No description available.

Made available in DSpace on 2014-10-09T12:42:19Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:00:09Z (GMT). No. of bitstreams: 0 / Tese (Doutoramento) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Made available in DSpace on 2014-10-09T12:42:19Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:00:09Z (GMT). No. of bitstreams: 0 / O trabalho apresenta uma aplicação da ferramenta metodológica conhecida como FMEA (Failure Mode Effect Analysis) ao processo de identificação de aspectos e impactos ambientais. Tal processo é parte importante na implantação e na manutenção de Sistemas de Gestão Ambiental (SGA), baseados na norma NBR ISO 14001. Além disso, pode contribuir, de forma complementar, para a avaliação e aperfeiçoamento da segurança nuclear da instalação analisada. Como objeto de estudo elegeu-se o Centro de Combustíveis Nucleares (CCN) do IPEN/CNEN-SP, localizado junto ao Campus da Universidade de São Paulo-Brasil, destinado à realização de pesquisas científicas e à produção de elementos combustíveis para o Reator IEA-R1. A partir de um levantamento sistemático de dados, obtidos por meio de entrevistas, documentos e registros operacionais foi possível identificar os processos, suas interações e atividades, cuja análise permitiu definir os diversos modos de falhas potenciais, as respectivas causas e conseqüências para o meio ambiente. Como resultado da avaliação criteriosa dos modos causas foi possível identificar e classificar os principais impactos ambientais potenciais, que constitui uma etapa essencial para a implantação e manutenção de um Sistema de Gestão Ambiental para a instalação em estudo. Os resultados obtidos permitiram demonstrar a validade da aplicação da técnica FMEA aos processos de instalações nucleares, identificando aspectos e impactos ambientais, cujos controles são essenciais para a obtenção da conformidade com os requisitos ambientais do Sistema de Gestão Integrada do IPEN (SGI). Contribuíram também para fornecer uma ferramenta gerencial poderosa para a solução de questões relacionadas ao processo de atendimento de exigências legais aplicáveis no âmbito da Comissão Nacional de Energia Nuclear (CNEN) e do Instituto Brasileiro do Meio Ambiente (IBAMA). / Tese (Doutoramento) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Life presents fascinating examples of self-organization and emergent phenomena. In multi-cellular organisms, a multitude of cells interact to form and maintain highly complex body plans. This requires reliable communication between cells on various length scales. First, there has to be the right number of cells to preserve the integrity of the body and its size. Second, there have to be the right types of cells at the right positions to result in a functional body layout. In this thesis, we investigate theoretical feedback mechanisms for both self-organized body plan patterning and size control. The thesis is inspired by the astonishing scaling and regeneration abilities of flatworms. These worms can perfectly regrow their entire body plan even from tiny amputation fragments like the tip of the tail. Moreover, they can grow and actively de-grow by more than a factor of 40 in length depending on feeding conditions, scaling up and down all body parts while maintaining their functionality. These capabilities prompt for remarkable physical mechanisms of pattern formation. First, we explore pattern scaling in mechanisms previously proposed to describe biological pattern formation. We systematically extract requirements for scaling and highlight the limitations of these previous models in their ability to account for growth and regeneration in flatworms. In particular, we discuss a prominent model for the spontaneous formation of biological patterns introduced by Alan Turing. We characterize the hierarchy of steady states of such a Turing mechanism and demonstrate that Turing patterns do not naturally scale. Second, we present a novel class of patterning mechanisms yielding entirely self-organized and self-scaling patterns. Our framework combines a Turing system with our derived principles of pattern scaling and thus captures essential features of body plan regeneration and scaling in flatworms. We deduce general signatures of pattern scaling using dynamical systems theory. These signatures are discussed in the context of experimental data. Next, we analyze shape and motility of flatworms. By monitoring worm motility, we can identify movement phenotypes upon gene knockout, reporting on patterning defects in the locomotory system. Furthermore, we adapt shape mode analysis to study 2D body deformations of wildtype worms, which enables us to characterize two main motility modes: a smooth gliding mode due to the beating of their cilia and an inchworming behavior based on muscle contractions. Additionally, we apply this technique to investigate shape variations between different flatworm species. With this approach, we aim at relating form and function in flatworms. Finally, we investigate the metabolic control of cell turnover and growth. We establish a protocol for accurate measurements of growth dynamics in flatworms. We discern three mechanisms of metabolic energy storage; theoretical descriptions thereof can explain the observed organism growth by rules on the cellular scale. From this, we derive specific predictions to be tested in future experiments. In a close collaboration with experimental biologists, we combine minimal theoretical descriptions with state-of-the-art experiments and data analysis. This allows us to identify generic principles of scalable body plan patterning and growth control in flatworms. / Die belebte Natur bietet uns zahlreiche faszinierende Beispiele für die Phänomene von Selbstorganisation und Emergenz. In Vielzellern interagieren Millionen von Zellen miteinander und sind dadurch in der Lage komplexe Körperformen auszubilden und zu unterhalten. Dies verlangt nach einer zuverlässigen Kommunikation zwischen den Zellen auf verschiedenen Längenskalen. Einerseits ist stets eine bestimmte Zellanzahl erforderlich, sodass der Körper intakt bleibt und seine Größe erhält. Anderseits muss für einen funktionstüchtigen Körper aber auch der richtige Zelltyp an der richtigen Stelle zu finden sein. In der vorliegenden Dissertation untersuchen wir beide Aspekte, die Kontrolle von Wachstum sowie die selbstorganisierte Ausbildung des Körperbaus. Die Dissertation ist inspiriert von den erstaunlichen Skalierungs- und Regenerationsfähigkeiten von Plattwürmern. Diese Würmer können ihren Körper selbst aus winzigen abgetrennten Fragmenten -wie etwa der Schwanzspitze- komplett regenerieren. Darüberhinaus können sie auch, je nach Fütterungsbedingung, um mehr als das 40fache in der Länge wachsen oder schrumpfen und passen dabei alle Körperteile entsprechend an, wobei deren Funktionalität erhalten bleibt. Diese Fähigkeiten verlangen nach bemerkenswerten physikalischen Musterbildungsmechanismen. Zunächst untersuchen wir das Skalierungsverhalten von früheren Ansätzen zur Beschreibung biologischer Musterbildung. Wir leiten daraus Voraussetzung für das Skalieren ab und zeigen auf, dass die bekannten Modelle nur begrenzt auf Wachstum und Regeneration von Plattwürmern angewendet werden können. Insbesondere diskutieren wir ein wichtiges Modell für die spontane Entstehung von biologischen Strukturen, das von Alan Turing vorgeschlagen wurde. Wir charakterisieren die Hierarchie von stationären Zuständen solcher Turing Mechanismen und veranschaulichen, dass diese Turingmuster nicht ohne weiteres skalieren. Daraufhin präsentieren wir eine neuartige Klasse von Musterbildungsmechanismen, die vollständig selbstorgansierte und selbstskalierende Muster erzeugen. Unser Ansatz vereint ein Turing System mit den zuvor hergeleiteten Prinzipien für das Skalieren von Mustern und beschreibt dadurch wesentliche Aspekte der Regeneration und Skalierung von Plattwürmern. Mit Hilfe der Theorie dynamischer Systeme leiten wir allgemeine Merkmale von skalierenden Mustern ab, die wir im Hinblick auf experimentelle Daten diskutieren. Als nächstes analysieren wir Form und Fortbewegung der Würmer. Die Auswertung des Bewegungsverhaltens, nachdem einzelne Gene ausgeschaltet wurden, ermöglicht Rückschlüsse auf die Bedeutung dieser Gene für den Bewegungsapparat. Darüber hinaus wenden wir eine Hauptkomponentenanalyse auf die Verformungen des zweidimensionalen Wurmkörpers während der natürlichen Fortbewegung an. Damit sind wir in der Lage, zwei wichtige Fortbewegungsstrategien der Würmer zu charakterisieren: eine durch den Zilienschlag angetriebene gleichmässige Gleitbewegung und eine raupenartige Bewegung, die auf Muskelkontraktionen beruht. Zusätzlich wenden wir diese Analysetechnik auch an, um Unterschiede in der Gestalt von verschiedenen Plattwurmarten zu untersuchen. Grundsätzlich zielen alle diese Ansätze darauf ab, das Aussehen der Plattwürmer mit den damit verbundenen Funktionen verschiedener Körperteile in Beziehung zu setzen. Schlussendlich erforschen wir den Einfluss des Stoffwechsels auf den Zellaustausch und das Wachstum. Dazu etablieren wir Messungen der Wachstumsdynamik in Plattwürmern. Wir unterscheiden drei Mechanismen für das Speichern von Stoffwechselenergie, deren theoretische Beschreibung es uns ermöglicht, das beobachtete makroskopische Wachstum des Organismus mit dem Verhalten der einzelnen Zellen zu erklären. Basierend darauf leiten wir Vorhersagen ab, die nun experimentell getestet werden. In enger Zusammenarbeit mit Kollegen aus der experimentellen Biologie führen wir minimale theoretische Beschreibungen mit modernsten Experimenten und Analysetechniken zusammen. Dadurch sind wir in der Lage, Grundlagen sowohl der skalierbaren Ausbildung des Körperbaus als auch der Wachstumskontrolle bei Plattwürmern herauszuarbeiten.

At the Dawn of the 21st century, the field of specialty optical fibers experienced a scientific revolution with the introduction of the stack-and-draw technique, a multi-steps and advanced fiber fabrication method, which enabled the creation of well-controlled micro-structured designs. Since then, an extremely wide variety of finely tuned fiber structures have been demonstrated including novel materials and novel designs. As the complexity of the fiber design increased, highly-controlled fabrication processes became critical. To determine the ability of a novel fiber design to deliver light with properties tailored according to a specific application, several mode analysis techniques were reported, addressing the recurring needs for in-depth fiber characterization. The first part of this dissertation details a novel experiment that was demonstrated to achieve modal decomposition with extended capabilities, reaching beyond the limits set by the existing mode analysis techniques. As a result, individual transverse modes carrying between ~0.01% and ~30% of the total light were resolved with unmatched accuracy. Furthermore, this approach was employed to decompose the light guided in Large-Mode Area (LMA) fiber, Photonic Crystal Fiber (PCF) and Leakage Channel Fiber (LCF). The single-mode performances were evaluated and compared. As a result, the suitability of each specialty fiber design to be implemented for power-scaling applications of fiber laser systems was experimentally determined. The second part of this dissertation is dedicated to novel specialty fiber laser systems. First, challenges related to the monolithic integration of novel and complex specialty fiber designs in all-fiber systems were addressed. The poor design and size compatibility between specialty fibers and conventional fiber-based components limits their monolithic integration due to high coupling loss and unstable performances. Here, novel all-fiber Mode-Field Adapter (MFA) devices made of selected segments of Graded Index Multimode Fiber (GIMF) were implemented to mitigate the coupling losses between a LMA PCF and a conventional Single-Mode Fiber (SMF), presenting an initial 18-fold mode-field area mismatch. It was experimentally demonstrated that the overall transmission in the mode-matched fiber chain was increased by more than 11 dB (the MFA was a 250 ?m piece of 50 ?m core diameter GIMF). This approach was further employed to assemble monolithic fiber laser cavities combining an active LMA PCF and fiber Bragg gratings (FBG) in conventional SMF. It was demonstrated that intra-cavity mode-matching results in an efficient (60%) and narrow-linewidth (200 pm) laser emission at the FBG wavelength. In the last section of this dissertation, monolithic Multi-Core Fiber (MCF) laser cavities were reported for the first time. Compared to existing MCF lasers, renown for high-brightness beam delivery after selection of the in-phase supermode, the present new generation of 7-coupled-cores Yb-doped fiber laser uses the gain from several supermodes simultaneously. In order to uncover mode competition mechanisms during amplification and the complex dynamics of multi-supermode lasing, novel diagnostic approaches were demonstrated. After characterizing the laser behavior, the first observations of self-mode-locking in linear MCF laser cavities were discovered.

La dynamique structurelle des biomolécules est intimement liée à leur fonction, mais très coûteuse à étudier expériementalement. Pour cette raison, de nombreuses méthodologies computationnelles ont été développées afin de simuler la dynamique structurelle biomoléculaire. Toutefois, lorsque l'on s'intéresse à la modélisation des effects de milliers de mutations, les méthodes de simulations classiques comme la dynamique moléculaire, que ce soit à l'échelle atomique ou gros-grain, sont trop coûteuses pour la majorité des applications. D'autre part, les méthodes d'analyse de modes normaux de modèles de réseaux élastiques gros-grain (ENM pour "elastic network model") sont très rapides et procurent des solutions analytiques comprenant toutes les échelles de temps. Par contre, la majorité des ENMs considèrent seulement la géométrie du squelette biomoléculaire, ce qui en fait de mauvais choix pour étudier les effets de mutations qui ne changeraient pas cette géométrie. Le "Elastic Network Contact Model" (ENCoM) est le premier ENM sensible à la séquence de la biomolécule à l'étude, ce qui rend possible son utilisation pour l'exploration efficace d'espaces conformationnels complets de variants de séquence. La présente thèse introduit le pipeline computationel ENCoM-DynaSig-ML, qui réduit les espaces conformationnels prédits par ENCoM à des Signatures Dynamiques qui sont ensuite utilisées pour entraîner des modèles d'apprentissage machine simples. ENCoM-DynaSig-ML est capable de prédire la fonction de variants de séquence avec une précision significative, est complémentaire à toutes les méthodes existantes, et peut générer de nouvelles hypothèses à propos des éléments importants de dynamique structurelle pour une fonction moléculaire donnée. Nous présentons trois exemples d'étude de relations séquence-dynamique-fonction: la maturation des microARN, le potentiel d'activation de ligands du récepteur mu-opioïde et l'efficacité enzymatique de l'enzyme VIM-2 lactamase. Cette application novatrice de l'analyse des modes normaux est rapide, demandant seulement quelques secondes de temps de calcul par variant de séquence, et est généralisable à toute biomolécule pour laquelle des données expérimentale de mutagénèse sont disponibles. / The dynamics of biomolecules are intimately tied to their functions but experimentally elusive, making their computational study attractive. When modelling the effects of thousands of mutations, time-stepping methods such as classical or enhanced sampling molecular dynamics are too costly for most applications. On the other hand, normal mode analysis of coarse-grained elastic network models (ENMs) provides fast analytical dynamics spanning all timescales. However, the vast majority of ENMs consider backbone geometry alone, making them a poor choice to study point mutations which do not affect the equilibrium structure. The Elastic Network Contact Model (ENCoM) is the first sequence-sensitive ENM, enabling its use for the efficient exploration of full conformational spaces from sequence variants. The present work introduces the ENCoM-DynaSig-ML computational pipeline, in which the ENCoM conformational spaces are reduced to Dynamical Signatures and coupled to simple machine learning algorithms. ENCoM-DynaSig-ML predicts the function of sequence variants with significant accuracy, is complementary to all existing methods, and can generate new hypotheses about which dynamical features are important for the studied biomolecule's function. Examples given are the maturation efficiency of microRNA variants, the activation potential of mu-opioid receptor ligands and the effect of point mutations on VIM-2 lactamase's enzymatic efficiency. This novel application of normal mode analysis is very fast, taking a few seconds CPU time per variant, and is generalizable to any biomolecule on which experimental mutagenesis data exist.