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.

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

27 CFR 4.23 - Varietal (grape type) labeling.

Science.gov (United States)

2010-04-01

... 27 Alcohol, Tobacco Products and Firearms 1 2010-04-01 2010-04-01 false Varietal (grape type) labeling. 4.23 Section 4.23 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE BUREAU... Varietal (grape type) labeling. (a) General. The names of one or more grape varieties may be used as the...

Iodine-125 metaraminol: A new platelet specific labeling agent

International Nuclear Information System (INIS)

Ohmomo, Y.; Yokoyama, A.; Kawaii, K.; Horiuchi, K.; Saji, H.; Torizuka, K.

1984-01-01

In the search for a platelet specific labeling agent, Metaraminol (MA), which is a sympatomimetic amine used for the treatment of hypotension, cardiogenic shock and well recognized as a drug actively incorporated and accumulated in platelet, attracted the authors' attention. Using the classical chloramine-T iodination method, a high labeling efficiency near 98%, reaching a specific activity up to about 1000 Ci/mmole was obtained. Upon the harvest of platelet, only as platelet rich plasma (PRP), the labeling with this radiopharmaceutical was easily performed by incubation at 37 0 C for 10 min. Labeling efficiency as high as 63.0 +- 3.1% at 24 x 10/sup 8/ cells/ml was obtained. In in-vitro studies, the unaltered state of I-125 MA labeled platelet, with their cellular functions fully retained was demonstrated. Pharmacological study indicated a specific incorporation of I-125 MA by active transport system similar to that of 5-HT, along with passive diffusion. Then the in-vivo study carried out in rabbits with induced thrombi on the femoral artery, showed rather rapid disappearance of the I-125 MA labeled autologous platelet radioactivity, from circulating blood reaching as high thrombus-to-blood activity ratio as 19.8+-4.3 within 30 min post-administration. This new platelet labeling agent, I-125 MA, has many advantages over the use of IN-111 oxine and holds considerable promise for thrombus imaging with single photon emission CT upon the availability of I-123 MA

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...

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.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

Science.gov (United States)

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

Science.gov (United States)

Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

Directory of Open Access Journals (Sweden)

Xin Tang

Full Text Available Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC. Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our

Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

Science.gov (United States)

Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

2015-09-01

Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

The Non-Specific Binding of Fluorescent-Labeled MiRNAs on Cell Surface by Hydrophobic Interaction.

Science.gov (United States)

Lu, Ting; Lin, Zongwei; Ren, Jianwei; Yao, Peng; Wang, Xiaowei; Wang, Zhe; Zhang, Qunye

2016-01-01

MicroRNAs are small noncoding RNAs about 22 nt long that play key roles in almost all biological processes and diseases. The fluorescent labeling and lipofection are two common methods for changing the levels and locating the position of cellular miRNAs. Despite many studies about the mechanism of DNA/RNA lipofection, little is known about the characteristics, mechanisms and specificity of lipofection of fluorescent-labeled miRNAs. Therefore, miRNAs labeled with different fluorescent dyes were transfected into adherent and suspension cells using lipofection reagent. Then, the non-specific binding and its mechanism were investigated by flow cytometer and laser confocal microscopy. The results showed that miRNAs labeled with Cy5 (cyanine fluorescent dye) could firmly bind to the surface of adherent cells (Hela) and suspended cells (K562) even without lipofection reagent. The binding of miRNAs labeled with FAM (carboxyl fluorescein) to K562 cells was obvious, but it was not significant in Hela cells. After lipofectamine reagent was added, most of the fluorescently labeled miRNAs binding to the surface of Hela cells were transfected into intra-cell because of the high transfection efficiency, however, most of them were still binding to the surface of K562 cells. Moreover, the high-salt buffer which could destroy the electrostatic interactions did not affect the above-mentioned non-specific binding, but the organic solvent which could destroy the hydrophobic interactions eliminated it. These results implied that the fluorescent-labeled miRNAs could non-specifically bind to the cell surface by hydrophobic interaction. It would lead to significant errors in the estimation of transfection efficiency only according to the cellular fluorescence intensity. Therefore, other methods to evaluate the transfection efficiency and more appropriate fluorescent dyes should be used according to the cell types for the accuracy of results.

The Non-Specific Binding of Fluorescent-Labeled MiRNAs on Cell Surface by Hydrophobic Interaction.

Directory of Open Access Journals (Sweden)

Ting Lu

Full Text Available MicroRNAs are small noncoding RNAs about 22 nt long that play key roles in almost all biological processes and diseases. The fluorescent labeling and lipofection are two common methods for changing the levels and locating the position of cellular miRNAs. Despite many studies about the mechanism of DNA/RNA lipofection, little is known about the characteristics, mechanisms and specificity of lipofection of fluorescent-labeled miRNAs.Therefore, miRNAs labeled with different fluorescent dyes were transfected into adherent and suspension cells using lipofection reagent. Then, the non-specific binding and its mechanism were investigated by flow cytometer and laser confocal microscopy. The results showed that miRNAs labeled with Cy5 (cyanine fluorescent dye could firmly bind to the surface of adherent cells (Hela and suspended cells (K562 even without lipofection reagent. The binding of miRNAs labeled with FAM (carboxyl fluorescein to K562 cells was obvious, but it was not significant in Hela cells. After lipofectamine reagent was added, most of the fluorescently labeled miRNAs binding to the surface of Hela cells were transfected into intra-cell because of the high transfection efficiency, however, most of them were still binding to the surface of K562 cells. Moreover, the high-salt buffer which could destroy the electrostatic interactions did not affect the above-mentioned non-specific binding, but the organic solvent which could destroy the hydrophobic interactions eliminated it.These results implied that the fluorescent-labeled miRNAs could non-specifically bind to the cell surface by hydrophobic interaction. It would lead to significant errors in the estimation of transfection efficiency only according to the cellular fluorescence intensity. Therefore, other methods to evaluate the transfection efficiency and more appropriate fluorescent dyes should be used according to the cell types for the accuracy of results.

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.

Increase in the specific radioactivity of tritium-labeled compounds obtained by tritium thermal activation method

International Nuclear Information System (INIS)

Badun, G.A.; Chernysheva, M.G.; Ksenofontov, A.L.

2012-01-01

A method of tritium introduction into different types of organic molecules that is based on the interaction of atomic tritium with solid organic target is described. Tritium atoms are formed on the hot W-wire, which is heated by the electric current. Such an approach is called 'tritium thermal activation method'. Here we summarize the results of labeling globular proteins (lysozyme, human and bovine serum albumins); derivatives of pantothenic acid and amino acids; ionic surfactants (sodium dodecylsulfate and alkyltrimethylammonium bromides) and nonionic high-molecular weight surfactants - pluronics. For the first time it is observed that if the target-compound is fixed and its radicals are stable the specific radioactivity of the labeled product can be drastically increased (up to 400 times) when the target temperature is ca. 295 K compared with the results obtained at 77 K. The influence of labeling parameters as tritium gas pressure, exposure time and W-wire temperature was tested for each target temperature that results in the optimum labeling conditions with high specific radioactivity and chemical yield of the resulting compound. (orig.)

Combining position-specific 13C labeling with compound-specific isotope analysis: first steps towards soil fluxomics

Science.gov (United States)

Dippold, Michaela; Kuzyakov, Yakov

2015-04-01

Understanding the soil organic matter (SOM) dynamics is one of the most important challenges in soil science. Transformation of low molecular weight organic substances (LMWOS) is a key step in biogeochemical cycles because 1) all high molecular substances pass this stage during their decomposition and 2) only LMWOS will be taken up by microorganisms. Previous studies on LMWOS were focused on determining net fluxes through the LMWOS pool, but they rarely identified transformations. As LMWOS are the preferred C and energy source for microorganisms, the transformations of LMWOS are dominated by biochemical pathways of the soil microorganisms. Thus, understanding fluxes and transformations in soils requires a detailed knowledge on the biochemical pathways and its controlling factors. Tracing C fate in soil by isotopes became on of the most applied and promising biogeochemistry tools. Up to now, studies on LMWOS were nearly exclusively based on uniformly labeled organic substances i.e. all C atoms in the molecules were labeled with 13C or 14C. However, this classical approach did not allow the differentiation between use of intact initial substances in any process, or whether they were transformed to metabolites. The novel tool of position-specific labeling enables to trace molecule atoms separately and thus to determine the cleavage of molecules - a prerequisite for metabolic tracing. Position-specific labeling of LMWOS and quantification of 13CO2 and 13C in bulk soil enabled following the basic metabolic pathways of soil microorganisms. However, only the combination of position-specific 13C labeling with compound-specific isotope analysis of microbial biomarkers and metabolites allowed 1) tracing specific anabolic pathways in diverse microbial communities in soils and 2) identification of specific pathways of individual functional microbial groups. So, these are the prerequisites for soil fluxomics. Our studies combining position-specific labeled glucose with amino

A convenient method to synthesize specifically labelled cholesterol with tritium

International Nuclear Information System (INIS)

Malik, S.; Kenny, M.; Ahmad, S.; Washington Univ., Seattle, WA

1992-01-01

A simple method is described to label cholesterol with tritium. Cholesterol was first oxidized to 5-cholesten-3-one which was then purified by HPLC. Its structure was established by electron impact (EI) mass spectrometry and 1 H-NMR spectroscopy. The ketone was reduced with NaB 3 H 4 to give specifically labelled cholesterol (C-3 3 H) at low specific activity. (author)

Site-Specific Bioorthogonal Labeling for Fluorescence Imaging of Intracellular Proteins in Living Cells.

Science.gov (United States)

Peng, Tao; Hang, Howard C

2016-11-02

Over the past years, fluorescent proteins (e.g., green fluorescent proteins) have been widely utilized to visualize recombinant protein expression and localization in live cells. Although powerful, fluorescent protein tags are limited by their relatively large sizes and potential perturbation to protein function. Alternatively, site-specific labeling of proteins with small-molecule organic fluorophores using bioorthogonal chemistry may provide a more precise and less perturbing method. This approach involves site-specific incorporation of unnatural amino acids (UAAs) into proteins via genetic code expansion, followed by bioorthogonal chemical labeling with small organic fluorophores in living cells. While this approach has been used to label extracellular proteins for live cell imaging studies, site-specific bioorthogonal labeling and fluorescence imaging of intracellular proteins in live cells is still challenging. Herein, we systematically evaluate site-specific incorporation of diastereomerically pure bioorthogonal UAAs bearing stained alkynes or alkenes into intracellular proteins for inverse-electron-demand Diels-Alder cycloaddition reactions with tetrazine-functionalized fluorophores for live cell labeling and imaging in mammalian cells. Our studies show that site-specific incorporation of axial diastereomer of trans-cyclooct-2-ene-lysine robustly affords highly efficient and specific bioorthogonal labeling with monosubstituted tetrazine fluorophores in live mammalian cells, which enabled us to image the intracellular localization and real-time dynamic trafficking of IFITM3, a small membrane-associated protein with only 137 amino acids, for the first time. Our optimized UAA incorporation and bioorthogonal labeling conditions also enabled efficient site-specific fluorescence labeling of other intracellular proteins for live cell imaging studies in mammalian cells.

The acquisition of gender labels in infancy: Implications for sex-typed play

Science.gov (United States)

Zosuls, Kristina M.; Ruble, Diane N.; Tamis-LeMonda, Catherine S.; Shrout, Patrick E.; Bornstein, Marc H.; Greulich, Faith K.

2009-01-01

Two aspects of children’s early gender development - the spontaneous production of gender labels and sex-typed play - were examined longitudinally in a sample of 82 children. Survival analysis, a statistical technique well suited to questions involving developmental transitions, was used to investigate the timing of the onset of children’s gender labeling as based on mothers’ biweekly reports on their children’s language from 9 through 21 months. Videotapes of children’s play both alone and with mother at 17 and 21 months were independently analyzed for play with gender stereotyped and neutral toys. Finally, the relation between gender labeling and sex-typed play was examined. Children transitioned to using gender labels at approximately 19 months on average. Although girls and boys showed similar patterns in the development of gender labeling, girls began labeling significantly earlier than boys. Modest sex differences in play were present at 17 months and increased at 21 months. Gender labeling predicted increases in sex-typed play, suggesting that knowledge of gender categories might influence sex-typing before the age of 2. PMID:19413425

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

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.

Preparation of a high specific activity I-125 labeled styryl dye for leukocyte membrane labeling

International Nuclear Information System (INIS)

Lambert, C.; Mease, R.C.; Le, T.; Sabet, H.; Avren, L.I.; McAfee, J.G.

1994-01-01

The purpose of this work was to develop a high specific activity radioiodinated cell membrane probe for tracking lymphocytes in-vivo to replace the nucleus localizing, cytotoxic lipophilic chelates (In-111 oxine and Tc-99m HMPAO) currently used. Alkylation of parent dye 4-[2-[-N,N-didecylamino]phenyl]ethenyl pyridine with E-1-tributylstannyl-3-tosylpropene (prepared form E-1-tributylstannyl-1-propene-3-ol), gave a tributyltin precursor 1. Radiolabeled 3-[4-[2-[4-(N,N-didecylamino)phenyl]ethenyl]pyridino] E-[I-125]-1-iodopropene (2), was prepared from 1 using peracetic acid in acetonitrile/water. Labeling yields and specific activities achieved were 26% (∼2170 Ci/mmol), 40% (1220 Ci/mmol), and 55% (200 Ci/mmol) for nca, 0.4, and 2 nanomole carrier iodide runs respectively. Canine mixed leukocytes (0.5-1.0 x 10 8 cells) were labeled with 2 (67% and 42% yields for 200 Ci/mol and 1220 Ci/mmol preparations) and showed blood clearance similar to In 111 oxine. Radioiodinated styryl dye 2 appears to be a promising leukocyte labeling agent. Imaging studies with I-131 labeled 2 are in progress

HaloTag protein-mediated specific labeling of living cells with quantum dots

International Nuclear Information System (INIS)

So, Min-kyung; Yao Hequan; Rao Jianghong

2008-01-01

Quantum dots emerge as an attractive alternative to small molecule fluorophores as fluorescent tags for in vivo cell labeling and imaging. This communication presents a method for specific labeling of live cells using quantum dots. The labeling is mediated by HaloTag protein expressed at the cell surface which forms a stable covalent adduct with its ligand (HaloTag ligand). The labeling can be performed in one single step with quantum dot conjugates that are functionalized with HaloTag ligand, or in two steps with biotinylated HaloTag ligand first and followed by streptavidin coated quantum dots. Live cell fluorescence imaging indicates that the labeling is specific and takes place at the cell surface. This HaloTag protein-mediated cell labeling method should facilitate the application of quantum dots for live cell imaging

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.

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.

The acquisition of gender labels in infancy: implications for gender-typed play.

Science.gov (United States)

Zosuls, Kristina M; Ruble, Diane N; Tamis-Lemonda, Catherine S; Shrout, Patrick E; Bornstein, Marc H; Greulich, Faith K

2009-05-01

Two aspects of children's early gender development-the spontaneous production of gender labels and gender-typed play-were examined longitudinally in a sample of 82 children. Survival analysis, a statistical technique well suited to questions involving developmental transitions, was used to investigate the timing of the onset of children's gender labeling as based on mothers' biweekly telephone interviews regarding their children's language from 9 through 21 months. Videotapes of children's play both alone and with mother during home visits at 17 and 21 months were independently analyzed for play with gender-stereotyped and gender-neutral toys. Finally, the relation between gender labeling and gender-typed play was examined. Children transitioned to using gender labels at approximately 19 months, on average. Although girls and boys showed similar patterns in the development of gender labeling, girls began labeling significantly earlier than boys. Modest sex differences in play were present at 17 months and increased at 21 months. Gender labeling predicted increases in gender-typed play, suggesting that knowledge of gender categories might influence gender typing before the age of 2. Copyright 2009 APA, all rights reserved

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.

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

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

Memory for product sounds: the effect of sound and label type.

Science.gov (United States)

Ozcan, Elif; van Egmond, René

2007-11-01

The (mnemonic) interactions between auditory, visual, and the semantic systems have been investigated using structurally complex auditory stimuli (i.e., product sounds). Six types of product sounds (air, alarm, cyclic, impact, liquid, mechanical) that vary in spectral-temporal structure were presented in four label type conditions: self-generated text, text, image, and pictogram. A memory paradigm that incorporated free recall, recognition, and matching tasks was employed. The results for the sound type suggest that the amount of spectral-temporal structure in a sound can be indicative for memory performance. Findings related to label type suggest that 'self' creates a strong bias for the retrieval and the recognition of sounds that were self-labeled; the density and the complexity of the visual information (i.e., pictograms) hinders the memory performance ('visual' overshadowing effect); and image labeling has an additive effect on the recall and matching tasks (dual coding). Thus, the findings suggest that the memory performances for product sounds are task-dependent.

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

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.

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...

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...

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.

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

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.

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.

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.

[125I] radioiodinated metaraminol: A new platelet-specific labeling agent

International Nuclear Information System (INIS)

Ohmomo, Y.; Yokoyama, A.; Kawai, K.; Arano, Y.; Horiuchi, K.; Saji, H.; Torizuka, K.

1985-01-01

In our search for a platelet-specific labeling agent, metaraminol (MA), a low-toxic pharmaceutical for the treatment of hypotension and cardiogenic shock, attracted our attention. Its active incorporation and accumulation by platelets have been recognized. At first, the preparation of 125 I radioiodinated metaraminol ( 125 I-MA) was carried out using the chloramine-T method. Then, upon the harvest of platelets as platelet-rich plasma (PRP), their labeling with this new radiopharmaceutical was easily performed by incubation for 10 min at 37 0 C. The cell-labeling efficiency was dependent on cell density, reaching 63.0%+-3.1% at 2.4x10 9 cells/ml. The specific incorporation of 125 I-MA by an active transport system similar to that of 5-hydroxytryptamine (5-HT) as well as by passive diffusion was demonstrated. In vitro studies, the unaltered state of 125 I-MA-labeled platelets with their cellular functions fully retained was estimated. In vivo studies carried out in rabbits with induced thrombi in the femoral artery showed a rather rapid disappearance of the radioactivity from circulating blood, reaching a high thrombus-to-blood activity ratio of 19.8+-4.3 within 30 min of the administration of 125 I-MA-labeled autologous platelets. Thus, with the potential availability of 123 I, 123 I-MA-labeled platelets appear to be a promising agent for thrombus imaging using single-emission computed tomography (CT) studies. (orig.)

Synthesis of specifically labelled L-phenylalanines using phenylalanine ammonia lyase activity

International Nuclear Information System (INIS)

Haedener, A.; Tamm, Ch.

1987-01-01

Specifically labelled L-phenylalanines have been prepared using a variety of classical synthetic methods in combination with phenylalanine ammonia lyase (PAL) enzyme activity of the yeast Rhodosporidium toruloides ATCC 10788 or Rhodotorula glutinis IFO 0559, respectively. Thus, L-[2- 2 H]phenyl-[2- 2 H]alanine was formed from (E) -[2,2'- 2 H 2 ]cinnamic acid and ammonia in 46% yield, whereas L-phenyl-[2- 13 C, 15 N]alanine was obtained from (E)-[2- 13 C]cinnamic acid in 45% overall yield. Generally, labelled cinnamic acids were recovered in pure form from the reaction mixture, with a loss of 6-8%. Likewise, unchanged 15 NH 3 was reisolated as 15 NH 4 Cl after steam distillation with overall losses of less than 4%. Labelled cinnamic acids were prepared by Knoevenagel condensations between appropriately labelled benzaldehydes and malonic acids. [2- 2 H]Benzaldehyde was obtained from 2-bromotoluene by decomposition of the corresponding Grignard reagent with 2 H 2 O and subsequent oxidation. Since simple molecules, most of them commercially available in labelled form or otherwise easily accessible, may serve as starting material, and due to its defined stereochemistry, the reaction catalysed by PAL opens a short and attractive route to specifically labelled L-phenylalanines. (author)

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.

Site-Specific Biomolecule Labeling with Gold Clusters

OpenAIRE

Ackerson, Christopher J.; Powell, Richard D.; Hainfeld, James F.

2010-01-01

Site-specific labeling of biomolecules in vitro with gold clusters can enhance the information content of electron cryomicroscopy experiments. This chapter provides a practical overview of well-established techniques for forming biomolecule/gold cluster conjugates. Three bioconjugation chemistries are covered: Linker-mediated bioconjugation, direct gold–biomolecule bonding, and coordination-mediated bonding of nickel(II) nitrilotriacetic acid (NTA)-derivatized gold clusters to polyhistidine (...

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...

Visualization of antigen-specific human cytotoxic T lymphocytes labeled with superparamagnetic iron-oxide particles

Energy Technology Data Exchange (ETDEWEB)

Beer, Ambros J. [Technical University of Munich (TUM), Department of Nuclear Medicine, Klinikum rechts der Isar, Munich (Germany); Holzapfel, Konstantin; Settles, Marcus; Rummeny, Ernst J. [Technical University of Munich, Department of Radiology, Klinikum rechts der Isar, Munich (Germany); Neudorfer, Juliana; Kroenig, Holger; Peschel, Christian; Bernhard, Helga [TUM, Munich, Department of Hematology/Oncology, Klinikum rechts der Isar, Munich (Germany); Piontek, Guido; Schlegel, Juergen [TUM, Munich, Division of Neuropathology, Institute of Pathology, Klinikum rechts der Isar, Munich (Germany)

2008-06-15

New technologies are needed to characterize the migration and survival of antigen-specific T cells in vivo. In this study, we developed a novel technique for the labeling of human cytotoxic T lymphocytes with superparamagnetic iron-oxide particles and the subsequent depiction with a conventional 1.5-T magnetic resonance scanner. Antigen-specific CD8{sup +} T lymphocytes were labeled with ferucarbotran by lipofection. The uptake of ferucarbotran was confirmed by immunofluorescence microscopy using a dextran-specific antibody, and the intracellular enrichment of iron was measured by atomic absorption spectrometry. The imaging of T cells was performed by magnetic resonance on day 0, 2, 7 and 14 after the labeling procedure. On day 0 and 2 post labeling, a pronounced shortening of T2*-relaxation times was observed, which diminished after 7 days and was not detectable anymore after 14 days, probably due to the retained mitotic activity of the labeled T cells. Of importance, the antigen-specific cytolytic activity of the T cells was preserved following ferucarbotran labeling. Efficient ferucarbotran labeling of functionally active T lymphocytes and their detection by magnetic resonance imaging allows the in vivo monitoring of T cells and, subsequently, will impact the further development of T cell-based therapies. (orig.)

Visualization of antigen-specific human cytotoxic T lymphocytes labeled with superparamagnetic iron-oxide particles

International Nuclear Information System (INIS)

Beer, Ambros J.; Holzapfel, Konstantin; Settles, Marcus; Rummeny, Ernst J.; Neudorfer, Juliana; Kroenig, Holger; Peschel, Christian; Bernhard, Helga; Piontek, Guido; Schlegel, Juergen

2008-01-01

New technologies are needed to characterize the migration and survival of antigen-specific T cells in vivo. In this study, we developed a novel technique for the labeling of human cytotoxic T lymphocytes with superparamagnetic iron-oxide particles and the subsequent depiction with a conventional 1.5-T magnetic resonance scanner. Antigen-specific CD8 + T lymphocytes were labeled with ferucarbotran by lipofection. The uptake of ferucarbotran was confirmed by immunofluorescence microscopy using a dextran-specific antibody, and the intracellular enrichment of iron was measured by atomic absorption spectrometry. The imaging of T cells was performed by magnetic resonance on day 0, 2, 7 and 14 after the labeling procedure. On day 0 and 2 post labeling, a pronounced shortening of T2*-relaxation times was observed, which diminished after 7 days and was not detectable anymore after 14 days, probably due to the retained mitotic activity of the labeled T cells. Of importance, the antigen-specific cytolytic activity of the T cells was preserved following ferucarbotran labeling. Efficient ferucarbotran labeling of functionally active T lymphocytes and their detection by magnetic resonance imaging allows the in vivo monitoring of T cells and, subsequently, will impact the further development of T cell-based therapies. (orig.)

Site-specific labeling of proteins with NMR-active unnatural amino acids

International Nuclear Information System (INIS)

Jones, David H.; Cellitti, Susan E.; Hao Xueshi; Zhang Qiong; Jahnz, Michael; Summerer, Daniel; Schultz, Peter G.; Uno, Tetsuo; Geierstanger, Bernhard H.

2010-01-01

A large number of amino acids other than the canonical amino acids can now be easily incorporated in vivo into proteins at genetically encoded positions. The technology requires an orthogonal tRNA/aminoacyl-tRNA synthetase pair specific for the unnatural amino acid that is added to the media while a TAG amber or frame shift codon specifies the incorporation site in the protein to be studied. These unnatural amino acids can be isotopically labeled and provide unique opportunities for site-specific labeling of proteins for NMR studies. In this perspective, we discuss these opportunities including new photocaged unnatural amino acids, outline usage of metal chelating and spin-labeled unnatural amino acids and expand the approach to in-cell NMR experiments.

Nutrition labelling is a trade policy issue: lessons from an analysis of specific trade concerns at the World Trade Organization.

Science.gov (United States)

Thow, Anne Marie; Jones, Alexandra; Hawkes, Corinna; Ali, Iqra; Labonté, Ronald

2017-01-12

Interpretive nutrition labels provide simplified nutrient-specific text and/or symbols on the front of pre-packaged foods, to encourage and enable consumers to make healthier choices. This type of labelling has been proposed as part of a comprehensive policy response to the global epidemic of non-communicable diseases. However, regulation of nutrition labelling falls under the remit of not just the health sector but also trade. Specific Trade Concerns have been raised at the World Trade Organization's Technical Barriers to Trade Committee regarding interpretive nutrition labelling initiatives in Thailand, Chile, Indonesia, Peru and Ecuador. This paper presents an analysis of the discussions of these concerns. Although nutrition labelling was identified as a legitimate policy objective, queries were raised regarding the justification of the specific labelling measures proposed, and the scientific evidence for effectiveness of such measures. Concerns were also raised regarding the consistency of the measures with international standards. Drawing on policy learning theory, we identified four lessons for public health policy makers, including: strategic framing of nutrition labelling policy objectives; pro-active policy engagement between trade and health to identify potential trade issues; identifying ways to minimize potential 'practical' trade concerns; and engagement with the Codex Alimentarius Commission to develop international guidance on interpretative labelling. This analysis indicates that while there is potential for trade sector concerns to stifle innovation in nutrition labelling policy, care in how interpretive nutrition labelling measures are crafted in light of trade commitments can minimize such a risk and help ensure that trade policy is coherent with nutrition action. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Synthesis of specifically labelled L-phenylalanines using phenylalanine ammonia lyase activity

Energy Technology Data Exchange (ETDEWEB)

Haedener, A.; Tamm, Ch.

1987-11-01

Specifically labelled L-phenylalanines have been prepared using a variety of classical synthetic methods in combination with phenylalanine ammonia lyase (PAL) enzyme activity of the yeast Rhodosporidium toruloides ATCC 10788 or Rhodotorula glutinis IFO 0559, respectively. Thus, L-(2-/sup 2/H)phenyl-(2-/sup 2/H)alanine was formed from (E) -(2,2'-/sup 2/H/sub 2/)cinnamic acid and ammonia in 46% yield, whereas L-phenyl-(2-/sup 13/C, /sup 15/N)alanine was obtained from (E)-(2-/sup 13/C)cinnamic acid in 45% overall yield. Generally, labelled cinnamic acids were recovered in pure form from the reaction mixture, with a loss of 6-8%. Likewise, unchanged /sup 15/NH/sub 3/ was reisolated as /sup 15/NH/sub 4/Cl after steam distillation with overall losses of less than 4%. Labelled cinnamic acids were prepared by Knoevenagel condensations between appropriately labelled benzaldehydes and malonic acids. (2-/sup 2/H)Benzaldehyde was obtained from 2-bromotoluene by decomposition of the corresponding Grignard reagent with /sup 2/H/sub 2/O and subsequent oxidation. Since simple molecules, most of them commercially available in labelled form or otherwise easily accessible, may serve as starting material, and due to its defined stereochemistry, the reaction catalysed by PAL opens a short and attractive route to specifically labelled L-phenylalanines.

The radioactive labeling of monocytes

International Nuclear Information System (INIS)

Ensing, G.J.

1985-01-01

With the aim of studying a possible relationship between circulating monocytes and Sternberg-Reed cells investigations were started on the specific labeling of monocytes. In this thesis the literature on the pertinent data has been reviewed and a series of experiments on the monocyte labeling procedure has been described. The principles of cell labeling with radioactive compounds were discussed. 1. Total separation of the particular cell population to be labeled and subsequent labeling with a non-specific radiopharmaceutical. 2. Specific cell labeling in a mixture of cell types based on a well defined affinity of the cell under study for the radiopharmaceutical used. Next the radionuclides that can be used for cell labeling purposes were discussed with special attention for 111 In and its chelates. The principles of radiodosimetry were also discussed shortly. This section was focussed on the radiation dose the labeled cells receive because of the intracellular localized radioactivity. The radiation burden is high in comparison to amounts of radiation known to affect cell viability. A newly developed method for labeling monocytes specifically by phagocytosis of 111 In-Fe-colloid without apparent loss of cells was described in detail. (Auth.)

Site-Specific Biomolecule Labeling with Gold Clusters

Science.gov (United States)

Ackerson, Christopher J.; Powell, Richard D.; Hainfeld, James F.

2013-01-01

Site-specific labeling of biomolecules in vitro with gold clusters can enhance the information content of electron cryomicroscopy experiments. This chapter provides a practical overview of well-established techniques for forming biomolecule/gold cluster conjugates. Three bioconjugation chemistries are covered: Linker-mediated bioconjugation, direct gold–biomolecule bonding, and coordination-mediated bonding of nickel(II) nitrilotriacetic acid (NTA)-derivatized gold clusters to polyhistidine (His)-tagged proteins. PMID:20887859

Preparation of [In-111]-labeled-DTPA-bombesin conjugates at high specific activity and stability: Evaluation of labeling parameters and potential stabilizers

Energy Technology Data Exchange (ETDEWEB)

Pujatti, P.B., E-mail: pujatti.pb@gmail.com [Directory of Radiopharmacy, Nuclear and Energy Research Institute (IPEN/CNEN), Av. Prof. Lineu Prestes, 2242 - Cidade Universitaria da USP - Butanta, Sao Paulo - SP - Brazil - CEP: 05508-000 (Brazil); Massicano, A.V.F.; Mengatti, J.; Araujo, E.B. de [Directory of Radiopharmacy, Nuclear and Energy Research Institute (IPEN/CNEN), Av. Prof. Lineu Prestes, 2242 - Cidade Universitaria da USP - Butanta, Sao Paulo - SP - Brazil - CEP: 05508-000 (Brazil)

2012-05-15

The aim of the present work was to obtain stabilized high specific activity (HSA) {sup 111}In-labeled bombesin conjugates for preclinical evaluations. Parameters influencing the kinetics of labeling were investigated and the effect of stabilizers on HSA radiopeptides stability at room temperature were systematically categorized applying chromatography techniques. A SA of 174 GBq/{mu}mol was achieved with high radiochemical purity, but the labeled compounds exhibited low stability. The addition of stabilizers avoided their radiolysis and significantly increased their stability. - Highlights: Black-Right-Pointing-Pointer We aimed to obtain stabilized high specific activity (SA) {sup 111}In-labeled bombesin conjugates. Black-Right-Pointing-Pointer The effect of stabilizers on high SA radiopeptides stability were investigated. Black-Right-Pointing-Pointer A maximum specific activity of 174 GBq/{mu}mol was achieved. Black-Right-Pointing-Pointer The studied stabilizers significantly increased the stability of high SA radiopeptides. Black-Right-Pointing-Pointer These stabilized bombesin conjugates will be applied in preclinical studies.

Exploring whether Students' Use of Labelling Depends upon the Type of Activity

Science.gov (United States)

Bures, Eva Mary; Abrami, Philip C.; Schmid, Richard F.

2010-01-01

This paper explores a labelling feature designed to support higher-level online dialogue. It investigates whether students use labels less often during a structured online dialogue than during an unstructured one, and looks at students' reactions to labelling and to both types of tasks. Participants are from three successive course offerings of a…

40 CFR 600.306-86 - Labeling requirements.

Science.gov (United States)

2010-07-01

... general to specific labels or vice versa within a model type, the manufacturer shall, within five calendar... legibility of the fuel economy label is maintained. For this purpose, all fuel economy label information must... cause to be maintained on each automobile: (1) A general fuel economy label (initial, or updated as...

DNA-specific labelling by deoxyribonucleoside 5'-monophosphates in Saccharomyces cerevisiae

International Nuclear Information System (INIS)

Brendel, M.; Faeth, W.W.; Toper, R.

1975-01-01

Growth of 5'-dTMP low-requiring strains is inhibited by exogenous 5'-dGMP and 5'-GMP at concentrations higher than 5 x 10 -4 M. Synthesis of nucleic acids ceases and cells remain fixed in their respective place in the cell cycle. At concentrations lower than 10 -5 M deoxyribonucleoside 5'-monophosphates may be employed for radioactive labelling, the label being preferentially used for DNA synthesis. Affinity to DNA of the 5'-dNMPs is in the order of 5'-dAMPS > 5'-dGMP > 5'-dCMP > 5'-dUMP. DNA-specific label is achieved with 5'-dAMP when the medium is supplemented with adenine and deoxyadenosine. (orig.) [de

Comparison between sparsely distributed memory and Hopfield-type neural network models

Science.gov (United States)

Keeler, James D.

1986-01-01

The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns. The SDM is extended to include time delays so that contextual information can be used to cover sequences. Finally, it is shown how a generalization of the SDM allows storage of correlated input pattern vectors.

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.

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.

Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data.

Science.gov (United States)

Rahman, M Shafiqur; Sultana, Mahbuba

2017-02-23

When developing risk models for binary data with small or sparse data sets, the standard maximum likelihood estimation (MLE) based logistic regression faces several problems including biased or infinite estimate of the regression coefficient and frequent convergence failure of the likelihood due to separation. The problem of separation occurs commonly even if sample size is large but there is sufficient number of strong predictors. In the presence of separation, even if one develops the model, it produces overfitted model with poor predictive performance. Firth-and logF-type penalized regression methods are popular alternative to MLE, particularly for solving separation-problem. Despite the attractive advantages, their use in risk prediction is very limited. This paper evaluated these methods in risk prediction in comparison with MLE and other commonly used penalized methods such as ridge. The predictive performance of the methods was evaluated through assessing calibration, discrimination and overall predictive performance using an extensive simulation study. Further an illustration of the methods were provided using a real data example with low prevalence of outcome. The MLE showed poor performance in risk prediction in small or sparse data sets. All penalized methods offered some improvements in calibration, discrimination and overall predictive performance. Although the Firth-and logF-type methods showed almost equal amount of improvement, Firth-type penalization produces some bias in the average predicted probability, and the amount of bias is even larger than that produced by MLE. Of the logF(1,1) and logF(2,2) penalization, logF(2,2) provides slight bias in the estimate of regression coefficient of binary predictor and logF(1,1) performed better in all aspects. Similarly, ridge performed well in discrimination and overall predictive performance but it often produces underfitted model and has high rate of convergence failure (even the rate is higher than that

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.

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...

Specificity of Facial Expression Labeling Deficits in Childhood Psychopathology

Science.gov (United States)

Guyer, Amanda E.; McClure, Erin B.; Adler, Abby D.; Brotman, Melissa A.; Rich, Brendan A.; Kimes, Alane S.; Pine, Daniel S.; Ernst, Monique; Leibenluft, Ellen

2007-01-01

Background: We examined whether face-emotion labeling deficits are illness-specific or an epiphenomenon of generalized impairment in pediatric psychiatric disorders involving mood and behavioral dysregulation. Method: Two hundred fifty-two youths (7-18 years old) completed child and adult facial expression recognition subtests from the Diagnostic…

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.

Co-Labeling for Multi-View Weakly Labeled Learning.

Science.gov (United States)

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi

The effect of label affinity on the sensitivity and specificity of a hapten radioimmunoassay: A comparison of three [125I]diphenylhydantoin radioligands with the 14C-labelled drug

International Nuclear Information System (INIS)

Rowell, F.J.

1979-01-01

The effects on the sensitivity and specificity of a radioimmunoassay for diphenylhydantoin (DPH) has been investigated using three 125 I-labelled tyrosine ester derivatives of DPH having different bridge lengths between the tyrosine moiety and the DPH moeity and 14 C-labelled DPH. The results demonstrate that for a hapten which does not completely fill the antibody binding sites, greatest sensitivity is achieved when the bridge of the iodine label is most dissimilar to that present in the original immunogen, when the hapten and label affinities are nearly equivalent. Greatest specificity is achieved with the label which most resembles the original immunogen. These results illustrate the difficulty of designing satisfactory labels for assays of both high specificity and sensitivity since minimal changes in label structure may produce greatly amplified changes in the subsequent affinity of the label for the antiserum. (Auth.)

A Sparse Auto Encoder Deep Process Neural Network Model and its Application

Directory of Open Access Journals (Sweden)

Xu Shaohua

2017-01-01

Full Text Available Aiming at the problem of time-varying signal pattern classification, a sparse auto-encoder deep process neural network (SAE-DPNN is proposed. The input of SAE-DPNN is time-varying process signal and the output is pattern category. It combines the time-varying signal classification method of process neural network (PNN and the data feature extraction and hierarchical sparse representation mechanism of sparse automatic encoder (SAE. Based on the feedforward PNN model, SAE-DPNN is constructed by stacking the process neurons, SAE network and softmax classifier. It can maintain the time-sequence and structure of the input signal, express and synthesize the process distribution characteristics of multidimensional time-varying signals and their combinations. SAE-DPNN improves the identification of complex features and distinguishes between different types of signals, realizes the direct classification of time-varying signals. In this paper, the feature extraction and representation mechanism of time-varying signal in SAE-DPNN are analyzed, and a specific learning algorithm is given. The experimental results verify the effectiveness of the model and algorithm.

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.

Differential Isotope Labeling of Glycopeptides for Accurate Determination of Differences in Site-Specific Glycosylation.

Science.gov (United States)

Pabst, Martin; Benešová, Iva; Fagerer, Stephan R; Jacobsen, Mathias; Eyer, Klaus; Schmidt, Gregor; Steinhoff, Robert; Krismer, Jasmin; Wahl, Fabian; Preisler, Jan; Zenobi, Renato

2016-01-04

We introduce a stable isotope labeling approach for glycopeptides that allows a specific glycosylation site in a protein to be quantitatively evaluated using mass spectrometry. Succinic anhydride is used to specifically label primary amino groups of the peptide portion of the glycopeptides. The heavy form (D4(13)C4) provides an 8 Da mass increment over the light natural form (H4(12)C4), allowing simultaneous analysis and direct comparison of two glycopeptide profiles in a single MS scan. We have optimized a protocol for an in-solution trypsin digestion, a one-pot labeling procedure, and a post-labeling solid-phase extraction to obtain purified and labeled glycopeptides. We provide the first demonstration of this approach by comparing IgG1 Fc glycopeptides from polyclonal IgG samples with respect to their galactosylation and sialylation patterns using MALDI MS and LC-ESI-MS.

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.

Consumer perceptions of specific design characteristics for front-of-package nutrition labels.

Science.gov (United States)

Acton, R B; Vanderlee, L; Roberto, C A; Hammond, D

2018-04-01

An increasing number of countries are developing front-of-package (FOP) labels; however, there is limited evidence examining the impact of specific design characteristics for these labels. The current study investigated consumer perceptions of several FOP label design characteristics, including potential differences among sociodemographic sub-groups. Two hundred and thirty-four participants aged 16 years or older completed nine label rating tasks on a laptop at a local shopping mall in Canada. The rating tasks asked participants to rate five primary design characteristics (border, background presence, background colour, 'caution' symbol and government attribution) on their noticeability, readability, believability and likelihood of changing their beverage choice. FOP labels with a border, solid background and contrasting colours increased noticeability. A solid background increased readability, while a contrasting background colour reduced it. Both a 'caution' symbol and a government attribution increased the believability of the labels and the perceived likelihood of influencing beverage choice. The effect of the design characteristics was generally similar across sociodemographic groups, with modest differences in five of the nine outcomes. Label design characteristics, such as the use of a border, colour and symbols can enhance the salience of FOP nutrition labels and may increase the likelihood that FOP labels are used by consumers.

In Vivo Imaging of Xenograft Tumors Using an Epidermal Growth Factor Receptor-Specific Affibody Molecule Labeled with a Near-infrared Fluorophore

Directory of Open Access Journals (Sweden)

Haibiao Gong

2010-02-01

Full Text Available Overexpression of epidermal growth factor receptor (EGFR is associated with many types of cancers. It is of great interest to noninvasively image the EGFR expression in vivo. In this study, we labeled an EGFR-specific Affibody molecule (Eaff with a near-infrared (NIR dye IRDye800CW maleimide and tested the binding of this labeled molecule (Eaff800 in cell culture and xenograft mouse tumor models. Unlike EGF, Eaff did not activate the EGFR signaling pathway. Results showed that Eaff800 was bound and taken up specifically by EGFR-overexpressing A431 cells. When Eaff800 was intravenously injected into nude mice bearing A431 xenograft tumors, the tumor could be identified 1 hour after injection and it became most prominent after 1 day. Images of dissected tissue sections demonstrated that the accumulation of Eaff800 was highest in the liver, followed by the tumor and kidney. Moreover, in combination with a human EGFR type 2 (HER2-specific probe Haff682, Eaff800 could be used to distinguish between EGFR- and HER2-overexpressing tumors. Interestingly, the organ distribution pattern and the clearance rate of Eaff800 were different from those of Haff682. In conclusion, Eaff molecule labeled with a NIR fluorophore is a promising molecular imaging agent for EGFR-overexpressing tumors.

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...

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.

Site-Specific Antibody Labeling by Covalent Photoconjugation of Z Domains Functionalized for Alkyne-Azide Cycloaddition Reactions.

Science.gov (United States)

Perols, Anna; Arcos Famme, Melina; Eriksson Karlström, Amelie

2015-11-01

Antibodies are extensively used in research, diagnostics, and therapy, and for many applications the antibodies need to be labeled. Labeling is typically performed by using amine-reactive probes that target surface-exposed lysine residues, resulting in heterogeneously labeled antibodies. An alternative labeling strategy is based on the immunoglobulin G (IgG)-binding protein domain Z, which binds to the Fc region of IgG. Introducing the photoactivable amino acid benzoylphenylalanine (BPA) into the Z domain makes it possible for a covalent bond to be be formed between the Z domain and the antibody on UV irradiation, to produce a site-specifically labeled product. Z32 BPA was synthesized by solid-phase peptide synthesis and further functionalized to give alkyne-Z32 BPA and azide-Z32 BPA for Cu(I) -catalyzed cycloaddition, as well as DBCO-Z32 BPA for Cu-free strain-promoted cycloaddition. The Z32 BPA variants were conjugated to the human IgG1 antibody trastuzumab and site-specifically labeled with biotin or fluorescein. The fluorescently labeled trastuzumab showed specific staining of the membranes of HER2-expressing cells in immunofluorescence microscopy. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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.

Technique of leukocyte harvesting and labeling: problems and perspectives

International Nuclear Information System (INIS)

McAfee, J.G.; Subramanian, G.; Gagne, G.

1984-01-01

Mixed leukocyte suspensions obtained after gravity sedimentation of red cells and labeled with 111 In lipophilic chelates are now widely used clinically for abscess localization at many medical centers. So far, labeling with 111 In-oxine or tropolone has been more successful than any 99 mTc method. More sophisticated approaches are available for isolation and labeling of specific leukocyte cell types, to study their migration in vivo. The most significant advances in cell harvesting include newer density gradients for isopyknic centrifugation, centrifugal elutriation, and flow cytometry. Unlike current radioactive agents which label many cell types indiscriminately, more selective ligands are being developed which bind to specific cell surface receptors. These will label certain leukocyte populations or subtypes while not reacting with others, thereby avoiding laborious separation techniques. Monoclonal antibodies against leukocyte cell-surface antigens appear particularly promising as agents for selective cell labeling

Sensing site-specific structural characteristics and chirality using vibrational circular dichroism of isotope labeled peptides.

Science.gov (United States)

Keiderling, Timothy A

2017-12-01

Isotope labeling has a long history in chemistry as a tool for probing structure, offering enhanced sensitivity, or enabling site selection with a wide range of spectroscopic tools. Chirality sensitive methods such as electronic circular dichroism are global structural tools and have intrinsically low resolution. Consequently, they are generally insensitive to modifications to enhance site selectivity. The use of isotope labeling to modify vibrational spectra with unique resolvable frequency shifts can provide useful site-specific sensitivity, and these methods have been recently more widely expanded in biopolymer studies. While the spectral shifts resulting from changes in isotopic mass can provide resolution of modes from specific parts of the molecule and can allow detection of local change in structure with perturbation, these shifts alone do not directly indicate structure or chirality. With vibrational circular dichroism (VCD), the shifted bands and their resultant sign patterns can be used to indicate local conformations in labeled biopolymers, particularly if multiple labels are used and if their coupling is theoretically modeled. This mini-review discusses selected examples of the use of labeling specific amides in peptides to develop local structural insight with VCD spectra. © 2017 Wiley Periodicals, Inc.

Specific Labeling of Zinc Finger Proteins using Non-canonical Amino Acids and Copper-free Click Chemistry

Science.gov (United States)

Kim, Younghoon; Kim, Sung Hoon; Ferracane, Dean; Katzenellenbogen, John A.

2012-01-01

Zinc finger proteins (ZFPs) play a key role in transcriptional regulation and serve as invaluable tools for gene modification and genetic engineering. Development of efficient strategies for labeling metalloproteins such as ZFPs is essential for understanding and controlling biological processes. In this work, we engineered ZFPs containing cysteine-histidine (Cys2-His2) motifs by metabolic incorporation of the unnatural amino acid azidohomoalanine (AHA), followed by specific protein labeling via click chemistry. We show that cyclooctyne promoted [3 + 2] dipolar cycloaddition with azides, known as copper-free click chemistry, provides rapid and specific labeling of ZFPs at high yields as determined by mass spectrometry analysis. We observe that the DNA-binding activity of ZFPs labeled by conventional copper-mediated click chemistry was completely abolished, whereas ZFPs labeled by copper-free click chemistry retain their sequence-specific DNA-binding activity under native conditions, as determined by electrophoretic mobility shift assays, protein microarrays and kinetic binding assays based on Förster resonance energy transfer (FRET). Our work provides a general framework to label metalloproteins such as ZFPs by metabolic incorporation of unnatural amino acids followed by copper-free click chemistry. PMID:22871171

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

The antibody approach of labeling blood cells

International Nuclear Information System (INIS)

Srivastava, S.C.

1992-01-01

Although the science of blood cell labeling using monoclonal antibodies directed against specific cellular antigens is still in its early stages, considerable progress has recently been accomplished in this area. The monoclonal antibody approach offers the promise of greater selectivity and enhanced convenience since specific cell types can be labeled in vivo, thus eliminating the need for complex and damaging cell separation procedures. This article focuses on these developments with primary emphasis on antibody labeling of platelets and leukocytes. The advantages and the shortcomings of the recently reported techniques are critically assessed and evaluated

The antibody approach of labeling blood cells

International Nuclear Information System (INIS)

Srivastava, S.C.

1991-01-01

Although the science of blood cell labeling using monoclonal antibodies directed against specific cellular antigens is still in its early stages, considerable progress has recently been accomplished in this area. The monoclonal antibody approach offers the promise of greater selectivity and enhanced convenience since specific cell types can be labeled in vivo, thus eliminating the need for complex and damaging cell separation procedures. This article focuses on these developments with primary emphasis on antibody labeling of platelets and leukocytes. The advantages and the shortcomings of the recently reported techniques are criticality assessed and evaluated

The antibody approach of labeling blood cells

Energy Technology Data Exchange (ETDEWEB)

Srivastava, S.C.

1991-12-31

Although the science of blood cell labeling using monoclonal antibodies directed against specific cellular antigens is still in its early stages, considerable progress has recently been accomplished in this area. The monoclonal antibody approach offers the promise of greater selectivity and enhanced convenience since specific cell types can be labeled in vivo, thus eliminating the need for complex and damaging cell separation procedures. This article focuses on these developments with primary emphasis on antibody labeling of platelets and leukocytes. The advantages and the shortcomings of the recently reported techniques are criticality assessed and evaluated.

The antibody approach of labeling blood cells

Energy Technology Data Exchange (ETDEWEB)

Srivastava, S.C.

1991-01-01

Although the science of blood cell labeling using monoclonal antibodies directed against specific cellular antigens is still in its early stages, considerable progress has recently been accomplished in this area. The monoclonal antibody approach offers the promise of greater selectivity and enhanced convenience since specific cell types can be labeled in vivo, thus eliminating the need for complex and damaging cell separation procedures. This article focuses on these developments with primary emphasis on antibody labeling of platelets and leukocytes. The advantages and the shortcomings of the recently reported techniques are criticality assessed and evaluated.

The antibody approach of labeling blood cells

Energy Technology Data Exchange (ETDEWEB)

Srivastava, S.C.

1992-12-31

Although the science of blood cell labeling using monoclonal antibodies directed against specific cellular antigens is still in its early stages, considerable progress has recently been accomplished in this area. The monoclonal antibody approach offers the promise of greater selectivity and enhanced convenience since specific cell types can be labeled in vivo, thus eliminating the need for complex and damaging cell separation procedures. This article focuses on these developments with primary emphasis on antibody labeling of platelets and leukocytes. The advantages and the shortcomings of the recently reported techniques are critically assessed and evaluated.

Proteins labelling with 125I and experimental determination of their specific activity

International Nuclear Information System (INIS)

Caro, R.A.; Ciscato, V.A.; Giacomini, S.M.V. de; Quiroga, S.; Radicella, R.

1975-11-01

A standardization of the labelling technique of proteins with 125 I and the control of the obtained products, principally their specific activities was performed, in order to utilize them correctly in radioimmunoassays. The quantities of chloramine-T and sodium metabisulphite were lowered, with regard to the original method, to 3.6 and 9.6 μg respectively. Under these conditions, optimal yields and radioiodinated proteins with good immunological activities were obtained. It was found that the specific activity calculated, as usual, from the yield obtained by electrophoresis, is higher than the real value. For these reasons the yields and the corresponding specific activities were determined from ascending chromatographies performed with 70 per cent methanol as solvent, during two hours in darkness. The radioimmunoassay displacement curves obtained with proteins labelled which the proposed method and the specific activities of which were calculated from their radiochromatographic patterns, were reproducible and gave a percentage of bound radioiodinated protein in the absence of cold protein of 50 +- 4. (author) [es

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.

21 CFR 862.2050 - General purpose laboratory equipment labeled or promoted for a specific medical use.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false General purpose laboratory equipment labeled or... TOXICOLOGY DEVICES Clinical Laboratory Instruments § 862.2050 General purpose laboratory equipment labeled or promoted for a specific medical use. (a) Identification. General purpose laboratory equipment labeled or...

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.

Exploiting E. coli auxotrophs for leucine, valine, and threonine specific methyl labeling of large proteins for NMR applications

Energy Technology Data Exchange (ETDEWEB)

Monneau, Yoan R. [Rutgers University, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology (United States); Ishida, Yojiro [Rutgers University, Center for Advanced Biotechnology and Medicine (United States); Rossi, Paolo; Saio, Tomohide; Tzeng, Shiou-Ru [Rutgers University, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology (United States); Inouye, Masayori, E-mail: inouye@cabm.rutgers.edu [Rutgers University, Center for Advanced Biotechnology and Medicine (United States); Kalodimos, Charalampos G., E-mail: ckalodim@umn.edu [Rutgers University, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology (United States)

2016-06-15

A simple and cost effective method to independently and stereo-specifically incorporate [{sup 1}H,{sup 13}C]-methyls in Leu and Val in proteins is presented. Recombinant proteins for NMR studies are produced using a tailored set of auxotrophic E. coli strains. NMR active isotopes are routed to either Leu or Val methyl groups from the commercially available and scrambling-free precursors α-ketoisovalerate and acetolactate. The engineered strains produce deuterated proteins with stereospecific [{sup 1}H,{sup 13}C]-methyl labeling separately at Leu or Val amino acids. This is the first method that achieves Leu-specific stereospecific [{sup 1}H,{sup 13}C]-methyl labeling of proteins and scramble-free Val-specific labeling. Use of auxotrophs drastically decreases the amount of labeled precursor required for expression without impacting the yield. The concept is extended to Thr methyl labeling by means of a Thr-specific auxotroph that provides enhanced efficiency for use with the costly L-[4-{sup 13}C,2,3-{sup 2}H{sub 2},{sup 15}N]-Thr reagent. The Thr-specific strain allows for the production of Thr-[{sup 13}CH{sub 3}]{sup γ2} labeled protein with an optimal isotope incorporation using up to 50 % less labeled Thr than the traditional E. coli strain without the need for {sup 2}H-glycine to prevent scrambling.

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.

China Refrigerator Information Label: Specification Development and Potential Impact

Energy Technology Data Exchange (ETDEWEB)

Fridley, David; Fridley, David; Zheng, Nina; Zhou, Nan; Aden, Nathaniel; Lin, Jiang; Jianhong, Cheng; Sakamoto, Tomoyuki

2008-02-01

In the last five years, China's refrigerator market has grown rapidly, and now urban markets are showing signs of saturation, with ownership rates in urban households reaching 92%. Rural markets continue to grow from a much lower base. As a result of this growth, the Chinese government in 2006 decided to revise the refrigerator standards and its associated efficiency grades for the mandatory energy information label. In the Chinese standards process, the efficiency grades for the information label are tied to the minimum standards. Work on the minimum standards revision began in 2006 and continued through the first half of 2007, when the draft standard was completed under the direction of the China National Institute of Standardization (CNIS). Development of the information label grades required consideration of stakeholder input, continuity with the previous grade classification, ease of implementation, and potential impacts on the market. In this process, CLASP, with the support of METI/IEEJ, collaborated with CNIS to develop the efficiency grades, providing technical input to the process, comment and advice on particular technical issues, and evaluation of the results. After three months of effort and three drafts of the final grade specifications, this work was completed. In addition, in order to effectively evaluate the impact of the label on China's market, CLASP further provided assistance to CNIS to collect data on both the efficiency distribution and product volume distribution of refrigerators on the market. The new information label thresholds to be implemented in 2008 maintain the approach first adopted in 2005 of establishing efficiency levels relative to the minimum standard, but increased the related required efficiency levels by 20% over those established in 2003 and implemented in 2005. The focus of improvement was on the standard refrigerator/freezer (class 5), which constitutes the bulk of the Chinese market. Indeed, the new

49 CFR 172.407 - Label specifications.

Science.gov (United States)

2010-10-01

...) Design. (1) Except for size and color, the printing, inner border, and symbol on each label must be as... withstand, without deterioration or a substantial change in color, a 30-day exposure to conditions incident... shown in the appropriate section of this subpart. (d) Color. (1) The background color on each label must...

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.

Transcranial passive acoustic mapping with hemispherical sparse arrays using CT-based skull-specific aberration corrections: a simulation study

International Nuclear Information System (INIS)

Jones, Ryan M; O’Reilly, Meaghan A; Hynynen, Kullervo

2013-01-01

The feasibility of transcranial passive acoustic mapping with hemispherical sparse arrays (30 cm diameter, 16 to 1372 elements, 2.48 mm receiver diameter) using CT-based aberration corrections was investigated via numerical simulations. A multi-layered ray acoustic transcranial ultrasound propagation model based on CT-derived skull morphology was developed. By incorporating skull-specific aberration corrections into a conventional passive beamforming algorithm (Norton and Won 2000 IEEE Trans. Geosci. Remote Sens. 38 1337–43), simulated acoustic source fields representing the emissions from acoustically-stimulated microbubbles were spatially mapped through three digitized human skulls, with the transskull reconstructions closely matching the water-path control images. Image quality was quantified based on main lobe beamwidths, peak sidelobe ratio, and image signal-to-noise ratio. The effects on the resulting image quality of the source’s emission frequency and location within the skull cavity, the array sparsity and element configuration, the receiver element sensitivity, and the specific skull morphology were all investigated. The system’s resolution capabilities were also estimated for various degrees of array sparsity. Passive imaging of acoustic sources through an intact skull was shown possible with sparse hemispherical imaging arrays. This technique may be useful for the monitoring and control of transcranial focused ultrasound (FUS) treatments, particularly non-thermal, cavitation-mediated applications such as FUS-induced blood–brain barrier disruption or sonothrombolysis, for which no real-time monitoring techniques currently exist. (paper)

Transcranial passive acoustic mapping with hemispherical sparse arrays using CT-based skull-specific aberration corrections: a simulation study

Science.gov (United States)

Jones, Ryan M.; O’Reilly, Meaghan A.; Hynynen, Kullervo

2013-01-01

The feasibility of transcranial passive acoustic mapping with hemispherical sparse arrays (30 cm diameter, 16 to 1372 elements, 2.48 mm receiver diameter) using CT-based aberration corrections was investigated via numerical simulations. A multi-layered ray acoustic transcranial ultrasound propagation model based on CT-derived skull morphology was developed. By incorporating skull-specific aberration corrections into a conventional passive beamforming algorithm (Norton and Won 2000 IEEE Trans. Geosci. Remote Sens. 38 1337–43), simulated acoustic source fields representing the emissions from acoustically-stimulated microbubbles were spatially mapped through three digitized human skulls, with the transskull reconstructions closely matching the water-path control images. Image quality was quantified based on main lobe beamwidths, peak sidelobe ratio, and image signal-to-noise ratio. The effects on the resulting image quality of the source’s emission frequency and location within the skull cavity, the array sparsity and element configuration, the receiver element sensitivity, and the specific skull morphology were all investigated. The system’s resolution capabilities were also estimated for various degrees of array sparsity. Passive imaging of acoustic sources through an intact skull was shown possible with sparse hemispherical imaging arrays. This technique may be useful for the monitoring and control of transcranial focused ultrasound (FUS) treatments, particularly non-thermal, cavitation-mediated applications such as FUS-induced blood-brain barrier disruption or sonothrombolysis, for which no real-time monitoring technique currently exists. PMID:23807573

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....

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

Synthesis of molecules of biological interest labelled with high specific activity tritium

International Nuclear Information System (INIS)

Petillot, Yves

1975-01-01

Labelled molecules are artificial organic compounds possessing one or several radioactive or steady isotopic atoms. Using tritium to label molecules presents several benefits: a raw material easy to obtain with a high purity and at reasonable cost; synthesised labelled molecules displaying high specific activities very interesting in molecular biology; high resolution of radiographies; relatively simple and quick introduction of tritium atoms in complex molecules. Thus, this report for graduation in organic chemistry addresses the synthesis and study of new labelled molecules which belong to families of organic compounds which have fundamental activities in biology: uridine 3 H-5,6 and thymidine 3 H-methyl which are nucleotides which intervene under the form of phosphates in the synthesis of nucleic acids, oestradiol 3 H-2,4,6,7 which is a powerful estrogenic hormone which naturally secreted by the ovary; and noradrenaline 3 H-1,1' and dopamine 3 H-1,2 which are usually secreted by adrenal medulla and have multiple actions on the nervous system

Spin-labelling study of interactions of ovalbumin with multilamellar liposomes and specific anti-ovalbumin antibodies.

Science.gov (United States)

Brgles, Marija; Mirosavljević, Krunoslav; Noethig-Laslo, Vesna; Frkanec, Ruza; Tomasić, Jelka

2007-03-10

Ovalbumin (OVA) has been used continuously as the model antigen in numerous studies of immune reactions and antigen processing, very often encapsulated into liposomes. The purpose of this work was to study the possible interactions of spin-labelled OVA and lipids in liposomal membranes using electron spin resonance (ESR) spectroscopy. OVA was covalently spin-labelled with 4-maleimido-2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO-maleimide), characterized and encapsulated into multilamellar, negatively charged liposomes. ESR spectra of this liposomal preparation gave evidence for the interaction of OVA with the lipid bilayers. Such an interaction was also evidenced by the ESR spectra of liposomal preparation containing OVA, where liposomes were spin-labelled with n-doxyl stearic acids. The spin-labelled OVA retains its property to bind specific anti-OVA antibodies, as shown by ESR spectroscopy, but also in ELISA for specific anti-OVA IgG.

40 CFR 170.232 - Knowledge of labeling and site-specific information.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Knowledge of labeling and site-specific information. 170.232 Section 170.232 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS WORKER PROTECTION STANDARD Standard for Pesticide Handlers § 170.232 Knowledge...

Efficient implementations of block sparse matrix operations on shared memory vector machines

International Nuclear Information System (INIS)

Washio, T.; Maruyama, K.; Osoda, T.; Doi, S.; Shimizu, F.

2000-01-01

In this paper, we propose vectorization and shared memory-parallelization techniques for block-type random sparse matrix operations in finite element (FEM) applications. Here, a block corresponds to unknowns on one node in the FEM mesh and we assume that the block size is constant over the mesh. First, we discuss some basic vectorization ideas (the jagged diagonal (JAD) format and the segmented scan algorithm) for the sparse matrix-vector product. Then, we extend these ideas to the shared memory parallelization. After that, we show that the techniques can be applied not only to the sparse matrix-vector product but also to the sparse matrix-matrix product, the incomplete or complete sparse LU factorization and preconditioning. Finally, we report the performance evaluation results obtained on an NEC SX-4 shared memory vector machine for linear systems in some FEM applications. (author)

Transport of C-13-labelled linoleic and C-13-labelled caprylic acid in rat plasma after administration of specific structured triacylglycerols

DEFF Research Database (Denmark)

Vistisen, Bodil; Høy, Carl-Erik

2004-01-01

the transport of dietary C-13-labelled fatty acids in rat plasma to compare the chylomicron fatty acid metabolism after administration of specific structured, long chain and medium chain triacylglycerols. Rats were fed ML*M, M*LM*, L*L*L* or M*M*M* (L=linoleic acid, 18:2n-6, M=caprylic acid, 8:0, * = C-13......-labelled fatty acid) by gavage. A maximum transport of 0.5% of the administered C-13-labelled 18:2n-6 was observed in 1mL rat plasma both after administration of L*L*L* and ML*M, while approximately 0.04% of the administered C-13-labelled 8:0 was detected in 1mL plasma following administration of M......*M*M* or M*LM*. After L*L*L* administration C-13-labelled 20:4n-6 was observed in plasma, probably formed by elongation and desaturation of 18:2n-6 in the enterocyte or liver cells. Furthermore, C-13-labelled 16:0, 48:0, 18: 1n-9 and 20:4n-6 were observed in plasma of rats fed M*M*M* and M*LM* due...

Tc-99m labeled Sparfloxacin: A specific infection imaging agent

International Nuclear Information System (INIS)

Singh, A.K.; Verma, J.; Bhatnagar, A.; Ali, A.

2003-01-01

Radiolabeled antibiotics are being used for the specific diagnosis of infection by exploiting their specific binding properties to the bacterial component, thereby making it possible to differentiate infection from sterile lesions. A new radiopharmaceutical, Tc-99m Sparfloxacin has been developed for infection imaging. Sparfloxacin is a quinolone based broad-spectrum antibiotic, which is more potent than Ciprofloxacin. Radiolabeling of Sparfloxacin with Tc-99m was standardized using direct labeling protocol. Labeling efficiency, in-vitro and in-vivo stability, blood kinetics and organ distribution studies (in balb/c mice and New Zealand White Rabbits at different time interval up to 24hrs) were carried out. Biological activity of Sparfloxacin after its labeling with Tc-99m was evaluated with S.aureus using Peptone water (DIFCO) as media. Turpentine oil (100 μl) in left thigh and S.aureus (100μl of 3x10 7 cells) in right thigh were injected intramuscularly to create sterile and infective inflammation respectively in six New Zealand white rabbits. The localization kinetics of the radiolabeled complex were studied in the animal model by injecting 70-75MBq of Tc-99m Sparfloxacin intravenously in the ear of rabbit and the images were taken with a Gamma-camera (ECIL) at different post-injection time intervals. Standardized protocol produced >95% labeled complex. About 8% of tracer leached out at 24 hrs when incubated in serum at 37 0 C, confirming high stability of the complex. Blood clearance in rabbit revealed biphasic pattern and 50% of the complex clears from the blood within 5 min. Biodistribution studies in balb/c mice showed hepatobiliary route of excretion. Presence of insignificant amount of tracer at 24 hrs in the stomach confirmed high in vivo stability of the complex. Imaging in rabbits showed significant concentration of tracer in lesions with infection. Typical imaging patterns revealed initial accumulation of radiotracer in both sterile inflammatory

A two emulsion autoradiographic technique and the discriminating of the three different types of labelling after double labelling with 3H- and 14C-thymidine

International Nuclear Information System (INIS)

Schultze, B.; Maurer, W.; Hagenbusch, H.

1976-01-01

The first part of the paper deals with a two emulsion autoradiographic technique for double labelling experiments with 3 H- and 14 C-thymidine which permits a clear discrimination of the different types of labelling. In the second part the application of this technique to cell kinetics studies is discussed. Accurate discrimination between the different types of labelling, namely purely 3 H-, purely 14 C- and double ( 3 H + 14 C) labelling, is only possible if the activity ratio of 3 H- to 14 C-thymidine is sufficiently high. This condition is necessary for a reliable distinction between those grains in the first emulsion which are due to true 3 H-labelling and spurious grains which are simultaneously produced in the same emulsion by 14 C-β- particles. Experiments are described to determine the required activity ratio of 3 H- to 14 C-thymidine. (author)

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...

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.

Specific photoaffinity labeling of two plasma membrane polypeptides with an azido auxin

International Nuclear Information System (INIS)

Hicks, G.R.; Rayle, D.L.; Jones, A.M.; Lomax, T.L.

1989-01-01

Plasma membrane vesicles were isolated from zucchini (Cucurbita pepo) hypocotyl tissue by aqueous phase partitioning and assessed for homogeneity by the use of membrane-specific enzyme assays. The highly pure plasma membrane vesicles maintained a pH differential across the membrane and accumulated a tritiated azido analogue of 3-indoleacetic acid (IAA), 5-azido-[7- 3 H]IAA([ 3 H]N 3 IAA), in a manner similar to the accumulation of [ 3 H]IAA. The association of the [ 3 H]N 3 IAA with membrane vesicles was saturable and subject to competition by IAA and auxin analogues. Auxin-binding proteins were photoaffinity labeled by addition of [ 3 H]N 3 IAA to plasma membrane vesicles prior to exposure to UV light and detected by subsequent NaDodSO 4 /PAGE and fluorography. When the reaction temperature was lowered to -196 degree C, high-specific-activity labeling of a 40-kDa and a 42-kDa polypeptide was observed. Collectively, these results suggest that the radiolabeled polypeptides are auxin receptors. The covalent nature of the label should facilitate purification and further characterization of the receptors

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.

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.

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.

Synthesis of high specific activity tritium labelled [2-3H]-adenosine-5'-triphosphate

International Nuclear Information System (INIS)

Jaiswal, D.K.; Morimoto, H.; Trump, E.L.; Williams, P.G.; Wemmer, D.E.

1996-01-01

A procedure for high level tritium labelling at the C2-H position of adenosine 5'-triphosphate ([2- 3 H]-ATP, 1), based on the tritiodehalogenation reaction of 2-bromoadenosine 5'-triphosphate (2) has been elaborated. This precursor was prepared in a six-step synthesis from guanosine. The tritiodehalogenation of (2) for three hours over palladium oxide in phosphate buffer yielded tritium labelled ATP with high specific activity, in good chemical yield. (author)

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.

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.

Localization of 131I-labeled p97-specific Fab fragments in human melanoma as a basis for radiotherapy

International Nuclear Information System (INIS)

Larson, S.M.; Carrasquillo, J.A.; Krohn, K.A.

1983-01-01

33 patients with advanced malignant melanoma were studied after intravenous administration of 131 I-labeled Fab fragments specific for p97, an oncofetal glycoprotein of human melanoma. In all, 47 gamma camera imaging studies were performed for the purpose of localization of metastatic deposits. In addition to tumor, 131 I-Fab uptake was also seen in liver and kidney. 20 of these studies included simultaneous administration of both an 131 I-labeled Fab specific for p97, and an 125 I-labeled Fab not specific for p97. Blood clearance of p97-specific Fab was significantly more rapid than for nonspecific Fab. Eight of these patients had biopsies of subcutaneous nodules at 48 and 72 h postinjection in order to assess whether localization of radioactivity was antigen specific. Antigen-specific localization was observed with average ratios of specific/nonspecific uptake of 3.7 (48 h) and 3.4 (72 h); uptake was strongly correlated with tumor p97 concentration (r . 0.81, P less than 0.01). Also, imaging studies of the bio-distribution of 131 I-labeled anti-p97 Fab in patients selected for high p97 tumor concentration showed avid tumor uptake and more prolonged retention of labeled Fab in tumor than in normal tissues. Based on these studies, we estimated that total 131 I doses of 500 mCi could be safely given to patients before dose-limiting toxicity would be observed

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.

Probing Xylan-Specific Raman Bands for Label-Free Imaging Xylan in Plant Cell Wall

Energy Technology Data Exchange (ETDEWEB)

Zeng, Yining; Yarbrough, John M.; Mittal, Ashutosh; Tucker, Melvin P.; Vinzant, Todd; Himmel, Michael E.

2015-06-15

Xylan constitutes a significant portion of biomass (e.g. 22% in corn stover used in this study). Xylan is also an important source of carbohydrates, besides cellulose, for renewable and sustainable energy applications. Currently used method for the localization of xylan in biomass is to use fluorescence confocal microscope to image the fluorescent dye labeled monoclonal antibody that specifically binds to xylan. With the rapid adoption of the Raman-based label-free chemical imaging techniques in biology, identifying Raman bands that are unique to xylan would be critical for the implementation of the above label-free techniques for in situ xylan imaging. Unlike lignin and cellulose that have long be assigned fingerprint Raman bands, specific Raman bands for xylan remain unclear. The major challenge is the cellulose in plant cell wall, which has chemical units highly similar to that of xylan. Here we report using xylanase to specifically remove xylan from feedstock. Under various degree of xylan removal, with minimum impact to other major cell wall components, i.e. lignin and cellulose, we have identified Raman bands that could be further tested for chemical imaging of xylan in biomass in situ.

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.

Carbon-14 labeling of phytoplankton carbon and chlorophyll a carbon: determination of specific growth rates

International Nuclear Information System (INIS)

Welschmeyer, N.A.; Lorenzen, C.J.

1984-01-01

The pattern of photosynthetic 14 C labeling over time is described for phytoplankton. The carbon-specific growth rate (d -1 ) is defined explicitly by changes in the specific activity (dpm μg -1 C) of the algae. For Skeletonema costatum, growing in axenic batch culture, the specific activities of both total cellular carbon and chlorophyll carbon increase at equal rates and the change in specific activity with time follows the predicted pattern. The specific activity of 14 C-labeled chlorophyll a was used to estimate phytoplankton growth rates and C:Chl ratios of field samples in Dabob Bay (Puget Sound), Washington. Growth rates decreased with depth and C:Chl ratios were higher for samples incubated under high light intensity. In several instances the C:Chl ratio increased from the beginning to the end of the incubation; this trend was most conspicuous near surface light intensities and for days of high total incident radiation. On these occasions, Chl a was actively 14 C labeled, yet little (or even negative) change was noted in the concentration of Chl a. These results suggest that some process (or processes) of chlorophyll degradation must be active at the same time that chlorophyll is being synthesized

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.

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

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.

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.

Mutation rules and the evolution of sparseness and modularity in biological systems.

Directory of Open Access Journals (Sweden)

Tamar Friedlander

Full Text Available Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity--the near decomposability of the system into modules with distinct functionality. Recent work suggests that modularity can evolve in a variety of circumstances, including goals that vary in time such that they share the same subgoals (modularly varying goals, or when connections are costly. Here, we studied the origin of modularity and sparseness focusing on the nature of the mutation process, rather than on connection cost or variations in the goal. We use simulations of evolution with different mutation rules. We found that commonly used sum-rule mutations, in which interactions are mutated by adding random numbers, do not lead to modularity or sparseness except for in special situations. In contrast, product-rule mutations in which interactions are mutated by multiplying by random numbers--a better model for the effects of biological mutations--led to sparseness naturally. When the goals of evolution are modular, in the sense that specific groups of inputs affect specific groups of outputs, product-rule mutations also lead to modular structure; sum-rule mutations do not. Product-rule mutations generate sparseness and modularity because they tend to reduce interactions, and to keep small interaction terms small.

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.

Specific labeling and assignment strategies of valine methyl groups for NMR studies of high molecular weight proteins

Energy Technology Data Exchange (ETDEWEB)

Mas, Guillaume; Crublet, Elodie [Univ. Grenoble Alpes, Institut de Biologie Structurale (IBS) (France); Hamelin, Olivier [CNRS (France); Gans, Pierre; Boisbouvier, Jérôme, E-mail: jerome.boisbouvier@ibs.fr [Univ. Grenoble Alpes, Institut de Biologie Structurale (IBS) (France)

2013-09-28

The specific protonation of valine and leucine methyl groups in proteins is typically achieved by overexpressing proteins in M9/D{sub 2}O medium supplemented with either labeled α-ketoisovalerate for the labeling of the four prochiral methyl groups or with 2-acetolactate for the stereospecific labeling of the valine and leucine side chains. However, when these labeling schemes are applied to large protein assemblies, significant overlap between the correlations of the valine and leucine methyl groups occurs, hampering the analysis of 2D methyl-TROSY spectra. Analysis of the leucine and valine biosynthesis pathways revealed that the incorporation of labeled precursors in the leucine pathway can be inhibited by the addition of exogenous l-leucine-d{sub 10}. We exploited this property to label stereospecifically the pro-R and pro-S methyl groups of valine with minimal scrambling to the leucine residues. This new labeling protocol was applied to the 468 kDa homododecameric peptidase TET2 to decrease the complexity of its NMR spectra. All of the pro-S valine methyl resonances of TET2 were assigned by combining mutagenesis with this innovative labeling approach. The assignments were transferred to the pro-R groups using an optimally labeled sample and a set of triple resonance experiments. This improved labeling scheme enables us to overcome the main limitation of overcrowding in the NMR spectra of prochiral methyl groups, which is a prerequisite for the site-specific measurement of the structural and dynamic parameters or for the study of interactions in very large protein assemblies.

Simultaneous neuron- and astrocyte-specific fluorescent marking

Energy Technology Data Exchange (ETDEWEB)

Schulze, Wiebke [Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871 (Japan); Hayata-Takano, Atsuko [Molecular Research Center for Children' s Mental Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, 2-2 Yamadaoka, Suita, Osaka 565-0871 (Japan); Kamo, Toshihiko [Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871 (Japan); Nakazawa, Takanobu, E-mail: takanobunakazawa-tky@umin.ac.jp [iPS Cell-based Research Project on Brain Neuropharmacology and Toxicology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871 (Japan); Nagayasu, Kazuki [iPS Cell-based Research Project on Brain Neuropharmacology and Toxicology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871 (Japan); Kasai, Atsushi; Seiriki, Kaoru [Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871 (Japan); Interdisciplinary Program for Biomedical Sciences, Institute for Academic Initiatives, Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871 (Japan); Shintani, Norihito [Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871 (Japan); Ago, Yukio [Laboratory of Medicinal Pharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871 (Japan); Farfan, Camille [Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871 (Japan); and others

2015-03-27

Systematic and simultaneous analysis of multiple cell types in the brain is becoming important, but such tools have not yet been adequately developed. Here, we aimed to generate a method for the specific fluorescent labeling of neurons and astrocytes, two major cell types in the brain, and we have developed lentiviral vectors to express the red fluorescent protein tdTomato in neurons and the enhanced green fluorescent protein (EGFP) in astrocytes. Importantly, both fluorescent proteins are fused to histone 2B protein (H2B) to confer nuclear localization to distinguish between single cells. We also constructed several expression constructs, including a tandem alignment of the neuron- and astrocyte-expression cassettes for simultaneous labeling. Introducing these vectors and constructs in vitro and in vivo resulted in cell type-specific and nuclear-localized fluorescence signals enabling easy detection and distinguishability of neurons and astrocytes. This tool is expected to be utilized for the simultaneous analysis of changes in neurons and astrocytes in healthy and diseased brains. - Highlights: • We develop a method for the specific fluorescent labeling of neurons and astrocytes. • Neuron-specific labeling is achieved using Scg10 and synapsin promoters. • Astrocyte-specific labeling is generated using the minimal GFAP promoter. • Nuclear localization of fluorescent proteins is achieved with histone 2B protein.

Simultaneous neuron- and astrocyte-specific fluorescent marking

International Nuclear Information System (INIS)

Schulze, Wiebke; Hayata-Takano, Atsuko; Kamo, Toshihiko; Nakazawa, Takanobu; Nagayasu, Kazuki; Kasai, Atsushi; Seiriki, Kaoru; Shintani, Norihito; Ago, Yukio; Farfan, Camille

2015-01-01

Systematic and simultaneous analysis of multiple cell types in the brain is becoming important, but such tools have not yet been adequately developed. Here, we aimed to generate a method for the specific fluorescent labeling of neurons and astrocytes, two major cell types in the brain, and we have developed lentiviral vectors to express the red fluorescent protein tdTomato in neurons and the enhanced green fluorescent protein (EGFP) in astrocytes. Importantly, both fluorescent proteins are fused to histone 2B protein (H2B) to confer nuclear localization to distinguish between single cells. We also constructed several expression constructs, including a tandem alignment of the neuron- and astrocyte-expression cassettes for simultaneous labeling. Introducing these vectors and constructs in vitro and in vivo resulted in cell type-specific and nuclear-localized fluorescence signals enabling easy detection and distinguishability of neurons and astrocytes. This tool is expected to be utilized for the simultaneous analysis of changes in neurons and astrocytes in healthy and diseased brains. - Highlights: • We develop a method for the specific fluorescent labeling of neurons and astrocytes. • Neuron-specific labeling is achieved using Scg10 and synapsin promoters. • Astrocyte-specific labeling is generated using the minimal GFAP promoter. • Nuclear localization of fluorescent proteins is achieved with histone 2B protein

Fluorescently labeled dengue viruses as probes to identify antigen-specific memory B cells by multiparametric flow cytometry.

Science.gov (United States)

Woda, Marcia; Mathew, Anuja

2015-01-01

Low frequencies of memory B cells in the peripheral blood make it challenging to measure the functional and phenotypic characteristics of this antigen experienced subset of B cells without in vitro culture. To date, reagents are lacking to measure ex vivo frequencies of dengue virus (DENV)-specific memory B cells. We wanted to explore the possibility of using fluorescently labeled DENV as probes to detect antigen-specific memory B cells in the peripheral blood of DENV immune individuals. Alexa Fluor dye-labeled DENV yielded viable virus that could be stored at -80°C for long periods of time. Using a careful gating strategy and methods to decrease non-specific binding, we were able to identify a small frequency of B cells from dengue immune individuals that bound labeled DENV. Sorted DENV(+) B cells from immune, but not naïve donors secreted antibodies that bound DENV after in vitro stimulation. Overall, Alexa Fluor dye-labeled DENVs are useful reagents to enable the detection and characterization of memory B cells in DENV immune individuals. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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.

21-Methylpyrenyl-cholesterol stably and specifically associates with lipoprotein peripheral hemi-membrane: A new labelling tool

Energy Technology Data Exchange (ETDEWEB)

Gaibelet, Gérald [INSERM U563, CHU Purpan, Toulouse (France); CEA, SB2SM and UMR8221 CNRS, IBiTec-Saclay, Gif-sur-Yvette (France); Tercé, François [Université Toulouse III, UMR 1048, Toulouse (France); INSERM U1048, Toulouse (France); Bertrand-Michel, Justine [Université Toulouse III, UMR 1048, Toulouse (France); INSERM U1048, Lipidomic Platform Metatoul, Toulouse (France); Allart, Sophie [Plateau Technique d’Imagerie Cellulaire, INSERM U1043, Toulouse (France); Azalbert, Vincent [Université Toulouse III, UMR 1048, Toulouse (France); INSERM U1048, Toulouse (France); Lecompte, Marie-France [INSERM U563, Faculté de Médecine de Rangueil, Toulouse (France); Collet, Xavier [Université Toulouse III, UMR 1048, Toulouse (France); INSERM U1048, Toulouse (France); Orlowski, Stéphane, E-mail: stephane.orlowski@cea.fr [INSERM U563, CHU Purpan, Toulouse (France); CEA, SB2SM and UMR8221 CNRS, IBiTec-Saclay, Gif-sur-Yvette (France)

2013-11-01

Highlights: •21-Methylpyrenyl-cholesterol specifically and stably associates to lipoproteins. •It is not esterified by LCAT, and thus reliably labels their peripheral hemi-membrane. •HDL vs. LDL are well distinguishable by various fluorescent labelling characteristics. •LDL peripheral hemi-membrane harbors cholesterol-rich ordered lipid (micro)domains. •Cultured cells can be stained by such labelled lipoproteins-mediated delivery. -- Abstract: Lipoproteins are important biological components. However, they have few convenient fluorescent labelling probes currently reported, and their physiological reliability can be questioned. We compared the association of two fluorescent cholesterol derivatives, 22-nitrobenzoxadiazole-cholesterol (NBD-Chol) and 21-methylpyrenyl-cholesterol (Pyr-met-Chol), to serum lipoproteins and to purified HDL and LDL. Both lipoproteins could be stably labelled by Pyr-met-Chol, but virtually not by NBD-Chol. At variance with NBD-Chol, LCAT did not esterify Pyr-met-Chol. The labelling characteristics of lipoproteins by Pyr-met-Chol were well distinguishable between HDL and LDL, regarding dializability, associated probe amount and labelling kinetics. We took benefit of the pyrene labelling to approach the structural organization of LDL peripheral hemi-membrane, since Pyr-met-Chol-labelled LDL, but not HDL, presented a fluorescence emission of pyrene excimers, indicating that the probe was present in an ordered lipid micro-environment. Since the peripheral membrane of LDL contains more sphingomyelin (SM) than HDL, this excimer formation was consistent with the existence of cholesterol- and SM-enriched lipid microdomains in LDL, as already suggested in model membranes of similar composition and reminiscent to the well-described “lipid rafts” in bilayer membranes. Finally, we showed that Pyr-met-Chol could stain cultured PC-3 cells via lipoprotein-mediated delivery, with a staining pattern well different to that observed with NBD

21-Methylpyrenyl-cholesterol stably and specifically associates with lipoprotein peripheral hemi-membrane: A new labelling tool

International Nuclear Information System (INIS)

Gaibelet, Gérald; Tercé, François; Bertrand-Michel, Justine; Allart, Sophie; Azalbert, Vincent; Lecompte, Marie-France; Collet, Xavier; Orlowski, Stéphane

2013-01-01

Highlights: •21-Methylpyrenyl-cholesterol specifically and stably associates to lipoproteins. •It is not esterified by LCAT, and thus reliably labels their peripheral hemi-membrane. •HDL vs. LDL are well distinguishable by various fluorescent labelling characteristics. •LDL peripheral hemi-membrane harbors cholesterol-rich ordered lipid (micro)domains. •Cultured cells can be stained by such labelled lipoproteins-mediated delivery. -- Abstract: Lipoproteins are important biological components. However, they have few convenient fluorescent labelling probes currently reported, and their physiological reliability can be questioned. We compared the association of two fluorescent cholesterol derivatives, 22-nitrobenzoxadiazole-cholesterol (NBD-Chol) and 21-methylpyrenyl-cholesterol (Pyr-met-Chol), to serum lipoproteins and to purified HDL and LDL. Both lipoproteins could be stably labelled by Pyr-met-Chol, but virtually not by NBD-Chol. At variance with NBD-Chol, LCAT did not esterify Pyr-met-Chol. The labelling characteristics of lipoproteins by Pyr-met-Chol were well distinguishable between HDL and LDL, regarding dializability, associated probe amount and labelling kinetics. We took benefit of the pyrene labelling to approach the structural organization of LDL peripheral hemi-membrane, since Pyr-met-Chol-labelled LDL, but not HDL, presented a fluorescence emission of pyrene excimers, indicating that the probe was present in an ordered lipid micro-environment. Since the peripheral membrane of LDL contains more sphingomyelin (SM) than HDL, this excimer formation was consistent with the existence of cholesterol- and SM-enriched lipid microdomains in LDL, as already suggested in model membranes of similar composition and reminiscent to the well-described “lipid rafts” in bilayer membranes. Finally, we showed that Pyr-met-Chol could stain cultured PC-3 cells via lipoprotein-mediated delivery, with a staining pattern well different to that observed with NBD

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...

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.

A rapid and convenient method for specific 11C-labelling of synthetic polypeptides containing methionine

International Nuclear Information System (INIS)

Laengstroem, B.; Sjoeberg, S.; Ragnarsson, U.

1981-01-01

11 C-labelling of methionine residues in a synthetic peptide via the preparation of the corresponding protected, pure homocysteine peptide has been investigated. Complete deprotection of the peptide and specific methylation of the homocysteine residue can be performed in one step in liquid ammonia. As a first application of this method the synthesis of the tripeptide, Z-Gly-L-Hcy(Bzl)-Gly-O-Bzl, and its conversion to Gly-Met-Gly and the corresponding labelled Gly-([ 11 C]-methyl)-Met-Gly, is reported. Starting with the protected peptide the labelling was performed in 20 +- 5 min (starting with 11 CO 2 ), yielding the labelled peptide in 92 +- 5 % radiochemical yield. Analyses and preparative LC can be performed within 6 min. (author)

In vivo tumor angiogenesis imaging with site-specific labeled 99mTc-HYNIC-VEGF

International Nuclear Information System (INIS)

Blankenberg, Francis G.; Backer, Marina V.; Patel, Vimalkumar; Backer, Joseph M.; Levashova, Zoia

2006-01-01

We recently developed a cysteine-containing peptide tag (C-tag) that allows for site-specific modification of C-tag-containing fusion proteins with a bifunctional chelator, HYNIC (hydrazine nicotinamide)-maleimide. We then constructed and expressed C-tagged vascular endothelial growth factor (VEGF) and labeled it with HYNIC. We wished to test 99m Tc-HYNIC-C-tagged VEGF ( 99m Tc-HYNIC-VEGF) for the imaging of tumor vasculature before and after antiangiogenic (low continuous dosing, metronomic) and tumoricidal (high-dose) cyclophosphamide treatment. HYNIC-maleimide was reacted with the two thiol groups of C-tagged VEGF without any effect on biologic activity in vitro. 99m Tc-HYNIC-VEGF was prepared using tin/tricine as an exchange reagent, and injected via the tail vein (200-300 μCi, 1-2 μg protein) followed by microSPECT imaging 1 h later. Sequencing analysis of HYNIC-containing peptides obtained after digestion confirmed the site-specific labeling of the two accessible thiol groups of C-tagged VEGF. Tumor vascularity was easily visualized with 99m Tc/VEGF in Balb/c mice with 4T1 murine mammary carcinoma 10 days after implantation into the left axillary fat pad in controls (12.3±5.0 tumor/bkg, n=27) along with its decrease following treatment with high (150 mg/kg q.o.d. x 4; 1.14±0.48 tumor/bkg, n=9) or low (25 mg/kg q.d. x 7; 1.03±0.18 tumor/bkg, n=9) dose cyclophosphamide. Binding specificity was confirmed by observing a 75% decrease in tumor uptake of 99m Tc/biotin-inactivated VEGF, as compared with 99m Tc-HYNIC-VEGF. 99m Tc can be loaded onto C-tagged VEGF in a site-specific fashion without reducing its bioactivity. 99m Tc-HYNIC-VEGF can be rapidly prepared for the imaging of tumor vasculature and its response to different types of chemotherapy. (orig.)

The in vivo fate of a 211At labelled monoclonal antibody with known specificity in a murine system

International Nuclear Information System (INIS)

Vaughan, A.T.M.; Bateman, W.J.; Fisher, D.R.

1982-01-01

A monoclonal antibody reactive against the human transferrin receptor has been labelled with the alpha and X ray emitting isotope Astatine 211. The labelling procedure does not affect the ability of the product to bind to the transferrin receptor on the human leukemic cell line HL60. Using a direct binding assay, 211 At labelled antibody can be specifically inhibited from binding to its target cells by excess unlabelled antibody. Furthermore, the binding inhibition demonstrated in this system correlates to enhanced clonogenic survival of these cells, indicating that very few atoms of 211 At/cell are required for cell death. Data obtained from labelled antibody injected into mice show that the labelled product in serum retains the ability to bind to HL60 cells in vitro, although tissue distributions of the injected activity implies that some of the radiolabel is lost from the protein. Despite this loss of label, preliminary experiments on the localization of labelled antibody to HL60 cells growing s/c in nude mice show that tumor tissue has a higher specific activity than all other tissues, other than blood, after 12 hours. This suggests that further work on the nature of label degradation in vivo is warranted in the context of potential therapeutic and diagnostic studies

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...

Dual-mode fluorophore-doped nickel nitrilotriacetic acid-modified silica nanoparticles combine histidine-tagged protein purification with site-specific fluorophore labeling.

Science.gov (United States)

Kim, Sung Hoon; Jeyakumar, M; Katzenellenbogen, John A

2007-10-31

We present the first example of a fluorophore-doped nickel chelate surface-modified silica nanoparticle that functions in a dual mode, combining histidine-tagged protein purification with site-specific fluorophore labeling. Tetramethylrhodamine (TMR)-doped silica nanoparticles, estimated to contain 700-900 TMRs per ca. 23 nm particle, were surface modified with nitrilotriacetic acid (NTA), producing TMR-SiO2-NTA-Ni2+. Silica-embedded TMR retains very high quantum yield, is resistant to quenching by buffer components, and is modestly quenched and only to a certain depth (ca. 2 nm) by surface-attached Ni2+. When exposed to a bacterial lysate containing estrogen receptor alpha ligand binding domain (ERalpha) as a minor component, these beads showed very high specificity binding, enabling protein purification in one step. The capacity and specificity of these beads for binding a his-tagged protein were characterized by electrophoresis, radiometric counting, and MALDI-TOF MS. ERalpha, bound to TMR-SiO2-NTA-Ni++ beads in a site-specific manner, exhibited good activity for ligand binding and for ligand-induced binding to coactivators in solution FRET experiments and protein microarray fluorometric and FRET assays. This dual-mode type TMR-SiO2-NTA-Ni2+ system represents a powerful combination of one-step histidine-tagged protein purification and site-specific labeling with multiple fluorophore species.

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.

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.

Radiation-induced tritium labelling and product analysis

Energy Technology Data Exchange (ETDEWEB)

Peng, C.T. (California Univ., San Francisco, CA (United States). Dept. of Pharmaceutical Chemistry)

1993-05-01

By-products formed in radiation-induced tritium labelling are identified by co-chromatography with authentic samples or by structure prediction using a quantitative structure-retention index relationship. The by-products, formed from labelling of steroids, polynuclear aromatic hydrocarbons, 7-membered heterocyclic ring structures, 1,4-benzodiazepines, 1-haloalkanes, etc. with activated tritium and adsorbed tritium, are shown to be specifically labelled and anticipated products from known chemical reactions. From analyses of the by-products, one can conclude that the hydrogen abstraction by tritium atoms and the substitution by tritium ions are the mechanisms of labelling. Classification of the tritium labelling methods, on the basis of the type of tritium reagent, clearly shows the active role played by tritium atoms and ions in radiation-induced methods. (author).

Labeling capability of the eluent of gel-type 99Mo-99mTc generator

International Nuclear Information System (INIS)

Wu Xiuduo; Zhang Jingu

1995-01-01

The results of more than 7909 imaging of various organs which have been carried out on domestic gel-type 99 Mo- 99m Tc generators in the last seven years are presented. In all the 220 times of determination for the radiochemical purity of a portion of imaging agents, there were 15 times in which the labeling efficiency was lower than 90% and only 3 times in which the radiolabeling failed. In comparison with labeling capability of nuclear fission generators, it is showed that there are no evident differences between the eluent's labeling efficiency of the two kinds of generators

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

Science.gov (United States)

Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

2016-02-01

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Acridinium esters as high-specific-activity labels in immunoassay

International Nuclear Information System (INIS)

Weeks, I.; Beheshti, I.; McCapra, F.; Campbell, A.K.; Woodhead, J.S.

1983-01-01

A chemiluminescent acridinium ester has been synthesized that reacts spontaneously with proteins to yield stable, immunoreactive derivatives of high specific activity. The compound has been used to prepare chemiluminescent monoclonal antibodies to human alpha 1-fetoprotein having average incorporation ratios as great as 2.8 mol of label per mole of antibody, which corresponds to a detection limit of approximately 8 X 10(-19) mol. These antibodies have been used in the preliminary development of a two-site immunochemiluminometric assay for human alpha 1-fetoprotein, which requires only a 30-min incubation and a quantification time of 5 s per sample

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.

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

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.

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

Group-sparse representation with dictionary learning for medical image denoising and fusion.

Science.gov (United States)

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Sparse linear systems: Theory of decomposition, methods, technology, applications and implementation in Wolfram Mathematica

Energy Technology Data Exchange (ETDEWEB)

Pilipchuk, L. A., E-mail: pilipchik@bsu.by [Belarussian State University, 220030 Minsk, 4, Nezavisimosti avenue, Republic of Belarus (Belarus); Pilipchuk, A. S., E-mail: an.pilipchuk@gmail.com [The Natural Resources and Environmental Protestion Ministry of the Republic of Belarus, 220004 Minsk, 10 Kollektornaya Street, Republic of Belarus (Belarus)

2015-11-30

In this paper we propose the theory of decomposition, methods, technologies, applications and implementation in Wol-fram Mathematica for the constructing the solutions of the sparse linear systems. One of the applications is the Sensor Location Problem for the symmetric graph in the case when split ratios of some arc flows can be zeros. The objective of that application is to minimize the number of sensors that are assigned to the nodes. We obtain a sparse system of linear algebraic equations and research its matrix rank. Sparse systems of these types appear in generalized network flow programming problems in the form of restrictions and can be characterized as systems with a large sparse sub-matrix representing the embedded network structure.

Sparse linear systems: Theory of decomposition, methods, technology, applications and implementation in Wolfram Mathematica

International Nuclear Information System (INIS)

Pilipchuk, L. A.; Pilipchuk, A. S.

2015-01-01

In this paper we propose the theory of decomposition, methods, technologies, applications and implementation in Wol-fram Mathematica for the constructing the solutions of the sparse linear systems. One of the applications is the Sensor Location Problem for the symmetric graph in the case when split ratios of some arc flows can be zeros. The objective of that application is to minimize the number of sensors that are assigned to the nodes. We obtain a sparse system of linear algebraic equations and research its matrix rank. Sparse systems of these types appear in generalized network flow programming problems in the form of restrictions and can be characterized as systems with a large sparse sub-matrix representing the embedded network structure

Progress of new label-free techniques for biosensors: a review.

Science.gov (United States)

Sang, Shengbo; Wang, Yajun; Feng, Qiliang; Wei, Ye; Ji, Jianlong; Zhang, Wendong

2016-01-01

The detection techniques used in biosensors can be broadly classified into label-based and label-free. Label-based detection relies on the specific properties of labels for detecting a particular target. In contrast, label-free detection is suitable for the target molecules that are not labeled or the screening of analytes which are not easy to tag. Also, more types of label-free biosensors have emerged with developments in biotechnology. The latest developed techniques in label-free biosensors, such as field-effect transistors-based biosensors including carbon nanotube field-effect transistor biosensors, graphene field-effect transistor biosensors and silicon nanowire field-effect transistor biosensors, magnetoelastic biosensors, optical-based biosensors, surface stress-based biosensors and other type of biosensors based on the nanotechnology are discussed. The sensing principles, configurations, sensing performance, applications, advantages and restriction of different label-free based biosensors are considered and discussed in this review. Most concepts included in this survey could certainly be applied to the development of this kind of biosensor in the future.

Bacterial production of site specific {sup 13}C labeled phenylalanine and methodology for high level incorporation into bacterially expressed recombinant proteins

Energy Technology Data Exchange (ETDEWEB)

Ramaraju, Bhargavi; McFeeters, Hana; Vogler, Bernhard; McFeeters, Robert L., E-mail: robert.mcfeeters@uah.edu [University of Alabama in Huntsville, Department of Chemistry (United States)

2017-01-15

Nuclear magnetic resonance spectroscopy studies of ever larger systems have benefited from many different forms of isotope labeling, in particular, site specific isotopic labeling. Site specific {sup 13}C labeling of methyl groups has become an established means of probing systems not amenable to traditional methodology. However useful, methyl reporter sites can be limited in number and/or location. Therefore, new complementary site specific isotope labeling strategies are valuable. Aromatic amino acids make excellent probes since they are often found at important interaction interfaces and play significant structural roles. Aromatic side chains have many of the same advantages as methyl containing amino acids including distinct {sup 13}C chemical shifts and multiple magnetically equivalent {sup 1}H positions. Herein we report economical bacterial production and one-step purification of phenylalanine with {sup 13}C incorporation at the Cα, Cγ and Cε positions, resulting in two isolated {sup 1}H-{sup 13}C spin systems. We also present methodology to maximize incorporation of phenylalanine into recombinantly overexpressed proteins in bacteria and demonstrate compatibility with ILV-methyl labeling. Inexpensive, site specific isotope labeled phenylalanine adds another dimension to biomolecular NMR, opening new avenues of study.

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.

Detection of magnetic-labeled antibody specific recognition events by combined atomic force and magnetic force microscopy

International Nuclear Information System (INIS)

Hong Xia; Liu Yanmei; Li Jun; Guo Wei; Bai Yubai

2009-01-01

Atomic force (AFM) and magnetic force microscopy (MFM) were developed to detect biomolecular specific interaction. Goat anti-mouse immunoglobulin (anti-IgG) was covalently attached onto gold substrate modified by a self-assembly monolayer of thioctic acid via 1-ethyl-3-[3-(dimethylamino) propyl] carbodiimide (EDC) activation. Magnetic-labeled IgG then specifically adsorbed onto anti-IgG surface. The morphological variation was identified by AFM. MFM was proved to be a fine assistant tool to distinguish the immunorecognized nanocomposites from the impurities by detection of the magnetic signal from magnetic-labeled IgG. It would enhance the understanding of biomolecular recognition process.

Detection of magnetic-labeled antibody specific recognition events by combined atomic force and magnetic force microscopy

Energy Technology Data Exchange (ETDEWEB)

Hong Xia [Center for Advanced Optoelectronic Functional Materials Research, Key Laboratory of UV Light-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun 130024 (China); College of Chemistry, Jilin University, Changchun 130023 (China)], E-mail: xiahong@nenu.edu.cn; Liu Yanmei; Li Jun; Guo Wei; Bai Yubai [College of Chemistry, Jilin University, Changchun 130023 (China)

2009-09-15

Atomic force (AFM) and magnetic force microscopy (MFM) were developed to detect biomolecular specific interaction. Goat anti-mouse immunoglobulin (anti-IgG) was covalently attached onto gold substrate modified by a self-assembly monolayer of thioctic acid via 1-ethyl-3-[3-(dimethylamino) propyl] carbodiimide (EDC) activation. Magnetic-labeled IgG then specifically adsorbed onto anti-IgG surface. The morphological variation was identified by AFM. MFM was proved to be a fine assistant tool to distinguish the immunorecognized nanocomposites from the impurities by detection of the magnetic signal from magnetic-labeled IgG. It would enhance the understanding of biomolecular recognition process.

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...

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

Analysis Sparse Representation for Nonnegative Signals Based on Determinant Measure by DC Programming

Directory of Open Access Journals (Sweden)

Yujie Li

2018-01-01

Full Text Available Analysis sparse representation has recently emerged as an alternative approach to the synthesis sparse model. Most existing algorithms typically employ the l0-norm, which is generally NP-hard. Other existing algorithms employ the l1-norm to relax the l0-norm, which sometimes cannot promote adequate sparsity. Most of these existing algorithms focus on general signals and are not suitable for nonnegative signals. However, many signals are necessarily nonnegative such as spectral data. In this paper, we present a novel and efficient analysis dictionary learning algorithm for nonnegative signals with the determinant-type sparsity measure which is convex and differentiable. The analysis sparse representation can be cast in three subproblems, sparse coding, dictionary update, and signal update, because the determinant-type sparsity measure would result in a complex nonconvex optimization problem, which cannot be easily solved by standard convex optimization methods. Therefore, in the proposed algorithms, we use a difference of convex (DC programming scheme for solving the nonconvex problem. According to our theoretical analysis and simulation study, the main advantage of the proposed algorithm is its greater dictionary learning efficiency, particularly compared with state-of-the-art algorithms. In addition, our proposed algorithm performs well in image denoising.

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.

Improved Sparse Channel Estimation for Cooperative Communication Systems

Directory of Open Access Journals (Sweden)

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.

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

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...

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

Graceful, harmonious and magic type labelings relations and techniques

CERN Document Server

López, Susana C

2017-01-01

Aimed toward upper undergraduate and graduate students in mathematics, this book examines the foremost forms of graph labelings including magic, harmonious, and graceful labelings. An overview of basic graph theory concepts and notation is provided along with the origins of graph labeling. Common methods and techniques are presented introducing readers to links between graph labels. A variety of useful techniques are presented to analyze and understand properties of graph labelings. The classical results integrated with new techniques, complete proofs, numerous exercises, and a variety of open problems, will provide readers with a solid understanding of graph labelings.

16 CFR 306.12 - Labels.

Science.gov (United States)

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Labels. 306.12 Section 306.12 Commercial..., CERTIFICATION AND POSTING Label Specifications § 306.12 Labels. All labels must meet the following specifications: (a) Layout—(1) For gasoline labels. The label is 3″ (7.62 cm) wide × 21/2″ (6.35 cm) long. The...

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

KAUST Repository

Labschutz, Matthias

2015-08-12

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

KAUST Repository

Labschutz, Matthias; Bruckner, Stefan; Groller, M. Eduard; Hadwiger, Markus; Rautek, Peter

2015-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

Science.gov (United States)

Labschütz, Matthias; Bruckner, Stefan; Gröller, M Eduard; Hadwiger, Markus; Rautek, Peter

2016-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

Optimizing labeling conditions for cysteine-based peptides with 99mTc

International Nuclear Information System (INIS)

Sabahnoo, Hamideh; Hosseinimehr, Seyed Jalal

2016-01-01

Radiolabelled peptides have attracted a great deal of attention due to their wide applicability in the development of target-specific radiopharmaceuticals. They can easily be used in diagnostic imaging as carriers for the delivery of radionuclides to tumors as well as for therapy. Previous investigations revealed that technetium(V) could form stable complexes with peptide-based ligands of N 3 S type such as Cys-Gly-Gly-Gly. Herein, a targeting HER-2 receptor peptide was labeled with technetium- 99m ( 99m Tc) with two different types of tetrapeptide-based ligands, Cys-Gly-Gly-Gly and Cys-Ser-Ser-Ser. The effect of experimental parameters in the labeling procedure such as type of buffer solutions, pH of media, and type of exchange ligands were optimized toward obtaining maximum labeling yield. The optimum labeling conditions were different for two peptides. Shelf life of both labeled peptides was determined by analytical reversed-phase high-performance liquid chromatography (RP-HPLC) and thin layer chromatography (TLC) that showed radiochemical yield up to 95% even after 4 h. (author)

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.

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.

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...

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...

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.

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.

Site-Specific Protein Labeling Utilizing Lipoic Acid Ligase (LplA) and Bioorthogonal Inverse Electron Demand Diels-Alder Reaction.

Science.gov (United States)

Baalmann, Mathis; Best, Marcel; Wombacher, Richard

2018-01-01

Here, we describe a two-step protocol for selective protein labeling based on enzyme-mediated peptide labeling utilizing lipoic acid ligase (LplA) and bioorthogonal chemistry. The method can be applied to purified proteins, protein in cell lysates, as well as living cells. In a first step a W37V mutant of the lipoic acid ligase (LplA W37V ) from Escherichia coli is utilized to ligate a synthetic chemical handle site-specifically to a lysine residue in a 13 amino acid peptide motif-a short sequence that can be genetically expressed as a fusion with any protein of interest. In a second step, a molecular probe can be attached to the chemical handle in a bioorthogonal Diels-Alder reaction with inverse electron demand (DA inv ). This method is a complementary approach to protein labeling using genetic code expansion and circumvents larger protein tags while maintaining label specificity, providing experimental flexibility and straightforwardness.

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.)

Fast sparsely synchronized brain rhythms in a scale-free neural network.

Science.gov (United States)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For Dtypes of realistic statistical-mechanical measures. Only for the partial and sparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of

Fast sparsely synchronized brain rhythms in a scale-free neural network

Science.gov (United States)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D types of realistic statistical-mechanical measures. Only for the partial and sparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically homogeneous

Structure-guided approach to site-specific fluorophore labeling of the lac repressor LacI.

Directory of Open Access Journals (Sweden)

Kalle Kipper

Full Text Available The lactose operon repressor protein LacI has long served as a paradigm of the bacterial transcription factors. However, the mechanisms whereby LacI rapidly locates its cognate binding site on the bacterial chromosome are still elusive. Single-molecule fluorescence imaging approaches are well suited for the study of these mechanisms but rely on a functionally compatible fluorescence labeling of LacI. Particularly attractive for protein fluorescence labeling are synthetic fluorophores due to their small size and favorable photophysical characteristics. Synthetic fluorophores are often conjugated to natively occurring cysteine residues using maleimide chemistry. For a site-specific and functionally compatible labeling with maleimide fluorophores, the target protein often needs to be redesigned to remove unwanted native cysteines and to introduce cysteines at locations better suited for fluorophore attachment. Biochemical screens can then be employed to probe for the functional activity of the redesigned protein both before and after dye labeling. Here, we report a mutagenesis-based redesign of LacI to enable a functionally compatible labeling with maleimide fluorophores. To provide an easily accessible labeling site in LacI, we introduced a single cysteine residue at position 28 in the DNA-binding headpiece of LacI and replaced two native cysteines with alanines where derivatization with bulky substituents is known to compromise the protein's activity. We find that the redesigned LacI retains a robust activity in vitro and in vivo, provided that the third native cysteine at position 281 is retained in LacI. In a total internal reflection microscopy assay, we observed individual Cy3-labeled LacI molecules bound to immobilized DNA harboring the cognate O1 operator sequence, indicating that the dye-labeled LacI is functionally active. We have thus been able to generate a functional fluorescently labeled LacI that can be used to unravel mechanistic

Demosaicing and Superresolution for Color Filter Array via Residual Image Reconstruction and Sparse Representation

OpenAIRE

Sun, Guangling

2012-01-01

A framework of demosaicing and superresolution for color filter array (CFA) via residual image reconstruction and sparse representation is presented.Given the intermediate image produced by certain demosaicing and interpolation technique, a residual image between the final reconstruction image and the intermediate image is reconstructed using sparse representation.The final reconstruction image has richer edges and details than that of the intermediate image. Specifically, a generic dictionar...

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.

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.

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.

Sparse feature selection identifies H2A.Z as a novel, pattern-specific biomarker for asymmetrically self-renewing distributed stem cells

Directory of Open Access Journals (Sweden)

Yang Hoon Huh

2015-03-01

Full Text Available There is a long-standing unmet clinical need for biomarkers with high specificity for distributed stem cells (DSCs in tissues, or for use in diagnostic and therapeutic cell preparations (e.g., bone marrow. Although DSCs are essential for tissue maintenance and repair, accurate determination of their numbers for medical applications has been problematic. Previous searches for biomarkers expressed specifically in DSCs were hampered by difficulty obtaining pure DSCs and by the challenges in mining complex molecular expression data. To identify such useful and specific DSC biomarkers, we combined a novel sparse feature selection method with combinatorial molecular expression data focused on asymmetric self-renewal, a conspicuous property of DSCs. The analysis identified reduced expression of the histone H2A variant H2A.Z as a superior molecular discriminator for DSC asymmetric self-renewal. Subsequent molecular expression studies showed H2A.Z to be a novel “pattern-specific biomarker” for asymmetrically self-renewing cells, with sufficient specificity to count asymmetrically self-renewing DSCs in vitro and potentially in situ.

A New Type of Graphical Passwords Based on Odd-Elegant Labelled Graphs

Directory of Open Access Journals (Sweden)

Hongyu Wang

2018-01-01

Full Text Available Graphical password (GPW is one of various passwords used in information communication. The QR code, which is widely used in the current world, is one of GPWs. Topsnut-GPWs are new-type GPWs made by topological structures (also, called graphs and number theory, but the existing GPWs use pictures/images almost. We design new Topsnut-GPWs by means of a graph labelling, called odd-elegant labelling. The new Topsnut-GPWs will be constructed by Topsnut-GPWs having smaller vertex numbers; in other words, they are compound Topsnut-GPWs such that they are more robust to deciphering attacks. Furthermore, the new Topsnut-GPWs can induce some mathematical problems and conjectures.

Towards kit formulation of 99mTc labelled somatostatin receptor binding peptides of high specific activity for tumour localization

International Nuclear Information System (INIS)

Behe, M.; Powell, P.; Maecke, H.R.

2001-01-01

The project aimed to develop 99m Tc octreotide analogue for use in nuclear oncology. Several attempts to label SRIF analogues with 99m Tc have used a direct labelling approach but, for this project, HYNIC was chosen as a technetium ligand. A comparison of two different SRIF analogues designed for high specific activity labelling with 99m Tc was done. HYNIC-Octreotide and HYNIC-TOC were prepared and a kit formulation that can be labelled conveniently is currently being studied in a clinical setting. (author)

Stability Analysis on Sparsely Encoded Associative Memory with Short-Term Synaptic Dynamics

Science.gov (United States)

Xu, Muyuan; Katori, Yuichi; Aihara, Kazuyuki

This study investigates the stability of sparsely encoded associative memory in a network composed of stochastic neurons. The incorporation of short-term synaptic dynamics significantly changes the stability with respect to synaptic properties. Various states including static and oscillatory states are found in the network dynamics. Specifically, the sparseness of memory patterns raises the problem of spurious states. A mean field model is used to analyze the detailed structure in the stability and show that the performance of memory retrieval is recovered by appropriate feedback.

Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors

International Nuclear Information System (INIS)

Lucka, Felix

2012-01-01

Sparsity has become a key concept for solving of high-dimensional inverse problems using variational regularization techniques. Recently, using similar sparsity-constraints in the Bayesian framework for inverse problems by encoding them in the prior distribution has attracted attention. Important questions about the relation between regularization theory and Bayesian inference still need to be addressed when using sparsity promoting inversion. A practical obstacle for these examinations is the lack of fast posterior sampling algorithms for sparse, high-dimensional Bayesian inversion. Accessing the full range of Bayesian inference methods requires being able to draw samples from the posterior probability distribution in a fast and efficient way. This is usually done using Markov chain Monte Carlo (MCMC) sampling algorithms. In this paper, we develop and examine a new implementation of a single component Gibbs MCMC sampler for sparse priors relying on L1-norms. We demonstrate that the efficiency of our Gibbs sampler increases when the level of sparsity or the dimension of the unknowns is increased. This property is contrary to the properties of the most commonly applied Metropolis–Hastings (MH) sampling schemes. We demonstrate that the efficiency of MH schemes for L1-type priors dramatically decreases when the level of sparsity or the dimension of the unknowns is increased. Practically, Bayesian inversion for L1-type priors using MH samplers is not feasible at all. As this is commonly believed to be an intrinsic feature of MCMC sampling, the performance of our Gibbs sampler also challenges common beliefs about the applicability of sample based Bayesian inference. (paper)

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

16 CFR 309.17 - Labels.

Science.gov (United States)

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Labels. 309.17 Section 309.17 Commercial... ALTERNATIVE FUELS AND ALTERNATIVE FUELED VEHICLES Requirements for Alternative Fuels Label Specifications § 309.17 Labels. All labels must meet the following specifications: (a) Layout: (1) Non-liquid...

Optimizing labeling conditions for cysteine-based peptides with {sup 99m}Tc

Energy Technology Data Exchange (ETDEWEB)

Sabahnoo, Hamideh; Hosseinimehr, Seyed Jalal, E-mail: sjhosseinim@yahoo.com [Department of Radiopharmacy, Faculty of Pharmacy, Pharmaceutical Sciences Research Center, Mazandaran University of Medical Sciences, Sari (Iran, Islamic Republic of)

2016-07-01

Radiolabelled peptides have attracted a great deal of attention due to their wide applicability in the development of target-specific radiopharmaceuticals. They can easily be used in diagnostic imaging as carriers for the delivery of radionuclides to tumors as well as for therapy. Previous investigations revealed that technetium(V) could form stable complexes with peptide-based ligands of N{sub 3}S type such as Cys-Gly-Gly-Gly. Herein, a targeting HER-2 receptor peptide was labeled with technetium-{sup 99m} ({sup 99m}Tc) with two different types of tetrapeptide-based ligands, Cys-Gly-Gly-Gly and Cys-Ser-Ser-Ser. The effect of experimental parameters in the labeling procedure such as type of buffer solutions, pH of media, and type of exchange ligands were optimized toward obtaining maximum labeling yield. The optimum labeling conditions were different for two peptides. Shelf life of both labeled peptides was determined by analytical reversed-phase high-performance liquid chromatography (RP-HPLC) and thin layer chromatography (TLC) that showed radiochemical yield up to 95% even after 4 h. (author)

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.

Maternal Label and Gesture Use Affects Acquisition of Specific Object Names

Science.gov (United States)

Zammit, Maria; Schafer, Graham

2011-01-01

Ten mothers were observed prospectively, interacting with their infants aged 0 ; 10 in two contexts (picture description and noun description). Maternal communicative behaviours were coded for volubility, gestural production and labelling style. Verbal labelling events were categorized into three exclusive categories: label only; label plus…

Iodoinsulin specifically labelled with 125I and 127I for use as insulin tracer

International Nuclear Information System (INIS)

Bahrami, S.

1981-01-01

In order to describe for the first time iodine-labelled in the B1 position starting with inactive Bolton-Hunter reagents, the essential intermediate steps with secure sup(125,127) iodine positions were synthesized. This labelling position is essential to obtain the structural and biological porperties of insulin on the one hand, and to enable one to trace the metabolism further of the B chain after splitting the insulin in the organism on the other hand. The preparate was made with high specific activity of 420 Ci/mMol. A new high-pressure liquid chromatographic method was developed to separate the products. (RB) [de

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.

Research on segmentation based on multi-atlas in brain MR image

Science.gov (United States)

Qian, Yuejing

2018-03-01

Accurate segmentation of specific tissues in brain MR image can be effectively achieved with the multi-atlas-based segmentation method, and the accuracy mainly depends on the image registration accuracy and fusion scheme. This paper proposes an automatic segmentation method based on the multi-atlas for brain MR image. Firstly, to improve the registration accuracy in the area to be segmented, we employ a target-oriented image registration method for the refinement. Then In the label fusion, we proposed a new algorithm to detect the abnormal sparse patch and simultaneously abandon the corresponding abnormal sparse coefficients, this method is made based on the remaining sparse coefficients combined with the multipoint label estimator strategy. The performance of the proposed method was compared with those of the nonlocal patch-based label fusion method (Nonlocal-PBM), the sparse patch-based label fusion method (Sparse-PBM) and majority voting method (MV). Based on our experimental results, the proposed method is efficient in the brain MR images segmentation compared with MV, Nonlocal-PBM, and Sparse-PBM methods.

Sandwich nucleic acid hybridization: a method with a universally usable labeled probe for various specific tests

International Nuclear Information System (INIS)

Wolf, H.; Leser, U.; Haus, M.; Gu, S.Y.; Pathmanathan, R.

1986-01-01

The use of recombinant m13 phages as hybridization probes offers a considerable advantage over the commonly used recombinant plasmids as the preparation of the DNA probe is very simple and it can easily be labeled directly, e.g. with isotopes with long half-life like 125 I and used for hybridization. However, as the application of nucleic acid hybridization for diagnostic and epidemiological purposes becomes almost unavoidable, the logistic problems of keeping numerous individually labeled hybridization probes increase considerably and may reach prohibitory levels in less well-equipped laboratories. In a new sandwich technique, the first step involves hybridization with an unlabeled recombinant m13 DNA carrying an insert of the desired specificity. In a second step a universally usable labeled probe directed against the m13 part of the recombinant phage DNA is applied. This reduces considerably the problem of preparing and keeping multiple labeled probes in stock. (Auth.)

Lung transit of /sup 111/Indium-labelled granulocytes. Relationship to labelling techniques

Energy Technology Data Exchange (ETDEWEB)

Saverymuttu, S.H.; Peters, A.M.; Danpure, H.J.; Reavy, H.J.; Osman, S.; Lavender, J.P. (Hammersmith Hospital, London, England)

1983-01-01

The early in vivo distribution of /sup 111/Indium-labelled granulocytes, recorded by dynamic imaging using a gamma camera and computer, varied according to the separation and labelling technique. Following i.v. bolus injection, 4 kinetic patterns could be identified: (A) rapid transit through the pulmonary vasculature, (B) delayed transit through the lung with clearance by about 30 min, (C) complete retention by the lung, for up to 10 min, followed by slow release over a period of 1 to 2 h, (D) delayed transit through the lung with a similar time course to (B) but with subsequent heavy liver uptake. Granulocytes labelled with /sup 111/In-tropolonate and maintained in plasma throughout the labelling procedure, whether injected as a 'pure' (separated by plasma-enriched density gradient centrifugation) or 'crude' (seprated by differential centrifugation) preparation, displayed type A kinetics, thought to most closely represent the normal behaviour of granulocytes. 'Crude' cells labelled in saline with /sup 111/In-acetylacetonate displayed type B kinetics. 'Pure' cells isolated on Percoll-saline and labelled in saline with /sup 111/In-acetylacetonate displayed type C kinetics, thought to represent granulocyte 'stimulation' and/or damage, or type D kientics, thought to represent severe damage. The importance is stressed of labelling granulocytes for kinetic studies with a technique that results in minimal alteration of cell behaviour.

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.

Synthesis of high specific activity carbon-11 labeled tracers for neuroreceptor studies

International Nuclear Information System (INIS)

Dannals, R.F.; Ravert, H.T.; Wilson, A.A.; Wagner, H.N. Jr; Johns Hopkins Medical Institutions, Baltimore, MD

1989-01-01

The use of short-lived positron-emitting radiotracers together with positron emission tomography (PET) has allowed scientists to acquire previously inaccessible information regarding problems in physiology, biochemistry, and pharmacology in the living human body. In the past five years, successes in the application of PET to the non-invasive determination of the spatial distribution and regional concentration of a variety of neurotransmitter binding sites within the living brain often followed the successful selections and syntheses of appropriately radiolabeled ligands. This presentation concentrates on the synthesis of these high specific activity radiotracers for neuroreceptor PET studies labeled specifically with carbon-11. (author). 15 refs.; 1 fig

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

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...

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.

Broad substrate tolerance of tubulin tyrosine ligase enables one-step site-specific enzymatic protein labeling.

Science.gov (United States)

Schumacher, Dominik; Lemke, Oliver; Helma, Jonas; Gerszonowicz, Lena; Waller, Verena; Stoschek, Tina; Durkin, Patrick M; Budisa, Nediljko; Leonhardt, Heinrich; Keller, Bettina G; Hackenberger, Christian P R

2017-05-01

The broad substrate tolerance of tubulin tyrosine ligase is the basic rationale behind its wide applicability for chemoenzymatic protein functionalization. In this context, we report that the wild-type enzyme enables ligation of various unnatural amino acids that are substantially bigger than and structurally unrelated to the natural substrate, tyrosine, without the need for extensive protein engineering. This unusual substrate flexibility is due to the fact that the enzyme's catalytic pocket forms an extended cavity during ligation, as confirmed by docking experiments and all-atom molecular dynamics simulations. This feature enabled one-step C-terminal biotinylation and fluorescent coumarin labeling of various functional proteins as demonstrated with ubiquitin, an antigen binding nanobody, and the apoptosis marker Annexin V. Its broad substrate tolerance establishes tubulin tyrosine ligase as a powerful tool for in vitro enzyme-mediated protein modification with single functional amino acids in a specific structural context.

Library designs for generic C++ sparse matrix computations of iterative methods

Energy Technology Data Exchange (ETDEWEB)

Pozo, R.

1996-12-31

A new library design is presented for generic sparse matrix C++ objects for use in iterative algorithms and preconditioners. This design extends previous work on C++ numerical libraries by providing a framework in which efficient algorithms can be written *independent* of the matrix layout or format. That is, rather than supporting different codes for each (element type) / (matrix format) combination, only one version of the algorithm need be maintained. This not only reduces the effort for library developers, but also simplifies the calling interface seen by library users. Furthermore, the underlying matrix library can be naturally extended to support user-defined objects, such as hierarchical block-structured matrices, or application-specific preconditioners. Utilizing optimized kernels whenever possible, the resulting performance of such framework can be shown to be competitive with optimized Fortran programs.

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

Energy Technology Data Exchange (ETDEWEB)

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

2017-12-01

Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scienti c 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.

New approach for in vivo detection of insulitis in type I diabetes: activated lymphocyte targeting with 123I-labelled interleukin 2

International Nuclear Information System (INIS)

Signore, S.; Chianelli, M.; Ferretti, E.; Toscano, A.; Britton, K.E.; Andreani, D.; Gale, E.A.M.; Pozzilli, P.

1994-01-01

Insulitis is considered the histopathological hallmark of type I diabetes. In the non-obese diabetic (NOD) mouse, diabetes has never been observed in the absence of insulitis. The in vivo detection of insulitis could be of relevance for early prediction of diabetes. As approximately 15% of islet-infiltrating lymphocytes express interleukin 2 receptors, the authors have labelled recombinant inter-leukin 2 with 123 I and used this radiopharmaceutical to detect insulitis by gamma camera imaging. The authors studied 71 prediabetic NOD and 27 normal Balb/c mice. Labelled α-lactalbumin was used as the control protein. In the first set of experiments the tissue distribution of radiolabelled interleukin 2 in isolated organs from animals sacrificed at different time points was studied. Higher radioactivity was detected in the pancreas of NOD mice injected with labelled interleukin 2, as compared to NOD mice receiving labelled α-lactalbumin. In another set of experiments, gamma camera images have been acquired after injection of 123 I-labelled interleukin 2. Radioactivity in the pancreatic region of prediabetic NOD and Balb/c mice showed similar kinetics to those observed by single organ counting, with higher accumulation in the pancreatic region of NOD mice. Finally, a positive correlation was found between the radioactivity in the pancreas and the extent of lymphocytic infiltration. This study demonstrates that 123 I-labelled interleukin 2 administered intravenously accumulates specifically in the inflamed pancreas of diabetes-prone NOD mice, suggesting its potential application in human insulin-dependent diabetes mellitus. 34 refs., 6 figs., 1 tab

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.

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.

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).

Method of preparing tritium-labelled thymidine-5'-monophosphates of high specific activity

International Nuclear Information System (INIS)

Filip, J.; Vesely, J.; Cihak, A.

1976-01-01

A method is described of preparing thymidine-5'-monophosphates labelled with tritium of high specific activity based on enzyme synthesis in vitro. Phosphorylation was carried out using the catalytic effect of an enzyme contained in the supernatant fraction prepared from Yoshida ascites carcinoma in rats. The course of the enzyme reaction can be controlled by the concentration of the individual reaction mixture components. The method described allows obtaining thymidine-5'-monophosphate of radiochemical purity better than 95%. (J.B.)

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....

Protocols for 16S rDNA Array Analyses of Microbial Communities by Sequence-Specific Labeling of DNA Probes

Directory of Open Access Journals (Sweden)

Knut Rudi

2003-01-01

Full Text Available Analyses of complex microbial communities are becoming increasingly important. Bottlenecks in these analyses, however, are the tools to actually describe the biodiversity. Novel protocols for DNA array-based analyses of microbial communities are presented. In these protocols, the specificity obtained by sequence-specific labeling of DNA probes is combined with the possibility of detecting several different probes simultaneously by DNA array hybridization. The gene encoding 16S ribosomal RNA was chosen as the target in these analyses. This gene contains both universally conserved regions and regions with relatively high variability. The universally conserved regions are used for PCR amplification primers, while the variable regions are used for the specific probes. Protocols are presented for DNA purification, probe construction, probe labeling, and DNA array hybridizations.

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

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.

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.

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.

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.

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

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

Surface-Enhanced Raman Scattering Nanoparticles as Optical Labels for Imaging Cell Surface Proteins

Science.gov (United States)

MacLaughlin, Christina M.

Assaying the expression of cell surface proteins has widespread application for characterizing cell type, developmental stage, and monitoring disease transformation. Immunophenotyping is conducted by treating cells with labelled targeting moieties that have high affinity for relevant surface protein(s). The sensitivity and specificity of immunophenotyping is defined by the choice of contrast agent and therefore, the number of resolvable signals that can be used to simultaneously label cells. Narrow band width surface-enhanced Raman scattering (SERS) nanoparticles are proposed as optical labels for multiplexed immunophenotying. Two types of surface coatings were investigated to passivate the gold nanoparticles, incorporate SERS functionality, and to facilitate attachment of targeting antibodies. Thiolated poly(ethylene glycol) forms dative bonds with the gold surface and is compatible with multiple physisorbed Raman-active reporter molecules. Ternary lipid bilayers are used to encapsulate the gold nanoparticles particles, and incorporate three different classes of Raman reporters. TEM, UV-Visible absorbance spectroscopy, DLS, and electrophoretic light scattering were used characterize the particle coating. Colourimetric protein assay, and secondary antibody labelling were used to quantify the antibody conjugation. Three different in vitromodels were used to investigate the binding efficacy and specificity of SERS labels for their biomarker targets. Primary human CLL cells, LY10 B lymphoma, and A549 adenocarcinoma lines were targeted. Dark field imaging was used to visualize the colocalization of SERS labels with cells, and evidence of receptor clustering was obtained based on colour shifts of the particles' Rayleigh scattering. Widefield, and spatially-resolved Raman spectra were used to detect labels singly, and in combination from labelled cells. Fluorescence flow cytometry was used to test the particles' binding specificity, and SERS from labelled cells was also

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

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....

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.

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

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.

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.

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.

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.

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.

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.

Site-Specific Three-Color Labeling of α-Synuclein via Conjugation to Uniquely Reactive Cysteines during Assembly by Native Chemical Ligation.

Science.gov (United States)

Lee, Taehyung C; Moran, Crystal R; Cistrone, Philip A; Dawson, Philip E; Deniz, Ashok A

2018-04-12

Single-molecule fluorescence is widely used to study conformational complexity in proteins, and has proven especially valuable with intrinsically disordered proteins (IDPs). Protein studies using dual-color single-molecule Förster resonance energy transfer (smFRET) are now quite common, but many could benefit from simultaneous measurement of multiple distances through multi-color labeling. Such studies, however, have suffered from limitations in site-specific incorporation of more than two dyes per polypeptide. Here we present a fully site-specific three-color labeling scheme for α-synuclein, an IDP with important putative functions and links to Parkinson disease. The convergent synthesis combines native chemical ligation with regiospecific cysteine protection of expressed protein fragments to permit highly controlled labeling via standard cysteine-maleimide chemistry, enabling more global smFRET studies. Furthermore, this modular approach is generally compatible with recombinant proteins and expandable to accommodate even more complex experiments, such as by labeling with additional colors. Copyright © 2018 Elsevier Ltd. All rights reserved.

Practical cell labeling with magnetite cationic liposomes for cell manipulation.

Science.gov (United States)

Ito, Hiroshi; Nonogaki, Yurika; Kato, Ryuji; Honda, Hiroyuki

2010-07-01

Personalization of the cell culture process for cell therapy is an ideal strategy to obtain maximum treatment effects. In a previous report, we proposed a strategy using a magnetic manipulation device that combined a palm-top size device and a cell-labeling method using magnetite cationic liposomes (MCLs) to enable feasible personalized cell processing. In the present study, we focused on optimizing the MCL-labeling technique with respect to cell manipulation in small devices. From detailed analysis with different cell types, 4 pg/cell of MCL-label was found to be obtained immediately after mixing with MCLs, which was sufficient for magnetic cell manipulation. The amount of label increased within 24 h depending on cell type, although in all cases it decreased along with cell doubling, indicating that the labeling potential of MCLs was limited. The role of free MCLs not involved in labeling was also investigated; MCLs' role was found to be a supportive one that maximized the manipulation performance up to 100%. We also determined optimum conditions to manipulate adherent cells by MCL labeling using the MCL dispersed in trypsin solution. Considering labeling feasibility and practical performance with 10(3)-10(5) cells for personalized cell processing, we determined that 10 microg/ml of label without incubation time (0 h incubation) was the universal MCL-labeling condition. We propose the optimum specifications for a device to be combined with this method. 2010. Published by Elsevier B.V.

Sparse PDF maps for non-linear multi-resolution image operations

KAUST Repository

Hadwiger, Markus

2012-11-01

We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters. © 2012 ACM.

Fluorescent humanized anti-CEA antibody specifically labels metastatic pancreatic cancer in a patient-derived orthotopic xenograft (PDOX) mouse model

Science.gov (United States)

Lwin, Thinzar M.; Miyake, Kentaro; Murakami, Takashi; DeLong, Jonathan C.; Yazaki, Paul J.; Shivley, John E.; Clary, Bryan; Hoffman, Robert M.; Bouvet, Michael

2018-03-01

Specific tumor targeting can result in selective labeling of cancer in vivo for surgical navigation. In the present study, we show that the use of an anti-CEA antibody conjugated to the near-infrared (NIR) fluorescent dye, IRDye800CW, can selectively target and label pancreatic cancer and its metastases in a clinically relevant patient derived xenograft mouse model.

Sparse genetic tracing reveals regionally specific functional organization of mammalian nociceptors.

Science.gov (United States)

Olson, William; Abdus-Saboor, Ishmail; Cui, Lian; Burdge, Justin; Raabe, Tobias; Ma, Minghong; Luo, Wenqin

2017-10-12

The human distal limbs have a high spatial acuity for noxious stimuli but a low density of pain-sensing neurites. To elucidate mechanisms underlying regional differences in processing nociception, we sparsely traced non-peptidergic nociceptors across the body using a newly generated Mrgprd CreERT2 mouse line. We found that mouse plantar paw skin is also innervated by a low density of Mrgprd + nociceptors, while individual arbors in different locations are comparable in size. Surprisingly, the central arbors of plantar paw and trunk innervating nociceptors have distinct morphologies in the spinal cord. This regional difference is well correlated with a heightened signal transmission for plantar paw circuits, as revealed by both spinal cord slice recordings and behavior assays. Taken together, our results elucidate a novel somatotopic functional organization of the mammalian pain system and suggest that regional central arbor structure could facilitate the "enlarged representation" of plantar paw regions in the CNS.

New approach for in vivo detection of insulitis in type I diabetes: activated lymphocyte targeting with [sup 123]I-labelled interleukin 2

Energy Technology Data Exchange (ETDEWEB)

Signore, S.; Chianelli, M.; Ferretti, E.; Toscano, A.; Britton, K.E.; Andreani, D.; Gale, E.A.M.; Pozzilli, P. (Clinical Medica II, Univ. of Rome (Italy))

1994-10-01

Insulitis is considered the histopathological hallmark of type I diabetes. In the non-obese diabetic (NOD) mouse, diabetes has never been observed in the absence of insulitis. The in vivo detection of insulitis could be of relevance for early prediction of diabetes. As approximately 15% of islet-infiltrating lymphocytes express interleukin 2 receptors, the authors have labelled recombinant inter-leukin 2 with [sup 123]I and used this radiopharmaceutical to detect insulitis by gamma camera imaging. The authors studied 71 prediabetic NOD and 27 normal Balb/c mice. Labelled [alpha]-lactalbumin was used as the control protein. In the first set of experiments the tissue distribution of radiolabelled interleukin 2 in isolated organs from animals sacrificed at different time points was studied. Higher radioactivity was detected in the pancreas of NOD mice injected with labelled interleukin 2, as compared to NOD mice receiving labelled [alpha]-lactalbumin. In another set of experiments, gamma camera images have been acquired after injection of [sup 123]I-labelled interleukin 2. Radioactivity in the pancreatic region of prediabetic NOD and Balb/c mice showed similar kinetics to those observed by single organ counting, with higher accumulation in the pancreatic region of NOD mice. Finally, a positive correlation was found between the radioactivity in the pancreas and the extent of lymphocytic infiltration. This study demonstrates that [sup 123]I-labelled interleukin 2 administered intravenously accumulates specifically in the inflamed pancreas of diabetes-prone NOD mice, suggesting its potential application in human insulin-dependent diabetes mellitus. 34 refs., 6 figs., 1 tab.

Molecular organization in bacterial cell membranes. Specific labelling and topological distribution of glycoproteins and proteins in Streptomyces albus membranes

Energy Technology Data Exchange (ETDEWEB)

Larraga, V; Munoz, E [Consejo Superior de Investigaciones Cientificas, Madrid (Spain). Instituto de Biologia Celular

1975-05-01

The paper reports about an investigation into the question of the specific labelling and topological distribution of glycoproteins and proteins in Streptomyces albus membranes. The method of sample preparation is described: Tritium labelling of glycoproteins in protoplasts and membranes, iodination of proteins, trypsin treatment and polyacrylamide gel electrophoresis. The findings suggest an asymmetrical distribution of the glycoproteins in membranes and a weak accessibility to iodine label. A structural model of the plasma membranes of Streptomyces albus is proposed similar to the general 'fluid mosaic' model of Singer and Nicholson.

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.

The diagnosis of the gastric cancer using catheter-type semiconductor radiation detector. Comparison of diagnostic values of. beta. -emitting radionuclide label with. gamma. -emitting label

Energy Technology Data Exchange (ETDEWEB)

Sassa, R; Iwase, T [Asahi Life Foundation, Tokyo (Japan). Inst. for Adult Diseases; Sugita, T; Iio, M

1975-06-01

The diagnostic usefulness of /sup 32/P-phosphate for human gastric cancer, using a catheter-type semiconductor radiation detector (CASRAD) combined with gastrofiberscope technique, has already been reported by the authors. They have in addition used sup(99m)Tc-bleomycin, sup(99m)Tc-tetracycline in the diagnosis of experimental rabbit gastric cancer, too. In the present study, further refinement of the technique for the ..beta..-ray labeled substance (/sup 32/P-phosphate) for detection of the gastric cancer was compared with that of ..gamma..-ray labeled substance (sup(99m)Tc-tetracycline). A more correct diagnosis of the gastric cancer by in vivo measurement of beta activity could be obtained, when the collimater, made of stainless steel, was attached to the top of the detector. In this way contribution to the count from the adjacent tissues or organs could be eliminated. They were unable to produce an effective and useful collimater for ..gamma..-ray labeled substance which could to be used safely in vivo. Because of the unsatisfactory collimater, radioactivities of the adjacent organs caused on increase in the radioactivity of the background. Therefore inspite of the recent introduction of various sup(99m)Tc-tumor agents, these labels were not applicable to the CASRAD method. For such a small detector system, ..beta..-labels, represented by /sup 32/P-phosphate, was still prefererable to ..gamma..-labels.

Automated sequence- and stereo-specific assignment of methyl-labeled proteins by paramagnetic relaxation and methyl–methyl nuclear overhauser enhancement spectroscopy

International Nuclear Information System (INIS)

Venditti, Vincenzo; Fawzi, Nicolas L.; Clore, G. Marius

2011-01-01

Methyl-transverse relaxation optimized spectroscopy is rapidly becoming the preferred NMR technique for probing structure and dynamics of very large proteins up to ∼1 MDa in molecular size. Data interpretation, however, necessitates assignment of methyl groups which still presents a very challenging and time-consuming process. Here we demonstrate that, in combination with a known 3D structure, paramagnetic relaxation enhancement (PRE), induced by nitroxide spin-labels incorporated at only a few surface-exposed engineered cysteines, provides fast, straightforward and robust access to methyl group resonance assignments, including stereoassignments for the methyl groups of leucine and valine. Neither prior assignments, including backbone assignments, for the protein, nor experiments that transfer magnetization between methyl groups and the protein backbone, are required. PRE-derived assignments are refined by 4D methyl–methyl nuclear Overhauser enhancement data, eliminating ambiguities and errors that may arise due to the high sensitivity of PREs to the potential presence of sparsely-populated transient states.

Automated sequence- and stereo-specific assignment of methyl-labeled proteins by paramagnetic relaxation and methyl-methyl nuclear overhauser enhancement spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Venditti, Vincenzo; Fawzi, Nicolas L.; Clore, G. Marius, E-mail: mariusc@mail.nih.gov [National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Laboratory of Chemical Physics (United States)

2011-11-15

Methyl-transverse relaxation optimized spectroscopy is rapidly becoming the preferred NMR technique for probing structure and dynamics of very large proteins up to {approx}1 MDa in molecular size. Data interpretation, however, necessitates assignment of methyl groups which still presents a very challenging and time-consuming process. Here we demonstrate that, in combination with a known 3D structure, paramagnetic relaxation enhancement (PRE), induced by nitroxide spin-labels incorporated at only a few surface-exposed engineered cysteines, provides fast, straightforward and robust access to methyl group resonance assignments, including stereoassignments for the methyl groups of leucine and valine. Neither prior assignments, including backbone assignments, for the protein, nor experiments that transfer magnetization between methyl groups and the protein backbone, are required. PRE-derived assignments are refined by 4D methyl-methyl nuclear Overhauser enhancement data, eliminating ambiguities and errors that may arise due to the high sensitivity of PREs to the potential presence of sparsely-populated transient states.

Hierarchical Nanogold Labels to Improve the Sensitivity of Lateral Flow Immunoassay

Science.gov (United States)

Serebrennikova, Kseniya; Samsonova, Jeanne; Osipov, Alexander

2018-06-01

Lateral flow immunoassay (LFIA) is a widely used express method and offers advantages such as a short analysis time, simplicity of testing and result evaluation. However, an LFIA based on gold nanospheres lacks the desired sensitivity, thereby limiting its wide applications. In this study, spherical nanogold labels along with new types of nanogold labels such as gold nanopopcorns and nanostars were prepared, characterized, and applied for LFIA of model protein antigen procalcitonin. It was found that the label with a structure close to spherical provided more uniform distribution of specific antibodies on its surface, indicative of its suitability for this type of analysis. LFIA using gold nanopopcorns as a label allowed procalcitonin detection over a linear range of 0.5-10 ng mL-1 with the limit of detection of 0.1 ng mL-1, which was fivefold higher than the sensitivity of the assay with gold nanospheres. Another approach to improve the sensitivity of the assay included the silver enhancement method, which was used to compare the amplification of LFIA for procalcitonin detection. The sensitivity of procalcitonin determination by this method was 10 times better the sensitivity of the conventional LFIA with gold nanosphere as a label. The proposed approach of LFIA based on gold nanopopcorns improved the detection sensitivity without additional steps and prevented the increased consumption of specific reagents (antibodies).

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.

Disclaimer labels on fashion magazine advertisements: effects on social comparison and body dissatisfaction.

Science.gov (United States)

Tiggemann, Marika; Slater, Amy; Bury, Belinda; Hawkins, Kimberley; Firth, Bonny

2013-01-01

Recent proposals across a number of Western countries have suggested that idealised media images should carry some sort of disclaimer informing readers when these images have been digitally enhanced. The present studies aimed to experimentally investigate the impact on women's body dissatisfaction of the addition of such warning labels to fashion magazine advertisements. Participants were 120 and 114 female undergraduate students in Experiment 1 and Experiment 2 respectively. In both experiments, participants viewed fashion magazine advertisements with either no warning label, a generic warning label, or a specific more detailed warning label. In neither experiment was there a significant effect of type of label. However, state appearance comparison was found to predict change in body dissatisfaction irrespective of condition. Unexpectedly, trait appearance comparison moderated the effect of label on body dissatisfaction, such that for women high on trait appearance comparison, exposure to specific warning labels actually resulted in increased body dissatisfaction. In sum, the present results showed no benefit of warning labels in ameliorating the known negative effect of viewing thin-ideal media images, and even suggested that one form of warning (specific) might be harmful for some individuals. Accordingly, it was concluded that more extensive research is required to guide the most effective use of disclaimer labels. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

A sparse neural code for some speech sounds but not for others.

Directory of Open Access Journals (Sweden)

Mathias Scharinger

Full Text Available The precise neural mechanisms underlying speech sound representations are still a matter of debate. Proponents of 'sparse representations' assume that on the level of speech sounds, only contrastive or otherwise not predictable information is stored in long-term memory. Here, in a passive oddball paradigm, we challenge the neural foundations of such a 'sparse' representation; we use words that differ only in their penultimate consonant ("coronal" [t] vs. "dorsal" [k] place of articulation and for example distinguish between the German nouns Latz ([lats]; bib and Lachs ([laks]; salmon. Changes from standard [t] to deviant [k] and vice versa elicited a discernible Mismatch Negativity (MMN response. Crucially, however, the MMN for the deviant [lats] was stronger than the MMN for the deviant [laks]. Source localization showed this difference to be due to enhanced brain activity in right superior temporal cortex. These findings reflect a difference in phonological 'sparsity': Coronal [t] segments, but not dorsal [k] segments, are based on more sparse representations and elicit less specific neural predictions; sensory deviations from this prediction are more readily 'tolerated' and accordingly trigger weaker MMNs. The results foster the neurocomputational reality of 'representationally sparse' models of speech perception that are compatible with more general predictive mechanisms in auditory perception.

Quantitative Proteomic Analysis of the Response to Zinc, Magnesium, and Calcium Deficiency in Specific Cell Types of Arabidopsis Roots

Directory of Open Access Journals (Sweden)

Yoichiro Fukao

2016-01-01

Full Text Available The proteome profiles of specific cell types have recently been investigated using techniques such as fluorescence activated cell sorting and laser capture microdissection. However, quantitative proteomic analysis of specific cell types has not yet been performed. In this study, to investigate the response of the proteome to zinc, magnesium, and calcium deficiency in specific cell types of Arabidopsis thaliana roots, we performed isobaric tags for relative and absolute quantification (iTRAQ-based quantitative proteomics using GFP-expressing protoplasts collected by fluorescence-activated cell sorting. Protoplasts were collected from the pGL2-GFPer and pMGP-GFPer marker lines for epidermis or inner cell lines (pericycle, endodermis, and cortex, respectively. To increase the number of proteins identified, iTRAQ-labeled peptides were separated into 24 fractions by OFFGFEL electrophoresis prior to high-performance liquid chromatography coupled with mass spectrometry analysis. Overall, 1039 and 737 proteins were identified and quantified in the epidermal and inner cell lines, respectively. Interestingly, the expression of many proteins was decreased in the epidermis by mineral deficiency, although a weaker effect was observed in inner cell lines such as the pericycle, endodermis, and cortex. Here, we report for the first time the quantitative proteomics of specific cell types in Arabidopsis roots.

From position-specific isotope labeling towards soil fluxomics: a novel toolbox to assess the microbial impact on biogeochemical cycles

Science.gov (United States)

Apostel, C.; Dippold, M. A.; Kuzyakov, Y.

2015-12-01

Understanding the microbial impact on C and nutrient cycles is one of the most important challenges in terrestrial biogeochemistry. Transformation of low molecular weight organic substances (LMWOS) is a key step in all biogeochemical cycles because 1) all high molecular substances pass the LMWOS pool during their degradation and 2) only LMWOS can be taken up by microorganisms intact. Thus, the transformations of LMWOS are dominated by biochemical pathways of the soil microorganisms. Thus, understanding fluxes and transformations in soils requires a detailed knowledge on the microbial metabolic network and its control mechanism. Tracing C fate in soil by isotopes became on of the most applied and promising biogeochemistry tools but studies were nearly exclusively based on uniformly labeled substances. However, such tracers do not allow the differentiation of the intact use of the initial substances from its transformation to metabolites. The novel tool of position-specific labeling enables to trace molecule atoms separately and thus to determine the cleavage of molecules - a prerequisite for metabolic tracing. Position-specific labeling of basic metabolites and quantification of isotope incorporation in CO2 and bulk soil enabled following the basic metabolic pathways of microorganisms. However, the combination of position-specific 13C labeling with compound-specific isotope analysis of microbial biomarkers and metabolites like phospholipid fatty acids (PLFA) or amino sugars revealed new insights into the soil fluxome: First, it enables tracing specific anabolic pathways in diverse microbial communities in soils e.g. carbon starvation pathways versus pathways reflecting microbial growth. Second, it allows identification of specific pathways of individual functional microbial groups in soils in situ. Tracing metabolic pathways and understanding their regulating factors are crucial for soil C fluxomics i.e. the unravaling of the complex network of C transformations

Synthesis of high specific activity tritium-labelled chloroethylcyclohexylnitrosourea and its application to the study of DNA modification

International Nuclear Information System (INIS)

Siew, E.L.; Habraken, Yvette; Ludlum, D.B.

1991-01-01

A small-scale synthesis of high specific activity, N-(2-chloro-2-[ 3 H-ethyl)-N'-cyclohexyl-N-nitrosourea ([ 3 H]-CCNU) has been accomplished from tritium-labelled ethanolamine. The product is pure by TLC and HPLC analysis and has been used successfully to modify DNA. The overall yield on radioactivity including losses in HPLC purification is approximately 4 percent. The availability of this tritium-labelled compound makes studies of DNA repair and of cellular resistance to N-(2-chloroethyl)-N'-cyclohexyl-N-nitrosourea possible. (author)

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.

Characterization of the omega-conotoxin target. Evidence for tissue-specific heterogeneity in calcium channel types

International Nuclear Information System (INIS)

Cruz, L.J.; Johnson, D.S.; Olivera, B.M.

1987-01-01

Omega-Conotoxin GVIA (omega-CgTx-VIA) is a 27 amino acid peptide from the venom of the fish-hunting snail, Conus geographus, that blocks voltage-activated Ca channels. The characterization of a biologically active, homogeneous 125 I-labeled monoiodinated Tyr 22 derivative of omega-conotoxin GVIA and its use in binding and cross-linking studies are described. The 125 I-labeled toxin is specifically cross-linked to a receptor protein with an apparent M/sub r/ of 135,000. The stoichiometry between omega-conotoxin and nitrendipine binding sites in different chick tissues was determined. Skeletal muscle has a high concentration of [ 3 H]nitrendipine binding sites but no detectable omega-conotoxin sites. Brain microsomes have both binding sites, but omega-conotoxin targets are in excess. These results, combined with recent electrophysiological studies define four types of Ca channels in chick tissues, N, T, L/sub n/ (omega sensitive), and L/sub m/ (omega insensitive), and are consistent with the hypothesis that the α-subunits of certain neuronal Ca 2+ channels (L/sub n/, N) are the molecular targets of omega-conotoxin GVIA

Dynamics of growth/mature-related substances in vegetables using specific triple labeled compound

International Nuclear Information System (INIS)

Yamato, Yoichi; Hamano, Megumi; Yamazaki, Hiroko; Miura, Hiroyuki

2000-01-01

To progress physiological studies of vegetables, development of biosynthetic method for production of triple labeled compounds was attempted in this study and such method for vegetables using specifically labeled sugars was examined. As a sugar compound, 6-C 14 -glucose (14-CG) and 1-H 3 -glucose (3-HG) were given to culture medium for cells derived from tomato embryonic axis and the changes of these compounds were monitored. Tomato embryonic cells were harvested 20 and 44 hours after the addition of 14-CG or 3-CG into the culture medium the cells. The cells were homogenized and the supernatant after centrifugation was applied onto HPLC. Radio analyzer revealed major two peaks in the chromatography of the sugar fraction from the cells after 20 hours from the addition of 14-CG. One was the peak of glucose, itself and the other was estimated to be that of fructose based on the retention time. Whereas in the elution pattern of the sugar fraction after 44 hours from the addition, a peak of sucrose was found along with the peak of glucose. These results indicate that C 14 in 14-CG but not H 3 in 3-HG was transferred into fructose after the metabolism in tomato. Moreover, in both elution patterns, there was a peak positioned at the same retention time, indicating that the compound in this peak was produced from either of 14-CG or 3-HG. Therefore, it is thought that H 3 and C 14 double-labeled compound could be produced from the cell culture added with both labeled compounds; 14-CG and 3-HG. (M.N.)

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.

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.

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.

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

An exploratory study of drinkers views of health information and warning labels on alcohol containers.

Science.gov (United States)

Thomson, Lisa M; Vandenberg, Brian; Fitzgerald, John L

2012-03-01

To identify general and specific features of health information warning labels on alcohol beverage containers that could potentially inform the development and implementation of a new labelling regime in Australia. Mixed methods, including a cross-sectional population survey and a qualitative study of knowledge, attitudes and behaviours regarding alcohol beverage labelling. The population survey used computer-assisted telephone interviews of 1500 persons in Victoria, Australia to gauge the level of support for health information and warning labels. The qualitative study used six focus groups to test the suitability of 12 prototype labels that were placed in situ on a variety of alcohol beverage containers. The telephone survey found 80% to 90% support for a range of information that could potentially be mandated by government authorities for inclusion on labels (nutritional information, alcohol content, health warning, images). Focus group testing of the prototype label designs found that labels should be integrated with other alcohol-related health messages, such as government social advertising campaigns, and specific labels should be matched appropriately to specific consumer groups and beverage types. There are high levels of public support for health information and warning labels on alcohol beverages. This study contributes much needed empirical guidance for developing alcohol beverage labelling strategies in an Australian context. © 2011 Australasian Professional Society on Alcohol and other Drugs.

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.

Ultrafast layer based computer-generated hologram calculation with sparse template holographic fringe pattern for 3-D object.

Science.gov (United States)

Kim, Hak Gu; Man Ro, Yong

2017-11-27

In this paper, we propose a new ultrafast layer based CGH calculation that exploits the sparsity of hologram fringe pattern in 3-D object layer. Specifically, we devise a sparse template holographic fringe pattern. The holographic fringe pattern on a depth layer can be rapidly calculated by adding the sparse template holographic fringe patterns at each object point position. Since the size of sparse template holographic fringe pattern is much smaller than that of the CGH plane, the computational load can be significantly reduced. Experimental results show that the proposed method achieves 10-20 msec for 1024x1024 pixels providing visually plausible results.

Synthesis of high specific activity tritium-labelled chloroethylcyclohexylnitrosourea and its application to the study of DNA modification

Energy Technology Data Exchange (ETDEWEB)

Siew, E.L. (State Univ. of New York, Albany, NY (USA). Dept. of Chemistry); Habraken, Yvette; Ludlum, D.B. (Massachusetts Univ., Worcester, MA (USA). Medical School)

1991-02-01

A small-scale synthesis of high specific activity, N-(2-chloro-2-{sup 3}H-ethyl)-N'-cyclohexyl-N-nitrosourea ({sup 3}H-CCNU) has been accomplished from tritium-labelled ethanolamine. The product is pure by TLC and HPLC analysis and has been used successfully to modify DNA. The overall yield on radioactivity including losses in HPLC purification is approximately 4 percent. The availability of this tritium-labelled compound makes studies of DNA repair and of cellular resistance to N-(2-chloroethyl)-N'-cyclohexyl-N-nitrosourea possible. (author).

Effects of radiation type and delivery mode on a radioresistant eukaryote Cryptococcus neoformans

International Nuclear Information System (INIS)

Shuryak, Igor; Bryan, Ruth A.; Broitman, Jack; Marino, Stephen A.; Morgenstern, Alfred; Apostolidis, Christos; Dadachova, Ekaterina

2015-01-01

Introduction: Most research on radioresistant fungi, particularly on human pathogens such as Cryptococcus neoformans, involves sparsely-ionizing radiation. Consequently, fungal responses to densely-ionizing radiation, which can be harnessed to treat life-threatening fungal infections, remain incompletely understood. Methods: We addressed this issue by quantifying and comparing the effects of densely-ionizing α-particles (delivered either by external beam or by 213 Bi-labeled monoclonal antibodies), and sparsely-ionizing 137 Cs γ-rays, on Cryptococus neoformans. Results: The best-fit linear-quadratic parameters for clonogenic survival were the following: α = 0.24 × 10 −2 Gy −1 for γ-rays and 1.07 × 10 −2 Gy −1 for external-beam α-particles, and β = 1.44 × 10 −5 Gy −2 for both radiation types. Fungal cell killing by radiolabeled antibodies was consistent with predictions based on the α-particle dose to the cell nucleus and the linear-quadratic parameters for external-beam α-particles. The estimated RBE (for α-particles vs. γ-rays) at low doses was 4.47 for the initial portion of the α-particle track, and 7.66 for the Bragg peak. Non-radiological antibody effects accounted for up to 23% of cell death. Conclusions: These results quantify the degree of C. neoformans resistance to densely-ionizing radiations, and show how this resistance can be overcome with fungus-specific radiolabeled antibodies

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.

Preparation of riboflavin specifically labeled in the 5'-hydroxymethyl terminus using a vitamin B2-aldehyde-forming enzyme from Schizophyllum commune

International Nuclear Information System (INIS)

Kekelidze, T.N.; Edmondson, D.E.; McCormick, D.B.

1995-01-01

A method is described for synthesis of riboflavin selectively labeled in the hydrogens at the 5'-hydroxymethyl position. In this method, a vitamin B 2 -aldehyde-forming enzyme from Schizophyllum commune is used to specifically and completely oxidize the 5'-hydroxymethyl of riboflavin to the 5'-aldehyde. This reaction is monitored spectrophotometrically by the reduction of 2,6-dichlorophenolindophenol at 600 nm. Appearance of aldehyde product was directly quantitated by reverse-phase high-performance liquid chromatography. Product is extracted from the incubation mixture by phenol after saturation with (NH 4 ) 2 SO 4 and then further purified by benzyl alcohol extraction. The 5'-aldehyde is reduced with appropriately labeled sodium borohydride to yield the vitamin specifically labeled in the 5'-hydroxymethyl group. (author)

Application of the quantitative autoradiography for determination of specific activity of labelled non-metallic inclusions

International Nuclear Information System (INIS)

Kowalczyk, J.T.; Wilczynski, A.W.

1983-01-01

The knowledge of specific activity of labelled non-metallic inclusions, i.e. the knowledge of the content of the radiotracer in a single inclusion, allows to obtain new information about the mechanism and the kinetics of steel deoxidation. In order to determine this specific activity quantitative autoradiography was used. Fo; this purpose, various standards of aluminium oxides with different amounts of cerium oxide Ce 2 O 3 and an aluminium-cerium alloy were prepared. The standards and the alloy were activated with thermal neutrons. Then several autoradiographs were made for these standards (ORWO AF-3 films were used). The autoradiographs served as the basis for evaluation of the standardization curves: optical density versus dimension of particles for a constant cerium concentration; optical density versus concentration of cerium for a constant dimension of particle. The samples of liquid steel were deoxidated with Al-Ce alloy. After labelled non-metallic inclusions had been isolated, the autoradiographs were made under the same conditions as for the standards. The standardization curves were used to determine the cerium content in the single inclusions. (author)

Image Classification Based on Convolutional Denoising Sparse Autoencoder

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Shuangshuang Chen

2017-01-01

Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.

Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging.

Science.gov (United States)

Wang, Shanshan; Tan, Sha; Gao, Yuan; Liu, Qiegen; Ying, Leslie; Xiao, Taohui; Liu, Yuanyuan; Liu, Xin; Zheng, Hairong; Liang, Dong

2018-01-01

The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an - - minimization objective with an norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.

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.

Labelling and Self-Esteem: The Impact of Using Specific vs. Generic Labels

Science.gov (United States)

Taylor, Laura Marie; Hume, Ian Robert; Welsh, Nikki

2010-01-01

The aim of this study is to investigate the relationship between being labelled either as having dyslexia or as having general special educational needs (SEN) and a child's self-esteem. Seventy-five children aged between 8 and 15 years categorised as having dyslexia (N = 26), as having general SEN (N = 26) or as having no learning difficulties (N…

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....

Application of product life cycle concept to private label management

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Sandra Horvat

2013-06-01

Full Text Available Private labels have recorded significant growth rates worldwide, becoming a serious threat to manufacturer brands. Development of private labels in many different product categories increased the complexity of their management. Therefore, this paper examines the possibility of using the product life cycle concept in private label management. Given that private labels are a specific brand type, it is necessary to adjust certain elements of the product life cycle concept, as it was developed on the basis of manufacturer brands. For instance, in the growth stage of the product life cycle, retailers expand private labels to a number of product categories and use the push strategy while manufacturers tend to expand their distribution network in the expansion of their brands and predominantly use the pull strategy in doing so. Furthermore, there is a focus shift from low-price strategy, predominantly used in the introduction phase, to increasing the quality and private label value in the later stages of the product life cycle.

Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction

Science.gov (United States)

Niu, Shanzhou; Yu, Gaohang; Ma, Jianhua; Wang, Jing

2018-02-01

Spectral computed tomography (CT) has been a promising technique in research and clinics because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often affected by serious quantum noise because of the limited number of photons used in the corresponding energy bin. To address this problem, we present an iterative reconstruction method for spectral CT using nonlocal low-rank and sparse matrix decomposition (NLSMD), which exploits the self-similarity of patches that are collected in multi-energy images. Specifically, each set of patches can be decomposed into a low-rank component and a sparse component, and the low-rank component represents the stationary background over different energy bins, while the sparse component represents the rest of the different spectral features in individual energy bins. Subsequently, an effective alternating optimization algorithm was developed to minimize the associated objective function. To validate and evaluate the NLSMD method, qualitative and quantitative studies were conducted by using simulated and real spectral CT data. Experimental results show that the NLSMD method improves spectral CT images in terms of noise reduction, artifact suppression and resolution preservation.

The 18F-labelled alkylating agent 2,2,2-trifluoroethyl triflate: synthesis and specific activity

International Nuclear Information System (INIS)

Johnstroem, P.; Stone-Elander, S.

1995-01-01

A method for synthesizing the alkylating agent 2,2,2-trifluoroethyl triflate labelled with [ 18 ]fluoride in the two position is presented. Ethyl [2- 18 )F]-trifluoroacetate was synthesized by the nucleophilic reaction of [ 18 F]F - with ethyl bromodifluoroacetate in DMSO (45-60%, 5 min, 80 o C) and subsequently converted to [2- 18 F]-2,2,2-trifluoroethanol using alane in THF (85-95%, 2 min, 40 o C. Reaction with triflic anhydride in 2,6-lutidine produced [2- 18 F]-2,2,2-trifluoroethyl triflate (70-80%, 1 min, 0 o C. In all three cases the product was removed from the reaction vessel by heating to distil under a stream of nitrogen. [2- 18 F]-2,2,2-Trifluoroethyl triflate was used to label 2-oxoquazepam by N-alkylation in a toulene:DMF mixture (80-85%, 20 min, 120 o C). Although no-carrier-added [ 18 )F]F - was used, considerable unlabelled ethyl trifluoroacetate was produced in the first reaction. Varying the conditions for the fluoro-debromination reaction did not appreciably improve the relative ratio of labelled to unlabelled ester. The specific activity of the labelled 1,4-benzodiazepine-2-one obtained from 1850 MBq [ 18 F]F - was found to be ≅37 MBq/μmol (1mCi/μmol). (Author)

Site-specific fluorescent labeling of nascent proteins on the translating ribosome.

Science.gov (United States)

Saraogi, Ishu; Zhang, Dawei; Chandrasekaran, Sandhya; Shan, Shu-ou

2011-09-28

As newly synthesized proteins emerge from the ribosome, they interact with a variety of cotranslational cellular machineries that facilitate their proper folding, maturation, and localization. These interactions are essential for proper function of the cell, and the ability to study these events is crucial to understanding cellular protein biogenesis. To this end, we have developed a highly efficient method to generate ribosome-nascent chain complexes (RNCs) site-specifically labeled with a fluorescent dye on the nascent polypeptide. The fluorescent RNC provides real-time, quantitative information on its cotranslational interaction with the signal recognition particle and will be a valuable tool in elucidating the role of the translating ribosome in numerous biochemical pathways.

Preparation of ⁶⁸Ga-labelled DOTA-peptides using a manual labelling approach for small-animal PET imaging.

Science.gov (United States)

Romero, Eduardo; Martínez, Alfonso; Oteo, Marta; García, Angel; Morcillo, Miguel Angel

2016-01-01

(68)Ga-DOTA-peptides are a promising PET radiotracers used in the detection of different tumours types due to their ability for binding specifically receptors overexpressed in these. Furthermore, (68)Ga can be produced by a (68)Ge/(68)Ga generator on site which is a very good alternative to cyclotron-based PET isotopes. Here, we describe a manual labelling approach for the synthesis of (68)Ga-labelled DOTA-peptides based on concentration and purification of the commercial (68)Ga/(68)Ga generator eluate using an anion exchange-cartridge. (68)Ga-DOTA-TATE was used to image a pheochromocytoma xenograft mouse model by a microPET/CT scanner. The method described provides satisfactory results, allowing the subsequent (68)Ga use to label DOTA-peptides. The simplicity of the method along with its implementation reduced cost, makes it useful in preclinical PET studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes

DEFF Research Database (Denmark)

Bentzen, Amalie Kai; Marquard, Andrea Marion; Lyngaa, Rikke Birgitte

2016-01-01

-major histocompatibility complex (MHC) multimers labeled with individual DNA barcodes to screen >1,000 peptide specificities in a single sample, and detect low-frequency CD8 T cells specific for virus- or cancer-restricted antigens. When analyzing T-cell recognition of shared melanoma antigens before and after adoptive...... cell therapy in melanoma patients, we observe a greater number of melanoma-specific T-cell populations compared with cytometry-based approaches. Furthermore, we detect neoepitope-specific T cells in tumor-infiltrating lymphocytes and peripheral blood from patients with non-small cell lung cancer...

Solving large-scale sparse eigenvalue problems and linear systems of equations for accelerator modeling

International Nuclear Information System (INIS)

Gene Golub; Kwok Ko

2009-01-01

The solutions of sparse eigenvalue problems and linear systems constitute one of the key computational kernels in the discretization of partial differential equations for the modeling of linear accelerators. The computational challenges faced by existing techniques for solving those sparse eigenvalue problems and linear systems call for continuing research to improve on the algorithms so that ever increasing problem size as required by the physics application can be tackled. Under the support of this award, the filter algorithm for solving large sparse eigenvalue problems was developed at Stanford to address the computational difficulties in the previous methods with the goal to enable accelerator simulations on then the world largest unclassified supercomputer at NERSC for this class of problems. Specifically, a new method, the Hemitian skew-Hemitian splitting method, was proposed and researched as an improved method for solving linear systems with non-Hermitian positive definite and semidefinite matrices.

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.

Synthesis of high specific activity tritium labelled 1S,2S-(-)-trans-2-isothiocyanato-N-methyl-N-(2-(1-pyrrolidinyl)-cyclohexyl)benzene acetamide, a specific irreversible ligand for kappa opioid receptors

Energy Technology Data Exchange (ETDEWEB)

Costa, B.R. de; Thurkauf, A.; Rothman, R.R. (National Inst. of Mental Health, Bethesda, MD (USA)); Jacobson, A.E.; Rice, K.C. (National Inst. of Digestive Diabetes, and Kidney Diseases, Bethesda, MD (USA))

1990-11-01

Optically pure tritium labeled 1S,2S-(-)-trans-2-isothiocyanato-N-methyl-N-(2-(1-pyrrolidinyl)cyclohexyl )benzeneacetamide, an affinity ligand specific for the kappa opioid receptor was synthesized from optically pure 1S,2S-(-)-trans-2-amino-N-methyl-N-(2-(1-pyrrolidinyl)cyclohexyl)benzeneacetamide via the sequence of dibromination (57%) followed by catalytic tritiation of the dibromide. The resulting tritium labelled aniline (14% yield, specific activity 31.2 Ci/mmol) was transformed to the title compound in 13.3% yield and 99+% radiochemical purity by treatment with thiophosgene. (author).

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.

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...

Sparse Pseudo Spectral Projection Methods with Directional Adaptation for Uncertainty Quantification

KAUST Repository

Winokur, J.

2015-12-19

We investigate two methods to build a polynomial approximation of a model output depending on some parameters. The two approaches are based on pseudo-spectral projection (PSP) methods on adaptively constructed sparse grids, and aim at providing a finer control of the resolution along two distinct subsets of model parameters. The control of the error along different subsets of parameters may be needed for instance in the case of a model depending on uncertain parameters and deterministic design variables. We first consider a nested approach where an independent adaptive sparse grid PSP is performed along the first set of directions only, and at each point a sparse grid is constructed adaptively in the second set of directions. We then consider the application of aPSP in the space of all parameters, and introduce directional refinement criteria to provide a tighter control of the projection error along individual dimensions. Specifically, we use a Sobol decomposition of the projection surpluses to tune the sparse grid adaptation. The behavior and performance of the two approaches are compared for a simple two-dimensional test problem and for a shock-tube ignition model involving 22 uncertain parameters and 3 design parameters. The numerical experiments indicate that whereas both methods provide effective means for tuning the quality of the representation along distinct subsets of parameters, PSP in the global parameter space generally requires fewer model evaluations than the nested approach to achieve similar projection error. In addition, the global approach is better suited for generalization to more than two subsets of directions.

Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

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Jie Xu

2013-01-01

Full Text Available New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA and sparse support vector machine (SVM. The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA. The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.

Monoclonal antibodies and coupling reagents to cell membrane proteins for leukocyte labeling

International Nuclear Information System (INIS)

McAfee, J.G.; Gagne, G.; Subramanian, G.; Schneider, R.F.

1984-01-01

Current gamma-emitting agents for tagging leukocytes, In-111 oxine or tropolone, label all cell types indiscriminantly, and nuclear localization in lymphocytes results in radiation damage. Coupling reagents and murine monoclonal antibodies (Mab) specific for cell surface antigens of human leukocytes were tried as cell labeling agents to avoid nuclear localization. 10/sup 8/ mixed human leukocytes in Hepes buffer were added to tubes coated with 5 mg of dry cyclic dianhydride of DTPA for 15 minutes at room temperature. After washing, 0.1 ml of In-111 Cl in ACD (pH 6.8) was added. After 30 minutes, a cell labeling yield of 23% was obtained. Washing the cells in an elutriation centrifuge showed that this label was irreversible. Mab for cell surface antigens of human granulocytes were labeled with 300 μCi of I-125 using the Iodobead technic and unbound activity was removed by gel column chromatography. 1-10 μg were added to 10/sup 8/ mixed leukocytes in 0.5 ml plasma or saline for 1 hr. With Mab anti-leu M4 (clone G7 E11), an IgM, the cell labeling yield was 21%, irreversible, and specific for granulocytes. With anti-human leukocyte Mab NEI-042 (clone 9.4), and IgG2a, and anti-granulocyte Mab MAS-065 (clone FMCl1) an IgG1, the cell labeling was relatively unstable. Labeling of leukocyte subpopulations with Mab is feasible, and the binding of multivalent IgM is stronger than that of other immunoglobulins. DTPA cyclic anhydride is firmly bound to cell membranes, but the labeling is non-specific

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

Compressive sensing for sparse time-frequency representation of nonstationary signals in the presence of impulsive noise

Science.gov (United States)

Orović, Irena; Stanković, Srdjan; Amin, Moeness

2013-05-01

A modified robust two-dimensional compressive sensing algorithm for reconstruction of sparse time-frequency representation (TFR) is proposed. The ambiguity function domain is assumed to be the domain of observations. The two-dimensional Fourier bases are used to linearly relate the observations to the sparse TFR, in lieu of the Wigner distribution. We assume that a set of available samples in the ambiguity domain is heavily corrupted by an impulsive type of noise. Consequently, the problem of sparse TFR reconstruction cannot be tackled using standard compressive sensing optimization algorithms. We introduce a two-dimensional L-statistics based modification into the transform domain representation. It provides suitable initial conditions that will produce efficient convergence of the reconstruction algorithm. This approach applies sorting and weighting operations to discard an expected amount of samples corrupted by noise. The remaining samples serve as observations used in sparse reconstruction of the time-frequency signal representation. The efficiency of the proposed approach is demonstrated on numerical examples that comprise both cases of monocomponent and multicomponent signals.

Syntheses of 18F-labeled reduced haloperidol and 11C-labeled reduced 3-N-methylspiperone

International Nuclear Information System (INIS)

Ravert, H.T.; Dannals, R.F.; Wilson, A.A.; Wong, D.F.; Wagner, H.N. Jr.

1991-01-01

18 F-Labeled reduced haloperidol and 11 C-labeled reduced 3-N-methylspiperone were synthesized in a convenient and quantitative one step reduction from 18 F-labeled haloperidol and 11 C-labeled N-methylspiperone, respectively. Both products were purified by semipreparative HPLC and were obtained at high specific activity and radiochemical purity. (author)

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...

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...

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

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

Directory of Open Access Journals (Sweden)

Wei Jin

2016-12-01

Full Text Available Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC, atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

Science.gov (United States)

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-01-01

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

Preparation of labelled antituberculotics for clarifying specific problems in therapy optimization. 1

International Nuclear Information System (INIS)

Winsel, K.; Iwainsky, H.; Mittag, E.; Kiessling, M.; Koehler, H.

1985-01-01

The preparation of tritium labelled isoniazid according to the Wilzbach method and by catalytic exchange is described. The purified labelled isoniazid is adjusted to an activity of 37 MBq/300 mg isoniazid. It meets the requirements for an injectable pharmaceutical. The tritium labelling is stable under in vitro conditions and in the macroorganism. (author)

Sparse-sampling with time-encoded (TICO) stimulated Raman scattering for fast image acquisition

Science.gov (United States)

Hakert, Hubertus; Eibl, Matthias; Karpf, Sebastian; Huber, Robert

2017-07-01

Modern biomedical imaging modalities aim to provide researchers a multimodal contrast for a deeper insight into a specimen under investigation. A very promising technique is stimulated Raman scattering (SRS) microscopy, which can unveil the chemical composition of a sample with a very high specificity. Although the signal intensities are enhanced manifold to achieve a faster acquisition of images if compared to standard Raman microscopy, there is a trade-off between specificity and acquisition speed. Commonly used SRS concepts either probe only very few Raman transitions as the tuning of the applied laser sources is complicated or record whole spectra with a spectrometer based setup. While the first approach is fast, it reduces the specificity and the spectrometer approach records whole spectra -with energy differences where no Raman information is present-, which limits the acquisition speed. Therefore, we present a new approach based on the TICO-Raman concept, which we call sparse-sampling. The TICO-sparse-sampling setup is fully electronically controllable and allows probing of only the characteristic peaks of a Raman spectrum instead of always acquiring a whole spectrum. By reducing the spectral points to the relevant peaks, the acquisition time can be greatly reduced compared to a uniformly, equidistantly sampled Raman spectrum while the specificity and the signal to noise ratio (SNR) are maintained. Furthermore, all laser sources are completely fiber based. The synchronized detection enables a full resolution of the Raman signal, whereas the analogue and digital balancing allows shot noise limited detection. First imaging results with polystyrene (PS) and polymethylmethacrylate (PMMA) beads confirm the advantages of TICO sparse-sampling. We achieved a pixel dwell time as low as 35 μs for an image differentiating both species. The mechanical properties of the applied voice coil stage for scanning the sample currently limits even faster acquisition.

ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

KAUST Repository

Wu, Baoyuan

2015-12-07

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). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges. Unlike most MLML methods, We formulate this learning problem transductively as a convex quadratic matrix optimization problem that encourages training label consistency and encodes both types of label dependencies (i.e. undirected and directed edges) using quadratic terms and hard linear constraints. The alternating direction method of multipliers (ADMM) can be used to exactly and efficiently solve this problem. To evaluate our proposed method, we consider two popular applications (image and video annotation), where the label hierarchy can be derived from Wordnet. Experimental results show that our method achieves a significant improvement over state-of-the-art methods in performance and robustness to missing labels.

A hedonic analysis of nutrition labels across product types and countries

DEFF Research Database (Denmark)

Edenbrandt, Anna Kristina; Smed, Sinne; Jansen, Leon

2018-01-01

show positive values attached to the label across countries and most product categories, suggesting that consumers do value the guidance that the nutrition labels provide. Policy implications from these results indicate that credible nutrition labels are relevant tools in the effort to combat diet...

User's Manual for PCSMS (Parallel Complex Sparse Matrix Solver). Version 1.

Science.gov (United States)

Reddy, C. J.

2000-01-01

PCSMS (Parallel Complex Sparse Matrix Solver) is a computer code written to make use of the existing real sparse direct solvers to solve complex, sparse matrix linear equations. PCSMS converts complex matrices into real matrices and use real, sparse direct matrix solvers to factor and solve the real matrices. The solution vector is reconverted to complex numbers. Though, this utility is written for Silicon Graphics (SGI) real sparse matrix solution routines, it is general in nature and can be easily modified to work with any real sparse matrix solver. The User's Manual is written to make the user acquainted with the installation and operation of the code. Driver routines are given to aid the users to integrate PCSMS routines in their own codes.

Gold Nanoparticle Labels Amplify Ellipsometric Signals

Science.gov (United States)

Venkatasubbarao, Srivatsa

2008-01-01

The ellipsometric method reported in the immediately preceding article was developed in conjunction with a method of using gold nanoparticles as labels on biomolecules that one seeks to detect. The purpose of the labeling is to exploit the optical properties of the gold nanoparticles in order to amplify the measurable ellipsometric effects and thereby to enable ultrasensitive detection of the labeled biomolecules without need to develop more-complex ellipsometric instrumentation. The colorimetric, polarization, light-scattering, and other optical properties of nanoparticles depend on their sizes and shapes. In the present method, these size-and-shape-dependent properties are used to magnify the polarization of scattered light and the diattenuation and retardance of signals derived from ellipsometry. The size-and-shape-dependent optical properties of the nanoparticles make it possible to interrogate the nanoparticles by use of light of various wavelengths, as appropriate, to optimally detect particles of a specific type at high sensitivity. Hence, by incorporating gold nanoparticles bound to biomolecules as primary or secondary labels, the performance of ellipsometry as a means of detecting the biomolecules can be improved. The use of gold nanoparticles as labels in ellipsometry has been found to afford sensitivity that equals or exceeds the sensitivity achieved by use of fluorescence-based methods. Potential applications for ellipsometric detection of gold nanoparticle-labeled biomolecules include monitoring molecules of interest in biological samples, in-vitro diagnostics, process monitoring, general environmental monitoring, and detection of biohazards.

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....

Dual-process theory and consumer response to front-of-package nutrition label formats.

Science.gov (United States)

Sanjari, S Setareh; Jahn, Steffen; Boztug, Yasemin

2017-11-01

Nutrition labeling literature yields fragmented results about the effect of front-of-package (FOP) nutrition label formats on healthy food choice. Specifically, it is unclear which type of nutrition label format is effective across different shopping situations. To address this gap, the present review investigates the available nutrition labeling literature through the prism of dual-process theory, which posits that decisions are made either quickly and automatically (system 1) or slowly and deliberately (system 2). A systematically performed review of nutrition labeling literature returned 59 papers that provide findings that can be explained according to dual-process theory. The findings of these studies suggest that the effectiveness of nutrition label formats is influenced by the consumer's dominant processing system, which is a function of specific contexts and personal variables (eg, motivation, nutrition knowledge, time pressure, and depletion). Examination of reported findings through a situational processing perspective reveals that consumers might prefer different FOP nutrition label formats in different situations and can exhibit varying responses to the same label format across situations. This review offers several suggestions for policy makers and researchers to help improve current FOP nutrition label formats. © The Author(s) 2017. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices.

Science.gov (United States)

Zhao, Wenfeng; Sun, Biao; Wu, Tong; Yang, Zhi

2018-02-01

On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and -sparse random binary matrix [-SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and -SRBM encoders with reduced area and total power consumption.

Scintigraphy with In-111 labeled leukocytes

International Nuclear Information System (INIS)

Itoh, Kazuo; Tsukamoto, Eriko; Furudate, Masayori; Saito, Chihoko.

1987-01-01

With increasing necessity for In-111 labeled leukocyte scintigraphy (ILLS) as a routine examination, a problem of complicated labeling of leukocytes has arisen. In this study, simplified labeling of leukocytes was examined with respect to its ability to detect abscesses. Simplified labeling method yielded significantly satisfactory results for recovery and labeling rates of leukocytes, as compared with conventional recommended method. Therefore, ILLS by simplified technique was clinically applied in 58 patients with suppurative or non-suppurative diseases who gave informed consent. In an analysis of ILLS for detecting suppurative region, the sensitivity, specificity, and corrected specificity were found to be 81 %, 75 %, and 82 %, respectively. (Namekawa, K.)

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...

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.

Synaptic learning rules and sparse coding in a model sensory system.

Directory of Open Access Journals (Sweden)

Luca A Finelli

2008-04-01

Full Text Available Neural circuits exploit numerous strategies for encoding information. Although the functional significance of individual coding mechanisms has been investigated, ways in which multiple mechanisms interact and integrate are not well understood. The locust olfactory system, in which dense, transiently synchronized spike trains across ensembles of antenna lobe (AL neurons are transformed into a sparse representation in the mushroom body (MB; a region associated with memory, provides a well-studied preparation for investigating the interaction of multiple coding mechanisms. Recordings made in vivo from the insect MB demonstrated highly specific responses to odors in Kenyon cells (KCs. Typically, only a few KCs from the recorded population of neurons responded reliably when a specific odor was presented. Different odors induced responses in different KCs. Here, we explored with a biologically plausible model the possibility that a form of plasticity may control and tune synaptic weights of inputs to the mushroom body to ensure the specificity of KCs' responses to familiar or meaningful odors. We found that plasticity at the synapses between the AL and the MB efficiently regulated the delicate tuning necessary to selectively filter the intense AL oscillatory output and condense it to a sparse representation in the MB. Activity-dependent plasticity drove the observed specificity, reliability, and expected persistence of odor representations, suggesting a role for plasticity in information processing and making a testable prediction about synaptic plasticity at AL-MB synapses.

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.

What's in a Name? Effect of Breed Perceptions & Labeling on Attractiveness, Adoptions & Length of Stay for Pit-Bull-Type Dogs.

Directory of Open Access Journals (Sweden)

Lisa M Gunter

Full Text Available Previous research has indicated that certain breeds of dogs stay longer in shelters than others. However, exactly how breed perception and identification influences potential adopters' decisions remains unclear. Current dog breed identification practices in animal shelters are often based upon information supplied by the relinquishing owner, or staff determination based on the dog's phenotype. However, discrepancies have been found between breed identification as typically assessed by welfare agencies and the outcome of DNA analysis. In Study 1, the perceived behavioral and adoptability characteristics of a pit-bull-type dog were compared with those of a Labrador Retriever and Border Collie. How the addition of a human handler influenced those perceptions was also assessed. In Study 2, lengths of stay and perceived attractiveness of dogs that were labeled as pit bull breeds were compared to dogs that were phenotypically similar but were labeled as another breed at an animal shelter. The latter dogs were called "lookalikes." In Study 3, we compared perceived attractiveness in video recordings of pit-bull-type dogs and lookalikes with and without breed labels. Lastly, data from an animal shelter that ceased applying breed labeling on kennels were analyzed, and lengths of stay and outcomes for all dog breeds, including pit bulls, before and after the change in labeling practice were compared. In total, these findings suggest that breed labeling influences potential adopters' perceptions and decision-making. Given the inherent complexity of breed assignment based on morphology coupled with negative breed perceptions, removing breed labels is a relatively low-cost strategy that will likely improve outcomes for dogs in animal shelters.

What's in a Name? Effect of Breed Perceptions & Labeling on Attractiveness, Adoptions & Length of Stay for Pit-Bull-Type Dogs.

Science.gov (United States)

Gunter, Lisa M; Barber, Rebecca T; Wynne, Clive D L

2016-01-01

Previous research has indicated that certain breeds of dogs stay longer in shelters than others. However, exactly how breed perception and identification influences potential adopters' decisions remains unclear. Current dog breed identification practices in animal shelters are often based upon information supplied by the relinquishing owner, or staff determination based on the dog's phenotype. However, discrepancies have been found between breed identification as typically assessed by welfare agencies and the outcome of DNA analysis. In Study 1, the perceived behavioral and adoptability characteristics of a pit-bull-type dog were compared with those of a Labrador Retriever and Border Collie. How the addition of a human handler influenced those perceptions was also assessed. In Study 2, lengths of stay and perceived attractiveness of dogs that were labeled as pit bull breeds were compared to dogs that were phenotypically similar but were labeled as another breed at an animal shelter. The latter dogs were called "lookalikes." In Study 3, we compared perceived attractiveness in video recordings of pit-bull-type dogs and lookalikes with and without breed labels. Lastly, data from an animal shelter that ceased applying breed labeling on kennels were analyzed, and lengths of stay and outcomes for all dog breeds, including pit bulls, before and after the change in labeling practice were compared. In total, these findings suggest that breed labeling influences potential adopters' perceptions and decision-making. Given the inherent complexity of breed assignment based on morphology coupled with negative breed perceptions, removing breed labels is a relatively low-cost strategy that will likely improve outcomes for dogs in animal shelters.

Labelled compounds for agrochemical residue studies in developing countries

International Nuclear Information System (INIS)

1977-01-01

Potential applications of stable and radioactive isotopic tracers for assessing undesirable contaminants in agriculture, fisheries and food are discussed as related to developing countries. Sources and types of residues are considered, and their local implications; also, the availability of suitably labelled compounds, including possible international cooperation to facilitate more centralized and economic preparation, and the distribution of labelled intermediates and compounds for use by local scientists. The provision of training courses and their syllabus are reviewed. Experience in the Joint FAO/IAEA chemical residue and pollution programme has indicated a need for longer-lived radioisotopically labelled pesticides (insecticides, acaricides, fungicides, herbicides, fumigants, etc.) for studying their behaviour. 15 N-, 13 C- or 2 H-labelled fertilizers and fertilizer additives such as nitrification inhibitors will shortly be needed, for studying the behaviour of fertilizer nitrogen residues, and their regulation and conservation, under conditions prevailing in the developing countries. Compounds labelled with stable isotopes are considered particularly valuable under field conditions. The report reviews the present situation and presents specific recommendations to the Directors General of FAO and IAEA

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.

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

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.

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

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.

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.

Site-specific conjugation and labelling of prostate antibody 7E11C5.3 (CYT-351) with technetium-99m

International Nuclear Information System (INIS)

Stalteri, M.A.; Mather, S.J.; Belinka, B.A.; Coughlin, D.J.; Chengazi, V.U.; Britton, K.E.

1997-01-01

Attachment of chelating agents to the sugar residues of antibodies for subsequent radiolabelling is an attractive approach since it may have less effect on the immunoreactivity than attachment through lysine residues, which are distributed throughout the antibody and may be present near the antigen binding site. We have attached a new hydrazide-linked chelator CYT-395 (Cytogen Corp., Princeton, N.J.) to the sugar residues of the anti-prostate monoclonal antibody 7E11C5.3 and optimised the conditions for labelling the conjugate with technetium-99m in order to compare the conjugate to 7E11C5.3 antibody labelled directly with technetium using a mercaptoethanol reduction technique. Labelling yields of 70%-90% were obtained at specific activities up to 2000 MBq/mg antibody. The stability of the technetium-labelled conjugate in plasma or to a challenge with 0.1 or 1.0 mM cysteine was similar to that of direct-labelled antibody. In nine patients with prostate cancer, the plasma clearance of the labelled conjugate followed a two-compartment model, with an average β-phase half-life of 31.4±3.9 h. The average urinary clearance at 24 h was 15.3±5.0% of the injected dose. In this group of patients there was no significant difference between the blood and urine clearance of the labelled conjugate, and the clearances of the direct-labelled antibody. (orig.). With 5 figs

Human Vitamin B12 Absorption and Metabolism are Measured by Accelerator Mass Spectrometry Using Specifically Labeled 14C-Cobalamin

International Nuclear Information System (INIS)

Carkeet, C; Dueker, S R; Lango, J; Buchholz, B A; Miller, J W; Green, R; Hammock, B D; Roth, J R; Anderson, P J

2006-01-01

There is need for an improved test of human ability to assimilate dietary vitamin B 12 . Assaying and understanding absorption and uptake of B 12 is important because defects can lead to hematological and neurological complications. Accelerator mass spectrometry (AMS) is uniquely suited for assessing absorption and kinetics of 14 C-labeled substances after oral ingestion because it is more sensitive than decay counting and can measure levels of carbon-14 ( 14 C) in microliter volumes of biological samples, with negligible exposure of subjects to radioactivity. The test we describe employs amounts of B 12 in the range of normal dietary intake. The B 12 used was quantitatively labeled with 14 C at one particular atom of the DMB moiety by exploiting idiosyncrasies of Salmonellametabolism. In order to grow aerobically on ethanolamine, S. entericamust be provided with either pre-formed B 12 or two of its precursors: cobinamide and dimethylbenzimidazole (DMB). When provided with 14 C-DMB specifically labeled in the C2 position, cells produced 14 C-B 12 of high specific activity (2.1 GBq/mmol, 58 mCi/mmol) and no detectable dilution of label from endogenous DMB synthesis. In a human kinetic study, a physiological dose (1.5 mg, 2.2 KBq/59 nCi) of purified 14 C-B 12 was administered and showed plasma appearance and clearance curves consistent with the predicted behavior of the pure vitamin. This method opens new avenues for study of B 12 assimilation

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

Tuning a Protein-Labeling Reaction to Achieve Highly Site Selective Lysine Conjugation.

Science.gov (United States)

Pham, Grace H; Ou, Weijia; Bursulaya, Badry; DiDonato, Michael; Herath, Ananda; Jin, Yunho; Hao, Xueshi; Loren, Jon; Spraggon, Glen; Brock, Ansgar; Uno, Tetsuo; Geierstanger, Bernhard H; Cellitti, Susan E

2018-04-16

Activated esters are widely used to label proteins at lysine side chains and N termini. These reagents are useful for labeling virtually any protein, but robust reactivity toward primary amines generally precludes site-selective modification. In a unique case, fluorophenyl esters are shown to preferentially label human kappa antibodies at a single lysine (Lys188) within the light-chain constant domain. Neighboring residues His189 and Asp151 contribute to the accelerated rate of labeling at Lys188 relative to the ≈40 other lysine sites. Enriched Lys188 labeling can be enhanced from 50-70 % to >95 % by any of these approaches: lowering reaction temperature, applying flow chemistry, or mutagenesis of specific residues in the surrounding protein environment. Our results demonstrated that activated esters with fluoro-substituted aromatic leaving groups, including a fluoronaphthyl ester, can be generally useful reagents for site-selective lysine labeling of antibodies and other immunoglobulin-type proteins. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Thrombus imaging with indium-111 and iodine-131-labeled fibrin-specific monoclonal antibody and its F(ab')2 and Fab fragments

International Nuclear Information System (INIS)

Rosebrough, S.F.; Grossman, Z.D.; McAfee, J.G.

1988-01-01

We have previously reported successful imaging of fresh (2-4 hr old) and aged (1-5 days old) canine thrombi with 131 I-labeled intact monoclonal antibody (MAb) specific for fibrin. We now report thrombus imaging with 131 I-labeled F(ab')2 and Fab and 111 In-labeled intact MAb, F(ab')2, and Fab. Indium-111-labeled F(ab')2 proved to be the best imaging agent due to less nonspecific binding in the liver than whole IgG. Image quality was improved by the higher administered dose permissible with 111 In and its better physical characteristics for imaging, compared to 131 I. Immunofluorescence of fresh human histologic sections showed intact MAb and F(ab')2 binding to thrombi, pulmonary emboli, and atherosclerotic plaques, strengthening the feasibility of clinical thrombus imaging

Detection of acute synthetic vascular graft infection with IN-111 labeled leukocyte imaging

International Nuclear Information System (INIS)

Alazraki, N.; Dries, D.; Lawrence, P.; Murphy, K.; Kercher, J.; Datz, F.; Christian, P.; Taylor, A.

1985-01-01

Synthetic vascular graft infection is characterized by late diagnosis due to indolent and nonspecific symptoms. Reported data on accuracy of In-111 labeled leukocyte imaging to identify vascular graft infection is sparse and conflicting. The purpose of this animal study was to clarify the accuracy of detection of early graft infection using a mixed population of In-111 labeled leukocytes. Twelve mongrel dogs received dacron aortic interposition grafts. Seven grafts were contaminated at surgery by topical ATCC S. aureus, 10/sup 8/ organisms per ml. Six control animals received no graft contamination Mixed population In-111 homologous leukocyte labeling was performed followed by imaging at 24 and 48 hours following intravenous injection of 250 μCi In-111 leukocytes. Scans were done on Day 2 post-surgery. Infected dogs were sacrificed following Indium imaging; control dogs were rescanned at 3 weeks postop and sacrificed thereafter. Autopsy results were correlated with scans, yielding sensitivity 71%, specificity 100%, accuracy 85% for In-111 leukocyte imaging to detect early graft infection. False positive leukocyte imaging in the early postop period was not a problem. At autopsy all 5 dogs with infected grafts and positive scans had gross pus. The 2 dogs with false negative scans showed no gross pus at autopsy; cultures were positive for S. aureus in all 7 dogs. Scans at 2 days and 3 weeks post-surgery were true negatives in all 6 control dogs. These data suggest a high level of clinical reliability of leukocyte imaging for early graft infection detection

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.

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.

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.

Label-free aptamer-based sensor for specific detection of malathion residues by surface-enhanced Raman scattering

Science.gov (United States)

Nie, Yonghui; Teng, Yuanjie; Li, Pan; Liu, Wenhan; Shi, Qianwei; Zhang, Yuchao

2018-02-01

A novel label-free aptamer surface-enhanced Raman scattering (SERS) sensor for trace malathion residue detection was proposed. In this process, the binding of malathion molecule with aptamer is identified directly. The silver nanoparticles modified with positively charged spermine served as enhancing and capture reagents for the negatively charged aptamer. Then, the silver nanoparticles modified by aptamer were used to specifically capture the malathion. The SERS background spectra of spermine, aptamer, and malathion were recorded and distinguished with the spectrum of malathion-aptamer. To enhance the characteristic peak signal of malathion captured by the aptamer, the aggregate reagents (NaCl, KCl, MgCl2) were compared and selected. The selectivity of this method was verified in the mixed-pesticide standard solution, which included malathion, phosmet, chlorpyrifos-methyl, and fethion. Results show that malathion can be specifically identified when the mixed-pesticide interferences existed. The standard curve was established, presenting a good linear range of 5 × 10- 7 to 1 × 10- 5 mol·L- 1. The spiked experiments for tap water show good recoveries from 87.4% to 110.5% with a relative standard deviation of less than 4.22%. Therefore, the proposed label-free aptamer SERS sensor is convenient, specifically detects trace malathion residues, and can be applied for qualitative and quantitative analysis of other pesticides.

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...

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...

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.

Quality evaluation of LC-MS/MS-based E. coli H antigen typing (MS-H) through label-free quantitative data analysis in a clinical sample setup.

Science.gov (United States)

Cheng, Keding; Sloan, Angela; McCorrister, Stuart; Peterson, Lorea; Chui, Huixia; Drebot, Mike; Nadon, Celine; Knox, J David; Wang, Gehua

2014-12-01

The need for rapid and accurate H typing is evident during Escherichia coli outbreak situations. This study explores the transition of MS-H, a method originally developed for rapid H antigen typing of E. coli using LC-MS/MS of flagella digest of reference strains and some clinical strains, to E. coli isolates in clinical scenario through quantitative analysis and method validation. Motile and nonmotile strains were examined in batches to simulate clinical sample scenario. Various LC-MS/MS batch run procedures and MS-H typing rules were compared and summarized through quantitative analysis of MS-H data output for a standard method development. Label-free quantitative data analysis of MS-H typing was proven very useful for examining the quality of MS-H result and the effects of some sample carryovers from motile E. coli isolates. Based on this, a refined procedure and protein identification rule specific for clinical MS-H typing was established and validated. With LC-MS/MS batch run procedure and database search parameter unique for E. coli MS-H typing, the standard procedure maintained high accuracy and specificity in clinical situations, and its potential to be used in a clinical setting was clearly established. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Labeling of multiple HIV-1 proteins with the biarsenical-tetracysteine system.

Directory of Open Access Journals (Sweden)

Cândida F Pereira

Full Text Available Due to its small size and versatility, the biarsenical-tetracysteine system is an attractive way to label viral proteins for live cell imaging. This study describes the genetic labeling of the human immunodeficiency virus type 1 (HIV-1 structural proteins (matrix, capsid and nucleocapsid, enzymes (protease, reverse transcriptase, RNAse H and integrase and envelope glycoprotein 120 with a tetracysteine tag in the context of a full-length virus. We measure the impact of these modifications on the natural virus infection and, most importantly, present the first infectious HIV-1 construct containing a fluorescently-labeled nucleocapsid protein. Furthermore, due to the high background levels normally associated with the labeling of tetracysteine-tagged proteins we have also optimized a metabolic labeling system that produces infectious virus containing the natural envelope glycoproteins and specifically labeled tetracysteine-tagged proteins that can easily be detected after virus infection of T-lymphocytes. This approach can be adapted to other viral systems for the visualization of the interplay between virus and host cell during infection.

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.

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

DATATOC: a novel conjugate for kit-type 68Ga labelling of TOC at ambient temperature.

Science.gov (United States)

Seemann, Johanna; Waldron, Bradley; Parker, David; Roesch, Frank

2017-01-01

The widespread acceptance and application of 68 Ga-PET depends on our ability to develop radiopharmaceuticals that can be prepared in a convenient and suitable manner. A kit-type labelling protocol provides such characteristics and requires chelators that can be radiolabelled under exceptionally mild conditions. Recently the DATA chelators have been introduced that fulfil these requirements. In continuing their development, the synthesis and radiolabelling of the first DATA bifunctional chelator (BFC) and peptide conjugate are described. A BFC derived from the DATA ligand (2,2'-(6-((carboxymethyl)amino)-1,4-diazepane-1,4-diyl)diacetic acid) has been synthesised in five steps from simple building blocks, with an overall yield of 8 %. DATA M5 -3 t Bu (5-[1,4-Bis-tert-butoxycarbonylmethyl-6-(tert-butoxycarbonylmethyl-methyl-amino)-[1, 4]diazepan-6-yl]-pentanoic acid) has been coupled to [DPhe 1 ][Tyr 3 ]-octreotide (TOC) and the resulting peptide conjugate (DATATOC) radiolabelled with purified 68 Ga derived via four different 68 Ge/ 68 Ga generator post-processing (PP) methods. The stability and lipophilicity of the radiotracer have been assessed and a kit-type formulation for radiolabelling evaluated. 68 Ga-DATATOC has been prepared with a > 95 % radiochemical yield (RCY) within 1 (fractionated and acetone-PP) and 10 min (ethanol- and NaCl-PP) at 23 °C (pH 4.2-4.9, 13 nmol). The radiolabelled peptide is stable in the presence of human serum. Lipophilicity of 68 Ga-DATATOC was calculated as logP = -3.2 ± 0.3, with a HPLC retention time ( t R  = 10.4 min) similar to 68 Ga-DOTATOC (logP = -2.9 ± 0.4, t R  = 10.3 min). Kit-type labelling from a lyophilised solid using acetone-PP based labelling achieves > 95 % RCY in 10 min at 23 °C. The favourable labelling properties of the DATA chelators have been retained for DATATOC. High radiochemical purity can be achieved at 23 °C in less than 1 min and from a kit formulation. The

3D label-free prostate specific antigen (PSA) immunosensor based on graphene-gold composites.

Science.gov (United States)

Jang, Hee Dong; Kim, Sun Kyung; Chang, Hankwon; Choi, Jeong-Woo

2015-01-15

Highly sensitive and label-free detection of the prostate specific antigen (PSA) remains a challenge in the diagnosis of prostate cancer. Here, a novel three-dimensional (3D) electrochemical immunosensor capable of sensitive and label-free detection of PSA is reported. This unique immunosensor is equipped with a highly conductive graphene (GR)-based gold (Au) composite modified electrode. The GR-based Au composite is prepared using aerosol spray pyrolysis and the morphology of the composite is the shape of a crumpled GR ball decorated with Au nanoparticles. Unlike the previous research, this novel 3D immunosensor functions very well over a broad linear range of 0-10 ng/mL with a low detection limit of 0.59 ng/mL; furthermore, it exhibits a significantly increased electron transfer and high sensitivity toward PSA. The highest rate of current change with respect to the PSA concentration is 5 μA/(ng/mL). Satisfactory selectivity, reproducibility, and stability of the 3D immunosensor are also exhibited. Copyright © 2014 Elsevier B.V. All rights reserved.

Specific uptake, dissociation, and degradation of 125I-labeled insulin in isolated turtle (Chrysemys dorbigni) thyroid glands

International Nuclear Information System (INIS)

Marques, M.; da Silva, R.S.; Turyn, D.; Dellacha, J.M.

1985-01-01

Thyroid glands from turtles (Chrysemys dorbigni) pretreated with potassium iodide were incubated with 125 I-insulin in the presence or absence of unlabeled insulin, in order to study its specific uptake. At 24 degrees, the specific uptake reached a plateau at 180 min of incubation. The dose of bovine insulin that inhibited 50% of the 125 I-insulin uptake was 2 micrograms/ml of incubation medium. Most of the radioactive material (71%) extracted from the gland, after 30 min incubation with 125 I-insulin, eluted in the same position as labeled insulin on Sephadex G-50. Only 24% eluted in the salt position. After 240 min incubation, increased amount of radioactivity appeared in the Na 125 I position. When bovine insulin was added together with the labeled hormone, a substantial reduction of radioactivity was observed in the insulin and Na 125 I elution positions. Dissociation studies were performed at 6 degrees in glands preincubated with 125 I-insulin either at 24 or 6 degrees. The percentage of trichloroacetic acid (TCA)-soluble radioactive material in the dissociation medium increased with incubation time at both temperatures. However, the degradation activity was lower at 6 than at 24 degrees. The addition of bovine insulin to the incubation buffer containing 125 I-insulin reduced the radioactive degradation products in the dissociated medium. Chloroquine or bacitracin inhibited the degradation activity. Incubation of thyroid glands with 125 I-hGH or 125 I-BSA showed values of uptake, dissociation, and degradation similar to those experiments in which an excess of bovine insulin was added together with the labeled hormone. Thus, by multiple criteria, such as specific uptake, dissociation, and degradation, the presence of insulin-binding sites in the turtle thyroid gland may be suggested

What’s in a Name? Effect of Breed Perceptions & Labeling on Attractiveness, Adoptions & Length of Stay for Pit-Bull-Type Dogs

Science.gov (United States)

Gunter, Lisa M.; Barber, Rebecca T.; Wynne, Clive D. L.

2016-01-01

Previous research has indicated that certain breeds of dogs stay longer in shelters than others. However, exactly how breed perception and identification influences potential adopters' decisions remains unclear. Current dog breed identification practices in animal shelters are often based upon information supplied by the relinquishing owner, or staff determination based on the dog's phenotype. However, discrepancies have been found between breed identification as typically assessed by welfare agencies and the outcome of DNA analysis. In Study 1, the perceived behavioral and adoptability characteristics of a pit-bull-type dog were compared with those of a Labrador Retriever and Border Collie. How the addition of a human handler influenced those perceptions was also assessed. In Study 2, lengths of stay and perceived attractiveness of dogs that were labeled as pit bull breeds were compared to dogs that were phenotypically similar but were labeled as another breed at an animal shelter. The latter dogs were called "lookalikes." In Study 3, we compared perceived attractiveness in video recordings of pit-bull-type dogs and lookalikes with and without breed labels. Lastly, data from an animal shelter that ceased applying breed labeling on kennels were analyzed, and lengths of stay and outcomes for all dog breeds, including pit bulls, before and after the change in labeling practice were compared. In total, these findings suggest that breed labeling influences potential adopters' perceptions and decision-making. Given the inherent complexity of breed assignment based on morphology coupled with negative breed perceptions, removing breed labels is a relatively low-cost strategy that will likely improve outcomes for dogs in animal shelters. PMID:27008213

Single-Trial Decoding of Bistable Perception Based on Sparse Nonnegative Tensor Decomposition

Science.gov (United States)

Wang, Zhisong; Maier, Alexander; Logothetis, Nikos K.; Liang, Hualou

2008-01-01

The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper, we develop a sparse nonnegative tensor factorization-(NTF)-based method to extract features from the local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequency representation of the intracortical LFP data. The advantages of the sparse NTF-based feature extraction approach lies in its capability to yield components common across the space, time, and frequency domains yet discriminative across different conditions without prior knowledge of the discriminating frequency bands and temporal windows for a specific subject. We employ the support vector machines (SVMs) classifier based on the features of the NTF components for single-trial decoding the reported perception. Our results suggest that although other bands also have certain discriminability, the gamma band feature carries the most discriminative information for bistable perception, and that imposing the sparseness constraints on the nonnegative tensor factorization improves extraction of this feature. PMID:18528515

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.

CFL Labeling Harmonization in the United States, China, Brazil andELI Member Countries: Specifications, Testing, and MutualRecognition

Energy Technology Data Exchange (ETDEWEB)

Fridley, David; Lin, Jiang; Denver, Andrea; Biermayer, Peter; Dillavou, Tyler

2005-07-20

This report examines critical differences among energy-efficient labeling programs for CFLs in Brazil, China, the United States, and the seven members of the international Efficient Lighting Initiative (ELI) in terms of technical specifications and test procedures, and review issues related to international harmonization of these standards.

Specific identification of human papillomavirus type in cervical smears and paraffin sections by in situ hybridization with radioactive probes: a preliminary communication

International Nuclear Information System (INIS)

Gupta, J.; Gendelman, H.E.; Naghashfar, Z.; Gupta, P.; Rosenshein, N.; Sawada, E.; Woodruff, J.D.; Shah, K.

1985-01-01

Cervical Papanicolaou smears and paraffin sections of biopsy specimens obtained from women attending dysplasia clinics were examined for viral DNA sequences by in situ hybridization technique using 35 S-labeled cloned recombinant DNA probes of human papillomavirus (HPV) types 6, 11, and 16. These and one unrelated DNA probe complementary to measles virus RNA were labeled by nick translation using either one or two 35 S-labeled nucleotides. Paraffin sections and cervical smears were collected on pretreated slides, hybridized with the probes under stringent or nonstringent conditions for 50 h, and autoradiographed. Additional cervical specimens from the same women were examined for the presence of genus-specific papillomavirus capsid antigen by the immunoperoxidase technique. Preliminary results may be summarized as follows. The infecting virus could be identified in smears as well as in sections. Viral DNA sequences were detected only when there were condylomatous cells in the specimen and in only a proportion of the condylomatous cells. Even under stringent conditions, some specimens reacted with both HPV-6 and HPV-11. In some instances, the cells did not hybridize with any of the three probes even when duplicate specimens contained frankly condylomatous, capsid antigen-positive cells. In situ hybridization of Papanicolaou smears or of tissue sections is a practical method for diagnosis and follow-up of specific papillomavirus infection using routinely collected material

Labelling fashion magazine advertisements: Effectiveness of different label formats on social comparison and body dissatisfaction.

Science.gov (United States)

Tiggemann, Marika; Brown, Zoe

2018-06-01

The experiment investigated the impact on women's body dissatisfaction of different forms of label added to fashion magazine advertisements. Participants were 340 female undergraduate students who viewed 15 fashion advertisements containing a thin and attractive model. They were randomly allocated to one of five label conditions: no label, generic disclaimer label (indicating image had been digitally altered), consequence label (indicating that viewing images might make women feel bad about themselves), informational label (indicating the model in the advertisement was underweight), or a graphic label (picture of a paint brush). Although exposure to the fashion advertisements resulted in increased body dissatisfaction, there was no significant effect of label type on body dissatisfaction; no form of label demonstrated any ameliorating effect. In addition, the consequence and informational labels resulted in increased perceived realism and state appearance comparison. Yet more extensive research is required before the effective implementation of any form of label. Copyright © 2018 Elsevier Ltd. All rights reserved.

Syntheses of sup 18 F-labeled reduced haloperidol and sup 11 C-labeled reduced 3-N-methylspiperone

Energy Technology Data Exchange (ETDEWEB)

Ravert, H T; Dannals, R F; Wilson, A A; Wong, D F; Wagner, Jr, H N [Johns Hopkins Medical Institutions, Baltimore, MD (USA)

1991-03-01

{sup 18}F-Labeled reduced haloperidol and {sup 11}C-labeled reduced 3-N-methylspiperone were synthesized in a convenient and quantitative one step reduction from {sup 18}F-labeled haloperidol and {sup 11}C-labeled N-methylspiperone, respectively. Both products were purified by semipreparative HPLC and were obtained at high specific activity and radiochemical purity. (author).

CdSe/ZnS Quantum Dots-Labeled Mesenchymal Stem Cells for Targeted Fluorescence Imaging of Pancreas Tissues and Therapy of Type 1 Diabetic Rats.

Science.gov (United States)

Liu, Haoqi; Tang, Wei; Li, Chao; Lv, Pinlei; Wang, Zheng; Liu, Yanlei; Zhang, Cunlei; Bao, Yi; Chen, Haiyan; Meng, Xiangying; Song, Yan; Xia, Xiaoling; Pan, Fei; Cui, Daxiang; Shi, Yongquan

2015-12-01

Mesenchymal stem cells (MSCs) have been used for therapy of type 1 diabetes mellitus. However, the in vivo distribution and therapeutic effects of transplanted MSCs are not clarified well. Herein, we reported that CdSe/ZnS quantum dots-labeled MSCs were prepared for targeted fluorescence imaging and therapy of pancreas tissues in rat models with type 1 diabetes. CdSe/ZnS quantum dots were synthesized, their biocompatibility was evaluated, and then, the appropriate concentration of quantum dots was selected to label MSCs. CdSe/ZnS quantum dots-labeled MSCs were injected into mouse models with type 1 diabetes via tail vessel and then were observed by using the Bruker In-Vivo F PRO system, and the blood glucose levels were monitored for 8 weeks. Results showed that prepared CdSe/ZnS quantum dots owned good biocompatibility. Significant differences existed in distribution of quantum dots-labeled MSCs between normal control rats and diabetic rats (p quantum dots-labeled MSC injection. Statistical differences existed between the blood glucose levels of the diabetic rat control group and MSC-injected diabetic rat group (p < 0.01), and the MSC-injected diabetic rat group displayed lower blood glucose levels. In conclusion, CdSe/ZnS-labeled MSCs can target in vivo pancreas tissues in diabetic rats, and significantly reduce the blood glucose levels in diabetic rats, and own potential application in therapy of diabetic patients in the near future.

THREE TYPES OF ETHNO-SPECIFICITY IN PHRASEOLOGY: ETHNO-LINGUISTIC, ETHNO-CULTURAL, AND ETHNO-COGNITIVE SPECIFICITY

Directory of Open Access Journals (Sweden)

Marina Gutovskaya

2015-04-01

Full Text Available The ethno-specific phraseology – the phraseology which manifests interlanguage differences – is contemplated in the paper considering the phraseology corpora of the Russian and English languages. The popular opinion that ethno-specificity in phraseology is predetermined solely by unique features of the national culture is questioned. The three types of phraseological ethno-specificity are differentiated: ethno-linguistic (ensured by distinctive features of the national language, ethno-cultural (connected with the originality of the national culture, and ethno-cognitive (ordained by the uniqueness of the national worldview. The characteristics of the phraseological units that belong to each of the three types of ethno-specificity are enumerated, and the circles of questions on the ethno-specific phraseology to be studied within traditional linguistics, cultural linguistics, and cognitive linguistics are outlined. The possibility of coexistence of several types of ethno-specificity in one phraseological unit is shown. It is noted that in order to comprehend ethno-specific phraseological units of the three types a different degree of immersion in the foreign linguistic-cultural-cognitive space is required.

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.

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.

New sparse matrix solver in the KIKO3D 3-dimensional reactor dynamics code

International Nuclear Information System (INIS)

Panka, I.; Kereszturi, A.; Hegedus, C.

2005-01-01

The goal of this paper is to present a more effective method Bi-CGSTAB for accelerating the large sparse matrix equation solution in the KIKO3D code. This equation system is obtained by using the factorization of the improved quasi static (IQS) method for the time dependent nodal kinetic equations. In the old methodology standard large sparse matrix techniques were considered, where Gauss-Seidel preconditioning and a GMRES-type solver were applied. The validation of KIKO3D using Bi-CGSTAB has been performed by solving of a VVER-1000 kinetic benchmark problem. Additionally, the convergence characteristics were investigated in given macro time steps of Control Rod Ejection transients. The results have been obtained by the old GMRES and new Bi-CGSTAB methods are compared. (author)

Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings

KAUST Repository

Shaheen, Sara

2017-12-25

Convolutional sparse coding (CSC) plays an essential role in many computer vision applications ranging from image compression to deep learning. In this work, we spot the light on a new application where CSC can effectively serve, namely line drawing analysis. The process of drawing a line drawing can be approximated as the sparse spatial localization of a number of typical basic strokes, which in turn can be cast as a non-standard CSC model that considers the line drawing formation process from parametric curves. These curves are learned to optimize the fit between the model and a specific set of line drawings. Parametric representation of sketches is vital in enabling automatic sketch analysis, synthesis and manipulation. A couple of sketch manipulation examples are demonstrated in this work. Consequently, our novel method is expected to provide a reliable and automatic method for parametric sketch description. Through experiments, we empirically validate the convergence of our method to a feasible solution.

PNA-COMBO-FISH: From combinatorial probe design in silico to vitality compatible, specific labelling of gene targets in cell nuclei

Energy Technology Data Exchange (ETDEWEB)

Müller, Patrick; Rößler, Jens; Schwarz-Finsterle, Jutta [University of Heidelberg, Kirchhoff Institute for Physics, Im Neuenheimer Feld 227, D-69120 Heidelberg (Germany); Schmitt, Eberhard, E-mail: eschmitt@kip.uni-heidelberg.de [University of Heidelberg, Kirchhoff Institute for Physics, Im Neuenheimer Feld 227, D-69120 Heidelberg (Germany); University of Göttingen, Institute for Numerical and Applied Mathematics, Lotzestraße 16-18, D-37083 Göttingen (Germany); Hausmann, Michael, E-mail: hausmann@kip.uni-heidelberg.de [University of Heidelberg, Kirchhoff Institute for Physics, Im Neuenheimer Feld 227, D-69120 Heidelberg (Germany)

2016-07-01

Recently, advantages concerning targeting specificity of PCR constructed oligonucleotide FISH probes in contrast to established FISH probes, e.g. BAC clones, have been demonstrated. These techniques, however, are still using labelling protocols with DNA denaturing steps applying harsh heat treatment with or without further denaturing chemical agents. COMBO-FISH (COMBinatorial Oligonucleotide FISH) allows the design of specific oligonucleotide probe combinations in silico. Thus, being independent from primer libraries or PCR laboratory conditions, the probe sequences extracted by computer sequence data base search can also be synthesized as single stranded PNA-probes (Peptide Nucleic Acid probes). Gene targets can be specifically labelled with at least about 20 PNA-probes obtaining visibly background free specimens. By using appropriately designed triplex forming oligonucleotides, the denaturing procedures can completely be omitted. These results reveal a significant step towards oligonucleotide-FISH maintaining the 3D-nanostructure and even the viability of the cell target. The method is demonstrated with the detection of Her2/neu and GRB7 genes, which are indicators in breast cancer diagnosis and therapy. - Highlights: • Denaturation free protocols preserve 3D architecture of chromosomes and nuclei. • Labelling sets are determined in silico for duplex and triplex binding. • Probes are produced chemically with freely chosen backbones and base variants. • Peptide nucleic acid backbones reduce hindering charge interactions. • Intercalating side chains stabilize binding of short oligonucleotides.

PNA-COMBO-FISH: From combinatorial probe design in silico to vitality compatible, specific labelling of gene targets in cell nuclei

International Nuclear Information System (INIS)

Müller, Patrick; Rößler, Jens; Schwarz-Finsterle, Jutta; Schmitt, Eberhard; Hausmann, Michael

2016-01-01

Recently, advantages concerning targeting specificity of PCR constructed oligonucleotide FISH probes in contrast to established FISH probes, e.g. BAC clones, have been demonstrated. These techniques, however, are still using labelling protocols with DNA denaturing steps applying harsh heat treatment with or without further denaturing chemical agents. COMBO-FISH (COMBinatorial Oligonucleotide FISH) allows the design of specific oligonucleotide probe combinations in silico. Thus, being independent from primer libraries or PCR laboratory conditions, the probe sequences extracted by computer sequence data base search can also be synthesized as single stranded PNA-probes (Peptide Nucleic Acid probes). Gene targets can be specifically labelled with at least about 20 PNA-probes obtaining visibly background free specimens. By using appropriately designed triplex forming oligonucleotides, the denaturing procedures can completely be omitted. These results reveal a significant step towards oligonucleotide-FISH maintaining the 3D-nanostructure and even the viability of the cell target. The method is demonstrated with the detection of Her2/neu and GRB7 genes, which are indicators in breast cancer diagnosis and therapy. - Highlights: • Denaturation free protocols preserve 3D architecture of chromosomes and nuclei. • Labelling sets are determined in silico for duplex and triplex binding. • Probes are produced chemically with freely chosen backbones and base variants. • Peptide nucleic acid backbones reduce hindering charge interactions. • Intercalating side chains stabilize binding of short oligonucleotides.

Retrieval-based Face Annotation by Weak Label Regularized Local Coordinate Coding.

Science.gov (United States)

Wang, Dayong; Hoi, Steven C H; He, Ying; Zhu, Jianke; Mei, Tao; Luo, Jiebo

2013-08-02

Retrieval-based face annotation is a promising paradigm of mining massive web facial images for automated face annotation. This paper addresses a critical problem of such paradigm, i.e., how to effectively perform annotation by exploiting the similar facial images and their weak labels which are often noisy and incomplete. In particular, we propose an effective Weak Label Regularized Local Coordinate Coding (WLRLCC) technique, which exploits the principle of local coordinate coding in learning sparse features, and employs the idea of graph-based weak label regularization to enhance the weak labels of the similar facial images. We present an efficient optimization algorithm to solve the WLRLCC task. We conduct extensive empirical studies on two large-scale web facial image databases: (i) a Western celebrity database with a total of $6,025$ persons and $714,454$ web facial images, and (ii)an Asian celebrity database with $1,200$ persons and $126,070$ web facial images. The encouraging results validate the efficacy of the proposed WLRLCC algorithm. To further improve the efficiency and scalability, we also propose a PCA-based approximation scheme and an offline approximation scheme (AWLRLCC), which generally maintains comparable results but significantly saves much time cost. Finally, we show that WLRLCC can also tackle two existing face annotation tasks with promising performance.

Environmental Labels and Declarations

DEFF Research Database (Denmark)

Frydendal, Jeppe; Hansen, Lisbeth; Bonou, Alexandra

2018-01-01

Based on the terminology and structure developed by the International Organization for Standardization, a description is given on the types of ecolabels that build on life cycle assessments. Focus is on type I labels that point out products and services with an overall environmental preferability...... of labelling, the use of ecolabels in marketing, and the way ecolabels help build a market for “greener products”. Type III labels—or Environmental Product Declarations—are also briefly described with indicative examples from the building sector, a declaration for office furniture, and an introduction is given...... to the European Commission’s programme for product—and organisational environmental footprints ....

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.

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.

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.

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

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

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

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

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.

Studies of labelling conditions for gentamicin with99mTc Biological uptake

International Nuclear Information System (INIS)

Carvalho, O.G. de; Almeida, M.A.T.M. de; Muramoto, E.

1989-10-01

Gentamicin sulphate is an aminoglycoside antibiotic type specifically used for treatment of infections produced by Gram-negative bacterias but on the hand it presents ototoxic reactions as a serious side effect. The optimal labelling conditions of gentamicin sulphate with 99m Tc, using sodium pertechnetate solutions eluted from a 99 Mo - 99m Tc generator, were stablished by testing differents masses of antibiotic and reducing agent (SnCl 2 .2H 2 O), and also different reaction times and final labelling pH. The labelling yields were determined through ascendent type crimatographic analysis using metylacetone and 0,9% NaCl solution as solvents. From the studies of the biological uptake of 99m Tc gentamicin sulphate per gram of eight different organs and tissues from Wistar rats, it was shown that for a dose of 0,3 mg of 99m Tc-gentamicin intravenously administered. The kidneys, presented the greatest affinity for the drug, being thus the main excretory organs of the product. (author) [pt

A terminal-labelling microcytotoxity assay with 125I-iododeoxyuridine as a label for target cells

International Nuclear Information System (INIS)

Stirrat, G.M.

1976-01-01

The development of a terminal-labelling microcytotoxicity assay is described in which target cells (fetal fibroblasts) were labelled with 125 I-iododeoxyuridine after effector (lymphoid) cells had been incubated with them for 24 h. The time-course for the development of cell-mediated cytotoxicity was assessed following allogeneic skin grafting. 'Non-specific' cytotoxicity detracts from the sensitivity of all microcytotoxicity assays and the terminal-labelling assay using 125 I is no exception. The non-specific effects can be reduced but not eliminated by the removal of adherent cells. The optimum target cell/effector cell ratio would seem to be between 1:100 and 1:250. Residual lymph node cells did not appear to incorporate enough label to affect the test results. In vivo correlates of in vitro findings are still not easy to determine

Preclinical evaluation of melanocortin-1 receptor (MC1-R) specific 68Ga- and 44Sc-labeled DOTA-NAPamide in melanoma imaging.

Science.gov (United States)

Nagy, Gábor; Dénes, Noémi; Kis, Adrienn; Szabó, Judit P; Berényi, Ervin; Garai, Ildikó; Bai, Péter; Hajdu, István; Szikra, Dezső; Trencsényi, György

2017-08-30

Alpha melanocyte stimulating hormone (α-MSH) enhances melanogenesis in melanoma malignum by binding to melanocortin-1 receptors (MC1-R). Earlier studies demonstrated that alpha-MSH analog NAPamide molecule specifically binds to MC1-R receptor. Radiolabeled NAPamide is a promising radiotracer for the non-invasive detection of melanin producing melanoma tumors by Positron Emission Tomography (PET). In this present study the MC1-R selectivity of the newly developed Sc-44-labeled DOTA-NAPamide was investigated in vitro and in vivo using melanoma tumors. DOTA-NAPamide was labeled with Ga-68 and Sc-44 radionuclides. The MC1-R specificity of Ga-68- and Sc-44-labeled DOTA-NAPamide was investigated in vitro and in vivo using MC1-R positive (B16-F10) and negative (A375) melanoma cell lines. For in vivo imaging studies B16-F10 and A375 tumor-bearing mice were injected with 44 Sc/ 68 Ga-DOTA-NAPamide (in blocking studies with α-MSH) and whole body PET/MRI scans were acquired. Radiotracer uptake was expressed in terms of standardized uptake values (SUVs). 44 Sc/ 68 Ga-labeled DOTA-NAPamide were produced with high specific activity (approx. 19 GBq/μmol) and with excellent radiochemical purity (99%DOTA-NAPamide (SUVmean: 0.38±0.02), and Sc-44-DOTA-NAPamide (SUVmean: 0.52±0.13) uptake was observed in subcutaneously growing B16-F10 tumors, than in receptor negative A375 tumors, where the SUVmean values of Ga-68-DOTA-NAPamide and Sc-44-DOTA-NAPamide were 0.04±0.01 and 0.07±0.01, respectively. Tumor-to-muscle (T/M SUVmean) ratios were approximately 15-fold higher in B16-F10 tumor-bearing mice, than that of A375 tumors, and this difference was also significant (p≤0.01) using both radiotracers after 60 min incubation time. Our newly synthesized 44 Sc-labeled DOTA-NAPamide probe showed excellent binding properties to melanocortin-1 receptor (MC1-R) positive melanoma cell and tumors. Due to its high specificity and sensitivity 44 Sc-DOTA-NAPamide is a promising radiotracer in

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.

Stability of rhenium-188 labeled antibody

International Nuclear Information System (INIS)

Lim, B. K.; Jung, J. M.; Jung, J. K.; Lee, D. S.; Lee, M. C.

1999-01-01

For clinical application of beta-emitter labeled antibody, high specific activity is important. Carrier-free Re-188 from W-188/Re-188 generator is an ideal radionuclide for this purpose. However, low stability of Re-188 labeled antibody, especially in high specific activity, due to radiolytic decomposition by high energy (2.1 MeV) beta ray was problem. We studied the stability of Re-188 labeled antibody, and stabilizing effect of several nontoxic radical-quenching agents. Pre-reduced monoclonal antibody (CEA79.4) was labeled with Re-188 by incubating with generator-eluted Re-188-perrhenate in the presence of stannous tartrate for 2 hr at room temperature. Radiochemical purity of each preparation was determined by chromatography (ITLC-SG/acetone, ITLC-SG/Umezawa, Whatman No.1/saline). Human serum albumin was added to the labeled antibodies(2%). Stability of Re-188-CEA79.4 was investigated in the presence of vitamin C, ethanol, or Tween 80 as radical-quenching agents. Specific activities of 4.29∼5.11 MBq/μg were obtained. Labeling efficiencies were 88±4%(n=12). Very low stability after removal of stannous tartrate from the preparation was observed. If stored after purging with N 2 , all the preparations were stable for 10 hr. However, if contacted with air, stability decreased. Perrhenate and Re-188-tartrate was major impurity in declined preparation (12∼47 and 9∼38% each, after 10 hr). Colloid-formation was not a significant problem in all cases. Addition of vitamin C stabilized the labeled antibodies either under N 2 or under air by reducing the formation of perrhenate. High specific activity Re-188 labeled antibody is unstable, especially, in the presence of oxygen. Addition of vitamin C increased the stability

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...

Radioactive labelling of peptidic hormones

International Nuclear Information System (INIS)

Fromageot, P.; Pradelles, P.; Morgat, J.L.; Levine, H.

1976-01-01

The labelling of peptidic hormones requires stability, specificity and sensitivity of the label. Introduction of a radioactive atome is one way to satisfy these criteria. Several processes have been described to prepare radioactive TRF: synthesis of the peptide with labelled aminoacids or introduction of the label into the hormone. In that approach, tritium can be substituted in the imidazole ring, via precursors activating the proper carbon. Monoiodo TRF leads essentially to tritium labelling of the 5 positions whereas monoazo TRF allows the preparation of 3 H TRF labelled in the 2 positions. Di-substituted TRF leads to labelling into the 2 and 5 carbons. Labelled analogs of TRF can be prepared with labelled iodine; further developments of peptide labelling, will be presented. In particular, the homolytic scission of the C-iodine, bond by photochemical activation. The nascent carbon radical can be stabilized by a tritiated scavenger. This approach eliminates the use of heavy metal catalysts

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.

Waveguide-type optical circuits for recognition of optical 8QAM-coded label

Science.gov (United States)

Surenkhorol, Tumendemberel; Kishikawa, Hiroki; Goto, Nobuo; Gonchigsumlaa, Khishigjargal

2017-10-01

Optical signal processing is expected to be applied in network nodes. In photonic routers, label recognition is one of the important functions. We have studied different kinds of label recognition methods so far for on-off keying, binary phase-shift keying, quadrature phase-shift keying, and 16 quadrature amplitude modulation-coded labels. We propose a method based on waveguide circuits to recognize an optical eight quadrature amplitude modulation (8QAM)-coded label by simple passive optical signal processing. The recognition of the proposed method is theoretically analyzed and numerically simulated by the finite difference beam propagation method. The noise tolerance is discussed, and bit-error rate against optical signal-to-noise ratio is evaluated. The scalability of the proposed method is also discussed theoretically for two-symbol length 8QAM-coded labels.

From Position-Specific Labeling to Environmental Fluxomics: Elucidating Biogeochemical Cycles from the Metabolic Perspective (BG Division Outstanding ECS Award Lecture)

Science.gov (United States)

Dippold, Michaela; Apostel, Carolin; Dijkstra, Paul; Kuzyakov, Yakov

2017-04-01

Understanding soil and sedimentary organic matter (SOM) dynamics is one of the most important challenges in biogeoscience. To disentangle the fluxes and transformations of C in soils a detailed knowledge on the biochemical pathways and its controlling factors is required. Biogeochemists' view on the C transformation of microorganisms in soil has rarely exceed a strongly simplified concept assuming that C gets either oxidized to CO2 via the microbial catabolism or incorporated into biomass via the microbial anabolism. Biochemists, however, thoroughly identified in the past decades the individual reactions of glycolysis, pentose-phosphate pathway and citric acid cycle underlying the microbial catabolism. At various points within that metabolic network the anabolic fluxes feeding biomass formation branch off. Recent studies on metabolic flux tracing by position-specific isotope labeling allowed tracing these C transformations in soils in situ, an approach which is qunatitatively complemented by metabolic flux modeling. This approach has reached new impact by the cutting-edge combination of position-specific 13C labeling with compound-specific isotope analysis of microbial biomarkers and metabolites which allows 1) tracing specific anabolic pathways in diverse microbial communities in soils and 2) identification of specific pathways of individual functional microbial groups. Thus, the combination of position-specific labeling, compound-specific isotope incorporation in biomarkers and quantitative metabolic flux modelling provide the toolbox for quantitative soil fluxomics. Our studies combining position-specific labeled glucose with amino sugar 13C analysis showed that up to 55% of glucose, incorporated into the glucose derivative glucosamine, first passed glycolysis before allocated back via gluconeogenesis. Similarly, glutamate-derived C is allocated via anaplerotic pathways towards fatty acid synthesis and in parallel to its oxidation in citric acid cycle. Thus

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

Histochemical evidence for the differential surface labeling, uptake, and intracellular transport of a colloidal gold-labeled insulin complex by normal human blood cells.

Science.gov (United States)

Ackerman, G A; Wolken, K W

1981-10-01

A colloidal gold-labeled insulin-bovine serum albumin (GIA) reagent has been developed for the ultrastructural visualization of insulin binding sites on the cell surface and for tracing the pathway of intracellular insulin translocation. When applied to normal human blood cells, it was demonstrated by both visual inspection and quantitative analysis that the extent of surface labeling, as well as the rate and degree of internalization of the insulin complex, was directly related to cell type. Further, the pathway of insulin (GIA) transport via round vesicles and by tubulo-vesicles and saccules and its subsequent fate in the hemic cells was also related to cell variety. Monocytes followed by neutrophils bound the greatest amount of labeled insulin. The majority of lymphocytes bound and internalized little GIA, however, between 5-10% of the lymphocytes were found to bind considerable quantities of GIA. Erythrocytes rarely bound the labeled insulin complex, while platelets were noted to sequester large quantities of the GIA within their extracellular canalicular system. GIA uptake by the various types of leukocytic cells appeared to occur primarily by micropinocytosis and by the direct opening of cytoplasmic tubulo-vesicles and saccules onto the cell surface in regions directly underlying surface-bound GIA. Control procedures, viz., competitive inhibition of GIA labeling using an excess of unlabeled insulin in the incubation medium, preincubation of the GIA reagent with an antibody directed toward porcine insulin, and the incorporation of 125I-insulin into the GIA reagent, indicated the specificity and selectivity of the GIA histochemical procedure for the localization of insulin binding sites.

Histochemical evidence for the differential surface labeling, uptake, and intracellular transport of a colloidal gold-labeled insulin complex by normal human blood cells

International Nuclear Information System (INIS)

Ackerman, G.A.; Wolken, K.W.

1981-01-01

A colloidal gold-labeled insulin-bovine serum albumin (GIA) reagent has been developed for the ultrastructural visualization of insulin binding sites on the cell surface and for tracing the pathway of intracellular insulin translocation. When applied to normal human blood cells, it was demonstrated by both visual inspection and quantitative analysis that the extent of surface labeling, as well as the rate and degree of internalization of the insulin complex, was directly related to cell type. Further, the pathway of insulin (GIA) transport via round vesicles and by tubulo-vesicles and saccules and its subsequent fate in the hemic cells was also related to cell variety. Monocytes followed by neutrophils bound the greatest amount of labeled insulin. The majority of lymphocytes bound and internalized little GIA, however, between 5-10% of the lymphocytes were found to bind considerable quantities of GIA. Erythrocytes rarely bound the labeled insulin complex, while platelets were noted to sequester large quantities of the GIA within their extracellular canalicular system. GIA uptake by the various types of leukocytic cells appeared to occur primarily by micropinocytosis and by the direct opening of cytoplasmic tubulo-vesicles and saccules onto the cell surface in regions directly underlying surface-bound GIA. Control procedures, viz., competitive inhibition of GIA labeling using an excess of unlabeled insulin in the incubation medium, preincubation of the GIA reagent with an antibody directed toward porcine insulin, and the incorporation of 125I-insulin into the GIA reagent, indicated the specificity and selectivity of the GIA histochemical procedure for the localization of insulin binding sites

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

Do nutrition labels influence healthier food choices? Analysis of label viewing behaviour and subsequent food purchases in a labelling intervention trial.

Science.gov (United States)

Ni Mhurchu, Cliona; Eyles, Helen; Jiang, Yannan; Blakely, Tony

2018-02-01

There are few objective data on how nutrition labels are used in real-world shopping situations, or how they affect dietary choices and patterns. The Starlight study was a four-week randomised, controlled trial of the effects of three different types of nutrition labels on consumer food purchases: Traffic Light Labels, Health Star Rating labels, or Nutrition Information Panels (control). Smartphone technology allowed participants to scan barcodes of packaged foods and receive randomly allocated labels on their phone screen, and to record their food purchases. The study app therefore provided objectively recorded data on label viewing behaviour and food purchases over a four-week period. A post-hoc analysis of trial data was undertaken to assess frequency of label use, label use by food group, and association between label use and the healthiness of packaged food products purchased. Over the four-week intervention, study participants (n = 1255) viewed nutrition labels for and/or purchased 66,915 barcoded packaged products. Labels were viewed for 23% of all purchased products, with decreasing frequency over time. Shoppers were most likely to view labels for convenience foods, cereals, snack foods, bread and bakery products, and oils. They were least likely to view labels for sugar and honey products, eggs, fish, fruit and vegetables, and meat. Products for which participants viewed the label and subsequently purchased the product during the same shopping episode were significantly healthier than products where labels were viewed but the product was not subsequently purchased: mean difference in nutrient profile score -0.90 (95% CI -1.54 to -0.26). In a secondary analysis of a nutrition labelling intervention trial, there was a significant association between label use and the healthiness of products purchased. Nutrition label use may therefore lead to healthier food purchases. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

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

Calendar Year 2007 Program Benefits for U.S. EPA Energy Star Labeled Products: Expanded Methodology

Energy Technology Data Exchange (ETDEWEB)

Sanchez, Marla; Homan, Gregory; Lai, Judy; Brown, Richard

2009-09-24

This report provides a top-level summary of national savings achieved by the Energy Star voluntary product labeling program. To best quantify and analyze savings for all products, we developed a bottom-up product-based model. Each Energy Star product type is characterized by product-specific inputs that result in a product savings estimate. Our results show that through 2007, U.S. EPA Energy Star labeled products saved 5.5 Quads of primary energy and avoided 100 MtC of emissions. Although Energy Star-labeled products encompass over forty product types, only five of those product types accounted for 65percent of all Energy Star carbon reductions achieved to date, including (listed in order of savings magnitude)monitors, printers, residential light fixtures, televisions, and furnaces. The forecast shows that U.S. EPA?s program is expected to save 12.2 Quads of primary energy and avoid 215 MtC of emissions over the period of 2008?2015.

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

KAUST Repository

Wang, Jim Jing-Yan

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear 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 graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.

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

Science.gov (United States)

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

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (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 graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

Design Patterns for Sparse-Matrix Computations on Hybrid CPU/GPU Platforms

Directory of Open Access Journals (Sweden)

Valeria Cardellini

2014-01-01

Full Text Available We apply object-oriented software design patterns to develop code for scientific software involving sparse matrices. Design patterns arise when multiple independent developments produce similar designs which converge onto a generic solution. We demonstrate how to use design patterns to implement an interface for sparse matrix computations on NVIDIA GPUs starting from PSBLAS, an existing sparse matrix library, and from existing sets of GPU kernels for sparse matrices. We also compare the throughput of the PSBLAS sparse matrix–vector multiplication on two platforms exploiting the GPU with that obtained by a CPU-only PSBLAS implementation. Our experiments exhibit encouraging results regarding the comparison between CPU and GPU executions in double precision, obtaining a speedup of up to 35.35 on NVIDIA GTX 285 with respect to AMD Athlon 7750, and up to 10.15 on NVIDIA Tesla C2050 with respect to Intel Xeon X5650.

An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data

DEFF Research Database (Denmark)

Liu, Weifeng; Vinter, Brian

2014-01-01

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method, breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM algorithm has to handle extra...... irregularity from three aspects: (1) the number of the nonzero entries in the result sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the result sparse matrix dominate the execution time, and (3) load balancing must account for sparse data in both input....... Load balancing builds on the number of the necessary arithmetic operations on the nonzero entries and is guaranteed in all stages. Compared with the state-of-the-art GPU SpGEMM methods in the CUSPARSE library and the CUSP library and the latest CPU SpGEMM method in the Intel Math Kernel Library, our...

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....

Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction

Directory of Open Access Journals (Sweden)

Dong Zhang

2017-01-01

Full Text Available Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

Radioimmunological detection of type-specific antibodies against polioviruses 1, 2 and 3 as well as the B4 Coxsackie virus

International Nuclear Information System (INIS)

Hartung Goetze, C.F.

1985-01-01

A simple radioimmunological procedure is described in which the qualitative and quantitative determination of serum antibodies against polioviruses 1, 2 and 3 and against the B4 Coxsackie virus is based on adsorption chromatography. It is comparable to the neutralisation test as regards sensitivity and specifity and has the additional advantage of being the faster, simplier and cheaper of those two methods. The radioactive labelling of the virsuses was achieved with types of 32 P or 3 H that had favourable half-lives and would thus permit the labelled compounds to be stored for prolonged periods of time. The immunological statuses and antibody titres of sera obtained from 45 female volunteers, where the vaccinations against polymyelitis had mostly been carried out by the oral route, showed remarkably little changes, which was evident from the fact that the proportion of study participants found to have antibodies against all three serological types was 91% in 1970 and still as large at 89% in 1983. When a total of 1016 sera were screened for Coxsackie virus B4, no age dependency could be determined for the age range between 5 and 35 years, nor were there any differences seen between the individual residential areas. (TRV) [de

LabelFlow Framework for Annotating Workflow Provenance

Directory of Open Access Journals (Sweden)

Pinar Alper

2018-02-01

Full Text Available Scientists routinely analyse and share data for others to use. Successful data (reuse relies on having metadata describing the context of analysis of data. In many disciplines the creation of contextual metadata is referred to as reporting. One method of implementing analyses is with workflows. A stand-out feature of workflows is their ability to record provenance from executions. Provenance is useful when analyses are executed with changing parameters (changing contexts and results need to be traced to respective parameters. In this paper we investigate whether provenance can be exploited to support reporting. Specifically; we outline a case-study based on a real-world workflow and set of reporting queries. We observe that provenance, as collected from workflow executions, is of limited use for reporting, as it supports queries partially. We identify that this is due to the generic nature of provenance, its lack of domain-specific contextual metadata. We observe that the required information is available in implicit form, embedded in data. We describe LabelFlow, a framework comprised of four Labelling Operators for decorating provenance with domain-specific Labels. LabelFlow can be instantiated for a domain by plugging it with domain-specific metadata extractors. We provide a tool that takes as input a workflow, and produces as output a Labelling Pipeline for that workflow, comprised of Labelling Operators. We revisit the case-study and show how Labels provide a more complete implementation of reporting queries.

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.

Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

Directory of Open Access Journals (Sweden)

François-B. Vialatte

2011-01-01

Full Text Available Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD. However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz, α1 (7.5–9.5 Hz, α2 (9.5–12.5 Hz, and β (12.5–25 Hz. The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

Labelling of HBV-DNA probe using reagent made in China

International Nuclear Information System (INIS)

Wang Quanshi

1991-01-01

The labelling hepatitis Bvirus DNA (HBV-DNA) probe was studied by using reagent made in China. The results showed that: (1) The dNTPs with high specific activity was necessary for the labelling of nigh specific activity HBV-DNA probe; (2) reaction of labelling HBV-DNA probe was completed in a few minutes; (3) 0.37 MBq 3 H dTTP (specific activity 1.554TBq/mmol) was enough to label 1 μg HBV-DNA and the specific activity of probe reached 3.4 x 10 cpm/μg; (4) 7 MBqα- 32 P dATP (specific activity > 111 TBq/mmol) can label HBV-DNA probe to specific activity 1.35 x 10 cpm/μg. It was concluded that the reagent made in China can be used for the study in molecular biology

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

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

Optimization of the synthesis of a high specific activity 125 I-labelled hapten for radioimmunoassays

International Nuclear Information System (INIS)

Suarez, C.; Simon, M.A.; Paz, D.; Romero del Hombrebueno, B.

1994-01-01

In this first report it is described the synthesis, separation and purification of the 2-radioiodinated histamine ''125 I-labelled histamine by a mixed anhydride reaction. About 75% incorporation of I''1125, from Na''125, I, was achieved with a molecular ratio of 1:1 mixed anhydride:histamine. The radiochemical purity of the conjugate by TLC was >99% and its theoretical specific activity, 3850 mu Ci/mug. Dissolved in ethanol and held at -20 degree centigree under darkness decomposition on storage did not exceed 1% per month

A nanoparticle label/immunochromatographic electrochemical biosensor for rapid and sensitive detection of prostate-specific antigen

Energy Technology Data Exchange (ETDEWEB)

Lin, Ying-Ying; Wang, Jun; Liu, Guodong; Wu, Hong; Wai, Chien M.; Lin, Yuehe

2008-06-15

We present a nanoparticle (NP) label/immunochromatographic electrochemical biosensor (IEB) for rapid and sensitive detection of prostate-specific antigen (PSA) in human serum. This IEB integrates the immunochromatographic strip with the electrochemical detector for transducing quantitative signals. The NP label, made of CdSe@ZnS, serves as a signal-amplifier vehicle. A sandwich immunoreaction was performed on the immunochromatographic strip. The captured NP labels in the test zone were determined by highly sensitive stripping voltammetric measurement of the dissolved metallic component (cadmium) with a disposable-screen-printed electrode, which is embedded underneath the membrane of the test zone. Experimental parameters (e.g., immunoreaction time, the amount of anti-PSA-NP conjugations applied) and electrochemical detection conditions (e.g., preconcentration potential and time) were optimized using this biosensor for PSA detection. The analytical performance of this biosensor was evaluated with serum PSA samples according to the “figure-of-merits” (e.g., dynamic range, reproducibility, and detection limit). The results were validated with enzyme-linked immunosorbent assay (ELISA) and show high consistency. It is found that this biosensor is very sensitive with the detection limit of 0.02 ng/mL PSA and is quite reproducible. This method is rapid, clinically accurate, and less expensive than other diagnosis tools for PSA; therefore, this IEB coupled with a portable electrochemical analyzer shows great promise for simple, sensitive, quantitative point-of-care testing of disease-related protein biomarkers.

Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

Directory of Open Access Journals (Sweden)

Ying Chen

2014-05-01

Full Text Available Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE database. Compared with other widely used methods such as linear support vector machines (SVM, sparse representation-based classifier (SRC, nearest subspace classifier (NSC, K-nearest neighbor (KNN and radial basis function neural networks (RBFNN, the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.

Algebraic Specifications, Higher-order Types and Set-theoretic Models

DEFF Research Database (Denmark)

Kirchner, Hélène; Mosses, Peter David

2001-01-01

, and power-sets. This paper presents a simple framework for algebraic specifications with higher-order types and set-theoretic models. It may be regarded as the basis for a Horn-clause approximation to the Z framework, and has the advantage of being amenable to prototyping and automated reasoning. Standard......In most algebraic  specification frameworks, the type system is restricted to sorts, subsorts, and first-order function types. This is in marked contrast to the so-called model-oriented frameworks, which provide higer-order types, interpreted set-theoretically as Cartesian products, function spaces...... set-theoretic models are considered, and conditions are given for the existence of initial reduct's of such models. Algebraic specifications for various set-theoretic concepts are considered....

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

Directory of Open Access Journals (Sweden)

Lu Bing

2017-01-01

Full Text Available We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL. After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM. Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.

Science.gov (United States)

Bing, Lu; Wang, Wei

2017-01-01

We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

Response of selected binomial coefficients to varying degrees of matrix sparseness and to matrices with known data interrelationships

Science.gov (United States)

Archer, A.W.; Maples, C.G.

1989-01-01

Numerous departures from ideal relationships are revealed by Monte Carlo simulations of widely accepted binomial coefficients. For example, simulations incorporating varying levels of matrix sparseness (presence of zeros indicating lack of data) and computation of expected values reveal that not only are all common coefficients influenced by zero data, but also that some coefficients do not discriminate between sparse or dense matrices (few zero data). Such coefficients computationally merge mutually shared and mutually absent information and do not exploit all the information incorporated within the standard 2 ?? 2 contingency table; therefore, the commonly used formulae for such coefficients are more complicated than the actual range of values produced. Other coefficients do differentiate between mutual presences and absences; however, a number of these coefficients do not demonstrate a linear relationship to matrix sparseness. Finally, simulations using nonrandom matrices with known degrees of row-by-row similarities signify that several coefficients either do not display a reasonable range of values or are nonlinear with respect to known relationships within the data. Analyses with nonrandom matrices yield clues as to the utility of certain coefficients for specific applications. For example, coefficients such as Jaccard, Dice, and Baroni-Urbani and Buser are useful if correction of sparseness is desired, whereas the Russell-Rao coefficient is useful when sparseness correction is not desired. ?? 1989 International Association for Mathematical Geology.

Global sensitivity analysis using sparse grid interpolation and polynomial chaos

International Nuclear Information System (INIS)

Buzzard, Gregery T.

2012-01-01

Sparse grid interpolation is widely used to provide good approximations to smooth functions in high dimensions based on relatively few function evaluations. By using an efficient conversion from the interpolating polynomial provided by evaluations on a sparse grid to a representation in terms of orthogonal polynomials (gPC representation), we show how to use these relatively few function evaluations to estimate several types of sensitivity coefficients and to provide estimates on local minima and maxima. First, we provide a good estimate of the variance-based sensitivity coefficients of Sobol' (1990) [1] and then use the gradient of the gPC representation to give good approximations to the derivative-based sensitivity coefficients described by Kucherenko and Sobol' (2009) [2]. Finally, we use the package HOM4PS-2.0 given in Lee et al. (2008) [3] to determine the critical points of the interpolating polynomial and use these to determine the local minima and maxima of this polynomial. - Highlights: ► Efficient estimation of variance-based sensitivity coefficients. ► Efficient estimation of derivative-based sensitivity coefficients. ► Use of homotopy methods for approximation of local maxima and minima.

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.

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.

Applications of site-specific labeling to study HAMLET, a tumoricidal complex of α-lactalbumin and oleic acid.

Science.gov (United States)

Mercer, Natalia; Ramakrishnan, Boopathy; Boeggeman, Elizabeth; Qasba, Pradman K

2011-01-01

Alpha-lactalbumin (α-LA) is a calcium-bound mammary gland-specific protein that is found in milk. This protein is a modulator of β1,4-galactosyltransferase enzyme, changing its acceptor specificity from N-acetyl-glucosamine to glucose, to produce lactose, milk's main carbohydrate. When calcium is removed from α-LA, it adopts a molten globule form, and this form, interestingly, when complexed with oleic acid (OA) acquires tumoricidal activity. Such a complex made from human α-LA (hLA) is known as HAMLET (Human A-lactalbumin Made Lethal to Tumor cells), and its tumoricidal activity has been well established. In the present work, we have used site-specific labeling, a technique previously developed in our laboratory, to label HAMLET with biotin, or a fluoroprobe for confocal microscopy studies. In addition to full length hLA, the α-domain of hLA (αD-hLA) alone is also included in the present study. We have engineered these proteins with a 17-amino acid C-terminal extension (hLA-ext and αD-hLA-ext). A single Thr residue in this extension is glycosylated with 2-acetonyl-galactose (C2-keto-galactose) using polypeptide-α-N-acetylgalactosaminyltransferase II (ppGalNAc-T2) and further conjugated with aminooxy-derivatives of fluoroprobe or biotin molecules. We found that the molten globule form of hLA and αD-hLA proteins, with or without C-terminal extension, and with and without the conjugated fluoroprobe or biotin molecule, readily form a complex with OA and exhibits tumoricidal activity similar to HAMLET made with full-length hLA protein. The confocal microscopy studies with fluoroprobe-labeled samples show that these proteins are internalized into the cells and found even in the nucleus only when they are complexed with OA. The HAMLET conjugated with a single biotin molecule will be a useful tool to identify the cellular components that are involved with it in the tumoricidal activity.

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.

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.

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.

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

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.

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.

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...

Cell-selective metabolic labeling of biomolecules with bioorthogonal functionalities.

Science.gov (United States)

Xie, Ran; Hong, Senlian; Chen, Xing

2013-10-01

Metabolic labeling of biomolecules with bioorthogonal functionalities enables visualization, enrichment, and analysis of the biomolecules of interest in their physiological environments. This versatile strategy has found utility in probing various classes of biomolecules in a broad range of biological processes. On the other hand, metabolic labeling is nonselective with respect to cell type, which imposes limitations for studies performed in complex biological systems. Herein, we review the recent methodological developments aiming to endow metabolic labeling strategies with cell-type selectivity. The cell-selective metabolic labeling strategies have emerged from protein and glycan labeling. We envision that these strategies can be readily extended to labeling of other classes of biomolecules. Copyright © 2013 Elsevier Ltd. All rights reserved.

CdSe/ZnS Quantum Dots-Labeled Mesenchymal Stem Cells for Targeted Fluorescence Imaging of Pancreas Tissues and Therapy of Type 1 Diabetic Rats

Science.gov (United States)

Liu, Haoqi; Tang, Wei; Li, Chao; Lv, Pinlei; Wang, Zheng; Liu, Yanlei; Zhang, Cunlei; Bao, Yi; Chen, Haiyan; Meng, Xiangying; Song, Yan; Xia, Xiaoling; Pan, Fei; Cui, Daxiang; Shi, Yongquan

2015-06-01

Mesenchymal stem cells (MSCs) have been used for therapy of type 1 diabetes mellitus. However, the in vivo distribution and therapeutic effects of transplanted MSCs are not clarified well. Herein, we reported that CdSe/ZnS quantum dots-labeled MSCs were prepared for targeted fluorescence imaging and therapy of pancreas tissues in rat models with type 1 diabetes. CdSe/ZnS quantum dots were synthesized, their biocompatibility was evaluated, and then, the appropriate concentration of quantum dots was selected to label MSCs. CdSe/ZnS quantum dots-labeled MSCs were injected into mouse models with type 1 diabetes via tail vessel and then were observed by using the Bruker In-Vivo F PRO system, and the blood glucose levels were monitored for 8 weeks. Results showed that prepared CdSe/ZnS quantum dots owned good biocompatibility. Significant differences existed in distribution of quantum dots-labeled MSCs between normal control rats and diabetic rats ( p pancreas of rats in the diabetes group, and was about 32 %, while that in the normal control group rats was about 18 %. The blood glucose levels were also monitored for 8 weeks after quantum dots-labeled MSC injection. Statistical differences existed between the blood glucose levels of the diabetic rat control group and MSC-injected diabetic rat group ( p pancreas tissues in diabetic rats, and significantly reduce the blood glucose levels in diabetic rats, and own potential application in therapy of diabetic patients in the near future.

A performance study of sparse Cholesky factorization on INTEL iPSC/860

Science.gov (United States)

Zubair, M.; Ghose, M.

1992-01-01

The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.

Covalent affinity labeling, radioautography, and immunocytochemistry localize the glucocorticoid receptor in rat testicular Leydig cells

International Nuclear Information System (INIS)

Stalker, A.; Hermo, L.; Antakly, T.

1989-01-01

The presence and distribution of glucocorticoid receptors in the rat testis were examined by using 2 approaches: in vivo quantitative radioautography and immunocytochemistry. Radioautographic localization was made possible through the availability of a glucocorticoid receptor affinity label, dexamethasone 21-mesylate, which binds covalently to the glucocorticoid receptor, thereby preventing dissociation of the steroid-receptor complex. Adrenalectomized adult rats were injected with a tritiated (3H) form of this steroid into the testis and the tissue was processed for light-microscope radioautography. Silver grains were observed primarily over the Leydig cells of the interstitial space and to a lesser extent, over the cellular layers which make up the seminiferous epithelium, with no one cell type showing preferential labeling. To determine the specificity of the labeling, a 25- or 50-fold excess of unlabeled dexamethasone was injected simultaneously with the same dose of (3H)-dexamethasone 21-mesylate. In these control experiments, a marked reduction in label intensity was noted over the Leydig as well as tubular cells. Endocytic macrophages of the interstitium were non-specifically labeled, indicating uptake of the ligand possibly by fluid-phase endocytosis. A quantitative analysis of the label confirmed the presence of statistically significant numbers of specific binding sites for glucocorticoids in both Leydig cells and the cellular layers of the seminiferous epithelium; 86% of the label was found over Leydig cells, and only 14% over the cells of the seminiferous epithelium. These binding data were confirmed by light-microscope immunocytochemistry using a monoclonal antibody to the glucocorticoid receptor

Clinical applications of cells labelling; Aplicaciones clinicas del marcado de celulas

Energy Technology Data Exchange (ETDEWEB)

Gonzalez, B M [Instituto Nacional de Pediatria (Mexico)

1994-12-31

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.

The Influence of Nutrition Labeling and Point-of-Purchase Information on Food Behaviours.

Science.gov (United States)

Volkova, Ekaterina; Ni Mhurchu, Cliona

2015-03-01

Point-of-purchase information on packaged food has been a highly debated topic. Various types of nutrition labels and point-of-purchase information have been studied to determine their ability to attract consumers' attention, be well understood and promote healthy food choices. Country-specific regulatory and monitoring frameworks have been implemented to ensure reliability and accuracy of such information. However, the impact of such information on consumers' behaviour remains contentious. This review summarizes recent evidence on the real-world effectiveness of nutrition labels and point-of-purchase information.