Panoramic view of the occurrence of Yersinia species other than Y. pestis in Brazil

Data on the occurrence of Yersinia species, other than Y. pestis in Brazil are presented. Over the past 40 years, 767 Yersinia strains have been identified and typed by the National Reference Center on Yersinia spp. other than Y. pestis, using the classical biochemical tests for species characterization. The strains were further classified into biotypes, serotypes and phagetypes when pertinent. These tests led to the identification of Yersinia cultures belonging to the species Y. enterocolitica, Y. pseudotuberculosis, Y. intermedia, Y. frederiksenii and Y. kristensenii. Six isolates could not be classified in any of the known Yersinia species and for this reason were defined as Non-typable (NT). The bio-sero-phagetypes of these strains were diverse. The following species of Yersinia were not identified among the Brazilian strains by the classical phenotypic or biochemical tests: Y. aldovae, Y. rhodei, Y. mollaretti, Y. bercovieri and Y. ruckeri. The Yersinia strains were isolated from clinical material taken from sick and/or healthy humans and animals, from various types of food and from the environment, by investigators of various Institutions localized in different cities and regions of Brazil.

Calibration uncertainty estimation for internal aerodynamic balance

Aerodynamic balances are employed in wind tunnels to estimate the forces and moments acting on the model under test. This paper proposes a methodology for the assessment of uncertainty in the calibration of an internal multi-component aerodynamic balance. In order to obtain a suitable model to provide aerodynamic loads from the balance sensor responses, a calibration is performed prior to the tests by applying known weights to the balance. A multivariate polynomial fitting by the least squares method is used to interpolate the calibration data points. The uncertainties of both the applied loads and the readings of the sensors are considered in the regression. The data reduction includes the estimation of the calibration coefficients, the predicted values of the load components and their corresponding uncertainties, as well as the goodness of fit.

Raquianestesia contínua em paciente submetido à gastrectomia parcial. Relato de caso

BACKGROUND AND OBJECTIVES: Due to the high incidence of technical and neurological complications, continuous spinal blocks were not performed for several years. With the advent of intermediate catheters the technique has been used more often and gaining acceptance among anesthesiologists. The objective of this report was to demonstrate the usefulness of the technique as a viable alternative for medium and major size surgeries. CASE REPORT: This is a 58 years old female patient, weighing 62 kg, physical status ASA I, with a history of migraines, low back pain, and prior surgeries under spinal block without intercurrence. The patient was scheduled for exploratory laparotomy for a probable pelvic tumor. After venoclysis with an 18G catheter, monitoring with cardioscope, non-invasive blood pressure and pulse oximetry was instituted; she was sedated with 2 mg of midazolam and 100 μg of fentanyl, and placed in left lateral decubitus. The patient underwent continuous spinal block through the median approach in L 3-L 4; 9 mg of 0.5% hyperbaric bupivacaine and 120 μ g of morphine sulfate were administered. Inspection of the abdominal cavity revealed a gastric stromal tumor that required an increase in the incision for a partial gastrectomy. A small dose of hyperbaric solution was required for the entire procedure, which was associated with complete hemodynamic stability. Postoperative admission to the ICU was not necessary; the patient presented a good evolution without complaints and with a high degree of satisfaction. She was discharged from the hospital after 72 hours without intercurrence. CONCLUSIONS: Intermediate catheters used in continuous spinal blocks have shown the potential to turn it an attractive and useful technique in medium and large size surgeries and it can even be an effective alternative in the management of critical patients to whom hemodynamic repercussions can be harmful.

Infrared face recogniton by optimum-path forest

This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

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.

Clinical assessment of ethinylestradiol and cyproterone acetate in menstrual irregularities of hyper-androgenic origin

We report on the results of a double-blind, randomized, controlled clinical trial comparing two preparations of ethinylestradiol and cyproterone acetate in the treatment of women of reproductive age presenting menstrual irregularities of hyper-androgenic origin. After obtaining informed consent, subjects were randomized to a 4-month treatment period consisting of one daily dose of 0.035mg ethinylestradiol + 2mg cyproterone acetate. The treatment regimen cycle consisted of one pill, once daily for 21 days, followed by a 7-day pill-free period. We compared the efficacy of two presentations of the drug combination after each treatment cycle (Visits 2, 3, 4, and 5) in establishment and maintenance of menstrual regulation, intensity of menstrual flow, and dysmenorrhea, as well as a comparison of the two presentations in terms of Global Satisfaction and Drug Satisfaction assessments performed by the patients and the investigating physician. At each study visit, drug compliance and use of concomitant medications, as well as incidence, severity and duration of adverse events were recorded. A total of 86 subjects were randomized to treatment, with 43 subjects in each treatment group. At Visit 2 and each subsequent visit, all patients in both treatment groups reported an episode of withdrawal bleeding during the 7-day hormone-free period. We observed a statistically significant (p<0.0001) decrease in the incidence of dysmenorrhea at each study visit in relation to the pretreatment assessment. There was a significant reduction (p<0.0001) in the number of subjects reporting intermenstrual bleeding at each study visit in both treatment groups. Global Satisfaction scores by the patient and physician increased significantly at each successive study visit in both treatment groups. There were no clinically significant changes in vital signs, weight, and body mass index throughout the study period in either group. The number of subjects reporting adverse events at each visit did not vary between treatment groups. The combined oral contraceptive pill containing ethinylestradiol and cyproterone acetate was found to be both effective and safe in the menstrual irregularities of hyper-androgenic origin (amenorrhea, dysmenorrhea, and intermenstrual bleeding) assessed in this study. © Copyright Moreira Jr. Editora. Todos os direitos reservados.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.

Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

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.

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.

What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

Optimum-Path Forest pruning parameter estimation through Harmony Search

Pattern recognition in large amount of data has been paramount in the last decade, since that is not straightforward to design interactive and real time classification systems. Very recently, the Optimum-Path Forest classifier was proposed to overcome such limitations, together with its training set pruning algorithm, which requires a parameter that has been empirically set up to date. In this paper, we propose a Harmony Search-based algorithm that can find near optimal values for that. The experimental results have showed that our algorithm is able to find proper values for the OPF pruning algorithm parameter. © 2011 IEEE.