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

Summary of the ATLAS experiment's sensitivity to supersymmetry after LHC Run 1-interpreted in the phenomenological MSSM

A summary of the constraints from the ATLAS experiment on R-parity-conserving supersymmetry is presented. Results from 22 separate ATLAS searches are considered, each based on analysis of up to 20.3 fb−1 of proton-proton collision data at centre-of-mass energies of s√=7 and 8 TeV at the Large Hadron Collider. The results are interpreted in the context of the 19-parameter phenomenological minimal supersymmetric standard model, in which the lightest supersymmetric particle is a neutralino, taking into account constraints from previous precision electroweak and flavour measurements as well as from dark matter related measurements. The results are presented in terms of constraints on supersymmetric particle masses and are compared to limits from simplified models. The impact of ATLAS searches on parameters such as the dark matter relic density, the couplings of the observed Higgs boson, and the degree of electroweak fine-tuning is also shown. Spectra for surviving supersymmetry model points with low fine-tunings are presented.

Z boson production in p plus Pb collisions at root S-NN=5.02 TeV measured with the ATLAS detector

The ATLAS Collaboration measures the inclusive production of Z bosons via their decays into electron and muon pairs in p+Pb collisions at sNN−−−√=5.02TeV at the Large Hadron Collider. The measurements are made using data corresponding to integrated luminosities of 29.4 and 28.1 nb−1 for Z→ee and Z→μμ, respectively. The results from the two channels are consistent and combined to obtain a cross section times the Z→ℓℓ branching ratio, integrated over the rapidity region ∣∣y∗Z|<3.5, of 139.8±4.8(statistical)±6.2(systematic)±3.8 (luminosity) nb. Differential cross sections are presented as functions of the Z boson rapidity and transverse momentum and compared with models based on parton distributions both with and without nuclear corrections. The centrality dependence of Z boson production in p+Pb collisions is measured and analyzed within the framework of a standard Glauber model and the model's extension for fluctuations of the underlying nucleon-nucleon scattering cross section.

Measurements of the top quark branching ratios into channels with leptons and quarks with the ATLAS detector

A measurement of the inclusive cross section for top quark pair production in pp collisions using events with an isolated lepton (muon or electron) and a τ lepton decaying to hadrons (τhad) is reported. Measurements of the branching ratios of top quark decays into leptons and jets using events with tt¯ (top antitop) pairs are also reported. Events were recorded with the ATLAS detector at the LHC in pp collisions at a center-of-mass energy of 7 TeV. The collected data sample corresponds to an integrated luminosity of 4.6 fb−1. The inclusive cross section measured using events with an isolated lepton and a τhad is σtt¯=183±9(stat.)±23(syst.)±3(lumi.)pb. The measured top quark branching ratios agree with the Standard Model predictions within the measurement uncertainties of a few percent.

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.

Magnetoliposomes based on manganese ferrite nanoparticles as nanocarriers for antitumor drugs

Manganese ferrite nanoparticles with a size distribution of 26 ± 7 nm (from TEM measurements) were synthesized by the coprecipitation method. The obtained nanoparticles exhibit a superparamagnetic behaviour at room temperature with a magnetic squareness of 0.016 and a coercivity field of 6.3 Oe. These nanoparticles were either entrapped in liposomes (aqueous magnetoliposomes, AMLs) or covered with a lipid bilayer, forming solid magnetoliposomes (SMLs). Both types of magnetoliposomes, exhibiting sizes below or around 150 nm, were found to be suitable for biomedical applications. Membrane fusion between magnetoliposomes (both AMLS and SMLs) and GUVs (giant unilamellar vesicles), the latter used as models of cell membranes, was confirmed by F¨orster Resonance Energy Transfer (FRET) assays, using a NBD labeled lipid as the energy donor and Nile Red or rhodamine B-DOPE as the energy acceptor. A potential antitumor thienopyridine derivative was successfully incorporated into both aqueous and solid magnetoliposomes, pointing to a promising application of these systems in oncological therapy, simultaneously as hyperthermia agents and nanocarriers for antitumor drugs.

Single line for assembly just-in-sequence multiple models

Programa Doutoral em Líderes para as Indústrias Tecnológicas

Biomaterials for cartilage tissue engineering under mechanical stimulus

Tese de Doutoramento em Ciências (Especialidade de Física)

Nanocomposites: the contribution of multiscale modelling to improve the dispersion levels

Tese de Doutoramento em Ciência e Engenharia de Polímeros e Compósitos

O ajustamento psicossocial de jovens com consumos de substâncias psicoativas em escolas profissionais do grande Porto

Tese de Doutoramento em Estudos da Criança (Área de Especialidade Psicologia do Desenvolvimento e Educação)

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.

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.