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.

Brazilian multicenter study on prevalence of preterm birth and associated factors

Abstract Background The occurrence of preterm birth remains a complex public health condition. It is considered the main cause of neonatal morbidity and mortality, resulting in a high likelihood of sequelae in surviving children. With variable incidence in several countries, it has grown markedly in the last decades. In Brazil, however, there are still difficulties to estimate its real occurrence. Therefore, it is essential to establish the prevalence and causes of this condition in order to propose prevention actions. This study intend to collect information from hospitals nationwide on the prevalence of preterm births, their associated socioeconomic and environmental factors, diagnostic and treatment methods resulting from causes such as spontaneous preterm labor, prelabor rupture of membranes, and therapeutic preterm birth, as well as neonatal results. Methods/Design This proposal is a multicenter cross-sectional study plus a nested case-control study, to be implemented in 27 reference obstetric centers in several regions of Brazil (North: 1; Northeast: 10; Central-west: 1; Southeast: 13; South: 2). For the cross sectional component, the participating centers should perform, during a period of six months, a prospective surveillance of all patients hospitalized to give birth, in order to identify preterm birth cases and their main causes. In the first three months of the study, an analysis of the factors associated with preterm birth will also be carried out, comparing women who have preterm birth with those who deliver at term. For the prevalence study, 37,000 births will be evaluated (at term and preterm), corresponding to approximately half the deliveries of all participating centers in 12 months. For the case-control study component, the estimated sample size is 1,055 women in each group (cases and controls). The total number of preterm births estimated to be followed in both components of the study is around 3,600. Data will be collected through a questionnaire all patients will answer after delivery. The data will then be encoded in an electronic form and sent online by internet to a central database. The data analysis will be carried out by subgroups according to gestational age at preterm birth, its probable causes, therapeutic management, and neonatal outcomes. Then, the respective rates, ratios and relative risks will be estimated for the possible predictors. Discussion These findings will provide information on preterm births in Brazil and their main social and biological risk factors, supporting health policies and the implementation of clinical trials on preterm birth prevention and treatment strategies, a condition with many physical and emotional consequences to children and their families.

"Evaluating the model of classification and valuation of disabilities used in Brazil and defining the elaboration and adoption of a unique model for all the country": Brazilian Interministerial Workgroup Task

The President of Brazil established an Interministerial Work Group in order to “evaluate the model of classification and valuation of disabilities used in Brazil and to define the elaboration and adoption of a unique model for all the country”. Eight Ministries and/or Secretaries participated in the discussion over a period of 10 months, concluding that a proposed model should be based on the United Nations Convention on the Rights of Person with Disabilities, the International Classification of Functioning, Disability and Health, and the ‘support theory’, and organizing a list of recommendations and necessary actions for a Classification, Evaluation and Certification Network with national coverage.

Spanish National GPS Reference Station Network (ERGPS)

The Instituto Geográfico Nacional de España, thought its geodesy department, since 1997 has carried out the establisment of a GPS Reference Station Network (ERGPS) delivered all around Spain which allows millimetric co-ordinate results, as well as velocity fields in a Global Reference System (ITRFxx). It serves as support for other geodetic networks. Some of these stations are being integrated into the EUREF (EUropean REference Frame) Permanent Station Network. The ERGPS forms the zero order of the Spanish new geodesy

Satellite laser ranging and the ETALON Geodetic satellites

The technique of Satellite Laser Ranging is today a mature, important tool with applications in many area of geodynamics, geodesy and satellite dynamics. A global network of some 40 stations regularly obtains range observations with sub-cm precision to more than twelve orbiting spacecraft. At such levels of precision it is important to minimise potential sources of range bias in the observations, and part of the thesis is a study of subtle effects caused by the extended nature of the arrays of retro-reflectors on the satellites. We develop models that give a precise correction of the range measurements to the centres of mass of the geodetic satellites Lageos and Etalon, appropriate to a variety of different ranging systems, and use the Etalon values, which were not determined during pre-launch tests, in an extended orbital analysis. We have fitted continuous 2.5 year orbits to range observations of the Etalons from the global network of stations, and analysed the results by mapping the range residuals from these orbits into equivalent corrections to orbital elements over short time intervals. From these residuals we have detected and studied large un-modelled along-track accelerations associated with periods during which the satellites are undergoing eclipse by the Earth's shadow. We also find that the eccentricity residuals are significantly different for the two satellites, with Etalon-2 undergoing a year-long eccentricity anomaly similar in character to that experienced at intervals by Lageos-1. The nodal residuals show that the satellites define a very stable reference frame for Earth rotation determination, with very little drift-off during the 2.5 year period. We show that an analysis of more than about eight years of tracking data would be required to derive a significant value for 2. The reference frame defined by the station coordinates derived from the analyses shows very good agreement with that of ITRF93.

Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire

Acknowledgements This study was possible by partial financial support from the following Brazilian government agencies: CNPq, CAPES, and FAPESP (2011/19296-1 and 2015/07311-7). We also wish thank Newton Fund and COFAP.

Suppression of phase synchronisation in network based on cat’s brain

We wish to acknowledge the support of the Brazilian agencies: CNPq, CAPES, and FAPESP (2015/07311-7 and 2011/19296-1).

Comparative performance between two photogrammetric systems and a reference laser tracker network for large-volume industrial measurement

This paper determines the capability of two photogrammetric systems in terms of their measurement uncertainty in an industrial context. The first system – V-STARS inca3 from Geodetic Systems Inc. – is a commercially available measurement solution. The second system comprises an off-the-shelf Nikon D700 digital camera fitted with a 28 mm Nikkor lens and the research-based Vision Measurement Software (VMS). The uncertainty estimate of these two systems is determined with reference to a calibrated constellation of points determined by a Leica AT401 laser tracker. The calibrated points have an average associated standard uncertainty of 12·4 μm, spanning a maximum distance of approximately 14·5 m. Subsequently, the two systems’ uncertainty was determined. V-STARS inca3 had an estimated standard uncertainty of 43·1 μm, thus outperforming its manufacturer's specification; the D700/VMS combination achieved a standard uncertainty of 187 μm.

The impact of climate change on the brazilian agriculture: a ricardian study at microregion level.

We use at microregion level from the Brazilian Census years 1975, 1985, 1995 and 2006 to assess the impact of climate change on Brazilian agriculture using a Ricardian model. We estimate the Ricardian model using repeated cross sections for each Census Year, a pooled model and a twostage model based on Hsiao 2003. Results show that a marginal increase of temperature is harmful for agriculture in all regions of Brazil, with the exception of the South. The most negative impacts are felt in the North and in the North-East. There is mixed evidence on the effect of a marginal impact of precipitation. Additional rainfall is beneficial in South, South-East and in the Center-West. It is harmful in other regions. Impact estimates with three GCM scenarios generated using the A2 SRES emission scenario show that climate change is expected to be generally harmful in 2060. In 2100 only the climate change scenario generated by the Hadley HADCM3 model predicts negative impacts; the MIMR model predicts that climate change will not significantly affect land values while the NCPCM model predicts significant beneficial effects using the Hsiao model and nonsignificant beneficial effects using the pooled model. Among Brazilian regions, only the South and some cases the South-East are expected to benefit from climate change.

Genomic structure and marker-derived gene networks for growth and meat quality traits of brazilian nelore beef cattle.

Nelore is the major beef cattle breed in Brazil with more than 130 million heads. Genome-wide association studies (GWAS) are often used to associate markers and genomic regions to growth and meat quality traits that can be used to assist selection programs. An alternative methodology to traditional GWAS that involves the construction of gene network interactions, derived from results of several GWAS is the AWM (Association Weight Matrices)/PCIT (Partial Correlation and Information Theory). With the aim of evaluating the genetic architecture of Brazilian Nelore cattle, we used high-density SNP genotyping data (~770,000 SNP) from 780 Nelore animals comprising 34 half-sibling families derived from highly disseminated and unrelated sires from across Brazil. The AWM/PCIT methodology was employed to evaluate the genes that participate in a series of eight phenotypes related to growth and meat quality obtained from this Nelore sample.