Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Exploring the emission intensities of ICP OES aided by chemometrics in the geographical discrimination of mineral waters

A rapid method for classification of mineral waters is proposed. The discrimination power was evaluated by a novel combination of chemometric data analysis and qualitative multi-elemental fingerprints of mineral water samples acquired from different regions of the Brazilian territory. The classification of mineral waters was assessed using only the wavelength emission intensities obtained by inductively coupled plasma optical emission spectrometry (ICP OES), monitoring different lines of Al, B, Ba, Ca, Cl, Cu, Co, Cr, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Sb, Si, Sr, Ti, V, and Zn, and Be, Dy, Gd, In, La, Sc and Y as internal standards. Data acquisition was done under robust (RC) and non-robust (NRC) conditions. Also, the combination of signal intensities of two or more emission lines for each element were evaluated instead of the individual lines. The performance of two classification-k-nearest neighbor (kNN) and soft independent modeling of class analogy (SIMCA)-and preprocessing algorithms, autoscaling and Pareto scaling, were evaluated for the ability to differentiate between the various samples in each approach tested (combination of robust or non-robust conditions with use of individual lines or sum of the intensities of emission lines). It was shown that qualitative ICP OES fingerprinting in combination with multivariate analysis is a promising analytical tool that has potential to become a recognized procedure for rapid authenticity and adulteration testing of mineral water samples or other material whose physicochemical properties (or origin) are directly related to mineral content.

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis-patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis-patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle-foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern. (C) Koninklijke Brill NV, Leiden, 2011

New prediction methods for CO2 evaporation inside tubes: Part I - A two-phase flow pattern map and a flow pattern based phenomenological model for two-phase flow frictional pressure drops

An updated flow pattern map was developed for CO2 on the basis of the previous Cheng-Ribatski-Wojtan-Thome CO2 flow pattern map [1,2] to extend the flow pattern map to a wider range of conditions. A new annular flow to dryout transition (A-D) and a new dryout to mist flow transition (D-M) were proposed here. In addition, a bubbly flow region which generally occurs at high mass velocities and low vapor qualities was added to the updated flow pattern map. The updated flow pattern map is applicable to a much wider range of conditions: tube diameters from 0.6 to 10 mm, mass velocities from 50 to 1500 kg/m(2) s, heat fluxes from 1.8 to 46 kW/m(2) and saturation temperatures from -28 to +25 degrees C (reduced pressures from 0.21 to 0.87). The updated flow pattern map was compared to independent experimental data of flow patterns for CO2 in the literature and it predicts the flow patterns well. Then, a database of CO2 two-phase flow pressure drop results from the literature was set up and the database was compared to the leading empirical pressure drop models: the correlations by Chisholm [3], Friedel [4], Gronnerud [5] and Muller-Steinhagen and Heck [6], a modified Chisholm correlation by Yoon et al. [7] and the flow pattern based model of Moreno Quiben and Thome [8-10]. None of these models was able to predict the CO2 pressure drop data well. Therefore, a new flow pattern based phenomenological model of two-phase flow frictional pressure drop for CO2 was developed by modifying the model of Moreno Quiben and Thome using the updated flow pattern map in this study and it predicts the CO2 pressure drop database quite well overall. (C) 2007 Elsevier Ltd. All rights reserved.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

On the classification of mixed construction and demolition waste aggregate by porosity and its impact on the mechanical performance of concrete

The properties of recycled aggregate produced from mixed (masonry and concrete) construction and demolition (C&D) waste are highly variable, and this restricts the use of such aggregate in structural concrete production. The development of classification techniques capable of reducing this variability is instrumental for quality control purposes and the production of high quality C&D aggregate. This paper investigates how the classification of C&D mixed coarse aggregate according to porosity influences the mechanical performance of concrete. Concretes using a variety of C&D aggregate porosity classes and different water/cement ratios were produced and the mechanical properties measured. For concretes produced with constant volume fractions of water, cement, natural sand and coarse aggregate from recycled mixed C&D waste, the compressive strength and Young modulus are direct exponential functions of the aggregate porosity. Sink and float technique is a simple laboratory density separation tool that facilitates the separation of cement particles with lower porosity, a difficult task when done only by visual sorting. For this experiment, separation using a 2.2 kg/dmA(3) suspension produced recycled aggregate (porosity less than 17%) which yielded good performance in concrete production. Industrial gravity separators may lead to the production of high quality recycled aggregate from mixed C&D waste for structural concrete applications.

Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method`s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright (C) 2009 John Wiley & Sons, Ltd.

Impacts of land-use pattern on soil water-content variability on the Loess Plateau of China

Long-term vegetation restoration carried out on the slopes of the Loess Plateau of China employed different spatial and temporal land-use patterns but very little is known about the effects of these patterns on soil water-content variability. For this study the small Donggou catchment was selected to investigate soil water-content distributions for three spatial scales, including the entire catchment area, sampling transects, and land-use systems. Gravimetric soil water contents were determined incrementally to a soil depth of 1.20 m, on 10 occasions from April to October, 2007, at approximately 20-day intervals. Results indicated that soil water contents were affected by the six land-use types, resulting in four distinct patterns of vertical distribution of soil moisture (uniform, increasing, decreasing, and fluctuating with soil depth). The soil water content and its variation were also influenced in a complex manner by five land-use patterns distributed along transects following the gradients of five similar slopes. These patterns with contrasting hydrological responses in different components, such as forage land (alfalfa)-cropland-shrubland or shrubland-grassland (bunge needlegrass)-cropland-grassland, showed the highest soil water-content variability. Soil water at the catchment scale exhibited a moderate variability for each measurement date, and the variability of soil water content decreased exponentially with increasing soil water content. The minimum sample size for accurate data for use in a hydrological model for the catchment, for example, required many more samples for drier (69) than for wet (10) conditions. To enhance erosion and runoff control, this study suggested two strategies for land management: (i) to create a mosaic pattern by land-use arrangement that located units with higher infiltration capacities downslope from those with lower soil infiltrabilities; and (ii) raising the soil-infiltration capacity of units within the spatial mosaic pattern where possible.

A Brazilian registry of juvenile dermatomyositis: onset features and classification of 189 cases

Objective To describe onset features, classification and treatment of juvenile dermatomyositis (JDM) and juvenile polymyositis (JPM) from a multicentre registry. Methods Inclusion criteria were onset age lower than 18 years and a diagnosis of any idiopathic inflammatory myopathy (IIM) by attending physician. Bohan & Peter (1975) criteria categorisation was established by a scoring algorithm to define JDM and JPM based oil clinical protocol data. Results Of the 189 cases included, 178 were classified as JDM, 9 as JPM (19.8: 1) and 2 did not fit the criteria; 6.9% had features of chronic arthritis and connective tissue disease overlap. Diagnosis classification agreement occurred in 66.1%. Medial? onset age was 7 years, median follow-up duration was 3.6 years. Malignancy was described in 2 (1.1%) cases. Muscle weakness occurred in 95.8%; heliotrope rash 83.5%; Gottron plaques 83.1%; 92% had at least one abnormal muscle enzyme result. Muscle biopsy performed in 74.6% was abnormal in 91.5% and electromyogram performed in 39.2% resulted abnormal in 93.2%. Logistic regression analysis was done in 66 cases with all parameters assessed and only aldolase resulted significant, as independent variable for definite JDM (OR=5.4, 95%CI 1.2-24.4, p=0.03). Regarding treatment, 97.9% received steroids; 72% had in addition at least one: methotrexate (75.7%), hydroxychloroquine (64.7%), cyclosporine A (20.6%), IV immunoglobulin (20.6%), azathioprine (10.3%) or cyclophosphamide (9.6%). In this series 24.3% developed calcinosis and mortality rate was 4.2%. Conclusion Evaluation of predefined criteria set for a valid diagnosis indicated aldolase as the most important parameter associated with de, methotrexate combination, was the most indicated treatment.

Spatial pattern of black spot incidence within citrus trees related to disease severity and pathogen dispersal

Guignardia citricarpa, the causal agent of citrus black spot, forms airborne ascospores on decomposing citrus leaves and water-spread conidia on fruits, leaves and twigs. The spatial pattern of diseased fruit in citrus tree canopies was used to assess the importance of ascospores and conidia in citrus black spot epidemics in Sao Paulo State, Brazil. The aggregation of diseased fruit in the citrus tree canopy was quantified by the binomial dispersion index (D) and the binary form of Taylor`s Power Law for 303 trees in six groves. D was significantly greater than 1 in 251 trees. The intercept of the regression line of Taylor`s Power Law was significantly greater than 0 and the slope was not different from 1, implying that diseased fruit was aggregated in the canopy independent of disease incidence. Disease incidence (p) and severity (S) were assessed in 2875 citrus trees. The incidence-severity relationship was described (R-2 = 88.7%) by the model ln(S) = ln(a) + bCLL(p) where CLL = complementary log-log transformation. The high severity at low incidence observed in many cases is also indicative of low distance spread of G. citricarpa spores. For the same level of disease incidence, some trees had most of the diseased fruit with many lesions and high disease severity, whereas other trees had most of the fruit with few lesions and low disease severity. Aggregation of diseased fruit in the trees suggests that splash-dispersed conidia have an important role in increasing the disease in citrus trees in Brazil.

Characterization of the electrocardiographic pattern of individuals with cerebral palsy

Background: Dentists of Lar Sao Francisco observed during dental treatment that children with cerebral palsy (CP) had increased heart rate (HR) and lower production of saliva. Despite the high prevalence of CP found in the literature (2.08-3.6/1000 individuals), little is known about the electrocardiographic (ECG) characteristics, especially HR, of individuals with CP. Objective: This study aimed to investigate the hypothesis that individuals with CP have a higher HR and to define other ECG characteristics of this population. Methods: Ninety children with CP underwent clinical examination and 12-lead rest ECG. Electrocardiographic data on rhythm, HR, PR interval, QRS duration, P/QRS/T axis, and QT, QTc and T(peak-end) intervals (minimum, mean, maximum, and dispersion) were measured and analyzed then compared with data from a control group with 35 normal children. Fisher and Mann-Whitney U tests were used, respectively, to compare categorical and continuous data. Results: Groups cerebral palsy and control did not significantly differ in age (9 +/- 3 x 9 +/- 4 years) and male gender (65% x 49%). Children with CP had a higher HR (104.0 +/- 20.6 x 84.2 +/- 13.3 beats per minute; P < .0001), shorter PR interval (128.8 +/- 15.0 x 138.1 +/- 15.1 milliseconds; P = .0018), shorter QRS duration (77.4 +/- 8.6 x 82.0 +/- 8.7 milliseconds; P = .0180), QRS axis (46.0 degrees +/- 26.3 degrees x 59.7 degrees +/- 24.8 degrees; P = .0024) and T-wave axis (34.3 degrees +/- 28.9 degrees x 42.9 degrees +/- 17.1 degrees; P = .034) more horizontally positioned, and greater mean QTc (418.1 +/- 18.4 x 408.5 +/- 19.4 milliseconds; P = .0110). All the electrocardiogram variables were within the reference range for the age group including those with significant differences. Conclusion: Children with CP showed increased HR and other abnormal ECG findings in the setting of this investigation. Further studies are needed to explain our findings and to correlate the increased HR with situations such as dehydration, stress, and autonomic nervous disorders. (C) 2011 Elsevier Inc. All rights reserved.

Conservation of dual-targeted proteins in Arabidopsis and rice points to a similar pattern of gene-family evolution

Gene duplication followed by acquisition of specific targeting information and dual targeting were evolutionary strategies enabling organelles to cope with overlapping functions. We examined the evolutionary trend of dual-targeted single-gene products in Arabidopsis and rice genomes. The number of paralogous proteins encoded by gene families and the dual-targeted orthologous proteins were analysed. The number of dual-targeted proteins and the corresponding gene-family sizes were similar in Arabidopsis and rice irrespective of genome sizes. We show that dual targeting of methionine aminopeptidase, monodehydroascorbate reductase, glutamyl-tRNA synthetase, and tyrosyl-tRNA synthetase was maintained despite occurrence of whole-genome duplications in Arabidopsis and rice as well as a polyploidization followed by a diploidization event (gene loss) in the latter.

The effect of storage on the solubilization pattern of bean hull non-starch polysaccharides

The storage of Carioca bean at 30 C and 75% relative humidity for eight months altered the solubilization pattern of hulls non-starch polysaccharides The polysaccharide physicochemical pattern changed resulting in a shift in the composition of water-soluble and water-insoluble polysaccharides caused by the insolubilization of galacturonans and xyloglucan Hulls make up 10% of whole beans which showed an increase of about 5% in water-insoluble polysaccharides and a decrease of about 1% in water-soluble polysaccharides with aging These values suggest that cotyledons and hulls together account for an increase of about 2 g of water-insoluble polysaccharides and a decrease of 1 5 g of water-soluble polysaccharides per 100 g of beans This change in the polysaccharide composition may produce a considerable difference in the dietary fiber profile The alterations observed in bean hull non-starch polysaccharide composition were similar to those previously observed in the cotyledon (C) 2010 Elsevier Ltd All rights reserved

Restoration of Pattern Recognition Receptor Costimulation to Treat Chromoblastomycosis, a Chronic Fungal Infection of the Skin

Chromoblastomycosis is a chronic skin infection caused by the fungus Fonsecaea pedrosoi. Exploring the reasons underlying the chronic nature of F. pedrosoi infection in a murine model of chromoblastomycosis, we find that chronicity develops due to a lack of pattern recognition receptor (PRR) costimulation. F. pedrosoi was recognized primarily by C-type lectin receptors (CLRs), but not by Toll-like receptors (TLRs), which resulted in the defective induction of proinflammatory cytokines. Inflammatory responses to F. pedrosoi could be reinstated by TLR costimulation, but also required the CLR Mincle and signaling via the Syk/CARD9 pathway. Importantly, exogenously administering TLR ligands helped clear F. pedrosoi infection in vivo. These results demonstrate how a failure in innate recognition can result in chronic infection, highlight the importance of coordinated PRR signaling, and provide proof of the principle that exogenously applied PRR agonists can be used therapeutically.