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This dissertation presents three essays advancing the theory of taxation.The first chapter presents a new method for evaluating proposed reforms of progressive piecewise linear tax schedules. Typically, estimates of the elasticity of taxable income (ETI) are used to predict taxpayer responses to changes in tax rates and/or tax bracket thresholds. In this chapter, I show that elasticities are not always needed for this task; the \"bunching mass'' at a bracket threshold (the share of taxpayers locating there) is a sufficient statistic for the revenue effect of behavioral responses to small changes of the threshold. Building on this finding, revenue forecasting and welfare analysis of threshold changes can be conducted using the pre-reform distribution of taxable income alone. I apply these results in an analysis of the Earned Income Tax Credit, an exercise which motivates extensions addressing taxpayer optimization errors, tax rate heterogeneity, large reforms, and income and participation effects. My approach complements existing bunching methods: it avoids key limitations of bunching-based ETI estimation, but addresses a relatively narrower set of policy questions.The second chapter explores the evolution of economic inequality and political inequality in a democratic society where these two types of inequality mutually reinforcing. I introduce a simple dynamic model of democratic redistribution where, in each period, two candidates compete in an election by proposing how a fixed amount of income will be divided amongst a group of citizens in the next period (i.e. pure redistribution policy). Campaign spending is financed by citizen political donations, leading to inequality of political influence favoring wealthier citizens. This creates a feedback loop through which the current distribution of income affects the future distribution. If the marginal dollar of income yields a sufficiently large increase in political influence, long run convergence to a plutocratic equilibrium can occur for arbitrarily small levels of initial economic inequality. The opposite scenario is also possible: a society which is initially extremely unequal may nonetheless be destined for egalitarianism. The long run distribution of income can exhibit extreme sensitivity to initial conditions: tiny differences in initial inequality may determine whether democratic redistribution leads to plutocracy or egalitarianism. Turning to a version of the model where elections are fought over a nonlinear income tax, I show that the same conditions that determine whether convergence to egalitarianism occurs in the pure redistribution model also dictate whether taxation of the rich is possible in the nonlinear tax model.The third chapter employs a variant of the election model from the second chapter to examine the optimal tax treatment of political contributions. Adopting the normative stance that inequality of political influence is undesirable, I characterize the optimal nonlinear tax schedule on political donations. Sufficient statistics for optimal policy include not only donation demand elasticities, but also the marginal efficacy of campaign spending, and the effect of taxes on the sensitivity of donations to candidate policy platforms. Using numerical simulations, I provide proof-of-concept results showing that this framework can rationalize real world policies such as the nonlinear subsidy schedules present in Canada. These feature generous marginal rates of subsidy on the first dollar of political donations, with rates of subsidy declines in donation amount.

How should taxes on externality-generating activities be adjusted if they are regressive? In our model, the government raises revenue using distortionary income and commodity taxes. If more or less productive people have identical tastes for externality-generating consumption, the government optimally imposes a Pigouvian tax equal to the marginal damage from the externality. This is true regardless of whether the tax is regressive. But, if regressivity reflects different preferences of people with different incomes rather than solely income effects, the optimal tax differs from the Pigouvian benchmark. We derive sufficient statistics for optimal policy, and use them to study carbon taxation in the United States. Our empirical results suggest an optimal carbon tax that is remarkably close to the Pigouvian level, but with higher carbon taxes for very high-income households if this is feasible. When we allow for heterogeneity in preferences at each income level as well as across the income distribution, our optimal tax schedules are further attenuated toward the Pigouvian benchmark.

Cells alter their mechanical properties in response to their local microenvironment; this plays a role in determining cell function and can even influence stem cell fate. Here, we identify a robust and unified relationship between cell stiffness and cell volume. As a cell spreads on a substrate, its volume decreases, while its stiffness concomitantly increases. We find that both cortical and cytoplasmic cell stiffness scale with volume for numerous perturbations, including varying substrate stiffness, cell spread area, and external osmotic pressure. The reduction of cell volume is a result of water efflux, which leads to a corresponding increase in intracellular molecular crowding. Furthermore, we find that changes in cell volume, and hence stiffness, alter stem-cell differentiation, regardless of the method by which these are induced. These observations reveal a surprising, previously unidentified relationship between cell stiffness and cell volume that strongly influences cell biology.

Microglia are resident macrophages of the central nervous system and significantly contribute to overall brain function by participating in phagocytosis during development, homeostasis, and diseased states. Phagocytosis is a highly complex process that is specialized for the uptake and removal of opsonized and non-opsonized targets, such as pathogens, apoptotic cells, and cellular debris. While the role of phagocytosis in mediating classical innate and adaptive immune responses has been known for decades, it is now appreciated that phagocytosis is also critical throughout early neural development, homeostasis, and initiating repair mechanisms. As such, modulating phagocytic processes has provided unexplored avenues with the intent of developing novel therapeutics that promote repair and regeneration in the CNS. Here, we review the functional consequences that phagocytosis plays in both the healthy and diseased CNS, and summarize how phagocytosis contributes to overall pathophysiological mechanisms involved in brain injury and repair.

Mutations in the human LMNA gene cause muscular dystrophy by mechanisms that are incompletely understood. The LMNA gene encodes A-type lamins, intermediate filaments that form a network underlying the inner nuclear membrane, providing structural support for the nucleus and organizing the genome. To better understand the pathogenesis caused by mutant lamins, we performed a structural and functional analysis on LMNA missense mutations identified in muscular dystrophy patients. These mutations perturb the tertiary structure of the conserved A-type lamin Ig-fold domain. To identify the effects of these structural perturbations on lamin function, we modeled these mutations in Drosophila Lamin C and expressed the mutant lamins in muscle. We found that the structural perturbations had minimal dominant effects on nuclear stiffness, suggesting that the muscle pathology was not accompanied by major structural disruption of the peripheral nuclear lamina. However, subtle alterations in the lamina network and subnuclear reorganization of lamins remain possible. Affected muscles had cytoplasmic aggregation of lamins and additional nuclear envelope proteins. Transcription profiling revealed upregulation of many Nrf2 target genes. Nrf2 is normally sequestered in the cytoplasm by Keap-1. Under oxidative stress Nrf2 dissociates from Keap-1, translocates into the nucleus, and activates gene expression. Unexpectedly, biochemical analyses revealed high levels of reducing agents, indicative of reductive stress. The accumulation of cytoplasmic lamin aggregates correlated with elevated levels of the autophagy adaptor p62/SQSTM1, which also binds Keap-1, abrogating Nrf2 cytoplasmic sequestration, allowing Nrf2 nuclear translocation and target gene activation. Elevated p62/SQSTM1 and nuclear enrichment of Nrf2 were identified in muscle biopsies from the corresponding muscular dystrophy patients, validating the disease relevance of our Drosophila model. Thus, novel connections were made between mutant lamins and the Nrf2 signaling pathway, suggesting new avenues of therapeutic intervention that include regulation of protein folding and metabolism, as well as maintenance of redox homoeostasis.

We investigate the impact of the US drone program in Pakistan on insurgent violence. Using details about US-Pakistan counterterrorism cooperation and geocoded violence data, we show that the program was associated with monthly reductions of around nine to thirteen insurgent attacks and fifty-one to eighty-six casualties in the area affected by the program. This change was sizable, as in the year before the program, the affected area experienced around twenty-one attacks and one hundred casualties per month. Additional quantitative and qualitative evidence suggests that this drop is attributable to the drone program. However, the damage caused in strikes during the program cannot fully account for the reduction. Instead, anticipatory effects induced by the program played a prominent role in subduing violence. These effects stemmed from the insurgents’ perception of the risk of being targeted in drone strikes; their efforts to avoid targeting severely compromised their movement and communication abilities, in addition to eroding within-group trust. These findings contrast with prominent perspectives on air-power, counterinsurgency, and US counterterrorism, suggesting select drone deployments can be an effective tool of counterinsurgency and counterterrorism.

Ocean acidification and warming are considered two of the greatest threats to marine biodiversity, yet the combined effect of these stressors on marine organisms remains largely unclear. Using a meta‐analytical approach, we assessed the biological responses of marine organisms to the effects of ocean acidification and warming in isolation and combination. As expected biological responses varied across taxonomic groups, life‐history stages, and trophic levels, but importantly, combining stressors generally exhibited a stronger biological (either positive or negative) effect. Using a subset of orthogonal studies, we show that four of five of the biological responses measured (calcification, photosynthesis, reproduction, and survival, but not growth) interacted synergistically when warming and acidification were combined. The observed synergisms between interacting stressors suggest that care must be made in making inferences from single‐stressor studies. Our findings clearly have implications for the development of adaptive management strategies particularly given that the frequency of stressors interacting in marine systems will be likely to intensify in the future. There is now an urgent need to move toward more robust, holistic, and ecologically realistic climate change experiments that incorporate interactions. Without them accurate predictions about the likely deleterious impacts to marine biodiversity and ecosystem functioning over the next century will not be possible. Meta‐analysis of the impacts of ocean acidification and warming for marine organisms demonstrates that these combined stressors generally led to stronger effects than when experienced in isolation. Moreover, the interaction of ocean warming and acidification led to synergistic effects for the majority of biological responses investigated. Such outcomes highlight the need to include multiple stressors in the development of effective adaptive management strategies, at the same time as highlighting some of the difficulties that need to be overcome to achieve this.

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu ) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN’s core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal’s origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN’s ability to associate signals across scales makes it a unique tool for translational neuroscience research. Neurons carry information in the form of electrical signals. Each of these signals is too weak to detect on its own. But the combined signals from large groups of neurons can be detected using techniques called EEG and MEG. Sensors on or near the scalp detect changes in the electrical activity of groups of neurons from one millisecond to the next. These recordings can also reveal changes in brain activity due to disease. But how do EEG/MEG signals relate to the activity of neural circuits? While neuroscientists can rarely record electrical activity from inside the human brain, it is much easier to do so in other animals. Computer models can then compare these recordings from animals to the signals in human EEG/MEG to infer how the activity of neural circuits is changing. But building and interpreting these models requires advanced skills in mathematics and programming, which not all researchers possess. Neymotin et al. have therefore developed a user-friendly software platform that can help translate human EEG/MEG recordings into circuit-level activity. Known as the Human Neocortical Neurosolver, or HNN for short, the open-source tool enables users to develop and test hypotheses on the neural origin of EEG/MEG signals. The model simulates the electrical activity of cells in the outer layers of the human brain, the neocortex. By feeding human EEG/MEG data into the model, researchers can predict patterns of circuit-level activity that might have given rise to the EEG/MEG data. The HNN software includes tutorials and example datasets for commonly measured signals, including brain rhythms. It is free to use and can be installed on all major computer platforms or run online. HNN will help researchers and clinicians who wish to identify the neural origins of EEG/MEG signals in the healthy or diseased brain. Likewise, it will be useful to researchers studying brain activity in animals, who want to know how their findings might relate to human EEG/MEG signals. As HNN is suitable for users without training in computational neuroscience, it offers an accessible tool for discoveries in translational neuroscience.

A CRISPR/Cas9 genome editing framework has been developed that allows controlled introduction of mono- and bi-allelic sequence changes, and is used to generate induced human pluripotent stem cells with heterozygous and homozygous dominant mutations in amyloid precursor protein and presenilin 1 that have been associated with early onset Alzheimer’s disease. CRISPR/Cas9 editing for stem cells Marc Tessier-Lavigne and colleagues have developed a CRISPR/Cas9-based genome-editing method that allows selective introduction of mono- and bi-allelic sequence changes with high efficiency and accuracy. The authors demonstrate the application of these methods in the generation of human induced pluripotent stem cells (iPS cells) with heterozygous and homozygous dominant mutations in amyloid precursor protein and presenilin that have been associated with early onset Alzheimer's disease. The bacterial CRISPR/Cas9 system allows sequence-specific gene editing in many organisms and holds promise as a tool to generate models of human diseases, for example, in human pluripotent stem cells 1 , 2 . CRISPR/Cas9 introduces targeted double-stranded breaks (DSBs) with high efficiency, which are typically repaired by non-homologous end-joining (NHEJ) resulting in nonspecific insertions, deletions or other mutations (indels) 2 . DSBs may also be repaired by homology-directed repair (HDR) 1 , 2 using a DNA repair template, such as an introduced single-stranded oligo DNA nucleotide (ssODN), allowing knock-in of specific mutations 3 . Although CRISPR/Cas9 is used extensively to engineer gene knockouts through NHEJ, editing by HDR remains inefficient 3 , 4 , 5 , 6 , 7 , 8 and can be corrupted by additional indels 9 , preventing its widespread use for modelling genetic disorders through introducing disease-associated mutations. Furthermore, targeted mutational knock-in at single alleles to model diseases caused by heterozygous mutations has not been reported. Here we describe a CRISPR/Cas9-based genome-editing framework that allows selective introduction of mono- and bi-allelic sequence changes with high efficiency and accuracy. We show that HDR accuracy is increased dramatically by incorporating silent CRISPR/Cas-blocking mutations along with pathogenic mutations, and establish a method termed ‘CORRECT’ for scarless genome editing. By characterizing and exploiting a stereotyped inverse relationship between a mutation’s incorporation rate and its distance to the DSB, we achieve predictable control of zygosity. Homozygous introduction requires a guide RNA targeting close to the intended mutation, whereas heterozygous introduction can be accomplished by distance-dependent suboptimal mutation incorporation or by use of mixed repair templates. Using this approach, we generated human induced pluripotent stem cells with heterozygous and homozygous dominant early onset Alzheimer’s disease-causing mutations in amyloid precursor protein (APP Swe ) 10 and presenilin 1 (PSEN1 M146V ) 11 and derived cortical neurons, which displayed genotype-dependent disease-associated phenotypes. Our findings enable efficient introduction of specific sequence changes with CRISPR/Cas9, facilitating study of human disease.