Matching Items (333)
Description
Students across the United States lack the necessary skills to be successful college students in Science, Technology and Math (STEM) majors and as a result post-secondary institutions are developing summer bridge programs to aid in their transition. As they develop these programs, effective theory and approach are critical to developing successful programs. Though there are a multitude of theories on successful student development, a focus on self-efficacy is critical. Summer Bridge programs across the country as well as the Bio Bridge summer program at Arizona State University were studied alone and through the lens of Cognitive Self-Efficacy Theory as mentioned in Albert Bandura's "Perceived Self-Efficacy in Cognitive Development and Functioning." Cognitive Self-Efficacy Theory provides a framework for self-efficacy development in academic settings. An analysis of fifteen bridge programs found that a large majority focused on developing academic capabilities and often overlooked development of community and social efficacy. An even larger number failed to focus on personal psychology in managing self-debilitating thought patterns based on published goals. Further, Arizona State University's Bio Bridge program could not be considered successful at developing cognitive self-efficacy or increasing retention as data was inconclusive. However, Bio Bridge was tremendously successful at developing social efficacy and community among participants and faculty. Further research and better evaluative techniques need to be developed to understand the program's effectiveness in cognitive self-efficacy development and retention.
ContributorsTummala, Sailesh Vardhan (Author) / Orchinik, Miles (Thesis director) / Brownell, Sara (Committee member) / Shortlidge, Erin (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Collaborative learning has been found to enhance student learning experiences through interaction with peers and instructors in a way that typically does not occur in a traditional lecture course. However, more than half of all collaborative learning structures have failed to last very long after their initial introductions which makes understanding the factors of collaboration that make it successful very important. The purpose of this study was to evaluate collaborative learning in a blended learning course to gauge student perceptions and the factors of collaboration and student demographics that impact that perception. This was done by surveying a sample of students in BIO 282 about their experiences in the BIO 281 course they took previously which was a new introductory Biology course with a blended learning structure. It was found that students agree that collaboration is beneficial as it provides an opportunity to gain additional insight from peers and improve students' understanding of course content. Also, differences in student gender and first generation status have less of an effect on student perceptions of collaboration than differences in academic achievement (grade) bracket.
ContributorsVu, Bethany Thao-Vy (Author) / Stout, Valerie (Thesis director) / Brownell, Sara (Committee member) / Wright, Christian (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
Identifying disease biomarkers may aid in the early detection of breast cancer and improve patient outcomes. Recent evidence suggests that tumors are immunogenic and therefore patients may launch an autoantibody response to tumor associated antigens. Single-chain variable fragments of autoantibodies derived from regional lymph node B cells of breast cancer patients were used to discover these tumor associated biomarkers on protein microarrays. Six candidate biomarkers were discovered from 22 heavy chain-only variable region antibody fragments screened. Validation tests are necessary to confirm the tumorgenicity of these antigens. However, the use of single-chain variable autoantibody fragments presents a novel platform for diagnostics and cancer therapeutics.
ContributorsSharman, M. Camila (Author) / Magee, Dewey (Mitch) (Thesis director) / Wallstrom, Garrick (Committee member) / Petritis, Brianne (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor) / Virginia G. Piper Center for Personalized Diagnostics (Contributor) / Biodesign Institute (Contributor)
Created2012-12
Description
Background: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer deaths in females worldwide, accounting for 23% of all new cancer cases and 14% of all total cancer deaths in 2008. Five tumor-normal pairs of primary breast epithelial cells were treated for infinite proliferation by using a ROCK inhibitor and mouse feeder cells. Methods: Raw paired-end, 100x coverage RNA-Seq data was aligned to the Human Reference Genome Version 19 using BWA and Tophat. Gene differential expression analysis was completed using Cufflinks and Cuffdiff. Interactive Genome Viewer was used for data visualization. Results: 15 genes were found to be down-regulated by at least one log-fold change in 4/5 of tumor samples. 75 genes were found to be down-regulated in 3/5 of our tumor samples by at least one log-fold change. 11 genes were found to be up-regulated in 4/5 of our tumor samples, and 68 genes were identified to be up-regulated in 3/5 of the tumor samples by at least one-fold change. Conclusion: Expression changes in genes such as AZGP1, AGER, ALG11, and S1007 suggest a disruption in the glycosylation pathway. No correlation was found between Cufflink's Her2 gene-expression and DAKO score classification.
ContributorsHernandez, Fernando (Author) / Anderson, Karen (Thesis director) / Mangone, Marco (Committee member) / Park, Jin (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2013-05
Description
We, a team of students and faculty in the life sciences at Arizona State University (ASU), currently teach an Introduction to Biology course in a Level 5, or maximum-security unit with the support of the Arizona Department of Corrections and the Prison Education Program at ASU. This course aims to enhance current programs at the unit by offering inmates an opportunity to practice literacy and math skills, while also providing exposure to a new academic field (science, and specifically biology). Numerous studies, including a 2005 study from the Arizona Department of Corrections (ADC), have found that vocational programs, including prison education programs, reduce recidivism rates (ADC 2005, Esperian 2010, Jancic 1988, Steurer et al. 2001, Ubic 2002) and may provide additional benefits such as engagement with a world outside the justice system (Duguid 1992), the opportunity for inmates to revise personal patterns of rejecting education that they may regret, and the ability of inmate parents to deliberately set a good example for their children (Hall and Killacky 2008). Teaching in a maximum security prison unit poses special challenges, which include a prohibition on most outside materials (except paper), severe restrictions on student-teacher and student-student interactions, and the inability to perform any lab exercises except limited computer simulations. Lack of literature discussing theoretical and practical aspects of teaching science in such environment has prompted us to conduct an ongoing study to generate notes and recommendations from this class through the use of surveys, academic evaluation of students' work and ongoing feedback from both teachers and students to inform teaching practices in future science classes in high-security prison units.
ContributorsLarson, Anika Jade (Author) / Mor, Tsafrir (Thesis director) / Brownell, Sara (Committee member) / Lockard, Joe (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Recent studies suggest a role for the microbiota in autism spectrum disorders (ASD), potentially arising from their role in modulating the immune system and gastrointestinal (GI) function or from gut–brain interactions dependent or independent from the immune system. GI problems such as chronic constipation and/or diarrhea are common in children with ASD, and significantly worsen their behavior and their quality of life. Here we first summarize previously published data supporting that GI dysfunction is common in individuals with ASD and the role of the microbiota in ASD. Second, by comparing with other publically available microbiome datasets, we provide some evidence that the shifted microbiota can be a result of westernization and that this shift could also be framing an altered immune system. Third, we explore the possibility that gut–brain interactions could also be a direct result of microbially produced metabolites.
ContributorsKrajmalnik-Brown, Rosa (Author) / Lozupone, Catherine (Author) / Kang, Dae Wook (Author) / Adams, James (Author) / Biodesign Institute (Contributor)
Created2015-03-12
Description
Background
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
ContributorsSun, Qian (Author) / Muckatira, Sherin (Author) / Yuan, Lei (Author) / Ji, Shuiwang (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Ira A. Fulton Schools of Engineering (Contributor)
Created2013-12-03
Description
Background
“Stoichioproteomics” relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (http://www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels.
Results
Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline.
Conclusions
The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa.
“Stoichioproteomics” relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (http://www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels.
Results
Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline.
Conclusions
The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa.
ContributorsGilbert, James D. J. (Author) / Acquisti, Claudia (Author) / Martinson, Holly M. (Author) / Elser, James (Author) / Kumar, Sudhir (Author) / Fagan, William F. (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2013-09-04
Description
Background
Improvements in sequencing technology now allow easy acquisition of large datasets; however, analyzing these data for phylogenetics can be challenging. We have developed a novel method to rapidly obtain homologous genomic data for phylogenetics directly from next-generation sequencing reads without the use of a reference genome. This software, called SISRS, avoids the time consuming steps of de novo whole genome assembly, multiple genome alignment, and annotation.
Results
For simulations SISRS is able to identify large numbers of loci containing variable sites with phylogenetic signal. For genomic data from apes, SISRS identified thousands of variable sites, from which we produced an accurate phylogeny. Finally, we used SISRS to identify phylogenetic markers that we used to estimate the phylogeny of placental mammals. We recovered eight phylogenies that resolved the basal relationships among mammals using datasets with different levels of missing data. The three alternate resolutions of the basal relationships are consistent with the major hypotheses for the relationships among mammals, all of which have been supported previously by different molecular datasets.
Conclusions
SISRS has the potential to transform phylogenetic research. This method eliminates the need for expensive marker development in many studies by using whole genome shotgun sequence data directly. SISRS is open source and freely available at https://github.com/rachelss/SISRS/releases.
Improvements in sequencing technology now allow easy acquisition of large datasets; however, analyzing these data for phylogenetics can be challenging. We have developed a novel method to rapidly obtain homologous genomic data for phylogenetics directly from next-generation sequencing reads without the use of a reference genome. This software, called SISRS, avoids the time consuming steps of de novo whole genome assembly, multiple genome alignment, and annotation.
Results
For simulations SISRS is able to identify large numbers of loci containing variable sites with phylogenetic signal. For genomic data from apes, SISRS identified thousands of variable sites, from which we produced an accurate phylogeny. Finally, we used SISRS to identify phylogenetic markers that we used to estimate the phylogeny of placental mammals. We recovered eight phylogenies that resolved the basal relationships among mammals using datasets with different levels of missing data. The three alternate resolutions of the basal relationships are consistent with the major hypotheses for the relationships among mammals, all of which have been supported previously by different molecular datasets.
Conclusions
SISRS has the potential to transform phylogenetic research. This method eliminates the need for expensive marker development in many studies by using whole genome shotgun sequence data directly. SISRS is open source and freely available at https://github.com/rachelss/SISRS/releases.
ContributorsSchwartz, Rachel (Author) / Harkins, Kelly (Author) / Stone, Anne (Author) / Cartwright, Reed (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Life Sciences (Contributor)
Created2015-06-11
Description
Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.
Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.
Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.
ContributorsJi, Shuiwang (Author) / Li, Ying-Xin (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2009-04-21