On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

Emergence of Swiss metropole and scaling properties of urban clusters

Defining the limits of an urban agglomeration is essential both for fundamental and applied studies in quantitative and theoretical geography. A simple and consistent way for defining such urban clusters is important for performing different statistical analysis and comparisons. Traditionally, agglomerations are defined using a rather qualitative approach based on various statistical measures. This definition varies generally from one country to another, and the data taken into account are different. In this paper, we explore the use of the City Clustering Algorithm (CCA) for the agglomeration definition in Switzerland. This algorithm provides a systemic and easy way to define an urban area based only on population data. The CCA allows the specification of the spatial resolution for defining the urban clusters. The results from different resolutions are compared and analysed, and the effect of filtering the data investigated. Different scales and parameters allow highlighting different phenomena. The study of Zipf's law using the visual rank-size rule shows that it is valid only for some specific urban clusters, inside a narrow range of the spatial resolution of the CCA. The scale where emergence of one main cluster occurs can also be found in the analysis using Zipf's law. The study of the urban clusters at different scales using the lacunarity measure - a complementary measure to the fractal dimension - allows to highlight the change of scale at a given range.

Paper Surface Image Processing for Roughness Quality Measurement

Diplomityössä on käsitelty paperin pinnankarkeuden mittausta, joka on keskeisimpiä ongelmia paperimateriaalien tutkimuksessa. Paperiteollisuudessa käytettävät mittausmenetelmät sisältävät monia haittapuolia kuten esimerkiksi epätarkkuus ja yhteensopimattomuus sileiden papereiden mittauksissa, sekä suuret vaatimukset laboratorio-olosuhteille ja menetelmien hitaus. Työssä on tutkittu optiseen sirontaan perustuvia menetelmiä pinnankarkeuden määrittämisessä. Konenäköä ja kuvan-käsittelytekniikoita tutkittiin karkeilla paperipinnoilla. Tutkimuksessa käytetyt algoritmit on tehty Matlab® ohjelmalle. Saadut tulokset osoittavat mahdollisuuden pinnankarkeuden mittaamiseen kuvauksen avulla. Parhaimman tuloksen perinteisen ja kuvausmenetelmän välillä antoi fraktaaliulottuvuuteen perustuva menetelmä.

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

Reconstruction and analysis of surface variation using photometric stereo

In many industrial applications, accurate and fast surface reconstruction is essential for quality control. Variation in surface finishing parameters, such as surface roughness, can reflect defects in a manufacturing process, non-optimal product operational efficiency, and reduced life expectancy of the product. This thesis considers reconstruction and analysis of high-frequency variation, that is roughness, on planar surfaces. Standard roughness measures in industry are calculated from surface topography. A fast and non-contact method to obtain surface topography is to apply photometric stereo in the estimation of surface gradients and to reconstruct the surface by integrating the gradient fields. Alternatively, visual methods, such as statistical measures, fractal dimension and distance transforms, can be used to characterize surface roughness directly from gray-scale images. In this thesis, the accuracy of distance transforms, statistical measures, and fractal dimension are evaluated in the estimation of surface roughness from gray-scale images and topographies. The results are contrasted to standard industry roughness measures. In distance transforms, the key idea is that distance values calculated along a highly varying surface are greater than distances calculated along a smoother surface. Statistical measures and fractal dimension are common surface roughness measures. In the experiments, skewness and variance of brightness distribution, fractal dimension, and distance transforms exhibited strong linear correlations to standard industry roughness measures. One of the key strengths of photometric stereo method is the acquisition of higher frequency variation of surfaces. In this thesis, the reconstruction of planar high-frequency varying surfaces is studied in the presence of imaging noise and blur. Two Wiener filterbased methods are proposed of which one is optimal in the sense of surface power spectral density given the spectral properties of the imaging noise and blur. Experiments show that the proposed methods preserve the inherent high-frequency variation in the reconstructed surfaces, whereas traditional reconstruction methods typically handle incorrect measurements by smoothing, which dampens the high-frequency variation.

Constitutive laws for strong geometric non-linearity

This paper is divided into two different parts. The first one provides a brief introduction to the fractal geometry with some simple illustrations in fluid mechanics. We thought it would be helpful to introduce the reader into this relatively new approach to mechanics that has not been sufficiently explored by engineers yet. Although in fluid mechanics, mainly in problems of percolation and binary flows, the use of fractals has gained some attention, the same is not true for solid mechanics, from the best of our knowledge. The second part deals with the mechanical behavior of thin wires subjected to very large deformations. It is shown that starting to a plausible conjecture it is possible to find global constitutive equations correlating geometrical end energy variables with the fractal dimension of the solid subjected to large deformations. It is pointed out the need to complement the present proposal with experimental work.

Synthèse et caractérisation physicochimique de peptides de polyglutamines

Neuf maladies neurodégénératives sont le produit de l’expression de gènes mutés, dans lesquels le codon CAG est répété au-delà d’un seuil pathologique. Ceci produit des protéines mutantes dans lesquelles sont insérés des segments de polyglutamines (polyGln), qui perdent leur activité et acquièrent une nouvelle fonction, ce qui est toxique pour le neurone. Ces altérations sont attribuables aux propriétés particulières de la polyGln. En effet, ces dernières possèdent la capacité de s’assembler pour former des corps d’inclusion intracellulaires. Cette propension à l’agrégation de la polyGln rend difficile l’étude de ces pathologies. C’est ainsi que l’utilisation de peptides peut s’avérer une approche avantageuse. Toutefois, la synthèse de polyGln est associée à de nombreuses délétions et nécessite l’ajout de groupements chargés afin de permettre leur purification. Cependant, ce prérequis donne lieu à des interactions électrostatiques qui biaisent la structure et la cinétique d’agrégation de ces peptides, en plus d’interférer avec l’évaluation d’éventuels agents thérapeutiques. L’objectif du projet est de développer un système permettant l’étude de la polyGln en s’affranchissant des effets de charges. Pour ce faire, deux approches ont été explorées, la première utilise la polyGln non chargée et la seconde utilise une structure polyGln-morpholine ayant des charges labiles en fonction du pH. Ces peptides ont été produits en utilisant une approche linéaire de synthèse peptidique sur support solide avec protection maximale des chaînes latérales. La purification a été effectuée par chromatographie de haute performance en phase inverse en milieu acide. Ces stratégies ont permis de produire des peptides de polyGln de grande pureté avec des rendements acceptables. Une procédure de solubilisation des peptides alliant sonication et lyophilisation a été développée afin d’étudier chacun de ces peptides à l’aide de diverses techniques physicochimiques, telles que la diffusion de la lumière, la spectroscopie de résonance magnétique nucléaire, Raman et UV-visible, le dichroïsme circulaire et la microscopie optique polarisée. La polyGln non chargée solubilisée dans le trifluoroéthanol-eau a montré que la taille des particules et la vitesse d’agrégation sont proportionnelles à la fraction volumique en eau. De plus, la structure secondaire en solution est à prédominance alpha et semble être peu sensible à la fraction d’eau jusqu’à un certain seuil (25%) après lequel la structure aléatoire prédomine. L’analyse des agrégats à l’état solide montre des structures hélicoïdales > aléatoires et ont les caractéristiques des fibrilles amyloïdes. Le peptide de polyGln-morpholines a un pKa de 7,3 en milieu aqueux. Il demeure en solution lorsque le pH < pKa et à faible force ionique, alors qu’il s’autoassemble lorsque ces conditions ne sont pas respectées. Ceci suggère que la répulsion électrostatique est responsable de la stabilisation du peptide en solution. La dimension fractale nous indique que le peptide forme des agrégats compacts dont les constituants ont une taille de 2,5 nm, compatibles avec une conformation aléatoire compacte, en coude bêta ou hélicoïdale. Ceci est en accord avec l’étude structurale des peptides en solution qui a montré des espèces aléatoires > bêta > alpha. De plus, en RMN, l’élargissement des signaux du 1Hγ en cours d’agrégation suggère une interaction via les chaînes latérales. Les analyses en phase solide ont plutôt montré une prédominance de structures bêta et alpha. L’inhibition de l’agrégation à pH 8 varie selon rouge de Congo > tréhalose, alors que le peptide liant la polyGln 1 et la thioflavine T ne semble pas avoir d’effet. Ces approches ont donc permis pour la première fois de s’affranchir des effets de charges auparavant inhérents à l’étude de la polyGln en solution et par conséquent d’obtenir des informations inédites quant à la solubilité, la structure et la cinétique d’agrégation. Enfin, le dispositif à charges labiles permet d’évaluer l’efficacité d’éventuels agents thérapeutiques à pH quasi physiologique.

Geometric characterization of red blood cells. Differentiation of normal and pathologic samples

Introduction. Fractal geometry measures the irregularity of abstract and natural objects with the fractal dimension. Fractal calculations have been applied to the structures of the human body and to quantifications in physiology from the theory of dynamic systems.Material and Methods. The fractal dimensions were calculated, the number of occupation spaces in the space border of box counting and the area of two red blood cells groups, 7 normal ones, group A, and 7 abnormal, group B, coming from patient and of bags for transfusion, were calculated using the method of box counting and a software developed for such effect. The obtained measures were compared, looking for differences between normal and abnormal red blood cells, with the purpose of differentiating samples.Results. The abnormality characterizes by a number of squares of occupation of the fractal space greater or equal to 180; values of areas between 25.117 and 33.548 correspond to normality. In case that the evaluation according to the number of pictures is of normality, must be confirmed with the value of the area applied to adjacent red blood cells within the sample, that in case of having values by outside established and/or the greater or equal spaces to 180, they suggest abnormality of the sample.Conclusions. The developed methodology is effective to differentiate the red globules alterations and probably useful in the analysis of bags of transfusion for clinical use 

Descripción matemática con dimensiones fractales de células normales y con anormalidades citológicas de cuello uterino

Introducción. La geometría fractal ha mostrado ser adecuada en la descripción matemática de objetos irregulares; esta medida se ha denominado dimensión fractal. La aplicación del análisis fractal para medir los contornos de las células normales así como aquellas que presentan algún tipo de anormalidad, ha mostrado la posibilidad de caracterización matemática de su irregularidad. Objetivos. Medir, a partir de la geometría fractal células del epitelio escamoso de cuello uterino clasificadas como normales, atipias escamosas de significado indeterminado (ASC-US) y lesiones intraepiteliales escamosas de bajo grado (LEIBG), diagnosticadas mediante observación microscópica, en busca de mediciones matemáticas que las distingan. Metodología. Este es un estudio exploratorio descriptivo en el que se calcularon las dimensiones fractales, con el método de box counting simplificado y convencional, de los contornos celular y nuclear de 13 células del epitelio escamoso de cuello uterino normales y con anormalidades como ASC-US y lesiones intraepiteliales de bajo grado (LEI BG), a partir de fotografías digitales de 7 células normales, 2 ASCUS y 4 LEI BG diagnosticadas con criterios citomorfológicos mediante observación microscópica convencional. Resultados. Se desarrolló una medida cuantitativa, objetiva y reproducible del grado de irregularidad en las células del epitelio escamoso de cuello uterino identificadas microscópicamente como normales, ASC-US y LEI BG. Conclusiones Se evidenció una organización fractal en la arquitectura celular normal, así como en células ASC-US y las lesiones intraepiteliales de bajo grado (LEI BG). No se encontraron diferencias entre los tipos celulares estudiados.

The population genetic structure of clonal organisms generated by exponentially bounded and fat-tailed dispersal

Long distance dispersal (LDD) plays an important role in many population processes like colonization, range expansion, and epidemics. LDD of small particles like fungal spores is often a result of turbulent wind dispersal and is best described by functions with power-law behavior in the tails ("fat tailed"). The influence of fat-tailed LDD on population genetic structure is reported in this article. In computer simulations, the population structure generated by power-law dispersal with exponents in the range of -2 to -1, in distinct contrast to that generated by exponential dispersal, has a fractal structure. As the power-law exponent becomes smaller, the distribution of individual genotypes becomes more self-similar at different scales. Common statistics like G(ST) are not well suited to summarizing differences between the population genetic structures. Instead, fractal and self-similarity statistics demonstrated differences in structure arising from fat-tailed and exponential dispersal. When dispersal is fat tailed, a log-log plot of the Simpson index against distance between subpopulations has an approximately constant gradient over a large range of spatial scales. The fractal dimension D-2 is linearly inversely related to the power-law exponent, with a slope of similar to -2. In a large simulation arena, fat-tailed LDD allows colonization of the entire space by all genotypes whereas exponentially bounded dispersal eventually confines all descendants of a single clonal lineage to a relatively small area.

Supramolecular structures of enzyme clusters

The structural characterization of subtilisin mesoscale clusters, which were previously shown to induce supramolecular order in biocatalytic self-assembly of Fmocdipeptides, was carried out by synchrotron small-angle X-ray, dynamic, and static light scattering measurements. Subtilisin molecules self-assemble to form supramolecular structures in phosphate buffer solutions. Structural arrangement of subtilisin clusters at 55 degrees Centigrade was found to vary systematically with increasing enzyme concentration. Static light scattering measurements showed the cluster structure to be consistent with a fractal-like arrangement, with fractal dimension varying from 1.8 to 2.6 with increasing concentration for low to moderate enzyme concentrations. This was followed by a structural transition around the enzyme concentration of 0.5 mg mL-1 to more compact structures with significantly slower relaxation dynamics, as evidenced by dynamic light scattering measurements. These concentration-dependent supramolecular enzyme clusters provide tunable templates for biocatalytic self-assembly.

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

Texture analysis and classification using deterministic tourist walk

In this paper, we present a study on a deterministic partially self-avoiding walk (tourist walk), which provides a novel method for texture feature extraction. The method is able to explore an image on all scales simultaneously. Experiments were conducted using different dynamics concerning the tourist walk. A new strategy, based on histograms. to extract information from its joint probability distribution is presented. The promising results are discussed and compared to the best-known methods for texture description reported in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.