Analyzing multi-fiber reconstruction in high angular resolution diffusion imaging using the tensor distribution function

High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the tradeoff between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusionsensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.

Numerical computation of an Evans function for travelling waves

We demonstrate a geometrically inspired technique for computing Evans functions for the linearised operators about travelling waves. Using the examples of the F-KPP equation and a Keller–Segel model of bacterial chemotaxis, we produce an Evans function which is computable through several orders of magnitude in the spectral parameter and show how such a function can naturally be extended into the continuous spectrum. In both examples, we use this function to numerically verify the absence of eigenvalues in a large region of the right half of the spectral plane. We also include a new proof of spectral stability in the appropriate weighted space of travelling waves of speed c≥sqrt(2δ) in the F-KPP equation.

The tensor distribution function

Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of six directions, second-order tensors (represented by three-by-three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high-angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corresponding eigenvalues.

A Quasi-Newton Adaptive Algorithm for Generalized Symmetric Eigenvalue Problem

In this paper, we first recast the generalized symmetric eigenvalue problem, where the underlying matrix pencil consists of symmetric positive definite matrices, into an unconstrained minimization problem by constructing an appropriate cost function, We then extend it to the case of multiple eigenvectors using an inflation technique, Based on this asymptotic formulation, we derive a quasi-Newton-based adaptive algorithm for estimating the required generalized eigenvectors in the data case. The resulting algorithm is modular and parallel, and it is globally convergent with probability one, We also analyze the effect of inexact inflation on the convergence of this algorithm and that of inexact knowledge of one of the matrices (in the pencil) on the resulting eigenstructure. Simulation results demonstrate that the performance of this algorithm is almost identical to that of the rank-one updating algorithm of Karasalo. Further, the performance of the proposed algorithm has been found to remain stable even over 1 million updates without suffering from any error accumulation problems.

Quasi-static and dynamic strain sensing using carbon nanotube/epoxy nanocomposite thin films

Thin films are developed by dispersing carbon black nanoparticles and carbon nanotubes (CNTs) in an epoxy polymer. The films show a large variation in electrical resistance when subjected to quasi-static and dynamic mechanical loading. This phenomenon is attributed to the change in the band-gap of the CNTs due to the applied strain, and also to the change in the volume fraction of the constituent phases in the percolation network. Under quasi-static loading, the films show a nonlinear response. This nonlinearity in the response of the films is primarily attributed to the pre-yield softening of the epoxy polymer. The electrical resistance of the films is found to be strongly dependent on the magnitude and frequency of the applied dynamic strain, induced by a piezoelectric substrate. Interestingly, the resistance variation is found to be a linear function of frequency and dynamic strain. Samples with a small concentration of just 0.57% of CNT show a sensitivity as high as 2.5% MPa-1 for static mechanical loading. A mathematical model based on Bruggeman's effective medium theory is developed to better understand the experimental results. Dynamic mechanical loading experiments reveal a sensitivity as high as 0.007% Hz(-1) at a constant small-amplitude vibration and up to 0.13%/mu-strain at 0-500 Hz vibration. Potential applications of such thin films include highly sensitive strain sensors, accelerometers, artificial neural networks, artificial skin and polymer electronics.

Imaging in turbid media using quasi-ballistic photons

We study by means of experiments and Monte Carlo simulations, the scattering of light in random media, to determine the distance up to which photons travel along almost undeviated paths within a scattering medium, and are therefore capable of casting a shadow of an opaque inclusion embedded within the medium. Such photons are isolated by polarisation discrimination wherein the plane of linear polarisation of the input light is continuously rotated and the polarisation preserving component of the emerging light is extracted by means of a Fourier transform. This technique is a software implementation of lock-in detection. We find that images may be recovered to a depth far in excess of that predicted by the diffusion theory of photon propagation. To understand our experimental results, we perform Monte Carlo simulations to model the random walk behaviour of the multiply scattered photons. We present a. new definition of a diffusing photon in terms of the memory of its initial direction of propagation, which we then quantify in terms of an angular correlation function. This redefinition yields the penetration depth of the polarisation preserving photons. Based on these results, we have formulated a model to understand shadow formation in a turbid medium, the predictions of which are in good agreement with our experimental results.

Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection

A modeling paradigm is proposed for covariate, variance and working correlation structure selection for longitudinal data analysis. Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection. This leads to a stepwise model selection procedure that deploys a combination of different model selection criteria. Although these criteria find a common theoretical root based on approximating the Kullback-Leibler distance, they are designed to address different aspects of model selection and have different merits and limitations. For example, the extended quasi-likelihood information criterion (EQIC) with a covariance penalty performs well for covariate selection even when the working variance function is misspecified, but EQIC contains little information on correlation structures. The proposed model selection strategies are outlined and a Monte Carlo assessment of their finite sample properties is reported. Two longitudinal studies are used for illustration.

Effects of variance-function misspecification in analysis of longitudinal data

The approach of generalized estimating equations (GEE) is based on the framework of generalized linear models but allows for specification of a working matrix for modeling within-subject correlations. The variance is often assumed to be a known function of the mean. This article investigates the impacts of misspecifying the variance function on estimators of the mean parameters for quantitative responses. Our numerical studies indicate that (1) correct specification of the variance function can improve the estimation efficiency even if the correlation structure is misspecified; (2) misspecification of the variance function impacts much more on estimators for within-cluster covariates than for cluster-level covariates; and (3) if the variance function is misspecified, correct choice of the correlation structure may not necessarily improve estimation efficiency. We illustrate impacts of different variance functions using a real data set from cow growth.

A quasi-likelihood approach for ordered categorical data with overdispersion

Quasi-likelihood (QL) methods are often used to account for overdispersion in categorical data. This paper proposes a new way of constructing a QL function that stems from the conditional mean-variance relationship. Unlike traditional QL approaches to categorical data, this QL function is, in general, not a scaled version of the ordinary log-likelihood function. A simulation study is carried out to examine the performance of the proposed QL method. Fish mortality data from quantal response experiments are used for illustration.

Space-Time Codes Achieving the DMD Tradeoff of the MIMO-ARQ Channel

For the quasi-static, Rayleigh-fading multiple-input multiple-output (MIMO) channel with n(t) transmit and n(r) receive antennas, Zheng and Tse showed that there exists a fundamental tradeoff between diversity and spatial-multiplexing gains, referred to as the diversity-multiplexing gain (D-MG) tradeoff. Subsequently, El Gamal, Caire, and Damen considered signaling across the same channel using an L-round automatic retransmission request (ARQ) protocol that assumes the presence of a noiseless feedback channel capable of conveying one bit of information per use of the feedback channel. They showed that given a fixed number L of ARQ rounds and no power control, there is a tradeoff between diversity and multiplexing gains, termed the diversity-multiplexing-delay (DMD) tradeoff. This tradeoff indicates that the diversity gain under the ARQ scheme for a particular information rate is considerably larger than that obtainable in the absence of feedback. In this paper, a set of sufficient conditions under which a space-time (ST) code will achieve the DMD tradeoff is presented. This is followed by two classes of explicit constructions of ST codes which meet these conditions. Constructions belonging to the first class achieve minimum delay and apply to a broad class of fading channels whenever n(r) >= n(t) and either L/n(t) or n(t)kslashL. The second class of constructions do not achieve minimum delay, but do achieve the DMD tradeoff of the fading channel for all statistical descriptions of the channel and for all values of the parameters n(r,) n(t,) L.

Quasi Crystalline A1-Mn Alloys: Pressure Induced Crystallization and Structural Studies

Irreversible, Pressure induced, quasicrystal-to-crystal transitions are observed for the first time in melt spun alloys at 4.9 GPa for Al 78 Mn22 and 9.3 GPa for Al86 Mn14 by monitoring the electrical resistivities of these alloys as a function of pressure. Electron diffraction and x-ray measurements are used to show that these quasicrystalline phases have icosohedral point group symmetry. The crystalline phases which appear at high pressures are identified as h.c.p. for Al78 Mn22 and orthorhombic for Al86 Mn14.

Characterization of the mitochondrial active-site serine protein LACTB: filaments in the mitochondrial intermembrane space

Mitochondria have evolved from endosymbiotic alpha-proteobacteria. During the endosymbiotic process early eukaryotes dumped the major component of the bacterial cell wall, the peptidoglycan layer. Peptidoglycan is synthesized and maintained by active-site serine enzymes belonging to the penicillin-binding protein and the β-lactamase superfamily. Mammals harbor a protein named LACTB that shares sequence similarity with bacterial penicillin-binding proteins and β-lactamases. Since eukaryotes lack the synthesis machinery for peptidoglycan, the physiological role of LACTB is intriguing. Recently, LACTB has been validated in vivo to be causative for obesity, suggesting that LACTB is implicated in metabolic processes. The aim of this study was to investigate the phylogeny, structure, biochemistry and cell biology of LACTB in order to elucidate its physiological function. Phylogenetic analysis revealed that LACTB has evolved from penicillin binding-proteins present in the bacterial periplasmic space. A structural model of LACTB indicates that LACTB shares characteristic features common to all penicillin-binding proteins and β-lactamases. Recombinat LACTB protein expressed in E. coli was recovered in significant quantities. Biochemical and cell biology studies showed that LACTB is a soluble protein localized in the mitochondrial intermembrane space. Further analysis showed that LACTB preprotein underwent proteolytic processing disclosing an N-terminal tetrapeptide motif also found in a set of cell death-inducing proteins. Electron microscopy structural studies revealed that LACTB can polymerize to form stable filaments with lengths ranging from twenty to several hundred nanometers. These data suggest that LACTB filaments define a distinct microdomain in the intermembrane space. A possible role of LACTB filaments is proposed in the intramitochondrial membrane organization and microcompartmentation. The implications of these findings offer novel insight into the evolution of mitochondria. Further studies of the LACTB function might provide a tool to treat mitochondria-related metabolic diseases.

Finite-amplitude love-waves on an isotropic layered half-space

A pair of semi-linear hyperbolic partial differential equations governing the slow variations in amplitude and phase of a quasi-monochromatic finite-amplitude Love-wave on an isotropic layered half-space is derived using the method of multiple-scales. The analysis of the exact solution of these equations for a signalling problem reveals that the amplitude of the wave remains constant along its characteristic and that the phase of the wave increases linearly behind the wave-front.

Renormalization methods in KAM theory

It is well known that an integrable (in the sense of Arnold-Jost) Hamiltonian system gives rise to quasi-periodic motion with trajectories running on invariant tori. These tori foliate the whole phase space. If we perturb an integrable system, the Kolmogorow-Arnold-Moser (KAM) theorem states that, provided some non-degeneracy condition and that the perturbation is sufficiently small, most of the invariant tori carrying quasi-periodic motion persist, getting only slightly deformed. The measure of the persisting invariant tori is large together with the inverse of the size of the perturbation. In the first part of the thesis we shall use a Renormalization Group (RG) scheme in order to prove the classical KAM result in the case of a non analytic perturbation (the latter will only be assumed to have continuous derivatives up to a sufficiently large order). We shall proceed by solving a sequence of problems in which theperturbations are analytic approximations of the original one. We will finally show that the approximate solutions will converge to a differentiable solution of our original problem. In the second part we will use an RG scheme using continuous scales, so that instead of solving an iterative equation as in the classical RG KAM, we will end up solving a partial differential equation. This will allow us to reduce the complications of treating a sequence of iterative equations to the use of the Banach fixed point theorem in a suitable Banach space.

Type III Secretion System of Phytopathogenic Bacterium Pseudomonas syringae:From Gene to Function

The type III secretion system (T3SS) is an essential requirement for the virulence of many Gram-negative bacteria which infect plants, animals and men. Pathogens use the T3SS to deliver effector proteins from the bacterial cytoplasm to the eukaryotic host cells, where the effectors subvert host defenses. The best candidates for directing effector protein traffic are the bacterial type III-associated appendages, called needles or pili. In plant pathogenic bacteria, the best characterized example of a T3SS-associated appendage is the HrpA pilus of the plant pathogen Pseudomonas syringae pv. tomato DC3000. The components of the T3SS in plant pathogens are encoded by a cluster of hrp (hypersensitive reaction and pathogenicity) genes. Two major classes of T3SS-secreted proteins are: harpin proteins such as HrpZ which are exported into extracellular space, and avirulence (Avr) proteins such as AvrPto which are translocated directly to the plant cytoplasm. This study deals with the structural and functional characterization of the T3SS-associated HrpA pilus and the T3SS-secreted harpins. By insertional mutagenesis analysis of HrpA, we located the optimal epitope insertion site in the amino-terminus of HrpA, and revealed the potential application of the HrpA pilus as a carrier of antigenic determinants for vaccination. By pulse-expression of proteins combined with immuno-electron microscopy, we discovered the Hrp pilus assembly strategy as addition of HrpA subunits to the distal end of the growing pilus, and we showed for the first time that secretion of HrpZ occurs at the tip of the pilus. The pilus thus functions as a conduit delivering proteins to the extracellular milieu. By using phage-display and scanning-insertion mutagenesis methods we identified a conserved HrpZ-binding peptide and localized the peptide-binding site to the central domain of HrpZ. We also found that the HrpZ specifically interacts with a host bean protein. Taken together, the current results provide deeper insight into the molecular mechanism of T3SS-associated pilus assembly and effector protein translocation, which will be helpful for further studies on the pathogenic mechanisms of Gram-negative bacteria and for developing new strategies to prevent bacterial infection.