Cylindrically symmetric models of gravitational collapse to black holes: a short review

We survey results about exact cylindrically symmetric models of gravitational collapse in General Relativity. We focus on models which result from the matching of two spacetimes having collapsing interiors which develop trapped surfaces and vacuum exteriors containing gravitational waves. We collect some theorems from the literature which help to decide a priori about eventual spacetime matchings. We revise, in more detail, some toy models which include some of the main mathematical and physical issues that arise in this context, and compute the gravitational energy flux through the matching boundary of a particular collapsing region. Along the way, we point out several interesting open problems.

Specification and testing of multiplicative time-varying GARCH models with applications

In this article, we develop a specification technique for building multiplicative time-varying GARCH models of Amado and Teräsvirta (2008, 2013). The variance is decomposed into an unconditional and a conditional component such that the unconditional variance component is allowed to evolve smoothly over time. This nonstationary component is defined as a linear combination of logistic transition functions with time as the transition variable. The appropriate number of transition functions is determined by a sequence of specification tests. For that purpose, a coherent modelling strategy based on statistical inference is presented. It is heavily dependent on Lagrange multiplier type misspecification tests. The tests are easily implemented as they are entirely based on auxiliary regressions. Finite-sample properties of the strategy and tests are examined by simulation. The modelling strategy is illustrated in practice with two real examples: an empirical application to daily exchange rate returns and another one to daily coffee futures returns.

Exploring the role of O-Mannosylation in the modulation of E-Cadherin function in Gastric Cancer cell line models

Dissertação de mestrado em Bioquímica Aplicada – Biomedicina

Evaluating biomaterial-and microfluidic-based 3D tumor models

Cancer is a major cause of morbidity and mortality worldwide, with a disease burden estimated to increase in the coming decades. Disease heterogeneity and limited information on cancer biology and disease mechanisms are aspects that 2D cell cultures fail to address. We review the current "state-of-the-art" in 3D Tissue Engineering (TE) models developed for and used in cancer research. Scaffold-based TE models and microfluidics, are assessed for their potential to fill the gap between 2D models and clinical application. Recent advances in combining the principles of 3D TE models and microfluidics are discussed, with a special focus on biomaterials and the most promising chip-based 3D models.

Optimization of filling systems for low pressure by Flow 3D

Dissertação de mestrado integrado em Engenharia Mecânica

Single line for assembly just-in-sequence multiple models

Programa Doutoral em Líderes para as Indústrias Tecnológicas

“PhenoWorld”: a new multidimensional strategy for studying behaviour in rodents

Tese de Doutoramento em Ciências da Saúde

A bio-inspired computational model for motion detection

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)

Comorbidities of mental and behavioral disorders in chemically dependent patients in different periods of abstinence

OBJECTIVE: To assess the frequency of comorbidities of mental and behavioral disorders (CMBD) in psychoactive substance (PAS)-dependent patients with different periods of abstinence cared for at Alcohol and Other Drug Psychosocial Care Centers (CAPS-ad). METHOD: All patients under treatment in the two CAPS-ad of the city of Uberlândia-MG, between April and September 2010, were consecutively assessed. The ICD-10 symptom checklist was used to diagnose CMBD; additional information was obtained from interviews and medical records. The patients were divided according to duration of abstinence: < 1 week (Group 1); 1-4 weeks (Group 2); and > 4 weeks (Group 3). RESULTS: Of all patients assessed, 62.8% were diagnosed with CMBD, which were more frequent (p < 0.05) in Group 1 (72%) than Group 3 (54.2%), and both groups were similar to Group 2 (61%). Depressive and anxiety disorders were more frequent among patients of Group 1. Mood disorders were more frequent (p < 0.05) in women [22/34 (65%) vs. 54/154 (35.1%)], whereas psychotic disorders were more frequent (p = 0.05) in men [16/154 (10.4%) vs. 0]. The presence of CMBD was associated with more severe clinical conditions. CONCLUSIONS: The higher frequency of diagnosis of CMBD in patients of Group 1 may have resulted from the difficulties in distinguishing mental disorders that are due to PAS intoxication or withdrawal from those that are not. However, to make the diagnosis of CMBD, even during detoxification, can increase the likelihood of better response to treatment.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Stress-triggered synaptic malfunction: a gate along the path from depressionto dementia

Tese de Doutoramento em Ciências da Saúde.

Cognitive-behavioral therapy for anxiety disorders in children and adolescents: a systematic review of follow-up studies

Objective To conduct a systematic review about the long-term response to cognitive-behavioral therapy (CBT) for anxiety disorders (ADs) in children and adolescents. Methods The PubMed and ISI Web of Science databases were consulted. Search in the databases was performed in November 2012 and included cohort studies after CBT for ADs in children and adolescents with a follow-up period over 12 months. Results A total of 10 papers met the inclusion criteria. The follow-up period ranged from 12 months to 13 years and the results generally showed maintenance of the short-term benefits with CBT. However, the studies presented limitations, especially regarding methods, such as lack of a control group and losses to follow-up. Conclusion The long-term benefits of CBT were identified, however it would be interesting to conduct other studies with more frequent assessment periods, in order to minimize losses to follow-up, in addition to evaluating children and adolescents in the various stages of their development.

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.