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5 results on '"Lefkaditis, Dionysios"'

1. Intelligent visual otolith classification for bony fish species recognition

Author

Lefkaditis, Dionysios

Subjects
333.95611
Abstract

The study of otoliths is a well-established source of information for understanding the life offish and fish populations. Conducting fish species identification from otolith samples found in the stomach contents of marine fish-eating animals finds interesting applications such as dietary studies, stock monitoring, assessment and management. Fish species identification can provide useful data for climatology, archaeology and palaeontology research, as otoliths can be sourced from geological sediments or archaeological excavations. Analysing an otolith is a highly complex and time-consuming procedure Therefore, an automated otolith classification system can prove to be a vital tool for a wide variety of scientific research. The aim of the programme of work seeks the development of a novel automated fish species identification system. The main focus of this investigation is on the commercially interesting fish of the Northern Aegean Sea. The methodology described in this thesis exploits the inherent shape variability offish otoliths according to their corresponding species. This is based on the processing and analysis of images acquired using a stereoscopic microscope fitted with a digital camera. A compact feature vector is then constructed out of a list of candidate descriptors derived from the morphology as well as the image statistics of the otoliths. The identification is carried out by an intelligent classifier based on an artificial neural network. Several configurations of multi-layer perceptron, radial basis function and hybrid neural networks are considered in pursuit of a practical and expandable classification system.

Published
2009

3. Central role and mechanisms of beta-cell dysfunction and death in Friedreich ataxia- associated diabetes

Author

Cnop, Miriam, Igoillo Esteve, Mariana, Rai, Myriam, Begu, Audrey, Serroukh, Yasmina, Depondt, Chantal, Musuaya, Anyishaï, Marhfour, Ihsane, Ladrière, Laurence, Moles Lopez, Xavier, Lefkaditis, Dionysios, Moore, Fabrice, Brion, Jean Pierre, Cooper, Mark J, Schapira, Anthony HV, Clark, Anne, Koeppen, A.H., Marchetti, Piero, Pandolfo, Massimo, Eizirik, Decio L., Fery, Françoise, Cnop, Miriam, Igoillo Esteve, Mariana, Rai, Myriam, Begu, Audrey, Serroukh, Yasmina, Depondt, Chantal, Musuaya, Anyishaï, Marhfour, Ihsane, Ladrière, Laurence, Moles Lopez, Xavier, Lefkaditis, Dionysios, Moore, Fabrice, Brion, Jean Pierre, Cooper, Mark J, Schapira, Anthony HV, Clark, Anne, Koeppen, A.H., Marchetti, Piero, Pandolfo, Massimo, Eizirik, Decio L., and Fery, Françoise

Abstract

Objective: Friedreich ataxia (FRDA) is an autosomal recessive neurodegenerative disease caused in almost all cases by homozygosity for a GAA trinucleotide repeat expansion in the frataxin gene. Frataxin is a mitochondrial protein involved in iron homeostasis. FRDA patients have a high prevalence of diabetes, the pathogenesis of which is not known. We aimed to evaluate the relative contribution of insulin resistance and β-cell failure and the pathogenic mechanisms involved in FRDA diabetes. Methods: Forty-one FRDA patients, 26 heterozygous carriers of a GAA expansion, and 53 controls underwent oral and intravenous glucose tolerance tests. β-Cell proportion was quantified in postmortem pancreas sections from 9 unrelated FRDA patients. Using an in vitro disease model, we studied how frataxin deficiency affects β-cell function and survival. Results: FRDA patients had increased abdominal fat and were insulin resistant. This was not compensated for by increased insulin secretion, resulting in a markedly reduced disposition index, indicative of pancreatic β-cell failure. Loss of glucose tolerance was driven by β-cell dysfunction, which correlated with abdominal fatness. In postmortem pancreas sections, pancreatic islets of FRDA patients had a lower β-cell content. RNA interference–mediated frataxin knockdown impaired glucose-stimulated insulin secretion and induced apoptosis in rat β cells and human islets. Frataxin deficiency sensitized β cells to oleate-induced and endoplasmic reticulum stress-induced apoptosis, which could be prevented by the incretins glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide. Interpretation: Pancreatic β-cell dysfunction is central to diabetes development in FRDA as a result of mitochondrial dysfunction and higher sensitivity to metabolic and endoplasmic reticulum stress-induced β-cell death. ANN NEUROL 2012;72:971–982, info:eu-repo/semantics/published

Published
2012

4. Expression of endoplasmic reticulum stress markers in the islets of patients with type 1 diabetes.

Author

Marhfour, Ihsane, Moles Lopez, Xavier, Lefkaditis, Dionysios, Salmon, Isabelle, Allagnat, Florent, Richardson, S J, Morgan, N G, Eizirik, Decio L., Marhfour, Ihsane, Moles Lopez, Xavier, Lefkaditis, Dionysios, Salmon, Isabelle, Allagnat, Florent, Richardson, S J, Morgan, N G, and Eizirik, Decio L.

Abstract

Endoplasmic reticulum (ER) stress may play a role in cytokine-mediated beta cell death in type 1 diabetes, but it remains controversial whether ER stress markers are present in islets from type 1 diabetic individuals. Therefore, we evaluated by immunostaining the expression of markers of the three main branches of the ER stress response in islets from 13 individuals with and 15 controls without type 1 diabetes (eight adults and seven children)., Journal Article, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2012

5. Morphological feature selection and neural classification for electronic components.

Author

Lefkaditis, Dionysios and Tsirigotis, Georgios

Subjects
ARTIFICIAL neural networks, ARTIFICIAL intelligence, SYSTEM analysis, ELECTRONICS, STATISTICAL correlation, ELECTRONIC systems, ALGORITHMS
Abstract

This paper presents the development procedure of the feature extraction and classification module of an intelligent sorting system for electronic components. This system was designed as a prototype to recognise six types of electronic components for the needs of the educational electronics laboratories of the Kavala Institute of Technology. A list of features that describe the morphology of the outline of the components was extracted from the images. Two feature selection strategies were examined for the production of a powerful yet concise feature vector. These were correlation analysis and an implementation of support vector machines. Moreover, two types of neural classifiers were considered. The multilayer perceptron trained with the back-propagation algorithm and the radial basis function network trained with the K-means method. The best results were obtained with the combination of SVMs with MLPs, which successfully recognised 92.3% of the cases. [ABSTRACT FROM AUTHOR]

Published
2009

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