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Imaging fluorescent determination of energetics of chloroplast division and related matters
During exploration of imaging photosynthetic fluorimetry of Arabidopsis thaliana mutants, we discovered a novel phenomenon wherein photosynthetic efficiency (defined in Ning et al., 1995) is shown to plot in discrete groups. This exploration resulted first in the development of a spectrofluorometric method that apparently allows for in vivo observation of division of chloroplast populations in leaves of Arabidopsis thaliana mutants and in the wild-type. Testing the phenomenon, we examined leaves of monocot plants in which the progression of leaf development and greening follows a linear course upwards along the leaf. The monocots chosen were sugarcane and especially Amaryllis; data from wheat, Narcissus, and other plants are mentioned but not described here. The above results showed that in these plants, chloroplast division phenomenon occurred only where chloroplast division is localized. We found this is also consistent with the postulate that the biphasic energetics observed correspond to the division of this organelle. To verify the phenomenon further, we performed preliminary confocal microscopy studies in Amaryllis; we saw what seemed to be chloroplast division in the zones where the leaves showed the multiple photosynthetic efficiencies and these results supported the concept that our spectroscopic technique is a real and useful method to observe chloroplast division. Here we also present a novel statistical approach allowing quantification of probability in two- dimensional in vivo fluorescence spectroscopy of these biological samples. To automate detection of chloroplast division for future use, we develop a digital image processing program we called a software "tool". This tool analyzes photographs of confocal images, identifies chloroplast division and shows statistical information of identified chloroplasts. The statistical information includes distribution of intensity, area and perimeter of each identified chloroplast. We used several image processing techniques to analyze confocal images, including filtering images, object extracting and algorithms in graph theory. We have implemented a "friendly" GUI (Graphical User Interface) that enables a user to perform operations such as correction, addition and deletion of group(s) easily during the execution of the program. By employing an innovative configuration of image analysis techniques, this software tool is able to identify where chloroplast division is occurring and answer related experimental questions
Exploring 3D Shapes through Real Functions
This thesis lays in the context of research on representation, modelling and coding knowledge related to digital shapes, where by shape it is meant any individual object having a visual appareance which exists in some two-, three- or higher dimensional space. Digital shapes are digital representations of either physically existing or virtual objects that can be processed by computer applications. While the technological advances in terms of hardware and software have made available plenty of tools for using and interacting with the geometry of shapes, to manipulate and retrieve huge amount of data it is necessary to define methods able to effectively code them. In this thesis a conceptual model is proposed which represents a given 3D object through the coding of its salient features and defines an abstraction of the object, discarding irrelevant details. The approach is based on the shape descriptors defined with respect to real functions, which provide a very useful shape abstraction method for the analysis and structuring of the information contained in the discrete shape model. A distinctive feature of these shape descriptors is their capability of combining topological and geometrical information properties of the shape, giving an abstraction of the main shape features. To fully develop this conceptual model, both theoretical and computational aspects have been considered, related to the definition and the extension of the different shape descriptors to the computational domain. Main emphasis is devoted to the application of these shape descriptors in computational settings; to this aim we display a number of application domains that span from shape retrieval, to shape classification and to best view selection.Questa tesi si colloca nell\u27ambito di ricerca riguardante la rappresentazione, la modellazione e la codifica della conoscenza connessa a forme digitali, dove per forma si intende l\u27aspetto visuale di ogni oggetto che esiste in due, tre o pi? dimensioni. Le forme digitali sono rappresentazioni di oggetti sia reali che virtuali, che possono essere manipolate da un calcolatore. Lo sviluppo tecnologico degli ultimi anni in materia di hardware e software ha messo a disposizione una grande quantit? di strumenti per acquisire, rappresentare e processare la geometria degli oggetti; tuttavia per gestire questa grande mole di dati ? necessario sviluppare metodi in grado di fornirne una codifica efficiente. In questa tesi si propone un modello concettuale che descrive un oggetto 3D attraverso la codifica delle caratteristiche salienti e ne definisce una bozza ad alto livello, tralasciando dettagli irrilevanti. Alla base di questo approccio ? l\u27utilizzo di descrittori basati su funzioni reali in quanto forniscono un\u27astrazione della forma molto utile per analizzare e strutturare l\u27informazione contenuta nel modello discreto della forma. Una peculiarit? di tali descrittori di forma ? la capacit? di combinare propriet? topologiche e geometriche consentendo di astrarne le principali caratteristiche. Per sviluppare questo modello concettuale, ? stato necessario considerare gli aspetti sia teorici che computazionali relativi alla definizione e all\u27estensione in ambito discreto di vari descrittori di forma. Particolare attenzione ? stata rivolta all\u27applicazione dei descrittori studiati in ambito computazionale; a questo scopo sono stati considerati numerosi contesti applicativi, che variano dal riconoscimento alla classificazione di forme, all\u27individuazione della posizione pi? significativa di un oggetto