Perfil clínico de pacientes chagásicos e não-chagásicos portadores de marca-passo cardíaco artificial

O objetivo do estudo foi comparar pacientes chagásicos e não-chagásicos com marca-passo cardíaco artificial uni ou bicameral quanto à fração de ejeção do ventrículo esquerdo, o limiar de estimulação ventricular e a incidência de arritmias cardíacas. Entre janeiro/2001 e novembro/2002 foram avaliados 45 pacientes chagásicos e 35 não-chagásicos portadores de marca-passo quanto à história clínica, radiografia do tórax, ecocardiograma, Holter 24h e análise telemétrica do marca-passo. Embora os chagásicos fossem mais jovens, os dois grupos foram semelhantes quanto a variáveis radiológicas e o limiar de estimulação ventricular direito. Os chagásicos apresentaram menor fração de ejeção do ventrículo esquerdo ao ecocardiograma e maior incidência de arritmia ventricular ao Holter 24h. Observou-se correlação positiva entre a baixa fração de ejeção do ventrículo esquerdo e a intensidade da arritmia ventricular ao Holter 24h. Em pacientes com marca-passo, a doença de Chagas está associada a marcadores cardíacos de prognóstico adverso.

A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima

INTRODUÇÃO: A malária é uma doença endêmica na Amazônia Legal Brasileira, apresentando riscos diferentes para cada região. O Município de Cantá, no Estado de Roraima, apresentou para todo o período estudado, um dos maiores índices parasitários anuais do Brasil, com valor sempre maior que 50. O presente estudo visa à utilização de uma rede neural artificial para previsão da incidência da malária nesse município, a fim de auxiliar os coordenadores de saúde no planejamento e gestão dos recursos. MÉTODOS: Os dados foram coletados no site do Ministério da Saúde, SIVEP - Malária entre 2003 e 2009. Estruturou-se uma rede neural artificial com três neurônios na camada de entrada, duas camadas intermediárias e uma camada de saída com um neurônio. A função de ativação foi à sigmoide. No treinamento, utilizou-se o método backpropagation, com taxa de aprendizado de 0,05 e momentum 0,01. O critério de parada foi atingir 20.000 ciclos ou uma meta de 0,001. Os dados de 2003 a 2008 foram utilizados para treinamento e validação. Comparam-se os resultados com os de um modelo de regressão logística. RESULTADOS: Os resultados para todos os períodos previstos mostraram-se que as redes neurais artificiais obtiveram um menor erro quadrático médio e erro absoluto quando comparado com o modelo de regressão para o ano de 2009. CONCLUSÕES: A rede neural artificial se mostrou adequada para um sistema de previsão de malária no município estudado, determinando com pequenos erros absolutos os valores preditivos, quando comparados ao modelo de regressão logística e aos valores reais.

Variation in Aedes aegypti (Linnaeus) (Diptera, Culicidae) infestation in artificial containers in Caxias, state of Maranhão, Brazil

INTRODUCTION: Dengue is a serious public health problem worldwide, with cases reported annually in tropical and subtropical regions. Aedes aegypti (Linnaeus, 1762), the main vector of dengue, is a domiciliary species with high dispersal and survival capacities and can use various artificial containers as breeding sites. We assessed potential container breeding sites of A. aegypti in the municipality of Caxias, Maranhão, Brazil. METHODS: In the initial phase, we analyzed 900 properties in 3 neighborhoods during the dry and rainy seasons (August-October 2005 and February-April 2006, respectively). During the second sampling period, September 2006-August 2007, we used 5 assessment cycles for 300 properties in a single neighborhood. RESULTS: During the dry and rainy seasons, water-storage containers comprised 55.7% (n = 1,970) and 48.5% (n = 1,836) of the total containers inspected, and showed the highest productivity of immature A. aegypti; we found 23.7 and 106.1 individuals/container, respectively, in peridomicile sites. In intradomicile sites, water-storage containers were also the most important breeding sites with 86.4% (n = 973) and 85.6% (n = 900) of all containers and a mean of 7.9 and 108.3 individuals/container in the dry and rainy seaso-October 2006 (1,342). The highest number of positives (70) was recorded in May, mostly (94%) in storage containers. CONCLUSIONS: Storage containers are the principal and most productive A. aegypti breeding sites and are a major contributing factor to the maintenance of this vector in Caxias.

What hides behind the fixed-term contracts wage gap?

In Portugal, about 20% of full-time workers are employed under a fixed-term contract. Using a rich longitudinal matched employer-employee dataset for Portugal, with more than 20 million observations and covering the 2002-2012 period, we confirm the common idea that fixed-term contracts are not desirable when compared to permanent ones, by estimating a conditional wage gap of -1.7 log points. Then, we evaluate the sources of that wage penalty by combining a three way high-dimensional fixed effects model with the decomposition of Gelbach (2014), in which the three dimensions considered are the worker’s unobserved ability, the firm’s compensation wage policy and the job title effect. It is shown that the average worker with a fixed-term contract is less productive than his/her permanent counterparts, explaining -3.92 log points of the FTC wage penalty. Additionally, the sorting of workers into lower-paid job titles is also responsible for -0.59 log points of the wage gap. Surprisingly, we found that the allocation of workers among firms mitigates the existing wage penalty (in 4.23 log points), as fixed-term workers are concentrated into firms with a more generous compensation policy. Finally, following Figueiredo et al. (2014), we further control for the worker-firm match characteristics and reach the conclusion that fixed-term employment relationships have an overrepresentation of low quality worker-firm matches, explaining 0.65 log points of the FTC wage penalty.

Search-for-yield in Portuguese fixed-income mutual funds and monetary policy

This paper studies the effects of monetary policy on mutual fund risk taking using a sample of Portuguese fixed-income mutual funds in the 2000-2012 period. Firstly I estimate time-varying measures of risk exposure (betas) for the individual funds, for the benchmark portfolio, as well as for a representative equally-weighted portfolio, through 24-month rolling regressions of a two-factor model with two systematic risk factors: interest rate risk (TERM) and default risk (DEF). Next, in the second phase, using the estimated betas, I try to understand what portion of the risk exposure is in excess of the benchmark (active risk) and how it relates to monetary policy proxies (one-month rate, Taylor residual, real rate and first principal component of a cross-section of government yields and rates). Using this methodology, I provide empirical evidence that Portuguese fixed-income mutual funds respond to accommodative monetary policy by significantly increasing exposure, in excess of their benchmarks, to default risk rate and slightly to interest risk rate as well. I also find that the increase in funds’ risk exposure to gain a boost in return (search-for-yield) is more pronounced following the 2007-2009 global financial crisis, indicating that the current historic low interest rates may incentivize excessive risk taking. My results suggest that monetary policy affects the risk appetite of non-bank financial intermediaries.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Artificial thymus: a tissue engineering strategy

The thymus is the central organ responsible for the generation of T lymphocytes (1). Various diseases cause the thymus to produce in- sufficient T cells, which can lead to immune-suppression (2). Since T cells are essential for the protection against pathogens, it is crucial to promote de novo differentiation of T cells on diseased individuals. The available clinical solutions are: 1) one protocol involving the transplant of thymic stroma from unrelated children only applicable for athymic children (3); 2) for patients with severe peripheral T cell depletion and reduced thymic activity, the administration of stimu- lating molecules stimulating the activity of the endogenous thymus (4). A scaffold (CellFoam) was suggested to support thymus regen- eration in vivo (5), although this research was discontinued. Herein, we propose an innovative strategy to generate a bioartificial thymus. We use a polycaprolactone nanofiber mesh (PCL-NFM) seeded and cultured with human thymic epithelial cells (hTECs). The cells were obtained from infant thymus collected during pediatric cardio-tho- racic surgeries. We report new data on the isolation and characterization of those cells and their interaction with PCL-NFM, by expanding hTECs into relevant numbers and by optimizing cell seeding methods.

As possíveis alterações microclimáticas devido a formação do lago artificial da hidrelétrica de Tucuruí -PA

Existem muitas preocupações ecológicas do impacto que a construção de grandes lagos na Amazônia podem provocar, principalmente relacionadas ao microclima. Este estudo visa aumentar o conhecimento científico sobre a distribuição de chuvas antes e depois da formação do lago artificial da UHE Tucuruí-PA. Foram utilizados dados diários de precipitação dos períodos de 1972 a 1983 (pré-enchimento) e de 1984 a 1996 (pós-enchimento) para as cidades de Tucuruí e Marabá-PA. Comparando-se os totais mensais (pré e pós-enchimento), não se observam diferenças estatisticamente significantes (foram aplicados os testes de Fisher e Man-Whitney). Analisando-se a ocorrência de dias com precipitação superior a 5 e 25 mm.dia-1, também não se observam diferenças estatisticamente significativas. Há um leve aumento do número de dias com chuvas leves no final período sêco após a formação do lago, talvez devido a alta evaporação do lago artificial. Também não se observou modificações do início ou final da estação chuvosa.

Generating timed trajectories foran autonomous robot

Tese de Doutoramento Programa Doutoral em Engenharia Electrónica e Computadores

Rapid assessment of fruit-color selection by birds using artificial fruits at local scale in Central Amazonia

Modeling clays have been used in several ecological experiments and have proved to be an important tool to variables control. The objective of our study was to determine if fruit color in isolated and grouped displays influences the fruit selection by birds in the field using artificial fruits. Data were collected in six plots distributed homogeneously in 3 km long trails with a minimum distance of 0.5 km. We used a paired experimental design to establish our experiments, so that all treatments were available to the local bird community in each plot. Overall, red was more pecked than brown and white. Isolated red and brown displays were significantly more pecked than others display. Even though our study was conducted in small spatial scales, artificial fruits appeared to be efficient in register fruit consumption attempts by bird. Although inconclusive about selective forces that sharp the dynamics of fruit color polymorphisms and choice by frugivorous birds, our findings corroborate recent studies wherein birds showed preferences by high- over low-contrast fruit signals.

Associação da medroxiprogesterona ao protocolo Ovsynch para inseminação artificial em tempo fixo de búfalas cíclicas (Bubalus bubalis) criadas na Amazônia Oriental

O presente estudo visou avaliar os efeitos da associação da medroxiprogesterona (análogo sintético da progesterona) ao protocolo Ovsynch sobre o crescimento folicular, a ovulação e a taxa de concepção de búfalas criadas na Amazônia Oriental (Tracuateua-PA). Vinte e sete fêmeas adultas (G1 n=14 e G2 n=13), cíclicas, sem bezerro ao pé e com ECC 3,5 foram submetidas a Ovsynch. Os animais do G2 receberam 60 mg de medroxiprogesterona entre D0 e D7 (D0=início do tratamento). A ultra-sonografia ovariana foi realizada nos D 0, 7, 9 e 10. O contingente de folículos pequenos diferiu no D7 (G1: 4,57±0,60 versus G2: 6,54±0,67; P=0,05). Tempo e tratamento influenciaram o diâmetro folicular no D7. O crescimento do folículo dominante entre D7 e D9 foi maior nos animais tratados (G1: 2,05±0,49 mm/dia versus 3,48±0,41 mm/dia; P<0,05). Mais animais do G1 ovularam precocemente (35,71% versus 30,77%), porém isso não afetou as taxas de concepção (G1: 50,00% e G2: 30,77%; P>0,05). Os achados sugerem que a medroxiprogesterona (1) aumenta recrutamento folicular e retarda o crescimento dos folículos com diâmetro maior que 5,0 mm entre D0 e D7; (2) sua retirada incrementa em 1,7 vezes o crescimento folicular do D7 ao D9; (3) pode contribuir para a ovulação de folículos maiores e, em tese, para maior formação de tecido luteínico; (4) não promove ovulação precoce após o Ovsynch; (5) não eleva as taxas de concepção após sincronização de fêmeas cíclicas e com bom escore corporal, devendo ser avaliada para uso em fêmeas acíclicas ou com ECC mais baixo.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.