On Barrier Islands: Drivers and Controls

Barrier islands are complex dynamic environments that support unique ecosystems and protect human developments on the mainland from the impacts of storms. Therefore, it is essential to assess their current and future state in terms of the processes driving barrier evolution and of the controls of barrier morphology, critically including the elevation and alongshore extent of coastal foredunes. This Dissertation examines the vulnerability of barrier islands through a stochastic framework addressing both short and long timescales. Chapter 1 offers a stochastic characterization of the water-driven nuisance flooding affecting the after-storm recovery of coastal dunes by analyzing time series of still-water level and deep-water wave data. This short to mid-term driver of barrier dynamics can be modeled as a marked Poisson process with exponentially distributed marks. Furthermore, a constant frequency of about 1 event per month seems to define a characteristic beach elevation suggesting a causal relationship between this driver and beach morphology. Chapter 2 investigates the role of the main controls of the stochastic barrier dynamics, namely elevation capital (defined as sand budget over an island) and barrier width. Through an analysis of Digital Elevation Models, critical thresholds of 100-200m in width and 0.5m in elevation are found to capture the transition from resilient to vulnerable barrier state. In Chapter 3 a reference stochastic model that ties drivers (Chapter 1) and controls (Chapter 2) of barrier steady state is first validated and then used to quantify the transition regime. Finally, the study of the role of barrier width as main control of elevation capital provides an insight into the long-term effects of Sea Level Rise and the onset of vulnerable barrier state. This Dissertation, therefore, should serve researchers, managers and policy-makers in the identification of the key drivers and controls of barrier vulnerability and provide an understanding of the future of barrier islands

Study of Tissue Heterogeneity and Classification using AI Techniques

The idea behind our project is to design an algorithm that utilizes artificial intelligence to detect tissue heterogeneity in patients without the need to carry out an invasive biopsy. We aim to make the cancer prognosis process based solely on the study of the scanned medical images such as MRI or CT. The algorithm will be written in Python and will utilize large data sets of radiomics biomarkers extracted from medical images of different modalities through a software called LIFEx. Radiomics biomarkers are huge amounts of quantitative features extracted from medical images that characterize tumor phenotypes like texture and shape. The objective that we want our algorithm to achieve is to classify the cancer stage. In this project, we will focus on cervix cancer as it is of great interest to our collaborators who are providing us with private data. Another benefit to our algorithm is that it will offer a noninvasive method for cancer diagnosis and will hence bypass biopsies as they are associated with many additional health risks and costs. This project will contribute to changing the way doctors diagnose cancer and make it a more efficient process using our robust, reliable detection of tissue heterogeneity

The Roles of Time and Disturbance Regimes in Savanna Plant Communities

Despite growing recognition that Earth’s biodiverse grasslands and savannas require long periods of time to accumulate their diversity, the idea that grasslands can be as ancient as forests (i.e., the old-growth grassland concept) is still not widely accepted. Consequently, biodiversity of tropical grasslands and savannas receives far less attention than tropical forests in studies of land-use change and environmental gradients. My dissertation addresses the ways that savanna plant diversity is shaped over long periods of time by fire and herbivores and over short periods of time by land-use change at the global, community, and organismal levels. In my first chapter, as a global test of the old-growth grassland concept, I performed a meta-analysis to compare plant species richness of old-growth grasslands and secondary grasslands (i.e., grasslands recovering after destruction by agriculture and afforestation). I found that old-growth grasslands had 37% more species compared to secondary grasslands, and that secondary grasslands need at least a century to recover their former richness. This analysis highlighted the paucity of studies on land-use change in tropical savannas, and so for my second chapter, I quantified the effects of land use on savanna plant communities in India. I sampled four land uses (old-growth savanna, tillage agriculture, fallows, and tree plantations) stratified across a 1500 mm precipitation gradient. I found that tillage agriculture and tree planting have consistent negative effects on old-growth savanna plant diversity across the precipitation gradient. These findings underscore an urgent need to recognize expanding agriculture and afforestation as existential threats to fire- and grazer-maintained tropical savanna biodiversity. In my third chapter, I studied how fire and grazing have shaped grass functional traits of 337 native Texas grasses. I found that fire and grazing have resulted in Texas grasses evolving strategies to either promote fire or promote grazing. Results highlight the legacy of Pleistocene megafauna and the undeniable role of fire in shaping the grass flora of Texas. In conclusion, my dissertation provides evidence to recognize fire and grazing as ancient forces shaping savanna biodiversity. Maintenance of these endogenous disturbance regimes in old-growth savannas and limiting land-use change should be a conservation priority

Development of Phage Display Techniques with Genetic Code Expansion for Peptide Drug Discovery

Phage display is one of the most widely-used techniques to develop peptide therapeutics. Its unique design links the displayed peptides to their encoding DNAs, providing a means for amplifying the peptide library and an easy way to identify selected ligands through sequencing. Although powerful, the conventional phage display technique replies on host cell’s translation machinery, therefore its chemical diversity is confined to twenty canonical amino acids. Further, phage-displayed peptides are also generally linear and unstructured leading to entropy penalty when binding to targets and proneness to proteolytic degradation. To resolve these drawbacks, we expand the functional and structural diversity of phage display using orthogonal aminoacyl-tRNA synthetase/tRNA pairs to incorporate non-canonical amino acids with diverse chemical functionalities into displayed peptide libraries. First, we develop a system to genetically incorporate an N^ε-acryloyl-L-lysine (AcrK) at the C-terminus of an 8-mer library; the non-canonical amino acid undergoes a proximity-driven Michael addition with the cysteine at the N-terminus to generate a phage-displayed cyclic-peptide library. The cyclic peptide library is applied to the affinity selection against histone deacetylase 8 (HDAC8), leading to the discovery of a potent cyclic peptide inhibitor that binds to target protein with a single digit micromolar affinity and displays better potency than its linear counterparts. Then, the same approach is applied to screen against the spike protein of the novel coronavirus SARS-CoV-2 to evolve peptide inhibitors. A 12-mer library is constructed and used in a displacement-based selection which gives two peptide ligands; both are shown to disrupt Spike-RBD/ACE2 binding. Lastly, a previously developed amber-obligate library is used to display an N^ε-butyryl-lysine (BuK) on 7-mer peptides. This lysine derivative is a naturally occurring lysine posttranslational modification that has target-ligand interactions with eleven-nineteen leukemia protein (ENL). During the selection, BuK serves as a warhead to guide displayed peptides towards the active site of ENL, thereby, increasing the selectivity and productivity of biopanning. We validate the selected peptides as ENL inhibitors, and further optimization and investigation have led to the discovery of a potent, cellular active peptide inhibitor that exhibits on-target effects in inhibiting ENL target gene expression and leukemia growth

Machine Learning-Based Multiscale Modeling and Control of Quantum Dot Manufacturing and Their Applications

In the past few years, there has been a major impetus in the search for quantum dots (QDs), which are a type of semiconducting nanocrystals (NCs) with tunable optical and optoelectronic properties for next-generation photonic devices. This can be attributed to their relatively high photoluminescence quantum yield, wide color gamut, tunable optoelectronic properties, and cost-effective solution processibilities. Furthermore, the rising market share of these applications has led to an increased demand for fast and scalable production of QDs and the associated optoelectronics devices. However, there are major commercialization challenges associated with the manufacturing of QDs: (a) lack of mechanistic understanding of the crystallization kinetics of various QD systems, which hinders the predictive control of the QD size distribution; (b) absence of a well-established paradigm for high-fidelity modeling and scale-up of various QD manufacturing processes (e.g., crystallization and thin-film deposition); and (c) no presence of computationally efficient solutions for control and optimization of QD processes. To address these knowledge gaps, in this work, we develop different models to describe the mechanism of QD crystal growth, enable fast-scalable manufacturing of QDs and the associated optoelectronic devices, and develop an appropriate control framework for various QD processes. First, a first-principled kinetic Monte Carlo (kMC) was developed and experimentally validated to describe the crystallization kinetics of QDs. Second, to resolve the various issues associated with the batch synthesis of QDs, continuous manufacturing of QDs using a plug flow crystallizer (PFC) was demonstrated using a multiscale modeling approach. Further, this approach was extended to two-phase slug flow crystallizers (SFCs) by combining the construction of a CFD-based multiscale model. Also, a highly efficient data-driven optimal control framework was formulated using a deep neural network (DNN) to control QD crystal size and distribution. Third, modeling of thin-film deposition required for manufacturing of solar cells and high-resolution displays was performed. Specifically, a multiscale model that combines the surface-level discrete element method (DEM) model of QD aggregation and macroscopic mass and energy balance equations was developed for describing the spray coating of QDs. Further, given the computational expense of this model, a surrogate DNN model was developed, which was integrated with a model predictive controller (MPC) to control the film characteristics (i.e., thickness and roughness). Next, although the resulting thin-films are of the desired quality, they are chemically labile and cannot withstand subsequent downstream processing during the manufacturing of LEDs or solar cells. Thus, a kMC model was developed to describe crosslinking of QD thin-films for increased chemical robustness resulting in the manufacturing of high-resolution displays. Moreover, it is important to note that all of the above-developed models were experimentally validated using appropriate experimental observations. Lastly, although the above-developed model accurately describes various processes related to QD manufacturing, these models are very system-specific, and cannot be easily extrapolated to other QD systems. To provide a concrete direction for addressing this issue in the future, we propose a transformer-based hybrid model, which can leverage the remarkable transfer learning properties of transformers for better generalization across different QD systems. Overall, the proposed work addresses three major challenges in the QD field (i.e., control of QD kinetics, continuous production of QDs, and designing manufacturing processes for fast scale-up of QD-based devices) by developing various experimentally validated multiscale models and combining them with an appropriate control framework

In Situ Tribochemical Characterization of Nanolubricants

Lubrication plays important roles in mechanical systems in motion. The challenge in understanding the characteristics of a lubricant under working conditions lies in its dynamic nature, and it is impossible to observe the target directly. The objective of this thesis research is to obtain better fundamental understanding of nanolubricants under shear. Specifically, a methodology that enables in situ detection of a rubbing pair is developed. Using this approach, the properties and performance of nanoluricants are studied. The resulting tribochemical products as tribofilms are investigated. This research consists investigation in three aspects. The first is to develop in situ triboelectrochemical techniques enabling basic study. An integrated tribotesting system combined a disc-on-disc tribotesting with electrochemical impedance measurement. The second is to study the properties of working lubricants, their electrical and thermal properties. The electrical conductivity against the oil film thickness was examined. Results showed the non-ohmic behavior of a lubricating film in the hydrodynamic regime. Properties of lubricants and testing conditions are some of the factors affecting the conductivity. The study on thermal performance over a mineral oil and polyalphaolefin (PAO) were carried out. Results showed that thermal properties of lubricants depended on the shear and they were not constants as being known. This research revealed the potential existence of dynamic properties of a working lubricant. The third is to investigate tribochemical interactions between nanolubricants and rubbing surfaces of a substrate. Using α-ZrP nanoparticles as additives, a nanolubricant produced a tribofilm with consistent electrical properties that reduced friction for 40% and wear 90%. Research results showed that under shear, a tribofilm consisting of pyrophosphate

Design of Wavelength Division Multiplexing Optical Interconnect Systems with Advanced Heterogeneous Integration

The dissertation presents the design of silicon photonic (SiP) optical transceiver and co-packaged photonic integrated circuit (PIC) for energy-efficient multi-channel optical data communication. The dissertation demonstrates 3 designs in silicon photonic CMOS co-design platform. The first design is a 2.5D integrated forward-clock 8-channel The dissertation presents the design of silicon photonic (SiP) optical transceiver and co-packaged photonic integrated circuit (PIC) for energy-efficient multi-channel optical data communication. The dissertation demonstrates 3 designs in silicon photonic CMOS co-design platform. The first design is a 2.5D integrated forward-clock 8-channel Wavelength Division Multiplexing (WDM) optical transceiver with co-designed electrical transceiver and implemented in 12nm FinFET technology. The second design is a 3D-integrated 32-channel WDM optical transceiver in state-of-the-art Direct Bond Interconnect (DBI) packaging aimed at maximal optimization of energy efficiency and Optical Amplitude Modulation (OMA) Sensitivity, also in 12nm FinFET technology. The 2 transceiver chips are to address the continuous demand in bandwidth, transmission distance and energy efficiency from modern-day data communication emerged from the high-performance computing and data center application. The third implemented chip is a silicon photonic based monolithic polarization controller and tracking loop for optical interconnect. The chip design targets the issue of random Polarization Mode rotation commonly encounter in long distance fiber transmission due to unwanted mechanical stress and motion of fiber which leads to data corruption in optical data communication. In contrast to prior arts which are discrete control loop via PIC and FPGA, the proposed polarization tracking loop is implemented in silicon-photonic-CMOS monolithic 90nm Silicon-On-Insulator (SOI) process which integrates the Silicon-Germanium photonic devices, CMOS digital logic and analog circuitry onto a single chip, with the potential scalability of multiple polarization tracker channel on a single chip for multiple wavelength polarization stabilization for WDM optical inter-connect. Wavelength Division Multiplexing (WDM) optical transceiver with co-designed electrical transceiver and implemented in 12nm FinFET technology. The second design is a 3D-integrated 32-channel WDM optical transceiver in state-of-the-art Direct Bond Interconnect (DBI) packaging aimed at maximal optimization of energy efficiency and Optical Amplitude Modulation (OMA) Sensitivity, also in 12nm FinFET technology. The 2 transceiver chips are to address the continuous demand in bandwidth, transmission distance and energy efficiency from modern-day data communication emerged from the high-performance computing and data center application. The third implemented chip is a silicon photonic based monolithic polarization controller and tracking loop for optical interconnect. The chip design targets the issue of random Polarization Mode rotation commonly encounter in long distance fiber transmission due to unwanted mechanical stress and motion of fiber which leads to data corruption in optical data communication. In contrast to prior arts which are discrete control loop via PIC and FPGA, the proposed polarization tracking loop is implemented in silicon-photonic-CMOS monolithic 90nm Silicon-On-Insulator (SOI) process which integrates the Silicon-Germanium photonic devices, CMOS digital logic and analog circuitry onto a single chip, with the potential scalability of multiple polarization tracker channel on a single chip for multiple wavelength polarization stabilization for WDM optical interconnect

Improving Our Understanding of Portopulmonary Hypertension

Pulmonary arterial hypertension (PAH) is characterized by elevated pulmonary artery pressure and pulmonary vascular resistance, right heart failure, exercise limitation, and an increased risk of death. Histopathologic examination reveals intimal proliferation, medial hypertrophy, and adventitial fibrosis in the small muscular pulmonary arteries. Plexiform lesions and in situ thrombosis are also seen. Most commonly idiopathic, PAH may also be associated with portal hypertension, termed portopulmonary hypertension (PoPH). Previous studies have shown a prevalence of histopathologic changes of PAH of 0.61% in autopsies of patients with cirrhosis, and PoPH was the third most common form of PAH in a population-based epidemiologic study in France. Recent cohort studies showed that the prevalence of POPH in patients presenting for liver transplant evaluation is between 5 and 6%. Patients with POPH have an increased risk of death, even with specific PAH treatment. In many cases, POPH greatly complicates or precludes liver transplantation, significantly affecting the course of hepatic failure in these patients. The etiology of PAH in patients with portal hypertension (characterized by systemic vasodilatation) is unclear. We have shown that female sex and autoimmune etiology of liver disease are associated with the risk of POPH. Although germline mutations in the gene that codes for bone morphogenetic protein receptor type II(BMPR2) have been associated with idiopathic and familial forms of PAH, they have not been found in patients with POPH. In this thesis, a step-by-step approach is proposed to advance our understanding of POPH, and the outcome of this project is to provide a comprehensive systemic review of POPH, diagnosis, evaluation and management. In addition, we want to understand the leading cause of mortality in patients with POPH and why liver transplant is so underutilized

Effect of Caffeine on Horizontal Gene Transfer

Microbiomes have drawn a large interest in the scientific community in recent years (Hornung et al, 2019). Currently, it is postulated that Horizontal Gene Transfer (HGT), a common phenomenon that occurs across many bacterial species, happens in human gut microbiome as well (Liu et al, 2012). The flow of genetic information is fluid amongst this domain and can be exchanged between organisms through mechanisms such as transformation. Many studies have attributed that bacteria have co-evolved with their’ environment to survive. Wang et al. 2020 proposed that an exterior stressor evoked bacteria’s sense of survival which enhanced the DNA to uptake the recombination that ultimately promotes transformation (Wang et al 2020. In this case, shifting away from a normal growth environment has been suspected to induce stress to many different bacteria in many ways. With anti-bacterial effects, caffeine can successfully act as an exterior stressor to stimulate the transformation process. Through experiment, with exposure to caffeine in Escherichia coli culture, caffeine showed an enhancement effect on bacterial transformation. With preliminary RNA sequencing results, it was found that fumC, genetic sequence that encodes fumarase enzyme, were upregulated drastically when the sample is exposed to caffeine, along with its counterpart, fumA and fumB being deactivated or downregulated. This showed the presence of increase in ROS level in the cell culture when exposed to caffeine. With reference to a study of similar interest, increase in ROS level and exterior stress level, the cell permeability was increased, enhancing horizontal gene transfer. Therefore, it was hypothesized that with the presence of caffeine, the antimicrobial effect served as an exterior stressor to the bacteria, and that the bacteria's responses to stress would increase DNA uptake and recombination, promoting the bacterial transformation