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The experiments conducted in this report supported previous evidence (Bethany et al., 2019) that a newly identified predatory bacterium causes a higher rate of mortality in the biological soil crust cyanobacterium M. vaginatus when in hot soils than in cold soils. I predicted that the extracellular propagules of this predatory

The experiments conducted in this report supported previous evidence (Bethany et al., 2019) that a newly identified predatory bacterium causes a higher rate of mortality in the biological soil crust cyanobacterium M. vaginatus when in hot soils than in cold soils. I predicted that the extracellular propagules of this predatory bacterium were inactivated at seasonally low temperatures, rendering them non-viable when introduced to M. vaginatus at room temperature. However, I found that the predatory bacterium became only transiently inactive at low temperatures, recovering its pathogenicity when later exposed to warmer temperatures. By contrast, inactivation of infectivity was complete by exposure in both liquid and dry conditions for five days at 40 °C. I also expected that its infectivity towards M. vaginatus was temperature dependent. Indeed, infection was hampered and did not cause high mortality when predator and prey were incubated at or below 10 °C, which could have been due to slowed metabolisms of M. vaginatus or to an inability of the predatory bacterium to attack in cold conditions. Above 10 °C, when M. vaginatus grew faster, time to full death of predator/prey incubations correlated with the rate of growth of healthy cultures.
The experiments in this study observed a correlation between the growth rate of uninfected cultures and the decay rate of infected cultures, meaning that temperatures that cultures that displayed a higher growth rate for uninfected M. vaginatus would die faster when infected with the predatory bacterium. Infected cultures that were incubated at temperatures 4 and 10 °C did not display death and this could have been due to lower activity of M. vaginatus at lower temperatures or the inability for the predatory bacterium to attack at lower temperatures.
ContributorsAhamed, Anisa Nour (Author) / Garcia-Pichel, Ferran (Thesis director) / Giraldo Silva, Ana Maria (Committee member) / Bethany Rakes, Julie (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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This thesis examines Care Not Cash, a welfare reform measure that replaced traditional cash General Assistance program payments for homeless persons in San Francisco with in-kind social services. Unlike most welfare reform measures, proponents framed Care Not Cash as a progressive policy, aimed at expanding social services and government care

This thesis examines Care Not Cash, a welfare reform measure that replaced traditional cash General Assistance program payments for homeless persons in San Francisco with in-kind social services. Unlike most welfare reform measures, proponents framed Care Not Cash as a progressive policy, aimed at expanding social services and government care for this vulnerable population. Drawing on primary and secondary documents, as well as interviews with homelessness policy experts, this thesis examines the historical and political success of Care Not Cash, and explores the potential need for implementation of a similar program in Phoenix, Arizona.
ContributorsMcCutcheon, Zachary Ryan (Author) / Lucio, Joanna (Thesis director) / Williams, David (Committee member) / Bretts-Jamison, Jake (Committee member) / School of Public Affairs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging

Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Description
The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the

The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the visual cortex of the brain). However, using the pRF model is very time consuming. Past research has focused on the creation of Convolutional Neural Networks (CNN) to mimic the pRF model in a fraction of the time, and they have worked well under highly controlled conditions. However, these models have not been thoroughly tested on real human data. This thesis focused on adapting one of these CNNs to accurately predict the retinotopy of a real human subject using a dataset from the Human Connectome Project. The results show promise towards creating a fully functioning CNN, but they also expose new challenges that must be overcome before the model could be used to predict the retinotopy of new human subjects.
ContributorsBurgard, Braeden (Author) / Wang, Yalin (Thesis director) / Ta, Duyan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05