Labeling methods for association rule clustering

Although association mining has been highlighted in the last years, the huge number of rules that are generated hamper its use. To overcome this problem, many post-processing approaches were suggested, such as clustering, which organizes the rules in groups that contain, somehow, similar knowledge. Nevertheless, clustering can aid the user only if good descriptors be associated with each group. This is a relevant issue, since the labels will provide to the user a view of the topics to be explored, helping to guide its search. This is interesting, for example, when the user doesn't have, a priori, an idea where to start. Thus, the analysis of different labeling methods for association rule clustering is important. Considering the exposed arguments, this paper analyzes some labeling methods through two measures that are proposed. One of them, Precision, measures how much the methods can find labels that represent as accurately as possible the rules contained in its group and Repetition Frequency determines how the labels are distributed along the clusters. As a result, it was possible to identify the methods and the domain organizations with the best performances that can be applied in clusters of association rules.

Multiplexed quantitative proteomics using differential metabolic 15N-labeling

There are several advantages of using metabolic labeling in quantitative proteomics. The early pooling of samples compared to post-labeling methods eliminates errors from different sample processing, protein extraction and enzymatic digestion. Metabolic labeling is also highly efficient and relatively inexpensive compared to commercial labeling reagents. However, methods for multiplexed quantitation in the MS-domain (or ‘non-isobaric’ methods), suffer from signal dilution at higher degrees of multiplexing, as the MS/MS signal for peptide identification is lower given the same amount of peptide loaded onto the column or injected into the mass spectrometer. This may partly be overcome by mixing the samples at non-uniform ratios, for instance by increasing the fraction of unlabeled proteins. We have developed an algorithm for arbitrary degrees of nonisobaric multiplexing for relative protein abundance measurements. We have used metabolic labeling with different levels of 15N, but the algorithm is in principle applicable to any isotope or combination of isotopes. Ion trap mass spectrometers are fast and suitable for LC-MS/MS and peptide identification. However, they cannot resolve overlapping isotopic envelopes from different peptides, which makes them less suitable for MS-based quantitation. Fourier-transform ion cyclotron resonance (FTICR) mass spectrometry is less suitable for LC-MS/MS, but provides the resolving power required to resolve overlapping isotopic envelopes. We therefore combined ion trap LC-MS/MS for peptide identification with FTICR LC-MS for quantitation using chromatographic alignment. We applied the method in a heat shock study in a plant model system (A. thaliana) and compared the results with gene expression data from similar experiments in literature.

Quantitative plant proteomics

Quantitation is an inherent requirement in comparative proteomics and there is no exception to this for plant proteomics. Quantitative proteomics has high demands on the experimental workflow, requiring a thorough design and often a complex multi-step structure. It has to include sufficient numbers of biological and technical replicates and methods that are able to facilitate a quantitative signal read-out. Quantitative plant proteomics in particular poses many additional challenges but because of the nature of plants it also offers some potential advantages. In general, analysis of plants has been less prominent in proteomics. Low protein concentration, difficulties in protein extraction, genome multiploidy, high Rubisco abundance in green tissue, and an absence of well-annotated and completed genome sequences are some of the main challenges in plant proteomics. However, the latter is now changing with several genomes emerging for model plants and crops such as potato, tomato, soybean, rice, maize and barley. This review discusses the current status in quantitative plant proteomics (MS-based and non-MS-based) and its challenges and potentials. Both relative and absolute quantitation methods in plant proteomics from DIGE to MS-based analysis after isotope labeling and label-free quantitation are described and illustrated by published studies. In particular, we describe plant-specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices, and discuss other potential advantages and challenges that may arise from the unique properties of plants.

Neue 18 F-markierte Liganden zur Visualisierung des GABA A, alpha 5-Rezeptorstatus mittels PET

Die heutige Verfügbarkeit der molekularen Bildgebung ermöglicht einen signifikanten Einfluss auf die Diagnostik und die Therapiekontrolle von neurodegenerativen Erkrankungen, die unter anderem durch Fehlsteuerungen im GABAergen System auftreten können. Die Visualisierung und Quantifizierung des GABAA-alpha5-Subtyps durch PET könnte dabei zu einem besseren Verständnis von Erkrankungen wie Alzheimer und traumatischen Neurosen (emotionales Langzeitgedächtnis) beitragen. Ferner eröffnen GABAA/alpha5-subtypselektive Liganden die Möglichkeit, wesentliche Grundlagen der elementaren Vorgänge von Lernen und Erinnern zu untersuchen. 7,8,9,10-Tetrahydro-(7,10-ethan)-1,2,4-triazol[3,4-alpha]phthalazine stellen sich als vielverspre-chende Leitstrukturen zur Entwicklung neuer 18F-markierter alpha5-subtypselektiver GABAA-Rezeptorliganden für die PET dar. Um diese neuartigen Substanzen hinsichtlich ihrer Potenz als GABAA-alpha5-subtypselektive Radioliganden zu verifizieren, wurden zunächst die entsprechenden 19F-Derivate TC07-TC12 synthetisiert. Diese Referenzverbindungen wurden in Rezeptor-bindungsassays und in Autoradiographien mit [3H]Ro 15-4513 als zu verdrängender Radioligand evaluiert. In beiden Experimenten als auch in in vivo-Verdrängungsexperimenten an Ratten konnte eine hohe Affinität im nanomolaren Bereich als auch eine hohe Selektivität bezüglich der GABAA/alpha5-Untereinheit für einige der dargestellten Referenzverbindungen nachgewiesen werden. Gemäß diesen vielversprechenden Ergebnissen wurden verschiedene Markie-rungsvorläufer für eine 18F-Direktmarkierung der relevantesten Substanz TC07 in einer mehrstufigen organischen Synthese dargestellt. Die anschließende 18F-Markierung erfolgte über eine nukleophile Substitution mit [18F]Fluorid. Die Reaktionsparameter wurden hinsichtlich Reaktionstemperatur und dauer, Markierungsvorläuferkonzentration, Basenabhängigkeit und verschiedenen Markierungsmethoden optimiert. Daraus resultierend konnte [18F]TC07 mit bis zu 45 % radiochemischer Ausbeute erhalten werden. Die zerfallskorrigierte, gesamtradiochemische Ausbeute von nca [18F]TC07 in isotonischer NaCl-Lösung betrug 15 %. Basierend auf den bisher erhaltenen Ergebnissen wurde der Radioligand in in vitro-, ex vivo- und in vivo µPET-Experimenten evaluiert. Die zunächst durchgeführten in vitro-Experimente deuteten auf eine homogene Verteilung der Aktivität hin und zeigten keine spezifische Anreicherung. Diese Ergebnisse wurden sowohl in ex vivo- als auch in in vivo-µPET-Studien bestätigt. Auch hier konnte nur eine niedrige Aktivitätsanreicherung, eine homogene Verteilung im gesamten Gehirn und keine Übereinstimmung mit der bekannten GABAA/alpha5-Subtypverteilung gefunden werden. Eine im Anschluss durchgeführte Metabolismusstudie zeigte eine langsame Metabolisierungsrate des [18F]TC07 und auch eine Organverteilungsstudie zeigte keine außergewöhnlichen Anreicherungen. Aus den erhaltenen Ergebnissen kann geschlossen werden, dass der Radioligand [18F]TC07 kein geeigneter Tracer zur in vivo-Visualisierung der alpha5-Untereinheit des GABAA-Rezeptors ist.

Little strokes fill big oaks: a simple in vivo stain of brain cells

High-resolution functional imaging of neural activity in vivo relies on appropriate labeling methods. In this issue of Neuron, Nagayama et al. introduce a simple procedure for staining subsets of neurons with organic calcium indicator dyes via local electroporation. Neuronal populations are sparsely labeled, preserving the ability to resolve calcium signals in dendrites and synaptic structures.

Triple-peptide receptor targeting in vitro allows detection of all tested gut and bronchial NETs.

UNLABELLED A high proportion of gut and bronchial neuroendocrine tumors (NETs) overexpresses somatostatin receptors, especially the sst2 subtype. It has also recently been observed that incretin receptors, namely glucagonlike peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) receptors, can be overexpressed in gut and bronchial NETs. However, because not all tumors can express these receptors in sufficient amounts, in vivo imaging with a single radioligand may not always be successful. We therefore evaluated with in vitro methods whether a cocktail of radioligands targeting these 3 receptors would improve tumor labeling. METHODS In vitro receptor autoradiography was performed on 55 NETs, comparing in each successive section of tumor the binding with a single radioligand, either (125)I-Tyr(3)-octreotide, (125)I-GLP-1(7-36)amide, or (125)I-GIP(1-30), with the binding using a cocktail of all 3 radioligands, given concomitantly under identical experimental conditions. RESULTS Using the cocktail of radioligands, all tumors without exception showed moderate to very high binding, with a receptor density corresponding to 1,000-10,000 dpm/mg of tissue; conversely, single-ligand binding, although identifying most tumors as receptor-positive, failed to detect receptors or measured only a low density of receptors below 1,000 dpm/mg in a significant number of tumors. In addition, the cocktail of radioligands always provided a homogeneous labeling of the whole tumor, whereas single radioligands occasionally showed heterogeneous labeling. CONCLUSION The study suggests that the use of a cocktail of 3 radioligands binding to somatostatin receptors, GLP-1 receptors, and GIP receptors would allow detecting virtually all NETs and labeling them homogeneously in vivo, representing a significant improvement for imaging and therapy in NETs.

Birefringence Imaging Directly Reveals Architectural Dynamics of Filamentous Actin in Living Growth ConesV⃞

We have investigated the dynamic behavior of cytoskeletal fine structure in the lamellipodium of nerve growth cones using a new type of polarized light microscope (the Pol-Scope). Pol-Scope images display with exquisite resolution and definition birefringent fine structures, such as filaments and membranes, without having to treat the cell with exogenous dyes or fluorescent labels. Furthermore, the measured birefringence of protein fibers in the thin lamellipodial region can be interpreted in terms of the number of filaments in the bundles. We confirmed that birefringent fibers are actin-based using conventional fluorescence-labeling methods. By recording movies of time-lapsed Pol-Scope images, we analyzed the creation and dynamic composition of radial fibers, filopodia, and intrapodia in advancing growth cones. The strictly quantitative information available in time-lapsed Pol-Scope images confirms previously deduced behavior and provides new insight into the architectural dynamics of filamentous actin.

Cluster labeling for multilingual scatter/gather using comparable corpora

Scatter/Gather systems are increasingly becoming useful in browsing document corpora. Usability of the present-day systems are restricted to monolingual corpora, and their methods for clustering and labeling do not easily extend to the multilingual setting, especially in the absence of dictionaries/machine translation. In this paper, we study the cluster labeling problem for multilingual corpora in the absence of machine translation, but using comparable corpora. Using a variational approach, we show that multilingual topic models can effectively handle the cluster labeling problem, which in turn allows us to design a novel Scatter/Gather system ShoBha. Experimental results on three datasets, namely the Canadian Hansards corpus, the entire overlapping Wikipedia of English, Hindi and Bengali articles, and a trilingual news corpus containing 41,000 articles, confirm the utility of the proposed system.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Species identification and authentication of some of the most important seafood products in Iranian markets using biotechnological methods based on DNA

This study describes the molecular identification of sixteen fish species present in processed products imported into Iran for human consumption. DNA barcoding using direct sequencing of about 650 bp of the mitochondrial Cytochrome Oxidase subunit I gene revealed incorrect labeling (31.25%). Substitution of fish species constitutes serious economic fraud, and our results increase concern regarding the trading of processed fish products in Iran from both health and conservation points of view.

Application of methyl parathion hydrolase (MPH) as a labeling enzyme

Methyl parathion hydrolase (MPH) is an enzyme that catalyzes the degradation of methyl parathion, generating a yellow product with specific absorption at 405 nm. The application of MPH as a new labeling enzyme was illustrated in this study. The key advantages of using MPH as a labeling enzyme are as follows: (1) unlike alkaline phosphatase (AP), horseradish peroxidase (HRP), and glucose oxidase (GOD), MPH is rarely found in animal cells, and it therefore produces less background noise; (2) its active form in solution is the monomer, with a molecular weight of 37 kDa; (3) its turnover number is 114.70 +/- 13.19 s(-1), which is sufficiently high to yield a significant signal for sensitive detection; and (4) its 3D structure is known and its C-terminal that is exposed to the surface can be easily subjected to the construction of genetic engineering monocloning antibody-enzyme fusion for enzyme-linked immunosorbent assay (ELISA). To demonstrate its utility, MPH was ligated to an single-chain variable fragment (scFv), known as A1E, against a white spot syndrome virus (WSSV) with the insertion of a [-(Gly-Ser)(5)-] linker peptide. The resulting fusion protein MPH-A1E possessed both the binding specificity of the scFv segment and the catalytic activity of the MPH segment. When MPH-A1E was used as an ELISA reagent, 25 ng purified WSSV was detected; this was similar to the detection sensitivity obtained using A1E scFv and the HRP/Anti-E Tag Conjugate protocol. The fusion protein also recognized the WSSV in 1 mu L hemolymph from an infected shrimp and differentiated it from a healthy shrimp.

Development of functionalized terbium fluorescent nanoparticles for antibody labeling and time-resolved fluoroimmunoassay application

Silica-based functionalized terbium fluorescent nanoparticles were prepared, characterized and developed as a fluorescence probe for antibody labeling and time-resolved fluoroimmunoassay. The nanoparticles were prepared in a water-in-oil (W/O) microemulsion containing a strongly fluorescent Tb3+ chelate. N,N.N-1,N-1-12,6-bis(3'-aminomethyl-1'-pyrazolyl)phenylpyridine] tetrakis(acetate)-Tb3+ (BPTA-Tb3+), Triton X-100, octanol, and cyclohexane by controlling copolymerization of tetraethyl orthosilicate (TEOS) and 3-[2-(2- aminoethylamino)-ethylamino]propyl-trimethoxysilane (AEPS) with ammonia water. The characterizations by transmission electron microscopy and fluorometric quantum methods show that the nanoparticles are spherical and uniform in size, 45 +/- 3 nm in diameter, strongly fluorescent with fluorescence yield of 10% and a long fluorescence lifetime of 2.0 ms. The amino groups directly introduced to the nanoparticle's surface by using AEPS in the preparation made the surface modification and bioconjugation of the nanoparticles easier. The nanoparticle-labeled anti-human alpha-fetoprotein antibody was prepared and used for time-resolved fluoroimmunoassay of (x-fetoprotein (AFP) in human serum samples. The assay response is linear from 0.10 ng ml(-1) to about 100 ng ml(-1) with the detection limit of 0.10 ng ml(-1). The coefficient variations (CVs) of the method are less than 9.0%. and the recoveries are in the range of 84-98% for human serum sample measurements. (C) 2004 Elsevier B.V. All rights reserved.

Validation of membrane protein topology models by oxidative labeling and mass spectrometry

Computer-assisted topology predictions are widely used to build low-resolution structural models of integral membrane proteins (IMPs). Experimental validation of these models by traditional methods is labor intensive and requires modifications that might alter the IMP native conformation. This work employs oxidative labeling coupled with mass spectrometry (MS) as a validation tool for computer-generated topology models. ·OH exposure introduces oxidative modifications in solvent-accessible regions, whereas buried segments (e.g., transmembrane helices) are non-oxidizable. The Escherichia coli protein WaaL (O-antigen ligase) is predicted to have 12 transmembrane helices and a large extramembrane domain (Pérez et al., Mol. Microbiol. 2008, 70, 1424). Tryptic digestion and LC-MS/MS were used to map the oxidative labeling behavior of WaaL. Met and Cys exhibit high intrinsic reactivities with ·OH, making them sensitive probes for solvent accessibility assays. Overall, the oxidation pattern of these residues is consistent with the originally proposed WaaL topology. One residue (M151), however, undergoes partial oxidation despite being predicted to reside within a transmembrane helix. Using an improved computer algorithm, a slightly modified topology model was generated that places M151 closer to the membrane interface. On the basis of the labeling data, it is concluded that the refined model more accurately reflects the actual topology of WaaL. We propose that the combination of oxidative labeling and MS represents a useful strategy for assessing the accuracy of IMP topology predictions, supplementing data obtained in traditional biochemical assays. In the future, it might be possible to incorporate oxidative labeling data directly as constraints in topology prediction algorithms.

Learning about probability from text and tables: Do color coding and labeling through an interactive-user interface help?

Learning from visual representations is enhanced when learners appropriately integrate corresponding visual and verbal information. This study examined the effects of two methods of promoting integration, color coding and labeling, on learning about probabilistic reasoning from a table and text. Undergraduate students (N = 98) were randomly assigned to learn about probabilistic reasoning from one of 4 computer-based lessons generated from a 2 (color coding/no color coding) by 2 (labeling/no labeling) between-subjects design. Learners added the labels or color coding at their own pace by clicking buttons in a computer-based lesson. Participants' eye movements were recorded while viewing the lesson. Labeling was beneficial for learning, but color coding was not. In addition, labeling, but not color coding, increased attention to important information in the table and time with the lesson. Both labeling and color coding increased looks between the text and corresponding information in the table. The findings provide support for the multimedia principle, and they suggest that providing labeling enhances learning about probabilistic reasoning from text and tables

The effect of energy and traffic light labeling on parents purchasing decisions of cereals for their children

Objective: Nutritional labeling systems are considered a tool to fight obesity since they aim to contribute for more informed food choices as well as assist consumers to make healthier nutrition options and in this manner, contribute to a decrease in the obesity rate. This study intends to analyze the effect of different types of labeling systems on parents’ purchasing decisions for their children on a specific product: breakfast cereals. More precisely, how labels affect parents’ perception of healthiness regarding cereals and if the nutritional information has an effect on intended purchases for their children. Participants and methods: We conducted a study with 135 Portuguese parents of children aged 4 to12 years. Parents answered a questionnaire with one of three hypothetical cereals menus. Menus only differed in their nutritional labeling technique: no labels (control group), reference intake labels or traffic light labels. In addition, we conducted 20 face-to-face interviews to a different group of parents in order to perform a recall task. Findings: This paper provides no evidence to suggest that energy labeling or traffic light labeling systems alone were successful in helping parents making healthy purchases of cereals for their children. Therefore, there is the need to promote supplementary policies to encourage the consumption of healthier food and help fight obesity.