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Efficacy and Utility of MPP51AA2-Traited Cotton as a Management Tool for Cotton Fleahoppers [Pseudatomoscelis seraitus (Reuter)]
No full textIn Texas the cotton fleahopper (Pseudatomoscelis seriatus (Reuter)) is considered a highly economically damaging pest of cotton (Gossypium hirsutum L.). Current control methods rely heavily on the use of foliar applied chemical insecticides during the growing season. The Mpp51Aa2.834_16 gene in cotton (ThryvOn) has proven effective against thrips and Lygus spp. with piercing and sucking feeding behaviors, suggesting the trait may also provide similar efficacy on cotton fleahoppers.
Field trials were conducted in 2019, 2020, and 2021 comparing a ThryvOn cultivar to a non-traited isoline under insecticide-treated and untreated conditions. While cotton fleahopper population differences between the traited and non-traited plants were not consistently noted during the pre-bloom squaring period, there was a consistent increase in square retention in cotton expressing Mpp51Aa2 relative to non-traited cotton. Additionally, cotton expressing Mpp51Aa2 offered similar square protection relative to non-traited cotton treated with insecticides for cotton fleahopper. These findings indicate that the Mpp51Aa2 protein should provide benefits of delayed nymphal growth, population suppression, and increased square retention.
In the choice assay, feeding by cotton fleahoppers significantly reduced square retention in the non-traited cotton to 46%, while ThryvOn cotton retained 60% of squares. In the no-choice assay, cotton fleahopper nymph feeding significantly reduced square retention in the non-traited cotton to 61%, whereas ThryvOn cotton was unaffected. Our findings indicate that the Mpp51Aa2 protein influences cotton fleahopper feeding preference and the susceptibility of cotton plants to damage caused by cotton fleahoppers.
To evaluate the feeding behavior of the cotton fleahoppers on ThryvOn cotton an electropenetrography (EPG) coupled with a Giga-8 DC EPG amplifier was used to monitor the probing activity cotton fleahopper nymphs on ThryvOn and non-traited cotton squares. Nymphs were placed on a square for 8 hours and waveforms were characterized as non-probing, cell rupturing, and ingestion. There were significantly more cell rupturing events on ThryvOn (14.8) than on non-traited squares (10.3) but were no differences in ingestion events. However, the duration of ingestion events were significantly shorter at 509s on ThryvOn compared to 914s on non-traited squares. The results of this study provide evidence that ThryvOn affects the feeding behavior of cotton fleahoppers
Human-Centered AI for Precision Medicine: Methods and Applications
No full textAI has emerged as a powerful tool in the healthcare and biomedical domains. In the field of medicine, AI must demonstrate strong performance while adhering to human ethics. Throughout my Ph.D., I focused on designing and developing human-centered AI tools for precision medicine, with a special emphasis on addressing ethical concerns in medical AI. To bridge the gap between AI and medicine, I delved into cutting-edge AI methods, including knowledge distillation, reinforcement learning, multi-task learning, multi-modality learning and contrastive learning. To make our contribution focused, I specialized in the phenotyping, disease diagnosis, organ transplant, and health event prediction scenario which are essential medical tasks. Four-folds challenges of designing human-centered AI framework towards precision medicine are summarized as (1).Trade-off between performance and fairness, (2).AI integration in clinical workflow, (3).Multi-task prediction on related medical indicators. (4).Multi-modality EHR data. To comprehensively investigate the fairness issue in the clinical prediction algorithm, I conduct extensive experiments on the disease diagnosis to benchmark the performance and bias in the electronic phenotyping. I design a twostep debiasing strategy with unbiased knowledge distillation to predict the graft failure after liver transplant fairly and precisely. In order to support the doctor���s clinical decision, FairAlloc framework is proposed to directly generate accurate and unbiased patient prioritization decisions with reinforcement learning. To simultaneously predict highly related medical indicators, CoD-MTL is designed to take advantage of multiple highly related tasks to predict multiple cause-of-death after the liver transplant. When it comes to multimodal EHR data, I design a cross-modality knowledge distillation framework to distill the knowledge from LLM into the predictive model on structured EHR. My research efforts paved a way to design powerful and trustworthy AI frameworks to support precision medicine with human-centered AI principles
Professional Mental Health Help-Seeking Behaviors
No full textThis dissertation examined the mental health help-seeking behaviors of undergraduate students and the retrospective help-seeking experiences of professors, while they were college students. Past research has noted disparate mental health outcomes for minoritized and first-generation students. However, research exploring their help-seeking attitudes, styles, and sources in general college populations is limited. This dissertation aims to address some of those gaps by including the perspectives of traditionally underrepresented groups.
The dissertation employed a mixed-methods approach. The first study used a quantitative approach with over 200 self-identified Black, Latine, and White students through surveys. Data was analyzed via numerous versions of Analysis of variances. The second study employed a qualitative focus group design with 10 students from the first study. The third study used a single case design via an exploratory, qualitative inquiry with 8 self-identified Black and Latine professors. Data collection methods for both qualitative studies included face-to-face interviews. Data for both qualitative studies were also analyzed via the constant comparative method.
Major findings from the first study indicated significantly higher avoidant help-seeking styles scores for first-generation students than their counterparts. Findings from the second study included various themes about generational differences, socioeconomic ideals, and differing help-seeking styles via ethnicity. Findings from the third study included various themes based on values, sociocultural factors, and how professors��� attitudes changed over time.
The data from this dissertation highlights the need for continued exploration and understanding the role that college settings, ethnicity, and first-generation status may or may not play in mental health help-seeking behaviors
Dynamic Covalent Polymer Networks for Additive Manufacturing and Supersonic Impact
No full textThe use of dynamic covalent bonds enables self-healing, shape morphing, and energy dissipation in materials. During the past decade, significant progress has been made in advancing both network chemistry and structural design. Diels-Alder (DA) ���click��� reactions introduce room-temperature stability and thermal response to the polymer networks. This thesis explores the roles of temperature and stress in the unique behavior of DA dynamic polymer (DAP) materials for their use in additive manufacturing and supersonic impact. In the second chapter, we report highly conductive nanocomposites made of DAP networks containing branched multi-wall carbon nanotubes (b-CNTs). The ability of liquified DAP to wet, infiltrate, and chemically stabilize bCNTs at increased temperature, and then ���lock��� well-dispersed nanotubes upon cooling via the ���click��� DA reaction results in a low percolation threshold of 0.04 wt% b-CNTs. Moreover, the nanocomposites exhibit controlled network plasticity for permanent shape reconfiguration at solid state via Joule-heating-induced dynamic bond exchanges, and these transformations can be triggered selectively at different locations in printed multi-material hybrid constructs, enabling spatiotemporal control of the material's permanent shape. In the third chapter, we demonstrate that large puncture healing of ultra-thin DAP dynamic networks under supersonic impact by microprojectiles outperforms that of traditional glassy polymers, while showing energy absorption comparable to those materials. Post-mortem microscopies reveal efficient puncture healing that is likely enabled by stress- and temperature-induced viscoelastic responses of DAP networks. The progression of impact events was observed using in-situ imaging with a nanosecond, microscale resolution, while the recovery of DA bonds after the impact was confirmed by infrared nanospectroscopy. In the fourth chapter, we present a procedure design for manufacturing DAP microspheres and analyze their high-strain-rate deformation behaviors under supersonic impacts against a rigid substrate. The dynamic responses of these microspheres are associated with the viscoelastic and viscoplastic characteristics of the microspheres as well as interfacial adhesion. Unlike traditional polymers, the interfacial adhesion properties are governed by the thermomechanical responses of DAP networks, due to temperature- and stress-sensitive DA bonds. The role of thermomechanical network responses in microsphere impacts was unveiled by in-situ observations, post-mortem morphological analysis, and finite element analysis simulations
Accuracy of Three Digital Impression Techniques for Implant-Fixed Complete Dentures
No full textEvidence comparing accuracy of implant-fixed complete denture (IFCD) impression techniques is unclear. The purpose of this in vitro study was to compare the accuracy of three digital impression techniques for IFCD.
A polyurethane edentulous mandible with four implant analogs served as the master model. A reference scan was made using a laboratory scanner. Test scans (n=10 per group) were made for the three groups: splinted IOS (Group S), non-splinted IOS (Group NS), and photogrammetry (Group PG). All scans were exported in standard tessellation language (STL) format and superimposed to compare linear, angular, and RMS deviations using a three-dimensional metrology software. Statistical analysis was performed using Kruskal-Wallis test for non-normally distributed data (a = 0.05).
No significant difference in overall accuracy was seen between the three groups. No significant difference in accuracy was seen when splinting ISBs. Significant differences in accuracy were seen within each group depending on position in the arch; higher angular deviation was seen at position RM1 in Group PG and Group S (p<.001).
Digital impressions for IFCD using either IOS or PG yielded similar results. Splinting ISBs did not seem to have a beneficial effect on accuracy. All three methods produced clinically acceptable results
Essays on Microeconometrics
No full textThis dissertation consists of three essays on Microeconometrics and Applied Econometrics.
Chapter 2 provides nonparametric identification results in first-price auctions with unknown collusion schemes. This chapter shows that regardless of the unknown collusion scheme, collusive bidders in a partial cartel can be identified from winning bids, identities of winners, and auction-specific covariates satisfying an exclusion restriction. It can be shown that the value distributions of collusive bidders are identified under two types of cartels characterized by McAfee and McMillan (1992). This chapter proposes a test procedure to recover the identities of collusive bidders. The test method is applied to California highway procurement auctions.
Chapter 3, a joint work with Yonghong An, Matthew Gentry, and Daiqiang Zhang, examines the economic impacts of anti-bid-shopping legislation. We exploit the policy changes in Washington state to evaluate the impacts of anti-bid-shopping legislation on both ex-ante and ex-post procurement auction outcomes. With the engineer���s estimate of the cost and work types of projects, we can classify the contracts affected or not affected by anti-bid-shopping legislation. We estimate the effects of the policy using Regression Discontinuity Design (RDD) or Difference-in-Difference (DID). Findings indicate that the requirement for subcontractor listing can increase the winning bid while reducing the final total payment to the primary contractor. Moreover, disallowing substitutions may expedite project completion time and reduce the final total payment.
Chapter 4 proposes nonparametric estimation and uniform inference on counterfactual distributions in the presence of proxy variables. A two-step series estimator is developed to consistently estimate counterfactual distributions and their functionals under a general framework with latent variables. The uniform asymptotic theory is built on strong approximations of Gaussian processes introduced in Chernozhukov et al. (2013). The two-sample series counterfactual distribution estimator is applied to both simulations and an empirical study of the wage gap, in which the Armed Forces Qualifying Test (AFQT) score is used as a proxy variable for premarket human capital. The difference in the distribution of premarket human capital explains the racial wage ga
Estimation of Evapotranspiration Using Remote Sensing Data and Sebal Model in Adana, Turkiye
No full textThe world's population has been steadily increasing in recent decades, resulting in a higher demand for water. This demand, combined with the effects of climate change and the depletion of natural resources, has made it crucial to monitor water resources. Agriculture is the largest user of water, and its impact on water usage is becoming a growing concern. Evapotranspiration, which is the combined loss of water through evaporation and plant transpiration, plays a crucial role in the water budget and energy balance. This study examined the use of the python SEBAL (PySEBAL) model for estimating evapotranspiration in agricultural areas by utilizing Landsat satellite imagery and meteorological data. The research centered on Seyhan Plain, Adana, Turkey, from 2017 to 2019 and analyzed both summer and winter seasons across five different crop types: cotton, wheat, corn, soybean, and citrus. The PySEBAL model generated daily actual evapotranspiration maps at 30m resolution for the study area. Analysis of R-square values across years and crops revealed positive correlations, particularly for cotton (R-square = 0.819) and wheat (R-square = 0.809). Corn and citrus also showed positive correlations (R-square = 0.736 and 0.708, respectively), while soybean2 displayed a weaker association (R-square = 0.481). These findings suggest that the PySEBAL model can accurately predict Penman-Monteith evapotranspiration (PM-ET) for some crops (cotton, wheat) compared to others (soybean-2). The results indicated an overestimation of ET by the model compared to literature values. However, a positive correlation was found between PySEBAL-ET and Penman-Monteith evapotranspiration (PM-ET) estimates across all crops and years, suggesting the potential of SEBAL for agricultural water resource monitoring
Structured MXene-Polymer Composites from Pickering Emulsion Templating
No full textStructured polymer composites have gained increasing attention due to their superior property enhancement (e.g., thermal, electrical conductivity) compared to their homogeneous counterparts, by designing the internal filler structures in the polymer matrix. The fabrication and design of structured polymer composites are still challenging and the common methods (e.g., solution casting, melt blending) have limited control over the internal filler structures. Pickering emulsion templating, in contrast, is an attractive approach to creating structured composites due to their well-defined interfaces, manageable structure and dimensions, and ease of scale-up. Among the common Pickering particles, MXenes are of great interest as they have the ability to not only stabilize emulsions but also introduce functional properties into structures, such as high electrical conductivity, high EMI shielding, and rapid radio frequency (RF) heating.
In this work, we focus on the development of MXene Pickering emulsions in diverse fluidfluid systems (e.g., oil-water and oil-oil) and their use as templates for fabrication of functional structured MXene-polymer composites. Pickering emulsions drive nanosheets to the fluid-fluid interfaces and subsequent localized polymerization creates diverse structured polymer composites (e.g., capsules, armored particles, and porous monoliths). The ability to access both aqueous and nonaqueous emulsion systems largely expands the possible polymer compositions. The MXene nanosheets are organized in these composites instead of being randomly distributed throughout. For instance, polymerization of the emulsion interfaces gives polymer shells with nanosheets embedded, polymerization of the dispersed phase gives polymer particles armored with nanosheets, and polymerization of the continuous phase gives porous monoliths with polymer struct and nanosheets coated pores. The incorporation of MXenes imparts functional properties into their structures for additional applications. For example, the MXene armored particles can be used as feedstock to fabricate segregated films for efficient EMI shielding applications at low MXene loadings due to the templated network within the polymers. MXene-polymer capsules and porous monoliths show excellent RF heating performance due to the highly locally conductive regions in these structures. The research work in this dissertation provides a simple platform to produce diverse structured MXene-polymer composites with well-controlled filler distribution, versatile compositions, and functional properties for potential advanced applications
Optimal Mass Screening and Quarantine Policies in Heterogeneous Populations Under Limited Budget and Resources
No full textMass screening of populations is an indispensable public health tool that is extensively utilized in a variety of settings (e.g., screening blood transfusion, gastric cancer, sexually transmitted diseases (STDs)). The main objective is to efficiently screen a large population to accurately classify them as positive or negative for a certain binary characteristic (e.g., presence of an infectious agent). Owing to the advent of the COVID-19 pandemic, the topic of mass screening has gained considerable attention as it is a crucial aspect in effectively mitigating the spread of infectious diseases. The objective of mass screening is to maximize the overall classification accuracy under limited budget and testing resources.
We study the problem through the development of optimization-based frameworks that account for various factors, including population heterogeneity, imperfect assays, budget constraints, diverse testing schemes (individual and/or Dorfman group testing), the presence of multiple competing assays, and different testing approaches (proactive and/or reactive). These comprehensive considerations give rise to distinct optimization models. By analyzing the resulting optimization problems, we take advantage of the structure of the problem and identify efficient solution schemes.
Using real-world data, we conduct geographic-based nationwide case studies on COVID-19 screening in the United States. Our results reveal that the identified screening strategies substantially outperform conventional practices by significantly lowering misclassifications. Moreover, our results provide valuable managerial insights with regard to the distribution of testing schemes, assays, and budget across different geographic regions. Such insights can inform policy-makers with tailored and implementable data-driven recommendations.
Since screening can identify infected individuals and assess the associated risk levels, these testing efforts can significantly influence quarantine policies aimed at isolating positive cases. Consequently, our research also delves into the development of risk-based quarantine strategies. Our model takes into account the trade-off between healthcare benefits and the economic implications of quarantine measures. We show our resulting formulation can be cast as a more tractable network flow problem solvable in polynomial-time. We then proceed to calibrate our model using real-life COVID-19 and census data for the state of Minnesota. Our optimal risk-based quarantine policies exhibit substantial reductions in disease spread while maintaining favorable economic outputs
Unveiling the Persistence of Residual Microislands of Calculus After Periodontal Procedures Using a Videoscope: An Initial Investigation into Clinical and Microbiological Associations
No full textPeriodontitis is a chronic disease which is addressed in cases with probing depths of more than 6mm with surgical intervention. In this study, our objective is to use an additional visualization tool, the videoscope, to understand the persistence of microislands of calculus and their clinical and microbial manifestations in human patients.
The videoscope was used to visualize residual calculus in patients after periodontal procedures, and three examiners identified the calculus in 25 micrographs. For the second part of the study, six patients underwent open flap debridement procedure, and clinical and microbiological parameters were compared.
92% of cases had residual calculus present after flapped periodontal procedures performed by multiple periodontics residents. The consensus between trained and untrained examiners on identifying residual microislands of calculus was not statistically different. Probing depths were significantly reduced prior to and after the procedure in both control and test groups; however, one patient with AA showed significantly better clinical results at 3 months in the test quadrant compared to the control.
Residual microislands of calculus are frequently observed when using conventional visualization tools such as surgical loupes. In addition, there is a trend indicating a potential advantage in incorporating additional visualization tools like videoscopes during open flap periodontal procedures