On arterial contractility, energetic requirements of the cardiovascular system, and the formation of arterial sclerosis

The aorta has been viewed as a passive distribution manifold for blood whose elasticity allows it to store blood during cardiac ejection (systole), and release it during relaxation (diastole). This capacitance, or compliance, lowers peak cardiac work input and maintains peripheral sanguine irrigation throughout the cardiac cycle. The compliance of the human and canine circulatory systems have been described either as constant throughout the cycle (Toy et al. 1985) or as some inverse function of pressure (Li et al. 1990, Cappelo et al. 1995). This work shows that a compliance value that is higher during systole than diastole (equivalent to a direct function of pressure) leads to a reduction in the energetic input to the cardiovascular system (CV), even when accounting for the energy required to change compliance. This conclusion is obtained numerically, based on a 3-element lumped-parameter model of the CV, then demonstrated in a physical model built for the purpose. It is then shown, based on the numerical and physical models, on analytical considerations of elastic tubes, and on the analysis of arterial volume as a function of pressure measured in vivo (Armentano et al. 1995), that the mechanical effects of a presupposed arterial contraction are consistent with those of energetically beneficial changes in compliance during the cardiac cycle. Although the amount of energy potentially saved with rhythmically contracting arteries is small (mean 0.55% for the cases studied) the importance of the phenomenon lies in its possible relation to another function of the arterial smooth muscle (ASM): synthesis of wall matrix macromolecules. It is speculated that a reduction in the rate of collagen synthesis by the ASM is implicated in the formation of arteriosclerosis. ^

Fluid dynamics and platelet deposition in a model arterial stenosis

The objective of this study was to gain further understanding and elucidation of the fluid dynamic factors and flow-induced mechanisms of the thrombogenic process of platelet deposition onto, and possible subsequent embolization from, the walls of an arterial stenosis. This has been accomplished by measurement of the axial dependence of platelet deposition within a modeled arterial stenosis for a transitional flow and a completely laminar flow field. The stenotic region of the model was collagen-coated to simulate a damaged endothelial lining of an artery. Fluid dynamics within a stenosis was studied using qualitative flow visualization, and was further compared to the in vitro platelet deposition studies. Normalized platelet density (NPD) measurements indicate decreased levels of NPD in the high shear throat region of the stenosis for a Reynolds number of 300 and a drastic increase in NPD at the throat for a Reynolds number of 175. This study provides further understanding of the flow dynamic effects on thrombus development within a stenosis. ^

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

The Effect of Carbohydrate Amount, Quality and Type on Arterial Pulse Pressure in Cuban-Americans with and Without Type 2 Diabetes

Background: Arterial pulse pressure, the difference between systolic and diastolic blood pressure, has been used as an indicator (surrogate measure) of arterial stiffness. High arterial pulse pressure (> 40) has been associated with increased cardiovascular disease and mortality. Several clinical trials have reported that the proportion of calories from carbohydrate has an effect on blood pressure. The primary objective of this study was to assess arterial pulse pressure and its association with carbohydrate quantity and quality (glycemic load) with diabetes status for a Cuban American population. Methods: A single point analysis included 367 participants. There was complete data for 365 (190 with and 175 without type 2 diabetes). The study was conducted in the investigator’s laboratory located in Miami, Florida. Demographic, dietary, anthropometric and laboratory data were collected. Arterial pulse pressure was calculated by the formula systolic minus the diastolic blood pressure. Glycemic load, fructose, sucrose, percent of average daily calories from carbohydrate, fat and protein, grams of fiber and micronutrient intakes were calculated from a validated food frequency questionnaire. Results: The mean arterial pulse pressure was significantly higher in participants with (52.9 ± 12.4) than without (48.6 ± 13.4) type 2 diabetes. The odds of persons with diabetes having high arterial pulse pressure (>40) was 1.85 (95% CI =1.09, 3.13); p=0.023. For persons with type 2 diabetes higher glycemic load was associated with lower arterial pulse pressure. Conclusions: Arterial pulse pressure and diet are modifiable risk factors of cardiovascular disease. Arterial pulse pressure may be associated with carbohydrate intake differently considering diabetes status. Results may be due to individuals with diabetes following dietary recommendations. The findings of this study suggest clinicians take into consideration how medical condition, ethnicity and diet are associated with arterial pulse pressure before developing a medical nutrition therapy plan in collaboration with the client.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.