890 resultados para Multimodal biometric fusion


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Canine distemper virus (CDV) produces a glycosylated type I fusion protein (F) with an internal hydrophobic signal sequence beginning around 115 residues downstream of the first AUG used for translation initiation. Cleavage of the signal sequence yields the F0 molecule, which is cleaved into the F1 and F2 subunits. Surprisingly, when all in-frame AUGs located in the first third of the F gene were mutated a protein of the same molecular size as the F0 molecule was still expressed from both the Onderstepoort (OP) and A75/17-CDV F genes. We designated this protein, which is initiated from a non-AUG codon protein Fx. Site-directed mutagenesis allowed to identify codon 85, a GCC codon coding for alanine, as the most likely position from which translation initiation of Fx occurs in OP-CDV. Deletion analysis demonstrated that at least 60 nucleotides upstream of the GCC codon are required for efficient Fx translation. This sequence is GC-rich, suggesting extensive folding. Secondary structure may therefore be important for translation initiation at codon 85.

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The registration of full 3-D models is an important task in computer vision. Range finders only reconstruct a partial view of the object. Many authors have proposed several techniques to register 3D surfaces from multiple views in which there are basically two aspects to consider. First, poor registration in which some sort of correspondences are established. Second, accurate registration in order to obtain a better solution. A survey of the most common techniques is presented and includes experimental results of some of them

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This work provides a general description of the multi sensor data fusion concept, along with a new classification of currently used sensor fusion techniques for unmanned underwater vehicles (UUV). Unlike previous proposals that focus the classification on the sensors involved in the fusion, we propose a synthetic approach that is focused on the techniques involved in the fusion and their applications in UUV navigation. We believe that our approach is better oriented towards the development of sensor fusion systems, since a sensor fusion architecture should be first of all focused on its goals and then on the fused sensors

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Arbuscular mycorrhizal fungi (AMF) form symbioses with the majority of plants and form extensive underground hyphal networks simultaneously connecting the roots of different plant species. No empirical evidence exists for either anastomosis between genetically different AMF or genetic exchange.Five isolates of one population of Glomus intraradices were used to study anastomosis between hyphae of germinating spores. We show that genetically distinct AMF, from the same field, anastomose, resulting in viable cytoplasmic connections through which genetic exchange could potentially occur.Pairs of genetically different isolates were then co-cultured in an in vitro system.Freshly produced spores were individually germinated to establish new cultures.Using several molecular tools, we show that genetic exchange occurred between genetically different AMF. Specific genetic markers from each parent were transmitted to the progeny. The progeny were viable, forming symbioses with plant roots. The phenotypes of some of the progeny were significantly different from either parent.Our results indicate that considerable promiscuity could occur in these fungi because nine out of 10 combinations of different isolates anastomosed. The ability to perform genetic crosses between AMF experimentally lays a foundation for understanding the genetics and evolutionary biology of these important plants symbionts.

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Recently, we described the improved immunogenicity of new malaria vaccine candidates based on the expression of fusion proteins containing immunodominant epitopes of merozoites and Salmonella enterica serovar Typhimurium flagellin (FliC) protein as an innate immune agonist. Here, we tested whether a similar strategy, based on an immunodominant B-cell epitope from malaria sporozoites, could also generate immunogenic fusion polypeptides. A recombinant His6-tagged FliC protein containing the C-terminal repeat regions of the VK210 variant of Plasmodium vivax circumsporozoite (CS) protein was constructed. This recombinant protein was successfully expressed in Escherichia coli as soluble protein and was purified by affinity to Ni-agarose beads followed by ion exchange chromatography. A monoclonal antibody specific for the CS protein of P. vivax sporozoites (VK210) was able to recognise the purified protein. C57BL/6 mice subcutaneously immunised with the recombinant fusion protein in the absence of any conventional adjuvant developed protein-specific systemic antibody responses. However, in mice genetically deficient in expression of TLR5, this immune response was extremely low. These results extend our previous observations concerning the immunogenicity of these recombinant fusion proteins and provide evidence that the main mechanism responsible for this immune activation involves interactions with TLR5, which has not previously been demonstrated for any recombinant FliC fusion protein.

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This paper is a joint effort between five institutionsthat introduces several novel similarity measures andcombines them to carry out a multimodal segmentationevaluation. The new similarity measures proposed arebased on the location and the intensity values of themisclassified voxels as well as on the connectivity andthe boundaries of the segmented data. We showexperimentally that the combination of these measuresimprove the quality of the evaluation. The study that weshow here has been carried out using four differentsegmentation methods from four different labs applied toa MRI simulated dataset of the brain. We claim that ournew measures improve the robustness of the evaluation andprovides better understanding about the differencebetween segmentation methods.

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Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.

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Invariant NKT (iNKT) cells are potent activators of DCs, NK cells, and T cells, and their antitumor activity has been well demonstrated. A single injection of the high-affinity CD1d ligand alpha-galactosylceramide (alphaGalCer) leads to short-lived iNKT cell activation followed, however, by long-term anergy, limiting its therapeutic use. In contrast, we demonstrated here that when alphaGalCer was loaded on a recombinant soluble CD1d molecule (alphaGalCer/sCD1d), repeated injections led to sustained iNKT and NK cell activation associated with IFN-gamma secretion as well as DC maturation in mice. Most importantly, when alphaGalCer/sCD1d was fused to a HER2-specific scFv antibody fragment, potent inhibition of experimental lung metastasis and established s.c. tumors was obtained when systemic treatment was started 2-7 days after the injection of HER2-expressing B16 melanoma cells. In contrast, administration of free alphaGalCer at this time had no effect. The antitumor activity of the CD1d-anti-HER2 fusion protein was associated with HER2-specific tumor localization and accumulation of iNKT, NK, and T cells at the tumor site. Targeting iNKT cells to the tumor site thus may activate a combined innate and adaptive immune response that may prove to be effective in cancer immunotherapy

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The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.

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SNARE complexes are required for membrane fusion in the endomembrane system. They contain coiled-coil bundles of four helices, three (Q(a), Q(b), and Q(c)) from target (t)-SNAREs and one (R) from the vesicular (v)-SNARE. NSF/Sec18 disrupts these cis-SNARE complexes, allowing reassembly of their subunits into trans-SNARE complexes and subsequent fusion. Studying these reactions in native yeast vacuoles, we found that NSF/Sec18 activates the vacuolar cis-SNARE complex by selectively displacing the vacuolar Q(a) SNARE, leaving behind a Q(bc)R subcomplex. This subcomplex serves as an acceptor for a Q(a) SNARE from the opposite membrane, leading to Q(a)-Q(bc)R trans-complexes. Activity tests of vacuoles with diagnostic distributions of inactivating mutations over the two fusion partners confirm that this distribution accounts for a major share of the fusion activity. The persistence of the Q(bc)R cis-complex and the formation of the Q(a)-Q(bc)R trans-complex are both sensitive to the Rab-GTPase inhibitor, GDI, and to mutations in the vacuolar tether complex, HOPS (HOmotypic fusion and vacuolar Protein Sorting complex). This suggests that the vacuolar Rab-GTPase, Ypt7, and HOPS restrict cis-SNARE disassembly and thereby bias trans-SNARE assembly into a preferred topology.

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The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.