We provide the modeling framework to enable a proposed new measurement of the Hubble constant, using the radial extent of galaxy clusters as a standard ruler. Observationally, we plan to measure the angular extent of the cluster and the velocity of galaxies around the cluster. More massive clusters have galaxies that orbit faster, so we can use the velocity of galaxies within clusters to estimate an effective cluster mass. To enable this, we have calibrated the relation between line-of-sight galaxy velocity and cluster mass using cosmological simulations. With an estimate of halo mass now in hand, we can infer the radius of the dark matter halo containing it. To enable this, we have also calibrated the relationship between halo mass and radius with simulations. We find that a halo whose mass is $1\times10^{14}$ $M_\odot/h$ has a physical radius of $596\pm3$ kPc/h, better than $1\%$ precision. Comparing the halo radius inferred from galaxies' velocities to their angular extent allows us to estimate the distance to the cluster, which in turn can be used to assemble a Hubble diagram for galaxy clusters. The high level of precision in the halo radius needed to establish this measurement is founded on a recent insight in halo modeling: galaxies in halos can be split into two populations: those orbiting their host dark matter halo, and those falling into the host for the first time. Here, we present an algorithm that uses the galaxies' accretion history to distinguish between them. We use our split catalog to generate fits for the orbiting and infalling galaxy phase space densities. Importantly, each can be described as depending on a single fundamental scale, the halo radius $r_h$. Both the orbiting and density profiles can be described with 5$\%$ accuracy using $r_h$ as the length scale. In velocity space, we show that the infalling velocity distribution has a bimodal appearance due to the impact of the Hubble flow on galaxy velocities. Our model of the distribution of galaxy line-of-sight velocities is also 5\% accurate. Finally, to prepare the calibration for application to galaxy clusters, we characterize the impact of cluster selection effects the phase space distribution of galaxies. To do so, we select clusters based on the galaxy counts in cylinders of height $\pm$20 h$^{-1}$~Mpc and $\pm$60 h$^{-1}$~Mpc along the line of sight. The distributions of line-of-sight velocities for both orbiting and infalling galaxies are robust to cluster selection; so is the projected orbiting surface density. The projected surface density of the infalling population, however, exhibits a strong scale-dependent bias, where the scale is set by the aperture used in the process of cluster selection. Finally, we suggest the next steps needed to characterize the dependence of the halo radius on the assumed cosmology, as well as the possible influence of baryonic processes on it. As a coda, we also include work on the broadband emission of galaxy clusters, and examine the possible detection of extra-solar neutrinos (via the ICECUBE and Auger experiments) from the Coma cluster of galaxies, as well as for $\gamma$-ray-bright clusters.

Wildfire size, severity, and frequency have been increasing in the Southwestern US since the mid-1980s as a direct result of anthropogenic climate change and land management practices. Significantly, high severity burn area in Arizona and New Mexico has been increasing at a rate of about 1,000 ha per year since 1985. This increase in more frequent, higher severity wildfire, combined with two decades of drought, threatens the persistence, regeneration, and resilience of conifer trees in the dry pine forests of Southern Arizona’s sky islands. Failure of conifers to recover may result in ecosystem conversion, where forested areas are replaced by oak or shrub woodlands. Here we report on radial tree growth, conifer regeneration, and community composition in the Santa Catalina Mountains (SCM) following wildfires in 2002, 2003 and 2020. For our tree growth analysis, we found a striking resilience to both drought and wildfire in three dominant conifers. Pines that burned at high and low severity in particular showed non-significant positive growth trajectories following wildfire exposure in 2003. Douglas-fir growth was more climate-dependent and less fire-dependent than Pinus growth. For areas that burned only in the earlier fires, conifer regeneration over the 17–18-year interval was found in the majority of burned plots, although density varied greatly. Community composition analysis in these areas found some loss of conifer overstory dominance in areas burned at high severity; in general, these were replaced mainly by Aspen (Populus tremuloides) in higher elevation stands, not deciduous or evergreen oaks. Community composition analysis for areas that burned in the most recent fire showed wide variability and may be seen as a starting point for future trajectories of change in Southwestern forests under the influence of changing climate and fire regimes.

Lesser long-nosed bats (Leptonycteris yerbabuenae) are important pollinators and seasonal residents along the U.S-Mexico border. Because they feed on flowering and fruiting plants, they are vulnerable to phenological shifts in those species. To evaluate synchrony between bats and their key food plants, we characterized seasonal abundance of bats and flowering and fruiting phenology of food plants at roosts in 2010, 2011, and 2021. Although phenology of bats and their food plants was generally consistent across years, we observed modest changes in aspects of flowering and fruiting of saguaro (Carnegiea gigantea) and organ pipe cacti (Stenocereus thurberi). At maternity roosts, bat abundance was synchronized more tightly with flowering phenology of saguaros in 2021 than in 2010-11. At post-maternity transient roosts, bat abundance was synchronized less tightly with flowering phenology of agaves (Agave palmeri) in 2021 than in 2010-11. We complemented these analyses by improving methods for non-invasive genetic sampling of this species. Specifically, we developed a two-step amplification approach to analyze microsatellite loci and identify individual bats via DNA extracted from fecal samples. As a proof of concept, five highly polymorphic microsatellite loci distinguished 434 individuals reliably. The probability of two closely related individuals having the same genotype at all five loci was 0.003, and the overall probability of identity was 7.5E-09. Addition of a multiplex step added minimal cost, improved amplification success, and conserved DNA extracts. Repeated analyses showed genotyping error was <2%. We explore the benefits and limits of our approaches for population studies of lesser long-nosed bats and other species that provide key ecosystem services and are commonly of conservation concern.

Black American composer R. Nathaniel Dett (1882–1943) is a pioneering figure of American classical music composed by Black musicians. During his career—as a composer, performer, and educator—Dett advocated for the preservation of Black American folk music and its development via its use in “serious” classical music. While initially not interested in doing so, after hearing the masterful use of Black American folk music in Dvořák’s American Quartet during his student years at Oberlin College, Dett’s perspective changed; Dvořák’s composition provided a conceptual foundation for this fusion. In light of this, Dett’s career is defined by his mission to preserve and develop Black American folk music, of which his oratorio, The Ordering of Moses (1937), represents this fulfillment. This dissertation asserts that R. Nathaniel Dett’s The Ordering of Moses embodies his distinct views on preserving and developing Black American folk music, as demonstrated through his utilization of Negro Spirituals as thematic material within this work. Similarly, this dissertation holds that Dett’s symphonic work preserves and develops broader Black American idioms on account of its use of the Exodus emblem as its narrative focus, consistent with its use in the cultural expressions of Black arts, letters, and religion (Christianity).

Migraine headache is a debilitating disorder of unknown etiology that has been documented in medical records dating back thousands of years. While advancements have been made in understanding the physiology that underlies migraine, medical scientists still struggle to fully explain the symptomology that corresponds with migraine. Trigeminal nociception is heavily implicated in migraine development, as is the role of vasodilation and alterations to blood-brain barrier integrity, yet the more that is elucidated about migraine, the more it becomes clear that migraine represents an incredibly complex neurovascular event. Current therapeutics rely primarily on inducing meningeal vasoconstriction with 5HT1B/D agonists and CGRP antagonism, but symptom relief is rarely absolute. Other options for migraine treatment include anti-inflammatory therapies, such as NSAIDs, however there is always the possibility of developing tolerance or even secondary overuse headache. Within the past two decades, the role of the endocannabinoid system has been increasingly investigated in migraine physiology. Evidence has shown that migraineurs have decreased central levels of the endocannabinoids, 2-AG and AEA, and that exogenous cannabinoid agonists can often help to alleviate migraine pain. The theory of Clinical Endocannabinoid Deficiency has emerged based off this evidence, touting that migraine, and other functional pain disorders, may stem from the observed decrease in endocannabinoid tone. Prior work in animal models has demonstrated that inhibition of AEA and 2-AG hydrolysis can ameliorate induced headache pain; however, clinical trials of AEA hydrolysis inhibitors were forced to halt due to fatal adverse effects and lack of efficacy. Therefore, investigation into endocannabinoid-based therapeutics has shifted to studies of 2-AG. In this thesis, we seek to validate reduced endocannabinoid tone during headache and to better understand the role that 2-AG plays during induced headache. As clinical prevalence of migraine shows a sex difference of roughly 3:1 for females: males, we began by investigating whether sex differences exist within the endocannabinoid system between female and male rodents. Females were shown to have reduced levels of 2-AG as compared to males within the periaqueductal gray (PAG), an important region for descending pain modulation. Furthermore, immunohistochemistry and proteomic analysis revealed that females have greater expression of the 2-AG hydrolyzing enzymes, MAGL and ABHD6, within the PAG. These results indicate that the increased prevalence of functional pain disorders in the female population may indeed arise from decreased 2-AG signaling within the PAG. We next sought to recapitulate reductions of 2-AG during migraine by utilizing three models of headache: KCl administration, sumatriptan overuse, and morphine overuse. In all three models, 2-AG was reduced within the PAG as compared to controls. Within the KCl model, we observed increased expression of MAGL and ABHD6 following headache induction, as well as increases in PGE2 and glial activation markers (GFAP). This demonstrates that currently utilized models of headache lead to reductions in endocannabinoid tone and increases in neuroinflammation in pertinent nociceptive regions. Following evidence that endocannabinoid tone is reduced during headache, and that females display reduced levels of 2-AG as compared to males, we investigated whether we could induce headache phenotypes by exogenously depleting 2-AG. DAGL synthesizes 2-AG from DAG, with DAGLα representing the primary 2-AG synthesizing enzyme within the central nervous system (CNS). We found that DAGL and specific DAGLα inhibition led to reductions of 2-AG within the PAG that corresponded to induction of periorbital allodynia without hind paw allodynia. Female and male rodents were utilized, and it was observed that females displayed greater allodynia for longer periods of time than males. DAGLα inhibition also led to increased photophobia and anxiety behaviors within animals. Taken together, these results indicate that depletion of 2-AG is sufficient to trigger headache, that females are more sensitive than males to 2-AG depletion, and that 2-AG depletion primarily induces cephalic allodynia. This validates 2-AG signaling as a player in headache development and DAGLα inhibition as a potential novel means of modeling episodic migraine phenotypes in rodents. To explore the potential of endocannabinoid-based therapeutics, we built off evidence that 2-AG is reduced during headache due to increased degradation. MAGL and ABHD6 inhibitors were employed before and after KCl induced headache to test for their abilities to prevent and reverse induced headache. We also examined for the receptor dependency of these effects by co-administering hydrolysis inhibitors with antagonists of the CB1R and CB2R. We observed that both MAGL and ABHD6 inhibition can prevent and reverse KCl induced headache. Furthermore, ABHD6 was shown to be independent of the cannabinoid receptors as a preventative treatment, while its reversal was primarily mediated via the CB1R. MAGL, on the other hand, showed dependence on the CB2R for both prevention and reversal. These findings indicate that 2-AG hydrolysis inhibitors may represent novel headache therapeutics. As the CB2R primarily signals at microglia to reduce neuroinflammation, MAGL inhibition is particularly appealing in its ability to avoid the psychoactive effects associated with CB1R agonism. Alterations to the blood-brain barrier are well documented during headache, so our final objective was to investigate the contributions of 2-AG signaling to barrier integrity. In vivo analysis of barrier permeability via carotid perfusion of 14C-sucrose demonstrated increased extravasation of 14C-sucrose into PAG tissue following both DAGLα inhibition and medication overuse, correlating with previous findings within a KCl model of headache. In vitro analysis of bEnd.3 cells following DAGLα inhibition demonstrated reduced trans-endothelial electrical resistance, increased trans-endothelial leak of 14C-sucrose, and changes in cell morphology that correlated with reductions of the tight junction protein VE-Cadherin. Further investigation of the tight junction effects elucidated that VE-Cadherin underwent cleavage following DAGLα inhibition. VE-Cadherin is primarily cleaved via a calcium dependent kinase, therefore we investigated whether DAGLα inhibition induced greater levels of intracellular calcium, which was confirmed with the calcium imaging dye Fura-2, AM. Finally, we confirmed that DAGLα inhibition was reducing endothelial cell levels of 2-AG via LC-MS. Based on this data, we conclude that DAGLα inhibition reduces integrity of the blood-brain barrier by altering the function of tight junction proteins. The results of this thesis demonstrate that 2-AG signaling plays a major role during headache development. Female rodents displayed reduced 2-AG levels in the PAG that correspond with the female prevalence of clinical migraine, currently utilized models of headache likewise reduced PAG 2-AG signaling, exogenous inhibition of 2-AG synthesis induced headache pain in a sex-dependent manner, inhibition of 2-AG hydrolysis alleviated induced headache, and depletion of 2-AG led to loss of integrity of the blood-brain barrier. This work translates well to clinical research, as the induced headache phenotypes following DAGLα inhibition correlate directly with episodic migraine. Further investigation of the effects of 2-AG depletion on the blood-brain barrier is warranted, as well as research into the potential of reversible 2-AG hydrolysis inhibitors, namely MAGL inhibitors, for clinical treatment of migraine.

The Coahuilteco language had been dormant for approximately 200 years. It was documented by two Franciscan priests, Gabriel De Vegara (1730) Cuadernillo de los Indios Pajalates and Bartholomé Garcia (1760). Manual para administrator los santos sacraments de penitencia, eucharistia, extrema-uncion, y matimonio: dar gracias despues de comulgar y ayudar a bien morir. These documents represent the limited corpus of the language. These documents were analyzed and described by two American linguists John Reed Swanton (1940) and Rudolph C. Troike (1959-2015). Their analyses and descriptions of the language posed many problems (reading and interpreting linguistic jargon) for the community of lineal descendants attempting to awaken and reclaim the language. Troike (p.c.2016) provided the community with valuable insights and decoding keys to help the community interpret the linguistic jargon; thus, helping the community answer the question of ‘‘How do we facilitate the teaching and learning of the scholarly research of the heritage language to the community?” Further knowledge of teaching the scholarly material has been provided by Wilson De Lima Silva (p.c.2022).

This paper provides a review of the construction of the metro in Mexico City and evaluates themetro as a production of culture. The incorporation and Mexicanization of foreign technology allowed Mexico to build and service a metro while making it uniquely Mexican. The naming of the stations and the use of icons to identify them project Mexican culture and power. The art within the stations and the activities travelers see and participate in are also examples of how Mexico’s metro is unique to its culture. The use of newspaper articles from major cities from around the world demonstrated that the metro was accepted as a successful technological marvel. Newspaper articles, songs, and local art show that Mexicans accepted the metro as their own. We conclude that the metro was a cultural production that propelled Mexico City to the same level as other modern capital cities.

INTRODUCTION: Dementia is a major contributor to disability and death. A risk factor of dementia is type 2 diabetes (T2D). Current evidence suggests shared causes of the two diseases and a potential to repurpose specific glucose-control agents (GCAs) for dementia prevention or treatment. However, clinical trials and population studies focusing on this topic are insufficient to conclude GCAs anti-dementia effects and compare effectiveness of different GCAs. This dissertation intended to compare the effects of monotherapy and concomitant use of GCAs on dementia. The results should be able to provide suggestions on GCAs selection in T2D, in terms of dementia risk management. METHODS: In this dissertation, study 1 and study 2 used medical records from the Veteran’s Affairs Healthcare (VAH) database. Glucose-control treatment histories were extracted from prescription records and were aggregated at drug class level. Disease conditions were extracted from outpatient diagnosis records, coded by ICD-9 or ICD-10. Four antidiabetic drug classes were assessed based on a literature review and VAH data richness. Among T2D, study 1 compared the associations of first-generation antidiabetic drug classes (metformin [MET], sulfonylureas [SU], and thiazolidinedione [TZD]) with the risk of dementia. And study 2 compared the associations of adding dipeptidyl peptidase-4 inhibitors (DPP-4is) to MET and/or SU (MET/SU) treatment with risks of dementia and other vascular events. To capture the trends of clinical evidence in relevant fields, study 3 reviewed phase II, III, and IV clinical trials repurposing GCAs for Alzheimer's disease (AD). RESULTS: In T2D without prior dementia, study 1 found that after at least one year of treatment, compared with MET monotherapy users, the risk of all-cause dementia was 22% lower in TZD monotherapy users (HR: 0.78, 95% CI 0·75-0·81), and 11% lower in MET and TZD dual therapy users (HR 0.89, 95% CI 0·86-0·93), whereas the risk was 12% higher in sulfonylurea monotherapy users (HR 1.12 95% CI 1·09-1·15). In study 2, compared with participants staying on MET/SU treatment, participants who added DPP-4is to MET/SU regiments had lower risks for the cerebrovascular outcome (HR, 0.68, 95% CI, 0.62-0.74), and the microvascular outcome (HR, 0.91; 95% CI, 0.88-0.94), but not associated with the risk for the renal outcome (HR, 1.03, 95% CI, 0.97-1.10). In study 3, 26 clinical trials were reviewed and summarized in a narrative way. Elders with mild cognitive impairment, AD, and other types of dementia were the main trial participants. Among studied GCAs, insulin, MET, and pioglitazone showed protective effects on subsets of cognitive function but findings on global cognition and AD biomarkers were neutral. But evidence of other GCAs was either insufficient to make conclusions or unavailable. CONCLUSION: These studies suggest that different GCAs had varied effects on dementia risk within elder T2D, although GCAs may be unable to modify AD progression after AD onset. Without disturbing diabetes management, clinicians may consider lowering patients’ dementia risk through GCAs selection. Additional research is warranted to exam whether our findings apply to younger populations and minority groups.

Human health is largely influenced by genetic architecture and living environments. Evolutionary processes, especially past adaptation to changing environments, shaped the genetic architecture and might deeply influence current disease risks. Advances in genomic sequencing dramatically improved our understanding of the genetic basis of diseases in the past ten years. Thousands of genes have been found to be associated with non-infectious and infectious diseases. However, the adaptation experienced by disease-associated genes is not well characterized, let alone the potential causal relationships between disease and genomic adaptation. Here, we use human genomic data to characterize the interplay between adaptation and human non-infectious diseases: what disease gene attributes may influence adaptation, and conversely how past adaptation may have shaped the landscape of disease variants. In the first chapter, I study an important prerequisite: accounting for confounders when studying adaptation in groups of genes, for example, disease genes, relative to the rest of the genome. I show how the lack of accounting for confounding factors other than the biological categories of interest can cause spurious results in the framework of Gene Set Enrichment Analysis (GSEA) of past adaptation. I propose a pipeline that specifically addresses the methodological problems of GSEA applied to recent adaptation in the form of selective sweeps. In the second chapter, I use the GSEA approach established in the first chapter to study the relationship between human non-infectious disease and recent adaptation. I specifically try to clarify the dominant causal direction of this relationship. Adaptation might increase the risk of diseases. For example, deleterious mutations may increase in frequency by hitchhiking with advantageous mutations and thus genes carrying deleterious variants may experience more recent adaptation compared to non-disease genes. Alternatively, pre-existing disease status associated with disease genes might affect the occurrence of selective sweeps at disease genes through the specific attributes that differentiate disease genes from non-disease genes. We find a deficit of selective sweeps in Mendelian non-infectious disease genes compared to non-disease genes in the human genome. This deficit is due to linked disease variants substantially slowing down adaptation at disease gene loci. This highlights a dominant causal relationship direction, without however excluding the possibility that selective sweeps have also increased the frequency of linked disease variants, albeit not at a sufficiently large number of genes to create a visible selective sweep enrichment to counteract the observed deficit, caused by the more predominant opposite action of disease variants slowing down linked adaptive variants. Thus, the picture that emerges from these results is that predominantly, some pre-existing specific attributes of disease genes have limited recent adaptation at their corresponding loci. Taking a step back to the definition of disease, disease is a phenotype that largely deviated from the optimum. What processes might increase the risk of having a largely deviated phenotype? Past strong adaptations, including those that took place a long evolutionary time ago, may have taken the associated phenotypes further from the current optimum compared to the hypothetical situation where these adaptations had not occurred. For example, for a protein whose optimal abundance is high in the current and most historical environments, past adaptation to one particular environment that lowers the abundance to the edge of the disease-causing value may increase the risk of association with diseases. Any mutation that slightly further decreases the abundance may push the abundance of the protein below the critical disease level. In this respect, past strong and rapid adaptation, as opposed to weak and slow adaptation, should have been particularly prone to cause pronounced shifts away from phenotypic optima. An important difficulty then is to first identify past strong adaptations in the human genome. This challenge presented an opportunity for me to connect my work on non-infectious diseases and adaptation to the work done by the rest of the lab on virus-driven adaptation. As mentioned, past strong adaptation should have been more prone to distance phenotypes away from the current optima. We happen to know that viruses drove strong adaptation in human host genomes during ancient viral epidemics, in genes that interact physically with viruses (VIPs for Virus-Interacting Proteins). This strong adaptation notably likely involved adaptive changes in gene expression and abundance, a phenotype that has been shown many times to be connected to genetic diseases. Although we do not have access to past changes in protein abundance directly, we can infer past changes in protein stability, the protein property that affects abundance of folded, functional proteins. In the third chapter, I therefore study host protein adaptations in response to viruses that were driven by changes in protein stability of VIPs. We find that past strong adaptation in VIPs mostly involved large stability changes. This result indicates that host VIP protein stability and thus protein abundance is a phenotype that was strongly selected during ancient viral epidemics. However, the optimal protein stability during past epidemics may be deviated from the current optimum after the selective pressure is weak or gone. In fact, we find compensatory evolution that keeps protein stability stable following viral epidemics in proviral VIPs which have broadly conserved non-immune host native functions. At the same time, specifically, many genetic diseases are known to carry disease variants that decrease thermodynamic stabilities. It is possible that strong past adaptation to viral infections that largely changed protein stability in VIPs increases the risk for following mutations to be deleterious. However, further research is needed to connect these virus-driven adaptive changes in VIP stability to the present occurrence of non-infectious disease variants at VIPs. This connection represents a logical further avenue of research to continue to characterize the relationship between non-infectious disease genes and adaptation.

Interpretability is very important for many NLP applications. Many such applications (e.g., information extraction, sentiment analysis) are applied to important decision-making areas like government policy, financial, law, and others. In these scenarios, machines must explain the information produced, if they are to be deployed in the real world. In this dissertation, we present some approaches for information extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. First we investigate an approach uses an encoder-decoder architecture, which jointly trains a classifier for information extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the classifier. We evaluate the proposed approach on two different information extraction tasks and show that the decoder generates interpretable rules that serve as accurate explanations for the classifier's decisions, and, importantly, that the joint training generally improves the performance of the classifier. We show that our approach can be used for semi-supervised learning, and that its performance improves when trained on automatically-labeled data generated by a rule-based system. Second, we investigate another approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relation that explain the decisions of the relation classifier. We also convert the model outputs to rules to bring global explanations to this approach. This sequence model is trained using a hybrid strategy: supervised, when supervision from pre-existing patterns is available, and semi-supervised otherwise. In the latter situation, we treat the sequence model's labels as latent variables, and learn the best assignment that maximizes the performance of the relation classifier. We evaluate the proposed approach on the two relation extraction datasets and show that the sequence model provides labels that serve as accurate explanations for the relation classifier's decisions, and, importantly, that the joint training generally improves the performance of the relation classifier. We also evaluate the performance of the generated rules and show that the new rules are great add-on to the manual rules and bring the rule-based system much closer to the neural models. Third, we also explore the usages of the model outputs in two ways: 1. Convert them to rules to bring global explanations to this approach; and 2. Use them for bootstrapping when we do not have enough data. Our globally-explainable models approach the performance of neural ones within a reasonable gap.