Office automation : debunking the myths concerning its impact on the individual and the organization, a case study

Moran, Lisa N Mich., UMI 1982 국내박사

The image of the Jew in German and Austrian drama, 1800-1850

Lea, Charlene A Mich., UMI 1977 국내박사

The rise of the 'Mencius' : historical interpretations of Mencian morality

황준걸 Mich., UMI 1980 국내박사

Reflections of status and intimacy in Korean speech levels, English code, and their use dyadic coversations between bilingual

Song-Bradford, Linda Marie Mich., UMI 1979 국내박사

A sector analysis of modern written Korean

이응호 Mich., UMI 1978 국내박사

The Evolution of Social Intelligence in Macaques

Bettle, Rosemary ProQuest Dissertations & Theses University of Mich 2021 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Primates lead complex social lives, and this complexity is thought to drive the evolution of social intelligence, including social cognitive abilities that are thought to be especially sophisticated in humans. However, it remains unclear (1) how different social styles promote the emergence of these abilities, and (2) how social cognition alters naturalistic patterns of social behavior. My PhD research examines the social cognition and social behavior of two closely related primate species that have different social styles—despotic rhesus macaques (Macaca mulatta) and socially tolerant Barbary macaques (Macaca sylvanus), to characterize the processes shaping social cognition and test several major hypotheses about the evolutionary contexts that support the emergence of complex cognition. The first focus of my dissertation is to characterize complex social cognition in primates. In Chapters 2 and 3, I assess whether rhesus and Barbary macaques––like humans––have a sophisticated understanding of others’ gaze, that accounts for their line-of-sight. In Chapter 4, I further assess whether rhesus macaques are sensitive to gaze as a communicative cue, which has been proposed to have been especially important during human evolution (Csibra & Gergely, 2011). Finally, I review evidence for different social cognitive abilities across primates in Chapter 6, and argue that human-unique social cognitive abilities may be linked to cooperation. The second focus of my dissertation is to test the role of social style in promoting sophisticated social cognition. In particular, while much work on primate social cognition has suggested that high rates of social competition drive social cognitive evolution (Byrne & Whiten, 1989; Dunbar, 1998), other work has argued that tolerance promotes sophisticated social cognition––and that despotic primate species are actually constrained to utilizing sophisticated social abilities within competitive contexts (Hare, 2001; Lyons & Santos, 2006). Chapters 2, 3, and 5 test these hypotheses, by assessing whether despotic rhesus macaques can flexibly use a sophisticated understanding of others’ gaze even in a non-competitive context (Chapter 2), testing whether tolerant Barbary macaques or rhesus macaques show greater skill in this task (Chapter 3), and examining the link between social behavior and cognition (Chapter 5). The final focus of my thesis is to examine how social cognition predicts natural social behavior. The social intelligence hypothesis posits that primate sociality selected for cognitive skills which allow individuals to respond adaptively to others’ behavior (Byrne & Whiten, 1989; Hare, 2017; Humphrey, 1976; Jolly, 1966), yet has not been empirically tested despite intense theoretical interest. In Chapter 5, I utilize a novel approach that combines both experimental assessments of cognitive abilities with naturalistic social behavior upon Barbary macaques. I then contrast three influential hypotheses about the relationship between intelligence and behavior: (1) does social intelligence enable interaction with a larger number of individuals (Dunbar 1998); (2) does social intelligence facilitate strong social bonds or other prosocial behavior (Cheney, Seyfarth, and Smuts 1986; Hare 2017; Dunbar and Shultz 2007); and (3) does social intelligence facilitate sophisticated competitive behavior? (Byrne and Whiten 1989). Overall, this dissertation examines the evolution of social intelligence in macaques, by contrasting cognition and behavior across two closely related species that vary in social tolerance. This will provide new insights into how complex social intelligence evolves across species, including humans.

Activation of Protein Ubiquitination by the Antiviral Enzyme, Viperin

Patel, Ayesha Maleeha ProQuest Dissertations & Theses University of Mich 2021 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Viperin (Virus Inhibitory Protein; Endoplasmic Reticulum-associated, Interferon iNducible) is an interferon-stimulated gene that is upregulated as a part of the innate immune response to viral infection. It has been shown to restrict the replication of a broad range of human viruses including influenza, hepatitis C, human immunodeficiency, Dengue, West Nile, Zika, and tick-borne encephalitis viruses. However, the mechanism with which viperin restricts infection varies dependent upon the type of virus. Viperin is a member of the radical S-adenosylmethionine (SAM) enzyme superfamily, and recently was shown to catalyze the dehydration of cytidine triphosphate (CTP) to form the antiviral nucleotide 3′-deoxy-3′,4′-didehydro-CTP (ddhCTP) through a SAM-dependent radical mechanism. ddhCTP acts as a chain terminating inhibitor of viral genome replication of some, but not all, viral RNA-dependent RNA polymerases. These recent findings are exciting but do not fully account for viperin’s antiviral activity against most other viruses.Viperin is also known to play a key role in the Toll-like receptor 7 and 9(TLR-7/9) immune signaling pathways. It recruits signaling proteins to lipid bodies, and thereby facilitates the downstream activation of numerous genes. However, evidence for activation of downstream genes comes from studies conducted with proteins transiently expressed in mammalian cells, and the interpretation of such data is complicated by the potential involvement of other cellular proteins. Therefore, this study focuses on reconstituting viperin’s interactions with two enzymes involved in TRL-7/9 signaling in vitro using purified proteins: TRAF6 (tumor necrosis factor receptor associated factor 6) and IRAK1 (interleukin receptor associated kinase 1). TRAF6 is an E3 ubiquitin ligase that catalyzes K63-linked polyubiquitination of a broad range of substrate proteins which are involved in several signaling pathways including TLR7/9, NF-kB, and MAPK signaling cascades. In addition, TRAF6 itself is auto-ubiquitinated to recruit downstream kinases into signaling complexes.Here, I describe the recombinant expression and purification of various domains of TRAF6, the ‘death’ domain of IRAK1, and an N-terminal truncation of viperin (viperin-ΔN50) from E. coli. This has allowed the interaction between viperin and TRAF6 to be directly demonstrated. It also allowed the auto-ubiquitination activity of TRAF6 to be reconstituted in vitro using purified enzymes. Using this system, viperin was shown to activate TRAF6 ubiquitin ligase activity, which provides a biochemical mechanism to explain viperin’s role in potentiating innate immune signaling.The interaction of viperin with IRAK1 has also been studied. IRAK1 is a serine/threonine kinase that is involved in TLR7/9 pathways. Viperin is predicted to facilitate the ubiquitination of IRAK1 by TRAF6 to activate the production of type I interferons. Using truncated IRAK1 constructs, transiently expressed in HEK293T cells, the interactions of IRAK1 with viperin was localized to the ‘death’ domain of IRAK1. Unfortunately, attempts to express and purify this IRAK1 domain in its soluble form in E. coli to facilitate in vitro studies proved unsuccessful.Lastly, I developed a method to purify the full-length, membrane associated form of viperin using lipid nanodiscs to maintain a membrane-like environment. These experiments represent the first time that full-length viperin has been purified in its active form. This work thus provides a new platform to facilitate structural studies on full-length viperin and study its interaction with other membrane-associated proteins that may contribute to its antiviral activity.

Purpose-First Programming: A Programming Learning Approach for Learners Who Care Most About What Code Achieves

Cunningham, Kathryn Irene ProQuest Dissertations & Theses University of Mich 2020 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Measuring Gender Essentialism in Children and Examining the Influence of Indirect Contact with Transgender People on Essentialist Beliefs

Fine, Rachel Danielle ProQuest Dissertations & Theses University of Mich 2021 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Recently, gender diversity has become more visible in the U.S. Yet many still struggle to understand gender identities outside of the binary of man and woman (Buck, 2016). One lay theory children and adults may use to think about gender and specific gender identities is essentialism. Essentialism is a set of beliefs that center around the idea that certain categories have an unknown or ill-defined essence. As a result of this presumed essence, the categories are thought to be biologically based, discrete from one another, informative about category members’ behaviors and preferences, and immutable. Although prior research has established the use of essentialist beliefs about gender from an early age, several questions remain, especially at a time when gender diversity is becoming more visible. In this dissertation, I: (1) developed a new scale of gender essentialism for children five to ten years of age, the Gender Essentialism Scale for Children (GES-C); and (2) examined the effect of stories about trans-identity characters on children’s understanding of transgender identities and gender essentialism. The GES-C is a 16-item measure of gender essentialism with four four-item subscales measuring the components of essentialism described above. I found the GES-C to be a reliable and valid scale with 316 participants aged five to ten years old. I also performed a confirmatory factory analysis (CFA) using structural equation modeling (SEM) and found my scale to have fit indices outside of commonly used cutoffs for good model fit but in line with the other scales for children specifically developed for use in developmental research psychology. Next, I conducted a study with 173 five- to six-year-old and nine- to ten-year-old children to test what children can learn about transgender identities from stories and whether this can lead to a reduction in gender essentialist beliefs. Participants in this study were assigned to one of three conditions, varying in the story that they heard: the Realistic story about a transgender girl socially transitioning from a boy to a girl; the Metaphorical/Fantastical story about an anthropomorphized, red-labeled marker who discovers their identity as blue (this story could be interpreted as a metaphor for being transgender); or No story (control). Hearing the realistic story about the transgender girl significantly improved understanding of transgender identities. And although I found no overall reduction in gender essentialism, essentialist beliefs about the immutability of gender were reduced after hearing the realistic story. These findings underscore the importance of examining gender essentialism, wholly and by component, in children. Being able to efficiently and effectively measure multiple components of gender essentialism at one time allows researchers to better measure when and how essentialist beliefs change in children. It will be especially important to understand how children’s gender essentialist beliefs may or may not change as a result of the increased visibility of gender diverse identities.

Provably Efficient Reinforcement Learning under Linear Model Structures: From Tabular to Feature Based Exploration

Modi, Aditya ProQuest Dissertations & Theses University of Mich 2021 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Reinforcement learning (RL) is a machine learning paradigm where an agent learns to interact with an environment in the presence of a reward signal as feedback. Recent breakthroughs have led to a renewed interest in building intelligent RL agents by combining the core RL algorithms with the expressivity of deep function approximators and advances in computation via simulation. Despite the recent advances, in most complex domains RL algorithms need a large amount of interaction data in order to learn a good policy. As a result, recent theoretical research has focused on problems pertaining to the quantification and/or improvement of sample efficiency of RL under various interaction protocols. These efforts are directed towards understanding the statistical aspects of reinforcement learning, which can be a key factor in making progress towards real world applications, ranging from healthcare and robotics to control of large scale web systems. The main theme of this thesis is the analysis of such information-theoretic aspects of RL in terms of the structural complexity of the environment by using tools from the learning theory literature. In this thesis, we consider a spectrum of scenarios: ranging from tabular to rich observation domains, single to multi-task settings and reward-specific to reward-free learning. Specifically, this thesis presents theoretical results in the following settings: the 1st part studies the sample efficiency of online learning in contextual Markov decision processes (MDPs) where the agent interacts with a sequence of tabular environments and has access to a high-dimensional representation that determines the dynamics of each MDP. The 2nd part studies the sample complexity of learning in a structured model class where the true model of an arbitrarily large MDP can be approximated using a feature-based linear combination of a known ensemble of models. The 3rd part investigates the problem of learning in a low-rank MDP where I design and analyze the first provably efficient model-free representation learning and exploration algorithm for reward-free learning. Lastly, in the 4th part, I provide results for online multi-task learning in linear quadratic regulators, under the assumption that the transition matrices for each system share a common basis. A common thread running through all results in this thesis is the effect of a linear/low-rank structure in the model on the sample efficiency of learning. Overall, this thesis proposes novel provably efficient exploration and model selection algorithms under various linear model structures, and highlights the role of environment complexity in the statistical efficiency of reinforcement learning.