ALTERNATING CURRENT DIELECTROPHORETIC MANIPULATION OF ERYTHROCYTES IN MEDICAL MICRODEVICE TECHNOLOGY

Medical microdevices have gained popularity in the past few decades because they allow the medical laboratory to be taken out into the field and for disease diagnostics to happen with a smaller sample volume, at a lower cost and much faster. Blood is the human body's most readily available and informative diagnostic fluid because of the wealth of information it provides about the body's general health including enzymatic, proteomic and immunological states. The purpose of this project is to optimize operating conditions and study ABO-Rh erythrocytes dielectrophoretic responses to alternating current electric signals. The end goal of this project is the creation of a relatively inexpensive microfluidic device, which can be used for the ABO-Rh typing of a blood sample. This dissertation presents results showing how blood samples of a known ABO- Rh blood type exhibit differing behavior to the same electrical stimulus based on their blood type. The first panel of donors and experiments, presented in Chapter 4 occurred when a sample of known blood type was injected into a microdevice with a T-shaped electrode configuration and the erythorcytes were found to rupture at a rate specific to their ABO-Rh blood type. The second set of experiments, presented in Chapter 5, were originally published in Electrophoresis in 20111. Novel in this work was the discovery that treatment of human erythrocytes with β-galactosidase successfully removed ABO surface antigens such that native A and B blood no longer agglutinated with the proper antibodies. This work was performed in a medium of conductivity 0.9S/m which is close to the measured conductivity of pooled plasma (~1.1S/m). The ability to perform dielectrophoresis experiments at physiological conductivities conditions is advantageous for future portable devices because the device/instrument would not need to store dilution buffers. The final results of this project, presented in Chapter 6, explore the entire dielectrophoretic spectra of the ABO-Rh erythrocytes including the cross-over frequency and the magnitudes of the positive or negative dielectrophoretic response. These were completed at lower medium conductivities of 0.1S/m and 0.01-0.04S/m. These results show that by using the sweep function built into the Agilent alternating current generator it is possible to explore how a single group of blood cells will react to rapid changes in frequency and will provide the user with curve that can be matched the theoretical dielectrophoretic response curves. As a whole this project shows that it is possible to distinguish human erythrocytes by their ABO-Rh blood type via three different dielectrophoretic methods. This work builds on the foundation of that it is possible to distinguish healthy from infected cells2-7, similar cell types1,7-14 and other work regarding the dielectrophoresis of human erythrocytes1,10,11. This work has implications in both medical diagnostics and future dielectrophoretic work because it has shown that ABO-Rh blood type is now a factor, which must be identified when working with a human blood sample. It also shows that the creation of a microfluidic device that subjects human erythrocytes to a dielectrophoretic impulse and then exports an ABO-Rh blood type is a near future possibility.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.

EXTRACTION OF PICTORIAL ENERGY INFORMATION FROM CAMPUS UNMETERED BUILDINGS USING IMAGE PROCESSING TECHNIQUES

In recent years, advanced metering infrastructure (AMI) has been the main research focus due to the traditional power grid has been restricted to meet development requirements. There has been an ongoing effort to increase the number of AMI devices that provide real-time data readings to improve system observability. Deployed AMI across distribution secondary networks provides load and consumption information for individual households which can improve grid management. Significant upgrade costs associated with retrofitting existing meters with network-capable sensing can be made more economical by using image processing methods to extract usage information from images of the existing meters. This thesis presents a new solution that uses online data exchange of power consumption information to a cloud server without modifying the existing electromechanical analog meters. In this framework, application of a systematic approach to extract energy data from images replaces the manual reading process. One case study illustrates the digital imaging approach is compared to the averages determined by visual readings over a one-month period.