964 resultados para latent class


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The first experimental demonstration of unique polarizatioon characteristics are reported. It is believed that the strong polarization effects reported result from the chirality imposed by the patterns of gammadions enhanced by plasmon effects due to the nanostructuring of the metal film in which they are cut. It is clear that such structures has the potential to yield many new and intriguing applications in optoelectronics and other areas.

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Major histocompatibility complex genes are thought to be involved in allogeneic graft rejection but not many reports are available on their functional analysis in fish. Analysis of available sequences of MHC genes suggests functions in antigen presentation similar to those found in higher vertebrates. In mammals, the MHC class I and class II molecules are major determinants of allogeneic graft rejection due to their polymorphism in conjunction with their antigen presenting function. In fish, MHC class H molecules are found to be involved in rejection of allogeneic scale grafts. The present study was designed to investigate the involvement of MHC class I molecules in allograft rejection. Erythrocytes were collected from donors of rainbow trout expressed different class MHC class I alleles, stained with two dyes, mixed and grafted to the recipients that were of the same sibling group as the donors. The grafts were rejected by allogeneic recipients and the MHC class I linkage group was the major determinant for the rejection.

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In this paper, we aim to reconstruct free-from 3D models from a single view by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: 1) a probabilistic framework for prior-based reconstruction we propose, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects, and 2) an attempt at automatic reconstruction of more complex 3D shapes, like human bodies, from 2D silhouettes only. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach. ©2009 IEEE.

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Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

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Hemorrhagic toxins are widely distributed in viperid and crotalid snake venoms. Envenomation of Trimeresurus stejnegeri, a member of Crotalidae family, caused potent systemic and local hemorrhage. Up to now, there is no report on hemorrhage toxins from th

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A new metalloproteinase-disintegrin, named Jerdonitin, was purified from Trimeresurus jerdonii venom with a molecular weight of 36 kDa on SDS-PAGE. It dose-dependently inhibited ADP-induced human platelet aggregation with IC50 of 120 nM. cDNA cloning and sequencing revealed that Jerdonitin belonged to the class II of snake venom metalloproteinases (SVMPs) (P-II class). Different from other P-II class SVMPs, metalloproteinase and disintegrin domains of its natural protein were not separated, confirmed by internal peptide sequencing. Compared to other P-II class SVMPs, Jerdonitin has two additional cysteines (Cys219 and Cys238) located in the spacer domain and disintegrin domain, respectively. They probably form a disulfide bond and therefore the metalloproteinase and disintegrin domains cannot be separated by posttranslationally processing. In summary, comparison of the amino acid sequences of Jerdonitin with those of other P-II class SVMPs by sequence alignment and phylogenetic analysis, in conjunction with natural protein structure data, suggested that it was a new type of P-II class SVMPs. (C) 2003 Elsevier Inc. All rights reserved.

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Four novel highly oxygenated trinortriterpenoids, sphenalactones A-D (1-4), were isolated from the leaves and stems of Schisandra sphenanthera and their structures were elucidated by extensive analysis of 1D and 2D NMR data. Compounds 1-4 featured a C-27

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Major histocompatibility complex (MHC) class I information is vital for understanding variance of immune responses in HIV vaccination and biomedical models. In this study, 9 Mamu-A and 13 Mamu-B alleles were identified from the cDNA products of 10 Chinese

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In this paper, a strategy for min-max Moving Horizon Estimation (MHE) of a class of uncertain hybrid systems is proposed. The class of hybrid systems being considered are Piecewise Affine systems (PWA) with both continuous valued and logic components. Furthermore, we consider the case when there is a (possibly structured) norm bounded uncertainty in each subsystem. Sufficient conditions on the time horizon and the penalties on the state at the beginning of the estimation horizon to guarantee convergence of the MHE scheme will be provided. The MHE scheme will be implemented as a mixed integer semidefinite optimisation for which an efficient algorithm was recently introduced.

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We identified a new class of human immunodeficiency virus type 1 (HIV-1) recombinants (00CN-HH069 and 00CN-HH086) in which further recombination occurred between two established circulating recombinant forms (CRFs). These two isolates were found among 57 HIV-1 samples from a cohort of injecting drug users in eastern Yunnan Province of China. Informative-site analysis in conjunction with bootscanning plots and exploratory tree analysis revealed that these two strains were closely related mosaics comprised of CRF07_BC and CRF08_BC, which are found in China. The genotype screening based on gag-reverse transcriptase sequences if 57 samples from eastern Yunnan identified 47 CRF08_BC specimens (82.5%), 5 CRF07_BC specimens (8.8%), and 3 additional specimens with the novel recombinant structure. These new "second-generation" recombinants thus constitute a substantial proportion (5 of 57; 8.8%) of HIV-1 strains in this population and may belong to a new but yet-undefined class of CRF. This might be the first example of CRFs recombining with each other, leading to the evolution of second-generation inter-CRF recombinants.

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Jerdonitin is a P-II class snake venom metalloproteinase comprising metalloproteinase and disintegrin domains. In this study, we established a high-level expression system in Pichia pastoris and developed a purification strategy for the recombinant Jerdonitin. This recombinant Jerdonitin degraded fibrinogen at a level of activity comparable with its wild type. The effects of recombinant Jerdonitin on inhibiting ADP-induced human platelet aggregation were in a dose-dependent manner with an IC50 of 248 nM. In addition, we reported here that Jerdonitin can significantly inhibit the growth of several cell lines, including human liver cancer cells (Bel7402), human leukemia cells (K562) and human gastric carcinoma cells (BGC823). This study offers recombinant Jerdonitin that will be valuable for further functional and structural studies of Jerdonitin. (C) 2009 Elsevier Ltd. All rights reserved.

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Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

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A superconducting fault current limiter (SFCL) for 6.6 kV and 400 A installed in a cubicle for a distribution network substation was conceptually designed. The SFCL consists of parallel- and series-connected superconducting YBCO elements and a limiting resistor. Before designing the SFCL, some tests were carried out. The width and length of each element used in the tests are 30 mm and 210 mm, respectively. The element consists of YBCO thin film of about 200 nm in thickness on cerium dioxide (CeO2) as a cap-layer on a sapphire substrate by metal-organic deposition with a protective metal coat. In the tests, characteristics of each element, such as over-current, withstand-voltage, and so on, were obtained. From these characteristics, series and parallel connections of the elements, called units, were considered. The characteristics of the units were obtained by tests. From the test results, a single phase prototype SFCL was manufactured and tested. Thus, an SFCL rated at 6.6 kV and 400 A can be designed. © 2009 IEEE.

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Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet process, are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly- suited for many modeling tasks. In this paper, we propose a class of exchangeable nonparametric priors obtained by restricting the domain of existing models. Such models allow us to specify the distribution over the number of features per data point, and can achieve better performance on data sets where the number of features is not well-modeled by the original distribution.