Edgedetection using wavelet transform and neural networks

The method exposed in this paper represents a new edge-detection tool of a grey-level image by the cooperation of two technics : wavelet decomposition and neural networks . The first part recalls the necessary background on mono and bidimensional wavelet decomposition and their main properties . The difficult phase of the algorithm lies in the optimal recomposition of différent resolutions, in the aim to obtain thin and noiseless edges . This work is given to a neural network which constitutes the object of the second part. The main interest of this new method is to give good resuits with images whose caracteristics are completly différent, without to modify any parameters .La méthode présentée dans cet article, constitue un nouvel outil d'extraction des contours d'une image en niveaux de gris, par coopération de techniques: décomposition en ondelettes et réseaux neuromimétiques. La première partie est consacrée aux rappels nécessaires quant au formalisme de la décomposition en ondelettes, ainsi que ses principales propriétés. La phase délicate de l'algorithme réside dans la recomposition optimale des différentes résolutions, afin d'obtenir des contours fins et sans bruit. Cette tâche est avantageusement confiée à un réseau de neurones, objet de la deuxième parti

Integrate and Fire Neural Networks, Piecewise Contractive Maps and Limit Cycles

We study the global dynamics of integrate and fire neural networks composed of an arbitrary number of identical neurons interacting by inhibition and excitation. We prove that if the interactions are strong enough, then the support of the stable asymptotic dynamics consists of limit cycles. We also find sufficient conditions for the synchronization of networks containing excitatory neurons. The proofs are based on the analysis of the equivalent dynamics of a piecewise continuous Poincar\'e map associated to the system. We show that for strong interactions the Poincar\'e map is piecewise contractive. Using this contraction property, we prove that there exist a countable number of limit cycles attracting all the orbits dropping into the stable subset of the phase space. This result applies not only to the Poincar\'e map under study, but also to a wide class of general n-dimensional piecewise contractive maps.Comment: 46 pages. In this version we added many comments suggested by the referees all along the paper, we changed the introduction and the section containing the conclusions. The final version will appear in Journal of Mathematical Biology of SPRINGER and will be available at http://www.springerlink.com/content/0303-681

A Nuclear Localization of the Infectious Haematopoietic Necrosis Virus NV Protein Is Necessary for Optimal Viral Growth

The nonvirion (NV) protein of infectious hematopoietic necrosis virus (IHNV) has been previously reported to be essential for efficient growth and pathogenicity of IHNV. However, little is known about the mechanism by which the NV supports the viral growth. In this study, cellular localization of NV and its role in IHNV growth in host cells was investigated. Through transient transfection in RTG-2 cells of NV fused to green fluorescent protein (GFP), a nuclear localization of NV was demonstrated. Deletion analyses showed that the 32EGDL35 residues were essential for nuclear localization of NV protein, and fusion of these 4 amino acids to GFP directed its transport to the nucleus. We generated a recombinant IHNV, rIHNV-NV-ΔEGDL in which the 32EGDL35 was deleted from the NV. rIHNVs with wild-type NV (rIHNV-NV) or with the NV gene replaced with GFP (rIHNV-ΔNV-GFP) were used as controls. RTG-2 cells infected with rIHNV-ΔNV-GFP and rIHNV-NV-ΔEGDL yielded 12- and 5-fold less infectious virion, respectively, than wild type rIHNV-infected cells at 48 h post-infection (p.i.). While treatment with poly I∶C at 24 h p.i. did not inhibit replication of wild-type rIHNVs, replication rates of rIHNV-ΔNV-GFP and rIHNV-NV-ΔEGDL were inhibited by poly I∶C. In addition, both rIHNV-ΔNV and rIHNV-NV-ΔEGDL induced higher levels of expressions of both IFN1 and Mx1 than wild-type rIHNV. These data suggest that the IHNV NV may support the growth of IHNV through inhibition of the INF system and the amino acid residues of 32EGDL35 responsible for nuclear localization are important for the inhibitory activity of NV

Aplicación de rabdovirus DNA infectivo para el desarrollo de un nuevo vector para tratar ictiopatologías infecciosas

La reciente elucidación de la secuencia completa del genoma RNA negativo (~12 Kb) del rabdovirus de la necrosis hematopoiética infecciosa (NHI) de salmónidos y el desarrollo de métodos que permiten el rescate de virus infectivos a partir de cDNA tanto en los rabdovirus de la rabia como en el de la estomatitis vesicular (EV), ha puesto al alcance de la mano la posibilidad de obtener rabdovirus de peces a partir de cDNA infectivo. Se abren con ello importantes posibilidades de manipulación de estos genomas RNA hasta ahora imposibles. Dicha manipulación puede hacerse con el objetivo de obtener virus mutantes de bajas probabilidades de reversión que pudieran utilizarse como vacunas. Otra posibilidad es la de diseñar rabdovirus NHI heterólogos que expresaran las proteínas G de otros rabdovirus (septicemia hemorrágica vírica, SHV, viremia primaveral de la carpa, VPC, etc.) (proyecto UE FAIR CT98-4398). Además sería posible clonar cualquier otra proteína vírica o bacteriana en rabdovirus DNA infectivos. Su uso potencial como vectores aumenta el ámbito de la posible aplicación de rabdovirus DNA infectivos para controlar las enfermedades en Acuicultura

Contribution of neural network to an edge-detection tool using wavelets transformation

The method exposed in this paper represents a new edge-detection tool of a greylevel image by the cooperation of two technics : wavelet decomposition and neural networks. The first part recalls the necessary background on mono and bidimensional wavelet decomposition and their main properties. The difficult phase of the algorithm lies in the optimal recomposition of different resolutions, in the aim to obtain thin and noiseless edges. This work is given to a neural network which constitutes the object of the second part The main interest of this new method is to give good results with images whose caracteristics are completly different, without to modify any parametersLa méthode présentée dans cet article, constitue un nouvel outil d'extraction des contours d'une image en niveaux de gris, par coopération de techniques : décomposition en ondelettes et réseaux neuromimétiques. La première partie est consacrée aux rappels nécessaires quant au formalisme de la décomposition en ondelettes, ainsi que ses principales propriétés. La phase délicate de l'algorithme réside dans la recomposition optimale des différentes résolutions, afin d'obtenir des contours fins et sans bruit. Cette tâche est avantageusement confiée à un réseau de neurones, objet de la deuxième partie. L'attrait majeur de cette nouvelle technique, est sa capacité à traiter correctement des images aux caractéristiques très différentes, sans avoir à modifier de paramètre