352 research outputs found

    Hybrid System Identification of Manual Tracking Submovements in Parkinson\u27s Disease

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    Seemingly smooth motions in manual tracking, (e.g., following a moving target with a joystick input) are actually sequences of submovements: short, open-loop motions that have been previously learned. In Parkinson\u27s disease, a neurodegenerative movement disorder, characterizations of motor performance can yield insight into underlying neurological mechanisms and therefore into potential treatment strategies. We focus on characterizing submovements through Hybrid System Identification, in which the dynamics of each submovement, the mode sequence and timing, and switching mechanisms are all unknown. We describe an initialization that provides a mode sequence and estimate of the dynamics of submovements, then apply hybrid optimization techniques based on embedding to solve a constrained nonlinear program. We also use the existing geometric approach for hybrid system identification to analyze our model and explain the deficits and advantages of each. These methods are applied to data gathered from subjects with Parkinson\u27s disease (on and off L-dopa medication) and from age-matched control subjects, and the results compared across groups demonstrating robust differences. Lastly, we develop a scheme to estimate the switching mechanism of the modeled hybrid system by using the principle of maximum margin separating hyperplane, which is a convex optimization problem, over the affine parameters describing the switching surface and provide a means o characterizing when too many or too few parameters are hypothesized to lie in the switching surface

    REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

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    Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

    Synthesis of hybrid automata with affine dynamics from time-series data

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    Formal design of embedded and cyber-physical systems relies on mathematical modeling. In this paper, we consider the model class of hybrid automata whose dynamics are defined by affine differential equations. Given a set of time-series data, we present an algorithmic approach to synthesize a hybrid automaton exhibiting behavior that is close to the data, up to a specified precision, and changes in synchrony with the data. A fundamental problem in our synthesis algorithm is to check membership of a time series in a hybrid automaton. Our solution integrates reachability and optimization techniques for affine dynamical systems to obtain both a sufficient and a necessary condition for membership, combined in a refinement framework. The algorithm processes one time series at a time and hence can be interrupted, provide an intermediate result, and be resumed. We report experimental results demonstrating the applicability of our synthesis approach

    New Statistical Learning Approaches with Applications to RNA-seq Data

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    This dissertation examines statistical learning problems in both the supervised and unsupervised settings. The dissertation is composed of three major parts. In the first two, we address the important question of significance of clustering, and in the third, we describe a novel framework for unifying hard and soft classification through a spectrum of binary learning problems. In the unsupervised task of clustering, determining whether the identified clusters represent important underlying structure, or are artifacts of natural sampling variation, has been a critical and challenging question. In this dissertation, we introduce two new methods for addressing this question using statistical significance. In the first part of the dissertation, we describe SigFuge, an approach for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. In the second part of this dissertation, we describe statistical Significance of Hierarchical Clustering (SHC), a Monte Carlo based approach for testing significance in hierarchical clustering, and demonstrate the power of the method to identify significant clustering using two cancer gene expression datasets. Both methods were implemented and made available as open source packages in R. In the final part of this dissertation, we propose a spectrum of supervised learning problems which spans the hard and soft classification tasks based on fitting multiple decision rules to a dataset. By doing so, we reveal a novel collection of binary supervised learning problems. We study the problems using the framework of large-margin classification and a class of piecewise linear surrogate losses, for which we derive statistical properties. We evaluate our approach using simulations and a magnetic resonance imaging (MRI) dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.Doctor of Philosoph

    Mitmemõõtmeliste andmete statistiline analüüs bioinformaatikas

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    Väitekirja elektrooniline versioon ei sisalda publikatsioone.Valgud on organismide ühed tähtsaimad ehituskivid. Nende kogust ja omavahelisi seoseid uurides on võimalik saada infot organismi seisundi kohta. Tänapäevased seadmed võimaldavad koguda lühikese ajaga palju valkudega seotud andmeid. Nende analüüs on aga suhteliselt keerukas ja on loonud uue teadusharu nimega bioinformaatika. Käesoleva doktoritöö eesmärgiks on kirjeldada mitmemõõtmeliste andmete statistilise analüüsiga seotud probleeme ja nende lahendusi. Näidatakse, kuidas sellised andmed saab esitada maatriksi kujul. Antakse ülevaade andmeallikatest ja analüüsimeetoditest ning näidatakse, kuidas neid saab praktikas kasutada. Kirjeldatakse üleeuroopalist vähiuuringute projekti PREDECT, kus paljud organisatsioonid osalevad vähimudelite täiustamises. Antakse ülevaade metaandmete kogumisest paljudelt partneritelt, samuti veebitööriistadest, mis loodi esmaseks andmeanalüüsiks. Kirjeldatakse uudse rinnavähi mudeliga seotud analüüsi ja koelõikude võrdlust erinevates laboritingimustes. Tutvustatakse vabalt kasutatavat veebitööriista, millega saab teha kirjeldavat andmeanalüüsi. Järgmistes peatükkides kirjeldatakse andmeanalüüsi erinevates uuringutes. Inimese platsentas leiti mitmeid uusi alleelispetsiifilise ekspressiooniga geene. Uuriti atoopilise dermatiidi molekulaarseid mehhanisme, täpsemalt valgu gamma-interferoon mõju sellele haigusele. Leiti mikroRNAsid, mida saab kasutada endometrioosi markeritena, ja loodi klassifitseerija endometrioosihaigete eristamiseks tervetest.Proteins are one of the most important building blocks of an organism. By investigating the abundance and relations between different proteins, it is possible to get information about the current state of the organism. Modern technologies allow to collect a large amount of data related to proteins in a short period of time. This type of analysis is quite complicated and has created a new field of science called bioinformatics. The aim of the dissertation is to describe problems and solutions related to statistical analysis of multivariate data. It is shown how this type of data can be presented as a matrix. An overview of data sources and analysis methods is given and it is shown how they can be used in practice. A pan-European project PREDECT is described where many organizations are contributing to develop better cancer models. An overview is given about collecting metadata from multiple partners, and about web tools created for initial data analysis. An analysis concerning a novel breast cancer model is described, and a comparison of tissue slices in different cultivation conditions is made. A freely available web tool is introduced which allows to perform exploratory data analysis. Next chapters describe data analysis in various projects. Multiple novel genes were found in the human placenta that have an allele-specific expression. Molecular mechanisms of a disease called atopic dermatitis were examined, more specifically the influence of the protein interferon-gamma. MicroRNAs were found that can be used as markers for a disease called endometriosis, and a classifier was built to differentiate people with endometriosis from healthy people
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