Recent Submissions

  • De novo assembly of the Tamarindus indica genome as part of the Kingdom of Saudi Arabia Native Genome Project

    Navarrete Rodriguez, Maria Eugenia (2022-08-10) [Thesis]
    Advisor: Wing, Rod Anthony
    Committee members: Blilou, Ikram; Merzaban, Jasmeen
    The Kingdom of Saudi Arabia Native Genome project aims to generate genomic resources for all the plants, animals, and associated microbiome species in the Kingdom. Tamarindus indica was pointed out by the MEWA as an endangered native species in the KSA and forms part of the first 15 plant species to be studied in the NGP. A voucher tree was identified in the Rijal Almaa region, from which leaf samples were collected. HMW DNA was extracted from this tissue and sequenced using CCS with the Pac-Bio Sequel II platform. The raw data obtained from the sequencing was assembled using HIFIASM, contaminant contigs were removed, and the 15 largest contigs were selected as the primary T. indica assembly. The genome sequence of Sindora glabra was used as reference guide for primary scaffolding, and T. indica optical maps were used for super-scaffolding. Secondary scaffolding utilized Hi-C data to produce a chromosome level assembly of the T. indica genome. Transposable element analysis and a preliminary annotation were performed on the final assembly. This project represents the first step in studying T. indica for the NGP. The final assembly can be used as a foundation for more genetic studies on this species, as a possible reference for other legume species from the Detarioideae family, and for Neo-domestication and reforestation. The pipeline developed for this project can also be used as a template for sequencing and assembling the remaining species in the NGP.
  • Xeno-pumice from Harrat Rahat: Understanding magma-crust interaction

    Garcia, Evelyn R. Garcia Paredes (2022-07-28) [Thesis]
    Advisor: Van der Zwan, Froukje M.
    Committee members: Jonsson, Sigurjon; Troll, Valentin R; van Buchem, Frans
    “Xeno-pumice” describes a pumice-like material, high in silica content and vesiculation, found as a xenolith in a more mafic rock. A xeno-pumice is an indicator of magma-crust interaction; however, the origin, nature, and processes behind this xenolith are still debated. Xeno-pumice has been described in a few places worldwide, including the Canary Islands in Spain, Indonesia, Iceland, the USA, Chile and Mexico. This thesis, for the first time, presents and analyzes the mineralogy, textural features, whole-rock geochemistry (major and trace element), and oxygen isotopes of xeno-pumice samples found in Harrat Rahat, Saudi Arabia. Harrat Rahat is a volcanic field whose last eruption was in 1256A.D. and reached the outskirts of Madinah, one of the main cities in Saudi Arabia. Harrat Rahat is characterized by a wide range of volcanic products: from basalts to trachyte. Previous studies suggested that this chemical variation has its source in the mantle and minor crustal contamination; however, the xeno-pumice samples found indicate magma-crust interaction. Thus, in this thesis, the crust-melt interaction hypothesis is addressed as a process that could modify the composition of the melt and thus the resulting volcanic products and eruptive style of the volcanic field. Indeed, the chemistry and oxygen isotope values of the studied volcanic rocks show a variation in composition, which is suggested to be the consequence of crust-melt interaction. The petrology, chemistry and oxygen isotope values suggest that the melt interacted with either the metamorphosed plutonic portion of the upper Arabian crust or with (meta-) sediments below Harrat Rahat. Finally, this thesis proved that magma-crust interaction occurred at Harrat Rahat, which has important implications for interpreting eruption mechanisms and mantle sources.
  • Establishment of 3D culture protocols for the maintenance and expansion of human pluripotent stem cell aggregates in a low scale platform and in the DASbox® Mini-Bioreactor System

    Hernandez-Bautista, Carlos Alberto (2022-07-27) [Thesis]
    Advisor: Adamo, Antonio
    Committee members: Merzaban, Jasmeen; Ibrahim, Leena Ali
    The human Embryonic Stem Cells (hESCs) and human induced Pluripotent Stem Cells (hiPSCs) have offered numerous advantages including but not limited to model diseases, high-throughput drug screening, and regenerative purposes. However, the employment of monolayer cultures has not been sufficient to mimic the in vivo stem cells niche. Thus, three-dimensional suspension cultures have helped us to advance our knowledge and ease the development of the human organs’ counterparts, commonly referred as organoids. Currently, the challenge is the generation of homogenous and reproducible human Pluripotent Stem Cell (hPSC) aggregates, the basic cellular unit to derive organoids. To date, the Ultra-Low Attachment (ULA) 6-well plates have been routinary used for the hPSC aggregates formation, which mainly relies on the inhibition of the Rho-associated kinase (ROCK) pathway resulting in the enhancement of cell survival coming from cryopreserved stocks or when passaging. However, little is known in this regard when analyzing the aggregate formation of hPSCs with two widely used compounds: RevitaCellTM Supplement and Y27632. Importantly, due to the high demand required from the regenerative medicine, I aimed to upscale the hPSC aggregates production in the DASbox® Mini-Bioreactor System. In this thesis, I established protocols for the hPSC aggregates formation by using two different types of media in two platforms being the ULA 6-well plates and the DASbox® Mini-Bioreactor System. In addition, I demonstrated that monolayer confluence cultures before single cell inoculations are paramount for the formation of bona fide hPSC aggregates in healthy and X aneuploid hiPSCs, precisely two hESCs and five hiPSCs.
  • Evaluative screening of kinetic models for simulating the performances of oxidative coupling of methane catalysts

    Gobouri, Abdullah (2022-07-27) [Thesis]
    Advisor: Castaño, Pedro
    Committee members: Ruiz-Martinez, Javier; Hauser, Charlotte
    In this work, multiple kinetic models have been screened as potential candidates for simulating the performances of three oxidative coupling of methane (OCM) catalysts. Two of the proposed models were subjected to testing and optimization. The types of models screened covered both kinetic and microkinetic type models, i.e., radical omitting and radical considering. Some of the models only accounted for catalytic heterogeneous pathways, while others have expanded on the homogeneous gas-phase mechanism of the OCM reaction. The optimization process was carried out in MATLAB® R2020a using an error minimization tool. The range of experimental conditions examined was as follows: 740–800◦C, 100 kPa, 2–4 CH4/O2 ratio, 1–6 gcat h molC –1 spacetime. The results show successful optimization of both models as well as discrepancies in terms of their performances in predicting experimentally obtained values of CH4 and O2 conversions, as well as selectivities towards COx and C2+ products. While a kinetic model served as an easy option to optimize, it expressed limits in terms of achievable performance, mainly failing to simulate experimental runs conducted at low spacetimes. A microkinetic model on the other hand, managed to simulate all experimental conditions, with less accuracy towards COx species and much greater computational demand.
  • Sars-Cov-2 Intra-Host Evolution in Immunocompromised Patients for the Emergence of Variants of Concerns, Including Omicron.

    Bantan, Azari I. (2022-07-21) [Thesis]
    Advisor: Gojobori, Takashi
    Committee members: Mineta, Katsuhiko; Pain, Arnab
    Unexpected high mutations detected in new emerging variants of concern (VOCs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), especially in the case of omicron, raises concerns and efforts to understand their evolutionary trajectory. Several hypotheses have been discussed in literature to conceptualize the source of their emergence, including intra-host viral evolution in immunocompromised patients. These patients grant opportunities for the emergence of new variants through a persisting virus winning against host immunity, and selection for viral mutations driven by treatment interventions. VOCs have in common high mutation rate exceeding the average rate of 1-2 mutations per month. Not many studies have investigated the evolutionary rate of SARS-CoV-2 in immunocompromised candidates. Therefore, the purpose of this study is to reveal potential mechanisms underlying the emergence of VOCs by exploring substitution rate of SARS-CoV-2 genomes from surveyed COVID-19 immunocompromised patient’s studies. First, SARS-CoV-2 genome sequences were collected at sequential time series throughout host infection, which were reported in the previous studies. Filtration criteria was applied to reanalyze patients with prolonged infection documented for ≥ 2 months, and comprehensive sequenced samples for ≥ 6 time points. Then, phylogenetic analysis was conducted using Nextclade (https://clades.nextstrain.org/), followed by mutation rate analysis using two substantial similar approaches to calculate the rate in i) substitutions per month and ii) substitutions per site (per year). The mutation tendency of SARS-CoV-2 in immunocompromised hosts was compared to reported VOCs, particularly to omicron. The highest observed mutation rate accounted for approximately 2.2 mutations per month, which is higher than the average rate. High mutation rate was due to prolonged infection and selection pressure by treatment interventions (i.e., convalescent plasma and antibodies). Here, higher rate of intra-host viral evolution in immunocompromised patients is detected, potentially leading to the emergence of VOC. Hence, this research highlights the need for sequencing efforts in high-risk individuals, updating treatment strategies along with further analysis on adaptive mutants pronounced due to intra-host evolution. Together, such findings provide an ultimate synergy for future public health guidelines and infection control measures.
  • De novo genome-scale prediction of protein-protein interaction networks using ontology-based background knowledge

    Niu, Kexin (2022-07-18) [Thesis]
    Advisor: Hoehndorf, Robert
    Committee members: Inal, Sahika; Moshkov, Mikhail
    Proteins and their function play one of the most essential roles in various biological processes. The study of PPI is of considerable importance. PPI network data are of great scientific value, however, they are incomplete and experimental identification is time and money consuming. Available computational methods perform well on model organisms’ PPI prediction but perform poorly for a novel organism. Due to the incompleteness of interaction data, it is challenging to train a model for a novel organism. Also, millions to billions of interactions need to be verified which is extremely compute-intensive. We aim to improve the performance of predicting whether a pair of proteins will interact, with only two sequences as input. And also efficiently predict a PPI network with a proteome of sequences as input. We hypothesize that information about cellular locations where proteins are active and proteins' 3D structures can help us to significantly improve predict performance. To overcome the lack of experimental data, we use predicted structures by AlphaFold2 and cellular locations by DeepGoPlus. We believe that proteins belonging to disjoint biological components have very little chance to interact. We manually choose several disjoint pairs and further confirmed it by experimental PPI. We generate new no-interaction pairs with disjoint classes to update the D-SCRIPT dataset. As result, the AUPR has improved by 10% compared to the D-SCRIPT dataset. Besides, we pre-filter the negatives instead of enumerating all the potential PPI for de-novo PPI network prediction. For E.coli, we can pass around a million negative interactions. To combine the structure and sequence information, we generate a graph for each protein. A graph convolution network using Self-Attention Graph Pooling in Siamese architecture is used to learn these graphs for PPI prediction. In this way, we can improve around 20% in AUPR compared to our baseline model D-SCRIPT.
  • Water Effect on the Methanol to Olefin Conversion over SSZ-13 Catalyst with an Operando Spectroscopy

    Alsindi, Mohammed (2022-07-18) [Thesis]
    Advisor: Ruiz-Martinez, Javier
    Committee members: Pinnau, Ingo; gomez-cabrero, David
    For more than 50 years, the methanol to olefins (MTO) reaction remains to be a hot topic within the catalysis community. The recent discoveries about it and the industrial implementation made it even receive more attention. The best way this process can be used is by hydrogenation of CO2 to make methanol and undergo after that the MTO reaction. This method will save energy, be more environmentally friendly, and be sustainable, but it requires advancement regarding carbon capture. The purpose of this paper is to understand the effect of water on SSZ-13 commercial zeolite when it is used for MTO reaction by a combination of gas chromatography (GC) analysis and operando UV – vis spectroscopy. It was observed that water with a ratio of 2:1 methanol to water would increase the lifetime of the catalyst from 3 h to 6.5 h, and the ratio of 1:1 would increase it to 9 h. However, a higher amount of water hadn’t been analyzed, but theoretically, it would cause dealumination to the zeolite invoking a different type of deactivation. This increase in catalyst lifetime was first due to the competitive adsorption between water and methanol; leading to a lower methanol reactivity toward methoxide formation. Second, because of the competition between water and propylene, it resulted in a longer induction period and a delay in the formation of hydrocarbon pool. Hence, less coke was formed from the reaction and more species were able to diffuse into the inner pores. Also, it was observed that ethylene selectivity increased with the addition of water to the feed. The UV-vis analysis proved the longer induction period and showed the formation of more species due to that. The deactivating materials were identified to be polyaromatic carbocation and phenanthrene, while the main activating species was methylated naphthalene carbocation. In addition, multiple characterization techniques, such as nitrogen physisorption, ammonia TPD, and SEM, were performed to understand the nature of the catalyst. It was found that it has weak and strong Bronsted acid sites, BET surface area of 665.7 m2/g, and crystal size of about 0.5 – 2 µm.
  • Benthic Habitat Mapping of Thuwal’s Reefs Using High-Resolution Acoustic Technologies and Imaging Data

    Watts, Marta A. Ezeta (2022-07-14) [Thesis]
    Advisor: Benzoni, Francesca
    Committee members: Berumen, Michael L.; Volker, Vahrenkamp
    Remote sensing studies based on satellite and aerial imagery have improved our understanding of the morphology and distribution of several shallow reefs along the Red Sea Arabian coast and of the benthic assemblages associated to them (Bruckner et al., 2011; Bruckner et al., 2012; Rowlands et al., 2016). However, data concerning the deeper benthic assemblages' composition and spatial distribution in the central Red Sea are still missing. Using high-resolution acoustic technology and an underwater remotely operated vehicle (ROV), we aim to map, describe, and classify the reefs found in Thuwal's coastal area, filling the information gap by producing the first benthic habitat map of this area and making progress towards the evaluation of shallow and upper mesophotic benthic resources in the Saudi Arabian Red Sea. High-resolution acoustic data was collected using a multibeam echosounder system, which generated a bathymetric model. Based on this, the seafloor features were classified into 12 morphotypes following a visual assessment. Based on the morphotypes classification, 28 sites were visually selected for ground-truthing data acquisition and characterization of the substrate and benthic assemblages using a remotely operated vehicle equipped with an ultra-short baseline (USBL) positioning system. With the information obtained from the bathymetry data and the ROV video transects, a Top-Down approach in which we analyzed, categorized, and classified the data was used to create Thuwal's reefs benthic habitat map in which 23 different benthic habitat types were identified. This research uncovered previously poorly studied reef morphologies in the Red Sea and their associated benthic assemblages. Moreover, this work will help improve the understanding of the spatial distribution of benthic communities located on Thuwal's reefs, giving a baseline with the potential to provide fundamental information that can be used for mapping, management, conservation, and future research at other Red Sea reef sites in Saudi Arabia.
  • Fast, Robust, Iterative Riemann Solvers for the Shallow Water and Euler Equations

    Muñoz-Moncayo, Carlos (2022-07-12) [Thesis]
    Advisor: Ketcheson, David I.
    Committee members: Tzavaras, Athanasios; Truscott, T. T.
    Riemann problems are of prime importance in computational fluid dynamics simulations using finite elements or finite volumes discretizations. In some applications, billions of Riemann problems might need to be solved in a single simulation, therefore it is important to have reliable and computationally efficient algorithms to do so. Given the nonlinearity of the flux function in most systems considered in practice, to obtain an exact solution for the Riemann problem explicitly is often not possible, and iterative solvers are required. However, because of issues found with existing iterative solvers like lack of convergence and high computational cost, their use is avoided and approximate solvers are preferred. In this thesis work, motivated by the advances in computer hardware and algorithms in the last years, we revisit the possibility of using iterative solvers to compute the exact solution for Riemann problems. In particular, we focus on the development, implementation, and performance comparison of iterative Riemann solvers for the shallow water and Euler equations. In a one-dimensional homogeneous framework for these systems, we consider several initial guesses and iterative methods for the computation of the Riemann solution. We find that efficient and reliable iterative solvers can be obtained by using recent estimates on the Riemann solution to modify and combine well-known methods. Finally, we consider the application of these solvers in finite volume simulations using the wave propagation algorithms implemented in Clawpack.
  • Estimation of Mercury Injection Capillary Pressure (MICP) from the Nuclear Magnetic Resonance (NMR) exponential decay with the Machine Learning (ML) Neural Network (NN) approach

    Ugolkov, Evgeny A. (2022-07-09) [Thesis]
    Advisor: Hoteit, Hussein
    Committee members: Santamarina, Carlos; Ahmed, Shehab
    Information about the capillary pressure has a wide range of applications in the petroleum industry, such as an estimation of irreducible water saturation, calculation of formation absolute permeability, determination of hydrocarbon-water contact and the thickness of the transition zone, evaluation of the seal capacity, and an estimation of relative permeability. All the listed parameters in the combination with petrophysical features, pressures, and fluid properties allow us to evaluate the economic viability of the well or the field overall. For this reason, capillary pressure curves are of great importance for petroleum engineers working on any stage of the field development: starting from exploration and finishing with production stages. Nowadays, capillary pressure experiments are provided either in the lab on the plugs of the rocks, either in the well on the certain stop points with the formation tester tools on the wire or tubes. Core extraction and formation testing are both laborious, expensive, and complicated processes since the newly-drilled well remain in the risky uncased condition during these operations, and for this reason, usually the listed works are provided in the exploration wells only. Afterward, the properties obtained from the exploration wells are assumed to be the same for the extraction or any other kinds of wells. Therefore, these days petroleum engineers have limited access to the capillary pressure curves: the modern tests are provided on the limited points of formation in the limited number of wells. An extension of capillary pressure measurements in the continuous mode for every well will dramatically expand the abilities of modern formation evaluation and significantly improve the field operation management by reducing the degree of uncertainty in the decision-making processes. This work is the first step toward continuous capillary pressure evaluation. Here we describe the procedure of finding the correlation between the results of the lab Nuclear Magnetic Resonance (NMR) experiment and lab Mercury Injection Capillary Pressure (MICP) measurements. Both experiments were provided on the 9 core plugs of the sandstone. Afterward, a Machine Learning (ML) algorithm was applied to generate additional samples of the porous media with different petrophysical properties representing the variations of the real cores of available sandstones. Overall, 405 additional digital rock models were generated. Thereafter, the digital simulations of MICP and NMR experiments were provided on the generated database of digital rocks. All the simulations were corrected for limited resolution of the CT scan. Based on the created database of experiments, we implemented a ML algorithm that found a correlation between the NMR echo data and MICP capillary pressure curves. Obtained correlation allows to calculate capillary pressure curve from the NMR echo data. Since NMR logging may be implemented in every well in the continuous mode, an extension of the created technique provides an opportunity for continuous estimation of capillary pressure for the whole logging interval. This extension is planned as future work.
  • An Experimental Investigation of Soot Formation in Laminar Inverse and Normal Diffusion Flames at Elevated Pressure

    Alsheikh, Ibrahim (2022-07-07) [Thesis]
    Advisor: Roberts, William L.
    Committee members: Sarathy, Mani; Hoteit, Hussein
    Hydrogen production from autothermal reforming (ATR) with Carbon Capture Utilization and Storage (CCUS) is gaining traction as prospect for a blue hydrogen economy. ATR is susceptible to catalyst poisoning and degradation from soot formation, which decrease H2 yield. In this work soot formation was examined thoroughly in conditions close to ATR, using an oxygen rich inverse diffusion flame (IDF) burner at elevated pressure. Normal diffusion flames (NDF) were also investigated against the same conditions to ultimately be compared alongside IDF. In NDF, soot formation and oxidation happen simultaneously, while in IDF soot oxidation is ignorable. Primary fuel was CO2 diluted methane, and the oxidizer stream has a 70%-by-mol O2 (30% N2) concentration. OH* chemiluminescence was used to find flame height against key parameters, PAH concentration and soot volume fraction were captured using Laser Induced Fluorescence (LIF) and Laser Induced Incandescence (LII) respectively. Key findings in this work were the dissimilarities between IDF and NDF against pressure and the effects of varying flame constituents on flame height in IDF.
  • Biophysical Characterization of the BIRD Complex and their Mode of Interaction

    Wang, Luyao (2022-07-06) [Thesis]
    Advisor: Arold, Stefan T.
    Committee members: Habuchi, Satoshi; Blilou, Ikram
    In Arabidopsis thaliana, the development and the defense system are precisely controlled by some proteins to allocate energy and resources as needed. JASMONATE-ZIM domain 3 protein is the repressor of the jasmonic acid defense pathway. JACKDAW (JKD), SHORTSHOOT (SHR), and SCARECROW (SCR) bind together to form the BIRD complex, which regulates root patterning. The transcription factor Teosinte branched1/Cycloidea/Proliferating cell factor 14 (TCP14) also regulates plant development. Recent data shows that JAZ3 and TCP14 interact with JKD and may form a ternary complex, which reveals the study of the five proteins mentioned above may help to understand how defense signals are interpreted during plant growth. The interactions of these five proteins provide a theoretical base to maximize plant fitness and increase crop yield. Using protein purification, microscale thermophoresis, isothermal titration calorimetry, negative staining, X-ray crystallography in this project, we identified JKD interacted with JAZ3, and JKD interacted with TCP14, but they could not form a ternary complex in vitro; SHR/SCR interacted with JAZ3. Those binding results suggests TCP14 and SHR/SCR may have very similar binding site of JKD, and JAZ3 may guide the degradation of the BIRD complex. In structural studies, we resolved the 2D class average that showed the outline of the BIRD complex and it potentially helped to identify how JKD bound to DNA. We also determined the crystal structure of the TCP14 domain, which was an intertwined dimer that possibly uses arginine residues in the N terminus to interact with DNA. These interaction and structure studies of the five proteins provide the basis to understand how defense signals affect plant development.
  • Predicting the future high-risk SARS-CoV-2 variants with deep learning

    Chen, NingNing (2022-07-04) [Thesis]
    Advisor: Gao, Xin
    Committee members: Gojobori, Takashi; Wang, Di
    SARS-CoV-2 has plagued the world since 2019 with continuously emergence of new variants, resulting in repeated waves of outbreak. Although the countermeasures like vaccination campaign has taken worldwide, the sophisticated virus mutated to escape immune system, threatening the public health. To win the race with the virus and ultimately end the pandemic, we have to take one step ahead to predict how the SARSCoV-2 might evolve and defeat it at the beginning of a new wave. Hence, we proposed a deep learning based framework to first build a deep learning model to shape the fitness landscape of the virus and then use genetic algorithm to predict the high-risk variants that might appear in the future. By combining pre-trained protein language model and structure modeling, the model is trained in a supervised way, predicting the viral transmissibility and antibodies escape ability to eight antibodies simultaneously. The prevenient virus evolution trajectory can be largely recovered by our model with high correlation to their sampling time. Novel mutations predicted by our model show high antibody escape through in silico simulation and overlapped with the mutations developed in prevenient infected patients. Overall, our scheme can provide insights into the evolution of SARS-CoV-2 and hopefully guide the development of vaccination and increase the preparedness.
  • Assessment of the Bacterial Growth Potential of Reverse Osmosis Produced Chlorinated Drinking Water

    Felix, Alejandra Ibarra (2022-07) [Thesis]
    Advisor: Vrouwenvelder, Johannes S.
    Committee members: Saikaly, Pascal; Burton, Jones; Farhat, Nadia
    Reverse Osmosis (RO) filtration is capable of producing high quality drinking water with an ultra-low nutrient level. Therefore, a very low bacterial growth potential (BGP). BGP is a key bioassay to evaluate microbial quality and the biological stability of drinking water. Current methods to assess BGP in drinking water need to be adapted to the wide variety of water types due to results could highly vary from each, providing unreliable insights to the biological stability of the assessed water. This study evaluates the application of an FCM-based BGP assay for RO produced chlorinated drinking water. The approach combines (i) the standardization of a quenching agent concentration, (ii) the impact of sample pre-treatment such as filtration and pasteurization on the BGP of RO produced chlorinated water, (iii) the effect of different inoculums (an indigenous community and a mixture with bottled water) on the bacterial growth and their longevity after being stored, (iv) the use of BGP to assess the performance of carbon filters in removing chlorine and (v) the use of BGP to assess the effect of the addition of magnesium on bacterial growth. The results showed that high concentrations of sodium metabisulphite (> 7.5 mg/L) decrease the pH levels of the water,thus, inhibiting bacterial growth. Filtration had a significant effect on BGP values (2.62 x10^5 intact cells/mL) in comparison to pasteurization (9.02 x 10^4 intact cells/mL), when compared to the control. Using a mixture of water types as inoculum might provide a better insight to bacterial growth potential in water due to a higher consumption of nutrients. BGP demonstrated to be a sensitive tool to test the performance of carbonfilters applied to remove chlorine and its applicability to evaluate the biological stability of RO produced chlorinated drinking water. The concentrations of magnesium chloride tested in this study did not have a significant effect on cell numbers.
  • The Effects of Gasoline Composition and Additive Concentration on the Lubricity of Gasoline Blends

    Al Ashkar, Youssef (2022-07) [Thesis]
    Advisor: Sarathy, Mani
    Committee members: Roberts, William L.; Szekely, Gyorgy
    Under current regulations, gasoline engines are facing lubricity and wear challenges that need to be met by enhanced gasoline lubricity. Gasoline lubricity can be enhanced by lubricity improvers such as heavy fatty acid methyl esters. This thesis presents the ‘High Frequency Reciprocating Rig’ (HFRR) tests carried out on a standardized tribological test rig as per a modified version of ASTM D6079, to account for the effects of volatility of gasoline. Testing 5 gasoline types (gasolines A-E) blended with 2 lubricity improver types (LI1-2) at 2 concentrations, 250 and 500 ppm, provided insights on the changes in lubrication behavior with different gasoline composition, LI type, and concentration. The gasoline types with higher aromatic content and average carbon number (lower volatility) resulted in less wear and better lubricity regardless of LI concentration. The highly aromatic gasoline “A” performed better with the fatty acid-based LI1. Gasolines “B-E”, which are less aromatic, resulted in less wear with the ester-based LI2. The decrease in wear volumes with LI2 was more pronounced with the highly volatile gasolines B and E. These insights were mainly challenged by the failure of some tests due to the high volatility of gasoline. To mitigate this effect and confirm the findings, less volatile gasoline surrogates were designed to mimic the composition of the gasoline types on functional group basis, and were blended with the same lubricity improvers, and then tested using the same method. This improved the results and showed that high aromaticity enhanced the lubricity of the gasoline blends, especially with fatty-acid based LI1, but degraded it beyond 50% aromatic content. The enhancement of lubricity with higher average carbon number was also highlighted. To create deeper understanding of the lubrication mechanisms involved, it is recommended to study the rheological properties of the blends, analyze the chemical composition of the deposits on the wear tracks, and repeat the tests with continuous supply of lubricant to further decrease the effect of gasoline volatility
  • Study of the Structure and Function of Rice De Novo Proteins

    Zheng, Yuanmin (2022-07) [Thesis]
    Advisor: Arold, Stefan T.
    Committee members: Inal, Sahika; Jaremko, Mariusz
    Proteins can form complex three-dimensional structures which allow them to perform many different functions, including efficient catalysis, sophisticated signal processing or dynamical cell restructuring. Hence, exploring the protein folding process is central to understand development, adaptation and disease states of organisms. It is becoming increasingly clear that proteins that arise “de novo” through translation of originally non-coding DNA regions play a significant role in the diversification and adaptation of species. However, the extent to which these de novo proteins can already form complex structures, and hence perform sophisticated functions, is unknown. In this study, bioinformatically predicted the structural features of 200 de novo sequence from rice, using AlphaFold and other computational tools. Based on the bioinformatic analytical results, we selected sequences for the small-scale screening and expression. Two proteins, de novo 18 and de novo 47 could be expressed recombinantly and experimentally analyzed using biophysical methods. Circular dichroism and Nuclear Magnetic Resonance measurements confirmed structural features in agreement with computational predictions. In addition, we designed protocols for the high-throughput analysis using robotics. Our results provide a stepping stone for comprehensive analysis of the structural landscape of de novo proteins, and raise the possibility that de novo proteins can produce more sophisticated folds through self-association. Thus, our work provides a hypothesis for the origin of complex protein folds that serve as a framework for complex protein functions.
  • Using LiDAR on a Ground-based Robotic Platform to Map Tree Structural Properties

    López Camargo, Omar Andrés (2022-07) [Thesis]
    Advisor: McCabe, Matthew
    Committee members: Johansen, Kasper; Jonsson, Sigurjon
    More efficient and reliable High-Throughput Field Phenotyping (HTFP) approaches are essential for the development of plant breeding and carbon storage studies, as well as the improvement of yield estimation in the food production sector. The use of ground-based platforms in combination with other data sources such as UAVs and satellites addresses constraints related to payload capacity restrictions and reduced below-canopy data collection. This study describes an early approach to the deployment of agile robots for HTFP that aims to estimate height, diameter at breast height (DBH), and volume for forty-three unique trees corresponding to two different species (E. variegata and F. altissima) occupying an urban-park. The data acquisition system consists of an agile robot from Boston Dynamics and a navigation enhancer LiDAR module from the same company. In order to obtain a point cloud using this system, it is necessary to overcome two challenges: a reduced vertical FoV of the LiDAR and limited management of the LiDAR module. A multiway registration approach is implemented to reconstruct a low-density digital twin of the experiment site, which is later georeferenced using points surveyed with a GNSS system. Subsequently, the point cloud is manually segmented using CloudCompare software to obtain individual tree point clouds. Three different algorithms are implemented to obtain height, DBH, and tree volume estimates from the individual point clouds. The results are statistically analyzed by species in order to characterize sources of error. The height estimation method had a Median Percentage Error (MPE) of 1.4% for E. variegata and 1.2% for F. altissima. The DBH estimation had an MPE of 20.1% for E. variegata and 13% for F. altissima. The volume estimation model returned an R2 of 0.86 for E. variegata and 0.98 for F. altissima. Finally, all three feature estimations are mapped into a GEOJson file. These findings, combined with the numerous advantages of using agile robots as mobile platforms in HTFP, enable more efficient and reliable estimation of important parameters such as aboveground biomass and carbon storage sequestration, as well as delivery the potential for improvements in crop monitoring and yield estimation.
  • Comparison of the NSD proteins dynamics and selectivity towards covalent inhibition

    Herrera Lozada, Bryan Daniel (2022-07) [Thesis]
    Advisor: Jaremko, Lukasz
    Committee members: Habuchi, Satoshi; Gallouzi, Imed
    Small-molecule drugs arise as a prospective area to treat different types of cancer. A promising target is the NSD protein family. These proteins have been related to cancers like myeloid leukemia, multiple myeloma, prostate, lung, and breast cancer. However, their treatment is limited to chemotherapy, radiotherapy, and surgical operation that could affect the patient's life quality. In 2020, Huang and collaborators developed a novel kind of inhibitor for NSD1 protein, BT5. This inhibitor covalently binds to the SET domain of the NSD family proteins. However, there is a high affinity for NSD1 than their counterparts. These proteins share a similar structure, but their dynamics could explain the affinity difference. In this project, we compare the NSD family protein dynamics by measuring NMR relaxation experiments. We identify a higher percentage binding for NSD1 and NSD3 to BT5 than NSD2. We also determine the perturbed chemical shifts under the presence of BT5 in NSD1, where the most affected regions are the SET and post-SET domain (auto-inhibitory loop) and the beginning region of the AWS domain. By comparing different NMR relaxation measurements, we identify that the three proteins share high dynamics in the auto-inhibitory loop region, especially in NSD1, and in the AWS domain for NSD1 and NSD3. These motions corresponds to the obtained results by adding BT5 in NSD1, which could indicated a relationship between the AWS dynamics and the auto-inhibitory loop, and the protein affinity.
  • Draft Assembly and Baseline Annotation of the Ziziphus spina-christi Genome

    Shuwaikan, Raghad H. (2022-07) [Thesis]
    Advisor: Wing, Rod Anthony
    Committee members: Krattinger, Simon G.; Zuccolo, Andrea
    Third generation sequencing has revolutionized our understanding of genomics, and enabled the in-depth discovery of complex plant genomes. In this project I aimed to assemble and annotate the genome of Z. spina-christi, a native plant to Saudi Arabia, as part of the the Kingdom of Saudi Arabia Native Genome Project established at the Center for Desert Agriculture at KAUST. Initially, a voucher plant was selected from the Al Lith region of Western Saudi Arabia. Fresh leaf tissue was collected for high-molecular weight (HMW) DNA extraction, as well as seed for greenhouse propagation. After HMW DNA extraction, library construction and PacBio HiFi sequencing, I generated a de novo assembly of the Z. spina-christi genome using the Hifiasm assembler, which yielded a 1.9 Gbp long assembly with high levels of duplication. The assembled contigs were scaffolded using an in-house script based on the software RagTag, that yielded a 406 Mbp long scaffold with 331 gaps (85.45% of estimated genome size). A preliminary analysis of the assembly for transposable elements revealed a TE content of 32.36%, with Long Terminal Repeats retrotransposons (LTR-RTs) being the major contributor to the total TE content. Basline annotation was completed using Omicsbox revealing 18,330 functional genes. This work describes the first genomic resource for the desert plant Z. spina-christi. To improve the assembly, I suggest the use of scaffolding using optical mapping, long Nanopore reads and Hi-C data to capture the spatial organization of the genome. Further experimental, genetic and TEs analysis is needed to explore the plant’s resilience to abiotic stresses in extreme environments.
  • Synthesis, Modification, and Evaluation of MXene as a Novel Anode Electrode Material for Bio-electrochemical Systems

    KOLUBAH, PEWEE DATOO (2022-07) [Thesis]
    Advisor: Castaño, Pedro
    Committee members: Szekely, Gyorgy; Saikaly, Pascal
    Bioelectrochemical systems (BESs) show promising prospects for recovering energy and chemicals from industrial and municipal wastewater. Despite the advances in the development of this technology, there is still a significant need for efficient electrode materials with high conductivity, hydrophilicity, and good biocompatibility to boost their performance and increase productivity. In this work, metal nanoparticle-two-dimensional MXene (W2N-Ti3C2Tx and Fe-W2N-Ti3C2Tx) composite electrocatalysts were synthesized using a simple impregnation method and deposited on carbon cloth (CC) to be used as a cheap and high performing anode in BESs. The plain CC, Ti3C2Tx-CC, W2N-Ti3C2Tx-CC, and Fe-W2N-Ti3C2Tx-CC were characterized using several techniques including scanning electron microscopy, transmission electron microscopy, water contact angle, and atomic force microscopy. The prepared anodes were tested in a single chamber air cathode microbial fuel cell inoculated with industrial wastewater for power generation and wastewater treatment simultaneously. The obtained results show that carbon cloth modified with W2N-Ti3C2Tx and Fe-W2N-Ti3C2Tx exhibited improved power density of 548 mW m-2 and 327 mW/m-2 with 81% and 44%, coulombic efficiency, respectively. The obtained power densities were 6 and 3.7 times higher, respectively than that achieved for pure carbon cloth (88 mW m-2). This study demonstrates the potential of combining two-dimensional MXene with metal nanoparticles to form an active composite anode catalyst for enhancing power generation and wastewater treatment using microbial fuel cells.

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