Effects of an exercise program on blood pressure in patients with treated hypertension and chronic Chagas' heart disease

INTRODUCTION: Previous studies describe an imbalance of the autonomic nervous system in Chagas' disease causing increased sympathetic activity, which could influence the genesis of hypertension. However, patients undergoing regular physical exercise could counteract this condition, considering that exercise causes physiological responses through autonomic and hemodynamic changes that positively affect the cardiovascular system. This study aimed to evaluate the effects of an exercise program on blood pressure in hypertensive patients with chronic Chagas' heart disease. METHODS: We recruited 17 patients to a 24-week regular exercise program and used ambulatory blood pressure monitoring before and after training. We determined the differences in the systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) from the beginning to the end of the study. RESULTS: The blood pressures were evaluated in general and during periods of wakefulness and sleep, respectively: SBP (p = 0.34; 0.23; 0.85), DBP (p = 0.46; 0.44; 0.94) and MBP (p = 0.41; 0.30; 0.97). CONCLUSIONS: There was no statistically significant change in blood pressure after the 24-week exercise program; however, we concluded that physical training is safe for patients with chronic Chagas' disease, with no incidence of increase in blood pressure.

Modeling and forecasting value-at-risk for the Portuguese stock market

The aim of this work project is to find a model that is able to accurately forecast the daily Value-at-Risk for PSI-20 Index, independently of the market conditions, in order to expand empirical literature for the Portuguese stock market. Hence, two subsamples, representing more and less volatile periods, were modeled through unconditional and conditional volatility models (because it is what drives returns). All models were evaluated through Kupiec’s and Christoffersen’s tests, by comparing forecasts with actual results. Using an out-of-sample of 204 observations, it was found that a GARCH(1,1) is an accurate model for our purposes.

Modeling and optimization of extracellular polysaccharides production by Enterobacter A47

Polysaccharides are gaining increasing attention as potential environmental friendly and sustainable building blocks in many fields of the (bio)chemical industry. The microbial production of polysaccharides is envisioned as a promising path, since higher biomass growth rates are possible and therefore higher productivities may be achieved compared to vegetable or animal polysaccharides sources. This Ph.D. thesis focuses on the modeling and optimization of a particular microbial polysaccharide, namely the production of extracellular polysaccharides (EPS) by the bacterial strain Enterobacter A47. Enterobacter A47 was found to be a metabolically versatile organism in terms of its adaptability to complex media, notably capable of achieving high growth rates in media containing glycerol byproduct from the biodiesel industry. However, the industrial implementation of this production process is still hampered due to a largely unoptimized process. Kinetic rates from the bioreactor operation are heavily dependent on operational parameters such as temperature, pH, stirring and aeration rate. The increase of culture broth viscosity is a common feature of this culture and has a major impact on the overall performance. This fact complicates the mathematical modeling of the process, limiting the possibility to understand, control and optimize productivity. In order to tackle this difficulty, data-driven mathematical methodologies such as Artificial Neural Networks can be employed to incorporate additional process data to complement the known mathematical description of the fermentation kinetics. In this Ph.D. thesis, we have adopted such an hybrid modeling framework that enabled the incorporation of temperature, pH and viscosity effects on the fermentation kinetics in order to improve the dynamical modeling and optimization of the process. A model-based optimization method was implemented that enabled to design bioreactor optimal control strategies in the sense of EPS productivity maximization. It is also critical to understand EPS synthesis at the level of the bacterial metabolism, since the production of EPS is a tightly regulated process. Methods of pathway analysis provide a means to unravel the fundamental pathways and their controls in bioprocesses. In the present Ph.D. thesis, a novel methodology called Principal Elementary Mode Analysis (PEMA) was developed and implemented that enabled to identify which cellular fluxes are activated under different conditions of temperature and pH. It is shown that differences in these two parameters affect the chemical composition of EPS, hence they are critical for the regulation of the product synthesis. In future studies, the knowledge provided by PEMA could foster the development of metabolically meaningful control strategies that target the EPS sugar content and oder product quality parameters.

Numerical modeling and assessment of the thermal environment in an industrial premise of automotive components

Dissertação de mestrado em Engenharia Industrial

Development of a thermoelectric generator for the exhaust of a vehicle

Dissertação de mestrado integrado em Engenharia Mecânica

Lowering Pulmonary Wedge Pressure after Heart Transplant: Pulmonary Compliance and Resistance Effect

AbstractBackground:Right ventricular (RV) afterload is an important risk factor for post-heart transplantation (HTx) mortality, and it results from the interaction between pulmonary vascular resistance (PVR) and pulmonary compliance (CPA). Their product, the RC time, is believed to be constant. An exception is observed in pulmonary hypertension because of elevated left ventricular (LV) filling pressures.Objective:Using HTx as a model for chronic lowering of LV filling pressures, our aim was to assess the variations in RV afterload components after transplantation.Methods:We retrospectively studied 159 patients with right heart catheterization before and after HTx. The effect of Htx on hemodynamic variables was assessed.Results:Most of the patients were male (76%), and the mean age was 53 ± 12 years. HTx had a significant effect on the hemodynamics, with normalization of the LV and RV filling pressures and a significant increase in cardiac output and heart rate (HR). The PVR decreased by 56% and CPA increased by 86%. The RC time did not change significantly, instead of increasing secondary to pulmonary wedge pressure (PWP) normalization after HTx as expected. The expected increase in RC time with PWP lowering was offset by the increase in HR (because of autonomic denervation of the heart). This effect was independent from the decrease of PWP.Conclusion:The RC time remained unchanged after HTx, notwithstanding the fact that pulmonary capillary wedge pressure significantly decreased. An increased HR may have an important effect on RC time and RV afterload. Studying these interactions may be of value to the assessment of HTx candidates and explaining early RV failure after HTx.

Design, modeling and fabrication of radio-frequency microelectromechanical switches and coplanar filters

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010

High Blood Pressure in Adolescents of Curitiba: Prevalence and Associated Factors

Abstract Background: Arterial hypertension is a major public health problem and has increased considerably in young individuals in past years. Thus, identifying factors associated with this condition is important to guide intervention strategies in this population. Objective: To determine high blood pressure prevalence and its associated factors in adolescents. Methods: A random sample of 1,242 students enrolled in public schools of the city of Curitiba (PR) was selected. Self-administered questionnaires provided family history of hypertension, daily energy expenditure, smoking habit, daily fat intake, and socioeconomic status. Waist circumference was measured following standardized procedures, and blood pressure was measured with appropriate cuffs in 2 consecutive days to confirm high blood pressure. Relative frequency and confidence interval (95%CI) indicated high blood pressure prevalence. Bivariate and multivariate analyses assessed the association of risk factors with high blood pressure. Results: The high blood pressure prevalence was 18.2% (95%CI 15.2-21.6). Individuals whose both parents had hypertension [odds ratio (OR), 2.22; 95%CI 1.28-3.85] and those with high waist circumference (OR, 2.1; 95%CI 1.34-3.28) had higher chances to develop high blood pressure. Conclusion: Positive family history of hypertension and high waist circumference were associated with high blood pressure in adolescents. These factors are important to guide future interventions in this population.

Mathematical modeling and evolution of signal transduction pathways and networks

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2013

Design, modeling and control of an autonomous field robot for white asparagus harvesting

Magdeburg, Univ., Fak. für Maschinenbau, Diss., 2014

Ab initio modeling and molecular dynamics simulation of the alpha 1b-adrenergic receptor activation.

This work describes the ab initio procedure employed to build an activation model for the alpha 1b-adrenergic receptor (alpha 1b-AR). The first version of the model was progressively modified and complicated by means of a many-step iterative procedure characterized by the employment of experimental validations of the model in each upgrading step. A combined simulated (molecular dynamics) and experimental mutagenesis approach was used to determine the structural and dynamic features characterizing the inactive and active states of alpha 1b-AR. The latest version of the model has been successfully challenged with respect to its ability to interpret and predict the functional properties of a large number of mutants. The iterative approach employed to describe alpha 1b-AR activation in terms of molecular structure and dynamics allows further complications of the model to allow prediction and interpretation of an ever-increasing number of experimental data.

3-Regime symmetric STAR modeling and exchange rate reversion

The breakdown of the Bretton Woods system and the adoption of generalized oating exchange rates ushered in a new era of exchange rate volatility and uncer- tainty. This increased volatility lead economists to search for economic models able to describe observed exchange rate behavior. In the present paper we propose more general STAR transition functions which encompass both threshold nonlinearity and asymmetric e¤ects. Our framework allows for a gradual adjustment from one regime to another, and considers threshold e¤ects by encompassing other existing models, such as TAR models. We apply our methodology to three di¤erent exchange rate data-sets, one for developing countries, and o¢ cial nominal exchange rates, the sec- ond emerging market economies using black market exchange rates and the third for OECD economies.

Technical Appendix-3-Regime asymmetric STAR modeling and exchange rate reversion

The breakdown of the Bretton Woods system and the adoption of generalized oating exchange rates ushered in a new era of exchange rate volatility and uncer- tainty. This increased volatility lead economists to search for economic models able to describe observed exchange rate behavior. The present is a technical Appendix to Cerrato et al. (2009) and presents detailed simulations of the proposed methodology and additional empirical results.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.