1,720,977 research outputs found
MR-imaging: a new approach for glioma characterization
No full textGliomas are the most common primary brain tumors. The diffuse infiltration of white matter tracts by cerebral gliomas is a major cause of their appalling prognosis: tumor cells invade, displace, and possibly destroy WM. An early diagnosis and a comprehensive evaluation of tumor extent and relationships with surrounding anatomical structures are crucial in determining prognosis and treatment planning. Conventional Magnetic Resonance (MR) sequences (e.g. T1- or T2—weighted images) have limited sensitivity and specificity in diagnosing brain tumors,[1] because they do not always allow precise delineation of tumor margins, or tumor differentiation from edema and /or treatment effects. In particular, contrast-enhanced MR images may underestimate lesion margins, which is critical for image-guided tumor resection, radiotherapy planning, and for assessing the response to chemotherapy. On the contrary, Diffusion Tensor Imaging (DTI) can identify peritumoral white—matter abnormalities, by detecting the presence of small areas with tumor—cell infiltration in WM around the edge of the gross tumor, as confirmed by image guided biopsies.
In particular the tumor core is characterized by reduced anisotropy and increased isotropy, while, around this area, tumor infiltration shows increased isotropy, but normal anisotropy. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by 3D statistical analysis. In order to investigate the pathological tissues, greyscale digital FLAIR images have been processed. Hence, several well—known statistical quantities have been used to gather meaningful information from the available dataset. The most commonly used indexes of location are mean, mode, median and quartiles. The dispersion (or variability) is given by the variance s2, which is related with its second order moment of the distribution, and its square root, the standard deviation s; dividing the latter by the absolute value of the mean one obtains the coefficient of variation CV, i.e. a non-dimensional measure of spread. Another feature of interest is the heterogeneity, usually characterized by the Gini concentration index and entropy, scaling range from 0 (minimum concentration) up to 1 (maximum concentration). Skewness and kurtosis represent the 3rd and 4th order moments of the distribution, and locate the asymmetry and the “distance” from a perfectly normally distributed variable. Finally, an estimation of the fractal dimension is performed using by box counting. Box counting is a method of gathering data for analyzing complex patterns by breaking a dataset, object, image, etc. into smaller and smaller pieces, typically "box"—shaped, and analyzing the pieces at each smaller scale[2]. This arsenal of instruments allowed us to determine the statistical differences among different gliomas
Persistent Homology Analysis of RNA
Full text linkTopological data analysis has been recently used to extract meaningful information from biomolecules. Here we introduce the application of persistent homology, a topological data analysis tool, for computing persistent features (loops) of the RNA folding space. The scaffold of the RNA folding space is a complex graph from which the global features are extracted by completing the graph to a simplicial complex via the notion of clique and Vietoris-Rips complexes. The resulting simplicial complexes are characterised in terms of topological invariants, such as the number of holes in any dimension, i.e. Betti numbers. Our approach discovers persistent structural features, which are the set of smallest components to which the RNA folding space can be reduced. Thanks to this discovery, which in terms of data mining can be considered as a space dimension reduction, it is possible to extract a new insight that is crucial for understanding the mechanism of the RNA folding towards the optimal secondary structure. This structure is composed by the components discovered during the reduction step of the RNA folding space and is characterized by minimum free energy
A topological approach for multivariate time series characterization: the epileptic brain
Full text linkIn this paper we propose a methodology based on Topogical Data Analysis (TDA) for capturing when a complex system, represented by a multivariate time series, changes its internal organization. The modification of the inner organization among the entities belonging to a complex system can induce a phase transition of the entire system. In order to identify these reorganizations, we designed a new methodology that is based on the representation of time series by simplicial complexes. The topologization of multivariate time series successfully pinpoints out when a complex system evolves. Simplicial complexes are characterized by persistent homo-logy techniques, such as the clique weight rank persistent homology and the topological invariants are used for computing a new entropy measure, the so-called weighted persistent entropy. With respect to the global invariants, e.g. the Betti numbers, the entropy takes into account also the topological noise and then it captures when a phase transition happens in a system. In order to verify the reliability of the methodology, we have analyzed the EEG signals of Phy-sioNet database and we have found numerical evidences that the methodology is able to detect the transition between the pre-ictal and ictal states
Topolnogical classifier for detecting the emergence of epileptic seizures
Full text linkObjective: An innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals.Results: The performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to 97.2% while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%
Characterisation of the Idiotypic Immune Network Through Persistent Entropy
No full textIn the present work we intend to investigate how to detect the behaviour of the immune system reaction to an external stimulus in terms of phase transitions. The immune model considered follows Jerne’s idiotypic network theory. We considered two graph complexity measures—the connectivity entropy and the approximate von Neumann entropy—and one entropy for topological spaces, the so-called persistent entropy. The simplicial complex is obtained enriching the graph structure of the weighted idiotypic network, and it is formally analyzed by persistent homology and persistent entropy. We obtained numerical evidences that approximate von Neumann entropy and persistent entropy detect the activation of the immune system. In addition, persistent entropy allows also to identify the antibodies involved in the immune memory
Topological classification of small DC motors
No full textIn this paper we propose a new methodology based on signal embedding and applied topology for studying real noisy signals. Even if signal embedding is a useful tool, it is not sufficient for studying long range noisy signals. We argue that embedded signal in m space can be properly analysed with topology based techniques. We obtained numerical evidences that our procedure properly classifies small DC motors into good/faulty, using the vibration data acquired from the bench. Small DC motors are largely employed in automotive to drive fans in HVAC (Heating, Ventilation and Air Conditioning) systems. Because of their high-speed, they can produce noise and vibration, perceived by the final users as a lack of quality. This problem is becoming relevant with the advent of the hybrid vehicles, in which the electrical motor is completely silent if compared to a traditional internal combustion engine, so all other noise and vibration source become noticeable by the passengers and felt as discomfort or a nuisanc
Survey of TOPDRIM applications of Topological Data Analysis
Full text linkEvery moment of our daily life belongs to the new era of "Big Data". We continuously produce, at an unpredictable rate, a huge amount of heterogeneous and distributed data. The classical techniques developed for knowledge discovery seem to be unsuitable for extracting information hidden in these volumes of data. Therefore, there is the need to design new computational techniques. In this paper we focus on a set of algorithms inspired by algebraic topology that are known as Topological Data Analysis (TDA). We brie y introduce the principal
techniques for building topological spaces from data and how these can be studied by persistent homology. Several case studies, collected within the TOPDRIM (Topology driven methods for complex systems) FP7-FET project, are used to illustrate the applicability of these techniques on different data sources and domains
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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