Comparative analysis of different meat traceability systems using multiple criteria and a social network approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Vereda and Murundu wetlands and changes in Brazilian environmental laws: challenges to conservation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

School inclusion: analyzing the establishment and organization of a special needs education service in a brazilian municipality

The present research integrates a network of studies called National Monitoring Center for Special Education (NMCSE) which studies the Multi-purpose Feature Rooms (MFRs) in regular schools. We aim to investigate whether the service offered by such rooms, maintained by the Department of Education of the Municipality of Araraquara, in São Paulo State, Brazil, is being successful at supporting the education of children and youth with special needs, pervasive developmental disorders and high skilled/gifted individuals. We have also investigated the limits and possibilities of such rooms concerning the set of services offered to their participants. In order to conduct the present research, we have performed: an interview with the Special Education Program manager from the abovementioned Department of Education; and the analysis of a Training Program that MFRs teachers must take. The training program consists of ten morning and afternoon shift meetings. The analyzed data leads us to conclude that the policy of implementation of MFRs, even in this relatively restricted universe is seen from different perspectives. Some interpretations are still permeated by the clinical model, considering individual action. The challenges observed in the classrooms show that the cooperation among teachers still occur randomly and, among other difficulties raised by them, is the selection of the right placement methods to identify eligible students who will benefit from the Specialized Educational Service (SES). In addition, teaching evaluation was considered fragile, as well as the training and the general requirements demanded in order to achieve the expected results.

Coastal and shelf circulation in the vicinity of Camamu Bay (14 degrees S), Eastern Brazilian Shelf

The Camamu Bay (CMB) is located on the narrowest shelf along the South American coastline and close to the formation of two major Western Boundary Currents (WBC), the Brazil/North Brazil Current (BC/NBC). These WBC flow close to the shelf break/slope region and are expected to interact with the shelf currents due to the narrowness of the shelf. The shelf circulation is investigated in terms of current variability based on an original data set covering the 2002-2003 austral summer and the 2003 austral autumn. The Results show that the currents at the shelf are mainly wind driven, experiencing a complete reversal between seasons due to a similar change in the wind field. Currents at the inner-shelf have a polarized nature, with the alongshore velocity mostly driven by forcings at the sub-inertial frequency band and the cross-shore velocity mainly supra-inertially forced, with the tidal currents playing an important role at this direction. The contribution of the forcing mechanisms at the mid-shelf changes between seasons. During the summer, forcings in the two frequency bands are important to drive the currents with a similar contribution of the tidal currents. On the other hand, during the autumn season, the alongshore velocity is mostly driven by sub-inertial forcings and tidally driven currents still remain important in both directions. Moreover, during the autumn when the stratification is weaker, the response of the shelf currents to the wind forcing presents a barotropic signature. The meso-scale processes related to the WBC flowing at the shelf/slope region also affect the circulation within the shelf, which contribute to cause significant current reversals during the autumn season. Currents at the shelf-estuary connection are clearly supra-inertially forced with the tidal currents playing a key role in the generation of the along-channel velocities. The sub-inertial forcings at this location act mainly to drive the weak ebb currents which were highly correlated with both local and remote wind forcing during the summer season. (C) 2010 Elsevier Ltd. All rights reserved.

Spatial reference of black capuchin monkeys in Brazilian Atlantic Forest: egocentric or allocentric?

Wild primates occupy large home ranges and travel long distances to reach goals. However, how primates are able to remember goal locations and travel efficiently is unclear. Few studies present consistent results regarding what reference system primates use to navigate, and what kind of spatial information they recognize. We analysed the pattern of navigation of one wild group of black capuchin monkeys, Cebus nigritus, at Atlantic Forest for 100 days in Carlos Botelho State Park (PECB), Brazil. We tested predictions based on the alternative hypotheses that black capuchin monkeys navigate using a sequence of landmarks as an egocentric reference system or an allocentric reference system, or both, depending on availability of food resources. The group location was recorded using a GPS device collecting coordinates at 5 min intervals, and route maps were generated using ArcView v9.3.1. The study group travelled through habitual routes during less than 30% of our study sample, and revisited resources from different starting points, using different paths and routes, even when prominent landmarks near feeding locations were not visible. The study group used habitual routes more frequently when high-quality foods were scarce, and navigated using different paths when revisiting food sources. Results support the hypothesis that black capuchin monkeys at PECB navigate using both egocentric and allocentric systems of reference, depending on the quality and distribution of the food resource they find. (C) 2010 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

On the pathogenesis of Plasmodium vivax malaria: Perspectives from the Brazilian field

Life-threatening Plasmodium vivax malaria cases, while uncommon, have been reported since the early 20th century. Unfortunately, the pathogenesis of these severe vivax malaria cases is still poorly understood. In Brazil, the proportion of vivax malaria cases has been steadily increasing, as have the number of cases presenting serious clinical complications. The most frequent syndromes associated with severe vivax malaria in Brazil are severe anaemia and acute respiratory distress. Additionally, P. vivax infection may also result in complications associated with pregnancy. Here, we review the latest findings on severe vivax malaria in Brazil. We also discuss how the development of targeted field research infrastructure in Brazil is providing clinical and ex vivo experimental data that benefits local and international efforts to understand the pathogenesis of P. vivax. (C) 2012 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Mistletoes Play Different Roles in a Modular Host-Parasite Network

Antagonistic interactions between host plants and mistletoes often form complex networks of interacting species. Adequate characterization of network organization requires a combination of qualitative and quantitative data. Therefore, we assessed the distribution of interactions between mistletoes and hosts in the Brazilian Pantanal and characterized the network structure in relation to nestedness and modularity. Interactions were highly asymmetric, with mistletoes presenting low host specificity (i.e., weak dependence) and with hosts being highly susceptible to mistletoe-specific infections. We found a non-nested and modular pattern of interactions, wherein each mistletoe species interacted with a particular set of host species. Psittacanthus spp. infected more species and individuals and also caused a high number of infections per individual, whereas the other mistletoes showed a more specialized pattern of infection. For this reason, Psittacanthus spp. were regarded as module hubs while the other mistletoe species showed a peripheral role. We hypothesize that this pattern is primarily the result of different seed dispersal systems. Although all mistletoe species in our study are bird dispersed, the frugivorous assemblage of Psittacanthus spp. is composed of a larger suite of birds, whereas Phoradendron are mainly dispersed by Euphonia species. The larger assemblage of bird species dispersing Psittacanthus seeds may also increase the number of hosts colonized and, consequently, its dominance in the study area. Nevertheless, other restrictions on the interactions among species, such as the differential capacity of mistletoe infections, defense strategies of hosts and habitat types, can also generate or enhance the observed pattern.

Complex network classification using partially self-avoiding deterministic walks

Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification relies on the use of representative measurements that describe topological structures. Although there are a large number of measurements, most of them are correlated. To overcome this limitation, this paper presents a new measurement for complex network classification based on partially self-avoiding walks. We validate the measurement on a data set composed by 40000 complex networks of four well-known models. Our results indicate that the proposed measurement improves correct classification of networks compared to the traditional ones. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4737515]

Research Training Program: Duke University and Brazilian Society of Cardiology

Background: Research coaching program focuses on the development of abilities and scientific reasoning. For health professionals, it may be useful to increase both the number and quality of projects and manuscripts. Objective: To evaluate the initial results and implementation methodology of the Research and Innovation Coaching Program of the Research on Research group of Duke University in the Brazilian Society of Cardiology. Methods: The program works on two bases: training and coaching. Training is done online and addresses contents on research ideas, literature search, scientific writing and statistics. After training, coaching favors the establishment of a collaboration between researchers and centers by means of a network of contacts. The present study describes the implementation and initial results in reference to the years 2011-2012. Results: In 2011, 24 centers received training, which consisted of online meetings, study and practice of the contents addressed. In January 2012, a new format was implemented with the objective of reaching more researchers. In six months, 52 researchers were allocated. In all, 20 manuscripts were published and 49 more were written and await submission and/or publication. Additionally, five research funding proposals have been elaborated. Conclusion: The number of manuscripts and funding proposals achieved the objectives initially proposed. However, the main results of this type of initiative should be measured in the long term, because the consolidation of the national production of high-quality research is a virtuous cycle that feeds itself back and expands over time. (Arq Bras Cardiol 2012;99(6):1075-1081)

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Phylogenetic and host-parasite relationship analysis of Henneguya multiplasmodialis n. sp infecting Pseudoplatystoma spp. in Brazilian Pantanal wetland

A new species of the genus Henneguya (Henneguya multiplasmodialis n. sp.) was found infecting the gills of three of 89 specimens (3.3%) of Pseudoplatystoma corruscans and two of 79 specimens (2.6%) of Pseudoplatystoma reticulatum from rivers in the Pantanal wetland, Brazil. Partial sequencing of the 18S rDNA gene of the spores obtained from one plasmodium from the gills of P. corruscans and other one from the gills of P. reticulatum, respectively, resulted in a total of 1560 and 1147 base pairs. As the spores of H. multiplasmodialis n. sp. resemble those of Henneguya corruscans, which is also a parasite of P. corruscans, sequencing of the 18S rDNA gene of the spores of H. corruscans found on P. corruscans caught in the Brazilian Pantanal wetland was also provided to avoid any taxonomic pendency between these two species, resulting in 1913 base pairs. The sequences of H. multiplasmodialis n. sp. parasite of P. corruscans and P. reticulatum and H. corruscans did not match any of the Myxozoa available in the GenBank. The similarity of H. multiplasmodialis n. sp. obtained from P. corruscans to that from P. reticulatum was of 99.7%. Phylogeny revealed a strong tendency among Henneguya species to form clades based on the order and/or family of the host fish. H. multiplasmodialis n. sp. clustered in a clade with Henneguya eirasi and H. corruscans, which are also parasites of siluriforms of the family Pimelodidae and, together with the clade composed of Henneguya spp. parasites of siluriforms of the family Ictaluridae, formed a monophyletic clade of parasites of siluriform hosts. The histological study revealed that the wall of the plasmodia of H. multiplasmodialis n. sp. were covered with a stratified epithelium rich in club cells and supported by a layer of connective tissue. The interior of the plasmodia had a network of septa that divided the plasmodia into numerous compartments. The septa were composed of connective tissue also covered on both sides with a stratified epithelium rich in club cells. Inflammatory infiltrate was found in the tissue surrounding the plasmodia as well as in the septa. (C) 2011 Elsevier B.V. All rights reserved.

STATE INSTITUTIONS, POWER, AND SOCIAL NETWORKS IN BRAZILIAN URBAN POLICIES

A large historiographic tradition has studied the Brazilian state, yet we know relatively little about its internal dynamics and particularities. The role of informal, personal, and unintentional ties has remained underexplored in most policy network studies, mainly because of the pluralist origin of that tradition. It is possible to use network analysis to expand this knowledge by developing mesolevel analysis of those processes. This article proposes an analytical framework for studying networks inside policy communities. This framework considers the stable and resilient patterns that characterize state institutions, especially in contexts of low institutionalization, particularly those found in Latin America and Brazil. The article builds on research on urban policies in Brazil to suggest that networks made of institutional and personal ties structure state organizations internally and insert them,into broader political scenarios. These networks, which I call state fabric, frame politics, influence public policies, and introduce more stability and predictability than the majority of the literature usually considers. They also form a specific power resource-positional power, associated with the positions that political actors occupy-that influences politics inside and around the state.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.