Nitric oxide modulates the firing rate of the rat supraoptic magnocellular neurons

In vitro, nitric oxide (NO) inhibits the firing rate of magnocellular neurosecretory cells (MNCs) of hypothalamic supraoptic and paraventricular nuclei and this effect has been attributed to GABAergic activation. However, little is known about the direct effects of NO in MNCs. We used the patch-clamp technique to verify the effect Of L-arginine, a precursor for NO synthesis, and N-omega-nitro-L-arginine methyl ester hydrochloride (L-NAME), an inhibitor of NOS, on spontaneous electrical activity of MNCs after glutamatergic and GABAergic blockade in Wistar rat brain slices. 6-Cyano-7-nitroquinoxaline-2,3-dione (CNQX) (10 mu M) and DL-2-amino-5-phosphonovaleric acid (DL-AP5) (30 mu M) were used to block postsynaptic glutamatergic currents, and picrotoxin (30 mu M) and saclofen (30 mu M) to block ionotropic and metabotropic postsynaptic GABAergic currents. Under these conditions, 500 mu M L-arginine decreased the firing rate from 3.7 +/- 0.6 Hz to 1.3 +/- 0.3 Hz. Conversely, 100 mu M L-NAME increased the firing rate from 3.0 +/- 0.3 Hz to 5.8 +/- 0.4 Hz. All points histogram analysis showed changes in resting potential from -58.1 +/- 0.8 mV to -62.2 +/- 1.1 mV in the presence of L-arginine and from -59.8 +/- 0.7 mV to -56.9 +/- 0.8 mV by L-NAME. Despite the nitrergic modulator effect on firing rate, some MNCs had no significant changes in their resting potential. In those neurons, hyperpolarizing after-potential (HAP) amplitude increased from 12.4 +/- 1.2 mV to 16.8 +/- 0.7 mV by L-arginine, but without significant changes by L-NAME treatment. To our knowledge, this is the first demonstration that NO can inhibit MNCs independent of GABAergic inputs. Further, our results point to HAP as a potential site for nitrergic modulation. (C) 2008 IBRO. Published by Elsevier Ltd. All rights reserved.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.

Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention

Classificação semântica de imagens com k-NN e RN

Um dos maiores desafios tecnológicos no presente é o de se conseguir gerar e manter, de uma maneira eficiente e consistente, uma base de dados de objectos multimédia, em particular, de imagens. A necessidade de desenvolver métodos de pesquisa automáticos baseados no conteúdo semântico das imagens tornou-se de máxima importância. MPEG-7 é um standard que descreve o contudo dos dados multimédia que suportam estes requisitos operacionais. Adiciona um conjunto de descritores audiovisuais de baixo nível. O histograma é a característica mais utilizada para representar as características globais de uma imagem. Neste trabalho é usado o “Edge Histogram Descriptor” (EHD), que resulta numa representação de baixo nível que permite a computação da similaridade entre imagens. Neste trabalho, é obtida uma caracterização semântica da imagem baseada neste descritor usando dois métodos da classificação: o algoritmo k Nearest Neighbors (k-NN) e uma Rede Neuronal (RN) de retro propagação. No algoritmo k-NN é usada a distância Euclidiana entre os descritores de duas imagens para calcular a similaridade entre imagens diferentes. A RN requer um processo de aprendizagem prévia, que inclui responder correctamente às amostras do treino e às amostras de teste. No fim deste trabalho, será apresentado um estudo sobre os resultados dos dois métodos da classificação.

Analysis and visualization of chromosome information

This paper analyzes the DNA code of several species in the perspective of information content. For that purpose several concepts and mathematical tools are selected towards establishing a quantitative method without a priori distorting the alphabet represented by the sequence of DNA bases. The synergies of associating Gray code, histogram characterization and multidimensional scaling visualization lead to a collection of plots with a categorical representation of species and chromosomes.

Comparação de diferentes técnicas de irradiação de mama em radioterapia com recurso a acelerador linear em modo de fotões

RESUMO: Introdução – A Radioterapia (RT) é uma abordagem terapêutica para tratamento de neoplasia de mama. Contudo, diferentes técnicas de irradiação (TI) podem ser usadas. Objetivos – Comparar 4 TI, considerando a irradiação dos volumes alvo (PTV) e dos órgãos de risco (OAR). Metodologia – Selecionaram-se 7 pacientes com indicação para RT de mama esquerda. Sobre tomografia computorizada foram feitos os contornos do PTV e dos OAR. Foram calculadas 4 planimetrias/paciente para as TI: conformacional externa (EBRT), intensidade modulada com 2 (IMRT2) e 5 campos (IMRT5) e arco dinâmico (DART). Resultados – Histogramas de dose volume foram comparados para todas as TI usando o software de análise estatística, IBM SPSS v20. Com IMRT5 e DART, os OAR recebem mais doses baixas. No entanto, IMRT5 apresenta melhores índices de conformidade e homogeneidade para o PTV. Conclusões – IMRT5 apresenta o melhor índice de conformidade; EBRT e IMRT2 apresentam melhores resultados que DART. Há d.e.s entre as TI, sobretudo em doses mais baixas nos OAR.

Avaliação de um sistema CAD na deteção de lesões em imagens mamográficas

Mestrado em Radiações Aplicadas às Tecnologias da Saúde - Área de especialização: Imagem Digital por Radiação X.

Comparação entre o pencil beam convolution algorithm e o analytical anisotropic algorithm em tumores de mama

Mestrado em Radioterapia

On cluster analysis of complex and heterogeneous data

3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon Portugal.

Detecção e seguimento de objectos em imagens termográficas: análise experimental de modelos de descrição

A instalação de sistemas de videovigilância, no interior ou exterior, em locais como aeroportos, centros comerciais, escritórios, edifícios estatais, bases militares ou casas privadas tem o intuito de auxiliar na tarefa de monitorização do local contra eventuais intrusos. Com estes sistemas é possível realizar a detecção e o seguimento das pessoas que se encontram no ambiente local, tornando a monitorização mais eficiente. Neste contexto, as imagens típicas (imagem natural e imagem infravermelha) são utilizadas para extrair informação dos objectos detectados e que irão ser seguidos. Contudo, as imagens convencionais são afectadas por condições ambientais adversas como o nível de luminosidade existente no local (luzes muito fortes ou escuridão total), a presença de chuva, de nevoeiro ou de fumo que dificultam a tarefa de monitorização das pessoas. Deste modo, tornou‐se necessário realizar estudos e apresentar soluções que aumentem a eficácia dos sistemas de videovigilância quando sujeitos a condições ambientais adversas, ou seja, em ambientes não controlados, sendo uma das soluções a utilização de imagens termográficas nos sistemas de videovigilância. Neste documento são apresentadas algumas das características das câmaras e imagens termográficas, assim como uma caracterização de cenários de vigilância. Em seguida, são apresentados resultados provenientes de um algoritmo que permite realizar a segmentação de pessoas utilizando imagens termográficas. O maior foco desta dissertação foi na análise dos modelos de descrição (Histograma de Cor, HOG, SIFT, SURF) para determinar o desempenho dos modelos em três casos: distinguir entre uma pessoa e um carro; distinguir entre duas pessoas distintas e determinar que é a mesma pessoa ao longo de uma sequência. De uma forma sucinta pretendeu‐se, com este estudo, contribuir para uma melhoria dos algoritmos de detecção e seguimento de objectos em sequências de vídeo de imagens termográficas. No final, através de uma análise dos resultados provenientes dos modelos de descrição, serão retiradas conclusões que servirão de indicação sobre qual o modelo que melhor permite discriminar entre objectos nas imagens termográficas.

Dosimetric effect of tissue heterogeneity for 125I prostate implants

Aim - To use Monte Carlo (MC) together with voxel phantoms to analyze the tissue heterogeneity effect in the dose distributions and equivalent uniform dose (EUD) for (125)I prostate implants. Background - Dose distribution calculations in low dose-rate brachytherapy are based on the dose deposition around a single source in a water phantom. This formalism does not take into account tissue heterogeneities, interseed attenuation, or finite patient dimensions effects. Tissue composition is especially important due to the photoelectric effect. Materials and Methods - The computed tomographies (CT) of two patients with prostate cancer were used to create voxel phantoms for the MC simulations. An elemental composition and density were assigned to each structure. Densities of the prostate, vesicles, rectum and bladder were determined through the CT electronic densities of 100 patients. The same simulations were performed considering the same phantom as pure water. Results were compared via dose-volume histograms and EUD for the prostate and rectum. Results - The mean absorbed doses presented deviations of 3.3-4.0% for the prostate and of 2.3-4.9% for the rectum, when comparing calculations in water with calculations in the heterogeneous phantom. In the calculations in water, the prostate D 90 was overestimated by 2.8-3.9% and the rectum D 0.1cc resulted in dose differences of 6-8%. The EUD resulted in an overestimation of 3.5-3.7% for the prostate and of 7.7-8.3% for the rectum. Conclusions - The deposited dose was consistently overestimated for the simulation in water. In order to increase the accuracy in the determination of dose distributions, especially around the rectum, the introduction of the model-based algorithms is recommended.

Fuzzy clustering não supervisionado na detecção automática de regiões de upwelling a partir de mapas de temperatura da superfície oceânica

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Characterizing the Risk Profiles of Intensive Care Units

OBJECTIVE: To develop a new method to evaluate the performance of individual ICUs through the calculation and visualisation of risk profiles. METHODS: The study included 102,561 patients consecutively admitted to 77 ICUs in Austria. We customized the function which predicts hospital mortality (using SAPS II) for each ICU. We then compared the risks of hospital mortality resulting from this function with the risks which would be obtained using the original function. The derived risk ratio was then plotted together with point-wise confidence intervals in order to visualise the individual risk profile of each ICU over the whole spectrum of expected hospital mortality. MAIN MEASUREMENTS AND RESULTS: We calculated risk profiles for all ICUs in the ASDI data set according to the proposed method. We show examples how the clinical performance of ICUs may depend on the severity of illness of their patients. Both the distribution of the Hosmer-Lemeshow goodness-of-fit test statistics and the histogram of the corresponding P values demonstrated a good fit of the individual risk models. CONCLUSIONS: Our risk profile model makes it possible to evaluate ICUs on the basis of the specific risk for patients to die compared to a reference sample over the whole spectrum of hospital mortality. Thus, ICUs at different levels of severity of illness can be directly compared, giving a clear advantage over the use of the conventional single point estimate of the overall observed-to-expected mortality ratio.

Hydrologic modeling and uncertainty analysis of an ungauged watershed using mapwindow-swat

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies