Matching Items (665)
Description
Once perceived as an unimportant occurrence in living organisms, cell degeneration was reconfigured as an important biological phenomenon in development, aging, health, and diseases in the twentieth century. This dissertation tells a twentieth-century history of scientific investigations on cell degeneration, including cell death and aging. By describing four central developments in cell degeneration research with the four major chapters, I trace the emergence of the degenerating cell as a scientific object, describe the generations of a variety of concepts, interpretations and usages associated with cell death and aging, and analyze the transforming influences of the rising cell degeneration research. Particularly, the four chapters show how the changing scientific practices about cellular life in embryology, cell culture, aging research, and molecular biology of Caenorhabditis elegans shaped the interpretations about cell degeneration in the twentieth-century as life-shaping, limit-setting, complex, yet regulated. These events created and consolidated important concepts in life sciences such as programmed cell death, the Hayflick limit, apoptosis, and death genes. These cases also transformed the material and epistemic practices about the end of cellular life subsequently and led to the formations of new research communities. The four cases together show the ways cell degeneration became a shared subject between molecular cell biology, developmental biology, gerontology, oncology, and pathology of degenerative diseases. These practices and perspectives created a special kind of interconnectivity between different fields and led to a level of interdisciplinarity within cell degeneration research by the early 1990s.
ContributorsJiang, Lijing (Author) / Maienschein, Jane (Thesis advisor) / Laubichler, Manfred (Thesis advisor) / Hurlbut, James (Committee member) / Creath, Richard (Committee member) / White, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or empirical verification and background data exist and are often well defined, these features are commonly lacking in social and other networks. Here, a novel algorithmic framework for statistical signal processing for graphs is presented. The framework is based on the analysis of spectral properties of the residuals matrix. The framework is applied to the detection of innovation patterns in publication networks, leveraging well-studied empirical knowledge from the history of science. Both the framework itself and the application constitute novel contributions, while advancing algorithmic and mathematical techniques for graph-based data and understanding of the patterns of emergence of novel scientific research. Results indicate the efficacy of the approach and highlight a number of fruitful future directions.
ContributorsBliss, Nadya Travinin (Author) / Laubichler, Manfred (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2015
Description
Leo Kanner first described autism in his 1943 article in Nervous Child titled "Autistic Disturbances of Affective Contact". Throughout, he describes the eleven children with autism in exacting detail. In the closing paragraphs, the parents of autistic children are described as emotionally cold. Yet, he concludes that the condition as he described it was innate. Since its publication, his observations about parents have been a source of controversy surrounding the original definition of autism.
Thus far, histories about autism have pointed to descriptions of parents of autistic children with the claim that Kanner abstained from assigning them causal significance. Understanding the theoretical context in which Kanner's practice was embedded is essential to sorting out how he could have held such seemingly contrary views simultaneously.
This thesis illustrates that Kanner held an explicitly descriptive frame of reference toward his eleven child patients, their parents, and autism. Adolf Meyer, his mentor at Johns Hopkins, trained him to make detailed life-charts under a clinical framework called psychobiology. By understanding that Kanner was a psychobiologist by training, I revisit the original definition of autism as a category of mental disorder and restate its terms. This history illuminates the theoretical context of autism's discovery and has important implications for the first definition of autism amidst shifting theories of childhood mental disorders and the place of the natural sciences in defining them.
Thus far, histories about autism have pointed to descriptions of parents of autistic children with the claim that Kanner abstained from assigning them causal significance. Understanding the theoretical context in which Kanner's practice was embedded is essential to sorting out how he could have held such seemingly contrary views simultaneously.
This thesis illustrates that Kanner held an explicitly descriptive frame of reference toward his eleven child patients, their parents, and autism. Adolf Meyer, his mentor at Johns Hopkins, trained him to make detailed life-charts under a clinical framework called psychobiology. By understanding that Kanner was a psychobiologist by training, I revisit the original definition of autism as a category of mental disorder and restate its terms. This history illuminates the theoretical context of autism's discovery and has important implications for the first definition of autism amidst shifting theories of childhood mental disorders and the place of the natural sciences in defining them.
ContributorsCohmer, Sean (Author) / Hurlbut, James B (Thesis advisor) / Maienschein, Jane (Committee member) / Laubichler, Manfred (Committee member) / Arizona State University (Publisher)
Created2014
Description
During the 1960s, the long-standing idea that traits or behaviors could be
explained by natural selection acting on traits that persisted "for the good of the group" prompted a series of debates about group-level selection and the effectiveness with which natural selection could act at or across multiple levels of biological organization. For some this topic remains contentious, while others consider the debate settled, even while disagreeing about when and how resolution occurred, raising the question: "Why have these debates continued?"
Here I explore the biology, history, and philosophy of the possibility of natural selection operating at levels of biological organization other than the organism by focusing on debates about group-level selection that have occurred since the 1960s. In particular, I use experimental, historical, and synthetic methods to review how the debates have changed, and whether different uses of the same words and concepts can lead to different interpretations of the same experimental data.
I begin with the results of a group-selection experiment I conducted using the parasitoid wasp Nasonia, and discuss how the interpretation depends on how one conceives of and defines a "group." Then I review the history of the group selection controversy and argue that this history is best interpreted as multiple, interrelated debates rather than a single continuous debate. Furthermore, I show how the aspects of these debates that have changed the most are related to theoretical content and empirical data, while disputes related to methods remain largely unchanged. Synthesizing this material, I distinguish four different "approaches" to the study of multilevel selection based on the questions and methods used by researchers, and I use the results of the Nasonia experiment to discuss how each approach can lead to different interpretations of the same experimental data. I argue that this realization can help to explain why debates about group and multilevel selection have persisted for nearly sixty years. Finally, the conclusions of this dissertation apply beyond evolutionary biology by providing an illustration of how key concepts can change over time, and how failing to appreciate this fact can lead to ongoing controversy within a scientific field.
explained by natural selection acting on traits that persisted "for the good of the group" prompted a series of debates about group-level selection and the effectiveness with which natural selection could act at or across multiple levels of biological organization. For some this topic remains contentious, while others consider the debate settled, even while disagreeing about when and how resolution occurred, raising the question: "Why have these debates continued?"
Here I explore the biology, history, and philosophy of the possibility of natural selection operating at levels of biological organization other than the organism by focusing on debates about group-level selection that have occurred since the 1960s. In particular, I use experimental, historical, and synthetic methods to review how the debates have changed, and whether different uses of the same words and concepts can lead to different interpretations of the same experimental data.
I begin with the results of a group-selection experiment I conducted using the parasitoid wasp Nasonia, and discuss how the interpretation depends on how one conceives of and defines a "group." Then I review the history of the group selection controversy and argue that this history is best interpreted as multiple, interrelated debates rather than a single continuous debate. Furthermore, I show how the aspects of these debates that have changed the most are related to theoretical content and empirical data, while disputes related to methods remain largely unchanged. Synthesizing this material, I distinguish four different "approaches" to the study of multilevel selection based on the questions and methods used by researchers, and I use the results of the Nasonia experiment to discuss how each approach can lead to different interpretations of the same experimental data. I argue that this realization can help to explain why debates about group and multilevel selection have persisted for nearly sixty years. Finally, the conclusions of this dissertation apply beyond evolutionary biology by providing an illustration of how key concepts can change over time, and how failing to appreciate this fact can lead to ongoing controversy within a scientific field.
ContributorsDimond, Christopher C (Author) / Collins, James P. (Thesis advisor) / Gadau, Juergen (Committee member) / Laubichler, Manfred (Committee member) / Armendt, Brad (Committee member) / Lynch, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
Natural history is, and was, dependent upon the collection of specimens. In the nineteenth century, American naturalists and institutions of natural history cultivated and maintained extensive collection networks comprised of numerous collectors that provided objects of natural history for study. Effective networks were collaborative in nature, with naturalists such as Spencer Baird of the Smithsonian trading their time and expertise for specimens. The incorporation of Darwinian and Neo-Lamarckian evolutionary theory into natural history in the middle of the century led to dramatic changes in the relationship between naturalists and collectors, as naturalists sought to reconcile their observations within the new evolutionary context. This dissertation uses the careers of collectors Robert Kennicott, Frank Stephens, Edward W. Nelson, E.A. Goldman, and Edmund Heller as case studies in order to evaluate how the changes in the theoretical framework of late nineteenth century natural history led to advances in field practice by assessing how naturalists trained their collectors to meet new demands within the field. Research focused on the correspondence between naturalists and collectors, along with the field notes and applicable publications by collectors. I argue that the changes in natural history necessitated naturalists training their collectors in the basics of biogeography - the study of geographic distribution of organisms, and systematics - the study of the diversity of life - leading to a collaborative relationship in which collectors played an active role in the formation of new biological knowledge. The project concludes that the changes in natural history with regard to theory and practice gradually necessitated a more professional cadre of collectors. Collectors became active agents in the formation of biological knowledge, and instrumental in the formation of a truly systematic natural history. As a result, collectors became de facto field naturalists, the forerunners of the field biologists that dominated the practice of natural history in the early and middle twentieth century.
ContributorsLaubacher, Matthew (Author) / Green, Monica (Thesis advisor) / Laubichler, Manfred (Thesis advisor) / Wright, Johnson Kent (Committee member) / Arizona State University (Publisher)
Created2011
Description
Gene-centric theories of evolution by natural selection have been popularized and remain generally accepted in both scientific and public paradigms. While gene-centrism is certainly parsimonious, its explanations fall short of describing two patterns of evolutionary and social phenomena: the evolution of sex and the evolution of social altruism. I review and analyze current theories on the evolution of sex. I then introduce the conflict presented to gene-centric evolution by social phenomena such as altruism and caste sterility in eusocial insects. I review gene-centric models of inclusive fitness and kin selection proposed by Hamilton and Maynard Smith. Based their assumptions, that relatedness should be equal between sterile workers and reproductives, I present several empirical examples that conflict with their models. Following that, I introduce a unique system of genetic caste determination (GCD) observed in hybrid populations of two sister-species of seed harvester ants, Pogonomyrmex rugosus and Pogonomyrmex barbatus. I review the evidence for GCD in those species, followed by a critique of the current gene-centric models used to explain it. In chapter two I present my own theoretical model that is both simple and extricable in nature to explain the origin, evolution, and maintenance of GCD in Pogonomyrmex. Furthermore, I use that model to fill in the gaps left behind by the contributing authors of the other GCD models. As both populations in my study system formed from inter-specific hybridization, I review modern discussions of heterosis (also called hybrid vigor) and use those to help explain the ecological competitiveness of GCD. I empirically address the inbreeding depression the lineages of GCD must overcome in order to remain ecologically stable, demonstrating that as a result of their unique system of caste determination, GCD lineages have elevated recombination frequencies. I summarize and conclude with an argument for why GCD evolved under selective mechanisms which cannot be considered gene-centric, providing evidence that natural selection can effectively operate on non-heritable genotypes appearing in groups and other social contexts.
ContributorsJacobson, Neal (Author) / Gadau, Juergen (Thesis advisor) / Laubichler, Manfred (Committee member) / Pratt, Stephen (Committee member) / Arizona State University (Publisher)
Created2012
Description
Design problem formulation is believed to influence creativity, yet it has received only modest attention in the research community. Past studies of problem formulation are scarce and often have small sample sizes. The main objective of this research is to understand how problem formulation affects creative outcome. Three research areas are investigated: development of a model which facilitates capturing the differences among designers' problem formulation; representation and implication of those differences; the relation between problem formulation and creativity.
This dissertation proposes the Problem Map (P-maps) ontological framework. P-maps represent designers' problem formulation in terms of six groups of entities (requirement, use scenario, function, artifact, behavior, and issue). Entities have hierarchies within each group and links among groups. Variables extracted from P-maps characterize problem formulation.
Three experiments were conducted. The first experiment was to study the similarities and differences between novice and expert designers. Results show that experts use more abstraction than novices do and novices are more likely to add entities in a specific order. Experts also discover more issues.
The second experiment was to see how problem formulation relates to creativity. Ideation metrics were used to characterize creative outcome. Results include but are not limited to a positive correlation between adding more issues in an unorganized way with quantity and variety, more use scenarios and functions with novelty, more behaviors and conflicts identified with quality, and depth-first exploration with all ideation metrics. Fewer hierarchies in use scenarios lower novelty and fewer links to requirements and issues lower quality of ideas.
The third experiment was to see if problem formulation can predict creative outcome. Models based on one problem were used to predict the creativity of another. Predicted scores were compared to assessments of independent judges. Quality and novelty are predicted more accurately than variety, and quantity. Backward elimination improves model fit, though reduces prediction accuracy.
P-maps provide a theoretical framework for formalizing, tracing, and quantifying conceptual design strategies. Other potential applications are developing a test of problem formulation skill, tracking students' learning of formulation skills in a course, and reproducing other researchers’ observations about designer thinking.
This dissertation proposes the Problem Map (P-maps) ontological framework. P-maps represent designers' problem formulation in terms of six groups of entities (requirement, use scenario, function, artifact, behavior, and issue). Entities have hierarchies within each group and links among groups. Variables extracted from P-maps characterize problem formulation.
Three experiments were conducted. The first experiment was to study the similarities and differences between novice and expert designers. Results show that experts use more abstraction than novices do and novices are more likely to add entities in a specific order. Experts also discover more issues.
The second experiment was to see how problem formulation relates to creativity. Ideation metrics were used to characterize creative outcome. Results include but are not limited to a positive correlation between adding more issues in an unorganized way with quantity and variety, more use scenarios and functions with novelty, more behaviors and conflicts identified with quality, and depth-first exploration with all ideation metrics. Fewer hierarchies in use scenarios lower novelty and fewer links to requirements and issues lower quality of ideas.
The third experiment was to see if problem formulation can predict creative outcome. Models based on one problem were used to predict the creativity of another. Predicted scores were compared to assessments of independent judges. Quality and novelty are predicted more accurately than variety, and quantity. Backward elimination improves model fit, though reduces prediction accuracy.
P-maps provide a theoretical framework for formalizing, tracing, and quantifying conceptual design strategies. Other potential applications are developing a test of problem formulation skill, tracking students' learning of formulation skills in a course, and reproducing other researchers’ observations about designer thinking.
ContributorsDinar, Mahmoud (Author) / Shah, Jami J. (Thesis advisor) / Langley, Pat (Committee member) / Davidson, Joseph K. (Committee member) / Lande, Micah (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this dissertation, three complex material systems including a novel class of hyperuniform composite materials, cellularized collagen gel and low melting point alloy (LMPA) composite are investigated, using statistical pattern characterization, stochastic microstructure reconstruction and micromechanical analysis. In Chapter 1, an introduction of this report is provided, in which a brief review is made about these three material systems. In Chapter 2, detailed discussion of the statistical morphological descriptors and a stochastic optimization approach for microstructure reconstruction is presented. In Chapter 3, the lattice particle method for micromechanical analysis of complex heterogeneous materials is introduced. In Chapter 4, a new class of hyperuniform heterogeneous material with superior mechanical properties is investigated. In Chapter 5, a bio-material system, i.e., cellularized collagen gel is modeled using correlation functions and stochastic reconstruction to study the collective dynamic behavior of the embed tumor cells. In chapter 6, LMPA soft robotic system is generated by generalizing the correlation functions and the rigidity tunability of this smart composite is discussed. In Chapter 7, a future work plan is presented.
ContributorsXu, Yaopengxiao (Author) / Jiao, Yang (Thesis advisor) / Liu, Yongming (Committee member) / Wang, Qing Hua (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
Description
Pipeline infrastructure forms a vital aspect of the United States economy and standard of living. A majority of the current pipeline systems were installed in the early 1900’s and often lack a reliable database reporting the mechanical properties, and information about manufacturing and installation, thereby raising a concern for their safety and integrity. Testing for the aging pipe strength and toughness estimation without interrupting the transmission and operations thus becomes important. The state-of-the-art techniques tend to focus on the single modality deterministic estimation of pipe strength and do not account for inhomogeneity and uncertainties, many others appear to rely on destructive means. These gaps provide an impetus for novel methods to better characterize the pipe material properties. The focus of this study is the design of a Bayesian Network information fusion model for the prediction of accurate probabilistic pipe strength and consequently the maximum allowable operating pressure. A multimodal diagnosis is performed by assessing the mechanical property variation within the pipe in terms of material property measurements, such as microstructure, composition, hardness and other mechanical properties through experimental analysis, which are then integrated with the Bayesian network model that uses a Markov chain Monte Carlo (MCMC) algorithm. Prototype testing is carried out for model verification, validation and demonstration and data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. With a view of providing a holistic measure of material performance in service, the fatigue properties of the pipe steel are investigated. The variation in the fatigue crack growth rate (da/dN) along the direction of the pipe wall thickness is studied in relation to the microstructure and the material constants for the crack growth have been reported. A combination of imaging and composition analysis is incorporated to study the fracture surface of the fatigue specimen. Finally, some well-known statistical inference models are employed for prediction of manufacturing process parameters for steel pipelines. The adaptability of the small datasets for the accuracy of the prediction outcomes is discussed and the models are compared for their performance.
ContributorsDahire, Sonam (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
Description
Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.
ContributorsCang, Ruijin (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018