Doctor of Philosophy

dissertationState and national policy makers for 150 years have promoted public access to higher education, supported through state tax funds and more recently through federal direct appropriations and tax expenditures. In the past 3 decades, state tax funding of higher education has declined, resulting in increased reliance on tuition and reduced college affordability, thereby raising barriers to access. There are also vast differences in how well states fund higher education, with some providing more generous tax funds and others steadily providing less. Higher education researchers have conducted ongoing inquiry regarding factors that may influence the level of state legislative support for higher education. These include institutional, political, economic, cultural, demographical, and fiscal factors. Several have pointed to what appears to be an inverse relationship between state funding of higher education and state funding of Medicaid. This study employs regression analyses of a 20-year, 50-state panel of data (1992-2011), considering the changes in budget share devoted to higher education, Medicaid, K-12 Public Education, and Corrections. During that 20-year period, higher education's share declined in 33 states, Medicaid's increased in 44 states, and 28 states experienced both a decrease in higher education's share and an increase in Medicaid's. Also considered were political party control of states, and changes in Gross State Product. The analysis tries to determine if increases in Medicaid's share is contributing to a decline in the share for higher education, and whether the share for each budget category explains state funding variations. A fixed effects regression model, taking into account both the differences within (across time) and between (across states), determined that 85% of the variation in the error term is due to the wide cross-sectional state differences. This calls into question much of the prior research that relied on ordinary least squares regression models, and did not account for what Zhu called "cross-unit heterogeneity." These findings indicate that additional research is needed, both quantitative (considering groupings of states rather than all 50 states), and interpretive case studies to elicit more insights and research questions that will yield more definitive answers about budgetary tradeoffs between higher education funding and other budgetary categories

Weed Seed Bank Emergence across the Corn Belt

Field experiments, conducted from 1991 to 1994, generated information on weed seedbank emergence for 22 site-years from Ohio to Colorado and Minnesota to Missouri. Early spring seedbank densities were estimated through direct extraction of viable seeds from soil cores. Emerged seedlings were recorded periodically, as were daily values for air and soil temperature, and precipitation. Percentages of weed seedbanks that emerged as seedlings were calculated from seedbank and seedling data for each species, and relationships between seedbank emergence and microclimatic variables were sought. Fifteen species were found in 3 or more site-years. Average emergence percentages (and coefficients of variation) of these species were as follows: giant foxtail, 31.2 (84%); velvetleaf, 28.2 (66); kochia, 25.7 (79); Pennsylvania smartweed, 25.1 (65); common purslane, 15.4 (135); common ragweed, 15.0 (110); green foxtail, 8.5 (72); wild proso millet, 6.6 (104); hairy nightshade, 5.2 (62); common sunflower, 5.0 (26); yellow foxtail, 3.4 (67); pigweed species, 3.3 (103); common lambsquarters, 2.7 (111); wild buckwheat, 2.5 (63), and prostrate knotweed, 0.6 (79). Variation among site-years, for some species, could be attributed to microclimate variables thought to induce secondary dormancy in spring. For example, total seasonal emergence percentage of giant foxtail was related positively to the 1st date at which average daily soil temperature at 5 to 10 cm soil depth reached 16 C. Thus, if soil warmed before mid April, secondary dormancy was induced and few seedlings emerged, whereas many seedlings emerged if soil remained cool until June

Weed Seed Bank Emergence across the Corn Belt

Field experiments, conducted from 1991 to 1994, generated information on weed seedbank emergence for 22 site-years from Ohio to Colorado and Minnesota to Missouri. Early spring seedbank densities were estimated through direct extraction of viable seeds from soil cores. Emerged seedlings were recorded periodically, as were daily values for air and soil temperature, and precipitation. Percentages of weed seedbanks that emerged as seedlings were calculated from seedbank and seedling data for each species, and relationships between seedbank emergence and microclimatic variables were sought. Fifteen species were found in 3 or more site-years. Average emergence percentages (and coefficients of variation) of these species were as follows: giant foxtail, 31.2 (84%); velvetleaf, 28.2 (66); kochia, 25.7 (79); Pennsylvania smartweed, 25.1 (65); common purslane, 15.4 (135); common ragweed, 15.0 (110); green foxtail, 8.5 (72); wild proso millet, 6.6 (104); hairy nightshade, 5.2 (62); common sunflower, 5.0 (26); yellow foxtail, 3.4 (67); pigweed species, 3.3 (103); common lambsquarters, 2.7 (111); wild buckwheat, 2.5 (63), and prostrate knotweed, 0.6 (79). Variation among site-years, for some species, could be attributed to microclimate variables thought to induce secondary dormancy in spring. For example, total seasonal emergence percentage of giant foxtail was related positively to the 1st date at which average daily soil temperature at 5 to 10 cm soil depth reached 16 C. Thus, if soil warmed before mid April, secondary dormancy was induced and few seedlings emerged, whereas many seedlings emerged if soil remained cool until June

The Holy Grail: A road map for unlocking the climate record stored within Mars' polar layered deposits

In its polar layered deposits (PLD), Mars possesses a record of its recent climate, analogous to terrestrial ice sheets containing climate records on Earth. Each PLD is greater than 2 ​km thick and contains thousands of layers, each containing information on the climatic and atmospheric state during its deposition, creating a climate archive. With detailed measurements of layer composition, it may be possible to extract age, accumulation rates, atmospheric conditions, and surface activity at the time of deposition, among other important parameters; gaining the information would allow us to “read” the climate record. Because Mars has fewer complicating factors than Earth (e.g. oceans, biology, and human-modified climate), the planet offers a unique opportunity to study the history of a terrestrial planet’s climate, which in turn can teach us about our own planet and the thousands of terrestrial exoplanets waiting to be discovered. During a two-part workshop, the Keck Institute for Space Studies (KISS) hosted 38 Mars scientists and engineers who focused on determining the measurements needed to extract the climate record contained in the PLD. The group converged on four fundamental questions that must be answered with the goal of interpreting the climate record and finding its history based on the climate drivers. The group then proposed numerous measurements in order to answer these questions and detailed a sequence of missions and architecture to complete the measurements. In all, several missions are required, including an orbiter that can characterize the present climate and volatile reservoirs; a static reconnaissance lander capable of characterizing near surface atmospheric processes, annual accumulation, surface properties, and layer formation mechanism in the upper 50 ​cm of the PLD; a network of SmallSat landers focused on meteorology for ground truth of the low-altitude orbiter data; and finally, a second landed platform to access ~500 ​m of layers to measure layer variability through time. This mission architecture, with two landers, would meet the science goals and is designed to save costs compared to a single very capable landed mission. The rationale for this plan is presented below. In this paper we discuss numerous aspects, including our motivation, background of polar science, the climate science that drives polar layer formation, modeling of the atmosphere and climate to create hypotheses for what the layers mean, and terrestrial analogs to climatological studies. Finally, we present a list of measurements and missions required to answer the four major questions and read the climate record. 1. What are present and past fluxes of volatiles, dust, and other materials into and out of the polar regions? 2. How do orbital forcing and exchange with other reservoirs affect those fluxes? 3. What chemical and physical processes form and modify layers? 4. What is the timespan, completeness, and temporal resolution of the climate history recorded in the PLD

The Holy Grail: A road map for unlocking the climate record stored within Mars' polar layered deposits

In its polar layered deposits (PLD), Mars possesses a record of its recent climate, analogous to terrestrial ice sheets containing climate records on Earth. Each PLD is greater than 2 ​km thick and contains thousands of layers, each containing information on the climatic and atmospheric state during its deposition, creating a climate archive. With detailed measurements of layer composition, it may be possible to extract age, accumulation rates, atmospheric conditions, and surface activity at the time of deposition, among other important parameters; gaining the information would allow us to “read” the climate record. Because Mars has fewer complicating factors than Earth (e.g. oceans, biology, and human-modified climate), the planet offers a unique opportunity to study the history of a terrestrial planet’s climate, which in turn can teach us about our own planet and the thousands of terrestrial exoplanets waiting to be discovered. During a two-part workshop, the Keck Institute for Space Studies (KISS) hosted 38 Mars scientists and engineers who focused on determining the measurements needed to extract the climate record contained in the PLD. The group converged on four fundamental questions that must be answered with the goal of interpreting the climate record and finding its history based on the climate drivers. The group then proposed numerous measurements in order to answer these questions and detailed a sequence of missions and architecture to complete the measurements. In all, several missions are required, including an orbiter that can characterize the present climate and volatile reservoirs; a static reconnaissance lander capable of characterizing near surface atmospheric processes, annual accumulation, surface properties, and layer formation mechanism in the upper 50 ​cm of the PLD; a network of SmallSat landers focused on meteorology for ground truth of the low-altitude orbiter data; and finally, a second landed platform to access ~500 ​m of layers to measure layer variability through time. This mission architecture, with two landers, would meet the science goals and is designed to save costs compared to a single very capable landed mission. The rationale for this plan is presented below. In this paper we discuss numerous aspects, including our motivation, background of polar science, the climate science that drives polar layer formation, modeling of the atmosphere and climate to create hypotheses for what the layers mean, and terrestrial analogs to climatological studies. Finally, we present a list of measurements and missions required to answer the four major questions and read the climate record. 1. What are present and past fluxes of volatiles, dust, and other materials into and out of the polar regions? 2. How do orbital forcing and exchange with other reservoirs affect those fluxes? 3. What chemical and physical processes form and modify layers? 4. What is the timespan, completeness, and temporal resolution of the climate history recorded in the PLD

Trf4 targets ncRNAs from telomeric and rDNA spacer regions and functions in rDNA copy number control

Trf4 is the poly(A) polymerase component of TRAMP4, which stimulates nuclear RNA degradation by the exosome. We report that in Saccharomyces cerevisiae strains lacking Trf4, cryptic transcripts are detected from regions of repressed chromatin at telomeres and the rDNA intergenic spacer region (IGS1-R), and at CEN3. Degradation of the IGS1-R transcript was reduced in strains lacking TRAMP components, the core exosome protein Mtr3 or the nuclear-specific exosome component Rrp6. IGS1-R has potential binding sites for the RNA-binding proteins Nrd1/Nab3, and was stabilized by mutation of Nrd1. IGS1-R passes through the replication fork barrier, a region required for rDNA copy number control. Strains lacking Trf4 showed sporadic changes in rDNA copy number, whereas loss of both Trf4 and either the histone deacetylase Sir2 or the topoisomerase Top1 caused dramatic loss of rDNA repeats. Chromatin immunoprecipitation analyses showed that Trf4 is co-transcriptionally recruited to IGS1-R, consistent with a direct role in rDNA stability. Co-transcriptional RNA binding by Trf4 may link RNA and DNA metabolism and direct immediate IGS1-R degradation by the exosome following transcription termination

The Evolutionary Analysis of Emerging Low Frequency HIV-1 CXCR4 Using Variants through Time—An Ultra-Deep Approach

Large-scale parallel pyrosequencing produces unprecedented quantities of sequence data. However, when generated from viral populations current mapping software is inadequate for dealing with the high levels of variation present, resulting in the potential for biased data loss. In order to apply the 454 Life Sciences' pyrosequencing system to the study of viral populations, we have developed software for the processing of highly variable sequence data. Here we demonstrate our software by analyzing two temporally sampled HIV-1 intra-patient datasets from a clinical study of maraviroc. This drug binds the CCR5 coreceptor, thus preventing HIV-1 infection of the cell. The objective is to determine viral tropism (CCR5 versus CXCR4 usage) and track the evolution of minority CXCR4-using variants that may limit the response to a maraviroc-containing treatment regimen. Five time points (two prior to treatment) were available from each patient. We first quantify the effects of divergence on initial read k-mer mapping and demonstrate the importance of utilizing population-specific template sequences in relation to the analysis of next-generation sequence data. Then, in conjunction with coreceptor prediction algorithms that infer HIV tropism, our software was used to quantify the viral population structure pre- and post-treatment. In both cases, low frequency CXCR4-using variants (2.5–15%) were detected prior to treatment. Following phylogenetic inference, these variants were observed to exist as distinct lineages that were maintained through time. Our analysis, thus confirms the role of pre-existing CXCR4-using virus in the emergence of maraviroc-insensitive HIV. The software will have utility for the study of intra-host viral diversity and evolution of other fast evolving viruses, and is available from http://www.bioinf.manchester.ac.uk/segminator/

Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes

OBJECTIVES: A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes. METHODS: Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset. RESULTS: Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2. CONCLUSION: Four discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk

Analysis of Biological Features Associated with Meiotic Recombination Hot and Cold Spots in Saccharomyces cerevisiae

Meiotic recombination is not distributed uniformly throughout the genome. There are regions of high and low recombination rates called hot and cold spots, respectively. The recombination rate parallels the frequency of DNA double-strand breaks (DSBs) that initiate meiotic recombination. The aim is to identify biological features associated with DSB frequency. We constructed vectors representing various chromatin and sequence-based features for 1179 DSB hot spots and 1028 DSB cold spots. Using a feature selection approach, we have identified five features that distinguish hot from cold spots in Saccharomyces cerevisiae with high accuracy, namely the histone marks H3K4me3, H3K14ac, H3K36me3, and H3K79me3; and GC content. Previous studies have associated H3K4me3, H3K36me3, and GC content with areas of mitotic recombination. H3K14ac and H3K79me3 are novel predictions and thus represent good candidates for further experimental study. We also show nucleosome occupancy maps produced using next generation sequencing exhibit a bias at DSB hot spots and this bias is strong enough to obscure biologically relevant information. A computational approach using feature selection can productively be used to identify promising biological associations. H3K14ac and H3K79me3 are novel predictions of chromatin marks associated with meiotic DSBs. Next generation sequencing can exhibit a bias that is strong enough to lead to incorrect conclusions. Care must be taken when interpreting high throughput sequencing data where systematic biases have been documented