Arterial mechanics considering the structural, mechanical, and biochemical contributions of elastin

Elastin provides many tissues with remarkable resilience and longevity. In elastic arteries such as aorta, elasticity is crucial for energy storage and transmission of the pulsatile blood flow. Human aorta is comprised of approximately 47% elastic fibers and undergoes several billion stretch cycles in the course of one's lifetime. Elastin is remarkably long lived, and it can suffer from cumulative effects of exposure to biochemical damages. Non-enzymatic glycation is one of the main mechanisms of aging and its effect is magnified in diabetic patients. The overall goal of this research is to advance the current understanding of the structural and mechanical roles of elastin in arterial mechanics with the effects of immediate biochemical environments using a coupled experimental and modeling approach. Such knowledge is integral to understanding the performance of elastin in living biological systems. Our study shows that there exists an intrinsic mechanical interaction among extracellular matrix (ECM) constituents that determines the mechanics of arteries and carries important implications to vascular mechanobiology. Considering the organization and engagement behavior of different ECM constituents in the arterial wall, we proposed a new constitutive model of ECM mechanics that considers the distinct structural and mechanical contributions of medial elastin, medial collagen, and adventitial collagen, to incorporate the constituent-specific fiber orientation and the sequential fiber engagement in arterial mechanics. Our study also reveals several interesting and important changes associated with non-enzymatic elastin glycation. Specifically, with in vitro glucose treatment, the stiffness of elastin increases significantly, and elastin exhibits a large hysteresis in the stress-stretch curves and an increase stress relaxation. Analysis of the relaxation time distribution spectra suggests that hydrogen bonding plays a major role in the relaxation behavior after glucose exposure. A multi-exponential model was developed to describe the relaxation behavior with material parameters obtained directly from continuous relaxation time distribution spectra. Elastin at different hydration levels was also studied to further understand the effects of immediate biochemical environments on the biomechanical behavior of elastin, and the close association of extra- and intrafibrillar water with the mechanical behavior of elastin

Signal Evolution Through Clustering of fMRI Data

The human brain is a large, complex organ comprised of billions of neurons and hundreds of trillions of connections, which makes the advanced cognitive functions possible. However, with various techniques including magnetic resonance imaging and electroencephalogram, the complexities in the brain are still largely unknown. In fact, the signals from these technologies are still under heavy debate in regard to their true meanings. In order to explore this problem, k-mean clustering was utilized as a method to evaluate functional magnetic resonance imaging data of subjects that were given repeated visual stimulus (\u3e100 times). It was found (after averaging 100 trials) that subjects had robust signals throughout the brain, which was not limited to just the visual cortex. In this project, clustering methods were applied on these scans to further explore the evolutional features of these signals invoked by visual stimulation. It was found from preliminary results that the evolution of these signals taken by subtracting voxels to adjacent voxels appears may be attributed to 5 different shapes. These shapes ultimately are similar to the base signals found in the gray matter of the brain. This could signify that there is an underlying meaning behind these functional magnetic resonance imaging signals which could have been overlooked

Charakterisierung von Serpinen des Flussneunauges (Lampetra fluviatilis): ein Beitrag zur Genese von Blutgerinnungskontrolle und Blutdruckregulation in Vertebraten

Wang Y. Charakterisierung von Serpinen des Flussneunauges (Lampetra fluviatilis): ein Beitrag zur Genese von Blutgerinnungskontrolle und Blutdruckregulation in Vertebraten. Universität Bielefeld: Universitätsbibliothek Bielefeld; 2014

Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis

Multi-function radars (MFRs) are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inference of MFR work modes. To address this issue, this paper proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this paper designed a fully-conjugate Bayesian non-parametric hidden Markov model with a designed prior distribution (agile BNP-HMM) to represent the MFR pulse agility characteristics. The proposed model allows fully-variational Bayesian inference. Then, the proposed framework is constructed by two main parts. The first part is the agile BNP-HMM model for automatically inferring the number of HMM hidden states and emission distribution of the corresponding hidden states. An estimation error lower bound on performance is derived and the proposed algorithm is shown to be close to the bound. The second part utilizes the streaming Bayesian updating to facilitate computation, and designed an online work mode change detection framework based upon a weighted sequential probability ratio test. We demonstrate that the proposed framework is consistently highly effective and robust to baseline methods on diverse simulated data-sets.Comment: 15 pages, 10 figures, submitted to IEEE transactions on signal processin