Exposição de culturas de fibroblastos de pterígios primários e recidivados à mitomicina c e ao 5-fluorouracil

Purpose: To evaluate the proliferation of the fibroblasts from primary and recurrent pterygium in culture of cells, after the exposure to mitomycin C and to 5-fluorouracil. Methods: Cultures of fibroblasts from primary and recurrent pterygiuns had been carried through. The cells of the third passage were exposed to mitomycin C 0.4% during 3 minutes and to 5-fluorouracil 25mg/ml that was kept in the nutritional medium. After 3, 6, 12 and 18 days of the exposure, cell countings were made in hemocytometer, in triplicate, with a control not treated with the drugs for each day. The data were submitted to statistical analysis.Results: Mitomicyn C had homogeneous behavior in the primary and recurrent groups, which inhibited the cellular proliferation since the first counting day. However, with 5-fluorouracil, the proliferation inhibition was verified only after the third day of evaluation (in the second counting day) in the recurrent group, and since the first counting day in the primary group. At the end of the experimental period, 5-fluorouracil and mitomycin C were equally efficient in the inhibition of the fibroblasts from primary and recurrent pterygium proliferation. In the controls not exposed, the cell's numbers were increasing during the experimental time.Conclusion: Mitomycin and 5-fluorouracil are equally able to inhibit fibroblasts proliferation from primary and recurrent pterygium Tenon's capsule in cell culture.

Fontes de distorções da rede geodésica Brasileira

The Brazilian Geodetic Network started to be established in the early 40's, employing classical surveying methods, such as triangulation and trilateration. With the introduction of satellite positioning systems, such as TRANSIT and GPS, that network was densified. That data was adjusted by employing a variety of methods, yielding distortions in the network that need to be understood. In this work, we analyze and interpret study cases in an attempt to understand the distortions in the Brazilian network. For each case, we performed the network adjustment employing the GHOST software suite. The results show that the distortion is least sensitive to the removal of invar baselines in the classical network. The network would be more affected by the inexistence of Laplace stations and Doppler control points, with differences up to 4.5 m.

Pesquisa de Salmonella e das condições sanitárias em frangos e lingüiças comercializados na cidade de Botucatu

In the present investigation were evaluated the sanitary conditions of poultry and several types of sausages retailed in Botucatu, São Paulo, Brazil for the determination of the most probable number of coliforms at 45°C/g besides the research of Salmonella using traditional methodology and PCR. In order to do so, 50 samples of poultry and 75 of sausages were collected from nine different establishments in the city, in the period of April to November of 2006. Of the 50 samples of chicken meat, 35 (70%) were out of the microbiologic parameters, according to Brazilian Sanitary Resolution RDC no 12 of Anvisa (>104 coliforms at 45°C/g). In this Resolution, the research of Salmonella is not demanded, but 4 samples (8%) presented the pathogen using the traditional methodology. That presence was confirmed by PCR, which was also positive in another 23, in a total of 27 positive samples (54%). Among 75 samples of sausages, 30 (40%) were out of the allowed limits, with 7 positive samples for Salmonella, using traditional methodology. However, if we consider PCR test, the number of positive samples increases to 42 (56%). Adding this number to coliforms microbiological limits, 86.7% of the analyzed sausages were inappropriate for the consumption.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Identification of foliar diseases in cotton crop

The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. © 2012 Taylor & Francis Group.

Fast identification of individuals based on iris characteristics for biometric systems

Nowadays, systems based on biométrie techniques have a wide acceptance in many different areas, due to their levels of safety and accuracy. A biometrie technique that is gaining prominence is the identification of individuals through iris recognition. However, to be proficiently used these systems must process their recognition task as fast as possible. The goal of this work has been the development of an iris recognition method to produce results rapidly, yet without losing the recognition accuracy. The experimental results show that the method is quite promising. © 2012 Taylor & Francis Group.

Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Effect of light curing modes on mechanical properties of direct and indirect composites

Objective. To evaluate the degree of conversion (DC), flexural strength (FS) and Knoop microhardness (KHN) of direct and indirect composite resins polymerized with different curing systems. Materials and methods. Specimens of direct (Z250, 3M/Espe) and indirect (Sinfony, 3M/Espe) restorative materials were made and polymerized using two light curing units: XL2500 (3M/Espe) and Visio system (3M/Espe). Absorption spectra of both composites were obtained on a FTIR spectrometer in order to calculate the DC. FS was evaluated in a universal testing machine and surface microhardness was performed in a microhardness tester (50gf/15s). DC, FS and KHN data were submitted to two-way ANOVA and Tukey's test (α = 0.05). Results. Z250 showed higher DC, FS and KHN compared with Sinfony when the polymerization was carried out with XL2500 (p < 0.05). However, there is no statistical difference in DC between the materials when Visio was used (p > 0.05). Visio showed higher DC and KHN for Z250 and Sinfony than the values obtained using XL2500 light curing (p < 0.05). For FS, no significant difference between curing units was found (p > 0.05). Conclusion. Even though the Visio system could increase DC and KHN for some direct and indirect composites, compared with the conventional halogen curing unit, a high number of monomers did not undergo conversion during the polymerization. © 2013 Informa Healthcare.

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.

Allelopathic effect of the peel of coffee fruit

In coffee (Coffea arabica)-producing areas, particularly in the southeastern region of Brazil, it is part of the agricultural practice to incorporate coffee fruit peels in organic substrates for the production of vegetables, fruit trees, and even in the coffee cultures, for use not only as an organic amendment but also as a way to control weeds. This study aimed to evaluate the allelopathic potential of dry and fresh coffee fruit peel extracts. Therefore, lettuce, Malaysian cabbage and beggar's tick seeds and seedlings were used as test subjects for the pre-emergence, post-emergence, and mitotic index of meristematic root cell tests. Additionally, the extracts' contents of phenols, flavonoids and caffeine, in addition to their antioxidant activity, were determined. The development of all the tested seedlings was inferred by the extracts from their roots and hypocotyls. The mitotic index was reduced in comparison to the negative control. A considerable quantity of phenols, flavonoids and caffeine was found in both of the extracts. A progressively growing antioxidant activity of the extracts was observed as their concentrations increased. Through the results obtained in this study, it is possible to conclude that C. arabica has allelopathic compounds. © 2013 Elsevier B.V.

Anita Garibaldi coberta por histórias

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O processo de auto-avaliação da UFMA (2004-2006) no contexto regulatório da Educação superior

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)