Matching Items (303)
Description
Bacteria are often regarded s pathogens, with deleterious impacts on the human body. However, it is known that the presence of trillions of bacteria on and in the human body impart beneficial effects on human health. Like a fingerprint, each individual’s microbiome is unique. The composition of bacteria in one person’s gut is different from the gut bacteria in another individual. Together, the human gut microbiome is a complex mix of organisms that is commonly referred to as “the second brain.� Its role in the human body goes beyond digestion and immune system function. The health of the microbiome factors into risk for illnesses as diverse as depression, obesity, bowel disorders and autism (Perlmutter et al., 2015). In context of the myriad of bacteria that live on and within the human body, the composition of bacteria in the gut may have the most significant impact on an individual’s well-being. This “superorganism� co-evolved with its host in order to provide essential and mutually beneficial functions (Ragonnaud et al., 2021).
Affecting millions of Americans, depression is one of the leading causes of the Global Burden of Disease (GBD), followed by anxiety (Gibson-Smith et al., 2018). Communication that occurs between the human brain and the gut microbiome has been found to be a major contributor towards mental health. The human gut microbiome is comprised of many microbes that can communicate with the brain through the gut-brain axis. However, factors such as stress and diets can interfere with this process, especially after increasing the permeability of the intestine (Khoshbin et al., 2020). Perturbation of the gut-brain axis has been implicated across a wide scale of neurodegenerative disorders, with respect to psychopathology (Bonaz et al., 2018). The environment of the gut, along with which species reside there, can help determine the link between gut function and disease. Therefore, it may be possible to prevent the degradation of an individual’s immune function and well-being through alteration of the gut microbiome. (abstract)
Affecting millions of Americans, depression is one of the leading causes of the Global Burden of Disease (GBD), followed by anxiety (Gibson-Smith et al., 2018). Communication that occurs between the human brain and the gut microbiome has been found to be a major contributor towards mental health. The human gut microbiome is comprised of many microbes that can communicate with the brain through the gut-brain axis. However, factors such as stress and diets can interfere with this process, especially after increasing the permeability of the intestine (Khoshbin et al., 2020). Perturbation of the gut-brain axis has been implicated across a wide scale of neurodegenerative disorders, with respect to psychopathology (Bonaz et al., 2018). The environment of the gut, along with which species reside there, can help determine the link between gut function and disease. Therefore, it may be possible to prevent the degradation of an individual’s immune function and well-being through alteration of the gut microbiome. (abstract)
ContributorsPisarczyk, Nicole (Author) / Penton, Christopher (Thesis director) / Huffman, Holly (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures.
ContributorsMielke, Clinton (Author) / Mandarino, Lawrence (Committee member) / LaBaer, Joshua (Committee member) / Magee, D. Mitchell (Committee member) / Dinu, Valentin (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
Description
The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.
ContributorsBrown, Justin Reed (Author) / Dinu, Valentin (Thesis advisor) / Johnson, William (Committee member) / Petitti, Diana (Committee member) / Arizona State University (Publisher)
Created2012
Description
Critical care environments are complex in nature. Fluctuating team dynamics and the plethora of technology and equipment create unforeseen demands on clinicians. Such environments become chaotic very quickly due to the chronic exposure to unpredictable clusters of events. In order to cope with this complexity, clinicians tend to develop ad-hoc adaptations to function in an effective manner. It is these adaptations or "deviations" from expected behaviors that provide insight into the processes that shape the overall behavior of the complex system. The research described in this manuscript examines the cognitive basis of clinicians' adaptive mechanisms and presents a methodology for studying the same. Examining interactions in complex systems is difficult due to the disassociation between the nature of the environment and the tools available to analyze underlying processes. In this work, the use of a mixed methodology framework to study trauma critical care, a complex environment, is presented. The hybrid framework supplements existing methods of data collection (qualitative observations) with quantitative methods (use of electronic tags) to capture activities in the complex system. Quantitative models of activities (using Hidden Markov Modeling) and theoretical models of deviations were developed to support this mixed methodology framework. The quantitative activity models developed were tested with a set of fifteen simulated activities that represent workflow in trauma care. A mean recognition rate of 87.5% was obtained in automatically recognizing activities. Theoretical models, on the other hand, were developed using field observations of 30 trauma cases. The analysis of the classification schema (with substantial inter-rater reliability) and 161 deviations identified shows that expertise and role played by the clinician in the trauma team influences the nature of deviations made (p<0.01). The results shows that while expert clinicians deviate to innovate, deviations of novices often result in errors. Experts' flexibility and adaptiveness allow their deviations to generate innovative ideas, in particular when dynamic adjustments are required in complex situations. The findings suggest that while adherence to protocols and standards is important for novice practitioners to reduce medical errors and ensure patient safety, there is strong need for training novices in coping with complex situations as well.
ContributorsVankipuram, Mithra (Author) / Greenes, Robert A (Thesis advisor) / Patel, Vimla L. (Thesis advisor) / Petitti, Diana B. (Committee member) / Dinu, Valentin (Committee member) / Smith, Marshall L. (Committee member) / Arizona State University (Publisher)
Created2012
Description
This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of the current implementation at the Southern Nevada Health District (SNHD). The result of the system deployment at SNHD was considered as a basis for projecting the practical application and benefits of an enterprise architecture. This approach has resulted in a sustainable platform to enhance the practice of public health by improving the quality and timeliness of data, effectiveness of an investigation, and reporting across the continuum.
ContributorsKriseman, Jeffrey Michael (Author) / Dinu, Valentin (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Arizona State University (Publisher)
Created2012
Description
Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state.
ContributorsKukreja, Muskan (Author) / Johnston, Stephen Albert (Thesis advisor) / Stafford, Phillip (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis looks at how Latinx communities in Wyoming, despite recognizing the impossibility of overcoming the traditional conservative autocracy, still utilize their identity as a political response to unify Latinx communities throughout the state. The project draws from oral histories conducted with Latinx/Chicanx community members in Wyoming, including professors, legislators, and everyday citizens.
ContributorsFranco, David (Author) / Fonseca-Chávez, Vanessa (Thesis director) / MartÃnez, Rafael (Committee member) / College of Integrative Sciences and Arts (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Uniforms and logos are an essential part of sports teams and are created with the intention of representing the city and state of their respective teams. More than a uniform: How culture influences the creation of Arizona sports logos and jerseys presents a look at the conversations and processes undergone before teams are able to unveil their new threads. Four local professional teams are involved with this project: Phoenix Suns, Arizona Diamondbacks, Arizona Coyotes and Arizona Cardinals. Members from each of the organizations were interviewed, in addition to Greg Fisher of Fisher Design. Information was gathered from each of those interviews in addition to research done on the history of each of the team’s uniforms. The information was then created into a documentary that consists of visual and verbal components. The film highlights how each team attempts to represent Arizona and its culture when it comes to what they are wearing on the field, court or ice. The interviews capture the mindset of creative teams as they explore growing new ideas and looks, in addition to a historical delve into two of the team’s debuts in the 1990s. Many of Arizona’s sports teams have much more behind their logos and jerseys than meets the eye. The project taught me how adapt broadcast skills into documentary style storytelling and how important visuals are for longer features. The interviews showed that so many things are taken into consideration when designing a sports logo or uniform and the process can take either months or years to finally reach fruition.
ContributorsNoel, Adam Jude (Author) / Dieffenbach, Paola (Thesis director) / Easley, Isaac (Committee member) / College of Integrative Sciences and Arts (Contributor) / Walter Cronkite School of Journalism and Mass Comm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The COVID-19 pandemic began in March of 2020 and drastically affected the global human population. Millions of people died due to a SARS-CoV-2 infection while many who survived developed devastating sequelae of the disease. In addition, the closure of schools and businesses led to international economic struggle in the year 2020 as global economies declined. Since the beginning of the pandemic, over 200,000 scientific articles have been published and compiled into a database that grows daily— a rare occurrence within the scientific community. This thesis uses natural language processing tools via Python and VOSviewer software to perform a bibliometric analysis on 205,712 papers published between January of 2020 and February of 2021 pertaining to COVID-19. We first investigate how to analyze these publications most effectively in terms of title versus abstract keyword searches, we further obtain the focus of the current scientific literature via co-occurrence analysis and clustering, and we at last discuss the time evolution of these topics over the course of 14 months.
ContributorsLovell, Madison Ray (Author) / Zheng, Wenwei (Thesis director) / Melkozernov, Alexander (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05