Matching Items (357)
Description
The following Student Sustainability Consultant's Portfolio was created with the intention of being duplicated and utilized by Arizona State University (ASU) students to build their own Portfolio and to help prepare them for success after graduation. Student Consultants in GreenLight Solutions (GLS) are in a unique position to prepare themselves to create value for organizations while in school, and then continue to after graduation. When I enrolled in the School of Sustainability as an undergraduate transfer student I heard some constructive criticism from graduates of the school. Those students shared that while they had attained a great theoretical understanding of the science of sustainability, they lacked the ability to apply their knowledge in a practical way. They were struggling with finding work in their field because they could not communicate to employers how their knowledge was useful. They did not know how to apply their sustainability knowledge to create value for an organization. I did not want to have that same problem when I graduated. Enter GreenLight Solutions.
ContributorsKeleher, Kevin Robert (Author) / Schoon, Michael (Thesis director) / Basile, George (Committee member) / Buch, Rajesh (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / Department of Supply Chain Management (Contributor)
Created2013-12
ContributorsChandler, N. Kayla (Author) / Neisewander, Janet (Thesis director) / Sanabria, Federico (Committee member) / Olive, M. Foster (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2013-05
Description
My project is an examination of the process ASU Tempe campus took to institute an organics collection program. Working from a sustainability science perspective I demonstrate the structural and logistical barriers faced during program creation and expansion. My examination lead to the creation of a manual designed as a tool for other organizations in which I document ASU's process and provide information on key steps and procedures necessary to implement a unique organics collection program.
ContributorsSchumacher, Katherine Marie (Author) / Schoon, Michael (Thesis director) / Brundiers, Katja (Committee member) / Levine, Alana (Committee member) / Barrett, The Honors College (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / School of Sustainability (Contributor)
Created2013-05
Description
This research paper assesses the effectiveness of a remote garden-based learning curriculum in teaching elementary students’ basic systems thinking concepts. Five remote lessons were designed, covering different garden topics, and in order to integrate systems thinking concepts, the Systems Thinking Hierarchical Model was used. This model includes eight emergent characteristics of systems thinking necessary for developing systems thinking competency. Five students were given the remote garden-based learning lessons. Student work was evaluated for systems thinking understanding and student outcomes were compared to anticipated learning outcomes. Results suggest that elementary students are able to understand basic systems thinking concepts because student work met anticipated outcomes for four systems thinking characteristics and exceeded anticipated outcomes for one characteristic. These results are significant because they further confirm that elementary-aged students do have the ability to understand systems thinking and they contribute to a growing movement to integrate sustainability education into elementary curriculum.
ContributorsDussault, Ashley (Author) / Weinberg, Andrea (Thesis director) / Schoon, Michael (Committee member) / School of Sustainability (Contributor) / Division of Teacher Preparation (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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
In this synthesis, we hope to accomplish two things: 1) reflect on how the analysis of the new archaeological cases presented in this special feature adds to previous case studies by revisiting a set of propositions reported in a 2006 special feature, and 2) reflect on four main ideas that are more specific to the archaeological cases: i) societal choices are influenced by robustness–vulnerability trade-offs, ii) there is interplay between robustness–vulnerability trade-offs and robustness–performance trade-offs, iii) societies often get locked in to particular strategies, and iv) multiple positive feedbacks escalate the perceived cost of societal change. We then discuss whether these lock-in traps can be prevented or whether the risks associated with them can be mitigated. We conclude by highlighting how these long-term historical studies can help us to understand current society, societal practices, and the nexus between ecology and society.
ContributorsSchoon, Michael (Author) / Fabricius, Christo (Author) / Anderies, John (Author) / Nelson, Margaret (Author) / College of Liberal Arts and Sciences (Contributor)
Created2011
Description
Major Depressive Disorder (MDD) affects over 300 million people worldwide, with the hippocampus showing decreased volume and activity in patients with MDD. The current study investigated whether a novel preclinical model of depression, unpredictable intermittent restraint (UIR), would decrease hippocampal neuronal dendritic complexity. Adult Sprague Dawley rats (24 male, 24 female) were equally divided into 4 groups: control males (CON-M), UIR males (UIR-M), control females (CON-F) and UIR females (UIR-F). UIR groups received restraint and shaking on an orbital shaker on a randomized schedule for 30 or 60 minutes/day for two to six days in a row for 26 days (21 total UIR days) before behavioral testing commenced. UIR continued and was interspersed between behavioral test days. At the end of behavioral testing, brains were processed. The behavior is published and not part of my honor’s thesis; my contribution involved quantifying and analyzing neurons in the hippocampus. Several neuronal types are found in the CA3 subregion of the hippocampus and I focused on short shaft (SS) neurons, which show different sensitivities to stress than the more common long shaft (LS) variety. Brains sections were mounted to slides and Golgi stained. SS neurons were drawn using a microscope with camera lucida attachment and quantified using the number of bifurcations and dendritic intersections as metrics for dendritic complexity in the apical and basal areas separately. The hypothesis that SS neurons in the CA3 region of the hippocampus would exhibit apical dendritic simplification in both sexes after UIR was not supported by our findings. In contrast, following UIR, SS apical dendrites were more complex in both sexes compared to controls. Although unexpected, we believe that the UIR paradigm was an effective stressor, robust enough to illicit neuronal adaptations. It appears that the time from the end of UIR to when the brain tissue was collected, or the post-stress recovery period, and/or repeated behavioral testing may have played a role in the observed increased neuronal complexity. Future studies are needed to parse out these potential effects.
ContributorsAcuna, Amanda Marie (Author) / Conrad, Cheryl (Thesis director) / Corbin, William (Committee member) / Olive, M. Foster (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
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