An interesting discovery in biology is that most genes in an organism are dispensable. That means these genes have minor effects on survival of the organism in standard laboratory conditions. One explanation of this discovery is that some genes play important roles in specific conditions and are essential genes under those conditions. E. coli is a model organism, which is widely used. It can adapt to many stress conditions, including temperature, pH, osmotic, antibiotic, etc. Underlying mechanisms and associated genes of each stress condition responses are usually different. In our analysis, we combined protein abundance data and mutant conditional fitness data into E. coli constraint-based metabolic models to study conditionally essential metabolic genes under temperature and pH stress conditions. Flux Balance Analysis was employed as the modeling method to analysis these data. We discovered lists of metabolic genes, which are E. coli dispensable genes, but conditionally essential under some stress conditions. Among these conditionally essential genes, atpA in low pH stress and nhaA in high pH stress found experimental evidences from previous studies. Our study provides new conditionally essential gene candidates for biologists to explore stress condition mechanisms.

A Cronobiologia estuda a origem e a manifestação de ritmos biológicos nos mais diversos táxons. A análise dos dados obtidos experimentalmente, contudo, é bastante complexa, haja vista a restrita gama de métodos disponíveis para tal. Os osciladores que determinam a existência dos ritmos biológicos são exemplos de sistemas dinâmicos não-lineares, os quais estão amplamente distribuídos nos seres vivos. Esses sistemas, por suas peculiaridades, são melhor analisados por métodos não-lineares. O objetivo deste trabalho é testar diferentes métodos de análise séries temporais, tanto alguns classicamente empregados na Cronobiologia quanto métodos não-lineares, verificando sua empregabilidade e funcionalidade para dados cronobiológicos, bem como as propriedades que permitem ou não seu uso. Para tanto, foram utilizados dados de ritmos de consumo de O2 obtidos para diferentes grupos etários de operárias de Melipona quadrifasciata (Hymenoptera, Meliponini). Tais dados foram submetidos às seguintes análises: Transformada Rápida de Fourier (TRF), Análise de Potência Espectral (PSA), Estatística Circular (teste de Rayleigh), Teste de Estacionariedade (Teste da Raiz Unitária de Dickey-Fuller Aumentado - ADF), Plot de Poincaré, Entropia Aproximada (ApEn) e Entropia Aproximada Volumétrica (vApEn). Os resultados obtidos demonstram que os métodos não-lineares de análise detectam a existência de ritmo metabólico, assim como os métodos lineares utilizados por Teixeira (2006) e Camargo (em andamento) indicam sua presença. Ressalta-se, contudo, que há certa dificuldade em distinguir se a variabilidade existente nas séries deve-se, possivelmente, a seu comportamento determinístico ou a ruídos externos a elas, cuja detecção não é possível. Outro importante fator limitante da aplicabilidade das análises não-lineares é o número de pontos das séries temporais em questão, que era bastante reduzido nesse estudo. Dessa maneira, conclui-se que alguns métodos não-lineares, e.g. TRF, também são eficazes na detecção de ritmos biológicos, devendo ser observada a restrição devido ao tamanho da série temporal. / Chronobiology is the study of the origin and manifestation of rhythmical phenomena in all sorts of taxons. The analysis of the experimental obtained data is, however, still very complex due to the lack of availability of methods and techniques. The oscillators that determine the existence of rhythmical biological phenomena are examples of non-linear dynamic systems, which are widely spread among organisms. This research intends to present the result of a sort of temporal series analysis methods, some already used for Chronobiology research, as non-linear methods, testing their use and functionality for chronobiological data as well as determining their gaps and limitations for this purpose. For this research, data regarding to rhythm of O2 consumption were obtained from different age groups of Melipona quadrifasciata (Hymenoptera, Meliponini) workers. Such data were submitted to the following analysis: Fast Fourier Transform (FFT), Power Spectrum Analysis (PSA), Circular Statistics (Rayleigh\'s test), Stationarity Test (Unit Root Augmented Dickey-Fuller Test - ADF), Poincaré Plot, Approximate Entropy (ApEn) e Volumetric Approximate Entropy (vApEn). The results indicate that nonlinear methods detect the presence of metabolic rhythm, such as previous researches where linear methods used by Teixeira (2006) and Camargo (ongoing research) indicate its presence. However, there is some difficulty in determining if the variability present in the series is possibly due to its deterministic behavior or to the external noise, from which the determination is still not possible. Another limiting factor is the number of points of analyzed temporal series, which was very small. We conclude that some nonlinear methods, e.g. FFT, are effective to detect biological rhythms, but the constraint to time series length must be observed.

Current monitoring tools in the intensive care units focus on displaying physiologically monitored parameters (e.g. vital signs such as heart rate, respiratory rate and blood pressure) at the present moment. Added clinical utility can be found by analyzing how the conditions of a patient evolve with time, and automatically relating that dynamics to population trends. Variability analysis consists of monitoring patterns of variation over intervals in time of physiological signals such as heart rate and respiratory rate. Given that illness has been associated in multiple studies with altered variability, most commonly lack of variation, variability monitoring represents a tool whose contribution at the bedside still needs to be explored. With the long term objective of improving care, this thesis promotes the use of variability analysis through three distinct types of analysis: facing the technical challenges involved with the dimensionality of variability analysis, enhancing the physiological understanding of variability, and showing its utility in real world clinical applications. In particular, the contributions of this thesis include: the review and classification into domains of a large array of measures of variability; the design of system and methods to integrate multiple measures of variability into a unique score, called composite measure, bringing relevant information to specific clinical problems; the comparison of patterns of heart rate variability during exercise and sepsis development, showing the inability of single measures of variability to discriminate between the two kinds of stressors; the analysis of variability produced from a physiologically-based model of the cardiovascular system, showing that each single measure of variability is an unspecific sensor of the body, thereby promoting multivariate analysis to the only means of understanding the physiology underlying variability; the study of heart rate variability in a population at high risk of sepsis development, showing the ability of variability to predict the occurrence of sepsis more than 48 hours in advance respect to the time of diagnosis of the clinical team; the study of heart and respiratory rate variability in intubated intensive care unit patients, showing how variability can provide a better way of assessing extubation readiness respect to commonly used clinical parameters. Overall, it is hoped that these novel contributions will help promoting bedside applications of variability monitoring to improve patient care.

A Cronobiologia estuda a origem e a manifestação de ritmos biológicos nos mais diversos táxons. A análise dos dados obtidos experimentalmente, contudo, é bastante complexa, haja vista a restrita gama de métodos disponíveis para tal. Os osciladores que determinam a existência dos ritmos biológicos são exemplos de sistemas dinâmicos não-lineares, os quais estão amplamente distribuídos nos seres vivos. Esses sistemas, por suas peculiaridades, são melhor analisados por métodos não-lineares. O objetivo deste trabalho é testar diferentes métodos de análise séries temporais, tanto alguns classicamente empregados na Cronobiologia quanto métodos não-lineares, verificando sua empregabilidade e funcionalidade para dados cronobiológicos, bem como as propriedades que permitem ou não seu uso. Para tanto, foram utilizados dados de ritmos de consumo de O2 obtidos para diferentes grupos etários de operárias de Melipona quadrifasciata (Hymenoptera, Meliponini). Tais dados foram submetidos às seguintes análises: Transformada Rápida de Fourier (TRF), Análise de Potência Espectral (PSA), Estatística Circular (teste de Rayleigh), Teste de Estacionariedade (Teste da Raiz Unitária de Dickey-Fuller Aumentado - ADF), Plot de Poincaré, Entropia Aproximada (ApEn) e Entropia Aproximada Volumétrica (vApEn). Os resultados obtidos demonstram que os métodos não-lineares de análise detectam a existência de ritmo metabólico, assim como os métodos lineares utilizados por Teixeira (2006) e Camargo (em andamento) indicam sua presença. Ressalta-se, contudo, que há certa dificuldade em distinguir se a variabilidade existente nas séries deve-se, possivelmente, a seu comportamento determinístico ou a ruídos externos a elas, cuja detecção não é possível. Outro importante fator limitante da aplicabilidade das análises não-lineares é o número de pontos das séries temporais em questão, que era bastante reduzido nesse estudo. Dessa maneira, conclui-se que alguns métodos não-lineares, e.g. TRF, também são eficazes na detecção de ritmos biológicos, devendo ser observada a restrição devido ao tamanho da série temporal. / Chronobiology is the study of the origin and manifestation of rhythmical phenomena in all sorts of taxons. The analysis of the experimental obtained data is, however, still very complex due to the lack of availability of methods and techniques. The oscillators that determine the existence of rhythmical biological phenomena are examples of non-linear dynamic systems, which are widely spread among organisms. This research intends to present the result of a sort of temporal series analysis methods, some already used for Chronobiology research, as non-linear methods, testing their use and functionality for chronobiological data as well as determining their gaps and limitations for this purpose. For this research, data regarding to rhythm of O2 consumption were obtained from different age groups of Melipona quadrifasciata (Hymenoptera, Meliponini) workers. Such data were submitted to the following analysis: Fast Fourier Transform (FFT), Power Spectrum Analysis (PSA), Circular Statistics (Rayleigh\'s test), Stationarity Test (Unit Root Augmented Dickey-Fuller Test - ADF), Poincaré Plot, Approximate Entropy (ApEn) e Volumetric Approximate Entropy (vApEn). The results indicate that nonlinear methods detect the presence of metabolic rhythm, such as previous researches where linear methods used by Teixeira (2006) and Camargo (ongoing research) indicate its presence. However, there is some difficulty in determining if the variability present in the series is possibly due to its deterministic behavior or to the external noise, from which the determination is still not possible. Another limiting factor is the number of points of analyzed temporal series, which was very small. We conclude that some nonlinear methods, e.g. FFT, are effective to detect biological rhythms, but the constraint to time series length must be observed.

One of the major tasks in electronic circuit design is the ability to predict the performance of general circuits in the presence of uncertainty in key design parameters. In the mathematical literature, such a task is referred to as uncertainty quantification. Uncertainty about the key design parameters arises mainly from the difficulty of controlling the physical or geometrical features of the underlying design, especially at the nanometer level. With the constant trend to scale down the process feature size, uncertainty quantification becomes crucial in shortening the design time. To achieve the uncertainty quantification, this thesis presents a new approach based on the concept of generalized Polynomial Chaos (gPC) to perform variability analysis of general nonlinear circuits. The proposed approach is built upon a decoupling formulation of the Galerkin projection (GP) technique, where the large matrix is transformed into a block-diagonal whose diagonal blocks can be factorized independently. The proposed methodology provides a general framework for decoupling the GP formulation based on a general system of orthogonal polynomials. Moreover, it provides a new insight into the error level that is caused by the decoupling procedure, enabling an assessment of the performance of a wide variety of orthogonal polynomials. For example, it is shown that, for the same order, the Chebyshev polynomials outperforms other commonly used gPC polynomials.

Current monitoring tools in the intensive care units focus on displaying physiologically monitored parameters (e.g. vital signs such as heart rate, respiratory rate and blood pressure) at the present moment. Added clinical utility can be found by analyzing how the conditions of a patient evolve with time, and automatically relating that dynamics to population trends. Variability analysis consists of monitoring patterns of variation over intervals in time of physiological signals such as heart rate and respiratory rate. Given that illness has been associated in multiple studies with altered variability, most commonly lack of variation, variability monitoring represents a tool whose contribution at the bedside still needs to be explored. With the long term objective of improving care, this thesis promotes the use of variability analysis through three distinct types of analysis: facing the technical challenges involved with the dimensionality of variability analysis, enhancing the physiological understanding of variability, and showing its utility in real world clinical applications. In particular, the contributions of this thesis include: the review and classification into domains of a large array of measures of variability; the design of system and methods to integrate multiple measures of variability into a unique score, called composite measure, bringing relevant information to specific clinical problems; the comparison of patterns of heart rate variability during exercise and sepsis development, showing the inability of single measures of variability to discriminate between the two kinds of stressors; the analysis of variability produced from a physiologically-based model of the cardiovascular system, showing that each single measure of variability is an unspecific sensor of the body, thereby promoting multivariate analysis to the only means of understanding the physiology underlying variability; the study of heart rate variability in a population at high risk of sepsis development, showing the ability of variability to predict the occurrence of sepsis more than 48 hours in advance respect to the time of diagnosis of the clinical team; the study of heart and respiratory rate variability in intubated intensive care unit patients, showing how variability can provide a better way of assessing extubation readiness respect to commonly used clinical parameters. Overall, it is hoped that these novel contributions will help promoting bedside applications of variability monitoring to improve patient care.

No description available.

No description available.

The handling of variability effects in structural models is a natural and necessary extension of deterministic analysis techniques. In the context of finite element and uncertainty modelling, the stochastic finite element method (SFEM), grouping the perturbation SFEM, the spectral SFEM and the Monte-Carlo simulation, has by far received the major attention. <br> The present work focuses on second moment approaches, in which the first two statistical moments of the structural response are estimated. Due to its efficiency for handling problems involving low variability levels, the perturbation method is selected for characterising the propagation of the parameter variability from an uncertain dynamic model to its structural response. A dynamic model excited by a time-harmonic loading is postulated and the extension of the perturbation SFEM to the frequency domain is provided. This method complements the deterministic analysis by a sensitivity analysis of the system response with respect to a finite set of random parameters and a response surface in terms of a Taylor series expansion truncated to the first or second order is built. Taking into account the second moment statistical data of the random design properties, the response sensitivities are appropriately condensed in order to obtain an estimation of the response mean value and covariance structure. <br> In order to handle a wide definition of variability, a computational tool is made available that is able to deal with material variability sources (material random variables and fields) as well as shape uncertainty sources. This second case requires an appropriate shape parameterisation and a shape design sensitivity analysis. The computational requirements of the tool are studied and optimised, by reducing the size of the random dimension of the problem and by improving the performances of the underlying deterministic analyses. In this context, modal approaches, which are known to provide efficient alternatives to direct approaches in frequency domain analyses, are developed. An efficient hybrid procedure, coupling the perturbation and the Monte-Carlo simulation SFEM, is proposed and analysed. <br> Finally, the developed methods are validated, by resorting mainly to the Monte-Carlo simulation technique, on different numerical applications: a cantilever beam structure, a plate bending problem (involving a 3-dimensional model), an articulated truss structure and a problem involving a plate with a random flatness default. The propagation of the model uncertainty in the response FRFs and the effects involved by random field modelling are examined. Some remarks are stated pertaining to the influence of the parameter PDF in simulation-based methods. <br> <br> La gestion de la variabilité présente dans les modèles structuraux est une extension naturelle et nécessaire des techniques de calcul déterministes. En incorporant la modélisation de l'incertitude dans le calcul aux éléments finis, la méthode des éléments finis stochastiques (groupant l'approche perturbative, l'approche spectrale et la technique de simulation Monte-Carlo) a reçu une large attention de la littérature scientifique. <br> Ce travail est orienté sur les approches dites de second moment, dans lesquelles les deux premiers moments statistiques de la réponse de la structure sont estimés. De par son aptitude à traiter des problèmes caractérisés par de faibles niveaux de variabilité, la méthode perturbative est choisie pour propager la variabilité des paramètres d'un modèle dynamique incertain sur sa réponse. Un modèle sous chargement dynamique harmonique est supposé et l'extension dans le domaine fréquentiel de l'approche perturbative est établie. Cette méthode complète l'analyse déterministe par une analyse de sensibilité de la réponse du système par rapport à un ensemble fini de variables aléatoires. Une surface de réponse en termes d'un développement de Taylor tronqué au premier ou second ordre peut alors être écrit. Les sensibilités de la réponse sont enfin condensées, en tenant compte des propriétés statistiques des paramètres de design aléatoires, pour obtenir une estimation de la valeur moyenne et de la structure de covariance de la réponse. <br> Un outil de calcul est développé avec la capacité de gestion d'une définition large de la variabilité: sources de variabilité matérielle (variables et champs aléatoires) ainsi que géométrique. Cette dernière source requiert une paramétrisation adéquate de la géométrie ainsi qu'une analyse de sensibilité à des paramètres de forme. Les exigences calcul de cet outil sont étudiées et optimisées, en réduisant la dimension aléatoire du problème et en améliorant les performances des analyses déterministes sous-jacentes. Dans ce contexte, des approches modales, fournissant une alternative efficace aux approches directes dans le domaine fréquentiel, sont dérivées. Une procédure hybride couplant la méthode perturbative et la technique de simulation Monte-Carlo est proposée et analysée. <br> Finalement, les méthodes étudiées sont validées, principalement sur base de résultats de simulations Monte-Carlo. Ces résultats sont relatifs à plusieurs applications numériques: une structure poutre-console, un problème de flexion de plaque (modèle tridimensionnel), une structure en treillis articulé et un problème de plaque présentant un défaut de planéité aléatoire. La propagation de l'incertitude du modèle dans les fonctions de réponse fréquentielle ainsi que les effets propres à la modélisation par champs aléatoires sont examinés. Quelques remarques relatives à l'influence de la loi de distribution des paramètres dans les méthodes de simulation sont évoquées.

Including the inherent temporal variability in a production system in planning and control processes can ensure the fulfillment of the production schedule and increase key performance indica- tors. This benefits the sustainable and efficient use of the system. The current lack of consideration of this inherent temporal variability in production planning leads to optimistic estimates and calcu- lations of planned values that cannot be met. To complete this information, the inherent temporal variability in a production system is investigated using a systematic approach. This approach detects, identifies, and quantifies inherent temporal variability and is applied to a data base created via an automated, event-driven procedure. The approach is tested in a smart factory laboratory. The results to date on improving production planning and control are promising as key performance indicators have been increased. There is still a need for action to ensure the fulfillment of the production schedule. Concluding, work on this topic has just begun, as can be seen from the discussion section. More data need to be collected and aggregated for future research. This publication is intended to motivate researchers to address this issue and better manage the existing uncertainty in production through the use of data.