Polymerase chain reaction and real-time PCR for diagnosing of Leishmania infantum chagasi in dogs

A importância do cão como reservatório de L. infantum chagasi no meio urbano tem estimulado a realização de inúmeros trabalhos de avaliação de técnicas de diagnóstico, uma vez que este procedimento, quando realizado corretamente, torna-se um importante passo na prevenção da doença em humanos. Dentre os métodos de diagnóstico, as técnicas moleculares têm adquirido destaque. Objetivou-se neste trabalho verificar o desempenho da Reação em Cadeia da Polimerase (PCR) e da PCR em tempo real (qPCR) para diagnóstico da Leishmaniose Visceral Canina (LVC) utilizando diferentes amostras biológicas. Para tanto foram utilizados 35 cães provenientes de uma área endêmica para LVC, onde foram utilizados para o diagnóstico molecular, aspirado de medula óssea, fragmentos de linfonodo e baço. Neste estudo a qPCR foi capaz de detectar um maior número de animais positivos quando comparada com a PCR. Já entre as diferentes amostras biológicas utilizadas não foi observada diferença significativa na detecção de DNA de L. infantumchagasi por meio da PCR e qPCR. Mesmo assim, considerando a facilidade de obtenção, o linfonodo pode ser considerada como a melhor amostra para diagnóstico molecular da infecção por L. infantum chagasi.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

Modelling and Design of Advanced Reliable Circuits and Devices for Energy Efficiency

Reliable electronic systems, namely a set of reliable electronic devices connected to each other and working correctly together for the same functionality, represent an essential ingredient for the large-scale commercial implementation of any technological advancement. Microelectronics technologies and new powerful integrated circuits provide noticeable improvements in performance and cost-effectiveness, and allow introducing electronic systems in increasingly diversified contexts. On the other hand, opening of new fields of application leads to new, unexplored reliability issues. The development of semiconductor device and electrical models (such as the well known SPICE models) able to describe the electrical behavior of devices and circuits, is a useful means to simulate and analyze the functionality of new electronic architectures and new technologies. Moreover, it represents an effective way to point out the reliability issues due to the employment of advanced electronic systems in new application contexts. In this thesis modeling and design of both advanced reliable circuits for general-purpose applications and devices for energy efficiency are considered. More in details, the following activities have been carried out: first, reliability issues in terms of security of standard communication protocols in wireless sensor networks are discussed. A new communication protocol is introduced, allows increasing the network security. Second, a novel scheme for the on-die measurement of either clock jitter or process parameter variations is proposed. The developed scheme can be used for an evaluation of both jitter and process parameter variations at low costs. Then, reliability issues in the field of “energy scavenging systems” have been analyzed. An accurate analysis and modeling of the effects of faults affecting circuit for energy harvesting from mechanical vibrations is performed. Finally, the problem of modeling the electrical and thermal behavior of photovoltaic (PV) cells under hot-spot condition is addressed with the development of an electrical and thermal model.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

COVARIATE-ADJUSTED NONPARAMETRIC ANALYSIS OF MAGNETIC RESONANCE IMAGES USING MARKOV CHAIN MONTE CARLO

Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain that are difficult to capture parametrically. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our non-parametric method to remove potential bias due to the observed covariates is presented.

Distribution of macrozooplankton at time series station MIGZOO-1

Vertical distributions and diel migrations of the main species of micronekton, four euphausiids, one mysid, one decapod and three fishes, were described in detail in the 0-1000 m water column on a fixed station in the Northwestern Mediterranean Sea. The euphausiids Euphausia krohni and Thysanopoda aequalis, the decapod Gennadas elegans and, to a lesser extent, the fish Argyropelecus hemigymnus were shown to perform clear diel vertical migrations. Results of horizontal hauls at a given depth around sunrise and sunset showed a marked diurnal symmetry of the migratory cycles, particularly for E.krohni, T.aequalis and G.elegans. The behaviour of the euphausiid Nematoscelis megalops was more complex: it presented a repetitive bimodal day distribution and only part of its population migrated. As very weak or non-migrators we found the euphausiid Stylocheiron longicorne and the bathypelagic mysid Eucopia unguiculata, for which migration concerned only some of the older individuals. The fishes Cyclothone braueri and Cyclothone pygmaea appeared to be non-migrants. As depth increased, C.braueri was replaced by C.pygmaea, with maximum concentrations at 350-550 and 550-700 m depth, respectively.

Anundance of phytoplankton at time series station DYNAPROC

A general study of biogeochemical processes (DYNAPROC cruise) was conducted in May 1995 at a time-series station in the open northwestern Mediterranean Sea where horizontal advection was weak. Short-term variations of the vertical distributions of pico- and nanophytoplankton were investigated over four 36-h cycles, along with parallel determinations of metabolic CO2 production rates and amino acid-containing colloid (AACC) concentrations at the chlorophyll maximum depth. The vertical (0-1000-m depth) distributions of (i) AACC, (ii) suspended particles and (iii) metabolic CO2 production rate were documented during the initial and final stages of these 36-h cycles. This study was concerned with diel vertical migration (DVM) of zooplankton, which provided periodic perturbations. Accordingly, the time scale of the experimental work varied from a few hours to a few days. Although all distributions exhibited a periodic behaviour, AACC distributions were generally not linked to diel vertical migrations. In the subsurface layer, Synechococcus made the most abundant population and large variations in concentration were observed both at day and at night. The corresponding integrated (over the upper 90 m) losses of Synechococcus during one night pointed to a potential source of exported organic matter amounting to 534 mg C/m**2. This study stresses the potential importance of organic matter export from the euphotic zone through the daily grazing activity of vertically migrating organisms, which would not be accounted for by measurements at longer time scales. The metabolic CO2 production exhibited a peak of activity below 500 m that was shifted downward, apparently in a recurrent way and independently of the vertical distributions of AACC or of suspended particulate material. To account for this phenomenon, a 'sustained wave train» hypothesis is proposed that combines the effect of the diel superficial faecal pellet production by swarming migrators and the repackaging activity of the nonmigrating midwater populations. Our results confirm the recent finding that the particulate compartment is not the major source of the observed instantaneous remineralisation rate and shed a new light on the fate of organic matter in the aphotic zone.

A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.

Therapeutic Assessment for an individual with Binge Eating Disorder: A single-case time-series design .

Therapeutic Assessment (TA) is a treatment approach that combines psychological assessment and psychotherapy. The study examines the efficacy of this approach with an individual with Binge Eating Disorder. A replicated single-case time-series design with daily measures is used to assess the effects of TA and to track the process of change during the TA. The individual experienced inconclusive benefits after participation in TA. Significant change occurred in all variables measured, though none of the changes occurred in the hypothesized direction. Further research is needed to determine if TA is an effective treatment for individuals diagnosed with Binge Eating Disorder.