Orphan nuclear receptor NGFI-B forms dimers with nonclassical interface

The orphan receptor nerve growth factor-induced B (NGFI-B) is a member of the nuclear receptor's subfamily 4A (Nr4a). NGFI-B was shown to be capable of binding both as a monomer to an extended half-site containing a single AAAGGTCA motif and also as a homodimer to a widely separated everted repeat, as opposed to a large number of nuclear receptors that recognize and bind specific DNA sequences predominantly as homo- and/or heterodimers. To unveil the structural organization of NGFI-B in solution, we determined the quaternary structure of the NGFI-B LBD by a combination of ab initio procedures from small-angle X-ray scattering (SAXS) data and hydrogen-deuterium exchange followed by mass spectrometry. Here we report that the protein forms dimers in solution with a radius of gyration of 2.9 nm and maximum dimension of 9.0 nm. We also show that the NGFI-B LBD dimer is V-shaped, with the opening angle significantly larger than that of classical dimer's exemplified by estrogen receptor (ER) or retinoid X receptor (RXR). Surprisingly, NGFI-B dimers formation does not occur via the classical nuclear receptor dimerization interface exemplified by ER and RXR, but instead, involves an extended surface area composed of the loop between helices 3 and 4 and C-terminal fraction of the helix 3. Remarkably, the NGFI-B dimer interface is similar to the dimerization interface earlier revealed for glucocorticoid nuclear receptor (GR), which might be relevant to the recognition of cognate DNA response elements by NGFI-B and to antagonism of NGFI-B-dependent transcription exercised by GR in cells. Published by Cold Spring Harbor Laboratory Press. Copyright © 2007 The Protein Society.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.

Dual path: A concept to improve the esthetic replacement of missing anterior teeth with a removable partial denture

The dual path of insertion concept for removable partial denture (RPD) design may be used in esthetically demanding situations. When compared to conventional RPDs, the main advantage of this design is the minimal use of clasps. This clinical report describes the treatment of a patient with an anterior maxillary edentulous area using a dual path RPD. The diagnostic cast was surveyed to ensure the adequacy of the undercuts on the mesial surfaces of the anterior abutments, where rigid minor connectors were placed. Inverted V-shaped canine cingulum rest seats were prepared to provide resistance to tooth movement during function. The dual path RPD concept allows excellent esthetic results, minimizes tooth preparation, and reduces the tendency toward plaque accumulation in a Kennedy class IV partially edentulous arch. © 2008 by The American College of Prosthodontists.

Investigation of the metal/porcelain interface in LASER-welded Ni-Cr-Mo alloy

The interface formed between the metal and the porcelain of laser-welded Ni-Cr-Mo alloy was studied on a metallurgical basis. The characterization was carried out by using optical microscope, electron scan microscopy and X-ray dispersive spectroscopy techniques and mechanical three-point flexion tests, in the laser-welded region, with and without porcelain. The union of the porcelain with the alloy is possible only after the oxidation of the metallic surface and the subsequent application of a bonding agent known as opaque. The porcelain applied to the base metal and weld bead showed different behaviours - after the flexion test, the base metal showed cracks, while that in the weld bead broke away completely. It was noted that the region subjected to laser welding had lower adherence to the porcelain than the base metal region, due to microstructural refinement of the weld bead. These results can be shown by the X-ray dispersive spectroscopy carried out on the regions studied. The flexion tests demonstrated that the Ni-Cr-Mo alloy subject to laser welding had significant alterations in its mechanical properties after application of the porcelain.

HPF boost interleaved operating in discontinuous conduction mode for trolleybus application

This paper deals with results of a research and development (R&D) project in cooperation with Electric Power Distribution Company in São Paulo (Brazil) regarding the development and experimental analysis of a new concept of power drive system suitable for application in traction systems of electrical vehicles pulled by electrical motors, which can be powered by urban DC or AC distribution networks. The proposed front-end structure is composed by five boost power cells in interleaving connection, operating in discontinuous conduction mode as AC-DC converter, or as DC-DC converter, in order to provide the proper DC output voltage range required by DC or AC adjustable speed drivers. Therefore, when supplied by single-phase AC distribution networks, and operating as AC-DC converter, it is capable to provide high power factor, reduced harmonic distortion in the input current, complying with the restrictions imposed by the IEC 61000-3-4 standards resulting in significant improvements for the trolleybuses systems efficiency and for the urban distribution network costs. Considering the compliance with input current restrictions imposed by IEC 61000-3-4 standards, two digital control strategies were evaluated. The digital controller has been implemented using a low cost FPGA (XC3S200) and developed totally using a hardware description language VHDL and fixed point arithmetic. Experimental results from a 15 kW low power scale prototype operating in DC and AC conditions are presented, in order to verify the feasibility and performance of the proposed system. © 2009 IEEE.

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.

Experimental study of the complex reflection coefficient of shear waves from the solid-liquid interface

The determination of the reflection coefficient of shear waves reflected from a solid-liquid interface is an important method in order to study the viscoelastic properties of liquids at high frequency. The reflection coefficient is a complex number. While the magnitude measurement is relatively easy and precise, the phase measurement is very difficult due to its strong temperature dependence. For that reason, most authors choose a simplified method in order to obtain the viscoelastic properties of liquids from the measured coefficient. In this simplified method, inconsistent viscosity results are obtained because pure viscous behavior is assumed and the phase is not measured. This work deals with an effort to improve the experimental technique required to measure both the magnitude and phase of the reflection coefficient and it intends to report realistic values for oils in a wide range of viscosity (0.092 - 6.7 Pa.s). Moreover, a device calibration process is investigated in order to monitor the dynamic viscosity of the liquid.

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.

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.

Path formulation for multiparameter D3-equivariant bifurcation problems

We implement a singularity theory approach, the path formulation, to classify D3-equivariant bifurcation problems of corank 2, with one or two distinguished parameters, and their perturbations. The bifurcation diagrams are identified with sections over paths in the parameter space of a Ba-miniversal unfolding f0 of their cores. Equivalence between paths is given by diffeomorphisms liftable over the projection from the zero-set of F0 onto its unfolding parameter space. We apply our results to degenerate bifurcation of period-3 subharmonics in reversible systems, in particular in the 1:1-resonance.

Latin America on its path into the digital age: where are we?

Includes bibliography

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.