No description available.

Στην παρούσα μελέτη η βιοτυπία και η ανάλυση του DNA με ηλεκτρο-φόρηση σε παλλόμενο ηλεκτρικό πεδίο (PFGE) εφαρμόσθηκαν σε ένα σύνο-λο εντε-ρο-κόκ-κων που απομονώθηκαν από νερά αναψυχής και πόσιμα νερά, προκει-μένου να προσδιορισθεί πιθανή γενετική συγγένεια. Τα 200 στελέχη εντε-ρο--κόκκου απομονώθηκαν από 246 δείγματα νερών αναψυχής και 903 δείγματα πόσιμου νερού, από θάλασσα, νερό δικτύου, ποταμούς και πηγές της Ν.Δ. Ελλάδας. Η ταυτοποίηση σε επίπεδο είδους έγινε με το σύστημα API 20strep. Ο έλεγχος της ευαισθησίας στα αντιβιοτικά έγινε με προσδιορισμό της ελάχιστης ανασταλτικής πυκνότητας (MIC) με την μέθοδο ταινιών E-test. Η ηλεκτροφό-ρηση σε παλλόμενο ηλεκτρικό πεδίο εφαρμόσθηκε για τις γονοτυπικές δοκιμασίες. Ο χαρακτηρισμός σε επίπεδο είδους ταξινόμησε τους εντερόκοκκους σε 22 βιότυπους. Στην πλειοψηφία τους τα στελέχη ήσαν E. faecium (142 ή 71%), ακολουθούσε ο E. faecalis (40 ή 20%), o E. durans (9 ή 4,5%), o E. gallinarum (5 ή 2,5%) και o E. avium (4 ή 2%). Μεταξύ των στελεχών του E. faecium υπήρ-χε σχέση των βιοτύπων και της πηγής του δείγματος. Αυτό δεν παρατηρή-θη-κε στα στελέχη του E. faecalis ούτε στα υπόλοιπα είδη. Κανένα στέλεχος δεν βρέθηκε να παράγει β-λακταμάση. Δεν παρατηρήθηκε καμία σχέση μεταξύ της αντοχής στα αντιβιοτικά και της προέλευσης των στελεχών. Κανένα είδος εντεροκόκκου δεν παρουσίασε αντοχή στα γλυκοπεπτίδια. Κλινικά στελέχη E. faecium περιελήφθησαν στα γονοτυπικά πειράματα ως μάρτυρες για τους ήδη ταυτοποιημένους κλώνους που ενδημούν στο Πανεπιστημιακό Νοσοκομείο της Πάτρας. Η ανάλυση των τύπων PFGE μεταξύ των 104 στελεχών αποκάλυψε την παρουσία 18 κλώνων μεταξύ του E. faecium, 14 του E. faecalis, 2 του E. durans, 3 του E. gallinarum και ενός του E. avium. Αν και μεταξύ των εξετασθέ-ντων στελεχών παρατηρήθηκε γενετική ποικιλία, προσδιορίσθηκαν κοινοί κλώ-νοι μεταξύ διαφορετικών δειγμάτων νερών. Τα δενδρογράμματα αποκά-λυ-ψαν γενετική σχέση μεταξύ συγκεκριμέ-νων περιβαλλοντικών στελεχών του E. faecium και αυτών που ήσαν νοσοκομειακής προέλευσης. Για τη μελέτη της παρουσίας κλώνων μεταξύ εντεροκόκκων που απομονώνονται από το περιβάλλον πρέπει να εφαρμόζονται μέθοδοι ανάλυσης χρωμοσωμικού DNA. / In this study we have identified the enterococcal species isolated from different environmental sources and we have characterized their biotypes, antibiotic resistance patterns and PFGE types. Biotyping and DNA fingerprinting by pulsed-field gel electrophoresis was applied to a collection of enterococci recovered form recreational and drinking water, in order to identify possible genetic relationships. Clinical strains of hospital origin were compared to the environmental isolates. A total of 200 enterococci were isolated from 246 recreational water, and 900 drinking water. One hundred forty two isolates were characterized as Enterococcus faecium recovered from all sources, 40 E. faecalis, 9 E. durans, 5. E. gallinarum and E. avium. Biotypes, determined with API 20 strep, among E. faecium were correlated with certain environmental sources, while antibiotypes, determined with Etest, did not reveal any relationship with the sample origin. Even though genetic diversity was observed among the studied strains, common clonal types were also identified in different sources, suggesting a possible common origin of the enterococci. Cluster analysis revealed a genetic relationship between certain environmental E. faecium and clinical strains.

Throughout the last decade, vehicle localization has been attracting significant attention in a wide range of applications, including Navigation Systems, Road Tolling, Smart Parking, and Collision Avoidance. To deliver on their requirements, these applications need specific localization accuracy. However, current localization techniques lack the required accuracy, especially for mission critical applications. Although various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources so as to achieve the highest accuracy possible, and to mitigate the impact of inadequate accuracy on the target application. In this thesis, a framework for fusing different localization techniques is introduced in order to estimate the location of a vehicle along with location integrity assessment that captures the impact of the measurement conditions on the localization quality. Knowledge about estimate integrity allows the system to plan the use of its localization resources so as to match the target accuracy of the application. The framework introduced provides the tools that would allow for modeling the impact of the operation conditions on estimate accuracy and integrity, as such it enables more robust system performance in three steps. First, localization system parameters are utilized to contrive a feature space that constitutes probable accuracy classes. Due to the strong overlap among accuracy classes in the feature space, a hierarchical classification strategy is developed to address the class ambiguity problem via the class unfolding approach (HCCU). HCCU strategy is proven to be superior with respect to other hierarchical configuration. Furthermore, a Context Based Accuracy Classification (CBAC) algorithm is introduced to enhance the performance of the classification process. In this algorithm, knowledge about the surrounding environment is utilized to optimize classification performance as a function of the observation conditions. Second, a task-driven integrity (TDI) model is developed to enable the applications modules to be aware of the trust level of the localization output. Typically, this trust level functions in the measurement conditions; therefore, the TDI model monitors specific parameter(s) in the localization technique and, accordingly, infers the impact of the change in the environmental conditions on the quality of the localization process. A generalized TDI solution is also introduced to handle the cases where sufficient information about the sensing parameters is unavailable. Finally, the produce of the employed localization techniques (i.e., location estimates, accuracy, and integrity level assessment) needs to be fused. Nevertheless, these techniques are hybrid and their pieces of information are conflicting in many situations. Therefore, a novel evidence structure model called Spatial Evidence Structure Model (SESM) is developed and used in constructing a frame of discernment comprising discretized spatial data. SESM-based fusion paradigms are capable of performing a fusion process using the information provided by the techniques employed. Both the location estimate accuracy and aggregated integrity resultant from the fusion process demonstrate superiority over the employing localization techniques. Furthermore, a context aware task-driven resource allocation mechanism is developed to manage the fusion process. The main objective of this mechanism is to optimize the usage of system resources and achieve a task-driven performance. Extensive experimental work is conducted on real-life and simulated data to validate models developed in this thesis. It is evident from the experimental results that task-driven integrity assessment and control is applicable and effective on hybrid localization systems.

Throughout the last decade, vehicle localization has been attracting significant attention in a wide range of applications, including Navigation Systems, Road Tolling, Smart Parking, and Collision Avoidance. To deliver on their requirements, these applications need specific localization accuracy. However, current localization techniques lack the required accuracy, especially for mission critical applications. Although various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources so as to achieve the highest accuracy possible, and to mitigate the impact of inadequate accuracy on the target application. In this thesis, a framework for fusing different localization techniques is introduced in order to estimate the location of a vehicle along with location integrity assessment that captures the impact of the measurement conditions on the localization quality. Knowledge about estimate integrity allows the system to plan the use of its localization resources so as to match the target accuracy of the application. The framework introduced provides the tools that would allow for modeling the impact of the operation conditions on estimate accuracy and integrity, as such it enables more robust system performance in three steps. First, localization system parameters are utilized to contrive a feature space that constitutes probable accuracy classes. Due to the strong overlap among accuracy classes in the feature space, a hierarchical classification strategy is developed to address the class ambiguity problem via the class unfolding approach (HCCU). HCCU strategy is proven to be superior with respect to other hierarchical configuration. Furthermore, a Context Based Accuracy Classification (CBAC) algorithm is introduced to enhance the performance of the classification process. In this algorithm, knowledge about the surrounding environment is utilized to optimize classification performance as a function of the observation conditions. Second, a task-driven integrity (TDI) model is developed to enable the applications modules to be aware of the trust level of the localization output. Typically, this trust level functions in the measurement conditions; therefore, the TDI model monitors specific parameter(s) in the localization technique and, accordingly, infers the impact of the change in the environmental conditions on the quality of the localization process. A generalized TDI solution is also introduced to handle the cases where sufficient information about the sensing parameters is unavailable. Finally, the produce of the employed localization techniques (i.e., location estimates, accuracy, and integrity level assessment) needs to be fused. Nevertheless, these techniques are hybrid and their pieces of information are conflicting in many situations. Therefore, a novel evidence structure model called Spatial Evidence Structure Model (SESM) is developed and used in constructing a frame of discernment comprising discretized spatial data. SESM-based fusion paradigms are capable of performing a fusion process using the information provided by the techniques employed. Both the location estimate accuracy and aggregated integrity resultant from the fusion process demonstrate superiority over the employing localization techniques. Furthermore, a context aware task-driven resource allocation mechanism is developed to manage the fusion process. The main objective of this mechanism is to optimize the usage of system resources and achieve a task-driven performance. Extensive experimental work is conducted on real-life and simulated data to validate models developed in this thesis. It is evident from the experimental results that task-driven integrity assessment and control is applicable and effective on hybrid localization systems.