Apert syndrome: Factors involved in the cognitive development

Apert syndrome is characterized by craniosynostosis, symmetric syndactyly and other systemic malformations, with mental retardation usually present. The objective of this study was to correlate brain malformations and timing for surgery with neuropsychological evaluation. We also tried to determine other relevant aspects involved in cognitive development of these patients such as social classification of families and parents' education. Eighteen patients with Apert syndrome were studied, whose ages were between 14 and 322 months. Brain abnormalities were observed in 55.6% of them. The intelligence quotient or developmental quotient values observed were between 45 and 108. Mental development was related to the quality of family environment and parents' education. Mental development was not correlated to brain malformation or age at time of operation. In conclusion, quality of family environment was the most significant factor directly involved in mental development of patients with Apert syndrome.

Risk factors for vascular dementia in elderly psychiatric outpatients with preserved cognitive functions

The aim of the study was to assess risk factors for vascular dementia (VaD) in elderly psychiatric outpatients without dementia, and to determine to what extent clinical interventions targeted such risk factors. Out of 250 clinical charts, 78 were selected of patients over 60 years old, who showed no signs of dementia. Information was obtained regarding demographics, clinical conditions (diagnosis according to ICD-10), complementary investigation, cognitive functions (via CAMCOG), neuroimaging, and the presence of risk factors for VaD. Depression was the most prevalent psychiatric disorder (74%). A great majority of the patients (86%) had at least one risk factor for VaD. One-third of the sample showed three or more risk factors for VaD. The clinical conditions related to risk factors for VaD were hypertension (48.7%), heart disease (30.8%), hypercholesterolemia (25.6%), diabetes mellitus (23.1%), stroke (12.8%), tryglyceride (12.8%), and obesity (5.1%). In terms of lifestyle, smoking (19.2%), alcohol abuse (16.7%), and sedentarism (14.1%) were other risk factors found. Definite risk factors for VaD were found in 83.3% of the patients. Previous interventions targeting risk factors were found in only 20% of the cases. The high rates of risk factors for VaD identified in this sample suggest that psychiatrists should be more attentive to these factors for the prevention of VaD. © 2007 Elsevier B.V. All rights reserved.

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.

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.

Integrating open source codes for solid modeling and automatic mesh generation in FEM applications

The applications of the Finite Element Method (FEM) for three-dimensional domains are already well documented in the framework of Computational Electromagnetics. However, despite the power and reliability of this technique for solving partial differential equations, there are only a few examples of open source codes available and dedicated to the solid modeling and automatic constrained tetrahedralization, which are the most time consuming steps in a typical three-dimensional FEM simulation. Besides, these open source codes are usually developed separately by distinct software teams, and even under conflicting specifications. In this paper, we describe an experiment of open source code integration for solid modeling and automatic mesh generation. The integration strategy and techniques are discussed, and examples and performance results are given, specially for complicated and irregular volumes which are not simply connected. © 2011 IEEE.

Land use image classification through optimum-path forest clustering

Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.

Automatic landslide recognition through Optimum-Path Forest

In this paper we shed light over the problem of landslide automatic recognition using supervised classification, and we also introduced the OPF classifier in this context. We employed two images acquired from Geoeye-MS satellite at March-2010 in the northwest (high steep areas) and north sides (pipeline area) covering the area of Duque de Caxias city, Rio de Janeiro State, Brazil. The landslide recognition rate has been assessed through a cross-validation with 10 runnings. In regard to the classifiers, we have used OPF against SVM with Radial Basis Function for kernel mapping and a Bayesian classifier. We can conclude that OPF, Bayes and SVM achieved high recognition rates, being OPF the fastest approach. © 2012 IEEE.

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.

Loss of sleep spindle frequency deceleration in Obstructive Sleep Apnea

Objective: Sleep spindles have been suggested as surrogates of thalamo-cortical activity. Internal frequency modulation within a spindle's time frame has been demonstrated in healthy subjects, showing that spindles tend to decelerate their frequency before termination. We investigated internal frequency modulation of slow and fast spindles according to Obstructive Sleep Apnea (OSA) severity and brain topography. Methods: Seven non-OSA subjects and 21 patients with OSA contributed with 30 min of Non-REM sleep stage 2, subjected to a Matching pursuit procedure with Gabor chirplet functions for automatic detection of sleep spindles and quantification of sleep spindle internal frequency modulation (chirp rate). Results: Moderate OSA patients showed an inferior percentage of slow spindles with deceleration when compared to Mild and Non-OSA groups in frontal and parietal regions. In parietal regions, the percentage of slow spindles with deceleration was negatively correlated with global apnea-hypopnea index (r s = -0.519, p = 0.005). Discussion: Loss of physiological sleep spindle deceleration may either represent a disruption of thalamo-cortical loops generating spindle oscillations or some compensatory mechanism, an interesting venue for future research in the context of cognitive dysfunction in OSA. Significance: Quantification of internal frequency modulation (chirp rate) is proposed as a promising approach to advance description of sleep spindle dynamics in brain pathology. © 2013 International Federation of Clinical Neurophysiology.

Modulation of HJURP (Holliday Junction-Recognizing Protein) Levels Is Correlated with Glioblastoma Cells Survival

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic

In this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.

Classification of histological images based on the stationary wavelet transform

Non-Hodgkin lymphomas are of many distinct types, and different classification systems make it difficult to diagnose them correctly. Many of these systems classify lymphomas only based on what they look like under a microscope. In 2008 the World Health Organisation (WHO) introduced the most recent system, which also considers the chromosome features of the lymphoma cells and the presence of certain proteins on their surface. The WHO system is the one that we apply in this work. Herewith we present an automatic method to classify histological images of three types of non-Hodgkin lymphoma. Our method is based on the Stationary Wavelet Transform (SWT), and it consists of three steps: 1) extracting sub-bands from the histological image through SWT, 2) applying Analysis of Variance (ANOVA) to clean noise and select the most relevant information, 3) classifying it by the Support Vector Machine (SVM) algorithm. The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels.

Modulation of synaptic transmission in hippocampal CA1 neurons by a novel neurotoxin (beta-pompilidotoxin) derived from wasp venom

We examined the effects of beta-pompilidotoxin (beta-PMTX), a neurotoxin derived from wasp venom. on synaptic transmission in the mammalian central nervous system (CNS). Using hippocampal slice preparations of rodents, we made both extracellular and intracellular recordings from the CA1 pyramidal neurons in response to stimulation of the Schaffer collateral/commissural fibers. Application of 5-10 muM beta-PMTX enhanced excitatory postsynaptic potentials (EPSPs) but suppressed the fast component of the inhibitory postsynaptic potentials (IPSPs). In the presence of 10 muM bicuculline, beta-PMTX potentiated EPSPs that were composed of both non-NMDA and NMDA receptor-mediated potentials. Potentiation of EPSPs was originated by repetitive firings of the prosynaptic axons, causing Summation of EPSPs. In the presence of 10 muM CNQX and 50 muM APV, beta-PMTX suppressed GABA(A) receptor-mediated fast IPSPs but retained GABA(B) receptor-mediated slow IPSPs. Our results suggest that beta-PMTX facilitates excitatory synaptic transmission by a presynaptic mechanism and that it causes overexcitation followed by block of the activity of some population of interneurons which regulate the activity of GABA(A) receptors. (C) 2001 Published by Elsevier B.V. Ireland Ltd and the Japan Neuroscience Society.