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.

Machine diagnosis based on artificial immune systems

Nowadays, many of the manufactory and industrial system has a diagnosis system on top of it, responsible for ensuring the lifetime of the system itself. It achieves this by performing both diagnosis and error recovery procedures in real production time, on each of the individual parts of the system. There are many paradigms currently being used for diagnosis. However, they still fail to answer all the requirements imposed by the enterprises making it necessary for a different approach to take place. This happens mostly on the error recovery paradigms since the great diversity that is nowadays present in the industrial environment makes it highly unlikely for every single error to be fixed under a real time, no production stop, perspective. This work proposes a still relatively unknown paradigm to manufactory. The Artificial Immune Systems (AIS), which relies on bio-inspired algorithms, comes as a valid alternative to the ones currently being used. The proposed work is a multi-agent architecture that establishes the Artificial Immune Systems, based on bio-inspired algorithms. The main goal of this architecture is to solve for a resolution to the error currently detected by the system. The proposed architecture was tested using two different simulation environment, each meant to prove different points of views, using different tests. These tests will determine if, as the research suggests, this paradigm is a promising alternative for the industrial environment. It will also define what should be done to improve the current architecture and if it should be applied in a decentralised system.

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.

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.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

A structured inventory of spiders (Arachnida, Araneae) in natural and artificial forest gaps at Porto Urucu, Western Brazilian Amazonia

A preliminary survey of the spider fauna in natural and artificial forest gap formations at “Porto Urucu”, a petroleum/natural gas production facility in the Urucu river basin, Coari, Amazonas, Brazil is presented. Sampling was conducted both occasionally and using a protocol composed of a suite of techniques: beating trays (32 samples), nocturnal manual samplings (48), sweeping nets (16), Winkler extractors (24), and pitfall traps (120). A total of 4201 spiders, belonging to 43 families and 393 morphospecies, were collected during the dry season, in July, 2003. Excluding the occasional samples, the observed richness was 357 species. In a performance test of seven species richness estimators, the Incidence Based Coverage Estimator (ICE) was the best fit estimator, with 639 estimated species. To evaluate differences in species richness associated with natural and artificial gaps, samples from between the center of the gaps up to 300 meters inside the adjacent forest matrix were compared through the inspection of the confidence intervals of individual-based rarefaction curves for each treatment. The observed species richness was significantly higher in natural gaps combined with adjacent forest than in the artificial gaps combined with adjacent forest. Moreover, a community similarity analysis between the fauna collected under both treatments demonstrated that there were considerable differences in species composition. The significantly higher abundance of Lycosidae in artificial gap forest is explained by the presence of herbaceous vegetation in the gaps themselves. Ctenidae was significantly more abundant in the natural gap forest, probable due to the increase of shelter availability provided by the fallen trees in the gaps themselves. Both families are identified as potential indicators of environmental change related to the establishment or recovery of artificial gaps in the study area.