Early detection and treatment strategies for vulnerable atherosclerotic plaques

Atherosclerotic plaque ruptures have been determined as the most common underlying cause of acute coronary syndromes and stroke. Currently, the standard of care for plaque rupture risk is based on the amount of luminal stenosis presented in a particular vessel; however, X-ray angiographic studies have shown that plaques at risk of rupture generally show <50% luminal narrowing. These findings explicate the need for other, more accurate methods of identifying problem lesions prior to the rupture event. Unfortunately, the study of thrombotic events and vulnerable plaque lesions in humans is difficult due to the spontaneity of rupture and the lengthy time course of disease progression. To further the understanding of plaque rupture risk in light of vulnerability detection, a rabbit model of atherothrombosis was used in conjunction with magnetic resonance imaging (MRI). MRI has been validated as a suitable imaging modality for in vivo, non-invasive detection of atherosclerosis and has provided quantitative predictors of plaques at risk of rupture. Additionally, the rabbit model has been shown, histologically, to present 6 of the 8 human plaque types classified by the American Heart Association. The first portion of this dissertation work focuses on using MRI to serially image rabbits undergoing the atherosclerotic protocol in order to assess rupture risk at the various time points. Previous work has determined that an increase in the vessel remodeling ratio (which hides a large plaque in the vessel wall) and contrast uptake (which indicates inflammation) are both characteristics of increased rupture risk. By obtaining these parameters at various time points in the disease progression, it was possible to determine when a certain plaque displays a heightened risk of rupture. The second portion of this work tested the efficacy of a pro-resolving molecule, lipoxin (an endogenous molecule), in reducing atherosclerotic disease state, specifically rupture with a luminal thrombus. Using chronic administration of this molecule in the same rabbit model of atherosclerosis yielded a faint reduction in atherosclerotic severity based on the parameters of decreased vessel lipid content and decreased thrombotic events presented in the treated group

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

Use of statistical outlier detection method in adaptive evolutionary algorithms

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to adaptive methods and soundly outperforms the non-adaptive case

Credit Assignment in Adaptive Evolutionary Algorithms

In this paper, a new method for assigning credit to search\ud operators is presented. Starting with the principle of optimizing\ud search bias, search operators are selected based on an ability to\ud create solutions that are historically linked to future generations.\ud Using a novel framework for defining performance\ud measurements, distributing credit for performance, and the\ud statistical interpretation of this credit, a new adaptive method is\ud developed and shown to outperform a variety of adaptive and\ud non-adaptive competitors

Investigation of Performance of Soil-Cement Pile in Support of Foundation Systems for High-Rise Buildings

This paper presents the experimental study of Soil-Cement Pile (SCpile) by wet mixing method in sandy soils, with the typical project at An Trung Complex apartment, Da Nang city, Vietnam. With the characteristic of soil layers is sandy soil, the strength of laboratory stabilized soils with the amount of cement from 150¸300 kg/m3 was determined. Simultaneously, the authors also performed the experiments of 20 test piles collected from the site which has cement content about 280 kg/m3 and the unconfined compressive strength qu= (4.5-6.0) MPa. After that, a full-scale model static axial compressive load tests of two single piles and a group of four piles with diameter 800 mm and 12 m length were also conducted. The experiment results show that the bearing capacity of every single pile is 1.200 kN with settlement 6.93 mm and the group of four CSpiles is 3.200 kN with settlement 5.03 mm. The results presented in the paper illustrate that SCpile is the suitable solution for foundation construction process with low cost and saving time for high rise buildings. The result shows a capable application of soil cement piles for support of high-rise buildings

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

Making and breaking power laws in evolutionary algorithm population dynamics

Deepening our understanding of the characteristics and behaviors of population-based search algorithms remains an important ongoing challenge in Evolutionary Computation. To date however, most studies of Evolutionary Algorithms have only been able to take place within tightly restricted experimental conditions. For instance, many analytical methods can only be applied to canonical algorithmic forms or can only evaluate evolution over simple test functions. Analysis of EA behavior under more complex conditions is needed to broaden our understanding of this population-based search process. This paper presents an approach to analyzing EA behavior that can be applied to a diverse range of algorithm designs and environmental conditions. The approach is based on evaluating an individual’s impact on population dynamics using metrics derived from genealogical graphs.\ud From experiments conducted over a broad range of conditions, some important conclusions are drawn in this study. First, it is determined that very few individuals in an EA population have a significant influence on future population dynamics with the impact size fitting a power law distribution. The power law distribution indicates there is a non-negligible probability that single individuals will dominate the entire population, irrespective of population size. Two EA design features are however found to cause strong changes to this aspect of EA behavior: i) the population topology and ii) the introduction of completely new individuals. If the EA population topology has a long path length or if new (i.e. historically uncoupled) individuals are continually inserted into the population, then power law deviations are observed for large impact sizes. It is concluded that such EA designs can not be dominated by a small number of individuals and hence should theoretically be capable of exhibiting higher degrees of parallel search behavior

Use of Statistical Outlier Detection Method in Adaptive\ud Evolutionary Algorithms

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to\ud adaptive methods and soundly outperforms the non-adaptive\ud case

A profiling analysis of contributions of cigarette smoking, dietary calcium intakes, and physical activity to fragility fracture in the elderly

Fragility fracture and bone mineral density (BMD) are influenced by common and modifiable lifestyle factors. In this study, we sought to define the contribution of lifestyle factors to fracture risk by using a profiling approach. The study involved 1683 women and 1010 men (50+ years old, followed up for up to 20 years). The incidence of new fractures was ascertained by X-ray reports. A “lifestyle risk score” (LRS) was derived as the weighted sum of effects of dietary calcium intake, physical activity index, and cigarette smoking. Each individual had a unique LRS, with higher scores being associated with a healthier lifestyle. Baseline values of lifestyle factors were assessed. In either men or women, individuals with a fracture had a significantly lower age-adjusted LRS than those without a fracture. In men, each unit lower in LRS was associated with a 66% increase in the risk of total fracture (non-adjusted hazard ratio [HR] 1.66; 95% CI, 1.26 to 2.20) and still significant after adjusting for age, weight or BMD. However, in women, the association was uncertain (HR 1.30; 95% CI, 1.11 to 1.53). These data suggest that unhealthy lifestyle habits are associated with an increased risk of fracture in men, but not in women, and that the association is mediated by BMD