The Effect of Dietary Tartrazine on Brain Dopamine and the Behavioral Symptoms of Attention Deficit Hyperactivity Disorder

Attention Deficit Hyperactivity Disorder is a neurodevelopmental disorder correlated with a decrease in brain dopamine and an increase in behavioral symptoms of hyperactivity and impulsivity. This experiment explored how tartrazine (Yellow #5) impacts these symptoms. After tartrazine administration to Spontaneously Hypertensive Rats (SHR), dopamine concentrations in regions of brain tissue were measured using Enzyme-Linked Immunosorbent Assay analysis. Behavioral testing with a T-maze and open field test measured impulsivity and hyperactivity, respectively. Results indicate that dietary tartrazine increases hyperactive behaviors in the SHR. However, results do not indicate a relationship between dietary tartrazine and brain dopamine. No conclusions regarding the relationship between dietary tartrazine and impulsivity were drawn.

Development Begins at Home: Women and the Domestic Economy in Brazil, 1945-1975

A number of historians of twentieth-century Latin America have identified ways that national labor laws, civil codes, social welfare programs, and business practices contributed to a gendered division of society that subordinated women to men in national economic development, household management, and familial relations. Few scholars, however, have critically explored women's roles as consumers and housewives in these intertwined realms. This work examines the Brazilian case after the Second World War, arguing that economic policies and business practices associated with “developmentalism” [Portuguese: desenvolvimentismo] created openings for women to engage in debates about national progress and transnational standards of modernity. While acknowledging that an asymmetry of gender relations persisted, the study demonstrates that urban women expanded their agency in this period, especially over areas of economic and family life deemed "domestic." This dissertation examines periodicals, consumer research statistics, public opinion surveys, personal interviews, corporate archives, the archives of key women’s organizations, and government officials’ records to identify the role that women and household economies played in Brazilian developmentalism between 1945 and 1975. Its principal argument is that business and political elites attempted to define gender roles for adult urban women as housewives and mothers, linking their management of the household to familial well-being and national modernization. In turn, Brazilian women deployed these idealized roles in public to advance their own economic interests, especially in the management of household finances and consumption, as well as to expand legal rights for married women, and increase women’s participation in the workforce. As the market for women's labor expanded with continued industrialization, these efforts defined a more active role for women in the economy and in debates about the trajectory of national development policies.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.