Partial Differential Equation Models of Collective Migration During Wound Healing

This dissertation is concerned with the derivation, analysis, and parameter inference of mathematical models of the collective migration of epithelial cells. During the wound healing process, epidermal keratinocytes collectively migrate from the wound edge into the wound area as a means to re-establish the outermost layer of skin. This migration into the wound is stimulated by the presence of epidermal growth factor. Accordingly, this dissertation focuses on the migratory response of epidermal keratinocytes in response to this growth factor. Such studies will suggest suitable clinical treatments to consider for chronic wounds and invasive cancers.We begin with a study into the role of cell-cell adhesions on keratinocyte migration during wound healing. We use an inverse problem methodology in combination with model validation to show that cells use these connections to promote migration by pulling on their follower cells as they migrate into the wound. We next derive a biochemically-structured version of Fisher's Equation that provides a framework to study how patterns of biochemical activation influence migration into the wound. We prove the existence of a self-similar traveling wave solution. In considering a more complicated scenario where cell migration depends on biochemical activity levels, we show numerically that the threshold parameter where all cells in the population become activated yields the simulations that migrate farthest into the wound. Lastly, we consider the role of numerical error on an inverse problem methodology. The numerical approximation of a cost function is dominated by either numerical or experimental error in computations, which leads to different rates of convergence as numerical resolution increases. We use residual analysis to derive an autocorrelative statistical model for cases where numerical error is the main source of error for first order schemes. This autocorrelative statistical model can correct confidence interval computation for these methods and hence improve uncertainty quantification

Pediatric anxiety and/or depression problems : associations with PM10, fly ash, and metal exposure.

Background: In the last several decades, the use of coal has become more prevalent in turn increasing the amount of coal ash being produced. Coal ash, the by-product of coal combustion, is composed of small particles that contain essential elements, hazardous metals, polycyclic aromatic hydrocarbons, and radioactive material. While a small proportion of coal ash is reused, the majority gets discarded in open-air landfills and ash ponds. Fly ash, the major component of coal ash, can become emitted into the air and potentially contribute to the air pollution and metal exposure in the surrounding community. Few studies, particularly in the United States, have investigated the relationship between coal ash and adverse health effects in children. Furthermore, because children are still developing both physically and neurologically they are more susceptible to the potential harms of coal ash and more vulnerable to the excess exposure of heavy metals and essential elements found in coal ash. The United States Environmental Protection Agency estimates that 1.5 million children are exposed to coal ash. Though the mechanisms are still unclear, metal exposure has been linked to mood disorders, such as anxiety and depression. The goal of this study was to examine the relationship between PM10, fly ash, and metal exposure and anxiety and/or depression problems in children aged 6-14 years, living near two coal ash storage facilities, and who were recruited in the first 16 months of an ongoing study. Methods: To determine anxiety and depression, the Child Behavior Checklist (CBCL) was completed for children residing in neighborhoods surrounding two large coal ash storage facilities. In-home air samples were collected and analyzed with Proton-Induced X-ray Emission (PIXE) and Scanning Electron Microscopy (SEM) to assess PM10, fly ash, and home environmental metal exposure. Toenail and fingernail samples were collected and analyzed with PIXE to assess metal body burden exposure. Logistic regression models, adjusting for potential covariates, were used to assess the relationship between in-home PM10, fly ash, metal exposure, and metal body burden and three primary outcomes determined from the CBCL: anxiety problems, withdrawn/depressed problems, and anxious/depressed problems. Results: High copper body burden was significantly associated with anxiety problems (AOR=10.3, 95% CI: 1.53-69.3, p-value=0.02), withdrawn/depressed problems (AOR=21.7, 95% CI: 1.96-240, p-value=0.01), and anxious/depressed problems (AOR=52.1, 95% CI: 2.96-919, p-value=0.01). Presence of manganese in the body was significantly associated with anxiety problems (AOR=9.03, 95% CI: 1.40-58.4, p-value=0.02) and anxious/depressed problems (AOR=8.72, 95% CI: 1.39-54.7, p-value=0.02). High filter metal score was significantly associated with withdrawn/depressed problems (AOR=0.14, 95% CI: 0.03-0.80, p-value=0.03). Conclusions: The results of this study use preliminary data from the overarching and ongoing study and should therefore by interpreted with caution. Findings are based on the recruited population from September 2015 through January 2017. These findings suggest that more studies are needed to comprehensively examine the relationship between PM10, fly ash, and metal exposure, in the home environment and metal body burden, and pediatric anxiety and/or depression problems, particularly in regards to exposure that may be from coal ash

Mycorrhizal development and effects on growth of the peanut (Arachis hypogaea L.)

The association between the growth of peanut (Arachis hypogaea L.) and arbuscular mycorrhizal (AM) fungi of the genus was investigated by measurements; • mycorrhizal status of Glomus spp in diverse substrate soil conditions. • mycorrhizal dependency and nutrient uptake. • potential for mycorrhizal biocontrol of a bacterial pathogen. • mycorrhizal response to salinity stress. •effect of fungicides on Glomus mosseae mycorrhizal association. Generally these investigations indicated that both the AM fungi Glomus mosseae and Glomusfasciculatum were infective to peanuts, but displayed a differential effectiveness depending on the soil microbial biomass content in the soil. Glomus mosseae gave the best overall results in improving peanut growth and therefore it was selected for peanut mycorrhization in further experiments. There appeared to be a threshold' phosphorus requirement level for nonmycorrhizal peanuts, below which relative mycorrhizal dependency of the peanut was inclined to be significantly pronounced. Glomus mosseae protected peanut seedlings against the pathogenic bacterium Erwinia carotovora, it suppressed the pathogen population, improved the nutritional status of the plant, decreased the susceptibility of peanut seedlings to the bacterial soft rot disease and significantly alleviated disease effects. The fungus also demonstrated an ability to reduce NaCl salt stress syndrome. Glomus mosseae/peanut association in soils treated with relatively high dosages of Aspor and Plantvax fungicides was seriously affected and did not improve peanut growth substantially and appears to result in the loss of mycorrhizal benefits. This study indicates that Glomus mosseae may be a potential component to improve peanut production in low-input sustainable agrosystems

MANAGING SOIL MICROBIAL COMMUNITIES WITH ORGANIC AMENDMENTS TO PROMOTE SOIL AGGREGATE FORMATION AND PLANT HEALTH

The effects of managing soil with organic amendments were examined with respect to soil microbial community dynamics, macroaggregate formation, and plant physio-genetic responses. The objective was to examine the possibility of managing soil microbial communities via soil management, such that the microbial community would provide agronomic benefits. In part one of this research, effects of three amendments (hairy vetch residue, manure, compost) on soil chemical and microbial properties were examined relative to formation of large macroaggregates in three different soils. Vetch and manure promoted fungal proliferation (measured via two biomarkers: fatty acid methyl ester 18:2ω6c and ergosterol) and also stimulated the greatest macroaggregate formation. In part two of this research, effects of soil management (same amendments as above, inorganic N fertilization, organic production) on soil chemical and microbial properties were examined relative to the expression of nitrogen assimilation and defense response genes in tomato (Solanum lycopersicum L.). Soil management affected expression of a nitrogen assimilation gene (GS1, glutamine synthetase) and several defense-related genes. The GS1 gene was downregulated with inorganic N fertilization, expression of the pathogenesis-related PR1b gene (which codes for the pathogenesis-related PR1b protein) was increased in plants grown in soil amended with compost, vetch, and N fertilizer, and expression of three other defense-related genes coding for chitinase (ChiB), osmotin (Osm), and β-1,3-glucanase (GluA) were decreased in plants from soil amended with manure and in plants from the organically managed soil. Differential expression of defense-related genes was inversely related to the relative abundance of Gram-negative bacteria. The relative abundance of the 18:1ω7c Gram‑negative bacterial biomarker was greatest in manure treated soil and in organically managed soil (which recieves seasonal manure applications). These treatments also had the lowest expression of ChiB, Osm, and GluA, leading to speculation that manure, through increases in Gram-negative bacteria, may have suppressed populations of soil organisms that induce a defense response in plants, possibly allowing for less-stressed plants. Outcomes of this research may be useful for those interested in developing management strategies for maintaining or improving soil structure as well as those interested in understanding management effects plant physio-genetic responses

Identifying The Key Variables for Assessing The Reclamation Success on Early Growth Vegetation in Ex-exploration of Oil and Gas Mining Areas

This paper examines the identification of key indicators that could be used to measure the success of reclamation plants in post-exploration oil and gas mining areas. The main objective of this research was to find key indicators or variables for evaluating the level success of reclamation results in the post-mining of oil and gas area. In this study, 44 environmental variables of the physical, biological, soil, water and air indicators were analyzed from 70 field plots of 6 reclamation and 2 natural forest sites. The analysis methods included (1) cluster analysis using the Agglomerative Hierarchical Clustering method with the Ward's method, and (2) quadratic discriminant analysis. The results of the clustering analysis showed that there were some clusters due to variation of biomass, water, soil and air conditions. The three clusters developed based on water and/or air variables provided high cophenetic correlation (0.80) with low within-cluster (14.5%) and high between-cluster variations (85.5%). Based on the multicollinearity analysis, average vector difference test, variance matrix variance test, unidimensional test of each variable and quadratic discriminant function, this study found that there were 3 key indicators determining variations of the quality of the reclamation plantations within the study sites, namely, biological indicator of biomass volume (Bio_B); soil indicator of P content in the soil (Tnh_P), saturation base of soil (Tnh_Kb), Manganese (Mn) content in the soil (Tnh_Mn), Sulfur content in the soil (Tnh_S), percentage of ash in the soil (Tnh_Ab), percentage of clay in the soil (Tnh_Li), and water indicator of chloride content in the surface water (Air_Cl). The examination on four classes of the reclamation quality showed that the classes were successfully classified having excellent cross-validation error matrix with overall accuracy more than 90%

Numerical Investigation of Parameters Impacting the Wall Thickness of Carbon Nanotubes Manufactured by Template-Based Chemical Vapor Deposition

Template-based chemical vapor deposition (TB-CVD) is a versatile technique for manufacturing carbon nanotubes (CNTs) or CNT-based devices for various applications. In this process, carbon is deposited by thermal decomposition of a carbon-based precursor gas inside the nanoscopic cylindrical pores of anodized aluminum oxide (AAO) templates to form CNTs. Experimental results show CNT formation in templates is controlled by TB-CVD process parameters, such as time, temperature and flow rate. Optimization of this process is done empirically, requiring tremendous time and effort. Moreover, there is a need for a more comprehensive and low cost way to characterize the flow in the furnace in order to understand how process parameters may affect CNT formation. In this report, we describe the development of four, three-dimensional numerical models, each varying in complexity, to elucidate the thermo-fluid behavior inside the TB-CVD process. Using computational fluid dynamic (CFD) commercial codes, the four models were compared to determine how the presence of the template and boat, composition of the precursor gas, and consumption of species at the template surface affect the temperature profiles and velocity fields in the system. The most accurate model will be used to conduct particle injection/tracking near the templates and to characterize the particle residence time as a function of time and consumption rate. The developments in this work build the groundwork for explaining how flow characteristics affect carbon deposition on templates in any CVD reactor

Work streaming / mainstreaming gendered land use and land cover change (GLUCC) : Afro-descendant communities in the Pacific Region of Colombia

textThis dissertation addresses gender dimensions of Land Use and Land Cover Change (GLULCC) in the last few decades in a collective land titled to Afro-descendant communities in the Pacific region of Colombia, South America, and examines socio-economic and political signifiers affecting land use decisions, rights, and responsibilities. It shows how contrasting but complementary subfields of investigation, Political Ecology and Land Use Science, have contributed ontological, epistemological and practical scholarly works to help better understand the Gender Dimensions of Land Use and Land Cover Change (GLULCC). Historical and current information on environmental, socioeconomic and settlement processes provided a comprehensive portrait of the study area. The remote sensing process (a mainstream method for identifying land use and land cover change) helped exploring the spatial setting of land cover/use, and to reflect on the opportunities and constrains of the steps undertaken during this procedure under the lenses of researching their gendered dimensions. Statistical analyses on both census data (secondary data) and survey sample data (fieldwork data) allowed to establish a set of three groups of gendered land uses, namely, women-akin, men-akin, and gender-blind uses. Exploratory statistics, pairwise correlations, and binary and multinomial logit regression models helped to reassert the latter gendered categories’ assertions. A concluding narrative perspective of GLULCC seeks to further contribute to work streaming/ mainstreaming what I consider may be a scholarly-fertile research line. It hopes to bond, with another perspective, previous theoretical, spatial and quantitative outcomes, under the lenses of the practical experience of fieldwork, which also by way of participatory observation and semi-unstructured interviews brought to the researcher (me) valuable insights and information besides the previous outcomes. Empirical evidence allowed identifying gender-based time allocation, resource-use power relations, and reproductive strategies. Finally, the found rearrangement of settlement spaces and production systems provides practical indications that women´s role on LULCC is well beyond the establishment of small gardens and orchards, or the collection of fuel wood to provide for their families. In contrast, inside this collective title, women’s decisions/strategies have also restructured settlement patterns, and thus, land use dynamics of larger areas at heterogeneous spatial and temporal scales.Geography and the Environmen

A METHOD FOR NON-INVASIVE, AUTOMATED BEHAVIOR CLASSIFICATION IN MICE, USING PIEZOELECTRIC PRESSURE SENSORS

While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well as active behavior states, using data acquired from a Signal Solutions (Lexington, KY) piezoelectric cage floor system. The NREM/REM classification system presented an 81% agreement with human EEG scorers, indicating a useful, high throughput alternative to the widely used EEG acquisition method

Large-scale clustering of CAGE tag expression data

Background: Recent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed. Results: We use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70-100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups. Conclusion: Our approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data