Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine learning (2)
- Acidification (1)
- Arabidopsis (1)
- Bayesian modeling (1)
- Biogeochemistry (1)
-
- CO2 enrichment (1)
- Column experiment (1)
- Competition (1)
- Cysteines (1)
- Digital Soil Mapping (1)
- Dissolution (1)
- Ecosystem service (1)
- Enlist crops (1)
- Environmental Covariates (1)
- FTIR (1)
- Gene expression oscillations (1)
- Glycine max (1)
- Gossypium hirsutum (1)
- Invasive Species (1)
- KNN (1)
- Leachate chemistry (1)
- Mineral weathering (1)
- Nitrogen (1)
- Nutrient availability (1)
- Oscillations (1)
- Phosphorus (1)
- Phytoremediation (1)
- Plant Immunity (1)
- Plants (1)
- Precipitation (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Investigating The Role Of Plant Traits And Interactions In Emergent Wetland Nutrient Removal, Andrew Ryan Sample
Investigating The Role Of Plant Traits And Interactions In Emergent Wetland Nutrient Removal, Andrew Ryan Sample
Theses and Dissertations
Increasing wetland restoration in the Lower Mississippi Alluvial Valley has been identified as a method to reduce nutrient loading in the Gulf of Mexico. Wetlands have historically been used to treat water through processes facilitated by wetland plants, and relatively few species and plant traits have been identified as important in carrying out these processes. This study focuses on some of those species and traits and aims to identify species differences and plant traits that may be important for wetland nutrient mitigation. Chapter I provides background information on nutrient pollution, wetland biogeochemical mechanisms for nutrient sequestration, and the focal species …
Dynamics Of Redox-Driven Molecular Processes In Local And Systemic Plant Immunity, Philip Berg
Dynamics Of Redox-Driven Molecular Processes In Local And Systemic Plant Immunity, Philip Berg
Theses and Dissertations
The work here presents two main parts. In the first part, chapters 1 – 3 focus on dynamical systems modeling in plant immunity, whereas chapters 4 – 6 describe contributions to computational modeling and analysis of proteomics and genomics data. Chapter 1 investigates dynamical and biochemical patterns of reversibly oxidized cysteines (RevOxCys) during effector-triggered immunity (ETI) in Arabidopsis, examines the regulatory patterns associated with Arabidopsis thimet oligopeptidase 1 and 2’s (TOP1 and TOP2), roles in the RevOxCys events during ETI, and analyzes the redox phenotype of the top1top2 mutant. The second chapter investigates the peptidome dynamics during ETI …
Determining The Effects Of Elevated Carbon Dioxide On Soil Acidification, Cation Depletion, And Soil Inorganic Carbon And Mapping Soil Carbons Using Artificial Intelligence, Jannatul Ferdush
Theses and Dissertations
Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO2 affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO2 and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO2 effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils …
Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn
Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn
Theses and Dissertations
With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the …