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Articles 1 - 7 of 7
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Exploring Soil Microbial Dynamics In Southern Appalachian Forests: A Systems Biology Approach To Prescribed Fire Impacts, Saad Abd Ar Rafie
Exploring Soil Microbial Dynamics In Southern Appalachian Forests: A Systems Biology Approach To Prescribed Fire Impacts, Saad Abd Ar Rafie
Doctoral Dissertations
Prescribed fires in Southern Appalachian forests are vital in ecosystem management and wildfire risk mitigation. However, understanding the intricate dynamics between these fires, soil microbial communities, and overall ecosystem health remains challenging. This dissertation addresses this knowledge gap by exploring selected aspects of this complex relationship across three interconnected chapters.
The first chapter investigates the immediate effects of prescribed fires on soil microbial communities. It reveals subtle shifts in porewater chemistry and significant increases in microbial species richness. These findings offer valuable insights into the interplay between soil properties and microbial responses during the early stages following a prescribed fire. …
Regulation Of Protein Synthesis In Arabidopsis Thaliana Through A Bioinformatic And Mathematical Lens, Ricardo Andres Urquidi Camacho
Regulation Of Protein Synthesis In Arabidopsis Thaliana Through A Bioinformatic And Mathematical Lens, Ricardo Andres Urquidi Camacho
Doctoral Dissertations
Organisms exist under constantly varying environmental and internal conditions, which necessitate the differential regulation of gene expression. To synthesize proteins, the ribosome translates the information encoded in the nucleotide sequence of an mRNA into the final, functional amino acid sequence. Knockouts of ribosomal proteins lead to lethality. One such protein is the ribosomal protein 6 of the small subunit (eS6/RPS6). We confirmed that the knockout of either one of two eS6 paralogs in Arabidopsis leads to stunted growth and chlorosis. Here, these phenotypes have been further characterized in seedlings by precisely quantifying the ribosome loading of mRNAs as well as …
Novel Microbial Guilds Implicated In N2o Reduction, Guang He
Novel Microbial Guilds Implicated In N2o Reduction, Guang He
Doctoral Dissertations
N2O is a long-recognized greenhouse gas (GHG) with potential in global warming and ozone depletion. Terrestrial ecosystems are a major source of N2O due to imbalanced N2O production and consumption. Soil pH is a chief modulating factor controlling net N2O emissions, and N2O consumption has been considered negligible under acidic conditions (pH <6). In this dissertation, we obtained solids-free cultures reducing N2O at pH 4.5. Furthermore, a co-culture (designated culture EV) comprising two interacting bacterial population was acquired via consecutive transfer in mineral salt medium. Integrated phenotypic, metagenomic and metabolomic analysis dictated that the Serratia population excreted certain …6).>
Understanding Host-Microbe Interactions In Maize Kernel And Sweetpotato Leaf Metagenomic Profiles., Alison K. Adams
Understanding Host-Microbe Interactions In Maize Kernel And Sweetpotato Leaf Metagenomic Profiles., Alison K. Adams
Doctoral Dissertations
Functional and quantitative metagenomic profiling remains challenging and limits our understanding of host-microbe interactions. This body of work aims to mediate these challenges by using a novel quantitative reduced representation sequencing strategy (OmeSeq-qRRS), development of a fully automated software for quantitative metagenomic/microbiome profiling (Qmatey: quantitative metagenomic alignment and taxonomic identification using exact-matching) and implementing these tools for understanding plant-microbe-pathogen interactions in maize and sweetpotato. The next generation sequencing-based OmeSeq-qRRS leverages the strengths of shotgun whole genome sequencing and costs lower that the more affordable amplicon sequencing method. The novel FASTQ data compression/indexing and enhanced-multithreading of the MegaBLAST in Qmatey allows …
Computational Analysis Of Microbial Sequence Data Using Statistics And Machine Learning, Zhixiu Lu
Computational Analysis Of Microbial Sequence Data Using Statistics And Machine Learning, Zhixiu Lu
Doctoral Dissertations
Since the discovery of the double helix of DNA in 1953, modern molecular biology has opened the door to a better understanding of how genes control chemical processes within cells, including protein synthesis. Although we are still far from claiming a complete understanding, recent advances in sequencing technologies, increased computational capacity, and more sophisticated computational methods have allowed the development of various new applications that provide further insight into DNA sequence data and how the information they encode impacts living organisms and their environment. Sequencing data can now be used to start identifying the relationships between microorganisms, where they live, …
The Genomic Dynamics Of Fermented Food Microbes, Katherine Chacón-Vargas
The Genomic Dynamics Of Fermented Food Microbes, Katherine Chacón-Vargas
Doctoral Dissertations
Along with the agriculture and domestication revolution, humans have utilized bacteria, yeasts, and molds for millennia in the production of traditionally fermented foods and beverages. Fermentation is a very ancient practice of high relevance nowadays since it contributes with a great variety of foods worldwide. Microbial fermentation allows metabolic transformation of the raw food materials leading to biochemical changes that played a key role in food preservation, health benefits, nutrition, flavors, and texture, among others. Food fermentation practices could diverge from traditional artisanal spontaneous fermentation to industrialize methods with specialized microbial starters and although fermented environments tend to be very …
Inverse Probability Weighting In Survival Analysis And Network Analysis, Yukun Lu
Inverse Probability Weighting In Survival Analysis And Network Analysis, Yukun Lu
Doctoral Dissertations
Inverse probability weighting is a popular technique to accommodate selection bias due to non-random sampling and missing data. In the first chapter, we develop an inverse probability weighted estimator and an augmented inverse probability weighted estimator of regression coefficients for a linear model with randomly censored covariates, when the censoring mechanism may be dependent on the outcome. We investigate the asymptotic properties of both estimators and evaluate their finite sample performance through extensive simulation studies. We apply the proposed methods to an Alzheimer’s disease study. In the second chapter, we present an application of network analysis in a study of …