986 resultados para mutual recognition
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We report a versatile but easy to make AC mutual inductance bridge. The bridge has been used over the temperature range 4-300 K and with applied magnetic fields up to 7 T. Representative data obtained on various types of magnetic systems are shown.
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Database schemes can be viewed as hypergraphs with individual relation schemes corresponding to the edges of a hypergraph. Under this setting, a new class of "acyclic" database schemes was recently introduced and was shown to have a claim to a number of desirable properties. However, unlike the case of ordinary undirected graphs, there are several unequivalent notions of acyclicity of hypergraphs. Of special interest among these are agr-, beta-, and gamma-, degrees of acyclicity, each characterizing an equivalence class of desirable properties for database schemes, represented as hypergraphs. In this paper, two complementary approaches to designing beta-acyclic database schemes have been presented. For the first part, a new notion called "independent cycle" is introduced. Based on this, a criterion for beta-acyclicity is developed and is shown equivalent to the existing definitions of beta-acyclicity. From this and the concept of the dual of a hypergraph, an efficient algorithm for testing beta-acyclicity is developed. As for the second part, a procedure is evolved for top-down generation of beta-acyclic schemes and its correctness is established. Finally, extensions and applications of ideas are described.
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Nested association mapping (NAM) offers power to dissect complex, quantitative traits. This study made use of a recently developed sorghum backcross (BC)-NAM population to dissect the genetic architecture of flowering time in sorghum; to compare the QTL identified with other genomic regions identified in previous sorghum and maize flowering time studies and to highlight the implications of our findings for plant breeding. A subset of the sorghum BC-NAM population consisting of over 1,300 individuals from 24 families was evaluated for flowering time across multiple environments. Two QTL analysis methodologies were used to identify 40 QTLs with predominately small, additive effects on flowering time; 24 of these co-located with previously identified QTL for flowering time in sorghum and 16 were novel in sorghum. Significant synteny was also detected with the QTL for flowering time detected in a comparable NAM resource recently developed for maize (Zea mays) by Buckler et al. (Science 325:714-718, 2009). The use of the sorghum BC-NAM population allowed us to catalogue allelic variants at a maximal number of QTL and understand their contribution to the flowering time phenotype and distribution across diverse germplasm. The successful demonstration of the power of the sorghum BC-NAM population is exemplified not only by correspondence of QTL previously identified in sorghum, but also by correspondence of QTL in different taxa, specifically maize in this case. The unification across taxa of the candidate genes influencing complex traits, such as flowering time can further facilitate the detailed dissection of the genetic control and causal genes.
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This paper suggests a scheme for classifying online handwritten characters, based on dynamic space warping of strokes within the characters. A method for segmenting components into strokes using velocity profiles is proposed. Each stroke is a simple arbitrary shape and is encoded using three attributes. Correspondence between various strokes is established using Dynamic Space Warping. A distance measure which reliably differentiates between two corresponding simple shapes (strokes) has been formulated thus obtaining a perceptual distance measure between any two characters. Tests indicate an accuracy of over 85% on two different datasets of characters.
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Rhipicephalus micro plus is an important bovine ectoparasite, widely distributed in tropical and subtropical regions of the world causing large economic losses to the cattle industry. Its success as an ectoparasite is associated with its capacity to disarm the antihemostatic and anti-inflammatory reactions of the host. Serpins are protease inhibitors with an important role in the modulation of host-parasite interactions. The cDNA that encodes for a R. microplus serpin was isolated by RACE and subsequently cloned into the pPICZ alpha A vector. Sequence analysis of the cDNA and predicted amino acid showed that this cDNA has a conserved serpin domain. B- and T-cell epitopes were predicted using bioinformatics tools. The recombinant R. microplus serpin (rRMS-3) was secreted into the culture media of Pichia pastoris after methanol induction at 0.2 mg l(-1) qRT-PCR expression analysis of tissues and life cycle stages demonstrated that RMS-3 was mainly expressed in the salivary glands of female adult ticks. Immunological recognition of the rRMS-3 and predicted B-cell epitopes was tested using tick-resistant and susceptible cattle sera. Only sera from tick-resistant bovines recognized the B-cell epitope AHYNPPPPIEFT (Seq7). The recombinant RMS-3 was expressed in P. pastoris, and ELISA screening also showed higher recognition by tick-resistant bovine sera. The results obtained suggest that RMS-3 is highly and specifically secreted into the bite site of R. microplus feeding on tick-resistant bovines. Capillary feeding of semi-engorged ticks with anti-AHYNPPPPIEFT sheep sera led to an 81.16% reduction in the reproduction capacity of R. microplus. Therefore, it is possible to conclude that R. microplus serpin (RMS-3) has an important role in the host-parasite interaction to overcome the immune responses in resistant cattle. (C) 2012 Elsevier GmbH. All rights reserved.
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The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).
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The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).
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Contains the papers of the Society founded in 1938 by recent German speaking Jewish immigrants to Boston to assist their initial adjustment to the economic, cultural, spiritual, and social life of the American community and subsequently, to provide mutual assistance to its membership and aid to other immigrants.
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This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.
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In this paper we propose a hypothetical scheme for recognizing the alphanumerics. The scheme is based on the known physiological structure of the visual cortex and the concept of a short Lino extractor nouron (SLEN). We assumo four basic typca of such units for extracting vertical, horizontal, right and left inclined straight line segments. The patterns reconstructed from the scheme show perfect agreement with the test patterns. The model indicates that the recognition of letters T and H requires extraction of the largest number of features.
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A galactose-specific protein (RC1) isolated from Ricinus communis beans was found to give a precipitin reaction with concanavalin A. Its carbohydrate content amounted to 8–9% of the total protein and was found to be rich in mannose. The interaction of RC1 with galactose and lactose was measured in 0.05 M phosphate buffer containing 0.2 M NaCl (pH 6.8) by the method of conventional equilibrium dialysis. From the analysis of the binding data according to Scatchard method the association constant (Ka) at 5°C was calculated as 3.8 mM−1 and 1.2 mM−1 for lactose and galactose, respectively. In both cases the number of binding sites per molecule of RC1 with molecular weight of 120000 was found to be 2. From the temperature-dependent Ka values for the binding of lactose, the values of –5.7 kcal/mol and –4.3 cal × mol−1× K−1 were calculated for ΔH and ΔS, respectively. The addition of concanavalin A to RC1 or vice versa led to the formation of the insoluble complex RC1· ConA4 containing one molecule of RC1 and one molecule of tetrameric concanavalin A (ConA4) which could be dissociated upon addition of concanavalin A-specific sugars. The complex formation results in a time-dependent appearance of turbidity in the time range from 10s to 10 min. From the measurement of the time-dependent appearance and disappearance of the turbidity the formation (kf) and dissociation (kd) rate constants were calculated as 3 mM−1× s−1 and 0.07 ks−1 respectively. The ratio kf/kd (43μM −1), that corresponds to the association constant of complex RC1· ConA4, is higher than that of mannoside · ConA4 and thereby suggests that protein-protein interaction contributes significantly in stabilising glycoprotein · lectin complexes. The relevance of this finding to the understanding of the chemical specificities that are involved in a model cell-lectin interaction is discussed.
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Mxr1p (methanol expression regulator 1) functions as a key regulator of methanol metabolism in the methylotrophic yeast Pichia pastoris. In this study, a recombinant Mxr1p protein containing the N-terminal zinc finger DNA binding domain was overexpressed and purified from E coli cells and its ability to bind to promoter sequences of AOXI encoding alcohol oxidase was examined. In the AOXI promoter, Mxr1p binds at six different regions. Deletions encompassing these regions result in a significant decrease in AOXI promoter activity in vivo. Based on the analysis of AOXI promoter sequences, a consensus sequence for Mxr1p binding consisting of a core 5' CYCC 3' motif was identified. When the core CYCC sequence is mutated to CYCA, CYCT or CYCM (M = 5-methylcytosine), Mxr1p binding is abolished. Though Mxr1p is the homologue of Saccharomyces cerevisiae Adr1p transcription factor, it does not bind to Adr1p binding site of S. cerevisiae alcohol dehydrogenase promoter (ADH2UAS1). However, two point mutations convert ADH2UAS1 into an Mxr1p binding site. The identification of key DNA elements involved in promoter recognition by Mxr1p is an important step in understanding its function as a master regulator of the methanol utilization pathway in P. pastoris.
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This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.
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The concept of a “mutualistic teacher” is introduced for unsupervised learning of the mean vectors of the components of a mixture of multivariate normal densities, when the number of classes is also unknown. The unsupervised learning problem is formulated here as a multi-stage quasi-supervised problem incorporating a cluster approach. The mutualistic teacher creates a quasi-supervised environment at each stage by picking out “mutual pairs” of samples and assigning identical (but unknown) labels to the individuals of each mutual pair. The number of classes, if not specified, can be determined at an intermediate stage. The risk in assigning identical labels to the individuals of mutual pairs is estimated. Results of some simulation studies are presented.