Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 3 of 3
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- All Subjects: Gene Expression
- Creators: Li, Baoxin
- Creators: Oyen, Emma
Description
Drosophila melanogaster, as an important model organism, is used to explore the mechanism which governs cell differentiation and embryonic development. Understanding the mechanism will help to reveal the effects of genes on other species or even human beings. Currently, digital camera techniques make high quality Drosophila gene expression imaging possible. On the other hand, due to the advances in biology, gene expression images which can reveal spatiotemporal patterns are generated in a high-throughput pace. Thus, an automated and efficient system that can analyze gene expression will become a necessary tool for investigating the gene functions, interactions and developmental processes. One investigation method is to compare the expression patterns of different developmental stages. Recently, however, the expression patterns are manually annotated with rough stage ranges. The work of annotation requires professional knowledge from experienced biologists. Hence, how to transfer the domain knowledge in biology into an automated system which can automatically annotate the patterns provides a challenging problem for computer scientists. In this thesis, the problem of stage annotation for Drosophila embryo is modeled in the machine learning framework. Three sparse learning algorithms and one ensemble algorithm are used to attack the problem. The sparse algorithms are Lasso, group Lasso and sparse group Lasso. The ensemble algorithm is based on a voting method. Besides that the proposed algorithms can annotate the patterns to stages instead of stage ranges with high accuracy; the decimal stage annotation algorithm presents a novel way to annotate the patterns to decimal stages. In addition, some analysis on the algorithm performance are made and corresponding explanations are given. Finally, with the proposed system, all the lateral view BDGP and FlyFish images are annotated and several interesting applications of decimal stage value are revealed.
ContributorsPan, Cheng (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Farin, Gerald (Committee member) / Arizona State University (Publisher)
Created2012
Description
It is well documented that menopause and the related decline in circulatory steroid hormones estrogen and progesterone are associated with memory alterations. Rodent models of surgical menopause can be used to study these effects, including ovariectomy (Ovx), or the surgical removal of the ovaries. This thesis aimed to characterize the effects of surgical menopause on spatial working and reference memory in rats and examine profiles of uterine gene expression alterations that may serve as indications of mechanisms underlying this association. Eighteen female rats were randomly assigned to one of two surgical treatment groups, either Ovx (the surgical menopause group) or sham (the control group). All subjects underwent testing on the water version of the radial arm maze (WRAM) which allows for the assessment of reference memory errors and two types of working memory errors. After behavioral testing, rat uterine tissues were dissected and RNA sequenced. The results showed that Ovx impaired spatial reference memory performance during a maze learning phase, with Ovx rats making reference memory failures earlier in the day, even before working memory load increased, as compared to control rats. There were no surgical menopause effects on spatial working memory, which may be due to the low working memory load and the young age of the rats. Post-hoc analyses showed that reference memory performance was correlated with nerve growth factor (NGF) and acetylcholinesterase (AChE) gene expression in uterine tissues. These findings add to the literature on the impact of estrogen and female cyclicity on memory and cognition. The results suggest that Ovx impairment of the ability to learn long-term spatial memory information relates to uterine gene expression underlying cellular functioning and that NGF and AChE genes are involved in pathways that give way to underlying cellular functioning that impacts cognition. Future studies should continue to evaluate the effects of menopause on memory function and the effectiveness of hormone therapy.
ContributorsOyen, Emma (Author) / Bimonte-Nelson, Heather (Thesis director) / Corbin, William (Committee member) / Wilson, Melissa (Committee member) / Lizik, Camryn (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2024-05
Description
Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy.
ContributorsZhao, Xinlin (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2016