Matching Items (455)
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Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a

The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description

Background: Autism spectrum disorders (ASD) are complex neurobiological disorders that impair social interactions and communication and lead to restricted, repetitive, and stereotyped patterns of behavior, interests, and activities. The causes of these disorders remain poorly understood, but gut microbiota, the 1013 bacteria in the human intestines, have been implicated because children

Background: Autism spectrum disorders (ASD) are complex neurobiological disorders that impair social interactions and communication and lead to restricted, repetitive, and stereotyped patterns of behavior, interests, and activities. The causes of these disorders remain poorly understood, but gut microbiota, the 1013 bacteria in the human intestines, have been implicated because children with ASD often suffer gastrointestinal (GI) problems that correlate with ASD severity. Several previous studies have reported abnormal gut bacteria in children with ASD. The gut microbiome-ASD connection has been tested in a mouse model of ASD, where the microbiome was mechanistically linked to abnormal metabolites and behavior. Similarly, a study of children with ASD found that oral non-absorbable antibiotic treatment improved GI and ASD symptoms, albeit temporarily. Here, a small open-label clinical trial evaluated the impact of Microbiota Transfer Therapy (MTT) on gut microbiota composition and GI and ASD symptoms of 18 ASD-diagnosed children.

Results: MTT involved a 2-week antibiotic treatment, a bowel cleanse, and then an extended fecal microbiota transplant (FMT) using a high initial dose followed by daily and lower maintenance doses for 7–8 weeks. The Gastrointestinal Symptom Rating Scale revealed an approximately 80% reduction of GI symptoms at the end of treatment, including significant improvements in symptoms of constipation, diarrhea, indigestion, and abdominal pain. Improvements persisted 8 weeks after treatment. Similarly, clinical assessments showed that behavioral ASD symptoms improved significantly and remained improved 8 weeks after treatment ended. Bacterial and phage deep sequencing analyses revealed successful partial engraftment of donor microbiota and beneficial changes in the gut environment. Specifically, overall bacterial diversity and the abundance of Bifidobacterium, Prevotella, and Desulfovibrio among other taxa increased following MTT, and these changes persisted after treatment stopped (followed for 8 weeks).

Conclusions: This exploratory, extended-duration treatment protocol thus appears to be a promising approach to alter the gut microbiome and virome and improve GI and behavioral symptoms of ASD. Improvements in GI symptoms, ASD symptoms, and the microbiome all persisted for at least 8 weeks after treatment ended, suggesting a long-term impact.

ContributorsKang, Dae Wook (Author) / Adams, James (Author) / Gregory, Ann C. (Author) / Borody, Thomas (Author) / Chittick, Lauren (Author) / Fasano, Alessio (Author) / Khoruts, Alexander (Author) / Geis, Elizabeth (Author) / Maldonado Ortiz, Juan (Author) / McDonough-Means, Sharon (Author) / Pollard, Elena (Author) / Roux, Simon (Author) / Sadowsky, Michael J. (Author) / Schwarzberg Lipson, Karen (Author) / Sullivan, Matthew B. (Author) / Caporaso, J. Gregory (Author) / Krajmalnik-Brown, Rosa (Author) / Biodesign Institute (Contributor)
Created2017-01-23
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Description

High proportions of autistic children suffer from gastrointestinal (GI) disorders, implying a link between autism and abnormalities in gut microbial functions. Increasing evidence from recent high-throughput sequencing analyses indicates that disturbances in composition and diversity of gut microbiome are associated with various disease conditions. However, microbiome-level studies on autism are

High proportions of autistic children suffer from gastrointestinal (GI) disorders, implying a link between autism and abnormalities in gut microbial functions. Increasing evidence from recent high-throughput sequencing analyses indicates that disturbances in composition and diversity of gut microbiome are associated with various disease conditions. However, microbiome-level studies on autism are limited and mostly focused on pathogenic bacteria. Therefore, here we aimed to define systemic changes in gut microbiome associated with autism and autism-related GI problems. We recruited 20 neurotypical and 20 autistic children accompanied by a survey of both autistic severity and GI symptoms. By pyrosequencing the V2/V3 regions in bacterial 16S rDNA from fecal DNA samples, we compared gut microbiomes of GI symptom-free neurotypical children with those of autistic children mostly presenting GI symptoms. Unexpectedly, the presence of autistic symptoms, rather than the severity of GI symptoms, was associated with less diverse gut microbiomes. Further, rigorous statistical tests with multiple testing corrections showed significantly lower abundances of the genera Prevotella, Coprococcus, and unclassified Veillonellaceae in autistic samples. These are intriguingly versatile carbohydrate-degrading and/or fermenting bacteria, suggesting a potential influence of unusual diet patterns observed in autistic children. However, multivariate analyses showed that autism-related changes in both overall diversity and individual genus abundances were correlated with the presence of autistic symptoms but not with their diet patterns. Taken together, autism and accompanying GI symptoms were characterized by distinct and less diverse gut microbial compositions with lower levels of Prevotella, Coprococcus, and unclassified Veillonellaceae.

ContributorsKang, Dae Wook (Author) / Park, Jin (Author) / Ilhan, Zehra (Author) / Wallstrom, Garrick (Author) / LaBaer, Joshua (Author) / Adams, James (Author) / Krajmalnik-Brown, Rosa (Author) / Biodesign Institute (Contributor)
Created2013-06-03
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Description
Agrobacterium tumefaciens has the ability to transfer its tumor inducing (Ti) plasmid into plant cells. In the last decade, agroinfiltration of Nicotiana benthamiana plants has shown promising results for recombinant protein production. However, A. tumefaciens produce endotoxins in the form of lipopolysaccharides (LPS), a component of their outer membrane that

Agrobacterium tumefaciens has the ability to transfer its tumor inducing (Ti) plasmid into plant cells. In the last decade, agroinfiltration of Nicotiana benthamiana plants has shown promising results for recombinant protein production. However, A. tumefaciens produce endotoxins in the form of lipopolysaccharides (LPS), a component of their outer membrane that can induce organ failure and septic shock. Therefore, we aimed to detoxify A. tumefaciens by modifying their Lipid A structure, the toxic region of LPS, via mutating the genes for lipid A biosynthesis. Two mutant strains of A. tumefaciens were infiltrated into N. benthamiana stems to test for tumor formation to ensure that the detoxifying process did not compromise the ability of gene transfer. Our results demonstrated that A. tumefaciens with both single and double mutations retained the ability to form tumors. Thus, these mutants can be utilized to generate engineered A. tumefaciens strains for the production of plant-based pharmaceuticals with low endotoxicity.
ContributorsHaseefa, Fathima (Author) / Chen, Qiang (Thesis director) / Mason, Hugh (Committee member) / Hurtado, Jonathan (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Skin elasticity, a key indicator of skin health, is influenced by various factors including diet and body composition. This study, led by Myka Williams as part of her Barrett, The Honors College Thesis Project at Arizona State University under the guidance of Dr. Carol Johnston and Dr. Sandy Mayol-Kreiser, investigates

Skin elasticity, a key indicator of skin health, is influenced by various factors including diet and body composition. This study, led by Myka Williams as part of her Barrett, The Honors College Thesis Project at Arizona State University under the guidance of Dr. Carol Johnston and Dr. Sandy Mayol-Kreiser, investigates the relationship between diet—specifically vegetarian and omnivorous patterns—and skin elasticity. Utilizing the ElastiMeter from Delfin Technologies, we assessed the skin elasticity of 38 individuals from the ASU community. Our findings revealed no significant difference in skin elasticity between the dietary groups. However, intriguing correlations emerged between participants' Body Mass Index (BMI) and skin elasticity. These initial findings suggest the potential influence of body composition on skin health, warranting further research with additional parameters to strengthen and expand upon these observations.
ContributorsWilliams, Myka (Author) / Johnston, Carol (Thesis director) / Mayol-Kreiser, Sandy (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2024-05