Matching Items (2)
Filtering by
- Genre: Masters Thesis
Description
Anxiety sensitivity (AS; the fear of anxiety-related bodily sensations) has been earmarked as a significant risk factor in the development and maintenance of pathological anxiety in adults and children. Given the potential implications of heightened AS, recent research has focused on investigating the etiology and developmental course of elevated AS; however, most of this work has been conducted with adults and is retrospective in nature. Data from college students show that early anxiety-related learning experiences may be a primary source of heightened AS levels, but it remains unclear whether AS in children is linked to their learning experiences (i.e., parental reinforcement, modeling, punishment, and/or transmission of information about anxiety-related behaviors). Based on AS theory and its iterations, an emerging theoretical model was developed to aid further exploration of the putative causes and consequences of heightened AS levels. Using a sample of 70 clinic-referred youth (ages 6 to 16 years old; 51.4% Hispanic/Latino), the present study sought to further explicate the role of learning in the development of AS and anxiety symptoms. Results suggest that childhood learning experiences may be an important precursor to heightened AS levels and, subsequently, increased experiences of anxiety symptoms. Findings also indicate that some youth may be more vulnerable to anxiety-related learning experiences and suggest that culture may play a role in the relations among learning, AS, and anxiety symptoms.
ContributorsHolly, Lindsay (Author) / Pina, Armando A (Thesis advisor) / Crnic, Keith A (Committee member) / Sanabria, Federico (Committee member) / Arizona State University (Publisher)
Created2012
Description
The past decade has seen a drastic increase in collaboration between Computer Science (CS) and Molecular Biology (MB). Current foci in CS such as deep learning require very large amounts of data, and MB research can often be rapidly advanced by analysis and models from CS. One of the places where CS could aid MB is during analysis of sequences to find binding sites, prediction of folding patterns of proteins. Maintenance and replication of stem-like cells is possible for long terms as well as differentiation of these cells into various tissue types. These behaviors are possible by controlling the expression of specific genes. These genes then cascade into a network effect by either promoting or repressing downstream gene expression. The expression level of all gene transcripts within a single cell can be analyzed using single cell RNA sequencing (scRNA-seq). A significant portion of noise in scRNA-seq data are results of extrinsic factors and could only be removed by customized scRNA-seq analysis pipeline. scRNA-seq experiments utilize next-gen sequencing to measure genome scale gene expression levels with single cell resolution.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
ContributorsBalachandran, Parithi (Author) / Wang, Xiao (Thesis advisor) / Brafman, David (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2017