Global ETD Search

71	Volumetrické efekty akcelerované na GPU / Volumetric Efects Accelerated on GPU Kubovčík, Tomáš January 2017 (has links) This thesis deals with simulation and rendering of fluid based volumetric effects, especially effect of fire and smoke. Computations are accelerated on graphics card using modern graphics API with motivation to achieve realistic visual results as well as physically correct calculations. Implemented volumetric effects are distributed as dynamic library which allows addition of these effects to existing applications.
72	Analyse statistique de populations pour l'interprétation d'images histologiques / Statistical analysis of populations for histological images interpretation Alsheh Ali, Maya 19 February 2015 (has links) Au cours de la dernière décennie, la pathologie numérique a été améliorée grâce aux avancées des algorithmes d'analyse d'images et de la puissance de calcul. Néanmoins, le diagnostic par un expert à partir d'images histopathologiques reste le gold standard pour un nombre considérable de maladies notamment le cancer. Ce type d'images préserve la structure des tissus aussi proches que possible de leur état vivant. Ainsi, cela permet de quantifier les objets biologiques et de décrire leur organisation spatiale afin de fournir une description plus précise des tissus malades. L'analyse automatique des images histopathologiques peut avoir trois objectifs: le diagnostic assisté par ordinateur, l'évaluation de la sévérité des maladies et enfin l'étude et l'interprétation des mécanismes sous-jacents des maladies et leurs impacts sur les objets biologiques. L'objectif principal de cette thèse est en premier lieu de comprendre et relever les défis associés à l'analyse automatisée des images histologiques. Ensuite, ces travaux visent à décrire les populations d'objets biologiques présents dans les images et leurs relations et interactions à l'aide des statistiques spatiales et également à évaluer la significativité de leurs différences en fonction de la maladie par des tests statistiques. Après une étape de séparation des populations d'objets biologiques basée sur la couleur des marqueurs, une extraction automatique de leurs emplacements est effectuée en fonction de leur type, qui peut être ponctuel ou surfacique. Les statistiques spatiales, basées sur la distance pour les données ponctuelles, sont étudiées et une fonction originale afin de mesurer les interactions entre deux types de données est proposée. Puisqu'il a été montré dans la littérature que la texture d'un tissu est altérée par la présence d'une maladie, les méthodes fondées sur les motifs binaires locaux sont discutées et une approche basée sur une modification de la résolution de l'image afin d'améliorer leur description est introduite. Enfin, les statistiques descriptives et déductives sont appliquées afin d'interpréter les caractéristiques extraites et d'étudier leur pouvoir discriminant dans le cadre de l'étude des modèles animaux de cancer colorectal. Ce travail préconise la mesure des associations entre différents types d'objets biologiques pour mieux comprendre et comparer les mécanismes sous-jacents des maladies et leurs impacts sur la structure des tissus. En outre, nos expériences confirment que l'information de texture joue un rôle important dans la différenciation des deux modèles d'implantation d'une même maladie. / During the last decade, digital pathology has been improved thanks to the advance of image analysis algorithms and calculus power. However, the diagnosis from histopathology images by an expert remains the gold standard in a considerable number of diseases especially cancer. This type of images preserves the tissue structures as close as possible to their living state. Thus, it allows to quantify the biological objects and to describe their spatial organization in order to provide a more specific characterization of diseased tissues. The automated analysis of histopathological images can have three objectives: computer-aided diagnosis, disease grading, and the study and interpretation of the underlying disease mechanisms and their impact on biological objects. The main goal of this dissertation is first to understand and address the challenges associated with the automated analysis of histology images. Then it aims at describing the populations of biological objects present in histology images and their relationships using spatial statistics and also at assessing the significance of their differences according to the disease through statistical tests. After a color-based separation of the biological object populations, an automated extraction of their locations is performed according to their types, which can be point or areal data. Distance-based spatial statistics for point data are reviewed and an original function to measure the interactions between point and areal data is proposed. Since it has been shown that the tissue texture is altered by the presence of a disease, local binary patterns methods are discussed and an approach based on a modification of the image resolution to enhance their description is introduced. Finally, descriptive and inferential statistics are applied in order to interpret the extracted features and to study their discriminative power in the application context of animal models of colorectal cancer. This work advocates the measure of associations between different types of biological objects to better understand and compare the underlying mechanisms of diseases and their impact on the tissue structure. Besides, our experiments confirm that the texture information plays an important part in the differentiation of two implemented models of the same disease. Pathologie numérique Modèles animaux de cancer colorectal Détection des objets biologiques Organisation spatiale Analyse de la texture Aide au diagnostic Glaucome Statistiques déductives Digital pathology Colorectal cancer animal models Biological object detection Spatial organization Texture analysis Compute-aided diagnosis Glaucoma Inferential statistics 004
73	Structure learning of Bayesian networks via data perturbation / Aprendizagem estrutural de Redes Bayesianas via perturbação de dados Gross, Tadeu Junior 29 November 2018 (has links) Structure learning of Bayesian Networks (BNs) is an NP-hard problem, and the use of sub-optimal strategies is essential in domains involving many variables. One of them is to generate multiple approximate structures and then to reduce the ensemble to a representative structure. It is possible to use the occurrence frequency (on the structures ensemble) as the criteria for accepting a dominant directed edge between two nodes and thus obtaining the single structure. In this doctoral research, it was made an analogy with an adapted one-dimensional random-walk for analytically deducing an appropriate decision threshold to such occurrence frequency. The obtained closed-form expression has been validated across benchmark datasets applying the Matthews Correlation Coefficient as the performance metric. In the experiments using a recent medical dataset, the BN resulting from the analytical cutoff-frequency captured the expected associations among nodes and also achieved better prediction performance than the BNs learned with neighbours thresholds to the computed. In literature, the feature accounted along of the perturbed structures has been the edges and not the directed edges (arcs) as in this thesis. That modified strategy still was applied to an elderly dataset to identify potential relationships between variables of medical interest but using an increased threshold instead of the predict by the proposed formula - such prudence is due to the possible social implications of the finding. The motivation behind such an application is that in spite of the proportion of elderly individuals in the population has increased substantially in the last few decades, the risk factors that should be managed in advance to ensure a natural process of mental decline due to ageing remain unknown. In the learned structural model, it was graphically investigated the probabilistic dependence mechanism between two variables of medical interest: the suspected risk factor known as Metabolic Syndrome and the indicator of mental decline referred to as Cognitive Impairment. In this investigation, the concept known in the context of BNs as D-separation has been employed. Results of the carried out study revealed that the dependence between Metabolic Syndrome and Cognitive Variables indeed exists and depends on both Body Mass Index and age. / O aprendizado da estrutura de uma Rede Bayesiana (BN) é um problema NP-difícil, e o uso de estratégias sub-ótimas é essencial em domínios que envolvem muitas variáveis. Uma delas consiste em gerar várias estruturas aproximadas e depois reduzir o conjunto a uma estrutura representativa. É possível usar a frequência de ocorrência (no conjunto de estruturas) como critério para aceitar um arco dominante entre dois nós e assim obter essa estrutura única. Nesta pesquisa de doutorado, foi feita uma analogia com um passeio aleatório unidimensional adaptado para deduzir analiticamente um limiar de decisão apropriado para essa frequência de ocorrência. A expressão de forma fechada obtida foi validada usando bases de dados de referência e aplicando o Coeficiente de Correlação de Matthews como métrica de desempenho. Nos experimentos utilizando dados médicos recentes, a BN resultante da frequência de corte analítica capturou as associações esperadas entre os nós e também obteve melhor desempenho de predição do que as BNs aprendidas com limiares vizinhos ao calculado. Na literatura, a característica contabilizada ao longo das estruturas perturbadas tem sido as arestas e não as arestas direcionadas (arcos) como nesta tese. Essa estratégia modificada ainda foi aplicada a um conjunto de dados de idosos para identificar potenciais relações entre variáveis de interesse médico, mas usando um limiar aumentado em vez do previsto pela fórmula proposta - essa cautela deve-se às possíveis implicações sociais do achado. A motivação por trás dessa aplicação é que, apesar da proporção de idosos na população ter aumentado substancialmente nas últimas décadas, os fatores de risco que devem ser controlados com antecedência para garantir um processo natural de declínio mental devido ao envelhecimento permanecem desconhecidos. No modelo estrutural aprendido, investigou-se graficamente o mecanismo de dependência probabilística entre duas variáveis de interesse médico: o fator de risco suspeito conhecido como Síndrome Metabólica e o indicador de declínio mental denominado Comprometimento Cognitivo. Nessa investigação, empregou-se o conceito conhecido no contexto de BNs como D-separação. Esse estudo revelou que a dependência entre Síndrome Metabólica e Variáveis Cognitivas de fato existe e depende tanto do Índice de Massa Corporal quanto da idade. Analytical threshold Aprendizado de estruturas robustas Associations discovery Bayesian network Cognitive impairment D-separação D-separation Data perturbation via bootstrap replicas Descoberta de associações Directed acyclic graph Envelhecimento da população Estabilidade de arcos Fatores de risco Grafo acíclico dirigido Learning of robust structures limiar analítico Média de modelos Metabolic syndrome Model averaging Perturbação de dados via bootstrap Population ageing Rede Bayesiana Risk factors Síndrome metabólica Stability of arcs Transtorno cognitivo
74	Structure learning of Bayesian networks via data perturbation / Aprendizagem estrutural de Redes Bayesianas via perturbação de dados Tadeu Junior Gross 29 November 2018 (has links) Structure learning of Bayesian Networks (BNs) is an NP-hard problem, and the use of sub-optimal strategies is essential in domains involving many variables. One of them is to generate multiple approximate structures and then to reduce the ensemble to a representative structure. It is possible to use the occurrence frequency (on the structures ensemble) as the criteria for accepting a dominant directed edge between two nodes and thus obtaining the single structure. In this doctoral research, it was made an analogy with an adapted one-dimensional random-walk for analytically deducing an appropriate decision threshold to such occurrence frequency. The obtained closed-form expression has been validated across benchmark datasets applying the Matthews Correlation Coefficient as the performance metric. In the experiments using a recent medical dataset, the BN resulting from the analytical cutoff-frequency captured the expected associations among nodes and also achieved better prediction performance than the BNs learned with neighbours thresholds to the computed. In literature, the feature accounted along of the perturbed structures has been the edges and not the directed edges (arcs) as in this thesis. That modified strategy still was applied to an elderly dataset to identify potential relationships between variables of medical interest but using an increased threshold instead of the predict by the proposed formula - such prudence is due to the possible social implications of the finding. The motivation behind such an application is that in spite of the proportion of elderly individuals in the population has increased substantially in the last few decades, the risk factors that should be managed in advance to ensure a natural process of mental decline due to ageing remain unknown. In the learned structural model, it was graphically investigated the probabilistic dependence mechanism between two variables of medical interest: the suspected risk factor known as Metabolic Syndrome and the indicator of mental decline referred to as Cognitive Impairment. In this investigation, the concept known in the context of BNs as D-separation has been employed. Results of the carried out study revealed that the dependence between Metabolic Syndrome and Cognitive Variables indeed exists and depends on both Body Mass Index and age. / O aprendizado da estrutura de uma Rede Bayesiana (BN) é um problema NP-difícil, e o uso de estratégias sub-ótimas é essencial em domínios que envolvem muitas variáveis. Uma delas consiste em gerar várias estruturas aproximadas e depois reduzir o conjunto a uma estrutura representativa. É possível usar a frequência de ocorrência (no conjunto de estruturas) como critério para aceitar um arco dominante entre dois nós e assim obter essa estrutura única. Nesta pesquisa de doutorado, foi feita uma analogia com um passeio aleatório unidimensional adaptado para deduzir analiticamente um limiar de decisão apropriado para essa frequência de ocorrência. A expressão de forma fechada obtida foi validada usando bases de dados de referência e aplicando o Coeficiente de Correlação de Matthews como métrica de desempenho. Nos experimentos utilizando dados médicos recentes, a BN resultante da frequência de corte analítica capturou as associações esperadas entre os nós e também obteve melhor desempenho de predição do que as BNs aprendidas com limiares vizinhos ao calculado. Na literatura, a característica contabilizada ao longo das estruturas perturbadas tem sido as arestas e não as arestas direcionadas (arcos) como nesta tese. Essa estratégia modificada ainda foi aplicada a um conjunto de dados de idosos para identificar potenciais relações entre variáveis de interesse médico, mas usando um limiar aumentado em vez do previsto pela fórmula proposta - essa cautela deve-se às possíveis implicações sociais do achado. A motivação por trás dessa aplicação é que, apesar da proporção de idosos na população ter aumentado substancialmente nas últimas décadas, os fatores de risco que devem ser controlados com antecedência para garantir um processo natural de declínio mental devido ao envelhecimento permanecem desconhecidos. No modelo estrutural aprendido, investigou-se graficamente o mecanismo de dependência probabilística entre duas variáveis de interesse médico: o fator de risco suspeito conhecido como Síndrome Metabólica e o indicador de declínio mental denominado Comprometimento Cognitivo. Nessa investigação, empregou-se o conceito conhecido no contexto de BNs como D-separação. Esse estudo revelou que a dependência entre Síndrome Metabólica e Variáveis Cognitivas de fato existe e depende tanto do Índice de Massa Corporal quanto da idade. Aprendizado de estruturas robustas D-separação Descoberta de associações Envelhecimento da população Estabilidade de arcos Fatores de risco Grafo acíclico dirigido limiar analítico Média de modelos Perturbação de dados via bootstrap Rede Bayesiana Síndrome metabólica Transtorno cognitivo Analytical threshold Associations discovery Bayesian network Cognitive impairment D-separation Data perturbation via bootstrap replicas Directed acyclic graph Learning of robust structures Metabolic syndrome Model averaging Population ageing Risk factors Stability of arcs
75	Modellierung und Visualisierung von Systemen zur Beschreibung der intra- und intermolekularen Wechselwirkungen in hydrophoben Peptiden Schneider, Alexander 11 November 2014 (has links) (PDF) Die vorliegende Arbeit beschäftigt sich mit der Untersuchung und Beschreibung der Eigenschaften der synthetischen Dekapeptide Cetrorelix und Ozarelix durch analytische Methoden und computergestützte Modellierung. Diese Moleküle sind hydrophobe, aggregierende Antagonisten des Gonadotropin-Releasing-Hormons (GnRH). Zusätzlich wurden amyloidbildende Peptidstrukturen als Modelle für die Assoziationsprozesse in hydrophoben Peptiden untersucht und visualisiert. Die intrinsische Fluoreszenz der GnRH-Antagonisten und zusätzlich der Peptide Teverelix und D-Phe6-GnRH sowie von verkürzten Fragmenten des Cetrorelix wurde untersucht. Ein Strukturmodell für die Beschreibung der Aggregation der Dekapeptide wurde erarbeitet. Der Aufbau eines Rechenclusters durch das Einbinden der Computer am Lehrstuhl in ein Linux-System zur Verteilung von Rechenprozessen über das Netzwerk ermöglichte die Bereitstellung der notwendigen Leistung zur Realisierung der Berechnungen. Es wurden Werkzeuge zur Modellierung der solvatisierten Aggregate von Peptiden ohne eindeutige Vorzugsstruktur programmiert und in ein Docking-System für beliebige Moleküle eingebunden. Verwendet wurde das Kraftfeld MMFF94 mit einer Erweiterung durch ein Verfahren zur dynamischen Berechnung von Partialladungen in Molekülstrukturen. Solvatisierte Aggregate der Dekapeptide und von bekannten amyloidbildenden Strukturen wurden modelliert (Docking). Berechnet wurden als aggregierend beschriebene Sequenzen und entsprechende Vergleichsstrukturen des Calcitonins, des Insel-Amyloid-Polypeptides, des beta2-Mikroglobulins, des Amyloid-beta-Proteins, des Lactoferrins und weitere Modellpeptide. Die wesentlichen Wechselwirkungen während der Aggregation konnten schließlich anhand von Dynamik-Simulationen der faltblattartigen Dimere des Cetrorelix und Ozarelix beschrieben werden. So wurden die Prozesse der hydrophoben Assoziation und Stabilisierung durch Wasserstoffbrücken von Peptiden veranschaulicht und auf molekularer Ebene erfolgreich analysiert. Die Visualisierung der erhaltenen Modellierungsergebnisse erfolgt durch die Darstellung der Strukturen und Dynamik-Simulationen als interaktive 3D-Modelle in einem für diese Arbeit aufgebauten Internetauftritt. / This work discusses the analysis of the aggregation properties of the gonadotropin releasing hormone antagonists Cetrorelix, Teverelix, Ozarelix and of small amyloid forming model peptides by analytical fluorescence spectroscopy and molecular modelling. A high performance linux compute cluster was developed for calculation of molecular structures. Solvated aggregate clusters of peptides without defined secondary structure were modelled by molecular mechanics methods (forcefield mmff94) in combination with an advanced charge equilibration and docking technique. Molecular dynamics of solvated peptide dimers were implemented and the role of hydrophic association and hydrogen bond formation in hydrophobic peptide aggregates was explained. Finally, an aggregation model for the directed association of hydrophobic peptides is presented. The modelling results, 3d structures and dynamic simulations are visualized in an interactive web material. Aggregation GnRH-Antagonist Peptidaggregation Gonadotropin-Releasinghormon-Analoga Cetrorelix Ozarelix Teverelix Fluoreszenzspektroskopie LINUX-Rechencluster Molekulardynamik Molekülmechanik Docking Solvatation Kraftfeldberechnung MMFF94 Naphthylalanin Tyrosin Tyrosinat Hydropathie Wasserstoffbrücken aggregation peptide aggregation peptide docking fluorescence spectroscopy amyloid molecular modelling molecular dynamics force field hydrogen bonds hydropathy hydrophobicity hydrophobic effect secondary structure beta sheet compute cluster ddc:540 rvk:WD 5250 Aggregation Aggregat <Chemie> Assoziation <Chemie> Gonadotropin-Releasinghormon-Analoga Cetrorelix Fluoreszenzspektroskopie Amyloid Gel Aminosäuren Nichtproteinogene Aminosäuren Peptide Strukturanalyse Faltblatt Sekundärstruktur Wasserstoffbrückenbindung Hydrophobe Wechselwirkung Hydrophobie Van-der-Waals-Kraft Aromaten LINUX Cluster <Rechnernetz> Hochleistungsrechnen Kraftfeld Kraftfeld-Rechnung Modellierung Molekulardesign Molekulardynamik Docking <Chemie> Solvatation Tyrosin Calcitonin Amylin Mikroglobulin <beta-2-> Amyloid <beta-> Amyloid-Peptid <beta-> Lactoferrin Visualisierung VRML Virtualisierung Visuelle Wahrnehmung
76	Modellierung und Visualisierung von Systemen zur Beschreibung der intra- und intermolekularen Wechselwirkungen in hydrophoben Peptiden Schneider, Alexander 08 October 2014 (has links) Die vorliegende Arbeit beschäftigt sich mit der Untersuchung und Beschreibung der Eigenschaften der synthetischen Dekapeptide Cetrorelix und Ozarelix durch analytische Methoden und computergestützte Modellierung. Diese Moleküle sind hydrophobe, aggregierende Antagonisten des Gonadotropin-Releasing-Hormons (GnRH). Zusätzlich wurden amyloidbildende Peptidstrukturen als Modelle für die Assoziationsprozesse in hydrophoben Peptiden untersucht und visualisiert. Die intrinsische Fluoreszenz der GnRH-Antagonisten und zusätzlich der Peptide Teverelix und D-Phe6-GnRH sowie von verkürzten Fragmenten des Cetrorelix wurde untersucht. Ein Strukturmodell für die Beschreibung der Aggregation der Dekapeptide wurde erarbeitet. Der Aufbau eines Rechenclusters durch das Einbinden der Computer am Lehrstuhl in ein Linux-System zur Verteilung von Rechenprozessen über das Netzwerk ermöglichte die Bereitstellung der notwendigen Leistung zur Realisierung der Berechnungen. Es wurden Werkzeuge zur Modellierung der solvatisierten Aggregate von Peptiden ohne eindeutige Vorzugsstruktur programmiert und in ein Docking-System für beliebige Moleküle eingebunden. Verwendet wurde das Kraftfeld MMFF94 mit einer Erweiterung durch ein Verfahren zur dynamischen Berechnung von Partialladungen in Molekülstrukturen. Solvatisierte Aggregate der Dekapeptide und von bekannten amyloidbildenden Strukturen wurden modelliert (Docking). Berechnet wurden als aggregierend beschriebene Sequenzen und entsprechende Vergleichsstrukturen des Calcitonins, des Insel-Amyloid-Polypeptides, des beta2-Mikroglobulins, des Amyloid-beta-Proteins, des Lactoferrins und weitere Modellpeptide. Die wesentlichen Wechselwirkungen während der Aggregation konnten schließlich anhand von Dynamik-Simulationen der faltblattartigen Dimere des Cetrorelix und Ozarelix beschrieben werden. So wurden die Prozesse der hydrophoben Assoziation und Stabilisierung durch Wasserstoffbrücken von Peptiden veranschaulicht und auf molekularer Ebene erfolgreich analysiert. Die Visualisierung der erhaltenen Modellierungsergebnisse erfolgt durch die Darstellung der Strukturen und Dynamik-Simulationen als interaktive 3D-Modelle in einem für diese Arbeit aufgebauten Internetauftritt. / This work discusses the analysis of the aggregation properties of the gonadotropin releasing hormone antagonists Cetrorelix, Teverelix, Ozarelix and of small amyloid forming model peptides by analytical fluorescence spectroscopy and molecular modelling. A high performance linux compute cluster was developed for calculation of molecular structures. Solvated aggregate clusters of peptides without defined secondary structure were modelled by molecular mechanics methods (forcefield mmff94) in combination with an advanced charge equilibration and docking technique. Molecular dynamics of solvated peptide dimers were implemented and the role of hydrophic association and hydrogen bond formation in hydrophobic peptide aggregates was explained. Finally, an aggregation model for the directed association of hydrophobic peptides is presented. The modelling results, 3d structures and dynamic simulations are visualized in an interactive web material. info:eu-repo/classification/ddc/540 ddc:540

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