Matching Items (49)
Description
In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.
ContributorsRai, Samita (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Borges, Chad (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2012
Description
Over the last fifty years, education funding has been litigated and debated in the United States. In an effort to uncover more into the effects of state dollars on education, I used the guiding research question: did differences in state-level education funding trends in the 1990s affect crime rates? With the help of literature on changing education-funding trends, I selected the timeframe of 1990-1995 because some states and jurisdictions increased their funding while others decreased it. For my research, I outlined the independent variable of per pupil expenditures in order to focus directly on the dollars that impact the individual student, and the dependent variables of burglary, robbery, and motor vehicle theft crime rates because juveniles typically commit these crimes. Unemployment rates and household income served as confounding variables, as these economic factors have been proven to affect crime rates. Using the difference-in-difference method, I was able to test the effect of the implementation of a treatment, increased education funding, on my control and treatment group over the 1990-1995-time period. After running a regression on each of my selected juvenile-specific crime rates, I found my results to be inconclusive; however, by factoring in more confounding variables, I believe this quasi-experimental approach can be repeated to find more solid results.
ContributorsWilson, Kelsey Marie (Author) / Dorn, Sherman (Thesis director) / Carter, Heather (Committee member) / Tatto, Maria (Committee member) / School of Public Affairs (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The transition from high school to college can cause an undue amount of attrition for fully qualified, college-intending first-generation students. Although the students may have overcome multiple obstacles to be accepted to a college and arrive at the transition over summer, it can feel overwhelming to complete the flood of tasks without access to a supportive network to guide and interpret the intricate steps. Many programs focus on college preparation and access to college but do not devote attention to the delicate transition from access to enrollment during the summer months. The term opportunity melt for students who confirm their enrollment and do not enroll in any institution of higher education in the fall semester. This study identified the influence of strategic peer mentor support during the summer months for Chico State students who applied, were admitted, and accepted their college admission. This action research intervention applies key concepts of academic capital theory and follows up on previous cycles of action research in the California State University system to identify barriers for those who intended to enroll but decided not to attend any Cal State or other institution of higher education in the Fall semester after high school graduation.
ContributorsRyan, Shawn (Author) / Dorn, Sherman (Thesis advisor) / Kim, Jeongeun (Committee member) / Weston, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2022
Description
In the Cold War era, educational broadcasting became a new technological instrument in less industrialized countries, to enhance the quality of education. Specifically, the use of mass media in classrooms was starting to be considered a modernized way for developing the educational system in less industrialized regions. This study argues that educational broadcasting in less industrialized areas, including South Korea, reveals the effects of Cold War politics in educational development in many countries. Through the concept of localized modernization, this study highlights American educational aid programs in establishing an educational broadcasting system in a foreign country and its effects on changing the entire educational system focusing on the case of South Korea.
By investigating various archival sources published by governmental agencies, international organizations, and local governments, this dissertation reveals how some less industrialized regions sought to change their educational system by using a new modern technology, educational broadcasting, and how a new educational idea—that the use of mass media in school instruction can change the entire educational system—influenced, changed, and was adopted in these areas.
Although the U.S. Agency for International Development introduced mass media to modernize education, this study shows how local people adapted a new educational broadcasting system to their own purposes and unique circumstances. Korean policymakers and educators agreed with some parts of the U.S. recommended system, but used them for their own needs. The educational broadcasting system in Korea proceeded differently from the U.S. recommended system. The author thus argues that the case of educational broadcasting in South Korea is an example of how local countries constructed their own educational broadcasting systems, how individual countries adapted U.S. systems during the Cold War era to their own needs, and how the localization of the modernization process can be an alternative lens for an overview of the historical pathway of U.S. educational aid projects in the Cold War era.
ContributorsKim, Woo Yeong (Author) / Dorn, Sherman (Thesis advisor) / Silova, Iveta (Committee member) / Powers, Jeanne M. (Committee member) / Arizona State University (Publisher)
Created2022
Description
The pandemic has not only increased economic inequities within variouscommunities, but it has also exacerbated the social, emotional, and math achievement
inequities of middle school students, creating an environment that increases the potential
for heightened anxiety and peer conflict. Now, more than ever, it is imperative that
educators not only understand the existence and impact of these social and emotional
inequities but have the knowledge and skills to effectively address them. Within this
study, I facilitated a 10-week online community of practice with three middle school
math teachers, entitled The More than Math Collective (MTMC), with the purpose of
improving participant self-efficacy with SEL, developing their professional capital,
discussing various strategies to address the social and emotional skill needs of students in
their classrooms, and providing time for implementation of the discussed strategies. At
the conclusion of the study, most participants reported an increase in self-efficacy, human
capital, and decisional capital while only one out of three participants reported an
increase in social capital. All participants described a positive impact on their students
and their professional growth due to their participation in the MTMC and the various
strategies that were learned and implemented in their classrooms. Given the small sample
size, more research can be done to determine if the results of this study may be
transferable to other educational settings.
ContributorsNorcini, Erica (Author) / Dorn, Sherman (Thesis advisor) / Firetto, Carla (Committee member) / Grant, Cara (Committee member) / Arizona State University (Publisher)
Created2023
Description
This study investigated the impact of learning about cultural intelligence (CQ) from senior U.S. Army Special Forces leaders (Group Commanders and Group Command Sergeants Major) on aspiring Special Forces Captains (students) at the Captains Career Course. Three research questions addressed the influence of senior leader interventions on students’ CQ scores, motivation to work with partner forces, and intentions to improve CQ. The study involved quantitative and qualitative data for each of the three comparison groups: control, face-to-face (in-person interaction with senior leaders), and podcast (audio-only recordings). The quantitative data measured CQ capabilities of motivation, cognition, metacognition, and behavior. Descriptive statistics revealed that from the pre-test to the post-test, the control and podcast groups experienced increased self-assessment scores on all four constructs but decreased observer assessment scores. By contrast, the face-to-face group experienced both a decrease in observer assessment scores as well as a marginal decrease in self-assessment scores (on motivation and metacognition).
Exploring motivation to work with partner forces, analysis of the group interview transcripts revealed that the control group attributed their motivation primarily to their prior experiences, while participants in the face-to-face group reported mixed feelings regarding prior experiences but highlighted the impact of senior Special Forces leaders' stories on their motivation. The podcast group credited their course experience and the senior leaders' narratives for their increased motivation.
Examining the influence of senior leader stories on intent to improve CQ, the control group provided generic responses focused on improving cognition. The face-to-face group offered more specific, action-oriented answers emphasizing business systems, sociolinguistics, and cultural values. The podcast group produced varying responses, with some sharing basic intent and others detailing specific strategies such as language fluency and cultural immersion. Participants across all three groups expressed a strong intention to seek out mentorship and stories from experienced individuals.
In conclusion, this study highlights the myriad influences on aspiring Special Forces Captains' CQ and the multifaceted impact of senior Special Forces leaders' stories. The narratives contributed to increased motivation, deeper understanding of the Special Forces mission, and specific strategies for improving CQ, providing valuable insights for military education and training programs.
ContributorsKohistany, Mahboba Lyla (Author) / Dorn, Sherman (Thesis advisor) / Livermore, David (Committee member) / Amrein-Beardsley, Audrey (Committee member) / Arizona State University (Publisher)
Created2023
Description
Online education and virtual learning spaces have become increasingly incorporated into the educational landscape. Even before the Covid-19 pandemic closed most institutions in 2020 for in-person learning, online education was growing rapidly. As the world emerged from the pandemic, online education and virtual learning environments of all types were commonplace in K-12, higher education, and professional development. This action research explored two main areas in the virtual learning space. First, this research focused on how to provide a more connected and social online learning experience. Specifically, the goal was to determine how participation in Supporting Online Connections in Asynchronous Learning (SOCIAL) helped faculty to provide opportunities for social interaction and learning within their online contexts. Second, this research sought to determine if the development experience itself impacts how the participants share and implement the knowledge they gain. Of particular interest was identifying if by providing this type of development within the same educational context (virtual, asynchronous, and diverse) there was a direct impact on their ability to develop and deliver similar virtual learning experiences to their students. This research provided asynchronous learning opportunities for educators within a private Facebook group designed to deliver professional development to educators in different countries. Participants learned about both Connectivism and Community of Inquiry/Social Presence through both content and explicit modeling within the various Facebook “events” structured around the group and learning objectives. Pre- and post-innovation surveys, one-on-one interviews, and materials collected during the events provided insight into what the participants experienced and what they were planning to implement in their own contexts. The data revealed that participants felt they gained knowledge and had a beneficial experience. In addition, they incorporated what they learned in their context in theory through their action plans and in practice through immediate implementation. Findings suggest that providing professional development in this structure was beneficial in knowledge acquisition through both content and modeling as well as facilitating the creation of similar experiences within the participants’ professional contexts.
ContributorsBuehler, Charles (Author) / Dorn, Sherman (Thesis advisor) / Henriksen, Danah (Committee member) / Farmakis, Heather (Committee member) / Arizona State University (Publisher)
Created2023
Educational Data Mining: An Application of a Predictive Model of Online Student Enrollment Decisions
Description
College and university enrollment has decreased nationwide every year for more than a decade as educational consumers increasingly question the value of higher education and discover alternatives to the traditional university system. Enrollment professionals seeking growth are tasked to develop and implement innovative solutions to address increasing enrollment challenges by being responsive to consumer values, interests, and needs. This multi-phase mixed methods action research study explores educational data mining and machine learning to understand and predict the enrollment decisions of admitted applicants (n=3,843) at the online campus of a public research university (phase one). Then, this innovation is distributed to understand how university enrollment professionals (n=7) interpret and are affected by the factors that influence online student enrollment decisions (phase two). Logistic regression was used to evaluate 24 independent variables to classify each applicant into a dichotomous dependent outcome: will an applicant enroll or will they not. The model identified 10 significant predictors and accurately categorized 81% of the enrollment outcomes at its peak. The population was comprised of online adult learners and the findings were carefully compared to the findings of previous studies which differed in institutional settings (on campus) and student populations (first-year students). Additionally, the study aimed to extend the work of previous literature through a second application phase within the local context. The second phase was guided by distributed leadership theory and the four-stage theory of organizational change and introduced the model to enrollment professionals within the local context through participation in a workshop coupled with a pre-/post-workshop survey. This convergent parallel mixed methods design resulted in themes that demonstrated enrollment managers had a genuine desire to understand and apply the model to assist in solving complex enrollment challenges and were interested in using the model to inform their perspectives, decision-making, and strategy development. This study concludes that educational data mining and machine learning can be used to predict the enrollment decisions of online adult students and that enrollment managers can use the data to inform the many enrollment challenges they are tasked to overcome.
ContributorsSinger, Cody Gene (Author) / Ross, Lydia (Thesis advisor) / Dorn, Sherman (Thesis advisor) / Cillay, David (Committee member) / Arizona State University (Publisher)
Created2023