SBD-RESP na busca ativa de casos de hanseníase

FUNDAMENTOS: A Sociedade Brasileira de Dermatologia Regional do Estado de São Paulo (SBD-RESP), apoiada pela Fundação Paulista Contra a Hanseníase, e em ação conjunta com os Serviços de Dermatologia do estado de São Paulo, credenciados pela Sociedade Brasileira de Dermatologia, lançou a campanha SBD-RESP na Busca Ativa de Casos de Hanseníase. OBJETIVOS: Auxiliar o Programa Nacional de Controle da Hanseníase no controle da doença. MÉTODO: Todos os Serviços de Dermatologia do estado de São Paulo, credenciados pela Sociedade Brasileira de Dermatologia, foram convidados e os 17 que participaram receberam uma planilha de dados e modelos de materiais informativos sobre a doença. A campanha foi realizada entre os meses de maio e julho de 2010. Ao término do período, cada serviço encaminhou a planilha de dados para análise estatística. RESULTADOS: Foram examinadas 1718 pessoas e diagnosticados, no total, 90 casos de hanseníase, a maioria do gênero masculino e da cor branca, com percentuais semelhantes de multibacilares e de paucibacilares. Doze por cento apresentavam história familiar de hanseníase. O maior número de casos detectados foi na capital, seguido, no interior, pela região de Presidente Prudente. O índice de detecção em menores de 15 anos foi 4%. CONCLUSÕES: Os resultados da campanha mostram a importância desta iniciativa da SBD-RESP. Sugere-se que ações semelhantes sejam repetidas e que se estendam a outras regionais da Sociedade Brasileira de Dermatologia

Photoluminescence in disordered sm-doped PbTiO3: Experimental and theoretical approach

Sm-doped PbTiO3 powder was synthesized by the polymeric precursor method, and was heat treated at different temperatures. The x-ray diffraction, photoluminescence, and UV-visible were used as a probe for the structural order degree short-, intermediate-, and long-range orders. Sm-3+ ions were used as markers of these order-disorder transformations in the PbTiO3 system. From the Rietveld refinement of the Sm-doped PbTiO3 x-ray diffraction data, structural models were obtained and analyzed by periodic ab initio quantum mechanical calculations using the CRYSTAL 98 package within the framework of density functional theory at the B3LYP level. This program can yield important information regarding the structural and electronic properties of crystalline and disordered structures. The experimental and theoretical results indicate the presence of the localized states in the band gap, due to the symmetry break, which is responsible for visible photoluminescence at room temperature in the disordered structure. (c) 2006 American Institute of Physics.

Eucalyptus ESTs related to genes for oxidative stress

Oxidative stress generating active oxygen species has been proved to be one of the underlying agents causing tissue injury after the exposure of Eucalyptus (Eucalyptus spp.) plants to a wide variety of stress conditions. The objective of this study was to perform data mining to identify favorable genes and alleles associated with the enzyme systems superoxide dismutase, catalase, peroxidases, and glutathione S-transferase that are related to tolerance for environmental stresses and damage caused by pests, diseases, herbicides, and by weeds themselves. This was undertaken by using the eucalyptus expressed-sequence database (https//forests.esalq.usp.br). The alignment results between amino acid and nucleotide sequences indicated that the studied enzymes were adequately represented in the ESTs database of the FORESTs project.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

An environment for knowledge discovery in biology

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported. © 2003 Elsevier Ltd. All rights reserved.

Eucalyptus ESTs associated with resistance to herbicide inhibitors of aromatic and branched-chain amino acid synthesis

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

Intelligent alarm processing

This paper presents the analysis that have been carried out in the alarm system of the DCRanger EMS. The intention of this study is to present the problem of alarm processing in electric energy control centers, its various aspects and operational difficulties due to operator needs. Some tests are produced in order to identify the desirable features an alarm system should possess in order to be of effective help in the operative duty. © 2006 IEEE.

Uso de polinômios segmentados para o estudo da curva de lactação de búfalos da raça Murrah e seus mestiços em sistema de criação semi-intensivo, no estado de São Paulo

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

Three-dimensional visualization of human hemoglobin phenotypes with HPLC

Hemoglobinopathies were included in the Brazilian Neonatal Screening Program on June 6, 2001. Automated high-performance liquid chromatography (HPLC) was indicated as one of the diagnostic methods. The amount of information generated by these systems is immense, and the behavior of groups cannot always be observed in individual analyses. Three-dimensional (3-D) visualization techniques can be applied to extract this information, for extracting patterns, trends or relations from the results stored in databases. We applied the 3-D visualization tool to analyze patterns in the results of hemoglobinopathy based on neonatal diagnosis by HPLC. The laboratory results of 2520 newborn analyses carried out in 2001 and 2002 were used. The Fast, F1, F and A peaks, which were detected by the analytical system, were chosen as attributes for mapping. To establish a behavior pattern, the results were classified into groups according to hemoglobin phenotype: normal (N = 2169), variant (N = 73) and thalassemia (N = 279). 3-D visualization was made with the FastMap DB tool; there were two distribution patterns in the normal group, due to variation in the amplitude of the values obtained by HPLC for the F1 window. It allowed separation of the samples with normal Hb from those with alpha thalassemia, based on a significant difference (P > 0.05) between the mean values of the Fast and A peaks, demonstrating the need for better evaluation of chromatograms; this method could be used to help diagnose alpha thalassemia in newborns.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

First measurement of the underlying event activity at the LHC with √ = 0.9 TeV

A measurement of the underlying activity in scattering processes with pT scale in the GeV region is performed in proton-proton collisions at √ = 0.9 TeV, using data collected by the CMS experiment at the LHC. Charged particle production is studied with reference to the direction of a leading object, either a charged particle or a set of charged particles forming a jet. Predictions of several QCD-inspired models as implemented in PYTHIA are compared, after full detector simulation, to the data. The models generally predict too little production of charged particles with pseudorapidity {pipe}η{pipe} < 2, pT > 0.5 GeV/c, and azimuthal direction transverse to that of the leading object. © 2010 CERN for benefit of the CMS collaboration.

Development banking in the Eastern Caribbean: the case of Saint Lucia and Grenada

Includes bibliography

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Optimization of algorithm to identification of duplicate tuples through similarity phonetic based on multithreading

Aiming to ensure greater reliability and consistency of data stored in the database, the data cleaning stage is set early in the process of Knowledge Discovery in Databases (KDD) and is responsible for eliminating problems and adjust the data for the later stages, especially for the stage of data mining. Such problems occur in the instance level and schema, namely, missing values, null values, duplicate tuples, values outside the domain, among others. Several algorithms were developed to perform the cleaning step in databases, some of them were developed specifically to work with the phonetics of words, since a word can be written in different ways. Within this perspective, this work presents as original contribution an optimization of algorithm for the detection of duplicate tuples in databases through phonetic based on multithreading without the need for trained data, as well as an independent environment of language to be supported for this. © 2011 IEEE.

Spatial clustering applied to health area

The significant volume of work accidents in the cities causes an expressive loss to society. The development of Spatial Data Mining technologies presents a new perspective for the extraction of knowledge from the correlation between conventional and spatial attributes. One of the most important techniques of the Spatial Data Mining is the Spatial Clustering, which clusters similar spatial objects to find a distribution of patterns, taking into account the geographical position of the objects. Applying this technique to the health area, will provide information that can contribute towards the planning of more adequate strategies for the prevention of work accidents. The original contribution of this work is to present an application of tools developed for Spatial Clustering which supply a set of graphic resources that have helped to discover knowledge and support for management in the work accidents area. © 2011 IEEE.