Kara L. Kruse

A high resolution computer model for sound propagation in the human thorax based on the Visible Human data set

A parallel supercomputer model based on realistic tissue data is developed for sound propagation in the human thorax and the sound propagation behavior is analyzed under various conditions using artificial sound sources. The model uses the Visible Human male data set for a realistic representation of the human thorax. The results were analyzed in time and frequency domains. The analysis suggests that lower frequencies of around 100 Hz are more effectively transmitted through the thorax and that the spatial confinement of sound waves within the thorax results in a resonance effect at around 1500 Hz. The results confirm previous studies that show the size of the thorax plays a significant role in the type of sound generated at the chest wall.

An interactive ontology-driven information system for simulating background radiation and generating scenarios for testing special nuclear materials detection algorithms

This paper describes an original approach to generate scenarios for the purpose of testing the algorithms used to detect special nuclear materials (SNM) that incorporates the use of ontologies. Separating the signal of SNM from the background requires sophisticated algorithms. To assist in developing such algorithms, there is a need for scenarios that capture a very wide range of variables affecting the detection process, depending on the type of detector being used. To provide such a capability, we developed an ontology-driven information system (ODIS) for generating scenarios that can be used for testing of algorithms for SNM detection. The Ontology-Driven Scenario Generator (ODSG) is an ODIS based on information supplied by subject matter experts and other documentation. The details of the creation of the ontology, the development of the ontology-driven information system, and the design of the web user interface (UI) are presented along with specific examples of scenarios generated using the ODSG. We demonstrate that the paradigm behind the ODSG is capable of addressing the problem of semantic complexity at both the user and developer levels. Compared to traditional approaches, an ODIS provides benefits such as faithful representation of the users? domain conceptualization, simplified management of very large and semantically diverse datasets, and the ability to handle frequent changes to the application and the UI. The approach makes possible the generation of a much larger number of specific scenarios based on limited user-supplied information.

Instrumental noise estimates stabilize and quantify endothelial cell micro-impedance barrier function parameter estimates

Abstract A novel transcellular micro-impedance biosensor, referred to as the electric cell-substrate impedance sensor or ECIS, has become increasingly applied to the study and quantification of endothelial cell physiology. In principle, frequency dependent impedance measurements obtained from this sensor can be used to estimate the cell–cell and cell–matrix impedance components of endothelial cell barrier function based on simple geometric models. Few studies, however, have examined the numerical optimization of these barrier function parameters and established their error bounds. This study, therefore, illustrates the implementation of a multi-response Levenberg–Marquardt algorithm that includes instrumental noise estimates and applies it to frequency dependent porcine pulmonary artery endothelial cell impedance measurements. The stability of cell–cell, cell–matrix and membrane impedance parameter estimates based on this approach is carefully examined, and several forms of parameter instability and refinement illustrated. Including frequency dependent noise variance estimates in the numerical optimization reduced the parameter value dependence on the frequency range of measured impedances. The increased stability provided by a multi-response non-linear fit over one-dimensional algorithms indicated that both real and imaginary data should be used in the parameter optimization. Error estimates based on single fits and Monte Carlo simulations showed that the model barrier parameters were often highly correlated with each other. Independently resolving the different parameters can, therefore, present a challenge to the experimentalist and demand the use of non-linear multivariate statistical methods when comparing different sets of parameters.

A computational model of cell migration in response to biochemical diffusion

The Computational Sciences and Engineering Division of the Oak Ridge National Laboratory is partnering with the University of Tennessee Graduate School of Medicine to design a computational model describing various factors related to the development of intimal hyperplasia (IH) in response to arterial injury. This research focuses on modeling the chemotactic and haptotactic processes that stimulate vascular smooth muscle cell migration into the intima. A hybrid discrete-continuous mathematical model of cell migration in response to biochemical diffusion was developed in C++. Chemoattractant diffusion is modeled as a continuous partial differential equation, whereas migration of the cells is modeled as a series of discrete events. Results obtained from the discrete state model for cell migration agree with those obtained from Boyden chamber experiments.

Flow simulation in a 3D model of pig airways and connection to lung sounds

Fundamental to the understanding of the various transport processes within the respiratory system, airway fluid dynamics plays an important role in biomedical research. When air flows through the respiratory tract, it is constantly changing direction through a complex system of curved and bifurcating tubes. As a result, numerical simulations of the airflow through this tracheobronchial system must be capable of resolving such fluid dynamic phenomena as flow separation, recirculation, secondary flows due to centrifugal instabilities, and shear stress variation along the airway surface. Anatomic complexities within the tracheobronchial tree, such as sharp carinal regions at asymmetric bifurcations, have motivated the application of the incompressible Computational Fluid Dynamics code PHI3D to the modeling of airflow. Developed at ORNL, PHI3D implements the new Continuity Constraint Method. Using a finite-element methodology, complex geometries can be easily simulated with PHI3D using unstructured grids. A time- accurate integration scheme allows the simulation of both transient and steady-state flows. A realistic geometry model of the central airways for the fluid flow studies was obtained from pig lungs using a new high resolution x-ray computed tomography system developed at ORNL for generating 3D images of the internal structure of laboratory animals.

Real time monitoring of endothelial cell actin filament disruption by cytochalasin D using a cellular impedance biosensor

Using a novel cellular impedance biosensor and confocal microscopy, this study has examined the dynamic and steady state cellular impedance response of porcine pulmonary endothelial cells to varying doses of cytochalasin D. Endothelial cell monolayer impedances were obtained using an array of gold microelectrodes coated with fibronectin to facilitate endothelial cell adhesion. Impedance measurements were acquired at 5.6 kHz by phase sensitive detection from a lock-in amplifier. The electrically measured cytochalasin D dose dependant actin disruption was successfully correlated with actin stained confocal microscopy images quantified using image-processing techniques. Based on this study, the cellular kinetic response to cytochalasin D increased systematically with the dose and saturated at a critical concentration. The real time quantification of pharmacological agents that target specific elements of the cytoskeleton using electrical impedance measurements therefore has a number of important applications in understanding the dynamic and steady state response of endothelial cells to toxins and drug induced cellular cytoskeletal micromechanics

Modeling the effect of soluble fibrin on the immune-tumor interaction

Cancer is the second leading cause of death in the United States. It is believed that many people develop cancers in their lifetime but the immune system kills these cells without the need for outside treatments. In cancer progression, however, tumor cells may evade the immune system by a mechanism that is not fully understood. Soluble fibrin (sFn), a marker for disseminated intravascular coagulation, may play a role in evasion by tumor cells. As sFn becomes the predominant substance of the matrix surrounding the tumor cells it becomes immunosuppressive. This change from a predominantly collagen matrix to a sFn matrix could play a major role in tumor evasion. Previous research has shown that when sFn is present during the immune-tumor interaction monocyte receptors (MAC-1 and LFA-1) that, interact with CD54 on the tumor cell are altered. When sFn is bound to CD54 the LFA-1 receptor can no longer bind to the tumor cell, however, MAC-1 is still functional. If sFn binds to both, MAC-1 and CD54, then the monocyte is no longer functional against the tumor cell. A mathematical model was developed using a series of scenarios to create a set of continuous ordinary differential equations. These equations were coded in MATLAB to obtain simulations. Parameters were altered to show that the equations validated the experimental research previously published.

Modeling the effect of tumor cell growth when in the presence of natural killer cells

Cancer is the second leading cause of death in the United States. It is believed that many humans develop cancers in their lifetime, but the immune system kills these cells without the need for outside treatments. In cancer progression, however, tumor cells may evade the immune system. The mechanism is not fully understood. Natural killer cells (NK) are one of many immune cells capable of tumor cell lysis. Specifically, the natural cytotoxicity receptors NKp46, NKp44 and NKp30, and the c-type lectin receptor NKG2D on NK cells, are crucial in lysing tumor cells. MHC Class-1 related chain A (MICA), one of several possible activating ligands for NKG2D, is broadly expressed on human tumor cells of epithelial origin. MICA, in combination with various matrix metalloproteinases (MMPs), appears to be involved in both tumor elimination and tumor growth. Tumor growth tends to increase in the presence of soluble MICA but decreases when high levels of MICA bind to a tumor cell. MMPs have been implicated in the shedding of MICA from the tumor surface. A mathematical model was developed to simulate the interactions between NK cells and tumor cells in the presence of MMPs. A set of twelve continuous partial differential equations were created based on data from published literature and solved. These simultaneous equations describe the relationship between twelve state variables and various parameters which will be determined through experimentation. Results of model simulations with three different initial concentration values of MMPs show how this biochemical affects the ability of NK cells to attack tumor cells.