Matching Items (287)
Description
For over a century, researchers have been investigating collective cognition, in which a group of individuals together process information and act as a single cognitive unit. However, I still know little about circumstances under which groups achieve better (or worse) decisions than individuals. My dissertation research directly addressed this longstanding question, using the house-hunting ant Temnothorax rugatulus as a model system. Here I applied concepts and methods developed in psychology not only to individuals but also to colonies in order to investigate differences of their cognitive abilities. This approach is inspired by the superorganism concept, which sees a tightly integrated insect society as the analog of a single organism. I combined experimental manipulations and models to elucidate the emergent processes of collective cognition. My studies show that groups can achieve superior cognition by sharing the burden of option assessment among members and by integrating information from members using positive feedback. However, the same positive feedback can lock the group into a suboptimal choice in certain circumstances. Although ants are obligately social, my results show that they can be isolated and individually tested on cognitive tasks. In the future, this novel approach will help the field of animal behavior move towards better understanding of collective cognition.
ContributorsSasaki, Takao (Author) / Pratt, Stephen C (Thesis advisor) / Amazeen, Polemnia (Committee member) / Liebig, Jürgen (Committee member) / Janssen, Marco (Committee member) / Fewell, Jennifer (Committee member) / Hölldobler, Bert (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation explores the use of bench-scale batch microcosms in remedial design of contaminated aquifers, presents an alternative methodology for conducting such treatability studies, and - from technical, economical, and social perspectives - examines real-world application of this new technology. In situ bioremediation (ISB) is an effective remedial approach for many contaminated groundwater sites. However, site-specific variability necessitates the performance of small-scale treatability studies prior to full-scale implementation. The most common methodology is the batch microcosm, whose potential limitations and suitable technical alternatives are explored in this thesis. In a critical literature review, I discuss how continuous-flow conditions stimulate microbial attachment and biofilm formation, and identify unique microbiological phenomena largely absent in batch bottles, yet potentially relevant to contaminant fate. Following up on this theoretical evaluation, I experimentally produce pyrosequencing data and perform beta diversity analysis to demonstrate that batch and continuous-flow (column) microcosms foster distinctly different microbial communities. Next, I introduce the In Situ Microcosm Array (ISMA), which took approximately two years to design, develop, build and iteratively improve. The ISMA can be deployed down-hole in groundwater monitoring wells of contaminated aquifers for the purpose of autonomously conducting multiple parallel continuous-flow treatability experiments. The ISMA stores all sample generated in the course of each experiment, thereby preventing the release of chemicals into the environment. Detailed results are presented from an ISMA demonstration evaluating ISB for the treatment of hexavalent chromium and trichloroethene. In a technical and economical comparison to batch microcosms, I demonstrate the ISMA is both effective in informing remedial design decisions and cost-competitive. Finally, I report on a participatory technology assessment (pTA) workshop attended by diverse stakeholders of the Phoenix 52nd Street Superfund Site evaluating the ISMA's ability for addressing a real-world problem. In addition to receiving valuable feedback on perceived ISMA limitations, I conclude from the workshop that pTA can facilitate mutual learning even among entrenched stakeholders. In summary, my doctoral research (i) pinpointed limitations of current remedial design approaches, (ii) produced a novel alternative approach, and (iii) demonstrated the technical, economical and social value of this novel remedial design tool, i.e., the In Situ Microcosm Array technology.
ContributorsKalinowski, Tomasz (Author) / Halden, Rolf U. (Thesis advisor) / Johnson, Paul C (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Bennett, Ira (Committee member) / Arizona State University (Publisher)
Created2013
Description
The spread of invasive species may be greatly affected by human responses to prior species spread, but models and estimation methods seldom explicitly consider human responses. I investigate the effects of management responses on estimates of invasive species spread rates. To do this, I create an agent-based simulation model of an insect invasion across a county-level citrus landscape. My model provides an approximation of a complex spatial environment while allowing the "truth" to be known. The modeled environment consists of citrus orchards with insect pests dispersing among them. Insects move across the simulation environment infesting orchards, while orchard managers respond by administering insecticide according to analyst-selected behavior profiles and management responses may depend on prior invasion states. Dispersal data is generated in each simulation and used to calculate spread rate via a set of estimators selected for their predominance in the empirical literature. Spread rate is a mechanistic, emergent phenomenon measured at the population level caused by a suite of latent biological, environmental, and anthropogenic. I test the effectiveness of orchard behavior profiles on invasion suppression and evaluate the robustness of the estimators given orchard responses. I find that allowing growers to use future expectations of spread in management decisions leads to reduced spread rates. Acting in a preventative manner by applying insecticide before insects are actually present, orchards are able to lower spread rates more than by reactive behavior alone. Spread rates are highly sensitive to spatial configuration. Spatial configuration is hardly a random process, consisting of many latent factors often not accounted for in spread rate estimation. Not considering these factors may lead to an omitted variables bias and skew estimation results. The ability of spread rate estimators to predict future spread varies considerably between estimators, and with spatial configuration, invader biological parameters, and orchard behavior profile. The model suggests that understanding the latent factors inherent to dispersal is important for selecting phenomenological models of spread and interpreting estimation results. This indicates a need for caution when evaluating spread. Although standard practice, current empirical estimators may both over- and underestimate spread rate in the simulation.
ContributorsShanafelt, David William (Author) / Fenichel, Eli P (Thesis advisor) / Richards, Timothy (Committee member) / Janssen, Marco (Committee member) / Arizona State University (Publisher)
Created2012
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
DescriptionThis paper provides an analysis of the differences in impacts made by companies that promote their sustainability efforts. A comparison of companies reveals that the ones with greater supply chain influence and larger consumer bases can make more concrete progress in terms of accomplishment for the sustainability realm.
ContributorsBeaubien, Courtney Lynn (Author) / Anderies, John (Thesis director) / Allenby, Brad (Committee member) / Janssen, Marco (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2013-05
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
The evolution of cooperation is a fundamental problem in biology, especially for non-relatives, where indirect fitness benefits cannot counter within-group inequalities. Multilevel selection models show how cooperation can evolve if it generates a group-level advantage, even when cooperators are disadvantaged within their group. This allows the possibility of group selection, but few examples have been described in nature. Here we show that group selection can explain the evolution of cooperative nest founding in the harvester ant Pogonomyrmex californicus. Through most of this species’ range, colonies are founded by single queens, but in some populations nests are instead founded by cooperative groups of unrelated queens. In mixed groups of cooperative and single-founding queens, we found that aggressive individuals had a survival advantage within their nest, but foundress groups with such non-cooperators died out more often than those with only cooperative members. An agent-based model shows that the between-group advantage of the cooperative phenotype drives it to fixation, despite its within-group disadvantage, but only when population density is high enough to make between-group competition intense. Field data show higher nest density in a population where cooperative founding is common, consistent with greater density driving the evolution of cooperative foundation through group selection.
ContributorsShaffer, Zachary (Author) / Sasaki, Takao (Author) / Haney, Brian (Author) / Janssen, Marco (Author) / Pratt, Stephen (Author) / Fewell, Jennifer (Author) / College of Liberal Arts and Sciences (Contributor)
Created2016-07-28
Description
Climate change presents the urgent need for effective sustainable water management that is capable of preserving natural resources while maintaining economical stability. States like California rely heavily on groundwater pumping for agricultural use, contributing to land subsidence and insufficient returns to water resources. The recent California drought has impacted agricultural production of certain crops. In this thesis, we present an agent-based model of farmers adapting to drought conditions by making crop choice decisions, much like the decisions Californian farmers have made. We use the Netlogo platform to capture the 2D spatial view of an agricultural system with changes in annual rainfall due to drought conditions. The goal of this model is to understand some of the simple rules farmers may follow to self-govern their consumption of a water resource. Farmer agents make their crop decisions based on deficit irrigation crop production function and a net present value discount rate. The farmers choose between a thirsty crop with a high production cost and a dry crop with a low production cost. Simulations results show that farmers switch crops in accordance with limited water and land resources. Farmers can maintain profit and yield by following simple rules of crop switching based on future yields and optimal irrigation. In drought conditions, individual agents expecting lower annual rainfall were able to increase their total profits. The maintenance of crop yield and profit is evidence of successful adaptation when farmers switch to crops that require less water.
ContributorsGokool, Rachael Shanta (Author) / Janssen, Marco (Thesis director) / Eakin, Hallie (Committee member) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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