Matching Items (323)
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
Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This document presents a new implementation of the Smoothed Particles Hydrodynamics algorithm using DirectX 11 and DirectCompute. The main goal of this document is to present to the reader an alternative solution to the largely studied and researched problem of fluid simulation. Most other solutions have been implemented using the NVIDIA CUDA framework; however, the proposed solution in this document uses the Microsoft general-purpose computing on graphics processing units API. The implementation allows for the simulation of a large number of particles in a real-time scenario. The solution presented here uses the Smoothed Particles Hydrodynamics algorithm to calculate the forces within the fluid; this algorithm provides a Lagrangian approach for discretizes the Navier-Stockes equations into a set of particles. Our solution uses the DirectCompute compute shaders to evaluate each particle using the multithreading and multi-core capabilities of the GPU increasing the overall performance. The solution then describes a method for extracting the fluid surface using the Marching Cubes method and the programmable interfaces exposed by the DirectX pipeline. Particularly, this document presents a method for using the Geometry Shader Stage to generate the triangle mesh as defined by the Marching Cubes method. The implementation results show the ability to simulate over 64K particles at a rate of 900 and 400 frames per second, not including the surface reconstruction steps and including the Marching Cubes steps respectively.
ContributorsFigueroa, Gustavo (Author) / Farin, Gerald (Thesis advisor) / Maciejewski, Ross (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (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