Scientific researchers have studied microorganisms since the emergence of the single lens microscope in the 17th century. Since then, researchers designed and published many thousands of images to record and share their observations, including hand-drawn diagrams, photomicrographs, and photographs. Images shaped how researchers conceived of microorganisms, their concepts of microorganisms shaped their images, and their images and concepts were shaped by the contexts in which they were working. Over time, the interplay of images and concepts in various research contexts participated in the development of new concepts related to microorganisms, like the “biofilm” concept, or the idea that bacteria exist in nature as complex aggregates attached to surfaces via extracellular polymeric matrices. Many histories of microbiology locate the origin of the biofilm concept in the 1970s, but that date obscures the rich history of research about attached microbial aggregates that occurred throughout the history of microbiology. I discovered how the interplay of images and concepts related to bacteria participated in the development of the biofilm concept by documenting when and why researchers used different visual features to represent changing concepts related to microorganisms. I specifically examined how and why scientists represented evolving concepts related to bacteria during the 17th century (Chapter 1), from the late 17th century to the early 20th century (Chapter 2), and during the first seventy-four years of the 20th century (Chapter 3). I discovered the biofilm concept developed in at least three unique research contexts during the 20th century, and how images reflected and shaped the concept’s development in each case. The narrative and collection of images generated from this work serve as a visual history of the development of scientists’ ideas about the nature of bacteria over 300 years.

Abortion is a controversial topic internationally. Most current debates about abortion concern when, if at all, it should be legal. However, researchers have shown many times that after an abortion ban, maternal and infant mortalities rise significantly, as women who seek out abortions do so regardless of abortion legality. So, is it possible to reduce abortions in a population without delegalizing abortion and, if so, how? Why do some countries have higher abortion rates than others in the presence of the same law?This dissertation answers both questions. First, I present historical evidence in the first comprehensive comparative analysis of all 15 post-Soviet countries, which have very similar abortion laws originating from the Union of Soviet Socialist Republics (USSR). Second, I use those findings to build the first agent-based model (ABM) of unintended pregnancies in a hypothetical artificial population. USSR was the only country in the world to complete its demographic transition through abortion instead of modern contraception, and the Soviet government passed the first law in the world to allow abortion upon request in 1920. After the USSR dissolution in 1991, post-Soviet countries maintained very similar abortion laws, but had very different abortion rates for most years. Analysis of fertility data from post-Soviet countries shows that the prevalence of some specific contraceptive methods, namely the rhythm method (r = 0.82), oral pill (r = 0.56), and male condom (r = 0.51) are most strongly correlated with high abortion rates, and that sex education is a factor that reduces the rates in otherwise similar countries (p = 0.02). The ABM shows that even basic sex education results in fewer abortions than no sex education or abstinence-based sex education (p < 0.01). In scenarios without sex education, basic quality of post-abortion contraceptive counseling (PACC) is better than no PACC or low-quality PACC at reducing abortions (p < 0.01). Still, the higher the quality of sex education or PACC, the fewer abortions in the artificial population. The ABM is adaptive and policy makers can use it as a decision-support tool to make evidence-based policy decisions regarding abortion, and, potentially, other sociobiological phenomena with some adjustments to the code.

In 1830, a dispute erupted in the halls of lÕAcad mie des Sciences in Paris between the two most prominent anatomists of the nineteenth century. Georges Cuvier and tienne Geoffroy Saint-Hilaire, once friends and colleagues at the Paris Museum, became arch rivals after this historical episode. Like many important disputes in the history of science, this debate echoes several points of contrasts between the two thinkers. The two French Ânaturalists not only disagreed about what sorts of comparisons between vertebrates were acceptable, but also about which principles ought to underlie a rational system of animal taxonomy and guide the study of animal anatomy. Digging deeper into their differences, their particular disagreements over specific issues within zoology and anatomy culminated in the articulation of two competing and divergent philosophical views on the aims and methods of the life sciences. The emergence of these two distinct positions has had a lasting impact in the development of evolutionary and developmental biology. This essay will provide an overview of the conceptual themes of the debate, its implications for the development of the life sciences, and its role in the history of embryology and developmental biology.

How is knowledge created at the intersections between basic science, biotechnology, and industry? Gene drives are an interesting example, as they combine a long-standing interest with a recent technological breakthrough and a new set of commercial applications. Gene drives are genes engineered such that they are preferentially inherited at a frequency greater than the typical Mendelian fifty percent ratio. During the historical and conceptual evolution of gene drives beginning in the 1960s, there have been many innovations and publications. Along with that, gene drive science developed considerable public attention, explosion of new scientists, and variation in the way the topic is discussed. It is now time to look at this new organization of science using a systematic approach to characterize the system that has enabled knowledge to grow in this scientific field. This project breaks new ground in how knowledge advances in genetic engineering science, and how scientists understand what a “gene drive” is through analysis of language, communities, and other social factors. In effect, this research will advance multiple fields and enable a deeper understanding of knowledge and complexity. This project documents patterns of publication, collaborative relationships, linguistic variation, innovation, and knowledge expansion. The results of computational analysis provide an in-depth and complete characterization of the structure, dynamics, and evolution of scientific knowledge found in the gene drive technology. Further, time series analysis of the multiple layers of discourse enabled a diachronic connective mapping of collaborative relationships and tracked linguistic variation and change, highlighting where ambiguous language may appear, improving and creating more cohesive scientific language. Overall, depicting the structure, dynamics, and evolution of scientific knowledge during a novel eruption of scientific complexity can shed light on the factors that can lead to: (1) improved scientific communication, (2) reduction of scientific progress, (3) new knowledge, and (4) novel collaborative relationships. Therefore, characterizing the current technological, methodological, and social contexts that can influence scientific knowledge.

Getting clear about what behavioral scientists mean when they invoke content presupposing concepts, like information, is necessary for understanding how humanity’s own behavioral capacities do or do not relate to those of non-human animals. Yet, producing a general naturalistic definition for representational content has proven notoriously difficult. Some have argued that Claude Shannon’s formal, mathematically defined notion of information is the proper starting point for building a biological theory of content. Others have sought to define content presupposing concepts in terms of the historical selection processes that drive evolution. However, neither approach has produced definitions that capture the way successful researchers in the behavioral sciences use content-presupposing concepts. In this dissertation, I examine an ethological tradition of insect navigation research that has consistently ascribed content to insects. To clarify the meaning of such ascriptions, I analyze the practices scientists use to justify new attributions of content and the way new attributions of content guide scientists’ future research activities. In chapter 1, I examine a series of insect navigation experiments performed in 2006–2007 that led to a novel ascription of content. I argue that researchers ascribe content to insects’ navigation behaviors when those behaviors reliably accomplish a difficult goal-directed function. I also argue that ascriptions of content help researchers achieve their epistemic aims by guiding hypothesis formation and aiding comparative theorizing. In chapter 2, I trace the history of the experimental strategy analyzed above back to the work of Karl von Frisch in the early 20th century. I argue that von Frisch has a complicated and understudied relationship to the discipline of ethology. I support that argument by highlighting features of von Frisch’s research that both comported with and differed from the program of classical ethology. In chapter 3, I examine the cognitive map debate in insects. I argue that the debate stems from competing research groups’ endorsement of different norms for justifying claims about the dynamics of representational contents. I then situate these different norms historically to show how the cognitive map debate is a continuation of longstanding divisions within the history of animal behavior research.

Mentions of diversity have become an essential part of every university and medical school’s mission statement. Yet, with such an emphasis on diversity, there is an evident absence of<br/>cultural competence education in the curricula of medical education. There is no clear answer of<br/>what is expected of physicians and no direct transitions for the different stages of medical<br/>training when it comes to cultural competence education. This is a vital issue, as there is a close<br/>relationship between the quality of patient care, patient adherence, and medical providers’ levels<br/>of cultural competence. This research analyzes the extent that cultural competence is taught at<br/>various points of the medical education cycle through a data analysis of an IRB approved<br/>questionnaire given to students within the medical education cycle and their value versus<br/>exposure of cultural competence.

Transgenic experiments in Drosophila have proven to be a useful tool aiding in the

determination of mammalian protein function. A CNS specific protein, dCORL is a

member of the Sno/Ski family. Sno acts as a switch between Dpp/dActivin signaling.

dCORL is involved in Dpp and dActivin signaling, but the two homologous mCORL

protein functions are unknown. Conducting transgenic experiments in the adult wings,

and third instar larval brains using mCORL1, mCORL2 and dCORL are used to provide

insight into the function of these proteins. These experiments show mCORL1 has a

different function from mCORL2 and dCORL when expressed in Drosophila. mCORL2

and dCORL have functional similarities that are likely conserved. Six amino acid

substitutions between mCORL1 and mCORL2/dCORL may be the reason for the

functional difference. The evolutionary implications of this research suggest the

conservation of a switch between Dpp/dActivin signaling that predates the divergence of

arthropods and vertebrates.

As the US and the rest of the world face a growing need for affordable and accessible higher education, we must more deeply examine the scalability of our universities: how do they change with size? How do different institutional types vary? What makes ASU number one in innovation? At least two of these questions have immediate relevance to not only higher education, but political economy and sustainability as well. We apply to institutions the exciting complex systems framework of scaling, which has led to deep theoretical insight into the structure of biological systems and cities (West, Brown and Enquist 1997, Bettencourt 2013). First we group universities into seven distinct sectors, from public research universities to professional schools. Then we examine the returns to scale of university revenues, expenditures, and graduation rates, by correlating these key variables versus total enrollment. We discover that the sectors exhibit some important similarities, but overall leverage different economies of scale to serve their own priorities. These results imply shared mechanisms and constraints among the entire class of institutions. Furthermore, the uniqueness of each sector reveals their "speciation" into diverse institutional models, offering a fresh (though limited) first look at their scale-dependent complementary roles and competitive advantages. Accordingly, we outline what additional data and analyses might sufficiently strengthen these results to make recommendations, at levels ranging from student and family decisions to individual university strategies to sector-wide and system-wide policies. Promising future directions include longitudinal analysis of university growth patterns, detailed outlier analysis, and deeper theoretical investigation of mechanisms that drive the observed scaling.

In the digital humanities, there is a constant need to turn images and PDF files into plain text to apply analyses such as topic modelling, named entity recognition, and other techniques. However, although there exist different solutions to extract text embedded in PDF files or run OCR on images, they typically require additional training (for example, scholars have to learn how to use the command line) or are difficult to automate without programming skills. The Giles Ecosystem is a distributed system based on Apache Kafka that allows users to upload documents for text and image extraction. The system components are implemented using Java and the Spring Framework and are available under an Open Source license on GitHub (https://github.com/diging/).

This work challenges the conventional perceptions surrounding the utility and use of the CMS Open Payments data. I suggest unconsidered methodologies for extracting meaningful information from these data following an exploratory analysis of the 2014 research dataset that, in turn, enhance its value as a public good. This dataset is favored for analysis over the general payments dataset as it is believed that generating transparency in the pharmaceutical and medical device R&D process would be of the greatest benefit to public health. The research dataset has been largely ignored by analysts and this may be one of the few works that have accomplished a comprehensive exploratory analysis of these data. If we are to extract valuable information from this dataset, we must alter both our approach as well as focus our attention towards re-conceptualizing the questions that we ask. Adopting the theoretical framework of complex systems serves as the foundation for our interpretation of the research dataset. This framework, in conjunction with a methodological toolkit for network analysis, may set a precedent for the development of alternative perspectives that allow for novel interpretations of the information that big data attempts to convey. By thus proposing a novel perspective in interpreting the information that this dataset contains, it is possible to gain insight into the emergent dynamics of the collaborative relationships that are established during the pharmaceutical and medical device R&D process.