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An unnamed valley in Memnonia Fossae region, Mars: Evidence for Hydrological Activities
No full textUnderstanding Martian valley networks is crucial for deciphering the planet's paleoclimate and exploring the possibility that past or present subsurface water reservoirs, could harbor microbial life. In this context, the present study focuses on an unnamed valley system in the Memnonia quadrangle region of Mars, comprising two distinct valleys: Valley 1 and Valley 2. The goal of this study is to conduct a comprehensive analysis of these valleys to unravel their formation processes and hydrological dynamics, contributing to a deeper understanding of Mars's hydrological history. To achieve this, orbital remote sensing data are utilized to examine the topography and geomorphology of the whole of the unnamed valley system. Image data from cameras onboard Mars Reconnaissance Orbiter (MRO) are utilized to analyze the geomorphology of the region. Additionally, the Mars Global Surveyor’s (MGS) topographic data is used. Observations and results reveal the complex geological setting of the unnamed valley system within the Memnonia quadrangle, which is characterized by diverse terrain features and evidence of past fluvial and volcanic activity. Valley 1 and Valley 2 exhibit distinct morphological characteristics, with Valley 1 showing signs of erosion and sedimentary infilling, whereas Valley 2 displays a flat floor with no discernible morphological features. Key craters in the vicinity provide valuable insights into the timing of fluvial activities within the valley system. Chronological analyses based on CSFD curves suggest that Valley 1 was active between approximately 3.9 billion years ago and 3.5 billion years ago, and likewise Valley 2 was likely active during the same period. Volcanic activity around the early Hesperian epoch marked the cessation of fluvial processes in the region. In conclusion, this study sheds light on the paleo-hydrological dynamics of Mars through a detailed examination of the unnamed valley system in the Memnonia quadrangle
Wetland Accretion Rate Model for Ecosystem Resilience and its Application to Coastal Transportation Infrastructure Along Alabama State Route 180
Get PDFFort Morgan Road (SR-180) on Alabama’s Gulf Coast is a vital coastal roadway
impacted by severe storms, high groundwater table, and future sea-level rise (SLR). The area
surrounding SR-180 supports a variety of ecological habitats for natural and nature-based
features (NNBF). This study focuses on the effects of SLR (ESLR) on surface transportation
infrastructure and the ability of NNBF to mitigate those effects. NNBF combine ecological with
conventional designs and are expected to change substantially over their lifespan through natural
processes. To inform modeling of NNBF scenarios, we applied the Wetland Accretion Rate
Model for Ecosystem Resilience (WARMER) to vegetated areas of proposed NNBF. WARMER
predicts changes in marsh surface elevation relative to mean sea-level (MSL) through its cohort
tracking method to capture critical marsh accretion processes. We parameterized WARMER
using marsh sediment cores collected by previous studies in Bon Secour Bay paired with NOAA
predictions for SLR in the northern Gulf Coast. Four coastal salt marsh vegetation species were
analyzed in WARMER to predict their effectiveness when incorporated into NNBF design.
Mean annual accretion rates computed by WARMER during the 150-year simulation
were 14.10 mm/yr for C. mariscus, 13.26 mm/yr for J. roemerianus, 11.61 mm/yr for S. patens,
and 14.31 mm/yr for T. domingensis. Under low and intermediate-low SLR scenarios, the NNBF
increases elevations for all marsh species. With intermediate SLR, only T. domingensis remains
above SLR. Under the intermediate-high and high scenarios, the NNBF will become inundated
within 22 years and 11 years, respectfully, for all species. Marsh surface elevations from
WARMER will be used in the ongoing NOAA ESLR project as inputs for hydrologic and
hydrodynamic (H & H) modeling of Fort Morgan Peninsula. WARMER will also be used to
predict how NNBF will evolve as part of a dynamic system while informing the better design of
NNBF to improve coastal transportation infrastructure resilience
Dioxygen reduction and Superoxide dismutase mimicry using first row transition metal complexes with redox active organic ligands.
No full textThe efficient activation of small molecules, such as dioxygen (O2) and superoxide (O2-), is key to solving several energy- and health-related challenges. The selective reduction of O2 to water (H2O) is integral to the development of state-of-the-art fuel cell electrocatalysts that could supplant fossil fuel sources as an alternative energy source. The effective dismutation of O2- to O2 and hydrogen peroxide (H2O2) can alleviate the oxidative stress correlated with aging and other health disorders. Although several transition metal complexes have been designed and studied to promote dioxygen reduction and O2- degradation, most of these metal complexes rely exclusively on transition metals to supply and accept electrons during these reactions. Prior work from the Goldsmith lab has found that covalently attaching quinols to coordination complexes can augment the redox activity of transition metals and even allow redox-inactive metal ions to catalyze new sorts of chemical reactions. This dissertation is primarily focused on using coordination complexes with quinol-containing ligands to catalyze the oxygen reduction reaction (ORR).
Chapter one provides an overview of recent advances in the development of molecular catalysts for the ORR. This chapter also details the current electrochemical and spectrophotometric methods that are used to study these catalysts as well as several performance metrics used to assess the activity, selectivity, and efficiency of electrocatalysts for the ORR.
Chapter two discusses how pendent quinol or phenol groups tune the selectivity of molecular Co(II)-based ORR electrocatalysts. Although the activities and effective overpotentials of the quinol- and phenol-containing catalysts are similar, the use of the quinol shifts the product selectivity from H2O2 to H2O.
Chapter three explores the ORR activity of several Fe(II) and Fe(III) complexes with additional quinol- and phenol-containing ligands. This chapter begins to establish structure-function relationships for this class of catalyst and illustrates how the numbers of pyridine and quinol groups on the electrocatalysts can influence their activity, product selectivity, and efficiency for the ORR. An additional quinol bolsters the activity and selectivity for water production, albeit at the cost of a higher effective overpotential.
In chapter four, Density Functional Theory (DFT) calculations are performed to better understand the mechanism of O2- dismutation catalyzed by a Zn(II) complex with a pendent quinol group. It also reports a Zn(II) complex with a ligand that instead contains a pendent phenol. The phenolic complex is catalytically inactive, confirming that the redox-active quinol is essential for the degradation of O2- by the Zn(II)-quinol catalyst
Data-Driven Optimal Irrigation Control Based on DSSAT and Optimization Methodologies
No full textThe escalating challenges posed by global climate change, coupled with growing concerns about agricultural water scarcity, necessitate innovative strategies for efficient and sustainable water use. Agriculture, a primary consumer of freshwater, accounted for 42\% of total freshwater withdrawals in the U.S. in 2015, highlighting the importance of optimized irrigation practices. Traditional rule-based irrigation management methods, which rely on empirical schedules, are increasingly inadequate in addressing the complexities of modern agriculture. This dissertation investigates data-driven optimal irrigation approaches by integrating a well-known dynamic crop growth simulator for over 40 crops, decision support system for agrotechnology transfer (DSSAT) with modern optimization technologies to foster agricultural sustainability.
The study explores two key optimization and control methodologies: simulation optimization (SO) and model predictive control (MPC). Decision support system for agrotechnology transfer(DSSAT), a cropping system software for over 42 crops, offers detailed simulations of crop growth, yield, and soil moisture dynamics under diverse environmental scenarios. As such, this work proposes to use DSSAT as a high fidelity digital twin (DT) for testing various irrigation optimization and control strategies to achieve enhanced irrigation efficiency with the goal of reducing irrigation water usage while maintaining or even increasing crop productivity.
In this dissertation, the SO approach plays a critical role in advancing precision irrigation through the use of heuristic and meta-heuristic techniques. Pattern search (PS) and the multi-objective genetic algorithm (MOGA) are two key methods employed to optimize irrigation schedules with a focus on maximizing crop yield and improving water use efficiency. PS, a direct search algorithm, is particularly suited for scenarios where gradient information is unavailable, allowing it to effectively handle the non-linear, non-differentiable nature of agricultural systems. MOGA, on the other hand, is a more complex evolutionary algorithm designed to balance competing objectives, such as yield and water use efficiency. By searching for solutions along the Pareto front, MOGA provides irrigation schedules that offer various trade-offs, giving farmers flexibility to choose a plan based on their priorities, whether that is minimizing water use or achieving maximum yield.
Each technique operates on a daily basis, where irrigation amount and scheduling decisions are evaluated and adjusted daily to respond to crop and its environmental changes dynamically. Specifically, the algorithms consider historical and forecast weather conditions, soil moisture, and crop growth to make decisions about irrigation timing and quantity. PS iteratively tests different irrigation schedules within a specified search space, adjusting decisions based on simulated crop responses. MOGA generates a population of potential irrigation plans and evolves them across generations using selection, crossover, and mutation, converging towards solutions that optimize both yield and irrigation efficiency. This iterative approach ensures that irrigation practices are responsive to the day-to-day variability in weather and soil conditions, aligning each irrigation event closely with crop requirements and its environment.
These techniques have demonstrated considerable effectiveness in achieving optimal irrigation schedules under varying environmental conditions. The long-term simulations enabled the examination of diverse weather patterns, soil properties, and seasonal variations, which are critical for assessing the robustness of PS and MOGA under realistic agricultural scenarios. Both techniques have shown their potential to improve crop yield by maintaining ideal soil moisture levels and preventing over-irrigation, thereby enhancing Irrigation Use Efficiency (IUE). These findings contribute valuable insights to precision irrigation management, suggesting that the combination can serve as a powerful tool for sustainable agriculture, helping to mitigate the impacts of climate variability on water resources.
MPC as an advanced and dynamic strategy for real-time irrigation management, enabling precise control over soil moisture levels throughout the crop growth cycle. Central to the MPC approach is the development of an estimated state-space model, grounded in the principles of the soil-water balance equation. This model captures the dynamic interactions between soil moisture, crop water demands, and environmental variables, allowing MPC to make accurate, data-driven decisions regarding irrigation scheduling. By accounting for these complex relationships, MPC facilitates an real-time data-driven irrigation strategy that aligns closely with crop requirements, optimizing water use and supporting sustainable agriculture.
The MPC framework operates on a receding horizon principle, where soil moisture predictions are continuously updated within a specified forecast period. By integrating the state-space model with DSSAT crop simulations, MPC anticipates crop water needs based on current soil conditions, recent irrigation activities, and future weather patterns. This proactive approach allows MPC to address potential water deficits or prevent over-irrigation before they impact crop health, thereby enhancing both crop yield and water use efficiency. The DSSAT simulations validate these predictions, enabling MPC to optimize soil moisture levels throughout the growing season in response to real-time environmental data.
To enhance predictive accuracy, the state-space model is refined using system identification techniques, particularly through the autoregressive with exogenous inputs (ARX) model. This approach incorporates essential inputs such as irrigation, precipitation, and evapotranspiration, to predict soil moisture. With these refinements, MPC can effectively adjust to short-term environmental changes as well as long-term seasonal trends, making it a robust tool for precision irrigation amidst climate variability. By integrating real-time data and predictive modeling, MPC promotes efficient water management, ensuring crops maintain ideal moisture levels—a critical step toward sustainable agriculture, especially in regions facing water scarcity.
In summary, this dissertation highlights the potential to address the pressing challenges of climate change and water scarcity. By harnessing predictive modeling and optimization, this research contributes substantially to the development of sustainable, efficient, and climate-resilient agricultural systems
Developing Genome Engineering Tools for Engineering Non-model Microorganisms for Biochemical Production and Sustainable Agriculture Applications
No full textNowadays, the energy crisis is getting worse in the world, and the environmental pollution resulting from the consumption of fossil fuels cannot be underestimated. Therefore, the discovery and optimization of renewable and clean fuel alternatives are currently sought after. Biofuels and biochemicals, derived from renewable biomass, represent a crucial and viable solution in alleviating the crisis. N-butanol (hereafter referred to as butanol) is a valuable biofuel with numerous advantages over ethanol, the commonly used gasoline blendstock. It also serves as a valuable chemical feedstock with many applications across various industries. Additionally, hexanoate and hexanol are valuable fuels or fuel precursors that can be used as aviation fuels and chemical feedstocks in industries such as pharmaceuticals and cosmetics. Non-pathogenic clostridia possess natural C4 pathways for butanol production and can be engineered to produce hexanoate and hexanol. The goal of this study was to genetically engineer Clostridium hosts to produce these biochemicals.
However, as Gram-positive anaerobic bacteria, these Clostridium species are generally challenging to engineer. Although CRISPR-Cas9-based genome engineering tools were developed previously, the engineering efficiency is still not high. Therefore, more efficient tools for genome engineering are needed. In this study, the CRISPR-AsCas12f1 system, which significantly reduces size of the plasmid carrying the CRISPR system compared to CRISPR-Cas9, was successfully developed for engineering C. beijerinckii, a prominent host for biobutanol production, with a higher genome editing efficiency. Remarkably, the resultant mutant with Cbei_1741 deleted with the CRISPR-AsCas12f1 system could produce 40% more butanol compared to the wild type strain. Characterization of the cell membrane indicated that the deletion of Cbei_1741, which encodes 1-acyl-sn-glycerol-3-phosphate acyltransferase, blocked the CDP-diacylglycerol production pathway. This blockage saved Acyl-CoA and reducing power, which in turn slightly enhanced butanol tolerance and butanol production in the engineered mutant strain.
A good genome editing tool should have a broad functionality. So, the CRISPR-AsCas12f1 based genome engineering system was further developed in C. tyrobutyricum, another strain which was much more difficult to genetically engineer based on our experiences. As an initial step, the results of in vitro and in vivo codon optimized AsCas12f1 cleavage activity assay demonstrated that the optimal CRISPR-AsCas12f1 system was functional in C. tyrobutyricum. This genome editing system realizes one-step screening of mutant strains with an efficiency of up to 100%. Simultaneously, with the support of the plasmid curing system under the regulation of the theophylline-dependent riboswitch inducible gene expression system, the comprehensive genome engineering tool could significantly shorten the cycle of knocking out a certain gene in C. tyrobutyricum. The engineered C. tyrobutyricum was developed by using the optimal CRISPR-AsCas12f1 system to be able to produce the hexenoate. The mutant with highest hexanoate production in this study was constructed by overexpressed thiolase from Ruminococcaceae bacterium CPB6 (ThlCPB6), which is a key gene for producing C6-acyl-CoA intermediate by condensing the acetyl-CoA and butyryl-CoA, and replaced the native Cat1 (butyryl-CoA:acetate CoA-transferase) with Cat from CPB6, resulting in hexanoate production to 4.4 g/L. In addition, the ThlCPB6 was introduced into butanol producing C. tyrobutyricum strain to produce hexanol, allowing the titer of 91 mg/L.
As a part of this dissertation work, the genome engineering technology has been further applied in plant-growth promoting microorganisms towards agricultural sustainability. Recently, due to soil drought caused by environmental issues or climate changes, food production has dropped significantly; however, this stands in stark contrast to the increasing demand for high food production driven by population growth in the world. Recently, ACC (1-aminocyclopropane-1-carboxylate) was demonstrated to improve drought tolerance of plants. Here, one Plant Growth Promoting Rhizobacteria (PGPR) with ACC deaminase activity was isolated from the root of peanut, and the sequence of the ACC deaminase encoded gene was determined. Subsequently, a sacB-based genome editing tool was developed for the isolated PGPR from peanut root, Bradyrhizobium sp. strain 9. With the SacB as the counter-selection marker, the gene encoding the ACC deaminase was successfully deleted with higher efficiency. The results of ACC deaminase activity assays and Sanger sequencing further illustrate the efficacy of the SacB-based genome editing tool as an effective method for genome editing in the PGPR. Furthermore, a 5X ACC deaminase mutant strain was developed to further enhance the ACC deaminase activity. Finally, the plant tests of successful nodulations for these two mutant strains indicate that engineered PGPR could nodulate the root of peanuts without affecting peanut growth
Diversity and Effects of Fungicides on the Pecan Phyllosphere Microbiome, and Fungal Interactions with a Novel Yeast
Get PDFThe phyllosphere microbiome is one of the largest habitats on earth, hosting any organism that can adapt to it. Phyllosphere yeasts in particular can dominate this habitat, but to this date have been chronically understudied with much novelty left to discover and classify. In the presence of fungicides, phyllosphere yeast taxa have been shown to have altered abundance. Pecans are native to the southeastern US, and are grown for nutrients like fiber, copper, and zinc. Fungal pathogens like powdery mildew and scab can affect the fitness of nuts. These pathogens are controlled by fungicide applications, up to and over 10 times a growing season. Under these high fungicide usage conditions, it is hypothesized that these chemical applications may alter phyllosphere yeast taxa abundance. Chapter 1 reviews previous literature discussing phyllosphere microbiomes and effects of fungicides on natural community members, as well as current knowledge of phyllosphere yeasts. In chapter 2, we seek to classify the pecan phyllosphere microbiome from a two-year experiment using molecular and bioinformatic tools for community profiling and analysis. We hypothesize that fungicide applications will have a non-targeted effect on the phyllosphere microbiome, specifically regarding yeast species. We also predict that fungicide-treated leaves will have a disrupted network structure compared to non-treated control leaves. Thus, 12 pecan trees were sampled before and after fungicide applications over the course of two years, 2021 and 2022, to provide community profiling and determine effects of fungicides on community members. Findings from this study support the hypothesis that fungicides have a non-targeted effect on the pecan phyllosphere microbiome, decreasing richness of fungicide-treated leaves and causing a significant difference in community composition in 2021. In addition, findings show that network hubs change between non-treated leaves and treated leaves. In chapter 3, we explore fungal-fungal interactions between a selection of environmental yeast species and a group of fungi to support the hypothesis that these environmental isolates can produce compounds that inhibit fungi. After this, we hone in on a novel Dothideomycete yeast, EMM_F3, to explore metabolic properties that may be causing inhibition of fungi—specifically Fusarium graminearum PH-1—, and taxonomically place it using comparative genomics techniques to other high quality Dothideomycete species. Initial findings support the hypothesis that environmental yeasts can have an inhibitory effect on various fungi. When focusing on EMM_F3, we determined that inhibition to PH-1 was due to metabolites, and we attempted to classify these metabolites through both genome sequencing and gas chromatography-mass spectrometry. Metabolites of interest included clavaric acid, tridecane, and nonadecane. Genome comparison placed EMM_F3 closely to relatives Delphinella strobiligena (a pine pathogen), and Aureobasidium pullulans (an extremophile yeast that has known biocontrol properties). In Chapter 4 we discuss the impacts of these experiments. Thus, this thesis promotes ideas for balancing pest management and preservation of biodiversity in agricultural systems and encourages exploration of the phyllosphere—specifically yeast species—to help understand species diversity and microbial interactions, as well as elucidate new biocontrol directions or new biologically useful metabolites or proteins
Development of Advanced Deep Learning Algorithms for Domain-Specific Challenges
No full textDeep learning has made significant advancements since the 1940s, utilizing large amounts of data and computational power to mimic human cognitive processes. However, current models often struggle with scalability and specificity for real-world applications in various fields. This research aims to tackle some of these challenges by developing advanced deep-learning algorithms to improve predictive analytics and decision-making across diverse domains. The entire thesis comprises four parts, each focusing on a specific challenge of deep learning. The first part focuses on improving traffic safety through geospatial intelligence. It involves refining spatial clustering techniques for urban network analysis and developing a temporal convolution model to learn spatiotemporal patterns in recent traffic accident trends across the United States. This study aims to enhance the accuracy and efficiency of traffic hotspot prediction, thereby improving traffic safety measures. The second part extends to environmental sciences, using deep learning models to predict the Leaf Area Index (LAI), a critical measure of vegetation density. Accurately forecasting LAI using globally collected data from 1982 to 2016 can help anticipate vegetation growth patterns, optimize agricultural productivity, and develop targeted conservation strategies to mitigate the adverse impacts of climate change on biodiversity and ecosystem services. The third part of our research addresses the pressing global food security challenge. We integrate cutting-edge deep learning techniques with spatiotemporal features of remote sensing data to forecast crop yields for three major crops in the United States. This project involves employing Gaussian processes to provide a probabilistic framework that captures uncertainty and enhances the prediction reliability of deep learning models. This segment emphasizes the pivotal role of sophisticated analytical techniques in boosting agricultural productivity and sustainability by providing more reliable and timely predictions of crop harvests for farmers. The fourth part of our research delves into an exploration of optimizing Graph Neural Networks (GNNs) through an innovative pruning algorithm called Iterative Gradient Rank Pruning, inspired by the Lottery Ticket Hypothesis. This approach aims to significantly reduce computational demands while maintaining robustness for large-scale graph data analysis. The integration of each research component plays a crucial role in establishing a comprehensive strategy that utilizes the power of deep learning to effectively address specific challenges. This study underscores the immense potential of deep learning to drive progress and innovation across a diverse range of domains and industries. Additionally, this dissertation outlines studies that enhance the privacy, security, and efficiency of location-based alert systems by using advanced encryption techniques
Effects of Litter Amendment in Reducing Ammonia and Greenhouse Gas Emissions from Broiler Litter
No full textThe United States is the largest poultry producer, consumer, and exporter of poultry products. The broiler industry constitutes 60% of the total poultry industry and produces approximately 9.17 billion broilers annually in the United States. The sector generates about 12.6 million metric tons of broiler litter (BL) annually. One of the major problems faced by poultry producers is the NH3 production inside the poultry houses. The NH3 concentration above 25 ppm harm the birds and reduces their productivity. Higher NH3 emissions also affects the environment. Besides NH3, BL is also a source of greenhouse gas emissions [GHG (N2O, CO2, and CH4)]. The higher emissions of NH3 and GHGs have environmental consequences such as global warming and climate change. Therefore, manure management becomes essential in reducing gaseous emissions into the atmosphere. Researchers have identified different methods to reduce NH3 and GHG emissions from BL. The use of amendments to reduce NH3 emissions from BL has been investigated previously; however, very few datasets exist that have reported the effectiveness of amendments to reduce GHG emissions from BL. Therefore, the objectives of this study were to evaluate the potential of biochar (B), zeolite (Z), FGD-gypsum (G), and sodium bisulfate (S) at different application rates in reducing NH3 and GHG emissions from BL. The BL samples used in the experiments were collected from the commercial poultry farms of Alabama. We conducted two experiments, one for NH3 emissions and another for GHG emissions. One hundred grams of BL samples were treated with litter amendments at four different rates, kept in mason jars, and incubated at 30 ºC for 40-days. The moisture content of BL was maintained at 40% throughout the experiment. Phosphoric acid traps were used to trap NH3 produced from BL using a 2.5 cm3 sponge. The acidified trap was prepared by dipping the sponge sequentially on 1M H3PO4, followed by 1M KOH, DI, and a mixture of 1M H3PO4 + Glycerol. The sponge was squeezed after dipping in each solution, and finally, 3 ml of 1M H3PO4 + Glycerol was added to the sponge to trap NH3, which was hung inside the jars. The trapped NH3 was extracted with 40 mL of 2M KCl solution and analyzed in a flow injection analyzer. A similar experimental setup was developed for GHG (N2O, CO2, and CH4) measurement, where GHGs samples were taken from the headspace of the jars in a pre-evacuated vial using a 10 ml syringe at 0-minute, 60-minute, and 120-minute intervals. The gas samples were analyzed using a Shimadzu Gas Chromatography equipped with a flame ionization detector (FID) and electron capture detector (ECD). The results showed that 13% and 17% B (w/w) reduced NH3 emission by 40.7 and 45.6%, respectively, compared to control. Among the different rates of Z, 8% reduced NH3 by 19.6% and 11% (w/w) reduced by 32.5%. There was no significant difference between the different rates of gypsum however, 15% G reduced NH3 by 9.1%. Application of S at rates of 2%, 4%, 6%, and 7% (w/w) significantly reduced NH3 emissions by 90.5, 98.6, 99.7, and 99.8 %, respectively, compared to control. Based on the percent reduction of NH3 emission compared to control, the efficiency of the amendments can be ranked as follows: S > B > Z > G. On the contratry, effect of amendments on GHG emissions showed a large variability in results. The application of 13% and 17% B significantly increased cumulative CO2 and N2O emissions whereas 5% and 9% B had no effect on GHG emissions compared to control. Zeolite and G application had no significant effect on GHG reduction. Sodium bisulfate at rates of 4, 6, and 7% reduced CO2 emissions by 13, 29, 32 % and N2O emissions by 33, 46, 48 %, respectively, compared to control. Among the amendments used, 4, 6, and 7 % of S were found promising for reducing GHG emissions from BL. Further studies with different combinations of amendments will provide additional insights in reducing NH3 and GHG emissions from BL. These findings contribute to ongoing efforts to identify amendments of choice for poultry producers to minimize emissions from poultry production systems as well as environmental impacts benefiting poultry, human health, and the environment
Bionomics of Crapemyrtle bark scale (Acanthococcus lagerstroemiae)
No full textCrape myrtle bark scale (CMBS, Acanthococcus lagerstroemiae) is an invasive pest of crape
myrtles (CM, Lagerstroemia) used in ornamental landscapes throughout the United States.
Infestations of CMBS diminish the aesthetic value of CMs and heavy infestations of CMBS can
kill young trees. This pest also affects the nursery industry due to increased management costs.
First reported in the US 20 years ago, there are still gaps in the knowledge of bionomics of CMBS,
specifically crawler activity and survival. Observing these gaps, these experiments noted various
traits and reactions of these scale insects both on and off host to better understand CMBS
phenology. Over multiple years, crawler movement in areas of known infestations were observed
at locations in north and south Alabama. The first major peaks occurred in late April at Baldwin
County in 2019 and mid-May at Madison County in 2020 with highest activity occurring between
April and June at both locations. To predict peak crawler hatch, degree-day models were assessed
revealing that the sine wave method with a base temperature of 1.7°C provided the lowest
coefficient of variation (CV) across both counties. Relative humidity (RH) and temperature heavily
impacted CMBS survival, with highest survival observed at 100% RH and 25°C, while low RH
significantly reduced survival. No significant difference in crawler mortality was found between
sealed and open bags in phytosanitary tests, but the presence of gravid females and newly hatched
crawlers skewed results. This suggests the need for longer bagging durations of CM clippings to
prevent future infestations. These findings provide better understanding of CMBS activity patterns,
survival strategies, and effective disposal practices, which help enhance management strategies for
this pest. Further research is recommended to explore the effects of solarization and temperature
on CMBS mortality in outdoor disposal settings
Novel Approaches for Cancer Subtypes Discovery and Pathway Analysis
No full textComplex diseases, particularly cancer, encompass a wide range of disorders, from ag- gressive and lethal to indolent lesions with low or delayed potential for progression to death. Treatment options and success heavily depend on the disease subtype of individual patients, which are often determined based on their molecular features. The advent of high-throughput platforms in the past decade has generated a wealth of molecular data, not only for gene expres- sion but also for other molecular data, including DNA methylation and non-coding microRNA. This has significantly increased the number of samples to cover the heterogeneity of the dis- eases and allowed for subtyping from a more holistic perspective, considering phenomena at different molecular levels in a single analysis. However, the stochastic nature of omics data and its high dimensionality have hindered consensus among different omic levels and the inter- pretability of the discovered subtypes, necessitating a powerful integrative technique to handle the noise, high dimensionality, and large sample sizes for improved subtyping.
Following subtyping, understanding the biological mechanisms driving subtype differ- ences in complex diseases remains crucial for developing effective treatments and therapies. Pathway analysis and gene set enrichment analysis are widely used to determine significantly impacted biological processes between conditions. However, current pathway analyses are biased towards well-studied diseases, sensitive to noise, and have limited validation across di- verse datasets and conditions, making their effectiveness unclear in analyzing new diseases, complex etiologies, or in analyzing data with weak signals compared to controls. Moreover, inconsistencies among different methods hinder interpretation and confidence in the results for downstream analyses.
This dissertation addresses these challenges by investigating a wide range of techniques for integrating multi-omics data to subtype cancer patients, including matrix factorization, genetic algorithms, and similarity-based methods. We introduce several novel subtyping frameworks, including Multi-objective Genetic K-means clustering Algorithm (MGKA), Disease Subtyp- ing using Community detection from Consensus networks (DSCC), PINSPlus, and Subtyping Multi-omics using a Randomized Transformation (SMRT). MGKA utilizes a multi-objective genetic algorithm to refine the k-means clustering algorithm and automatically determine the optimal number of subtypes. DSCC employs a consensus network approach, building patient similarity networks from individual data types and using community detection to identify ro- bust subtypes. PINSPlus is an extension of the original PINS method, integrating multiple data types and providing a more accurate and efficient subtyping analysis. SMRT is capable of in- tegrating a large number of omics data types to subtype cancer patients. Through an extensive analysis of over 11,000 patients across 37 cancer types, we demonstrate the ability of these methods to detect cancer subtypes with significant differences in patient risk and survival. No- tably, these methods easily handle a large number of data types and patients, are robust against noise and missing data, and gain accuracy as more data types are integrated.
For the second challenge, we first introduce a web interface that offers pathway analysis using multiple methods and datasets in a single session with rich visualization features, allow- ing life scientists to easily conduct pathway analysis, compare results from different methods and datasets, and reach better consensus for downstream analyses. We then introduce a novel consensus pathway analysis approach, Perturbation-based Gene Set Analysis (PGSA), which efficiently determines significantly impacted pathways across a wide range of diseases. We analyzed 421 datasets from more than 30 diseases, demonstrating PGSA’s superior perfor- mance compared to state-of-the-art methods in identifying significantly impacted pathways. This marks the first time a pathway analysis method has been tested on such a large number of datasets and diseases to prevent bias and overfitting to well-studied diseases