Quality evaluation of DNA obtained from stored human saliva and its applicability to identification in Forensic Dentistry

PURPOSE: This study evaluated the quality of DNA obtained from stored human saliva and its applicability to human identification. METHODS: The saliva samples of 20 subjects, collected in the form of saliva in natura and from mouth swabs and stored at -20ºC, were analyzed. After 7 days, the DNA was extracted from the 40 saliva samples and subjected to PCR and electrophoresis. After 180 days, the technique was repeated with the 20 swab samples. RESULTS: The first-stage results indicated that DNA was successfully extracted in 97.5% of reactions, 95% of saliva in natura and 100% of swab saliva samples, with no statistically significant difference between the forms of saliva. In the second phase, the result was positive for all 20 analyzed samples (100%). Subsequently, in order to analyze the quality of the DNA obtained from human saliva, the SIX3-2 gene was tested on the 20 mouth swab samples, and the PCR products were digested using the MbO1 restriction enzyme to evaluate polymorphisms in the ADRA-2 gene, with positive results for most samples. CONCLUSION: It was concluded that the quantity and quality of DNA from saliva and the techniques employed are adequate for forensic analysis of DNA.

Inteligência artificial aplicada à Zootecnia

Os sistemas biológicos são surpreendentemente flexíveis pra processar informação proveniente do mundo real. Alguns organismos biológicos possuem uma unidade central de processamento denominada de cérebro. O cérebro humano consiste de 10(11) neurônios e realiza processamento inteligente de forma exata e subjetiva. A Inteligência Artificial (IA) tenta trazer para o mundo da computação digital a heurística dos sistemas biológicos de várias maneiras, mas, ainda resta muito para que isso seja concretizado. No entanto, algumas técnicas como Redes neurais artificiais e lógica fuzzy tem mostrado efetivas para resolver problemas complexos usando a heurística dos sistemas biológicos. Recentemente o numero de aplicação dos métodos da IA em sistemas zootécnicos tem aumentado significativamente. O objetivo deste artigo é explicar os princípios básicos da resolução de problemas usando heurística e demonstrar como a IA pode ser aplicada para construir um sistema especialista para resolver problemas na área de zootecnia.

The impact of spatial data redundancy on SOLAP query performance

Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little effort has been focused on studying how spatial data redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial data redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

A hybrid heuristic for the multi-plant capacitated lot sizing problem with setup carry-over

This paper addresses the capacitated lot sizing problem (CLSP) with a single stage composed of multiple plants, items and periods with setup carry-over among the periods. The CLSP is well studied and many heuristics have been proposed to solve it. Nevertheless, few researches explored the multi-plant capacitated lot sizing problem (MPCLSP), which means that few solution methods were proposed to solve it. Furthermore, to our knowledge, no study of the MPCLSP with setup carry-over was found in the literature. This paper presents a mathematical model and a GRASP (Greedy Randomized Adaptive Search Procedure) with path relinking to the MPCLSP with setup carry-over. This solution method is an extension and adaptation of a previously adopted methodology without the setup carry-over. Computational tests showed that the improvement of the setup carry-over is significant in terms of the solution value with a low increase in computational time.

Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison

PURPOSE: The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used. METHODS: The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK. RESULTS: The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%. CONCLUSION: Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.

Resultados preliminares de um sistema de topografia de grande-ângulo usando videoceratógrafo com discos de Plácido

OBJETIVO: Desenvolver a instrumentação e o "software" para topografia de córnea de grande-ângulo usando o tradicional disco de Plácido. O objetivo é permitir o mapeamento de uma região maior da córnea para topógrafos de córnea que usem a técnica de Plácido, fazendo-se uma adaptação simples na mira. MÉTODOS: Utilizando o tradicional disco de Plácido de um topógrafo de córnea tradicional, 9 LEDs (Light Emitting Diodes) foram adaptados no anteparo cônico para que o paciente voluntário pudesse fixar o olhar em diferentes direções. Para cada direção imagens de Plácido foram digitalizadas e processadas para formar, por meio de algoritmo envolvendo elementos sofisticados de computação gráfica, um mapa tridimensional completo da córnea toda. RESULTADOS: Resultados apresentados neste trabalho mostram que uma região de até 100% maior pode ser mapeada usando esta técnica, permitindo que o clínico mapeie até próximo ao limbo da córnea. São apresentados aqui os resultados para uma superfície esférica de calibração e também para uma córnea in vivo com alto grau de astigmatismo, mostrando a curvatura e elevação. CONCLUSÃO: Acredita-se que esta nova técnica pode propiciar a melhoria de alguns processos, como por exemplo: adaptação de lentes de contato, algoritmos para ablações costumizadas para hipermetropia, entre outros.

ScriptLattes: an open-source knowledge extraction system from the Lattes platform

The Lattes platform is the major scientific information system maintained by the National Council for Scientific and Technological Development (CNPq). This platform allows to manage the curricular information of researchers and institutions working in Brazil based on the so called Lattes Curriculum. However, the public information is individually available for each researcher, not providing the automatic creation of reports of several scientific productions for research groups. It is thus difficult to extract and to summarize useful knowledge for medium to large size groups of researchers. This paper describes the design, implementation and experiences with scriptLattes: an open-source system to create academic reports of groups based on curricula of the Lattes Database. The scriptLattes system is composed by the following modules: (a) data selection, (b) data preprocessing, (c) redundancy treatment, (d) collaboration graph generation among group members, (e) research map generation based on geographical information, and (f) automatic report creation of bibliographical, technical and artistic production, and academic supervisions. The system has been extensively tested for a large variety of research groups of Brazilian institutions, and the generated reports have shown an alternative to easily extract knowledge from data in the context of Lattes platform. The source code, usage instructions and examples are available at http://scriptlattes.sourceforge.net/.

AcMus: an open, integrated platform for room acoustics research

This article describes the design, implementation, and experiences with AcMus, an open and integrated software platform for room acoustics research, which comprises tools for measurement, analysis, and simulation of rooms for music listening and production. Through use of affordable hardware, such as laptops, consumer audio interfaces and microphones, the software allows evaluation of relevant acoustical parameters with stable and consistent results, thus providing valuable information in the diagnosis of acoustical problems, as well as the possibility of simulating modifications in the room through analytical models. The system is open-source and based on a flexible and extensible Java plug-in framework, allowing for cross-platform portability, accessibility and experimentation, thus fostering collaboration of users, developers and researchers in the field of room acoustics.