### Recent Submissions

• #### To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

(2022-06) [Dissertation]
Committee members: Wang, Di; Moshkov, Mikhail; Feng, Zhuo
Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. Understanding and exploiting the Label Dependency (LD) is well accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack. From the perspective of generalization on clean data, previous works have proved the advantage of exploiting LD in multi-label classification. To further verify the positive role of LD in multi-label classification and address previous limitations, we originally propose an approach named Prototypical Networks for Multi- Label Learning (PNML). Specially, PNML addresses multi-label classification from the angle of estimating the positive and negative class distribution of each label in a shared nonlinear embedding space. PNML achieves the State-Of-The-Art (SOTA) classification performance on clean data. From the perspective of robustness under evasion attack, as a pioneer, we firstly define the attackability of an multi-label classifier as the expected maximum number of flipped decision outputs by injecting budgeted perturbations to the feature distribution of data. Denote the attackability of a multi-label classifier as C∗, and the empirical evaluation of C∗ is an NP-hard problem. We thus develop a method named Greedy Attack Space Exploration (GASE) to estimate C∗ efficiently. More interestingly, we derive an information-theoretic upper bound for the adversarial risk faced by multi-label classifiers. The bound unveils the key factors determining the attackability of multi-label classifiers and points out the negative role of LD in multi-label classifiers’ adversarial robustness, i.e. LD helps the transfer of attack across labels, which makes multi-label classifiers more attackable. One step forward, inspired by the derived bound, we propose a Soft Attackability Estimator (SAE) and further develop Adversarial Robust Multi-label learning with regularized SAE (ARM-SAE) to improve the adversarial robustness of multi-label classifiers. This work gives a more comprehensive understanding of LD in multi-label learning. The exploiting of LD should be encouraged since its positive role in models’ generalization on clean data, but be restricted because of its negative role in models’ adversarial robustness.
• #### Pillar[n]arene-based Porous and Smart Materials

(2022-04-26) [Dissertation]
Committee members: Mohammed, Omar F.; Da Costa, Pedro M. F. J.; Sue, Andrew C.-H.
Pillar[n]arenes are a class of macrocycles with outstanding properties given by its electron rich and symmetric cavity, and facile functionalization that allows to tune its solubility and host-guest properties. In this work, the versatility of pillar[n]arenes for the design of porous materials is studied. Pillar[n]arenes are stable to guest removal, giving solvent-free phases and thus resulting in permanent porous structures. From simple ethyl pillar[5,6]arenes, nonporous adaptive crystals are obtained and studied for the recognition and separation of butanol isomers. The cavity size of the pillar[n]arene hosts and the linear or branched characteristic of the butanol isomers influences the assembly of the pillararene to selectively adsorb an isomer. Then, an A1/A2 benzaldehyde-functionalized pillar[5]arene is used as a building block for the synthesis of an imine porous organic cage, which would result in material with intrinsic and extrinsic porosity. Finally, a lipoic acid modified pillar[5]arene is implemented as ligand for nanoclusters, improving their stability. Taking advantage of the cavity of the pillar[5]arene, a host-guest complex is formed, enhancing the optical properties of nanoclusters.
• #### Contributions to Data-driven and Fractional-order Model-based Approaches for Arterial Haemodynamics Characterization and Aortic Stiffness Estimation

(2022-04-26) [Dissertation]
Committee members: Tareq, Y. Al-Naffouri; Knio, Omar; Gao, Xin; Figueroa, C. Alberto
Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. Patients at risk of evolving CVDs are assessed by evaluating a risk factor-based score that incorporates different bio-markers ranging from age and sex to arterial stiffness (AS). AS depicts the rigidity of the arterial vessels and leads to an increase in the arterial pulse pressure, affecting the heart and vascular physiology. These facts have encouraged researchers to propose surrogate markers of cardiovascular risks and develop simple and non-invasive models to better understand cardiovascular system operations. This work thus fundamentally capitalizes on developing a novel class of low-dimensional physics-based fractional-order models of systemic arteries and exploring the feasibility of fractional differentiation order to portray the vascular stiffness. Fractional-order modeling is a successful paradigm to integrate multiscale and interconnected mechanisms of the complex arterial system. However, this type of modeling alone often fails to efficiently integrate altered variabilities in vascular physiology from various sources of large datasets, multi-modalities, and levels. In this regard, combining fractional-order-based approaches with machine learning techniques presents a unique opportunity to develop a powerful prediction framework that reveals the correlation between intertwined vascular events. This work is divided into three parts. The first part contributes to developing the fractional-order lumped parametric model of the arterial system. First, we propose fractional-order representations to model and characterize the complex and frequency-dependent apparent arterial compliance. Second, we propose fractional-order arterial Windkessel modeling the aortic input impedance and hemodynamic. Subsequently, the proposed models have been applied and validated using both human in-silico healthy datasets and real vascular aging and hypertension. The second part addresses the non-zero initial value problem for fractional differential equations (FDEs) and proposes an estimation technique for joint estimation of the input, parameters, and fractional differentiation order of non-commensurate FDEs. The performance of the proposed estimation techniques is illustrated on arterial and neurovascular hemodynamic response models. The third part explores the feasibility of using machine learning algorithms to estimate the gold-standard measurement of AS, carotid-to-femoral pulse wave velocity. Different modalities have been investigated to generate informative input features and reduce the dimensionality of the time series pulse waves.
• #### Learning-Based Approaches for Next-Generation Intelligent Networks

(2022-04-20) [Dissertation]
Committee members: Alouini, Mohamed-Slim; Fahmy, Suhaib A.; Stoleru, Radu
The next-generation (6G) networks promise to provide extended 5G capabilities with enhanced performance at high data rates, low latency, low energy consumption, and rapid adaptation. 6G networks are also expected to support the unprecedented Internet of Everything (IoE) scenarios with highly diverse requirements. With the emerging applications of autonomous driving, virtual reality, and mobile computing, achieving better performance and fulfilling the diverse requirements of 6G networks are becoming increasingly difficult due to the rapid proliferation of wireless data and heterogeneous network structures. In this regard, learning-based algorithms are naturally powerful tools to deal with the numerous data and are expected to impact the evolution of communication networks. This thesis employed learning-based approaches to enhance the performance and fulfill the diverse requirements of the next-generation intelligent networks under various network structures. Specifically, we design the trajectory of the unmanned aerial vehicle (UAV) to provide energy-efficient, high data rate, and fair service for the Internet of things (IoT) networks by employing on/off-policy reinforcement learning (RL). Thereafter, we applied a deep RL-based approach for heterogeneous traffic offloading in the space-air-ground integrated network (SAGIN) to cover the co-existing requirements of ultra-reliable low-latency communication (URLLC) traffic and enhanced mobile broadband (eMBB) traffic. Precise traffic prediction can significantly improve the performance of 6G networks in terms of intelligent network operations, such as predictive network configuration control, traffic offloading, and communication resource allocation. Therefore, we investigate the wireless traffic prediction problem in edge networks by applying a federated meta-learning approach. Lastly, we design an importance-oriented clustering-based high quality of service (QoS) system with software-defined networking (SDN) by adopting unsupervised learning.
• #### Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes

(2022-04-17) [Dissertation]
Committee members: Genton, Marc G.; Jasra, Ajay; Cooley, Daniel
The statistical modeling of extreme natural hazards is becoming increasingly important due to climate change, whose effects have been increasingly visible throughout the last decades. It is thus crucial to understand the dependence structure of rare, high-impact events over space and time for realistic risk assessment. For spatial extremes, max-stable processes have played a central role in modeling block maxima. However, the spatial tail dependence strength is persistent across quantile levels in those models, which is often not realistic in practice. This lack of flexibility implies that max-stable processes cannot capture weakening dependence at increasingly extreme levels, resulting in a drastic overestimation of joint tail risk. To address this, we develop new dependence models in this thesis from the class of max-infinitely divisible (max-id) processes, which contain max-stable processes as a subclass and are flexible enough to capture different types of dependence structures. Furthermore, exact simulation algorithms for general max-id processes are typically not straightforward due to their complex formulations. Both simulation and inference can be computationally prohibitive in high dimensions. Fast and exact simulation algorithms to simulate max-id processes are provided, together with methods to implement our models in high dimensions based on the Vecchia approximation method. These proposed methodologies are illustrated through various environmental datasets, including air temperature data in South-Eastern Europe in an attempt to assess the effect of climate change on heatwave hazards, and sea surface temperature data for the entire Red Sea. In another application focused on assessing how the spatial extent of extreme precipitation has changed over time, we develop new time-varying $r$-Pareto processes, which are the counterparts of max-stable processes for high threshold exceedances.
• #### Cellular and Polymeric Membranes for Separation and Delivery Applications

(2022-04-14) [Dissertation]
Committee members: Sessler, Jonathan; Cavallo, Luigi; Saikaly, Pascal
The primary focus of this research is to utilize cellular and polymeric membranes for biomedical applications: To date, several organic and inorganic materials have been used to synthesize nanoparticles (NPs). The question arises as to which criteria and design principles should be considered while selecting the best material. Based on the results of testing, three key roles of NPs have been identified. First, NPs need enough circulation time to reach their target. Then these NPs must be able to target diseased tissue while leaving healthy tissue unaffected. Finally, NPs must be biodegradable and easily eliminated from the body. Biomimetic nanoparticles based on cell membranes have been developed as an efficient way to fulfill the needs of drug delivery goals and achieve targeted delivery by actively interacting and communicating with the biological environment. In the first project, genome editing machinery was delivered to particular cells using biomimetic cancer cell coated zeolitic imidazolate frameworks. MCF-7 cells demonstrated the highest uptake of C3-ZIFMCF compared to HeLa, HDFn, and aTC cells. In terms of genome editing, MCF-7 cells transfected with C3-ZIFMCF showed 3-fold EGFP repression compared to C3-ZIFHELA cells transfected with 1-fold EGFP repression. In vivo tests demonstrated C3-ZIFMCF's affinity for MCF-7 tumor cells. This demonstrates the biomimetic approach's ability to target cells specifically, which is definitely the most essential step in future genome editing technology translation. In the second project, multimodal therapeutic nanowires (NWs D-ZIF) MCF-7 cancer cells were developed. D-ZIF coated NWs had higher cellular uptake and photothermal treatment efficiency than non-coated NWs. (NWs D-ZIF) MCF accumulates in MCF-7 tumor cells and enhances photothermal capability. On the other hand, chiral separation of enantiomers is becoming more important, particularly in pharmaceuticals. Because enzyme activities and other biological processes are stereoselective, chiral drugs' enantiomers often have different metabolic effects, pharmacological activity, metabolic rates, and toxicities. In an attempt to address this issue, we decided in the final project to study the capability of chiral polyamide membrane for efficient and energy-free chiral separation. In particular, to separate essential amino acid critical to all living organisms (DL-tryptophan).
• #### Optical Behavior of Perovskite Nanocrystals with Different Dimensionalities

(2022-04-12) [Dissertation]
Committee members: Huang, Kuo-Wei; Bakr, Osman; Malko, Anton V.
Metal halide perovskites have rapidly gained researchers’ attention and become one of the most promising materials today, with exciting properties and multiple optoelectronic applications. Regardless of the shape into which perovskite materials have been fashioned, the nanocrystals that constitute these materials have been the object of extensive research. Perovskite nanocrystals (PNCs) have rapidly developed due to their favorable optical and electronic properties, including high photoluminescence quantum yield, narrow emission bands, and tunable optical band gap. This dissertation discusses several chemical approaches to enhance optical performance properties of PNCs, including their photoluminescence quantum yield and stability.
• #### Reticular Chemistry Strategies: Design and Applications of Metal-Organic Frameworks

(2022-04) [Dissertation]
Committee members: Bakr, Osman; Hadjichristidis, Nikos; Maurin, Guillaume
• #### Analytical Modelling and Simulation of Drilling Lost-Circulation in Naturally Fractured Formation

(2022-04) [Dissertation]
Committee members: Patzek, Tadeusz; Sun, Shuyu; Yotov, Ivan
Drilling is crucial to many industries, including hydrocarbon extraction, CO2 sequestration, geothermal energy, and others. During penetrating the subsurface rocks, drilling fluid (mud) is used for drilling bit cooling, lubrication, removing rock cuttings, and providing wellbore mechanical stability. Significant mud loss from the wellbore into the surrounding formation causes fluid lost-circulation incidents. This phenomenon leads to cost overrun, environmental pollution, delays project time and causes safety issues. Although lost-circulation exacerbates wellbore conditions, prediction of the characteristics of subsurface formations can be obtained. Generally, four formation types cause lost-circulation: natural fractures, and induced fractures, vugs and caves, and porous/permeable medium. The focus in this work is on naturally fractured formations, which is the most common cause of lost circulation. In this work, a novel prediction tool is developed based on analytical solutions and type-curves (TC). Type-curves are derived from the Cauchy equation of motion and mass conservation for non-Newtonian fluid model, corresponding to Herschel-Bulkley model (HB). Experimental setup from literature mimicking a deformed fracture supports the establishment of the tool. Upscaling the model of a natural fracture at subsurface conditions is implemented into the equations to achieve a group of mud type-curves (MTC) alongside another set of derivative-based mud type-curves (DMTC). The developed approach is verified with numerical simulations. Further, verification is performed with other analytical solutions. This proposed tool serves various functionalities; It predicts the volume loss as a function of time, based on wellbore operating conditions. The time-dependent fluid loss penetration from the wellbore into the surrounding formation can be computed. Additionally, the hydraulic aperture of the fracture in the surrounding formation can be estimated. Due to the non-Newtonian behavior of the drilling mud, the tool can be used to assess the fluid loss stopping time. Validation of the tool is performed by using actual field datasets and published experimental measurements. Machine-Learning is finally investigated as a complementary approach to determine the flow behavior of mud loss and the corresponding fracture properties.
• #### Characterization of the role of MAP Kinases in stress induced responses

(2022-04) [Dissertation]
Committee members: Arold, Stefan T.; Blilou, Ikram; Kopka, Joachim
Biotic stresses such as infection by bacteria negatively affect plant growth and pose a severe threat to human food production. Improving our understanding of the immune systems of plants should help ensure food supplies in the years ahead. Bacterial infections induce Pattern-Triggered Immunity (PTI), a process in which plants perceive bacterial molecules and trigger an immune response. Mitogen- Activated Protein Kinase (MAPK) cascades are key players in this immunity process. Since the MAP Kinases (MPKs) 3/4/6 are mainly responsible for flg22- dependent phosphorylation events, we sought to find out how oxidation of MPK4 affects its ability to respond to stresses. Previous studies have shown varying kinase activity of MPK4 upon oxidation. Therefore, this project aims to provide an insight into the oxidative defense signaling mechanism of A. thaliana by investigating the role of MPK4 Cysteine181 in vitro and in vivo. Analysis of oxidation-mimicking as well as oxidation-dead mutants gave first hints that Cysteine181, which is located in the MPK4 substrate binding pocket, is a highly important regulatory residue of oxidative stress signaling by affecting MPK4 kinase activity and the activation of MPK3 and MPK6. Binding studies revealed that those events are due to sterical hindrance within the binding pocket of MPK4 and the blockage of upstream activator binding. The second part of this study characterizes compositional and post-translational changes of plant ribosomes during pathogen infection. Ribosomal proteins selectively participate in the formation of polysomes under different environmental and developmental conditions. However, the function of these changes still remains elusive. The current research project attempts to understand the plant ribosomal changes that occur upon exposure to bacterial pathogens. To observe ribosomal changes, A. thaliana plants were treated with a pathogen associated molecular pattern (PAMP), flg22. Mass spectrometric analysis identified quantitative changes of PAMP-induced ribosomal proteins in polysomes as well as changes in post-translational modifications. Spatial simulations of ribosomes revealed specific regions within the ribosomes to be PTI specific. This study demonstrates that MPK6 contributes to modification of P-stalk composition and phosphorylation status. The MPK6 mediated modifications may affect translation and in combination indicate a mechanism of PTI-related translational control.
• #### Multifunctional Flexible Laser-Scribed Graphene Sensors for Resilient and Sustainable Electronics

(2022-04) [Dissertation]
Committee members: Ooi, Boon S.; Duarte, Carlos M.; Dahiya, Ravinder
The Fourth Industrial Revolution is driven by cyber-physical systems, in which sensors link the real and virtual worlds. A global explosion of physical sensors seamlessly connected to networks is expected to produce trillions of sensors annually. To accommodate sustainable sensor production, it is crucial to minimize the consumption of raw materials, the complexity of fabrication, and waste discharge while improving sensor performance and wearability. Graphene has emerged as an excellent candidate material for its electrical and mechanical characteristics; however, its economic impact has been hindered by complex and energy-intensive processes. Meanwhile, printed electronics offer a compelling range of merits for scalable, high-yield, low-cost manufacturing of graphene. Among them, the one-step laser scribing process has enabled a simultaneous formation and patterning of porous graphene in a solid-state and opened new perspectives for versatile and widely tunable physical sensing platforms. This dissertation introduces flexible, lightweight, and robust Laser-Scribed Graphene sensor solutions for detecting various physical parameters, such as strain, flow, deflection, force, pressure, temperature, conductivity, and magnetic field. Multifunctionality was obtained by exploiting the direct laser scribing process combined with the flexible nature of polyimide and the piezoresistivity of porous graphene. The outstanding properties of LSG, such as low cytotoxicity, biocompatibility, corrosion resistance, and ability to function under extreme pressure and temperature conditions, allowed targeting diverse emerging applications. As a wearable device in healthcare, the LSG sensor was utilized to monitor motions involving joint bandings, such as finger folding, knee-related movements, microsleep detection, heart rate monitoring, and plantar pressure measurements. The marine ecosystem was used as an illustrative sensor application to cope with harsh environments. To this end, the sensor measured the velocity of underwater currents, pressure, salinity, and temperature while monitoring the movement of marine animals. The sensitivity to the magnetic field remained stable up to 400 °C, making the LSG sensor a viable option for high-temperature applications. In robotics, the LSG sensor was developed for velocity profile monitoring of drones and as a soft tactile sensor. The study provides insights into methods of improving sensor performance, opportunities, and challenges facing a tangible realization of LSG physical sensors.
• #### Energy-Efficient Devices and Circuits for Ultra-Low Power VLSI Applications

(2022-04) [Dissertation]
Committee members: Shamim, Atif; Sarathy, Mani; Weinstein, Dana
Nowadays, integrated circuits (IC) are mostly implemented using Complementary Metal Oxide Semiconductor (CMOS) transistor technology. This technology has allowed the chip industry to shrink transistors and thus increase the device density, circuit complexity, operation speed, and computation power of the ICs. However, in recent years, the scaling of transistor has faced multiple roadblocks, which will eventually lead the scaling to an end as it approaches physical and economic limits. The dominance of sub-threshold leakage, which slows down the scaling of threshold voltage VTH and the supply voltage VDD, has resulted in high power density on chips. Furthermore, even widely popular solutions such as parallel and multi-core computing have not been able to fully address that problem. These drawbacks have overshadowed the benefits of transistor scaling. With the dawn of Internet of Things (IoT) era, the chip industry needs adjustments towards ultra-low-power circuits and systems. In this thesis, energy-efficient Micro-/Nano-electromechanical (M/NEM) relays are introduced, their non-leaking property and abrupt switch ON/OFF characteristics are studied, and designs and applications in the implementation of ultra-low-power integrated circuits and systems are explored. The proposed designs compose of core building blocks for any functional microprocessor, for instance, fundamental logic gates; arithmetic adder circuits; sequential latch and flip-flop circuits; input/output (I/O) interface data converters, including an analog-to-digital converter (ADC), and a digital-to-analog converter (DAC); system-level power management DC-DC converters and energy management power gating scheme. Another contribution of this thesis is the study of device non-ideality and variations in terms of functionality of circuits. We have thoroughly investigated energy-efficient approximate computing with non-ideal transistors and relays for the next generation of ultra-low-power VLSI systems.
• #### Intrinsically Microporous Ladder Polymer-based Carbon Molecular Sieve Membranes for Gas Separation Applications

(2022-04) [Dissertation]
Committee members: Han, Yu; Koros, William J.; Lai, Zhiping
Industrial separations – primarily dominated by thermally driven distillation-based processes – consume 10-15% of the global energy production and emit more than 100 million tonnes of CO2 annually. Membrane technology, a 90% thermodynamically more energy-efficient than distillation processes, could be a desirable alternative with potentially lower energy consumption and lower carbon footprint. Industrial implementation of membrane technology, particularly for olefin/paraffin separations and hydrogen purification from syngas, remains challenging due to the substantially low mixed-gas selectivity of the currently available polymeric materials. Carbon molecular sieve (CMS) membranes – formed by high-temperature pyrolysis of solution-processable polymeric-based precursors at an oxygen-free atmosphere – have shown superior gas separation performance far beyond the polymeric upper bounds for many gas-pairs (e.g., CO2/CH4, N2/CH4). The ultimate goal of the research reported in this dissertation was to develop highly performing CMS membranes for industrially important but challenging gas separation applications (e.g., C2H4/C2H6, H2/CO2, etc.). This work successfully introduced a promising approach to fine-tune the pore size distribution of CMS membranes through a systematic modification of the contortion sites of highly aromatic ladder polymer of intrinsic microporosity (PIM) precursors. CMS membranes derived from Trip(Me2)-TB – a precursor with large and thermally stable triptycene units – demonstrated unprecedented pure- and-mixed C2H4/C2H6 selectivities of 96 and 57, respectively, with relatively higher ethylene permeability than other CMS membranes. Similarly, CMS membranes derived from an alternative ladder PIM-based precursor, EA(Me2)-TB, also showed an outstanding performance for C2H4/C2H6 with a pure-gas selectivity up to 77 but with, however, low ethylene permeability of 0.35 barrer. Furthermore, CMS membranes derived from ladder CANAL-TB-1 – a precursor with the smallest contortion site – exhibited superior pure- and-mixed H2/CO2 selectivities of 248 and 174, respectively, due to their tightly packed structure enabled by the lack of any shape-persistence unit such as triptycene. CMS membranes fabricated in this work also showed promising gas separation performance for many other important energy-intensive industrial applications, including CO2/CH4, O2/N2, N2/CH4, H2/CH4, etc. In summary, this dissertation frameworks a facile and effective approach to obtain CMS membranes with exceptional gas separation performance by rational design of the contortion sites of intrinsically microporous ladder polymer-based precursors.
• #### Bayesian Modeling of Sub-Asymptotic Spatial Extremes

(2022-04) [Dissertation]
Committee members: Genton, Marc G.; Jasra, Ajay; Naveau, Philippe
In many environmental and climate applications, extreme data are spatial by nature, and hence statistics of spatial extremes is currently an important and active area of research dedicated to developing innovative and flexible statistical models that determine the location, intensity, and magnitude of extreme events. In particular, the development of flexible sub-asymptotic models is in trend due to their flexibility in modeling spatial high threshold exceedances in larger spatial dimensions and with little or no effects on the choice of threshold, which is complicated with classical extreme value processes, such as Pareto processes. In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain. In the second part, we construct new marked point process models, where interest mostly lies in the extremes of the mark distribution. Our proposed joint models exploit intrinsic CAR priors to capture the spatial effects in landslide counts and sizes, while the mark distribution is assumed to take various parametric forms. We demonstrate that having a sub-asymptotic distribution for landslide sizes provides extra flexibility to accurately capture small to large and especially extreme, devastating landslides.
• #### Elucidating Mechanisms of Chromatin Crosstalk Using ‘Designer’ Nucleosomes

(2022-04) [Dissertation]
Committee members: Hamdan, Samir; Hirt, Heribert; Black, Ben
The molecular target of epigenetic signaling is chromatin. Histones are extensively post-translationally modified (PTM), and many of these individual modifications have been studied in depth. As PTMs occur at multiple positions within histones, the degree to which these modifications might influence each other remains one of the major challenges of chromatin biology. Although major discoveries in understanding the complex repertoire of histone modifications were achieved using reductionist experimental systems with synthetic histone peptides, they do not explain the role of putative PTM cross-talks in a chromatin context. However, generating chromatin substrates of defined modification status has proved to be a technically challenging task. In this thesis, I first demonstrate our work on establishing a novel approach to produce libraries of modified nucleosomes. We employed protein trans-splicing and sortase-mediated ligation strategies to incorporate chemical modifications on histone tails of ‘ligation-ready’ nucleosomes. Subsequently, the ‘designer’ nucleosome libraries were used for testing the binding of heterochromatin protein 1 (HP1) and elucidated the previously uncharacterized crosstalk of H3K9me2 and S28ph marks. Further investigations explained the mechanism of this crosstalk and highlighted the importance of developing chemical biology tools for elucidating complex chromatin signaling. Second, I describe our reconstitution systems for the assembly of semisynthetic recombinant chromatin carrying methylation marks on DNA and distinct modifications on histones, e.g. H3K9me3. I aimed to understand the mechanisms of the interplay between chromatin and one of the DNA maintenance methylation factors, UHRF1. I showed that UHRF1 strongly interacts with nucleosomes containing linker DNA. However, it exerts only residual enzymatic activity in this context. Based on functional H3 ubiquitylation assays in vitro, I found that hemi-methylated nucleosomes stimulate enzymatic activity of UHRF1, suggesting that the protein’s chromatin targeting and activation are a two-step process. The positioning of hemi-methylated CpG on nucleosome regulates UHRF1 target selectivity. Further, mutational analysis revealed that the PHD domain of the factor is indispensable for H3 binding and that its SRA domain is required for catalytic activation. Overall, our work adds a new layer of positional complexity to the me½CpG-dependent regulation of UHRF1 and expands the current model of DNA methylation maintenance.
• #### Impacts of polyaromatic hydrocarbons (PAHs) on oligotrophic tropical marine organisms and food-chains

(2022-04) [Dissertation]
Committee members: Duarte, Carlos M.; Daffonchio, Daniele
Polyaromatic hydrocarbons (PAHs) are oil derived toxic and persistent pollutants prevalent across the oceans from pelagic waters to coral reefs. The Great Barrier Reef (GBR) in Australia and the Red Sea are important oligotrophic marine ecosystems susceptible to oil contamination. This Ph.D. dissertation aims to advance our understanding on PAH tolerance, accumulation dynamics and trophic transfer in oligotrophic ecosystems where those aspects remain poorly explored. In this dissertation, a new, highly-sensitive method combining stable carbon isotope labelling and cavity ring-down spectroscopy (CRDS) was developed to quantify PAH accumulation and applied in a series of ex situ food chain experiments with two representative PAHs, 13C-phenanthrene and 13C-pyrene. The experiments conducted with Acropora millepora – a common reef-building coral in the GBR, showed faster accumulation of both PAHs by dissolved uptake, although dietary exposure caused more consistent accumulation. Phenanthrene was not toxic to the coral photosystem II in either exposure mode but biomagnification increased with increasing food-chain complexity. In contrary, pyrene led to loss of symbionts accompanied by reduction in photosynthetic efficiency and coral bleaching, especially via dietary uptake. Also, microbial communities and food webs are relevant components of oligotrophic waters. We identified contrasting sensitivities among key autotrophic and heterotrophic microbial populations in the chronically oil exposed Red Sea to a mixture of 16 PAHs recognized as priority pollutants. The differential tolerance pointed towards localized selection for resistant strains in some populations. Some PAH toxicity thresholds approached ambient PAHs concentrations suggesting that any increase in pollution loads will hold consequences for these important microbial groups and their ecological functions.
• #### Novel Misfit Functions for Full-waveform Inversion

(2022-04) [Dissertation]
Committee members: Keyes, David E.; Ravasi, Matteo; Fomel, Sergey
The main objective of this thesis is to develop novel misfit functions for full-waveform inversion such that (a) the estimation of the long-wavelength model will less likely stagnate in spurious local minima and (b) the inversion is immune to wavelet inaccuracy. First, I investigate the pros and cons of misfit functions based on optimal transport theory to indicate the traveltime discrepancy for seismic data. Even though the mathematically well-defined optimal transport theory is robust to highlight the traveltime difference between two probability distributions, it becomes restricted as applied to seismic data mainly because the seismic data are not probability distribution functions. We then develop a misfit function combining the local cross-correlation and dynamic time warping. This combination enables the proposed misfit automatically identify arrivals associated with a phase shift. Numerical and field data examples demonstrate its robustness for early arrivals and limitations for later arrivals.%, which means that a proper pre-processing step is still required. Next, we introduce differentiable dynamic time warping distance as the misfit function highlighting the traveltime discrepancy without non-trivial human intervention. Compared to the conventional warping distance, the differentiable version retains the property of representing the traveltime difference; moreover, it can eliminate abrupt changes in the adjoint source, which helps full-waveform inversion converge to geologically relevant estimates. Finally, we develop a misfit function entitled the deconvolutional double-difference measurement. The new misfit measures the first difference by deconvolution rather than cross-correlation. We also present the derivation of the adjoint source with the new misfit function. Numerical examples and mathematical proof demonstrate that this modification makes full-waveform inversion with the deconvolutional double-difference measurement immune to wavelet inaccuracy.
• #### BUILDING BETTER AQUEOUS ZINC BATTERIES

(2022-03-22) [Dissertation]
Committee members: Lai, Zhiping; Bakr, Osman; Chen, Wei
Aqueous zinc ion storage system has been deemed as one of the most promising alternatives due to its high capacity of zinc metal anode, low cost, and high safety characteristics. Recently, significant attempts have been made to produce highperformance aqueous Zn batteries. (AZBs) and great progress has been achieved. Yet there are a lot of issues still exist and need to be further optimized. In this thesis, we proposed several strategies to tackle these challenges and finally optimize the overall battery performance, including metal anode protection, cathode structural engineering, and rational electrolyte design. In the present thesis, we first developed the ZnF2 layer coated Zn metal anode via a simple plasma treatment method. The plasma treated Zn anode leads to dendrite-free Zn electrodeposition with lower overpotential. Density function theory calculation results demonstrate that the Zn diffusion energy barrier can be greatly reduced on the ZnF2 surface. Benefiting from these merits, the symmetric cell and full cell exhibited much improved electrolchemical performance and stability. Afterthen, We synthesised a layered Mg2+-intercalated V2O5 as the cathode material for AZBs. The large interlayer spacing reachs up to 13.4 A, allowing for efficient Zn2+ (de)insertion. As a result, the porous Mg0.34V2O5・nH2O cathodes can provide high capacities as well as long-term durability. We then recongnized that most of the parasitic side reactions are related to the aqueous electrolyte. We therefore further designed a hybrid electrolyte to realize the anode-free Zn metal batteries. It is demonstrated that in the presence of propylene carbonate, triflate anions are involved in the Zn2+ solvation sheath structure. The unique solvation structure results in the reduction of anions, thus forming a hydrophobic solid electrolyte interphase. Consequently, in the hybrid electrolyte, both Zn anodes and cathodes show excellent stability and reversibility. More importantly, we design an anode-free Zn metal battery, which exhibits good cycling stability (80% capacity retention after 275 cycles at 0.5 mA cm–2).
• #### Synthesis of 2D materials and their applications in advanced sodium ion batteries

(2022-03-22) [Dissertation]