Dental caries prevalence in children up to 36 months of age attending daycare centers in municipalities with different water fluoride content

This study determined the prevalence of cavitated caries lesions (CCL) and early childhood caries (ECC), and the contribution of some variables in children up to 36 months of age attending daycare centers in municipalities with different fluoride levels in the water supply: AFC (adequate fluoride content) and LFC (low fluoride content). After approval of the Ethics Committee, the parents were interviewed. The children were clinically examined using the same codes and criteria established by the WHO (World Health Organization) and the ADA (American Dental Association). Fisher's exact test (p<0.05) was applied for statistical analysis of data. The dmft indices calculated in the LFC and AFC municipalities were 0.57 and 0.68, respectively. Considering all children examined, 17.6% presented CCL and 33.8% ECC. The economic classification, mother's education level and duration of breastfeeding were considered statistically significant with regards to CCL prevalence. The age group, duration of the habit of drinking milk before bedtime and age at which oral hygiene started were considered statistically significant with regards to ECC prevalence.

Comparison of visual inspection, radiographic examination, laser fluorescence and their combinations on treatment decisions for occlusal surfaces

Purpose: To compare visual inspection (VI), radiographic examination (RX) and the laser fluorescence device DIAGNOdent (L), as well as their combinations in vitro regarding treatment decisions for occlusal surfaces. Methods: 72 extracted human permanent teeth (molars and premolars) were used. Treatment decisions were recorded by three calibrated examiners, and the options available were fissure sealant and conservative restoration. For validation of treatment decisions, the teeth were sectioned and examined in a stereomicroscope. Thereafter, dental slices were scanned and the images were edited to facilitate classification of existing carious lesions. Intra and inter-examiner reproducibility for the determination of treatment plans were calculated using Cohen's kappa test (95%-CI). Sensitivity, specificity, positive and negative predictive values, and the area under the ROC curve were also calculated. Results: VI and L provided on average the greatest intra- and inter-examiner reproducibility, respectively. Although the combination of diagnostic methods may decrease both intra- and inter examiners reproducibility, combination of VI, L and RX resulted in the greatest sensitivity, being statistically superior to RX and L. There was more inter-examiner agreement for the option of restorative treatment, while the use of sealants as a treatment option yielded the lowest values. Negative predictive values were numerically inferior to positive predictive values, indicating that the examiners preferred not to restore a carious tooth than to proceed operatively in an intact tooth. The combination of the three methods studied showed the best results in determining treatment plans for occlusal surfaces, when compared to the other types of exams. on the other hand, radiographic examination and laser fluorescence were less efficient when used alone.

O emprego de linguagens acessíveis para alunos com deficiência visual em aulas de Óptica

O presente artigo encontra-se inserido dentro de um estudo que busca compreender as principais alternativas para a inclusão de alunos com deficiência visual no contexto do ensino de física. Focalizando aulas de óptica, analisa as viabilidades comunicacionais entre licenciandos e discentes com deficiência visual. Para tal, enfatiza as estruturas empírica e semântico-sensorial das linguagens utilizadas, indicando fatores geradores de acessibilidade às informações veiculadas. Recomenda, ainda, alternativas que visam dar condições à participação efetiva do discente com deficiência visual no processo comunicativo, das quais se destacam: a identificação da estrutura semântico-sensorial dos significados veiculados, o conhecimento da história visual do aluno, a utilização de linguagens de estrutura empírica tátil-auditiva interdependente em contextos interativos, bem como, a exploração das potencialidades comunicacionais das linguagens constituídas de estruturas empíricas fundamental auditiva, e auditiva e visual independentes.

Conceptual model for adaptable and extensible visual data exploration

Interactive visual representations complement traditional statistical and machine learning techniques for data analysis, allowing users to play a more active role in a knowledge discovery process and making the whole process more understandable. Though visual representations are applicable to several stages of the knowledge discovery process, a common use of visualization is in the initial stages to explore and organize a sometimes unknown and complex data set. In this context, the integrated and coordinated - that is, user actions should be capable of affecting multiple visualizations when desired - use of multiple graphical representations allows data to be observed from several perspectives and offers richer information than isolated representations. In this paper we propose an underlying model for an extensible and adaptable environment that allows independently developed visualization components to be gradually integrated into a user configured knowledge discovery application. Because a major requirement when using multiple visual techniques is the ability to link amongst them, so that user actions executed on a representation propagate to others if desired, the model also allows runtime configuration of coordinated user actions over different visual representations. We illustrate how this environment is being used to assist data exploration and organization in a climate classification problem.

Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Image categorization through optimum path forest and visual words

Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.

Improving image classification through descriptor combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.

Delineation of specific management areas for coffee cultivation based on the soil-relief relationship and numerical classification

Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31. 7 ha from which samples were collected at depths of 0. 00-0. 20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30. 4l ha-1) and best coffee beverage quality (61 sacks ha-1). Based on the results, we believe that multivariate analysis, geostatistics and the soil-relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production. © 2012 Springer Science+Business Media New York.

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

Teor de clorofila, danos oxidativos e qualidade de sementes de soja

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Procedimentos de seleção de cor, percepção visual de diferença de cor e fluorescência em odontologia estética

Pós-graduação em Ciências Odontológicas - FOAR

Sensoriamento remoto e geoprocessamento aplicado ao estudo temporal do uso da terra e na comparação entre classificação não supervisionada e análise visual

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

O ensino de ciências e deficiência visual: uma investigação das percepções das professoras de ciências e da sala de recursos com relação à inclusão

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)