Higher-derivative non-Abelian gauge fields via the Faddeev-Jackiw formalism

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Técnica do Grupo de Renormalização Numérico (GRN) aplicada em quantum dots

Pós-graduação em Física - IGCE

Vibrational-rotational analysis of supersingular plus quadratic A/r(4)+r(2) potential

Most work on supersingular potentials has focused on the study of the ground state. In this paper, a global analysis of the ground and excited states for the successive values of the orbital angular momentum of the supersingular plus quadratic potential is carried out, making use of centrifugal plus quadratic potential eigenfunction bases. First, the radially nodeless states are variationally analyzed for each value of the orbital angular momentum using the corresponding functions of the bases; the output includes the centrifugal and frequency parameters of the auxiliary potentials and their eigenfunction bases. In the second stage, these bases are used to construct the matrix representation of the Hamiltonian of the system, and from its diagonalization the energy eigenvalues and eigenvectors of the successive states are obtained. The systematics of the accuracy and convergence of the overall results are discussed with emphasis on the dependence on the intensity of the supersingular part of the potential and on the orbital angular momentum.

On some Banach space constants arising in nonlinear fixed point and eigenvalue theory

[EN] As is well known, in any infinite-dimensional Banach space one may find fixed point free self-maps of the unit ball, retractions of the unit ball onto its boundary, contractions of the unit sphere, and nonzero maps without positive eigenvalues and normalized eigenvectors. In this paper, we give upper and lower estimates, or even explicit formulas, for the minimal Lipschitz constant and measure of noncompactness of such maps.

Inversion of low frequency seismo-volcanic events

The inversion of seismo-volcanic events is performed to retrieve the source geometry and to determine volumetric budgets of the source. Such observations have shown to be an important tool for the seismological monitoring of volcanoes. We developed a novel technique for the non-linear constrained inversion of low frequency seismo-volcanic events. Unconstrained linear inversion methods work well when a dense network of broadband seismometers is available. We propose a new constrained inversion technique, which has shown to be efficient also in a reduced network configuration and a low signal-noise ratio. The waveform inversion is performed in the frequency domain, constraining the source mechanism during the event to vary only in its magnitude. The eigenvectors orientation and the eigenvalue ratio are kept constant. This significantly reduces the number of parameters to invert, making the procedure more stable. The method has been tested over a synthetic dataset, reproducing realistic very-long-period (VLP) signals of Stromboli volcano. The information obtained by performing the synthetic tests is used to assess the reliability of the results obtained on a VLP dataset recorded on Stromboli volcano and on a low frequency events recorded at Vesuvius volcano.

PENALIZED FUNCTIONAL REGRESSION

We develop fast fitting methods for generalized functional linear models. An undersmooth of the functional predictor is obtained by projecting on a large number of smooth eigenvectors and the coefficient function is estimated using penalized spline regression. Our method can be applied to many functional data designs including functions measured with and without error, sparsely or densely sampled. The methods also extend to the case of multiple functional predictors or functional predictors with a natural multilevel structure. Our approach can be implemented using standard mixed effects software and is computationally fast. Our methodology is motivated by a diffusion tensor imaging (DTI) study. The aim of this study is to analyze differences between various cerebral white matter tract property measurements of multiple sclerosis (MS) patients and controls. While the statistical developments proposed here were motivated by the DTI study, the methodology is designed and presented in generality and is applicable to many other areas of scientific research. An online appendix provides R implementations of all simulations.

Aplicación de Métodos Meshless al Análisis de Problemas de Autovalores

La presente Tesis Doctoral aborda la aplicación de métodos meshless, o métodos sin malla, a problemas de autovalores, fundamentalmente vibraciones libres y pandeo. En particular, el estudio se centra en aspectos tales como los procedimientos para la resolución numérica del problema de autovalores con estos métodos, el coste computacional y la viabilidad de la utilización de matrices de masa o matrices de rigidez geométrica no consistentes. Además, se acomete en detalle el análisis del error, con el objetivo de determinar sus principales fuentes y obtener claves que permitan la aceleración de la convergencia. Aunque en la actualidad existe una amplia variedad de métodos meshless en apariencia independientes entre sí, se han analizado las diferentes relaciones entre ellos, deduciéndose que el método Element-Free Galerkin Method [Método Galerkin Sin Elementos] (EFGM) es representativo de un amplio grupo de los mismos. Por ello se ha empleado como referencia en este análisis. Muchas de las fuentes de error de un método sin malla provienen de su algoritmo de interpolación o aproximación. En el caso del EFGM ese algoritmo es conocido como Moving Least Squares [Mínimos Cuadrados Móviles] (MLS), caso particular del Generalized Moving Least Squares [Mínimos Cuadrados Móviles Generalizados] (GMLS). La formulación de estos algoritmos indica que la precisión de los mismos se basa en los siguientes factores: orden de la base polinómica p(x), características de la función de peso w(x) y forma y tamaño del soporte de definición de esa función. Se ha analizado la contribución individual de cada factor mediante su reducción a un único parámetro cuantificable, así como las interacciones entre ellos tanto en distribuciones regulares de nodos como en irregulares. El estudio se extiende a una serie de problemas estructurales uni y bidimensionales de referencia, y tiene en cuenta el error no sólo en el cálculo de autovalores (frecuencias propias o carga de pandeo, según el caso), sino también en términos de autovectores. This Doctoral Thesis deals with the application of meshless methods to eigenvalue problems, particularly free vibrations and buckling. The analysis is focused on aspects such as the numerical solving of the problem, computational cost and the feasibility of the use of non-consistent mass or geometric stiffness matrices. Furthermore, the analysis of the error is also considered, with the aim of identifying its main sources and obtaining the key factors that enable a faster convergence of a given problem. Although currently a wide variety of apparently independent meshless methods can be found in the literature, the relationships among them have been analyzed. The outcome of this assessment is that all those methods can be grouped in only a limited amount of categories, and that the Element-Free Galerkin Method (EFGM) is representative of the most important one. Therefore, the EFGM has been selected as a reference for the numerical analyses. Many of the error sources of a meshless method are contributed by its interpolation/approximation algorithm. In the EFGM, such algorithm is known as Moving Least Squares (MLS), a particular case of the Generalized Moving Least Squares (GMLS). The accuracy of the MLS is based on the following factors: order of the polynomial basis p(x), features of the weight function w(x), and shape and size of the support domain of this weight function. The individual contribution of each of these factors, along with the interactions among them, has been studied in both regular and irregular arrangement of nodes, by means of a reduction of each contribution to a one single quantifiable parameter. This assessment is applied to a range of both one- and two-dimensional benchmarking cases, and includes not only the error in terms of eigenvalues (natural frequencies or buckling load), but also of eigenvectors

Método computacional para la aceleración de análisis paramétricos de modificaciones locales en estructuras complejas sometidas a cargas dinámicas

Dentro del análisis y diseño estructural surgen frecuentemente problemas de ingeniería donde se requiere el análisis dinámico de grandes modelos de elementos finitos que llegan a millones de grados de libertad y emplean volúmenes de datos de gran tamaño. La complejidad y dimensión de los análisis se dispara cuando se requiere realizar análisis paramétricos. Este problema se ha abordado tradicionalmente desde diversas perspectivas: en primer lugar, aumentando la capacidad tanto de cálculo como de memoria de los sistemas informáticos empleados en los análisis. En segundo lugar, se pueden simplificar los análisis paramétricos reduciendo su número o detalle y por último se puede recurrir a métodos complementarios a los elementos .nitos para la reducción de sus variables y la simplificación de su ejecución manteniendo los resultados obtenidos próximos al comportamiento real de la estructura. Se propone el empleo de un método de reducción que encaja en la tercera de las opciones y consiste en un análisis simplificado que proporciona una solución para la respuesta dinámica de una estructura en el subespacio modal complejo empleando un volumen de datos muy reducido. De este modo se pueden realizar análisis paramétricos variando múltiples parámetros, para obtener una solución muy aproximada al objetivo buscado. Se propone no solo la variación de propiedades locales de masa, rigidez y amortiguamiento sino la adición de grados de libertad a la estructura original para el cálculo de la respuesta tanto permanente como transitoria. Adicionalmente, su facilidad de implementación permite un control exhaustivo sobre las variables del problema y la implementación de mejoras como diferentes formas de obtención de los autovalores o la eliminación de las limitaciones de amortiguamiento en la estructura original. El objetivo del método se puede considerar similar a los que se obtienen al aplicar el método de Guyan u otras técnicas de reducción de modelos empleados en dinámica estructural. Sin embargo, aunque el método permite ser empleado en conjunción con otros para obtener las ventajas de ambos, el presente procedimiento no realiza la condensación del sistema de ecuaciones, sino que emplea la información del sistema de ecuaciones completa estudiando tan solo la respuesta en las variables apropiadas de los puntos de interés para el analista. Dicho interés puede surgir de la necesidad de obtener la respuesta de las grandes estructuras en unos puntos determinados o de la necesidad de modificar la estructura en zonas determinadas para cambiar su comportamiento (respuesta en aceleraciones, velocidades o desplazamientos) ante cargas dinámicas. Por lo tanto, el procedimiento está particularmente indicado para la selección del valor óptimo de varios parámetros en grandes estructuras (del orden de cientos de miles de modos) como pueden ser la localización de elementos introducidos, rigideces, masas o valores de amortiguamientos viscosos en estudios previos en los que diversas soluciones son planteadas y optimizadas, y que en el caso de grandes estructuras, pueden conllevar un número de simulaciones extremadamente elevado para alcanzar la solución óptima. Tras plantear las herramientas necesarias y desarrollar el procedimiento, se propone un caso de estudio para su aplicación al modelo de elementos .nitos del UAV MILANO desarrollado por el Instituto Nacional de Técnica Aeroespacial. A dicha estructura se le imponen ciertos requisitos al incorporar un equipo en aceleraciones en punta de ala izquierda y desplazamientos en punta de ala derecha en presencia de la sustentación producida por una ráfaga continua de viento de forma sinusoidal. La modificación propuesta consiste en la adición de un equipo en la punta de ala izquierda, bien mediante un anclaje rígido, bien unido mediante un sistema de reducción de la respuesta dinámica con propiedades de masa, rigidez y amortiguamiento variables. El estudio de los resultados obtenidos permite determinar la optimización de los parámetros del sistema de atenuación por medio de múltiples análisis dinámicos de forma que se cumplan de la mejor forma posible los requisitos impuestos con la modificación. Se comparan los resultados con los obtenidos mediante el uso de un programa comercial de análisis por el método de los elementos .nitos lográndose soluciones muy aproximadas entre el modelo completo y el reducido. La influencia de diversos factores como son el amortiguamiento modal de la estructura original, el número de modos retenidos en la truncatura o la precisión proporcionada por el barrido en frecuencia se analiza en detalle para, por último, señalar la eficiencia en términos de tiempo y volumen de datos de computación que ofrece el método propuesto en comparación con otras aproximaciones. Por lo tanto, puede concluirse que el método propuesto se considera una opción útil y eficiente para el análisis paramétrico de modificaciones locales en grandes estructuras. ABSTRACT When developing structural design and analysis some projects require dynamic analysis of large finite element models with millions of degrees of freedom which use large size data .les. The analysis complexity and size grow if a parametric analysis is required. This problem has been approached traditionally in several ways: one way is increasing the power and the storage capacity of computer systems involved in the analysis. Other obvious way is reducing the total amount of analyses and their details. Finally, complementary methods to finite element analysis can also be employed in order to limit the number of variables and to reduce the execution time keeping the results as close as possible to the actual behaviour of the structure. Following this third option, we propose a model reduction method that is based in a simplified analysis that supplies a solution for the dynamic response of the structure in the complex modal space using few data. Thereby, parametric analysis can be done varying multiple parameters so as to obtain a solution which complies with the desired objetive. We propose not only mass, stiffness and damping variations, but also addition of degrees of freedom to the original structure in order to calculate the transient and steady-state response. Additionally, the simple implementation of the procedure allows an in-depth control of the problem variables. Furthermore, improvements such as different ways to obtain eigenvectors or to remove damping limitations of the original structure are also possible. The purpose of the procedure is similar to that of using the Guyan or similar model order reduction techniques. However, in our method we do not perform a true model order reduction in the traditional sense. Furthermore, additional gains, which we do not explore herein, can be obtained through the combination of this method with traditional model-order reduction procedures. In our procedure we use the information of the whole system of equations is used but only those nodes of interest to the analyst are processed. That interest comes from the need to obtain the response of the structure at specific locations or from the need to modify the structure at some suitable positions in order to change its behaviour (acceleration, velocity or displacement response) under dynamic loads. Therefore, the procedure is particularly suitable for parametric optimization in large structures with >100000 normal modes such as position of new elements, stiffness, mass and viscous dampings in previous studies where different solutions are devised and optimized, and in the case of large structures, can carry an extremely high number of simulations to get the optimum solution. After the introduction of the required tools and the development of the procedure, a study case is proposed with use the finite element model (FEM) of the MILANO UAV developed by Instituto Nacional de Técnica Aeroespacial. Due to an equipment addition, certain acceleration and displacement requirements on left wing tip and right wing tip, respectively, are imposed. The structure is under a continuous sinusoidal wind gust which produces lift. The proposed modification consists of the addition of an equipment in left wing tip clamped through a rigid attachment or through a dynamic response reduction system with variable properties of mass, stiffness and damping. The analysis of the obtained results allows us to determine the optimized parametric by means of multiple dynamic analyses in a way such that the imposed requirements have been accomplished in the best possible way. The results achieved are compared with results from a commercial finite element analysis software, showing a good correlation. Influence of several factors such as the modal damping of the original structure, the number of modes kept in the modal truncation or the precission given by the frequency sweep is analyzed. Finally, the efficiency of the proposed method is addressed in tems of computational time and data size compared with other approaches. From the analyses performed, we can conclude that the proposed method is a useful and efficient option to perform parametric analysis of possible local modifications in large structures.

Phenotypic divergence along lines of genetic variance

Natural populations inhabiting the same environment often independently evolve the same phenotype. Is this replicated evolution a result of genetic constraints imposed by patterns of genetic covariation? We looked for associations between directions of morphological divergence and the orientation of the genetic variance-covariance matrix (G) by using an experimental system of morphological evolution in two allopatric nonsister species of rainbow fish. Replicate populations of both Melanotaenia eachamensis and Melanotaenia duboulayi have independently adapted to lake versus stream hydrodynamic environments. The major axis of divergence (z) among all eight study populations was closely associated with the direction of greatest genetic variance (g(max)), suggesting directional genetic constraint on evolution. However, the direction of hydrodynamic adaptation was strongly associated with vectors of G describing relatively small proportions of the total genetic variance, and was only weakly associated with g(max). In contrast, divergence between replicate populations within each habitat was approximately proportional to the level of genetic variance, a result consistent with theoretical predictions for neutral phenotypic divergence. Divergence between the two species was also primarily along major eigenvectors of G. Our results therefore suggest that hydrodynamic adaptation in rainbow fish was not directionally constrained by the dominant eigenvector of G. Without partitioning divergence as a consequence of the adaptation of interest (here, hydrodynamic adaptation) from divergence due to other processes, empirical studies are likely to overestimate the potential for the major eigenvectors of G to directionally constrain adaptive evolution.

System identification of linear vibrating structures

Methods of dynamic modelling and analysis of structures, for example the finite element method, are well developed. However, it is generally agreed that accurate modelling of complex structures is difficult and for critical applications it is necessary to validate or update the theoretical models using data measured from actual structures. The techniques of identifying the parameters of linear dynamic models using Vibration test data have attracted considerable interest recently. However, no method has received a general acceptance due to a number of difficulties. These difficulties are mainly due to (i) Incomplete number of Vibration modes that can be excited and measured, (ii) Incomplete number of coordinates that can be measured, (iii) Inaccuracy in the experimental data (iv) Inaccuracy in the model structure. This thesis reports on a new approach to update the parameters of a finite element model as well as a lumped parameter model with a diagonal mass matrix. The structure and its theoretical model are equally perturbed by adding mass or stiffness and the incomplete number of eigen-data is measured. The parameters are then identified by an iterative updating of the initial estimates, by sensitivity analysis, using eigenvalues or both eigenvalues and eigenvectors of the structure before and after perturbation. It is shown that with a suitable choice of the perturbing coordinates exact parameters can be identified if the data and the model structure are exact. The theoretical basis of the technique is presented. To cope with measurement errors and possible inaccuracies in the model structure, a well known Bayesian approach is used to minimize the least squares difference between the updated and the initial parameters. The eigen-data of the structure with added mass or stiffness is also determined using the frequency response data of the unmodified structure by a structural modification technique. Thus, mass or stiffness do not have to be added physically. The mass-stiffness addition technique is demonstrated by simulation examples and Laboratory experiments on beams and an H-frame.

Principal component and second generation wavelet analysis of Treasury yield curve evolution

Prices of U.S. Treasury securities vary over time and across maturities. When the market in Treasurys is sufficiently complete and frictionless, these prices may be modeled by a function time and maturity. A cross-section of this function for time held fixed is called the yield curve; the aggregate of these sections is the evolution of the yield curve. This dissertation studies aspects of this evolution. ^ There are two complementary approaches to the study of yield curve evolution here. The first is principal components analysis; the second is wavelet analysis. In both approaches both the time and maturity variables are discretized. In principal components analysis the vectors of yield curve shifts are viewed as observations of a multivariate normal distribution. The resulting covariance matrix is diagonalized; the resulting eigenvalues and eigenvectors (the principal components) are used to draw inferences about the yield curve evolution. ^ In wavelet analysis, the vectors of shifts are resolved into hierarchies of localized fundamental shifts (wavelets) that leave specified global properties invariant (average change and duration change). The hierarchies relate to the degree of localization with movements restricted to a single maturity at the base and general movements at the apex. Second generation wavelet techniques allow better adaptation of the model to economic observables. Statistically, the wavelet approach is inherently nonparametric while the wavelets themselves are better adapted to describing a complete market. ^ Principal components analysis provides information on the dimension of the yield curve process. While there is no clear demarkation between operative factors and noise, the top six principal components pick up 99% of total interest rate variation 95% of the time. An economically justified basis of this process is hard to find; for example a simple linear model will not suffice for the first principal component and the shape of this component is nonstationary. ^ Wavelet analysis works more directly with yield curve observations than principal components analysis. In fact the complete process from bond data to multiresolution is presented, including the dedicated Perl programs and the details of the portfolio metrics and specially adapted wavelet construction. The result is more robust statistics which provide balance to the more fragile principal components analysis. ^

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Um estudo sobre polinômios matriciais

This research work aims to make a study of the algebraic theory of matrix monic polynomials, as well as the definitions, concepts and properties with respect to block eigenvalues, block eigenvectors and solvents of P(X). We investigte the main relations between the matrix polynomial and the Companion and Vandermonde matrices. We study the construction of matrix polynomials with certain solvents and the extention of the Power Method, to calculate block eigenvalues and solvents of P(X). Through the relationship between the dominant block eigenvalue of the Companion matrix and the dominant solvent of P(X) it is possible to obtain the convergence of the algorithm for the dominant solvent of the matrix polynomial. We illustrate with numerical examples for diferent cases of convergence.

Probing Tissue Microstructure Using Susceptibility Contrast Magnetic Resonance Imaging

Magnetic resonance imaging is a research and clinical tool that has been applied in a wide variety of sciences. One area of magnetic resonance imaging that has exhibited terrific promise and growth in the past decade is magnetic susceptibility imaging. Imaging tissue susceptibility provides insight into the microstructural organization and chemical properties of biological tissues, but this image contrast is not well understood. The purpose of this work is to develop effective approaches to image, assess, and model the mechanisms that generate both isotropic and anisotropic magnetic susceptibility contrast in biological tissues, including myocardium and central nervous system white matter.

This document contains the first report of MRI-measured susceptibility anisotropy in myocardium. Intact mouse heart specimens were scanned using MRI at 9.4 T to ascertain both the magnetic susceptibility and myofiber orientation of the tissue. The susceptibility anisotropy of myocardium was observed and measured by relating the apparent tissue susceptibility as a function of the myofiber angle with respect to the applied magnetic field. A multi-filament model of myocardial tissue revealed that the diamagnetically anisotropy α-helix peptide bonds in myofilament proteins are capable of producing bulk susceptibility anisotropy on a scale measurable by MRI, and are potentially the chief sources of the experimentally observed anisotropy.

The growing use of paramagnetic contrast agents in magnetic susceptibility imaging motivated a series of investigations regarding the effect of these exogenous agents on susceptibility imaging in the brain, heart, and kidney. In each of these organs, gadolinium increases susceptibility contrast and anisotropy, though the enhancements depend on the tissue type, compartmentalization of contrast agent, and complex multi-pool relaxation. In the brain, the introduction of paramagnetic contrast agents actually makes white matter tissue regions appear more diamagnetic relative to the reference susceptibility. Gadolinium-enhanced MRI yields tensor-valued susceptibility images with eigenvectors that more accurately reflect the underlying tissue orientation.

Despite the boost gadolinium provides, tensor-valued susceptibility image reconstruction is prone to image artifacts. A novel algorithm was developed to mitigate these artifacts by incorporating orientation-dependent tissue relaxation information into susceptibility tensor estimation. The technique was verified using a numerical phantom simulation, and improves susceptibility-based tractography in the brain, kidney, and heart. This work represents the first successful application of susceptibility-based tractography to a whole, intact heart.

The knowledge and tools developed throughout the course of this research were then applied to studying mouse models of Alzheimer’s disease in vivo, and studying hypertrophic human myocardium specimens ex vivo. Though a preliminary study using contrast-enhanced quantitative susceptibility mapping has revealed diamagnetic amyloid plaques associated with Alzheimer’s disease in the mouse brain ex vivo, non-contrast susceptibility imaging was unable to precisely identify these plaques in vivo. Susceptibility tensor imaging of human myocardium specimens at 9.4 T shows that susceptibility anisotropy is larger and mean susceptibility is more diamagnetic in hypertrophic tissue than in normal tissue. These findings support the hypothesis that myofilament proteins are a source of susceptibility contrast and anisotropy in myocardium. This collection of preclinical studies provides new tools and context for analyzing tissue structure, chemistry, and health in a variety of organs throughout the body.