes differentially regulate synaptic input to these two types of neurons, promoting inhibition onto excitatory neurons while inducing excitation onto inhibitory neurons. These findings suggest that the functional outcomes of activity-induced transcriptional responses are adapted in a cell-type-specific manner to achieve a circuit-wide homeostatic response. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Spatial separation of photosynthesis and ethanol production by cell type-specific metabolic engineering of filamentous cyanobacteria.

    Science.gov (United States)

    Ehira, Shigeki; Takeuchi, Takuto; Higo, Akiyoshi

    2018-02-01

    Cyanobacteria, which perform oxygenic photosynthesis, have drawn attention as hosts for the direct production of biofuels and commodity chemicals from CO 2 and H 2 O using light energy. Although cyanobacteria capable of producing diverse chemicals have been generated by metabolic engineering, anaerobic non-photosynthetic culture conditions are often necessary for their production. In this study, we conducted cell type-specific metabolic engineering of the filamentous cyanobacterium Anabaena sp. PCC 7120, which forms a terminally differentiated cell called a heterocyst with a semi-regular spacing of 10-15 cells. Because heterocysts are specialized cells for nitrogen fixation, the intracellular oxygen level of heterocysts is maintained very low even when adjacent cells perform oxygenic photosynthesis. Pyruvate decarboxylase of Zymomonas mobilis and alcohol dehydrogenase of Synechocystis sp. PCC 6803 were exclusively expressed in heterocysts. Ethanol production was concomitant with nitrogen fixation in genetically engineered Anabaena sp. PCC 7120. Engineering of carbon metabolism in heterocysts improved ethanol production, and strain ET14, with an extra copy of the invB gene expressed from a heterocyst-specific promoter, produced 130.9 mg L -1 of ethanol after 9 days. ET14 produced 1681.9 mg L -1 of ethanol by increasing the CO 2 supply. Ethanol production per heterocyst cell was approximately threefold higher than that per cell of unicellular cyanobacterium. This study demonstrates the potential of heterocysts for anaerobic production of biofuels and commodity chemicals under oxygenic photosynthetic conditions.

  5. Combinatorial Modulation of Signaling Pathways Reveals Cell-Type-Specific Requirements for Highly Efficient and Synchronous iPSC Reprogramming

    Directory of Open Access Journals (Sweden)

    Simon E. Vidal

    2014-10-01

    Full Text Available The differentiated state of somatic cells provides barriers for the derivation of induced pluripotent stem cells (iPSCs. To address why some cell types reprogram more readily than others, we studied the effect of combined modulation of cellular signaling pathways. Surprisingly, inhibition of transforming growth factor β (TGF-β together with activation of Wnt signaling in the presence of ascorbic acid allows >80% of murine fibroblasts to acquire pluripotency after 1 week of reprogramming factor expression. In contrast, hepatic and blood progenitors predominantly required only TGF-β inhibition or canonical Wnt activation, respectively, to reprogram at efficiencies approaching 100%. Strikingly, blood progenitors reactivated endogenous pluripotency loci in a highly synchronous manner, and we demonstrate that expression of specific chromatin-modifying enzymes and reduced TGF-β/mitogen-activated protein (MAP kinase activity are intrinsic properties associated with the unique reprogramming response of these cells. Our observations define cell-type-specific requirements for the rapid and synchronous reprogramming of somatic cells.

  6. Analysis of proteins of mouse sarcoma pseudotype viruses: type-specific radioimmunoassays for ecotropic virus p30's

    International Nuclear Information System (INIS)

    Kennel, S.J.; Tennant, R.W.

    1979-01-01

    Murine sarcoma virus pseudotypes were prepared by infection of nonproducer cells (A1-2), which were transformed by the Gazdar strain of mouse sarcoma virus, with Gross (N-tropic), WN1802B (B-tropic), or Moloney (NB-tropic) viruses. The respective host range pseudotype sarcoma viruses were defined by the tritration characteristics on cells with the appropriate Fv-1 genotype. Proteins from virus progeny were analyzed by sodium dodecyl sulfate--polyacrylamide gel electrophoresis. Bands present in both the 65,000- and the 10,000- to 20,000-molecular-weight regions of the gel distinguished the pseudotype viruses from their respective helpers. Furthermore, two protein bands were noted in the p30 region of murine sarcoma virus (Gross), one corresponding to Gross virus p30, and another of slightly slower mobility. However, since the mobility of the putative sarcoma p30 is nearly indentical to that of WN1802B, its presence could not be established by sodium dodecyl sulfate--polyacrylamide gel electrophoresis. Type-specific radioimmunossays for Gross virus p30 and for WN1802B p30 were applied for analysis of pseudotype preparations, and among several ecotropic viruses tested, only the homologous virus scored in the respective assay. By use of these assays, pseudotype viruses were found to contain only 8 to 48% helper-specific p30's; the remainder is presumably derived from the sarcoma virus

  7. Rhizoctonia solani and Bacterial Inoculants Stimulate Root Exudation of Antifungal Compounds in Lettuce in a Soil-Type Specific Manner

    Directory of Open Access Journals (Sweden)

    Saskia Windisch

    2017-06-01

    Full Text Available Previous studies conducted on a unique field site comprising three contrasting soils (diluvial sand DS, alluvial loam AL, loess loam LL under identical cropping history, demonstrated soil type-dependent differences in biocontrol efficiency against Rhizoctonia solani-induced bottom rot disease in lettuce by two bacterial inoculants (Pseudomonas jessenii RU47 and Serratia plymuthica 3Re-4-18. Disease severity declined in the order DS > AL > LL. These differences were confirmed under controlled conditions, using the same soils in minirhizotron experiments. Gas chromatography-mass spectrometry (GC-MS profiling of rhizosphere soil solutions revealed benzoic and lauric acids as antifungal compounds; previously identified in root exudates of lettuce. Pathogen inoculation and pre-inoculation with bacterial inoculants significantly increased the release of antifungal root exudates in a soil type-specific manner; with the highest absolute levels detected on the least-affected LL soil. Soil type-dependent differences were also recorded for the biocontrol effects of the two bacterial inoculants; showing the highest efficiency after double-inoculation on the AL soil. However, this was associated with a reduction of shoot growth and root hair development and a limited micronutrient status of the host plants. Obviously, disease severity and the expression of biocontrol effects are influenced by soil properties with potential impact on reproducibility of practical applications.

  8. Exercise in the fasted state facilitates fibre type-specific intramyocellular lipid breakdown and stimulates glycogen resynthesis in humans

    DEFF Research Database (Denmark)

    De Bock, K.; Richter, Erik; Russell, A.P.

    2005-01-01

    g (kg bw)-1 h-1) exercise. In both conditions, subjects ingested 5 g carbohydrates per kg body weight during recovery. Fibre type-specific relative IMTG content was determined by Oil red O staining in needle biopsies from m. vastus lateralis before, immediately after and 4 h after exercise. During F...... sessions with an interval of 3 weeks. In each session subjects performed 2 h of constant-load bicycle exercise (~75% VO2,max), followed by 4 h of controlled recovery. On one occasion they exercised after an overnight fast (F), and on the other (CHO) they received carbohydrates before (~150 g) and during (1...... but not during CHO, the exercise bout decreased IMTG content in type I fibres from 18 ± 2% to 6 ± 2% (P = 0.007) area lipid staining. Conversely, during recovery, IMTG in type I fibres decreased from 15 ± 2% to 10 ± 2% in CHO, but did not change in F. Neither exercise nor recovery changed IMTG in type IIa fibres...

  9. Cell-type-specific activation of mitogen-activated protein kinases in PAN-induced progressive renal disease in rats

    International Nuclear Information System (INIS)

    Park, Sang-Joon; Jeong, Kyu-Shik

    2004-01-01

    We examined the time-course activation and the cell-type specific role of MAP kinases in puromycin aminonucleoside (PAN)-induced renal disease. The maximal activation of c-Jun-NH 2 -terminal kinase (JNK), extracellular signal regulated kinase (ERK), and p38 MAP kinase was detected on Days 52, 38, and 38 after PAN-treatment, respectively. p-JNK was localized in mesangial and proximal tubular cells at the early renal injury. It was expressed, therefore, in the inflammatory cells of tubulointerstitial lesions. While, p-ERK was markedly increased in the glomerular regions and macrophages p-p38 was observed in glomerular endothelial cells, tubular cells, and some inflammatory cells. The results show that the activation of MAP kinases in the early renal injury by PAN-treatment involves cellular changes such as cell proliferation or apoptosis in renal native cells. The activation of MAP kinases in infiltrated inflammatory cells and fibrotic cells plays an important role in destructive events such as glomerulosclerosis and tubulointerstitial fibrosis

  10. Dynamics of bacterial communities in two unpolluted soils after spiking with phenanthrene: soil type specific and common responders

    Directory of Open Access Journals (Sweden)

    Guo-Chun eDing

    2012-08-01

    Full Text Available Considering their key role for ecosystem processes, it is important to understand the response of microbial communities in unpolluted soils to pollution with polycyclic aromatic hydrocarbons (PAH. Phenanthrene, a model compound for PAH, was spiked to a Cambisol and a Luvisol soil. Total community DNA from phenanthrene-spiked and control soils collected on days 0, 21 and 63 were analyzed based on PCR-amplified 16S rRNA genefragments. Denaturing gradient gel electrophoresis (DGGE fingerprints of bacterial communities increasingly deviated with time between spiked and control soils. In taxon specific DGGE, significant responses of Alphaproteobacteria and Actinobacteria became only detectable after 63 days, while significant effects on Betaproteobacteria were detectable in both soils after 21 days. Comparison of the taxonomic distribution of bacteria in spiked and control soils on day 63 as revealed by pyrosequencing indicated soil type specific negative effects of phenanthrene on several taxa, many of them belonging to the Gamma-, Beta- or Deltaproteobacteria. Bacterial richness and evenness decreased in spiked soils. Despite the significant differences in the bacterial community structure between both soils on day 0, similar genera increased in relative abundance after PAH spiking, especially Sphingomonas and Polaromonas. However, this did not result in an increased overall similarity of the bacterial communities in both soils.

  11. Comprehensive analysis of ultrasonic vocalizations in a mouse model of fragile X syndrome reveals limited, call type specific deficits.

    Directory of Open Access Journals (Sweden)

    Snigdha Roy

    Full Text Available Fragile X syndrome (FXS is a well-recognized form of inherited mental retardation, caused by a mutation in the fragile X mental retardation 1 (Fmr1 gene. The gene is located on the long arm of the X chromosome and encodes fragile X mental retardation protein (FMRP. Absence of FMRP in fragile X patients as well as in Fmr1 knockout (KO mice results, among other changes, in abnormal dendritic spine formation and altered synaptic plasticity in the neocortex and hippocampus. Clinical features of FXS include cognitive impairment, anxiety, abnormal social interaction, mental retardation, motor coordination and speech articulation deficits. Mouse pups generate ultrasonic vocalizations (USVs when isolated from their mothers. Whether those social ultrasonic vocalizations are deficient in mouse models of FXS is unknown. Here we compared isolation-induced USVs generated by pups of Fmr1-KO mice with those of their wild type (WT littermates. Though the total number of calls was not significantly different between genotypes, a detailed analysis of 10 different categories of calls revealed that loss of Fmr1 expression in mice causes limited and call-type specific deficits in ultrasonic vocalization: the carrier frequency of flat calls was higher, the percentage of downward calls was lower and that the frequency range of complex calls was wider in Fmr1-KO mice compared to their WT littermates.

  12. Samd7 is a cell type-specific PRC1 component essential for establishing retinal rod photoreceptor identity.

    Science.gov (United States)

    Omori, Yoshihiro; Kubo, Shun; Kon, Tetsuo; Furuhashi, Mayu; Narita, Hirotaka; Kominami, Taro; Ueno, Akiko; Tsutsumi, Ryotaro; Chaya, Taro; Yamamoto, Haruka; Suetake, Isao; Ueno, Shinji; Koseki, Haruhiko; Nakagawa, Atsushi; Furukawa, Takahisa

    2017-09-26

    Precise transcriptional regulation controlled by a transcription factor network is known to be crucial for establishing correct neuronal cell identities and functions in the CNS. In the retina, the expression of various cone and rod photoreceptor cell genes is regulated by multiple transcription factors; however, the role of epigenetic regulation in photoreceptor cell gene expression has been poorly understood. Here, we found that Samd7, a rod-enriched sterile alpha domain (SAM) domain protein, is essential for silencing nonrod gene expression through H3K27me3 regulation in rod photoreceptor cells. Samd7- null mutant mice showed ectopic expression of nonrod genes including S-opsin in rod photoreceptor cells and rod photoreceptor cell dysfunction. Samd7 physically interacts with Polyhomeotic homologs (Phc proteins), components of the Polycomb repressive complex 1 (PRC1), and colocalizes with Phc2 and Ring1B in Polycomb bodies. ChIP assays showed a significant decrease of H3K27me3 in the genes up-regulated in the Samd7 -deficient retina, showing that Samd7 deficiency causes the derepression of nonrod gene expression in rod photoreceptor cells. The current study suggests that Samd7 is a cell type-specific PRC1 component epigenetically defining rod photoreceptor cell identity.

  13. Succesful labelling schemes

    DEFF Research Database (Denmark)

    Juhl, Hans Jørn; Stacey, Julia

    2001-01-01

    . In the spring of 2001 MAPP carried out an extensive consumer study with special emphasis on the Nordic environmentally friendly label 'the swan'. The purpose was to find out how much consumers actually know and use various labelling schemes. 869 households were contacted and asked to fill in a questionnaire...... it into consideration when I go shopping. The respondent was asked to pick the most suitable answer, which described her use of each label. 29% - also called 'the labelling blind' - responded that they basically only knew the recycling label and the Government controlled organic label 'Ø-mærket'. Another segment of 6...

  14. Synthesizing labeled compounds

    International Nuclear Information System (INIS)

    London, R.E.; Matwiyoff, N.A.; Unkefer, C.J.; Walker, T.E.

    1983-01-01

    A metabolic study is presented of the chemical reactions provided by isotopic labeling and NMR spectroscopy. Synthesis of 13 C-labeled D-glucose, a 6-carbon sugar, involves adding a labeled nitrile group to the 5-carbon sugar D-arabinose by reaction with labeled hydrogen cyanide. The product of this reaction is then reduced and hydrolyzed to a mixture of the labeled sugars. The two sugars are separated by absorption chromotography. The synthesis of 13 C-labeled L-tyrosine, an amino acid, is also presented

  15. Completing sparse and disconnected protein-protein network by deep learning.

    Science.gov (United States)

    Huang, Lei; Liao, Li; Wu, Cathy H

    2018-03-22

    Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network

  16. Image fusion via nonlocal sparse K-SVD dictionary learning.

    Science.gov (United States)

    Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

    2016-03-01

    Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

  17. Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.

    Science.gov (United States)

    Wang, Ping; Jiang, Jin-Yang; Li, Na; Luo, Han-Wu; Li, Fang; Cui, Shi-Gang

    2017-07-01

    It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. The performance of the proposed dictionary was analyzed via a simulation and experimental data. The mean absolute error (MAE) was used to quantify the quality of the reconstructions. Experimental results indicate that the MAE associated with the proposed dictionary was always the smallest, the reconstruction time required was the shortest, and the lateral resolution and contrast of the reconstructed images were also the closest to the original images. The proposed sparse dictionary performed better than the other three sparse transforms. With the same sampling rate, the proposed dictionary achieved excellent reconstruction quality.

  18. A sparse matrix based full-configuration interaction algorithm

    International Nuclear Information System (INIS)

    Rolik, Zoltan; Szabados, Agnes; Surjan, Peter R.

    2008-01-01

    We present an algorithm related to the full-configuration interaction (FCI) method that makes complete use of the sparse nature of the coefficient vector representing the many-electron wave function in a determinantal basis. Main achievements of the presented sparse FCI (SFCI) algorithm are (i) development of an iteration procedure that avoids the storage of FCI size vectors; (ii) development of an efficient algorithm to evaluate the effect of the Hamiltonian when both the initial and the product vectors are sparse. As a result of point (i) large disk operations can be skipped which otherwise may be a bottleneck of the procedure. At point (ii) we progress by adopting the implementation of the linear transformation by Olsen et al. [J. Chem Phys. 89, 2185 (1988)] for the sparse case, getting the algorithm applicable to larger systems and faster at the same time. The error of a SFCI calculation depends only on the dropout thresholds for the sparse vectors, and can be tuned by controlling the amount of system memory passed to the procedure. The algorithm permits to perform FCI calculations on single node workstations for systems previously accessible only by supercomputers

  19. X-ray computed tomography using curvelet sparse regularization.

    Science.gov (United States)

    Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

    2015-04-01

    Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

  20. Selectivity and sparseness in randomly connected balanced networks.

    Directory of Open Access Journals (Sweden)

    Cengiz Pehlevan

    Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

  1. Low-count PET image restoration using sparse representation

    Science.gov (United States)

    Li, Tao; Jiang, Changhui; Gao, Juan; Yang, Yongfeng; Liang, Dong; Liu, Xin; Zheng, Hairong; Hu, Zhanli

    2018-04-01

    In the field of positron emission tomography (PET), reconstructed images are often blurry and contain noise. These problems are primarily caused by the low resolution of projection data. Solving this problem by improving hardware is an expensive solution, and therefore, we attempted to develop a solution based on optimizing several related algorithms in both the reconstruction and image post-processing domains. As sparse technology is widely used, sparse prediction is increasingly applied to solve this problem. In this paper, we propose a new sparse method to process low-resolution PET images. Two dictionaries (D1 for low-resolution PET images and D2 for high-resolution PET images) are learned from a group real PET image data sets. Among these two dictionaries, D1 is used to obtain a sparse representation for each patch of the input PET image. Then, a high-resolution PET image is generated from this sparse representation using D2. Experimental results indicate that the proposed method exhibits a stable and superior ability to enhance image resolution and recover image details. Quantitatively, this method achieves better performance than traditional methods. This proposed strategy is a new and efficient approach for improving the quality of PET images.

  2. Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.

    Science.gov (United States)

    She, Huajun; Chen, Rong-Rong; Liang, Dong; DiBella, Edward V R; Ying, Leslie

    2014-02-01

    To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Copyright © 2013 Wiley Periodicals, Inc.

  3. Layer- and cell-type-specific subthreshold and suprathreshold effects of long-term monocular deprivation in rat visual cortex.

    Science.gov (United States)

    Medini, Paolo

    2011-11-23

    Connectivity and dendritic properties are determinants of plasticity that are layer and cell-type specific in the neocortex. However, the impact of experience-dependent plasticity at the level of synaptic inputs and spike outputs remains unclear along vertical cortical microcircuits. Here I compared subthreshold and suprathreshold sensitivity to prolonged monocular deprivation (MD) in rat binocular visual cortex in layer 4 and layer 2/3 pyramids (4Ps and 2/3Ps) and in thick-tufted and nontufted layer 5 pyramids (5TPs and 5NPs), which innervate different extracortical targets. In normal rats, 5TPs and 2/3Ps are the most binocular in terms of synaptic inputs, and 5NPs are the least. Spike responses of all 5TPs were highly binocular, whereas those of 2/3Ps were dominated by either the contralateral or ipsilateral eye. MD dramatically shifted the ocular preference of 2/3Ps and 4Ps, mostly by depressing deprived-eye inputs. Plasticity was profoundly different in layer 5. The subthreshold ocular preference shift was sevenfold smaller in 5TPs because of smaller depression of deprived inputs combined with a generalized loss of responsiveness, and was undetectable in 5NPs. Despite their modest ocular dominance change, spike responses of 5TPs consistently lost their typically high binocularity during MD. The comparison of MD effects on 2/3Ps and 5TPs, the main affected output cells of vertical microcircuits, indicated that subthreshold plasticity is not uniquely determined by the initial degree of input binocularity. The data raise the question of whether 5TPs are driven solely by 2/3Ps during MD. The different suprathreshold plasticity of the two cell populations could underlie distinct functional deficits in amblyopia.

  4. Cell type-specific localization of Ephs pairing with ephrin-B2 in the rat postnatal pituitary gland.

    Science.gov (United States)

    Yoshida, Saishu; Kato, Takako; Kanno, Naoko; Nishimura, Naoto; Nishihara, Hiroto; Horiguchi, Kotaro; Kato, Yukio

    2017-10-01

    Sox2-expressing stem/progenitor cells in the anterior lobe of the pituitary gland form two types of micro-environments (niches): the marginal cell layer and dense cell clusters in the parenchyma. In relation to the mechanism of regulation of niches, juxtacrine signaling via ephrin and its receptor Eph is known to play important roles in various niches. The ephrin and Eph families are divided into two subclasses to create ephrin/Eph signaling in co-operation with confined partners. Recently, we reported that ephrin-B2 localizes specifically to both pituitary niches. However, the Ephs interacting with ephrin-B2 in these pituitary niches have not yet been identified. Therefore, the present study aims to identify the Ephs interacting with ephrin-B2 and the cells that produce them in the rat pituitary gland. In situ hybridization and immunohistochemistry demonstrated cell type-specific localization of candidate interacting partners for ephrin-B2, including EphA4 in cells located in the posterior lobe, EphB1 in gonadotropes, EphB2 in corticotropes, EphB3 in stem/progenitor cells and EphB4 in endothelial cells in the adult pituitary gland. In particular, double-immunohistochemistry showed cis-interactions between EphB3 and ephrin-B2 in the apical cell membranes of stem/progenitor cell niches throughout life and trans-interactions between EphB2 produced by corticotropes and ephrin-B2 located in the basolateral cell membranes of stem/progenitor cells in the early postnatal pituitary gland. These data indicate that ephrin-B2 plays a role in pituitary stem/progenitor cell niches by selective interaction with EphB3 in cis and EphB2 in trans.

  5. Synthesis of Heparan Sulfate with Cyclophilin B-binding Properties Is Determined by Cell Type-specific Expression of Sulfotransferases*

    Science.gov (United States)

    Deligny, Audrey; Denys, Agnès; Marcant, Adeline; Melchior, Aurélie; Mazurier, Joël; van Kuppevelt, Toin H.; Allain, Fabrice

    2010-01-01

    Cyclophilin B (CyPB) induces migration and adhesion of T lymphocytes via a mechanism that requires interaction with 3-O-sulfated heparan sulfate (HS). HS biosynthesis is a complex process with many sulfotransferases involved. N-Deacetylases/N-sulfotransferases are responsible for N-sulfation, which is essential for subsequent modification steps, whereas 3-O-sulfotransferases (3-OSTs) catalyze the least abundant modification. These enzymes are represented by several isoforms, which differ in term of distribution pattern, suggesting their involvement in making tissue-specific HS. To elucidate how the specificity of CyPB binding is determined, we explored the relationships between the expression of these sulfotransferases and the generation of HS motifs with CyPB-binding properties. We demonstrated that high N-sulfate density and the presence of 2-O- and 3-O-sulfates determine binding of CyPB, as evidenced by competitive experiments with heparin derivatives, soluble HS, and anti-HS antibodies. We then showed that target cells, i.e. CD4+ lymphocyte subsets, monocytes/macrophages, and related cell lines, specifically expressed high levels of NDST2 and 3-OST3 isoforms. Silencing the expression of NDST1, NDST2, 2-OST, and 3-OST3 by RNA interference efficiently decreased binding and activity of CyPB, thus confirming their involvement in the biosynthesis of binding sequences for CyPB. Moreover, we demonstrated that NDST1 was able to partially sulfate exogenous substrate in the absence of NDST2 but not vice versa, suggesting that both isoenzymes do not have redundant activities but do have rather complementary activities in making N-sulfated sequences with CyPB-binding properties. Altogether, these results suggest a regulatory mechanism in which cell type-specific expression of certain HS sulfotransferases determines the specific binding of CyPB to target cells. PMID:19940140

  6. Synthesis of heparan sulfate with cyclophilin B-binding properties is determined by cell type-specific expression of sulfotransferases.

    Science.gov (United States)

    Deligny, Audrey; Denys, Agnès; Marcant, Adeline; Melchior, Aurélie; Mazurier, Joël; van Kuppevelt, Toin H; Allain, Fabrice

    2010-01-15

    Cyclophilin B (CyPB) induces migration and adhesion of T lymphocytes via a mechanism that requires interaction with 3-O-sulfated heparan sulfate (HS). HS biosynthesis is a complex process with many sulfotransferases involved. N-Deacetylases/N-sulfotransferases are responsible for N-sulfation, which is essential for subsequent modification steps, whereas 3-O-sulfotransferases (3-OSTs) catalyze the least abundant modification. These enzymes are represented by several isoforms, which differ in term of distribution pattern, suggesting their involvement in making tissue-specific HS. To elucidate how the specificity of CyPB binding is determined, we explored the relationships between the expression of these sulfotransferases and the generation of HS motifs with CyPB-binding properties. We demonstrated that high N-sulfate density and the presence of 2-O- and 3-O-sulfates determine binding of CyPB, as evidenced by competitive experiments with heparin derivatives, soluble HS, and anti-HS antibodies. We then showed that target cells, i.e. CD4+ lymphocyte subsets, monocytes/macrophages, and related cell lines, specifically expressed high levels of NDST2 and 3-OST3 isoforms. Silencing the expression of NDST1, NDST2, 2-OST, and 3-OST3 by RNA interference efficiently decreased binding and activity of CyPB, thus confirming their involvement in the biosynthesis of binding sequences for CyPB. Moreover, we demonstrated that NDST1 was able to partially sulfate exogenous substrate in the absence of NDST2 but not vice versa, suggesting that both isoenzymes do not have redundant activities but do have rather complementary activities in making N-sulfated sequences with CyPB-binding properties. Altogether, these results suggest a regulatory mechanism in which cell type-specific expression of certain HS sulfotransferases determines the specific binding of CyPB to target cells.

  7. Cell type-specific variations in the induction of hsp70 in human leukocytes by feverlike whole body hyperthermia.

    Science.gov (United States)

    Oehler, R; Pusch, E; Zellner, M; Dungel, P; Hergovics, N; Homoncik, M; Eliasen, M M; Brabec, M; Roth, E

    2001-10-01

    Fever has been associated with shortened duration and improved survival in infectious disease. The mechanism of this beneficial response is still poorly understood. The heat-inducible 70-kDa heat shock protein (Hsp70) has been associated with protection of leukocytes against the cytotoxicity of inflammatory mediators and with improved survival of severe infections. This study characterizes the induction of Hsp70 by feverlike temperatures in human leukocytes in vitro and in vivo. Using flow cytometry, Hsp70 expression was determined in whole blood samples. This approach eliminated cell isolation procedures that would greatly affect the results. Heat treatment of whole blood in vitro for 2 hours at different temperatures revealed that Hsp70 expression depends on temperature and cell type; up to 41 degrees C, Hsp70 increased only slightly in lymphocytes and polymorphonuclear leukocytes. However, in monocytes a strong induction was already seen at 39 degrees C, and Hsp70 levels at 41 degrees C were 10-fold higher than in the 37 degrees C control. To be as close as possible to the physiological situation during fever, we immersed healthy volunteers in a hot water bath, inducing whole body hyperthermia (39 degrees C), and measured leukocyte Hsp70 expression. Hsp70 was induced in all leukocytes with comparable but less pronounced cell type-specific variations as observed in vitro. Thus, a systemic increase of body temperature as triggered by fever stimulates Hsp70 expression in peripheral leukocytes, especially in monocytes. This fever-induced Hsp70 expression may protect monocytes when confronted with cytotoxic inflammatory mediators, thereby improving the course of the disease.

  8. High intensity training may reverse the fiber type specific decline in myogenic stem cells in multiple sclerosis patients

    Directory of Open Access Journals (Sweden)

    Jean eFarup

    2016-05-01

    Full Text Available Multiple sclerosis (MS is associated with loss of skeletal muscle mass and function. The myogenic stem cells (satellite cells – SCs are instrumental to accretion of myonuclei, but remain to be investigated in MS. The present study aimed to compare the SC and myonuclei content between MS patients (n=23 and age matched healthy controls (HC, n=18. Furthermore, the effects of 12 weeks of high intensity training on SC and myonuclei content were explored in MS. Muscle biopsies were obtained from m. Vastus Lateralis at baseline (MS+HC and following 12 weeks of training (MS only. Frozen biopsies were sectioned followed by immunohistochemical analysis for fiber type specific SCs (Pax7+, myonuclei (MN and central nuclei content and fiber cross-sectional area (fCSA using ATPase histochemistry. At baseline the SCs per fiber was lower in type II compared to type I fiber in both MS (119%, p<0.01 and HC (69%, p<0.05, whereas the SCs per fCSA was lower in type II fibers compared to type I only in MS (72%, p<0.05. No differences were observed in MN or central nuclei between MS and HC. Following training the type II fiber SCs per fiber and fCSA in MS patients increased by 165% (p<0.05 and 135% (p<0.05, respectively. Furthermore, the type II fiber MN content increased by 35% (p<0.05 following training. In conclusion, the SC content is lower in type II compared to type I fibers in both MS and HC. Furthermore, high intensity training was observed to selectively increase the SC and myonuclei content in type II fibers in MS patients.

  9. Persistence of type-specific human papillomavirus infection and increased long-term risk of cervical cancer.

    Science.gov (United States)

    Chen, Hui-Chi; Schiffman, Mark; Lin, Ching-Yu; Pan, Mei-Hung; You, San-Lin; Chuang, Li-Chung; Hsieh, Chang-Yao; Liaw, Kai-Li; Hsing, Ann W; Chen, Chien-Jen

    2011-09-21

    Human papillomavirus (HPV) persistence is the pivotal event in cervical carcinogenesis. We followed a large-scale community-based cohort for 16 years to investigate the role of genotype-specific HPV persistence in predicting cervical cancer including invasive and in situ carcinoma. At the baseline examination in 1991-1992, 11,923 participants (aged 30-65 years) consented to HPV testing and cytology; 6923 participants were reexamined in 1993-1995. For HPV testing, we used a polymerase chain reaction-based assay that detected 39 HPV types. Women who developed cervical cancer were identified from cancer and death registries. Cumulative risks for developing cervical cancer among infected and persistently infected women were calculated by the Kaplan-Meier method. Of 10,123 women who were initially cytologically normal, 68 developed cervical cancer. The 16-year cumulative risks of subsequent cervical cancer for women with HPV16, HPV58 (without HPV16), or other carcinogenic HPV types (without HPV16 or HPV58) were 13.5%, 10.3%, and 4.0%, respectively, compared with 0.26% for HPV-negative women. Women with type-specific persistence of any carcinogenic HPV had greatly increased risk compared with women who were HPV-negative at both visits (hazard ratio = 75.4, 95% confidence interval = 31.8 to 178.9). The cumulative cervical cancer risks following persistent carcinogenic HPV infections increased with age: The risks were 5.5%, 14.4%, and 18.1% for women aged 30-44 years, 45-54 years, and 55 years and older, respectively. However, newly acquired infections were associated with a low risk of cervical cancer regardless of age. HPV negativity was associated with a very low long-term risk of cervical cancer. Persistent detection of HPV among cytologically normal women greatly increased risk. Thus, it is useful to perform repeated HPV testing following an initial positive test.

  10. In Vivo Senescence in the Sbds-Deficient Murine Pancreas: Cell-Type Specific Consequences of Translation Insufficiency.

    Directory of Open Access Journals (Sweden)

    Marina E Tourlakis

    2015-06-01

    Full Text Available Genetic models of ribosome dysfunction show selective organ failure, highlighting a gap in our understanding of cell-type specific responses to translation insufficiency. Translation defects underlie a growing list of inherited and acquired cancer-predisposition syndromes referred to as ribosomopathies. We sought to identify molecular mechanisms underlying organ failure in a recessive ribosomopathy, with particular emphasis on the pancreas, an organ with a high and reiterative requirement for protein synthesis. Biallelic loss of function mutations in SBDS are associated with the ribosomopathy Shwachman-Diamond syndrome, which is typified by pancreatic dysfunction, bone marrow failure, skeletal abnormalities and neurological phenotypes. Targeted disruption of Sbds in the murine pancreas resulted in p53 stabilization early in the postnatal period, specifically in acinar cells. Decreased Myc expression was observed and atrophy of the adult SDS pancreas could be explained by the senescence of acinar cells, characterized by induction of Tgfβ, p15(Ink4b and components of the senescence-associated secretory program. This is the first report of senescence, a tumour suppression mechanism, in association with SDS or in response to a ribosomopathy. Genetic ablation of p53 largely resolved digestive enzyme synthesis and acinar compartment hypoplasia, but resulted in decreased cell size, a hallmark of decreased translation capacity. Moreover, p53 ablation resulted in expression of acinar dedifferentiation markers and extensive apoptosis. Our findings indicate a protective role for p53 and senescence in response to Sbds ablation in the pancreas. In contrast to the pancreas, the Tgfβ molecular signature was not detected in fetal bone marrow, liver or brain of mouse models with constitutive Sbds ablation. Nevertheless, as observed with the adult pancreas phenotype, disease phenotypes of embryonic tissues, including marked neuronal cell death due to apoptosis

  11. In Vivo Senescence in the Sbds-Deficient Murine Pancreas: Cell-Type Specific Consequences of Translation Insufficiency

    Science.gov (United States)

    Tourlakis, Marina E.; Zhang, Siyi; Ball, Heather L.; Gandhi, Rikesh; Liu, Hongrui; Zhong, Jian; Yuan, Julie S.; Guidos, Cynthia J.; Durie, Peter R.; Rommens, Johanna M.

    2015-01-01

    Genetic models of ribosome dysfunction show selective organ failure, highlighting a gap in our understanding of cell-type specific responses to translation insufficiency. Translation defects underlie a growing list of inherited and acquired cancer-predisposition syndromes referred to as ribosomopathies. We sought to identify molecular mechanisms underlying organ failure in a recessive ribosomopathy, with particular emphasis on the pancreas, an organ with a high and reiterative requirement for protein synthesis. Biallelic loss of function mutations in SBDS are associated with the ribosomopathy Shwachman-Diamond syndrome, which is typified by pancreatic dysfunction, bone marrow failure, skeletal abnormalities and neurological phenotypes. Targeted disruption of Sbds in the murine pancreas resulted in p53 stabilization early in the postnatal period, specifically in acinar cells. Decreased Myc expression was observed and atrophy of the adult SDS pancreas could be explained by the senescence of acinar cells, characterized by induction of Tgfβ, p15Ink4b and components of the senescence-associated secretory program. This is the first report of senescence, a tumour suppression mechanism, in association with SDS or in response to a ribosomopathy. Genetic ablation of p53 largely resolved digestive enzyme synthesis and acinar compartment hypoplasia, but resulted in decreased cell size, a hallmark of decreased translation capacity. Moreover, p53 ablation resulted in expression of acinar dedifferentiation markers and extensive apoptosis. Our findings indicate a protective role for p53 and senescence in response to Sbds ablation in the pancreas. In contrast to the pancreas, the Tgfβ molecular signature was not detected in fetal bone marrow, liver or brain of mouse models with constitutive Sbds ablation. Nevertheless, as observed with the adult pancreas phenotype, disease phenotypes of embryonic tissues, including marked neuronal cell death due to apoptosis, were determined to

  12. Knockdown of the fat mass and obesity gene disrupts cellular energy balance in a cell-type specific manner.

    Directory of Open Access Journals (Sweden)

    Ryan T Pitman

    Full Text Available Recent studies suggest that FTO variants strongly correlate with obesity and mainly influence energy intake with little effect on the basal metabolic rate. We suggest that FTO influences eating behavior by modulating intracellular energy levels and downstream signaling mechanisms which control energy intake and metabolism. Since FTO plays a particularly important role in adipocytes and in hypothalamic neurons, SH-SY5Y neuronal cells and 3T3-L1 adipocytes were used to understand how siRNA mediated knockdown of FTO expression alters cellular energy homeostasis. Cellular energy status was evaluated by measuring ATP levels using a luminescence assay and uptake of fluorescent glucose. FTO siRNA in SH-SY5Y cells mediated mRNA knockdown (-82%, increased ATP concentrations by up to 46% (P = 0.013 compared to controls, and decreased phosphorylation of AMPk and Akt in SH-SY5Y by -52% and -46% respectively as seen by immunoblotting. In contrast, FTO siRNA in 3T3-L1 cells decreased ATP concentration by -93% (p<0.0005, and increased AMPk and Akt phosphorylation by 204% and 70%, respectively suggesting that FTO mediates control of energy levels in a cell-type specific manner. Furthermore, glucose uptake was decreased in both SH-SY5Y (-51% p = 0.015 and 3T3-L1 cells (-30%, p = 0.0002. We also show that FTO knockdown decreases NPY mRNA expression in SH-SY5Y cells (-21% through upregulation of pSTAT3 (118%. These results provide important evidence that FTO-variant linked obesity may be associated with altered metabolic functions through activation of downstream metabolic mediators including AMPk.

  13. Electronic Submission of Labels

    Science.gov (United States)

    Pesticide registrants can provide draft and final labels to EPA electronically for our review as part of the pesticide registration process. The electronic submission of labels by registrants is voluntary but strongly encouraged.

  14. Robust Active Label Correction

    DEFF Research Database (Denmark)

    Kremer, Jan; Sha, Fei; Igel, Christian

    2018-01-01

    for the noisy data lead to different active label correction algorithms. If loss functions consider the label noise rates, these rates are estimated during learning, where importance weighting compensates for the sampling bias. We show empirically that viewing the true label as a latent variable and computing......Active label correction addresses the problem of learning from input data for which noisy labels are available (e.g., from imprecise measurements or crowd-sourcing) and each true label can be obtained at a significant cost (e.g., through additional measurements or human experts). To minimize......). To select labels for correction, we adopt the active learning strategy of maximizing the expected model change. We consider the change in regularized empirical risk functionals that use different pointwise loss functions for patterns with noisy and true labels, respectively. Different loss functions...

  15. Pesticide Product Label System

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Pesticide Product Label System (PPLS) provides a collection of pesticide product labels (Adobe PDF format) that have been approved by EPA under Section 3 of the...

  16. Semiotic labelled deductive systems

    Energy Technology Data Exchange (ETDEWEB)

    Nossum, R.T. [Imperial College of Science, Technology and Medicine, London (United Kingdom)

    1996-12-31

    We review the class of Semiotic Models put forward by Pospelov, as well as the Labelled Deductive Systems developed by Gabbay, and construct an embedding of Semiotic Models into Labelled Deductive Systems.

  17. Mental Labels and Tattoos

    Science.gov (United States)

    Hyatt, I. Ralph

    1977-01-01

    Discusses the ease with which mental labels become imprinted in our system, six basic axioms for maintaining negative mental tattoos, and psychological processes for eliminating mental tattoos and labels. (RK)

  18. Soil Fumigant Labels - Dazomet

    Science.gov (United States)

    Updated labels include new safety requirements for buffer zones and related measures. Find information from the Pesticide Product Labeling System (PPLS) for products such as Basamid G, manufactured by Amvac.

  19. Soil Fumigant Labels - Chloropicrin

    Science.gov (United States)

    Search by EPA registration number, product name, or company name, and follow the link to the Pesticide Product Label System (PPLS) for details on each fumigant. Updated labels include new safety requirements for buffer zones and related measures.

  20. Metabolic characterization of isocitrate dehydrogenase (IDH) mutant and IDH wildtype gliomaspheres uncovers cell type-specific vulnerabilities.

    Science.gov (United States)

    Garrett, Matthew; Sperry, Jantzen; Braas, Daniel; Yan, Weihong; Le, Thuc M; Mottahedeh, Jack; Ludwig, Kirsten; Eskin, Ascia; Qin, Yue; Levy, Rachelle; Breunig, Joshua J; Pajonk, Frank; Graeber, Thomas G; Radu, Caius G; Christofk, Heather; Prins, Robert M; Lai, Albert; Liau, Linda M; Coppola, Giovanni; Kornblum, Harley I

    2018-01-01

    There is considerable interest in defining the metabolic abnormalities of IDH mutant tumors to exploit for therapy. While most studies have attempted to discern function by using cell lines transduced with exogenous IDH mutant enzyme, in this study, we perform unbiased metabolomics to discover metabolic differences between a cohort of patient-derived IDH1 mutant and IDH wildtype gliomaspheres. Using both our own microarray and the TCGA datasets, we performed KEGG analysis to define pathways differentially enriched in IDH1 mutant and IDH wildtype cells and tumors. Liquid chromatography coupled to mass spectrometry analysis with labeled glucose and deoxycytidine tracers was used to determine differences in overall cellular metabolism and nucleotide synthesis. Radiation-induced DNA damage and repair capacity was assessed using a comet assay. Differences between endogenous IDH1 mutant metabolism and that of IDH wildtype cells transduced with the IDH1 (R132H) mutation were also investigated. Our KEGG analysis revealed that IDH wildtype cells were enriched for pathways involved in de novo nucleotide synthesis, while IDH1 mutant cells were enriched for pathways involved in DNA repair. LC-MS analysis with fully labeled 13 C-glucose revealed distinct labeling patterns between IDH1 mutant and wildtype cells. Additional LC-MS tracing experiments confirmed increased de novo nucleotide synthesis in IDH wildtype cells relative to IDH1 mutant cells. Endogenous IDH1 mutant cultures incurred less DNA damage than IDH wildtype cultures and sustained better overall growth following X-ray radiation. Overexpression of mutant IDH1 in a wildtype line did not reproduce the range of metabolic differences observed in lines expressing endogenous mutations, but resulted in depletion of glutamine and TCA cycle intermediates, an increase in DNA damage following radiation, and a rise in intracellular ROS. These results demonstrate that IDH1 mutant and IDH wildtype cells are easily distinguishable

  1. On the Automatic Parallelization of Sparse and Irregular Fortran Programs

    Directory of Open Access Journals (Sweden)

    Yuan Lin

    1999-01-01

    Full Text Available Automatic parallelization is usually believed to be less effective at exploiting implicit parallelism in sparse/irregular programs than in their dense/regular counterparts. However, not much is really known because there have been few research reports on this topic. In this work, we have studied the possibility of using an automatic parallelizing compiler to detect the parallelism in sparse/irregular programs. The study with a collection of sparse/irregular programs led us to some common loop patterns. Based on these patterns new techniques were derived that produced good speedups when manually applied to our benchmark codes. More importantly, these parallelization methods can be implemented in a parallelizing compiler and can be applied automatically.

  2. Joint sparse representation for robust multimodal biometrics recognition.

    Science.gov (United States)

    Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

    2014-01-01

    Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.

  3. Sparse Representation Denoising for Radar High Resolution Range Profiling

    Directory of Open Access Journals (Sweden)

    Min Li

    2014-01-01

    Full Text Available Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.

  4. A Projected Conjugate Gradient Method for Sparse Minimax Problems

    DEFF Research Database (Denmark)

    Madsen, Kaj; Jonasson, Kristjan

    1993-01-01

    A new method for nonlinear minimax problems is presented. The method is of the trust region type and based on sequential linear programming. It is a first order method that only uses first derivatives and does not approximate Hessians. The new method is well suited for large sparse problems...... as it only requires that software for sparse linear programming and a sparse symmetric positive definite equation solver are available. On each iteration a special linear/quadratic model of the function is minimized, but contrary to the usual practice in trust region methods the quadratic model is only...... with the method are presented. In fact, we find that the number of iterations required is comparable to that of state-of-the-art quasi-Newton codes....

  5. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

    Science.gov (United States)

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

    2015-12-01

    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  6. Massively parallel sparse matrix function calculations with NTPoly

    Science.gov (United States)

    Dawson, William; Nakajima, Takahito

    2018-04-01

    We present NTPoly, a massively parallel library for computing the functions of sparse, symmetric matrices. The theory of matrix functions is a well developed framework with a wide range of applications including differential equations, graph theory, and electronic structure calculations. One particularly important application area is diagonalization free methods in quantum chemistry. When the input and output of the matrix function are sparse, methods based on polynomial expansions can be used to compute matrix functions in linear time. We present a library based on these methods that can compute a variety of matrix functions. Distributed memory parallelization is based on a communication avoiding sparse matrix multiplication algorithm. OpenMP task parallellization is utilized to implement hybrid parallelization. We describe NTPoly's interface and show how it can be integrated with programs written in many different programming languages. We demonstrate the merits of NTPoly by performing large scale calculations on the K computer.

  7. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2014-01-01

    Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

  8. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla

    2014-05-04

    Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

  9. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla

    2014-01-06

    Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

  10. Identification of MIMO systems with sparse transfer function coefficients

    Science.gov (United States)

    Qiu, Wanzhi; Saleem, Syed Khusro; Skafidas, Efstratios

    2012-12-01

    We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.

  11. A General Sparse Tensor Framework for Electronic Structure Theory.

    Science.gov (United States)

    Manzer, Samuel; Epifanovsky, Evgeny; Krylov, Anna I; Head-Gordon, Martin

    2017-03-14

    Linear-scaling algorithms must be developed in order to extend the domain of applicability of electronic structure theory to molecules of any desired size. However, the increasing complexity of modern linear-scaling methods makes code development and maintenance a significant challenge. A major contributor to this difficulty is the lack of robust software abstractions for handling block-sparse tensor operations. We therefore report the development of a highly efficient symbolic block-sparse tensor library in order to provide access to high-level software constructs to treat such problems. Our implementation supports arbitrary multi-dimensional sparsity in all input and output tensors. We avoid cumbersome machine-generated code by implementing all functionality as a high-level symbolic C++ language library and demonstrate that our implementation attains very high performance for linear-scaling sparse tensor contractions.

  12. Sparse dictionary learning of resting state fMRI networks.

    Science.gov (United States)

    Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

    2012-07-02

    Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

  13. Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

    Directory of Open Access Journals (Sweden)

    Qi Yuan(Alan

    2010-01-01

    Full Text Available Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( status and Estrogen Receptor negative ( status, respectively.

  14. P-SPARSLIB: A parallel sparse iterative solution package

    Energy Technology Data Exchange (ETDEWEB)

    Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)

    1994-12-31

    Iterative methods are gaining popularity in engineering and sciences at a time where the computational environment is changing rapidly. P-SPARSLIB is a project to build a software library for sparse matrix computations on parallel computers. The emphasis is on iterative methods and the use of distributed sparse matrices, an extension of the domain decomposition approach to general sparse matrices. One of the goals of this project is to develop a software package geared towards specific applications. For example, the author will test the performance and usefulness of P-SPARSLIB modules on linear systems arising from CFD applications. Equally important is the goal of portability. In the long run, the author wishes to ensure that this package is portable on a variety of platforms, including SIMD environments and shared memory environments.

  15. MULTISCALE SPARSE APPEARANCE MODELING AND SIMULATION OF PATHOLOGICAL DEFORMATIONS

    Directory of Open Access Journals (Sweden)

    Rami Zewail

    2017-08-01

    Full Text Available Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

  16. A Label to Regulate

    DEFF Research Database (Denmark)

    Tricoire, Aurélie; Boxenbaum, Eva; Laurent, Brice

    This paper examines the role labelling plays in the government of the contemporary economy.1Drawing on a detailed study of BBC-Effinergy, a French label for sustainable construction, we showhow the adoption and evolution of voluntary labels can be seen as emblematic of a governmentthrough experim...

  17. Labelling subway lines

    NARCIS (Netherlands)

    Garrido, M.A.; Iturriaga, C.; Márquez, A.; Portillo, J.R.; Reyes, P.; Wolff, A.; Eades, P.; Takaoka, T.

    2001-01-01

    Graphical features on map, charts, diagrams and graph drawings usually must be annotated with text labels in order to convey their meaning. In this paper we focus on a problem that arises when labeling schematized maps, e.g. for subway networks. We present algorithms for labeling points on a line

  18. Universal Regularizers For Robust Sparse Coding and Modeling

    OpenAIRE

    Ramirez, Ignacio; Sapiro, Guillermo

    2010-01-01

    Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding...

  19. Uniform sparse bounds for discrete quadratic phase Hilbert transforms

    Science.gov (United States)

    Kesler, Robert; Arias, Darío Mena

    2017-09-01

    For each α \\in T consider the discrete quadratic phase Hilbert transform acting on finitely supported functions f : Z → C according to H^{α }f(n):= \\sum _{m ≠ 0} e^{iα m^2} f(n - m)/m. We prove that, uniformly in α \\in T , there is a sparse bound for the bilinear form for every pair of finitely supported functions f,g : Z→ C . The sparse bound implies several mapping properties such as weighted inequalities in an intersection of Muckenhoupt and reverse Hölder classes.

  20. Sparse reconstruction by means of the standard Tikhonov regularization

    International Nuclear Information System (INIS)

    Lu Shuai; Pereverzev, Sergei V

    2008-01-01

    It is a common belief that Tikhonov scheme with || · ||L 2 -penalty fails in sparse reconstruction. We are going to show, however, that this standard regularization can help if the stability measured in L 1 -norm will be properly taken into account in the choice of the regularization parameter. The crucial point is that now a stability bound may depend on the bases with respect to which the solution of the problem is assumed to be sparse. We discuss how such a stability can be estimated numerically and present the results of computational experiments giving the evidence of the reliability of our approach.

  1. Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2015-01-01

    A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

  2. Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

    KAUST Repository

    Desmal, Abdulla

    2015-04-13

    A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

  3. Sparse grid techniques for particle-in-cell schemes

    Science.gov (United States)

    Ricketson, L. F.; Cerfon, A. J.

    2017-02-01

    We propose the use of sparse grids to accelerate particle-in-cell (PIC) schemes. By using the so-called ‘combination technique’ from the sparse grids literature, we are able to dramatically increase the size of the spatial cells in multi-dimensional PIC schemes while paying only a slight penalty in grid-based error. The resulting increase in cell size allows us to reduce the statistical noise in the simulation without increasing total particle number. We present initial proof-of-principle results from test cases in two and three dimensions that demonstrate the new scheme’s efficiency, both in terms of computation time and memory usage.

  4. Ordering sparse matrices for cache-based systems

    International Nuclear Information System (INIS)

    Biswas, Rupak; Oliker, Leonid

    2001-01-01

    The Conjugate Gradient (CG) algorithm is the oldest and best-known Krylov subspace method used to solve sparse linear systems. Most of the coating-point operations within each CG iteration is spent performing sparse matrix-vector multiplication (SPMV). We examine how various ordering and partitioning strategies affect the performance of CG and SPMV when different programming paradigms are used on current commercial cache-based computers. However, a multithreaded implementation on the cacheless Cray MTA demonstrates high efficiency and scalability without any special ordering or partitioning

  5. Sparse Matrix for ECG Identification with Two-Lead Features

    Directory of Open Access Journals (Sweden)

    Kuo-Kun Tseng

    2015-01-01

    Full Text Available Electrocardiograph (ECG human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.

  6. Low-rank and sparse modeling for visual analysis

    CERN Document Server

    Fu, Yun

    2014-01-01

    This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applic

  7. Label Review Training: Module 1: Label Basics, Page 15

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. Learn about the consequences of improper labeling.

  8. Label Review Training: Module 1: Label Basics, Page 23

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. Lists types of labels that do not require review.

  9. Label Review Training: Module 1: Label Basics, Page 16

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. Learn about the importance of labels and the role in enforcement.

  10. Label Review Training: Module 1: Label Basics, Page 14

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. Learn about positive effects from proper labeling.

  11. Label Review Training: Module 1: Label Basics, Page 21

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. Learn about types of labels.

  12. Label Review Training: Module 1: Label Basics, Page 22

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. Learn about what labels require review.

  13. Label Review Training: Module 1: Label Basics, Page 18

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. This section discusses the types of labels.

  14. Label Review Training: Module 1: Label Basics, Page 26

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. Learn about mandatory and advisory label statements.

  15. Label Review Training: Module 1: Label Basics, Page 24

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. This page is about which labels require review.

  16. Label Review Training: Module 1: Label Basics, Page 27

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. See examples of mandatory and advisory label statements.

  17. Label Review Training: Module 1: Label Basics, Page 19

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. This section covers supplemental distributor labeling.

  18. Label Review Training: Module 1: Label Basics, Page 17

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. See an overview of the importance of labels.

  19. Bonafide, type-specific human papillomavirus persistence among HIV-positive pregnant women: predictive value for cytological abnormalities, a longitudinal cohort study

    Directory of Open Access Journals (Sweden)

    Angela RI Meyrelles

    2016-02-01

    Full Text Available This study investigated the rate of human papillomavirus (HPV persistence, associated risk factors, and predictors of cytological alteration outcomes in a cohort of human immunodeficiency virus-infected pregnant women over an 18-month period. HPV was typed through L1 gene sequencing in cervical smears collected during gestation and at 12 months after delivery. Outcomes were defined as nonpersistence (clearance of the HPV in the 2nd sample, re-infection (detection of different types of HPV in the 2 samples, and type-specific HPV persistence (the same HPV type found in both samples. An unfavourable cytological outcome was considered when the second exam showed progression to squamous intraepithelial lesion or high squamous intraepithelial lesion. Ninety patients were studied. HPV DNA persistence occurred in 50% of the cases composed of type-specific persistence (30% or re-infection (20%. A low CD4+T-cell count at entry was a risk factor for type-specific, re-infection, or HPV DNA persistence. The odds ratio (OR was almost three times higher in the type-specific group when compared with the re-infection group (OR = 2.8; 95% confidence interval: 0.43-22.79. Our findings show that bonafide (type-specific HPV persistence is a stronger predictor for the development of cytological abnormalities, highlighting the need for HPV typing as opposed to HPV DNA testing in the clinical setting.

  20. ON Bipolar Cells in Macaque Retina: Type-Specific Synaptic Connectivity with Special Reference to OFF Counterparts

    Science.gov (United States)

    Tsukamoto, Yoshihiko; Omi, Naoko

    2016-01-01

    To date, 12 macaque bipolar cell types have been described. This list includes all morphology types first outlined by Polyak (1941) using the Golgi method in the primate retina and subsequently identified by other researchers using electron microscopy (EM) combined with the Golgi method, serial section transmission EM (SSTEM), and immunohistochemical imaging. We used SSTEM for the rod-dense perifoveal area of macaque retina, reconfirmed ON (cone) bipolar cells to be classified as invaginating midget bipolar (IMB), diffuse bipolar (DB)4, DB5, DB6, giant bipolar (GB), and blue bipolar (BB) types, and clarified their type-specific connectivity. DB4 cells made reciprocal synapses with a kind of ON-OFF lateral amacrine cell, similar to OFF DB2 cells. GB cells contacted rods and cones, similar to OFF DB3b cells. Retinal circuits formed by GB and DB3b cells are thought to substantiate the psychophysical finding of fast rod signals in mesopic vision. DB6 cell output synapses were directed to ON midget ganglion (MG) cells at 70% of ribbon contacts, similar to OFF DB1 cells that directed 60% of ribbon contacts to OFF MG cells. IMB cells contacted medium- or long-wavelength sensitive (M/L-) cones but not short-wavelength sensitive (S-) cones, while BB cells contacted S-cones but not M/L-cones. However, IMB and BB dendrites had similar morphological architectures, and a BB cell contacting a single S-cone resembled an IMB cell. Thus, both IMB and BB may be the ON bipolar counterparts of the OFF flat midget bipolar (FMB) type, likewise DB4 of DB2, DB5 of DB3a, DB6 of DB1, and GB of DB3b OFF bipolar type. The ON DB plus GB, and OFF DB cells predominantly contacted M/L-cones and their outputs were directed mainly to parasol ganglion (PG) cells but also moderately to MG cells. BB cells directed S-cone-driven outputs almost exclusively to small bistratified ganglion (SBG) cells. Some FMB cells predominantly contacted S-cones and their outputs were directed to OFF MG cells. Thus, two

  1. Drug and cell type-specific regulation of genes with different classes of estrogen receptor beta-selective agonists.

    Directory of Open Access Journals (Sweden)

    Sreenivasan Paruthiyil

    2009-07-01

    Full Text Available Estrogens produce biological effects by interacting with two estrogen receptors, ERalpha and ERbeta. Drugs that selectively target ERalpha or ERbeta might be safer for conditions that have been traditionally treated with non-selective estrogens. Several synthetic and natural ERbeta-selective compounds have been identified. One class of ERbeta-selective agonists is represented by ERB-041 (WAY-202041 which binds to ERbeta much greater than ERalpha. A second class of ERbeta-selective agonists derived from plants include MF101, nyasol and liquiritigenin that bind similarly to both ERs, but only activate transcription with ERbeta. Diarylpropionitrile represents a third class of ERbeta-selective compounds because its selectivity is due to a combination of greater binding to ERbeta and transcriptional activity. However, it is unclear if these three classes of ERbeta-selective compounds produce similar biological activities. The goals of these studies were to determine the relative ERbeta selectivity and pattern of gene expression of these three classes of ERbeta-selective compounds compared to estradiol (E(2, which is a non-selective ER agonist. U2OS cells stably transfected with ERalpha or ERbeta were treated with E(2 or the ERbeta-selective compounds for 6 h. Microarray data demonstrated that ERB-041, MF101 and liquiritigenin were the most ERbeta-selective agonists compared to estradiol, followed by nyasol and then diarylpropionitrile. FRET analysis showed that all compounds induced a similar conformation of ERbeta, which is consistent with the finding that most genes regulated by the ERbeta-selective compounds were similar to each other and E(2. However, there were some classes of genes differentially regulated by the ERbeta agonists and E(2. Two ERbeta-selective compounds, MF101 and liquiritigenin had cell type-specific effects as they regulated different genes in HeLa, Caco-2 and Ishikawa cell lines expressing ERbeta. Our gene profiling studies

  2. Coordinated cell type-specific epigenetic remodeling in prefrontal cortex begins before birth and continues into early adulthood.

    Directory of Open Access Journals (Sweden)

    Hennady P Shulha

    2013-04-01

    Full Text Available Development of prefrontal and other higher-order association cortices is associated with widespread changes in the cortical transcriptome, particularly during the transitions from prenatal to postnatal development, and from early infancy to later stages of childhood and early adulthood. However, the timing and longitudinal trajectories of neuronal gene expression programs during these periods remain unclear in part because of confounding effects of concomitantly occurring shifts in neuron-to-glia ratios. Here, we used cell type-specific chromatin sorting techniques for genome-wide profiling of a histone mark associated with transcriptional regulation--H3 with trimethylated lysine 4 (H3K4me3--in neuronal chromatin from 31 subjects from the late gestational period to 80 years of age. H3K4me3 landscapes of prefrontal neurons were developmentally regulated at 1,157 loci, including 768 loci that were proximal to transcription start sites. Multiple algorithms consistently revealed that the overwhelming majority and perhaps all of developmentally regulated H3K4me3 peaks were on a unidirectional trajectory defined by either rapid gain or loss of histone methylation during the late prenatal period and the first year after birth, followed by similar changes but with progressively slower kinetics during early and later childhood and only minimal changes later in life. Developmentally downregulated H3K4me3 peaks in prefrontal neurons were enriched for Paired box (Pax and multiple Signal Transducer and Activator of Transcription (STAT motifs, which are known to promote glial differentiation. In contrast, H3K4me3 peaks subject to a progressive increase in maturing prefrontal neurons were enriched for activating protein-1 (AP-1 recognition elements that are commonly associated with activity-dependent regulation of neuronal gene expression. We uncovered a developmental program governing the remodeling of neuronal histone methylation landscapes in the prefrontal

  3. Efficient coordinated recovery of sparse channels in massive MIMO

    KAUST Repository

    Masood, Mudassir

    2015-01-01

    This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots. Two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.

  4. Sparse Generalized Fourier Series via Collocation-based Optimization

    Science.gov (United States)

    2014-11-01

    Theory 51, 12 (2005) 4203– 4215. [6] P. CONSTANTINE , M. ELDRED AND E. PHIPPS, Sparse pseu- dospectral approximation method. Comput. Methods Appl. Mech...Visition XVI: Algorithms, Techniques, Active Vision , and Materials Handling, 224 (1997). [15] J. SHEN AND L. WANG, Some recent advances on spectral methods

  5. A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation

    DEFF Research Database (Denmark)

    Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri

    2014-01-01

    This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...

  6. Robust visual tracking via structured multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary

  7. Behavior of greedy sparse representation algorithms on nested supports

    DEFF Research Database (Denmark)

    Mailhé, Boris; Sturm, Bob L.; Plumbley, Mark

    2013-01-01

    is not locally nested: there is a dictionary and supports Γ ⊃ Γ′ such that OMP can recover all signals with support Γ, but not all signals with support Γ′. We also show that the support recovery optimality of OMP is globally nested: if OMP can recover all s-sparse signals, then it can recover all s...

  8. Sparse linear models: Variational approximate inference and Bayesian experimental design

    International Nuclear Information System (INIS)

    Seeger, Matthias W

    2009-01-01

    A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

  9. Inference algorithms and learning theory for Bayesian sparse factor analysis

    International Nuclear Information System (INIS)

    Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John

    2009-01-01

    Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

  10. Sparse linear models: Variational approximate inference and Bayesian experimental design

    Energy Technology Data Exchange (ETDEWEB)

    Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)

    2009-12-01

    A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

  11. Inference algorithms and learning theory for Bayesian sparse factor analysis

    Energy Technology Data Exchange (ETDEWEB)

    Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)

    2009-12-01

    Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

  12. Efficient coordinated recovery of sparse channels in massive MIMO

    KAUST Repository

    Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.

    2015-01-01

    on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation

  13. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu

    2012-01-01

    In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

  14. Structure-aware Local Sparse Coding for Visual Tracking

    KAUST Repository

    Qi, Yuankai; Qin, Lei; Zhang, Jian; Zhang, Shengping; Huang, Qingming; Yang, Ming-Hsuan

    2018-01-01

    with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we

  15. A Practical View on Tunable Sparse Network Coding

    DEFF Research Database (Denmark)

    Sørensen, Chres Wiant; Shahbaz Badr, Arash; Cabrera Guerrero, Juan Alberto

    2015-01-01

    Tunable sparse network coding (TSNC) constitutes a promising concept for trading off computational complexity and delay performance. This paper advocates for the use of judicious feedback as a key not only to make TSNC practical, but also to deliver a highly consistent and controlled delay perfor...

  16. Parallel and Scalable Sparse Basic Linear Algebra Subprograms

    DEFF Research Database (Denmark)

    Liu, Weifeng

    and heterogeneous processors. The thesis compares the proposed methods with state-of-the-art approaches on six homogeneous and five heterogeneous processors from Intel, AMD and nVidia. Using in total 38 sparse matrices as a benchmark suite, the experimental results show that the proposed methods obtain significant...

  17. SparseBeads data: benchmarking sparsity-regularized computed tomography

    DEFF Research Database (Denmark)

    Jørgensen, Jakob Sauer; Coban, Sophia B.; Lionheart, William R. B.

    2017-01-01

    -regularized reconstruction. A collection of 48 x-ray CT datasets called SparseBeads was designed for benchmarking SR reconstruction algorithms. Beadpacks comprising glass beads of five different sizes as well as mixtures were scanned in a micro-CT scanner to provide structured datasets with variable image sparsity levels...

  18. Hierarchical Bayesian sparse image reconstruction with application to MRFM.

    Science.gov (United States)

    Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

    2009-09-01

    This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

  19. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun

    2013-10-01

    When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

  20. Fast sparse matrix-vector multiplication by partitioning and reordering

    NARCIS (Netherlands)

    Yzelman, A.N.

    2011-01-01

    The thesis introduces a cache-oblivious method for the sparse matrix-vector (SpMV) multiplication, which is an important computational kernel in many applications. The method works by permuting rows and columns of the input matrix so that the resulting reordered matrix induces cache-friendly

  1. Sobol indices for dimension adaptivity in sparse grids

    NARCIS (Netherlands)

    Dwight, R.P.; Desmedt, S.G.L.; Shoeibi Omrani, P.

    2016-01-01

    Propagation of random variables through computer codes of many inputs is primarily limited by computational expense. The use of sparse grids mitigates these costs somewhat; here we show how Sobol indices can be used to perform dimension adaptivity to mitigate them further. The method is compared to

  2. Discriminative object tracking via sparse representation and online dictionary learning.

    Science.gov (United States)

    Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

    2014-04-01

    We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

  3. Fast Estimation of Optimal Sparseness of Music Signals

    DEFF Research Database (Denmark)

    la Cour-Harbo, Anders

    2006-01-01

    We want to use a variety of sparseness measured applied to ‘the minimal L1 norm representation' of a music signal in an overcomplete dictionary as features for automatic classification of music. Unfortunately, the process of computing the optimal L1 norm representation is rather slow, and we...

  4. Sparse principal component analysis in medical shape modeling

    Science.gov (United States)

    Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

    2006-03-01

    Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

  5. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

    KAUST Repository

    Zhang, Tianzhu; Liu, Si; Ahuja, Narendra; Yang, Ming-Hsuan; Ghanem, Bernard

    2014-01-01

    and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25

  6. Non-Cartesian MRI scan time reduction through sparse sampling

    NARCIS (Netherlands)

    Wajer, F.T.A.W.

    2001-01-01

    Non-Cartesian MRI Scan-Time Reduction through Sparse Sampling Magnetic resonance imaging (MRI) signals are measured in the Fourier domain, also called k-space. Samples of the MRI signal can not be taken at will, but lie along k-space trajectories determined by the magnetic field gradients. MRI

  7. Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit

    DEFF Research Database (Denmark)

    Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan

    2012-01-01

    We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures...

  8. Proportionate Minimum Error Entropy Algorithm for Sparse System Identification

    Directory of Open Access Journals (Sweden)

    Zongze Wu

    2015-08-01

    Full Text Available Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm.

  9. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

    KAUST Repository

    Zhang, Tianzhu

    2014-06-19

    Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.

  10. Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

    KAUST Repository

    Sicat, Ronell Barrera

    2014-12-31

    This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

  11. EEG Source Reconstruction using Sparse Basis Function Representations

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hansen, Lars Kai

    2014-01-01

    -validation this approach is more automated than competing approaches such as Multiple Sparse Priors (Friston et al., 2008) or Champagne (Wipf et al., 2010) that require manual selection of noise level and auxiliary signal free data, respectively. Finally, we propose an unbiased estimator of the reproducibility...

  12. Aliasing-free wideband beamforming using sparse signal representation

    NARCIS (Netherlands)

    Tang, Z.; Blacquière, G.; Leus, G.

    2011-01-01

    Sparse signal representation (SSR) is considered to be an appealing alternative to classical beamforming for direction-of-arrival (DOA) estimation. For wideband signals, the SSR-based approach constructs steering matrices, referred to as dictionaries in this paper, corresponding to different

  13. Deformable segmentation via sparse representation and dictionary learning.

    Science.gov (United States)

    Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N

    2012-10-01

    "Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach

    International Nuclear Information System (INIS)

    Ruan, D; Yang, Y; Cao, M; Hu, P; Low, D

    2014-01-01

    Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme

  15. SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach

    Energy Technology Data Exchange (ETDEWEB)

    Ruan, D; Yang, Y; Cao, M; Hu, P; Low, D [UCLA, Los Angeles, CA (United States)

    2014-06-01

    Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme

  16. Labelling of equipment dispensers.

    Science.gov (United States)

    Gray, D C

    1993-01-01

    A new labelling system for use on medical equipment dispensers is tested. This system uses one of the objects stored in each unit of the dispenser as the 'label', by attaching it to the front of the dispenser with tape. The new system was compared to conventional written labelling by timing subjects asked to select items from two dispensers. The new system was 27% quicker than the conventional system. Images Fig. 1 PMID:8110335

  17. Deuterium labeled cannabinoids

    International Nuclear Information System (INIS)

    Driessen, R.A.

    1979-01-01

    Complex reactions involving ring opening, ring closure and rearrangements hamper complete understanding of the fragmentation processes in the mass spectrometric fragmentation patterns of cannabinoids. Specifically labelled compounds are very powerful tools for obtaining more insight into fragmentation mechanisms and ion structures and therefore the synthesis of specifically deuterated cannabinoids was undertaken. For this, it was necessary to investigate the preparation of cannabinoids, appropriately functionalized for specific introduction of deuterium atom labels. The results of mass spectrometry with these labelled cannabinoids are described. (Auth.)

  18. Cell-type-specific H+-ATPase activity in root tissues enables K+ retention and mediates acclimation of barley (Hordeum vulgare) to salinity stress

    DEFF Research Database (Denmark)

    Shabala, Lana; Zhang, Jingyi; Pottosin, Igor

    2016-01-01

    While the importance of cell type specificity in plant adaptive responses is widely accepted, only a limited number of studies have addressed this issue at the functional level. We have combined electrophysiological, imaging, and biochemical techniques to reveal the physiological mechanisms confe...

  19. The role of myostatin and activin receptor IIB in the regulation of unloading-induced myofiber type-specific skeletal muscle atrophy.

    Science.gov (United States)

    Babcock, Lyle W; Knoblauch, Mark; Clarke, Mark S F

    2015-09-15

    Chronic unloading induces decrements in muscle size and strength. This adaptation is governed by a number of molecular factors including myostatin, a potent negative regulator of muscle mass. Myostatin must first be secreted into the circulation and then bind to the membrane-bound activin receptor IIB (actRIIB) to exert its atrophic action. Therefore, we hypothesized that myofiber type-specific atrophy observed after hindlimb suspension (HLS) would be related to myofiber type-specific expression of myostatin and/or actRIIB. Wistar rats underwent HLS for 10 days, after which the tibialis anterior was harvested for frozen cross sectioning. Simultaneous multichannel immunofluorescent staining combined with differential interference contrast imaging was employed to analyze myofiber type-specific expression of myostatin and actRIIB and myofiber type cross-sectional area (CSA) across fiber types, myonuclei, and satellite cells. Hindlimb suspension (HLS) induced significant myofiber type-specific atrophy in myosin heavy chain (MHC) IIx (P Myostatin staining associated with myonuclei was less in HLS rats compared with controls, while satellite cell staining for myostatin remained unchanged. In contrast, the total number myonuclei and satellite cells per myofiber was reduced in HLS compared with ambulatory control rats (P myostatin-induced myofiber type-selective atrophy observed during chronic unloading. Copyright © 2015 the American Physiological Society.

  20. Feature selection and multi-kernel learning for sparse representation on a manifold

    KAUST Repository

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2014-01-01

    combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity

  1. Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation

    KAUST Repository

    Murarasu, Alin; Weidendorfer, Josef

    2012-01-01

    bring a substantial contribution to the speedup. By identifying common patterns in the input data, we propose new algorithms for sparse grid interpolation that accelerate the state-of-the-art non-specialized version. Sparse grid interpolation

  2. A Non-static Data Layout Enhancing Parallelism and Vectorization in Sparse Grid Algorithms

    KAUST Repository

    Buse, Gerrit; Pfluger, Dirk; Murarasu, Alin; Jacob, Riko

    2012-01-01

    performance and facilitate the use of vector registers for our sparse grid benchmark problem hierarchization. Based on the compact data structure proposed for regular sparse grids in [2], we developed a new algorithm that outperforms existing implementations

  3. Group sparse canonical correlation analysis for genomic data integration.

    Science.gov (United States)

    Lin, Dongdong; Zhang, Jigang; Li, Jingyao; Calhoun, Vince D; Deng, Hong-Wen; Wang, Yu-Ping

    2013-08-12

    The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature

  4. Effective sample labeling

    International Nuclear Information System (INIS)

    Rieger, J.T.; Bryce, R.W.

    1990-01-01

    Ground-water samples collected for hazardous-waste and radiological monitoring have come under strict regulatory and quality assurance requirements as a result of laws such as the Resource Conservation and Recovery Act. To comply with these laws, the labeling system used to identify environmental samples had to be upgraded to ensure proper handling and to protect collection personnel from exposure to sample contaminants and sample preservatives. The sample label now used as the Pacific Northwest Laboratory is a complete sample document. In the event other paperwork on a labeled sample were lost, the necessary information could be found on the label

  5. Cell-type specific expression of constitutively-active Rheb promotes regeneration of bulbospinal respiratory axons following cervical SCI.

    Science.gov (United States)

    Urban, Mark W; Ghosh, Biswarup; Strojny, Laura R; Block, Cole G; Blazejewski, Sara M; Wright, Megan C; Smith, George M; Lepore, Angelo C

    2018-05-01

    Damage to respiratory neural circuitry and consequent loss of diaphragm function is a major cause of morbidity and mortality in individuals suffering from traumatic cervical spinal cord injury (SCI). Repair of CNS axons after SCI remains a therapeutic challenge, despite current efforts. SCI disrupts inspiratory signals originating in the rostral ventral respiratory group (rVRG) of the medulla from their phrenic motor neuron (PhMN) targets, resulting in loss of diaphragm function. Using a rat model of cervical hemisection SCI, we aimed to restore rVRG-PhMN-diaphragm circuitry by stimulating regeneration of injured rVRG axons via targeted induction of Rheb (ras homolog enriched in brain), a signaling molecule that regulates neuronal-intrinsic axon growth potential. Following C2 hemisection, we performed intra-rVRG injection of an adeno-associated virus serotype-2 (AAV2) vector that drives expression of a constitutively-active form of Rheb (cRheb). rVRG neuron-specific cRheb expression robustly increased mTOR pathway activity within the transduced rVRG neuron population ipsilateral to the hemisection, as assessed by levels of phosphorylated ribosomal S6 kinase. By co-injecting our novel AAV2-mCherry/WGA anterograde/trans-synaptic axonal tracer into rVRG, we found that cRheb expression promoted regeneration of injured rVRG axons into the lesion site, while we observed no rVRG axon regrowth with AAV2-GFP control. AAV2-cRheb also significantly reduced rVRG axonal dieback within the intact spinal cord rostral to the lesion. However, cRheb expression did not promote any recovery of ipsilateral hemi-diaphragm function, as assessed by inspiratory electromyography (EMG) burst amplitudes. This lack of functional recovery was likely because regrowing rVRG fibers did not extend back into the caudal spinal cord to synaptically reinnervate PhMNs that we retrogradely-labeled with cholera toxin B from the ipsilateral hemi-diaphragm. Our findings demonstrate that enhancing neuronal

  6. An in-depth study of sparse codes on abnormality detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

    2016-01-01

    Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality...... are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. The experiment results show that combining OMP codes with maximum coordinate...

  7. Sparse representation, modeling and learning in visual recognition theory, algorithms and applications

    CERN Document Server

    Cheng, Hong

    2015-01-01

    This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represen

  8. Porting of the DBCSR library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi systems

    OpenAIRE

    Bethune, Iain; Gloess, Andeas; Hutter, Juerg; Lazzaro, Alfio; Pabst, Hans; Reid, Fiona

    2017-01-01

    Multiplication of two sparse matrices is a key operation in the simulation of the electronic structure of systems containing thousands of atoms and electrons. The highly optimized sparse linear algebra library DBCSR (Distributed Block Compressed Sparse Row) has been specifically designed to efficiently perform such sparse matrix-matrix multiplications. This library is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. It is para...

  9. Dynamic map labeling.

    Science.gov (United States)

    Been, Ken; Daiches, Eli; Yap, Chee

    2006-01-01

    We address the problem of filtering, selecting and placing labels on a dynamic map, which is characterized by continuous zooming and panning capabilities. This consists of two interrelated issues. The first is to avoid label popping and other artifacts that cause confusion and interrupt navigation, and the second is to label at interactive speed. In most formulations the static map labeling problem is NP-hard, and a fast approximation might have O(nlogn) complexity. Even this is too slow during interaction, when the number of labels shown can be several orders of magnitude less than the number in the map. In this paper we introduce a set of desiderata for "consistent" dynamic map labeling, which has qualities desirable for navigation. We develop a new framework for dynamic labeling that achieves the desiderata and allows for fast interactive display by moving all of the selection and placement decisions into the preprocessing phase. This framework is general enough to accommodate a variety of selection and placement algorithms. It does not appear possible to achieve our desiderata using previous frameworks. Prior to this paper, there were no formal models of dynamic maps or of dynamic labels; our paper introduces both. We formulate a general optimization problem for dynamic map labeling and give a solution to a simple version of the problem. The simple version is based on label priorities and a versatile and intuitive class of dynamic label placements we call "invariant point placements". Despite these restrictions, our approach gives a useful and practical solution. Our implementation is incorporated into the G-Vis system which is a full-detail dynamic map of the continental USA. This demo is available through any browser.

  10. High-Order Sparse Linear Predictors for Audio Processing

    DEFF Research Database (Denmark)

    Giacobello, Daniele; van Waterschoot, Toon; Christensen, Mads Græsbøll

    2010-01-01

    Linear prediction has generally failed to make a breakthrough in audio processing, as it has done in speech processing. This is mostly due to its poor modeling performance, since an audio signal is usually an ensemble of different sources. Nevertheless, linear prediction comes with a whole set...... of interesting features that make the idea of using it in audio processing not far fetched, e.g., the strong ability of modeling the spectral peaks that play a dominant role in perception. In this paper, we provide some preliminary conjectures and experiments on the use of high-order sparse linear predictors...... in audio processing. These predictors, successfully implemented in modeling the short-term and long-term redundancies present in speech signals, will be used to model tonal audio signals, both monophonic and polyphonic. We will show how the sparse predictors are able to model efficiently the different...

  11. Sparse Covariance Matrix Estimation by DCA-Based Algorithms.

    Science.gov (United States)

    Phan, Duy Nhat; Le Thi, Hoai An; Dinh, Tao Pham

    2017-11-01

    This letter proposes a novel approach using the [Formula: see text]-norm regularization for the sparse covariance matrix estimation (SCME) problem. The objective function of SCME problem is composed of a nonconvex part and the [Formula: see text] term, which is discontinuous and difficult to tackle. Appropriate DC (difference of convex functions) approximations of [Formula: see text]-norm are used that result in approximation SCME problems that are still nonconvex. DC programming and DCA (DC algorithm), powerful tools in nonconvex programming framework, are investigated. Two DC formulations are proposed and corresponding DCA schemes developed. Two applications of the SCME problem that are considered are classification via sparse quadratic discriminant analysis and portfolio optimization. A careful empirical experiment is performed through simulated and real data sets to study the performance of the proposed algorithms. Numerical results showed their efficiency and their superiority compared with seven state-of-the-art methods.

  12. Sample size reduction in groundwater surveys via sparse data assimilation

    KAUST Repository

    Hussain, Z.

    2013-04-01

    In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.

  13. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer

    2017-12-25

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.

  14. Sample size reduction in groundwater surveys via sparse data assimilation

    KAUST Repository

    Hussain, Z.; Muhammad, A.

    2013-01-01

    In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.

  15. Sparse logistic principal components analysis for binary data

    KAUST Repository

    Lee, Seokho

    2010-09-01

    We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study. © Institute ol Mathematical Statistics, 2010.

  16. Statistical mechanics of semi-supervised clustering in sparse graphs

    International Nuclear Information System (INIS)

    Ver Steeg, Greg; Galstyan, Aram; Allahverdyan, Armen E

    2011-01-01

    We theoretically study semi-supervised clustering in sparse graphs in the presence of pair-wise constraints on the cluster assignments of nodes. We focus on bi-cluster graphs and study the impact of semi-supervision for varying constraint density and overlap between the clusters. Recent results for unsupervised clustering in sparse graphs indicate that there is a critical ratio of within-cluster and between-cluster connectivities below which clusters cannot be recovered with better than random accuracy. The goal of this paper is to examine the impact of pair-wise constraints on the clustering accuracy. Our results suggest that the addition of constraints does not provide automatic improvement over the unsupervised case. When the density of the constraints is sufficiently small, their only impact is to shift the detection threshold while preserving the criticality. Conversely, if the density of (hard) constraints is above the percolation threshold, the criticality is suppressed and the detection threshold disappears

  17. A sparse electromagnetic imaging scheme using nonlinear landweber iterations

    KAUST Repository

    Desmal, Abdulla

    2015-10-26

    Development and use of electromagnetic inverse scattering techniques for imagining sparse domains have been on the rise following the recent advancements in solving sparse optimization problems. Existing techniques rely on iteratively converting the nonlinear forward scattering operator into a sequence of linear ill-posed operations (for example using the Born iterative method) and applying sparsity constraints to the linear minimization problem of each iteration through the use of L0/L1-norm penalty term (A. Desmal and H. Bagci, IEEE Trans. Antennas Propag, 7, 3878–3884, 2014, and IEEE Trans. Geosci. Remote Sens., 3, 532–536, 2015). It has been shown that these techniques produce more accurate and sharper images than their counterparts which solve a minimization problem constrained with smoothness promoting L2-norm penalty term. But these existing techniques are only applicable to investigation domains involving weak scatterers because the linearization process breaks down for high values of dielectric permittivity.

  18. Semi-blind sparse image reconstruction with application to MRFM.

    Science.gov (United States)

    Park, Se Un; Dobigeon, Nicolas; Hero, Alfred O

    2012-09-01

    We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.

  19. Sparse data structure design for wavelet-based methods

    Directory of Open Access Journals (Sweden)

    Latu Guillaume

    2011-12-01

    Full Text Available This course gives an introduction to the design of efficient datatypes for adaptive wavelet-based applications. It presents some code fragments and benchmark technics useful to learn about the design of sparse data structures and adaptive algorithms. Material and practical examples are given, and they provide good introduction for anyone involved in the development of adaptive applications. An answer will be given to the question: how to implement and efficiently use the discrete wavelet transform in computer applications? A focus will be made on time-evolution problems, and use of wavelet-based scheme for adaptively solving partial differential equations (PDE. One crucial issue is that the benefits of the adaptive method in term of algorithmic cost reduction can not be wasted by overheads associated to sparse data management.

  20. Split-Bregman-based sparse-view CT reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Vandeghinste, Bert; Vandenberghe, Stefaan [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Goossens, Bart; Pizurica, Aleksandra; Philips, Wilfried [Ghent Univ. (Belgium). Image Processing and Interpretation Research Group (IPI); Beenhouwer, Jan de [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Antwerp Univ., Wilrijk (Belgium). The Vision Lab; Staelens, Steven [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Antwerp Univ., Edegem (Belgium). Molecular Imaging Centre Antwerp

    2011-07-01

    Total variation minimization has been extensively researched for image denoising and sparse view reconstruction. These methods show superior denoising performance for simple images with little texture, but result in texture information loss when applied to more complex images. It could thus be beneficial to use other regularizers within medical imaging. We propose a general regularization method, based on a split-Bregman approach. We show results for this framework combined with a total variation denoising operator, in comparison to ASD-POCS. We show that sparse-view reconstruction and noise regularization is possible. This general method will allow us to investigate other regularizers in the context of regularized CT reconstruction, and decrease the acquisition times in {mu}CT. (orig.)

  1. Sparse canonical correlation analysis: new formulation and algorithm.

    Science.gov (United States)

    Chu, Delin; Liao, Li-Zhi; Ng, Michael K; Zhang, Xiaowei

    2013-12-01

    In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms.

  2. Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm

    KAUST Repository

    Desmal, Abdulla

    2017-04-03

    An efficient electromagnetic inversion scheme for imaging sparse 3-D domains is proposed. The scheme achieves its efficiency and accuracy by integrating two concepts. First, the nonlinear optimization problem is constrained using L₀ or L₁-norm of the solution as the penalty term to alleviate the ill-posedness of the inverse problem. The resulting Tikhonov minimization problem is solved using nonlinear Landweber iterations (NLW). Second, the efficiency of the NLW is significantly increased using a steepest descent algorithm. The algorithm uses a projection operator to enforce the sparsity constraint by thresholding the solution at every iteration. Thresholding level and iteration step are selected carefully to increase the efficiency without sacrificing the convergence of the algorithm. Numerical results demonstrate the efficiency and accuracy of the proposed imaging scheme in reconstructing sparse 3-D dielectric profiles.

  3. Multi scales based sparse matrix spectral clustering image segmentation

    Science.gov (United States)

    Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

    2018-04-01

    In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

  4. Nonredundant sparse feature extraction using autoencoders with receptive fields clustering.

    Science.gov (United States)

    Ayinde, Babajide O; Zurada, Jacek M

    2017-09-01

    This paper proposes new techniques for data representation in the context of deep learning using agglomerative clustering. Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading to extraction of similar features, thus resulting in filtering redundancy. We propose a way to address this problem and show that such redundancy can be eliminated. This yields smaller networks and produces unique receptive fields that extract distinct features. It is also shown that autoencoders with nonnegativity constraints on weights are capable of extracting fewer redundant features than conventional sparse autoencoders. The concept is illustrated using conventional sparse autoencoder and nonnegativity-constrained autoencoders with MNIST digits recognition, NORB normalized-uniform object data and Yale face dataset. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery

    Science.gov (United States)

    Kim, Daeun; Haldar, Justin P.

    2016-01-01

    This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were designed to impose these constraints individually. Similar to previous greedy algorithms for sparse recovery, the proposed algorithms iteratively identify promising support indices. In contrast to previous approaches, the support index selection procedure has been adapted to prioritize indices that are consistent with both the nonnegativity and shared support constraints. Empirical results demonstrate for the first time that the combined use of simultaneous sparsity and nonnegativity constraints can substantially improve recovery performance relative to existing greedy algorithms that impose less signal structure. PMID:26973368

  6. Limited-memory trust-region methods for sparse relaxation

    Science.gov (United States)

    Adhikari, Lasith; DeGuchy, Omar; Erway, Jennifer B.; Lockhart, Shelby; Marcia, Roummel F.

    2017-08-01

    In this paper, we solve the l2-l1 sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and applying a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Numerical experiments show that our proposed approach eliminates spurious solutions more effectively while improving computational time.

  7. Obtaining sparse distributions in 2D inverse problems

    OpenAIRE

    Reci, A; Sederman, Andrew John; Gladden, Lynn Faith

    2017-01-01

    The mathematics of inverse problems has relevance across numerous estimation problems in science and engineering. L1 regularization has attracted recent attention in reconstructing the system properties in the case of sparse inverse problems; i.e., when the true property sought is not adequately described by a continuous distribution, in particular in Compressed Sensing image reconstruction. In this work, we focus on the application of L1 regularization to a class of inverse problems; relaxat...

  8. Sparse optimization for inverse problems in atmospheric modelling

    Czech Academy of Sciences Publication Activity Database

    Adam, Lukáš; Branda, Martin

    2016-01-01

    Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf

  9. Example-Based Image Colorization Using Locality Consistent Sparse Representation.

    Science.gov (United States)

    Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

    2017-11-01

    Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

  10. Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

    OpenAIRE

    Brown, Suzie; Johnson, Oliver; Tassi, Andrea

    2018-01-01

    Ultra-reliable Point-to-Multipoint (PtM) communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse Random Linear Network Coding (RLNC) techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kin...

  11. Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

    KAUST Repository

    Buse, Gerrit

    2014-01-01

    In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated grids, both with and without boundary grid points. Similar to the implicit data structures proposed in Feuersänger (Dünngitterverfahren für hochdimensionale elliptische partielle Differntialgleichungen. Diploma Thesis, Institut für Numerische Simulation, Universität Bonn, 2005) and Murarasu et al. (Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming. Cambridge University Press, New York, 2011, pp. 25–34) we also define a bijective mapping from the multi-dimensional space of grid points to a contiguous index, such that the grid data can be stored in a simple array without overhead. Our approach is especially well-suited to exploit all levels of current commodity hardware, including cache-levels and vector extensions. Furthermore, this kind of data structure is extremely attractive for today’s real-time applications, as it gives direct access to the hierarchical structure of the grids, while outperforming other common sparse grid structures (hash maps, etc.) which do not match with modern compute platforms that well. For dimensionality d ≤ 10 we achieve good speedups on a 12 core Intel Westmere-EP NUMA platform compared to the results presented in Murarasu et al. (Proceedings of the International Conference on Computational Science—ICCS 2012. Procedia Computer Science, 2012). As we show, this also holds for the results obtained on Nvidia Fermi GPUs, for which we observe speedups over our own CPU implementation of up to 4.5 when dealing with moderate dimensionality. In high-dimensional settings, in the order of tens to hundreds of dimensions, our sparse grid evaluation kernels on the CPU outperform any other known implementation.

  12. Iterative algorithms for large sparse linear systems on parallel computers

    Science.gov (United States)

    Adams, L. M.

    1982-01-01

    Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.

  13. Distributed coding of multiview sparse sources with joint recovery

    DEFF Research Database (Denmark)

    Luong, Huynh Van; Deligiannis, Nikos; Forchhammer, Søren

    2016-01-01

    In support of applications involving multiview sources in distributed object recognition using lightweight cameras, we propose a new method for the distributed coding of sparse sources as visual descriptor histograms extracted from multiview images. The problem is challenging due to the computati...... transform (SIFT) descriptors extracted from multiview images shows that our method leads to bit-rate saving of up to 43% compared to the state-of-the-art distributed compressed sensing method with independent encoding of the sources....

  14. Optimal deep neural networks for sparse recovery via Laplace techniques

    OpenAIRE

    Limmer, Steffen; Stanczak, Slawomir

    2017-01-01

    This paper introduces Laplace techniques for designing a neural network, with the goal of estimating simplex-constraint sparse vectors from compressed measurements. To this end, we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution) as the problem of computing the centroid of some polytope that results from the intersection of the simplex and an affine subspace determined by the measurements. Owing to the specific structure, it is shown that the centroid ca...

  15. Shape prior modeling using sparse representation and online dictionary learning.

    Science.gov (United States)

    Zhang, Shaoting; Zhan, Yiqiang; Zhou, Yan; Uzunbas, Mustafa; Metaxas, Dimitris N

    2012-01-01

    The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC by including as many training shapes in the repository. However, this strategy confronts two limitations in practice. First, since SSC involves an iterative sparse optimization at run-time, the more shape instances contained in the repository, the less run-time efficiency SSC has. Therefore, a compact and informative shape dictionary is preferred to a large shape repository. Second, in medical imaging applications, training shapes seldom come in one batch. It is very time consuming and sometimes infeasible to reconstruct the shape dictionary every time new training shapes appear. In this paper, we propose an online learning method to address these two limitations. Our method starts from constructing an initial shape dictionary using the K-SVD algorithm. When new training shapes come, instead of re-constructing the dictionary from the ground up, we update the existing one using a block-coordinates descent approach. Using the dynamically updated dictionary, sparse shape composition can be gracefully scaled up to model shape priors from a large number of training shapes without sacrificing run-time efficiency. Our method is validated on lung localization in X-Ray and cardiac segmentation in MRI time series. Compared to the original SSC, it shows comparable performance while being significantly more efficient.

  16. Color normalization of histology slides using graph regularized sparse NMF

    Science.gov (United States)

    Sha, Lingdao; Schonfeld, Dan; Sethi, Amit

    2017-03-01

    Computer based automatic medical image processing and quantification are becoming popular in digital pathology. However, preparation of histology slides can vary widely due to differences in staining equipment, procedures and reagents, which can reduce the accuracy of algorithms that analyze their color and texture information. To re- duce the unwanted color variations, various supervised and unsupervised color normalization methods have been proposed. Compared with supervised color normalization methods, unsupervised color normalization methods have advantages of time and cost efficient and universal applicability. Most of the unsupervised color normaliza- tion methods for histology are based on stain separation. Based on the fact that stain concentration cannot be negative and different parts of the tissue absorb different stains, nonnegative matrix factorization (NMF), and particular its sparse version (SNMF), are good candidates for stain separation. However, most of the existing unsupervised color normalization method like PCA, ICA, NMF and SNMF fail to consider important information about sparse manifolds that its pixels occupy, which could potentially result in loss of texture information during color normalization. Manifold learning methods like Graph Laplacian have proven to be very effective in interpreting high-dimensional data. In this paper, we propose a novel unsupervised stain separation method called graph regularized sparse nonnegative matrix factorization (GSNMF). By considering the sparse prior of stain concentration together with manifold information from high-dimensional image data, our method shows better performance in stain color deconvolution than existing unsupervised color deconvolution methods, especially in keeping connected texture information. To utilized the texture information, we construct a nearest neighbor graph between pixels within a spatial area of an image based on their distances using heat kernal in lαβ space. The

  17. Dentate Gyrus circuitry features improve performance of sparse approximation algorithms.

    Directory of Open Access Journals (Sweden)

    Panagiotis C Petrantonakis

    Full Text Available Memory-related activity in the Dentate Gyrus (DG is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2-4% of the total population. This sparsity is assumed to enhance the ability of DG to perform pattern separation, one of the most valuable contributions of DG during memory formation. In this work, we investigate how features of the DG such as its excitatory and inhibitory connectivity diagram can be used to develop theoretical algorithms performing Sparse Approximation, a widely used strategy in the Signal Processing field. Sparse approximation stands for the algorithmic identification of few components from a dictionary that approximate a certain signal. The ability of DG to achieve pattern separation by sparsifing its representations is exploited here to improve the performance of the state of the art sparse approximation algorithm "Iterative Soft Thresholding" (IST by adding new algorithmic features inspired by the DG circuitry. Lateral inhibition of granule cells, either direct or indirect, via mossy cells, is shown to enhance the performance of the IST. Apart from revealing the potential of DG-inspired theoretical algorithms, this work presents new insights regarding the function of particular cell types in the pattern separation task of the DG.

  18. Superresolving Black Hole Images with Full-Closure Sparse Modeling

    Science.gov (United States)

    Crowley, Chelsea; Akiyama, Kazunori; Fish, Vincent

    2018-01-01

    It is believed that almost all galaxies have black holes at their centers. Imaging a black hole is a primary objective to answer scientific questions relating to relativistic accretion and jet formation. The Event Horizon Telescope (EHT) is set to capture images of two nearby black holes, Sagittarius A* at the center of the Milky Way galaxy roughly 26,000 light years away and the other M87 which is in Virgo A, a large elliptical galaxy that is 50 million light years away. Sparse imaging techniques have shown great promise for reconstructing high-fidelity superresolved images of black holes from simulated data. Previous work has included the effects of atmospheric phase errors and thermal noise, but not systematic amplitude errors that arise due to miscalibration. We explore a full-closure imaging technique with sparse modeling that uses closure amplitudes and closure phases to improve the imaging process. This new technique can successfully handle data with systematic amplitude errors. Applying our technique to synthetic EHT data of M87, we find that full-closure sparse modeling can reconstruct images better than traditional methods and recover key structural information on the source, such as the shape and size of the predicted photon ring. These results suggest that our new approach will provide superior imaging performance for data from the EHT and other interferometric arrays.

  19. An Improved Information Hiding Method Based on Sparse Representation

    Directory of Open Access Journals (Sweden)

    Minghai Yao

    2015-01-01

    Full Text Available A novel biometric authentication information hiding method based on the sparse representation is proposed for enhancing the security of biometric information transmitted in the network. In order to make good use of abundant information of the cover image, the sparse representation method is adopted to exploit the correlation between the cover and biometric images. Thus, the biometric image is divided into two parts. The first part is the reconstructed image, and the other part is the residual image. The biometric authentication image cannot be restored by any one part. The residual image and sparse representation coefficients are embedded into the cover image. Then, for the sake of causing much less attention of attackers, the visual attention mechanism is employed to select embedding location and embedding sequence of secret information. Finally, the reversible watermarking algorithm based on histogram is utilized for embedding the secret information. For verifying the validity of the algorithm, the PolyU multispectral palmprint and the CASIA iris databases are used as biometric information. The experimental results show that the proposed method exhibits good security, invisibility, and high capacity.

  20. Two-dimensional sparse wavenumber recovery for guided wavefields

    Science.gov (United States)

    Sabeti, Soroosh; Harley, Joel B.

    2018-04-01

    The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples.

  1. Low-Rank Sparse Coding for Image Classification

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Xu, Changsheng; Ahuja, Narendra

    2013-01-01

    In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

  2. On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.

    Science.gov (United States)

    Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba

    2013-01-01

    Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝ d , and the dictionary is learned from the training data using the vector space structure of ℝ d and its Euclidean L 2 -metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis.

  3. Low-Rank Sparse Coding for Image Classification

    KAUST Repository

    Zhang, Tianzhu

    2013-12-01

    In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

  4. Efficient MATLAB computations with sparse and factored tensors.

    Energy Technology Data Exchange (ETDEWEB)

    Bader, Brett William; Kolda, Tamara Gibson (Sandia National Lab, Livermore, CA)

    2006-12-01

    In this paper, the term tensor refers simply to a multidimensional or N-way array, and we consider how specially structured tensors allow for efficient storage and computation. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms. Second, we study factored tensors, which have the property that they can be assembled from more basic components. We consider two specific types: a Tucker tensor can be expressed as the product of a core tensor (which itself may be dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor can be expressed as the sum of rank-1 tensors. We are interested in the case where the storage of the components is less than the storage of the full tensor, and we demonstrate that many elementary operations can be computed using only the components. All of the efficiencies described in this paper are implemented in the Tensor Toolbox for MATLAB.

  5. Stable isotopes labelled compounds

    International Nuclear Information System (INIS)

    1982-09-01

    The catalogue on stable isotopes labelled compounds offers deuterium, nitrogen-15, and multiply labelled compounds. It includes: (1) conditions of sale and delivery, (2) the application of stable isotopes, (3) technical information, (4) product specifications, and (5) the complete delivery programme

  6. Edge colouring by total labellings

    DEFF Research Database (Denmark)

    Brandt, Stephan; Rautenbach, D.; Stiebitz, M.

    2010-01-01

    We introduce the concept of an edge-colouring total k-labelling. This is a labelling of the vertices and the edges of a graph G with labels 1, 2, ..., k such that the weights of the edges define a proper edge colouring of G. Here the weight of an edge is the sum of its label and the labels of its...

  7. Radioiodine and its labelled compounds

    International Nuclear Information System (INIS)

    Robles, Ana Maria

    1994-01-01

    Chemical characteristics and their nuclear characteristics, types of labelled molecules,labelling procedures, direct labelling with various oxidizing agents, indirect labelling with various conjugates attached to protein molecules, purification and quality control. Iodination damage.Safe handling of labelling procedures with iodine radioisotopes.Bibliography

  8. 'Naturemade' -- a new label

    International Nuclear Information System (INIS)

    Niederhaeusern, A.

    2001-01-01

    This short article discusses the introduction of the 'Naturemade' two-level labelling scheme in the Swiss electricity market, which is to help provide transparency in the market for green power and promote the building of facilities for its production. In the form of an interview with the CEO of Swissolar and the president of Greenpeace Switzerland, the pros and contras of these labels are discussed. In particular, the interview partners' opinions on the possible misuse of the less stringent label and the influence of the labels on the construction of new installations for the generation of electricity from renewable sources are presented. The basic principles of the promotional model behind the labels are listed

  9. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.

    Directory of Open Access Journals (Sweden)

    Christopher D Brown

    Full Text Available Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL mapping has paralleled the adoption of genome-wide association studies (GWAS for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human

  10. Label Review Training: Module 1: Label Basics, Page 25

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review: clarity, accuracy, consistency with EPA policy, and enforceability.

  11. Label Review Training: Module 1: Label Basics, Page 29

    Science.gov (United States)

    This module of the pesticide label review training provides basic information about pesticides, their labeling and regulation, and the core principles of pesticide label review. This page is a quiz on Module 1.

  12. Fast Solution in Sparse LDA for Binary Classification

    Science.gov (United States)

    Moghaddam, Baback

    2010-01-01

    An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic

  13. Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

    Directory of Open Access Journals (Sweden)

    Jingbo Chen

    2018-02-01

    Full Text Available Semantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs, have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable.

  14. Soil Fumigant Labels - Methyl Bromide

    Science.gov (United States)

    Search soil fumigant pesticide labels by EPA registration number, product name, or company, and follow the link to The Pesticide Product Label System (PPLS) for details. Updated labels include new safety requirements for buffer zones and related measures.

  15. Storage of sparse files using parallel log-structured file system

    Science.gov (United States)

    Bent, John M.; Faibish, Sorin; Grider, Gary; Torres, Aaron

    2017-11-07

    A sparse file is stored without holes by storing a data portion of the sparse file using a parallel log-structured file system; and generating an index entry for the data portion, the index entry comprising a logical offset, physical offset and length of the data portion. The holes can be restored to the sparse file upon a reading of the sparse file. The data portion can be stored at a logical end of the sparse file. Additional storage efficiency can optionally be achieved by (i) detecting a write pattern for a plurality of the data portions and generating a single patterned index entry for the plurality of the patterned data portions; and/or (ii) storing the patterned index entries for a plurality of the sparse files in a single directory, wherein each entry in the single directory comprises an identifier of a corresponding sparse file.

  16. Development and Evaluation of a PCR and Mass Spectroscopy-based (PCR-MS) Method for Quantitative, Type-specific Detection of Human Papillomavirus

    Science.gov (United States)

    Patel, Divya A.; Shih, Yang-Jen; Newton, Duane W.; Michael, Claire W.; Oeth, Paul A.; Kane, Michael D.; Opipari, Anthony W.; Ruffin, Mack T.; Kalikin, Linda M.; Kurnit, David M.

    2010-01-01

    Knowledge of the central role of high-risk human papillomavirus (HPV) in cervical carcinogenesis, coupled with an emerging need to monitor the efficacy of newly introduced HPV vaccines, warrant development and evaluation of type-specific, quantitative HPV detection methods. In the present study, a prototype PCR and mass spectroscopy (PCR-MS)-based method to detect and quantitate 13 high-risk HPV types is compared to the Hybrid Capture 2 High Risk HPV DNA test (HC2; Digene Corp., Gaithersburg, MD) in 199 cervical scraping samples and to DNA sequencing in 77 cervical tumor samples. High-risk HPV types were detected in 76/77 (98.7%) cervical tumor samples by PCR-MS. Degenerate and type-specific sequencing confirmed the types detected by PCR-MS. In 199 cervical scraping samples, all 13 HPV types were detected by PCR-MS. Eighteen (14.5%) of 124 cervical scraping samples that were positive for high-risk HPV by HC2 were negative by PCR-MS. In all these cases, degenerate DNA sequencing failed to detect any of the 13 high-risk HPV types. Nearly half (46.7%) of the 75 cervical scraping samples that were negative for high-risk HPV by the HC2 assay were positive by PCR-MS. Type-specific sequencing in a subset of these samples confirmed the HPV type detected by PCR-MS. Quantitative PCR-MS results demonstrated that 11/75 (14.7%) samples contained as much HPV copies/cell as HC2-positive samples. These findings suggest that this prototype PCR-MS assay performs at least as well as HC2 for HPV detection, while offering the additional, unique advantages of type-specific identification and quantitation. Further validation work is underway to define clinically meaningful HPV detection thresholds and to evaluate the potential clinical application of future generations of the PCR-MS assay. PMID:19410602

  17. Development and evaluation of a PCR and mass spectroscopy (PCR-MS)-based method for quantitative, type-specific detection of human papillomavirus.

    Science.gov (United States)

    Patel, Divya A; Shih, Yang-Jen; Newton, Duane W; Michael, Claire W; Oeth, Paul A; Kane, Michael D; Opipari, Anthony W; Ruffin, Mack T; Kalikin, Linda M; Kurnit, David M

    2009-09-01

    Knowledge of the central role of high-risk human papillomavirus (HPV) in cervical carcinogenesis, coupled with an emerging need to monitor the efficacy of newly introduced HPV vaccines, warrant development and evaluation of type-specific, quantitative HPV detection methods. In the present study, a prototype PCR and mass spectroscopy (PCR-MS)-based method to detect and quantitate 13 high-risk HPV types is compared to the Hybrid Capture 2 High-Risk HPV DNA test (HC2; Digene Corp., Gaithersburg, MD) in 199 cervical scraping samples and to DNA sequencing in 77 cervical tumor samples. High-risk HPV types were detected in 76/77 (98.7%) cervical tumor samples by PCR-MS. Degenerate and type-specific sequencing confirmed the types detected by PCR-MS. In 199 cervical scraping samples, all 13 HPV types were detected by PCR-MS. Eighteen (14.5%) of 124 cervical scraping samples that were positive for high-risk HPV by HC2 were negative by PCR-MS. In all these cases, degenerate DNA sequencing failed to detect any of the 13 high-risk HPV types. Nearly half (46.7%) of the 75 cervical scraping samples that were negative for high-risk HPV by the HC2 assay were positive by PCR-MS. Type-specific sequencing in a subset of these samples confirmed the HPV type detected by PCR-MS. Quantitative PCR-MS results demonstrated that 11/75 (14.7%) samples contained as much HPV copies/cell as HC2-positive samples. These findings suggest that this prototype PCR-MS assay performs at least as well as HC2 for HPV detection, while offering the additional, unique advantages of type-specific identification and quantitation. Further validation work is underway to define clinically meaningful HPV detection thresholds and to evaluate the potential clinical application of future generations of the PCR-MS assay.

  18. Radioactive labelled orgotein

    International Nuclear Information System (INIS)

    1980-01-01

    The preparation and use of radioactively labelled orgotein, i.e. water-soluble protein congeners in pure, injectable form, is described. This radiopharmaceutical is useful in scintigraphy, especially for visualization of the kidneys where the orgotein is rapidly concentrated. Details of the processes for labelling bovine orgotein with sup(99m)Tc, 60 Co, 125 I or 131 I are specified. The pharmaceutical preparation of the labelled orgotein for intravenous and parenteral administration is also described. Examples using either sup(99m)TC or 125 I-orgotein in scintiscanning dogs' kidneys are given. (UK)

  19. On Online Labeling with Polynomially Many Labels

    DEFF Research Database (Denmark)

    Babka, Martin; Bulánek, Jan; Cunat, Vladimír

    2012-01-01

    be necessary to change the labels of some items; such changes may be done at any time at unit cost for each change. The goal is to minimize the total cost. An alternative formulation of this problem is the file maintenance problem, in which the items, instead of being labeled, are maintained in sorted order...... in an array of length m, and we pay unit cost for moving an item. For the case m = cn for constant c > 1, there are known algorithms that use at most O(n log(n)2) relabelings in total [9], and it was shown recently that this is asymptotically optimal [1]. For the case of m = θ(nC) for C > 1, algorithms...

  20. Clinical applications of cells labelling

    International Nuclear Information System (INIS)

    Gonzalez, B.M.

    1994-01-01

    Blood cells labelled with radionuclides are reviewed and main applications are described. Red blood cell labelling by both random and specific principle. A table with most important clinical uses, 99mTc labelling of RBC are described pre tinning and in vivo reduction of Tc, in vitro labelling and administration of labelled RBC and in vivo modified technique. Labelled leucocytes with several 99mTc-complex radiopharmaceuticals by in vitro technique and specific monoclonal s for white cells(neutrofiles). Labelled platelets for clinical use and research by in vitro technique and in vivo labelling

  1. FDA Online Label Repository

    Data.gov (United States)

    U.S. Department of Health & Human Services — The drug labels and other drug-specific information on this Web site represent the most recent drug listing information companies have submitted to the Food and Drug...

  2. Figuring Out Food Labels

    Science.gov (United States)

    ... It's also displayed in grocery stores near fresh foods, like fruits, vegetables, and fish. The nutrition facts label includes: a ... found in citrus fruits, other fruits, and some vegetables. Food companies might also list the amounts of other ...

  3. Energy efficiency labelling

    Energy Technology Data Exchange (ETDEWEB)

    1978-04-01

    This research assesses the likely effects on UK consumers of the proposed EEC energy-efficiency labeling scheme. Unless (or until) an energy-labeling scheme is introduced, it is impossible to do more than postulate its likely effects on consumer behavior. This report shows that there are indeed significant differences in energy consumption between different brands and models of the same appliance of which consumers are unaware. Further, the report suggests that, if a readily intelligible energy-labeling scheme were introduced, it would provide useful information that consumers currently lack; and that, if this information were successfully presented, it would be used and could have substantial effects in reducing domestic fuel consumption. Therefore, it is recommended that an energy labeling scheme be introduced.

  4. Like your labels?

    Science.gov (United States)

    Field, Michele

    2010-01-01

    The descriptive “conventions” used on food labels are always evolving. Today, however, the changes are so complicated (partly driven by legislation requiring disclosures about environmental impacts, health issues, and geographical provenance) that these labels more often baffle buyers than enlighten them. In a light-handed manner, the article points to how sometimes reading label language can be like deciphering runes—and how if we are familiar with the technical terms, we can find a literal meaning, but still not see the implications. The article could be ten times longer because food labels vary according to cultures—but all food-exporting cultures now take advantage of our short attention-span when faced with these texts. The question is whether less is more—and if so, in this contest for our attention, what “contestant” is voted off.

  5. Social biases determine spatiotemporal sparseness of ciliate mating heuristics.

    Science.gov (United States)

    Clark, Kevin B

    2012-01-01

    Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present

  6. Deploying temporary networks for upscaling of sparse network stations

    Science.gov (United States)

    Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane

    2016-10-01

    Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.

  7. Social biases determine spatiotemporal sparseness of ciliate mating heuristics

    Science.gov (United States)

    2012-01-01

    Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The

  8. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    Affara, Lama Ahmed

    2017-09-27

    Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.

  9. Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns

    Science.gov (United States)

    Aoyagi, Toshio; Nomura, Masaki

    1999-08-01

    Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.

  10. Sparse inverse covariance estimation with the graphical lasso.

    Science.gov (United States)

    Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert

    2008-07-01

    We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

  11. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    Affara, Lama Ahmed; Ghanem, Bernard; Wonka, Peter

    2017-01-01

    Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.

  12. Blind spectrum reconstruction algorithm with L0-sparse representation

    International Nuclear Information System (INIS)

    Liu, Hai; Zhang, Zhaoli; Liu, Sanyan; Shu, Jiangbo; Liu, Tingting; Zhang, Tianxu

    2015-01-01

    Raman spectrum often suffers from band overlap and Poisson noise. This paper presents a new blind Poissonian Raman spectrum reconstruction method, which incorporates the L 0 -sparse prior together with the total variation constraint into the maximum a posteriori framework. Furthermore, the greedy analysis pursuit algorithm is adopted to solve the L 0 -based minimization problem. Simulated and real spectrum experimental results show that the proposed method can effectively preserve spectral structure and suppress noise. The reconstructed Raman spectra are easily used for interpreting unknown chemical mixtures. (paper)

  13. Calculation of the inverse data space via sparse inversion

    KAUST Repository

    Saragiotis, Christos

    2011-01-01

    The inverse data space provides a natural separation of primaries and surface-related multiples, as the surface multiples map onto the area around the origin while the primaries map elsewhere. However, the calculation of the inverse data is far from trivial as theory requires infinite time and offset recording. Furthermore regularization issues arise during inversion. We perform the inversion by minimizing the least-squares norm of the misfit function by constraining the $ell_1$ norm of the solution, being the inverse data space. In this way a sparse inversion approach is obtained. We show results on field data with an application to surface multiple removal.

  14. Dynamic Stochastic Superresolution of sparsely observed turbulent systems

    International Nuclear Information System (INIS)

    Branicki, M.; Majda, A.J.

    2013-01-01

    Real-time capture of the relevant features of the unresolved turbulent dynamics of complex natural systems from sparse noisy observations and imperfect models is a notoriously difficult problem. The resulting lack of observational resolution and statistical accuracy in estimating the important turbulent processes, which intermittently send significant energy to the large-scale fluctuations, hinders efficient parameterization and real-time prediction using discretized PDE models. This issue is particularly subtle and important when dealing with turbulent geophysical systems with an vast range of interacting spatio-temporal scales and rough energy spectra near the mesh scale of numerical models. Here, we introduce and study a suite of general Dynamic Stochastic Superresolution (DSS) algorithms and show that, by appropriately filtering sparse regular observations with the help of cheap stochastic exactly solvable models, one can derive stochastically ‘superresolved’ velocity fields and gain insight into the important characteristics of the unresolved dynamics, including the detection of the so-called black swans. The DSS algorithms operate in Fourier domain and exploit the fact that the coarse observation network aliases high-wavenumber information into the resolved waveband. It is shown that these cheap algorithms are robust and have significant skill on a test bed of turbulent solutions from realistic nonlinear turbulent spatially extended systems in the presence of a significant model error. In particular, the DSS algorithms are capable of successfully capturing time-localized extreme events in the unresolved modes, and they provide good and robust skill for recovery of the unresolved processes in terms of pattern correlation. Moreover, we show that DSS improves the skill for recovering the primary modes associated with the sparse observation mesh which is equally important in applications. The skill of the various DSS algorithms depends on the energy spectrum

  15. Wind Noise Reduction using Non-negative Sparse Coding

    DEFF Research Database (Denmark)

    Schmidt, Mikkel N.; Larsen, Jan; Hsiao, Fu-Tien

    2007-01-01

    We introduce a new speaker independent method for reducing wind noise in single-channel recordings of noisy speech. The method is based on non-negative sparse coding and relies on a wind noise dictionary which is estimated from an isolated noise recording. We estimate the parameters of the model ...... and discuss their sensitivity. We then compare the algorithm with the classical spectral subtraction method and the Qualcomm-ICSI-OGI noise reduction method. We optimize the sound quality in terms of signal-to-noise ratio and provide results on a noisy speech recognition task....

  16. Sparse least-squares reverse time migration using seislets

    KAUST Repository

    Dutta, Gaurav

    2015-08-19

    We propose sparse least-squares reverse time migration (LSRTM) using seislets as a basis for the reflectivity distribution. This basis is used along with a dip-constrained preconditioner that emphasizes image updates only along prominent dips during the iterations. These dips can be estimated from the standard migration image or from the gradient using plane-wave destruction filters or structural tensors. Numerical tests on synthetic datasets demonstrate the benefits of this method for mitigation of aliasing artifacts and crosstalk noise in multisource least-squares migration.

  17. Predicting E-commerce Consumer Behaviour Using Sparse Session Data

    OpenAIRE

    Thorrud, Thorstein Kaldahl; Myklatun, Øyvind

    2015-01-01

    This thesis research consumer behavior in an e-commerce domain by using a data set of sparse session data collected from an anonymous European e-commerce site. The goal is to predict whether a consumer session results in a purchase, and if so, which items are purchased. The data is supplied by the ACM Recommender System Challenge, which is a yearly challenge held by the ACM Recommender System Conference. Classification is used for predicting whether or not a session made a purchase, as w...

  18. Anisotropic Third-Order Regularization for Sparse Digital Elevation Models

    KAUST Repository

    Lellmann, Jan

    2013-01-01

    We consider the problem of interpolating a surface based on sparse data such as individual points or level lines. We derive interpolators satisfying a list of desirable properties with an emphasis on preserving the geometry and characteristic features of the contours while ensuring smoothness across level lines. We propose an anisotropic third-order model and an efficient method to adaptively estimate both the surface and the anisotropy. Our experiments show that the approach outperforms AMLE and higher-order total variation methods qualitatively and quantitatively on real-world digital elevation data. © 2013 Springer-Verlag.

  19. Algorithms for sparse, symmetric, definite quadratic lambda-matrix eigenproblems

    International Nuclear Information System (INIS)

    Scott, D.S.; Ward, R.C.

    1981-01-01

    Methods are presented for computing eigenpairs of the quadratic lambda-matrix, M lambda 2 + C lambda + K, where M, C, and K are large and sparse, and have special symmetry-type properties. These properties are sufficient to insure that all the eigenvalues are real and that theory analogous to the standard symmetric eigenproblem exists. The methods employ some standard techniques such as partial tri-diagonalization via the Lanczos Method and subsequent eigenpair calculation, shift-and- invert strategy and subspace iteration. The methods also employ some new techniques such as Rayleigh-Ritz quadratic roots and the inertia of symmetric, definite, quadratic lambda-matrices

  20. Labelling of electricity

    International Nuclear Information System (INIS)

    Dettli, R.; Markard, J.

    2001-01-01

    This comprehensive report for the Swiss Federal Office of Energy (SFOE) presents a possible course of action to be taken to provide a means of declaring the sources of electrical power, as is foreseen in the draft of new Swiss electricity market legislation. The report presents the basic ideas behind the idea and defines the terms used such as labelling, certificates and declarations. Also, the legal situation in the European Union and in Switzerland is examined and a quantitative overview of electricity production and consumption is presented. Suggestions for a labelling scheme are made and some of the problems to be expected are looked at. The report also presents a series of examples of labelling schemes already implemented in other countries, such as Austria, Great Britain, Sweden and Germany. Tradable certificates and tracking systems are discussed as are initial quality labels like the Swiss 'Naturemade' label for green power. A concrete recommendation for the declaration and labelling of electricity in Switzerland is presented and various factors to be considered such as import/export, pumped storage, distribution losses, small-scale producers as well as the time-scales for introduction are discussed