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- Creators: School of Life Sciences
- Creators: Farin, Gerald
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
Vitellogenin (vg) is a precursor protein of egg yolk in honeybees, but it is also known to have immunological functions. The purpose of this experiment was to determine the effect of vg on the viral load of deformed wing virus (DWV) in worker honey bees (Apis mellifera). I hypothesized that a reduction in vg expression would lead to an increase in the viral load. I collected 180 worker bees and split them into four groups: half the bees were subjected to a vg gene knockdown by injections of double stranded vg RNA, and the rest were injected with green fluorescent protein (gfp) double stranded RNA. Half of each group was thereafter injected with DWV, and half given a sham injection. The rate of mortality in all four groups was higher than expected, leaving only 17 bees total. I dissected these bees' fat bodies and extracted their RNA to test for vg and DWV. PCR results showed that, out of the small group of remaining bees, the levels of vg were not statistically different. Furthermore, both groups of virus-injected bees showed similar viral loads. Because of the high mortality rate bees and the lack of differing levels of vg transcript between experimental and control groups, I could not draw conclusions from these results. The high mortality could be caused by several factors: temperature-induced stress, repeated stress from the two injections, and stress from viral infection. In addition, it is possible that the vg dsRNA batch I used was faulty. This thesis exemplifies that information cannot safely be extracted when loss of sampling units result in a small datasets that do not represent the original sampling population.
ContributorsCrable, Emma Lewis (Author) / Amdam, Gro (Thesis director) / Wang, Ying (Committee member) / Dahan, Romain (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The development of skeletal muscle during embryogenesis and repair in adults is dependent on the intricate balance between the proliferation of myogenic progenitor cells and the differentiation of those cells into functional muscle fibers. Recent studies demonstrate that the Drosophila melanogaster transcription factor CG9650 is expressed in muscle progenitor cells, where it maintains myoblast numbers. We are interested in the Mus musculus orthologs Bcl11a and Bcl11b (C2H2 zinc finger transcription factors), and understanding their role as molecular switches that control proliferation/differentiation decisions in muscle progenitor cells. Expression analysis revealed that Bcl11b, but not Bcl11a, is expressed in the region of the mouse embryo populated with myogenic progenitor cells; gene expression studies in muscle cell culture confirmed Bcl11b is also selectively transcribed in muscle. Furthermore, Bcl11b is down-regulated with differentiation, which is consistent with the belief that the gene plays a role in cell proliferation.
ContributorsDuong, Brittany Bach (Author) / Rawls, Alan (Thesis director) / Wilson-Rawls, Jeanne (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
This research project investigated known and novel differential genetic variants and their associated molecular pathways involved in Type II diabetes mellitus for the purpose of improving diagnosis and treatment methods. The goal of this investigation was to 1) identify the genetic variants and SNPs in Type II diabetes to develop a gene regulatory pathway, and 2) utilize this pathway to determine suitable drug therapeutics for prevention and treatment. Using a Gene Set Enrichment Analysis (GSEA), a set of 1000 gene identifiers from a Mayo Clinic database was analyzed to determine the most significant genetic variants related to insulin signaling pathways involved in Type II Diabetes. The following genes were identified: NRAS, KRAS, PIK3CA, PDE3B, TSC1, AKT3, SOS1, NEU1, PRKAA2, AMPK, and ACC. In an extensive literature review and cross-analysis with Kegg and Reactome pathway databases, novel SNPs located on these gene variants were identified and used to determine suitable drug therapeutics for treatment. Overall, understanding how genetic mutations affect target gene function related to Type II Diabetes disease pathology is crucial to the development of effective diagnosis and treatment. This project provides new insight into the molecular basis of the Type II Diabetes, serving to help untangle the regulatory complexity of the disease and aid in the advancement of diagnosis and treatment. Keywords: Type II Diabetes mellitus, Gene Set Enrichment Analysis, genetic variants, KEGG Insulin Pathway, gene-regulatory pathway
ContributorsBucklin, Lindsay (Co-author) / Davis, Vanessa (Co-author) / Holechek, Susan (Thesis director) / Wang, Junwen (Committee member) / Nyarige, Verah (Committee member) / School of Human Evolution & Social Change (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Inhibitor of growth factor 4 (ING4) is a tumor suppressor of which low expression has been associated with poor patient survival and aggressive tumor progression in breast cancer. ING4 is characterized as a transcription regulator of inflammatory genes. Among the ING4-regulated genes is CXCL10, a chemokine secreted by endothelial cells during normal inflammation response, which induces chemotactic migration of immune cells to the site. High expression of CXCL10 has been implicated in aggressive breast cancer, but the mechanism is not well understood. A potential signaling molecule downstream of Cxcl10 is Janus Kinase 2 (Jak2), a kinase activated in normal immune response. Deregulation of Jak2 is associated with metastasis, immune evasion, and tumor progression in breast cancer. Thus, we hypothesized that the Ing4/Cxcl10/Jak2 axis plays a key role in breast cancer progression. We first investigated whether Cxcl10 affected breast cancer cell migration. We also investigated whether Cxcl10-mediated migration is dependent on ING4 expression levels. We utilized genetically engineered MDAmb231 breast cancer cells with a CRISPR/Cas9 ING4-knockout construct or a viral ING4 overexpression construct. We performed Western blot analysis to confirm Ing4 expression. Cell migration was assessed using Boyden Chamber assay with or without exogenous Cxcl10 treatment. The results showed that in the presence of Cxcl10, ING4-deficient cells had a two-fold increase in migration as compared to the vector controls, suggesting Ing4 inhibits Cxcl10-induced migration. These findings support our hypothesis that ING4-deficient tumor cells have increased migration when Cxcl10 signaling is present in breast cancer. These results implicate Ing4 is a key regulator of a chemokine-induced tumor migration. Our future plan includes evaluation of Jak2 as an intermediate signaling molecule in Cxcl10/Ing4 pathway. Therapeutic implications of these findings are targeting Cxcl10 and/or Jak2 may be effective in treating ING4-deficient aggressive breast cancer.
ContributorsArnold, Emily (Author) / Kim, Suwon (Thesis director) / Blattman, Joseph (Thesis director) / Mason, Hugh (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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