An experimental and theoretical analysis of nitric oxide availability in the microcirculation

Nitric Oxide (NO) is produced in the vascular endothelium where it then diffuses to the adjacent smooth muscle cells (SMC) activating agents known to regulate vascular tone. The close proximity of the site of NO production to the red blood cells (RBC) and its known fast consumption by hemoglobin, suggests that the blood will scavenge most of the NO produced. Therefore, it is unclear how NO is able to play its role in accomplishing vasodilation. Investigation of NO production and consumption rates will allow insight into this paradox. DAF-FM is a sensitive NO fluorescence probe widely used for qualitative assessment of cellular NO production. With the aid of a mathematical model of NO/DAF-FM reaction kinetics, experimental studies were conducted to calibrate the fluorescence signal showing that the slope of fluorescent intensity is proportional to [NO]2 and exhibits a saturation dependence on [DAF-FM]. In addition, experimental data exhibited a Km dependence on [NO]. This finding was incorporated into the model elucidating NO 2 as the possible activating agent of DAF-FM. A calibration procedure was formed and applied to agonist stimulated cells, providing an estimated NO release rate of 0.418 ± 0.18 pmol/cm2s. To assess NO consumption by RBCs, measurements of the rate of NO consumption in a gas stream flowing on top of an RBC solution of specified Hematocrit (Hct) was performed. The consumption rate constant (kbl)in porcine RBCs at 25°C and 45% Hct was estimated to be 3500 + 700 s-1. kbl is highly dependent on Hct and can reach up to 9900 + 4000 s-1 for 60% Hct. The nonlinear dependence of kbl on Hct suggests a predominant role for extracellular diffusion in limiting NO uptake. Further simulations showed a linear relationship between varying NO production rates and NO availability in the SMCs utilizing the estimated NO consumption rate. The corresponding SMC [NO] level for the average NO production rate estimated was approximately 15.1 nM. With the aid of experimental and theoretical methods we were able to examine the NO paradox and exhibit that endothelial derived NO is able to escape scavenging by RBCs to diffuse to the SMCs.

Theoretical Analysis of "Academic Credibility and the Hospitality Curriculum" : The Image Problem

Social issues are assessed from different perspectives. The purpose here is to evaluate one short article in terms of interpretive social theory and then briefly assess it in terms of functionalism, conflic theory and critical theory.

A theoretical analysis of eyewitness identification: Dual -process theory, signal detection theory and eyewitness confidence

Given the growing number of wrongful convictions involving faulty eyewitness evidence and the strong reliance by jurors on eyewitness testimony, researchers have sought to develop safeguards to decrease erroneous identifications. While decades of eyewitness research have led to numerous recommendations for the collection of eyewitness evidence, less is known regarding the psychological processes that govern identification responses. The purpose of the current research was to expand the theoretical knowledge of eyewitness identification decisions by exploring two separate memory theories: signal detection theory and dual-process theory. This was accomplished by examining both system and estimator variables in the context of a novel lineup recognition paradigm. Both theories were also examined in conjunction with confidence to determine whether it might add significantly to the understanding of eyewitness memory. ^ In two separate experiments, both an encoding and a retrieval-based manipulation were chosen to examine the application of theory to eyewitness identification decisions. Dual-process estimates were measured through the use of remember-know judgments (Gardiner & Richardson-Klavehn, 2000). In Experiment 1, the effects of divided attention and lineup presentation format (simultaneous vs. sequential) were examined. In Experiment 2, perceptual distance and lineup response deadline were examined. Overall, the results indicated that discrimination and remember judgments (recollection) were generally affected by variations in encoding quality and response criterion and know judgments (familiarity) were generally affected by variations in retrieval options. Specifically, as encoding quality improved, discrimination ability and judgments of recollection increased; and as the retrieval task became more difficult there was a shift toward lenient choosing and more reliance on familiarity. ^ The application of signal detection theory and dual-process theory in the current experiments produced predictable results on both system and estimator variables. These theories were also compared to measures of general confidence, calibration, and diagnosticity. The application of the additional confidence measures in conjunction with signal detection theory and dual-process theory gave a more in-depth explanation than either theory alone. Therefore, the general conclusion is that eyewitness identifications can be understood in a more complete manor by applying theory and examining confidence. Future directions and policy implications are discussed. ^

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks

Understanding pathways of neurological disorders requires extensive research on both functional and structural characteristics of the brain. This dissertation introduced two interrelated research endeavors, describing (1) a novel integrated approach for constructing functional connectivity networks (FCNs) of brain using non-invasive scalp EEG recordings; and (2) a decision aid for estimating intracranial volume (ICV). The approach in (1) was developed to study the alterations of networks in patients with pediatric epilepsy. Results demonstrated the existence of statistically significant (p

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.