Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Neural network basis function center selection using cluster analysis

This paper deals with the selection of centres for radial basis function (RBF) networks. A novel mean-tracking clustering algorithm is described as a way in which centers can be chosen based on a batch of collected data. A direct comparison is made between the mean-tracking algorithm and k-means clustering and it is shown how mean-tracking clustering is significantly better in terms of achieving an RBF network which performs accurate function modelling.

Extreme wind storms over Europe in present and future climate: a cluster analysis approach

Boreal winter wind storm situations over Central Europe are investigated by means of an objective cluster analysis. Surface data from the NCEP-Reanalysis and ECHAM4/OPYC3-climate change GHG simulation (IS92a) are considered. To achieve an optimum separation of clusters of extreme storm conditions, 55 clusters of weather patterns are differentiated. To reduce the computational effort, a PCA is initially performed, leading to a data reduction of about 98 %. The clustering itself was computed on 3-day periods constructed with the first six PCs using "k-means" clustering algorithm. The applied method enables an evaluation of the time evolution of the synoptic developments. The climate change signal is constructed by a projection of the GCM simulation on the EOFs attained from the NCEP-Reanalysis. Consequently, the same clusters are obtained and frequency distributions can be compared. For Central Europe, four primary storm clusters are identified. These clusters feature almost 72 % of the historical extreme storms events and add only to 5 % of the total relative frequency. Moreover, they show a statistically significant signature in the associated wind fields over Europe. An increased frequency of Central European storm clusters is detected with enhanced GHG conditions, associated with an enhancement of the pressure gradient over Central Europe. Consequently, more intense wind events over Central Europe are expected. The presented algorithm will be highly valuable for the analysis of huge data amounts as is required for e.g. multi-model ensemble analysis, particularly because of the enormous data reduction.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles

Background: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. New method: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signalto-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA)to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). Results: After validating the pipeline on simulated data, we tested it on data from two experiments – a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.

Objective determination of the extratropical transition of tropical cyclones in the Northern Hemisphere

Extratropical transition (ET) has eluded objective identification since the realisation of its existence in the 1970s. Recent advances in numerical, computational models have provided data of higher resolution than previously available. In conjunction with this, an objective characterisation of the structure of a storm has now become widely accepted in the literature. Here we present a method of combining these two advances to provide an objective method for defining ET. The approach involves applying K-means clustering to isolate different life-cycle stages of cyclones and then analysing the progression through these stages. This methodology is then tested by applying it to five recent years from the European Centre of Medium-Range Weather Forecasting operational analyses. It is found that this method is able to determine the general characteristics for ET in the Northern Hemisphere. Between 2008 and 2012, 54% (±7, 32 of 59) of Northern Hemisphere tropical storms are estimated to undergo ET. There is great variability across basins and time of year. To fully capture all the instances of ET is necessary to introduce and characterise multiple pathways through transition. Only one of the three transition types needed has been previously well-studied. A brief description of the alternate types of transitions is given, along with illustrative storms, to assist with further study

Understanding climate change projections for precipitation over western Europe with a weather typing approach

Precipitation over western Europe (WE) is projected to increase (decrease) roughly northward (equatorward) of 50°N during the 21st century. These changes are generally attributed to alterations in the regional large-scale circulation, e.g., jet stream, cyclone activity, and blocking frequencies. A novel weather typing within the sector (30°W–10°E, 25–70°N) is used for a more comprehensive dynamical interpretation of precipitation changes. A k-means clustering on daily mean sea level pressure was undertaken for ERA-Interim reanalysis (1979–2014). Eight weather types are identified: S1, S2, S3 (summertime types), W1, W2, W3 (wintertime types), B1, and B2 (blocking-like types). Their distinctive dynamical characteristics allow identifying the main large-scale precipitation-driving mechanisms. Simulations with 22 Coupled Model Intercomparison Project 5 models for recent climate conditions show biases in reproducing the observed seasonality of weather types. In particular, an overestimation of weather type frequencies associated with zonal airflow is identified. Considering projections following the (Representative Concentration Pathways) RCP8.5 scenario over 2071–2100, the frequencies of the three driest types (S1, B2, and W3) are projected to increase (mainly S1, +4%) in detriment of the rainiest types, particularly W1 (−3%). These changes explain most of the precipitation projections over WE. However, a weather type-independent background signal is identified (increase/decrease in precipitation over northern/southern WE), suggesting modifications in precipitation-generating processes and/or model inability to accurately simulate these processes. Despite these caveats in the precipitation scenarios for WE, which must be duly taken into account, our approach permits a better understanding of the projected trends for precipitation over WE.

Causas de variação nos preços de bovinos nelore elite no Brasil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise multivariada e variabilidade espacial na estimativa da erodibilidade de um argissolo vermelho-amarelo

A erodibilidade é um fator de extrema importância na caracterização da perda de solo, representando os processos que regulam a infiltração de água e sua resistência à desagregação e o transporte de partículas. Assim, por meio da análise de dependência espacial dos componentes principais da erodibilidade (fator K), objetivou-se estimar a erodibilidade do solo em uma área de nascentes da microbacia do Córrego do Tijuco, Monte Alto-SP, e analisar a variabilidade espacial das variáveis granulométricas do solo ao longo do relevo. A erodibilidade média da área foi considerada alta, e a análise de agrupamento k-means apontou para uma formação de cinco grupos: no primeiro, os altos teores de areia grossa (AG) e média (AM) condicionaram sua distribuição nas áreas planas; o segundo, caracterizado pelo alto teor de areia fina (AF), distribui-se nos declives mais convexos; o terceiro, com altos teores de silte e areia muito fina (AMF), concentrou-se nos maiores declives e concavidades; o quarto, com maior teor de argila, seguiu as zonas de escoamento de água; e o quinto, com alto teor de matéria orgânica (MO) e areia grossa (AG), distribui-se nas proximidades da zona urbana. A análise de componentes principais (ACP) mostrou quatro componentes com 87,4 % das informações, sendo o primeiro componente principal (CP1) discriminado pelo transporte seletivo de partículas principalmente em zonas pontuais de maior declividade e acúmulo de sedimentos; o segundo (CP2), discriminado pela baixa coesão entre as partículas, mostra acúmulo da areia fina nas áreas de menor cota em toda a área de concentração de água; o terceiro (CP3), discriminado pela maior agregação do solo, concentra-se principalmente nas bases de grandes declives; e o quarto (CP4), discriminado pela areia muito fina, distribui-se ao longo das declividades nas maiores altitudes. Os resultados sugerem o comportamento granulométrico do solo, que se mostra suscetível ao processo erosivo devido às condições texturais superficiais e à movimentação do relevo.

The variability and predictors of quality of AIDS care services in Brazil

Background: Since establishing universal free access to antiretroviral therapy in 1996, the Brazilian Health System has increased the number of centers providing HIV/AIDS outpatient care from 33 to 540. There had been no formal monitoring of the quality of these services until a survey of 336 AIDS health centers across 7 Brazilian states was undertaken in 2002. Managers of the services were asked to assess their clinics according to parameters of service inputs and service delivery processes. This report analyzes the survey results and identifies predictors of the overall quality of service delivery.Methods: The survey involved completion of a multiple-choice questionnaire comprising 107 parameters of service inputs and processes of delivering care, with responses assessed according to their likely impact on service quality using a 3-point scale. K-means clustering was used to group these services according to their scored responses. Logistic regression analysis was performed to identify predictors of high service quality.Results: The questionnaire was completed by 95.8% (322) of the managers of the sites surveyed. Most sites scored about 50% of the benchmark expectation. K-means clustering analysis identified four quality levels within which services could be grouped: 76 services (24%) were classed as level 1 (best), 53 (16%) as level 2 (medium), 113 (35%) as level 3 (poor), and 80 (25%) as level 4 (very poor). Parameters of service delivery processes were more important than those relating to service inputs for determining the quality classification. Predictors of quality services included larger care sites, specialization for HIV/AIDS, and location within large municipalities.Conclusion: The survey demonstrated highly variable levels of HIV/AIDS service quality across the sites. Many sites were found to have deficiencies in the processes of service delivery processes that could benefit from quality improvement initiatives. These findings could have implications for how HIV/AIDS services are planned in Brazil to achieve quality standards, such as for where service sites should be located, their size and staffing requirements. A set of service delivery indicators has been identified that could be used for routine monitoring of HIV/AIDS service delivery for HIV/AIDS in Brazil (and potentially in other similar settings).

Ajuste de modelos não lineares e estimativas de parâmetros genéticos para produção de ovos de uma linhagem de poedeiras White Leghorn

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fibroblastos associados ao câncer e correlação com parãmetros patológicos em melanomas cutâneos caninos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multivariate analysis of the temporal variability of sugarcane ripening in south-eastern Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Biogeographic Distribution Patterns and Their Correlates in the Diverse Frog Fauna of the Atlantic Forest Hotspot

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