Molecular Characterization, Distribution, and Dynamics of Hepatitis C Virus Genotypes in Blood Donors in Colombia

Hepatitis C virus (HCV) is a frequent cause of acute and chronic hepatitis and a leading cause for cirrhosis of the liver and hepatocellular carcinoma. HCV is classified in six major genotypes and more than 70 subtypes. In Colombian blood banks, serum samples were tested for anti-HCV antibodies using a third-generation ELISA. The aim of this study was to characterize the viral sequences in plasma of 184 volunteer blood donors who attended the ""Banco Nacional de Sangre de la Cruz Roja Colombiana,`` Bogota, Colombia. Three different HCV genomic regions were amplified by nested PCR. The first of these was a segment of 180 bp of the 5`UTR region to confirm the previous diagnosis by ELISA. From those that were positive to the 5`UTR region, two further segments were amplified for genotyping and subtyping by phylogenetic analysis: a segment of 380 bp from the NS5B region; and a segment of 391 bp from the E1 region. The distribution of HCV subtypes was: 1b (82.8%), 1a (5.7%), 2a (5.7%), 2b (2.8%), and 3a (2.8%). By applying Bayesian Markov chain Monte Carlo simulation, it was estimated that HCV-1b was introduced into Bogota around 1950. Also, this subtype spread at an exponential rate between about 1970 to about 1990, after which transmission of HCV was reduced by anti-HCV testing of this population. Among Colombian blood donors, HCV genotype 1b is the most frequent genotype, especially in large urban conglomerates such as Bogota, as is the case in other South American countries. J. Med. Virol. 82: 1889-1898, 2010. (C) 2010 Wiley-Liss, Inc.

Electron beam current loss at the high-vacuum-high-pressure boundary in the environmental scanning electron microscope

A significant loss in electron probe current can occur before the electron beam enters the specimen chamber of an environmental scanning electron microscope (ESEM). This loss results from electron scattering in a gaseous jet formed inside and downstream (above) the pressure-limiting aperture (PLA), which separates the high-pressure and high-vacuum regions of the microscope. The electron beam loss above the PLA has been calculated for three different ESEMs, each with a different PLA geometry: an ElectroScan E3, a Philips XL30 ESEM, and a prototype instrument. The mass thickness of gas above the PLA in each case has been determined by Monte Carlo simulation of the gas density variation in the gas jet. It has been found that the PLA configurations used in the commercial instruments produce considerable loss in the electron probe current that dramatically degrades their performance at high chamber pressure and low accelerating voltage. These detrimental effects are minimized in the prototype instrument, which has an optimized thin-foil PLA design.

Design of clinical pharmacology trials

1. There are a variety of methods that could be used to increase the efficiency of the design of experiments. However, it is only recently that such methods have been considered in the design of clinical pharmacology trials. 2. Two such methods, termed data-dependent (e.g. simulation) and data-independent (e.g. analytical evaluation of the information in a particular design), are becoming increasingly used as efficient methods for designing clinical trials. These two design methods have tended to be viewed as competitive, although a complementary role in design is proposed here. 3. The impetus for the use of these two methods has been the need for a more fully integrated approach to the drug development process that specifically allows for sequential development (i.e. where the results of early phase studies influence later-phase studies). 4. The present article briefly presents the background and theory that underpins both the data-dependent and -independent methods with the use of illustrative examples from the literature. In addition, the potential advantages and disadvantages of each method are discussed.

Close packed transitions in slit-shaped pores: Density functional theory study of methane adsorption capacity in carbon

An important feature of improving lattice gas models and classical isotherms is the incorporation of a pore size dependent capacity, which has hitherto been overlooked. In this paper, we develop a model for predicting the temperature dependent variation in capacity with pore size. The model is based on the analysis of a lattice gas model using a density functional theory approach at the close packed limit. Fluid-fluid and solid-fluid interactions are modeled by the Lennard-Jones 12-6 potential and Steele's 10-4-3, potential respectively. The capacity of methane in a slit-shaped carbon pore is calculated from the characteristic parameters of the unit cell, which are extracted by minimizing the grand potential of the unit cell. The capacities predicted by the proposed model are in good agreement with those obtained from grand canonical Monte Carlo simulation, for pores that can accommodate up to three adsorbed layers. Single particle and pair distributions exhibit characteristic features that correspond to the sequence of buckling and rhombic transitions that occur as the slit pore width is increased. The model provides a useful tool to model continuous variation in the microstructure of an adsorbed phase, namely buckling and rhombic transitions, with increasing pore width. (C) 2002 American Institute of Physics.

Analysis of adsorption data of graphitized thermal carbon black with a DFT-lattice gas theory

In this paper we analyzed the adsorption of gases and vapors on graphitised thermal carbon black by using a modified DFT-lattice theory, in which we assume that the behavior of the first layer in the adsorption film is different from those of second and higher layers. The effects of various parameters on the topology of the adsorption isotherm were first investigated, and the model was then applied in the analysis of adsorption data of numerous substances on carbon black. We have found that the first layer in the adsorption film behaves differently from the second and higher layers in such a way that the adsorbate-adsorbate interaction energy in the first layer is less than that of second and higher layers, and the same is observed for the partition function. Furthermore, the adsorbate-adsorbate and adsorbate-adsorbent interaction energies obtained from the fitting are consistently lower than the corresponding values obtained from the viscosity data and calculated from the Lorentz-Berthelot rule, respectively.

The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.

A gestão do risco associado à negociação de energia eléctrica através de contratos bilaterais e mercado spot

Num mercado de electricidade competitivo onde existe um ambiente de incerteza, as empresas de geração adoptam estratégias que visam a maximização do lucro, e a minimização do risco. Neste contexto, é de extrema importância para desenvolver uma estratégia adequada de gestão de risco ter em conta as diferentes opções de negociação de energia num mercado liberalizado, de forma a suportar a tomada de decisões na gestão de risco. O presente trabalho apresenta um modelo que avalia a melhor estratégia de um produtor de energia eléctrica que comercializa num mercado competitivo, onde existem dois mercados possíveis para a transacção de energia: o mercado organizado (bolsa) e o mercado de contratos bilaterais. O produtor tenta maximizar seus lucros e minimizar os riscos correspondentes, seleccionando o melhor equilíbrio entre os dois mercados possíveis (bolsa e bilateral). O mercado de contratos bilaterais visa gerir adequadamente os riscos inerentes à operação de mercados no curto prazo (mercado organizado) e dar o vendedor / comprador uma capacidade real de escolher o fornecedor com que quer negociar. O modelo apresentado neste trabalho faz uma caracterização explícita do risco no que diz respeito ao agente de mercado na questão da sua atitude face ao risco, medido pelo Value at Risk (VaR), descrito neste trabalho por Lucro-em-Risco (PAR). O preço e os factores de risco de volume são caracterizados por um valor médio e um desvio padrão, e são modelizados por distribuições normais. Os resultados numéricos são obtidos utilizando a simulação de Monte Carlo implementado em Matlab, e que é aplicado a um produtor que mantém uma carteira diversificada de tecnologias de geração, para um horizonte temporal de um ano. Esta dissertação está organizada da seguinte forma: o capítulo 1, 2 e 3 descrevem o estado-da-arte relacionado com a gestão de risco na comercialização de energia eléctrica. O capítulo 4 descreve o modelo desenvolvido e implementado, onde é também apresentado um estudo de caso com uma aplicação do modelo para avaliar o risco de negociação de um produtor. No capítulo 5 são apresentadas as principais conclusões.

Nonmonotonic Magnetic Susceptibility of Dipolar Hard-Spheres at Low Temperature and Density

We investigate, via numerical simulations, mean field, and density functional theories, the magnetic response of a dipolar hard sphere fluid at low temperatures and densities, in the region of strong association. The proposed parameter-free theory is able to capture both the density and temperature dependence of the ring-chain equilibrium and the contribution to the susceptibility of a chain of generic length. The theory predicts a nonmonotonic temperature dependence of the initial (zero field) magnetic susceptibility, arising from the competition between magnetically inert particle rings and magnetically active chains. Monte Carlo simulation results closely agree with the theoretical findings. DOI: 10.1103/PhysRevLett.110.148306

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Dosimetria e análise de incertezas em braquiterapia ginecológica

Mestrado em Radiações Aplicadas às tecnologias da Saúde

Uncertainty assessment approach for composite structures based on global sensitivity indices

The problem of uncertainty propagation in composite laminate structures is studied. An approach based on the optimal design of composite structures to achieve a target reliability level is proposed. Using the Uniform Design Method (UDM), a set of design points is generated over a design domain centred at mean values of random variables, aimed at studying the space variability. The most critical Tsai number, the structural reliability index and the sensitivities are obtained for each UDM design point, using the maximum load obtained from optimal design search. Using the UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on supervised evolutionary learning. Finally, using the developed ANN a Monte Carlo simulation procedure is implemented and the variability of the structural response based on global sensitivity analysis (GSA) is studied. The GSA is based on the first order Sobol indices and relative sensitivities. An appropriate GSA algorithm aiming to obtain Sobol indices is proposed. The most important sources of uncertainty are identified.

Tissue composition and density impact on the clinical parameters for 125I prostate implants dosimetry

The MCNPX code was used to calculate the TG-43U1 recommended parameters in water and prostate tissue in order to quantify the dosimetric impact in 30 patients treated with (125)I prostate implants when replacing the TG-43U1 formalism parameters calculated in water by a prostate-like medium in the planning system (PS) and to evaluate the uncertainties associated with Monte Carlo (MC) calculations. The prostate density was obtained from the CT of 100 patients with prostate cancer. The deviations between our results for water and the TG-43U1 consensus dataset values were -2.6% for prostate V100, -13.0% for V150, and -5.8% for D90; -2.0% for rectum V100, and -5.1% for D0.1; -5.0% for urethra D10, and -5.1% for D30. The same differences between our water and prostate results were all under 0.3%. Uncertainties estimations were up to 2.9% for the gL(r) function, 13.4% for the F(r,θ) function and 7.0% for Λ, mainly due to seed geometry uncertainties. Uncertainties in extracting the TG-43U1 parameters in the MC simulations as well as in the literature comparison are of the same order of magnitude as the differences between dose distributions computed for water and prostate-like medium. The selection of the parameters for the PS should be done carefully, as it may considerably affect the dose distributions. The seeds internal geometry uncertainties are a major limiting factor in the MC parameters deduction.

Reabilitação de um imóvel industrial em Marvila: projeto de investimento

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Civil

Uncertainty propagation in inverse reliability-based design of composite structures

An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.