Characterization of a Novel Nuclear Specific Dicer-isoform in Human Cells(2019-09) [Thesis]
Advisor: Orlando, Valerio
Committee members: Froekjaer Jensen, Christian; Al-Babili, SalimFor more than a decade, studies focused on RNA interference (RNAi) pathway as a pivotal gene regulatory mechanism. RNAi components are attracting considerable interest due to the recent evidence demonstrating that they play a role not only in post-transcriptional regulation but also in transcriptional level. The involvement of RNAi components in heterochromatin formation and RNA Pol II processivity and alternative splicing in different organisms has been shown. Dicer protein, a highly conserved protein among kingdoms, is one of the main effectors in this pathway. There is a considerable amount of literature on Dicer’s role in the cytoplasm; however, there is still vast ambiguity concerning nuclear Dicer. More recent evidence reveals the existence of Dicer1 variants that are differentially expressed in some cancer cells. Our experiments set out to investigate one of these variants that we hypothesise is responsible for the nuclear function. We undertook genomic and biochemical approaches applied to HAP 1 cells as a model system to characterise Dicer1-s, taking advantage of a custom-made antibody in our research group. Here, as anticipated, our experiments proved that Dicer1-s is enriched in the nuclear compartment compared to full-length Dicer1, indicating that it might be a putative contributor to nuclear gene regulation activity. Unfortunately, it was not possible to establish a mutant cell line to investigate the significant nuclear function of Dicer1-s, due to the need for further optimisation of the methods used. Exploitation of previously optimised gene knock-out tools might accelerate shedding light on protein, DNA, and RNA partners, disclosing the exact nuclear mechanisms that might exhibit similar activity.
Automatic Protein Function Annotation Through Text Mining(2019-08-25) [Thesis]
Advisor: Hoehndorf, Robert
Committee members: Moshkov, Mikhail; Bajic, Vladimir B.The knowledge of a protein’s function is essential to many studies in molecular biology, genetic experiments and protein-protein interactions. The Gene Ontology (GO) captures gene products' functions in classes and establishes relationship between them. Manually annotating proteins with GO functions from the bio-medical litera- ture is a tedious process which calls for automation. We develop a novel, dictionary- based method to annotate proteins with functions from text. We extract text-based features from words matched against a dictionary of GO. Since classes are included upon any word match with their class description, the number of negative samples outnumbers the positive ones. To mitigate this imbalance, we apply strict rules before weakly labeling the dataset according to the curated annotations. Furthermore, we discard samples of low statistical evidence and train a logistic regression classifier. The results of a 5-fold cross-validation show a high precision of 91% and 96% accu- racy in the best performing fold. The worst fold showed a precision of 80% and an accuracy of 95%. We conclude by explaining how this method can be used for similar annotation problems.
Facade Segmentation in the Wild(2019-08-19) [Thesis]
Advisor: Wonka, Peter
Committee members: Alouini, Mohamed-Slim; Thabet, Ali KassemFacade parsing is a fundamental problem in urban modeling that forms the back- bone of a variety of tasks including procedural modeling, architectural analysis, urban reconstruction and quite often relies on semantic segmentation as the first step. With the shift to deep learning based approaches, existing small-scale datasets are the bot- tleneck for making further progress in fa ̧cade segmentation and consequently fa ̧cade parsing. In this thesis, we propose a new fa ̧cade image dataset for semantic segmenta- tion called PSV-22, which is the largest such dataset. We show that PSV-22 captures semantics of fa ̧cades better than existing datasets. Additionally, we propose three architectural modifications to current state of the art deep-learning based semantic segmentation architectures and show that these modifications improve performance on our dataset and already existing datasets. Our modifications are generalizable to a large variety of semantic segmentation nets, but are fa ̧cade-specific and employ heuris- tics which arise from the regular grid-like nature of fac ̧ades. Furthermore, results show that our proposed architecture modifications improve the performance compared to baseline models as well as specialized segmentation approaches on fa ̧cade datasets and are either close in, or improve performance on existing datasets. We show that deep models trained on existing data have a substantial performance reduction on our data, whereas models trained only on our data actually improve when evaluated on existing datasets. We intend to release the dataset publically in the future.
Development of Solution Processed Co-planar Nanogap Capacitors and Diodes for RF Applications Enabled Via Adhesion Lithography(2019-08-18) [Thesis]
Advisor: Anthopoulos, Thomas D.
Committee members: Laquai, Frédéric; McCulloch, IainFabrication process of capacitors and Schottky diodes with nanogap electrodes is explained in this Thesis. The Schottky diode is made with IGZO in the nanogap, whereas the capacitor is made with ZrO2 in the nanogap which acts as the dielectric. Moreover, the electric characterization of both the diode and capacitor was obtained for different frequencies and different diameters. The end result showed that as the frequency increases the diode performance increases, but the capacitance of the capacitors decreases. Also, the barrier height and concentration were obtained using the Mott-Schottky plot for different frequencies. The 10MHz had the highest carrier concentration (5.9E+18cm-3) and barrier height (1V).
Closing the Lab-to-Fab Gap with Inkjet-Printed Organic Photovoltaics(2019-08-08) [Thesis]
Advisor: Baran, Derya
Committee members: Inal, Sahika; Shamim, AtifInkjet printing promises to be an invaluable technique for processing organic solar cells with key advantages such as low material consumption, freedom of design and compatibility with different types of flexible substrates making it suitable for large-area production. However, one concern about inkjet printed organic solar cells is the common use of chlorinated solvents during the ink formulation process. While chlorinated solvents suit the inkjet printing process due to their high boiling points, suitable viscosity, and excellent solubility of organic donor and acceptor compounds, they still pose some risks for both human health and the environment, excluding them from being the ultimate choice for large-area production. As a step towards commercialization of OPV, we demonstrated the possibility to close the laboratory to fabrication gap, through the engineering of environmentally friendly inks, using a blend of non-halogenated benzene derivatives solvents optimized to meet the viscosity and surface tension requirements for the inkjet printing process. Starting from using the non-fullerene acceptor O-IDTBR combined with the commercially available donor polymer P3HT we obtained solar cell device with efficiency up to 4.73% - the best efficiency achieved by the P3HT:O-IDTBR system processed with all non-halogenated solvents via inkjet printing. We also delivered highly transparent active layer with device power conversion efficiency of up to 10% with a highly efficient blend of polymer donor PTB7-Th in combination with the ultranarrow band gap NFA IEICO-4F, using hydrocarbons solvent. Lastly, we demonstrated both high efficiency, transparency, and stability by presenting a novel approach based on NFAs consisting of lowering the donor:acceptor ratio in the photoactive layer ink formulations, resulting in more stable devices with comparable power conversion efficiencies to those achieved by lab methods. This breakthrough in ink engineering paves the way in closing the lab-to-fab gap in organic photovoltaic using the low-cost, high throughput inkjet printing technology while considering both environmental and health-conscious mass production and device stability of organic photovoltaics.
Expression of EZH1-Polycomb Repressive Complex 2 and MALAT1 lncRNA and their Combined Role in Epigenetic Adaptive Response(2019-08-04) [Thesis]
Advisor: Orlando, Valerio
Committee members: Orlando, Valerio; Arold, Stefan T.; Al-Babili, SalimLiving cells maintain stable transcriptional programs while exhibiting plasticity that allows them to respond to environmental stimuli. The Polycomb repressive complex 2 (PRC2) is a key regulator of chromatin structure that maintains gene silencing through the methylation of histone H3 on lysine 27 (H3K27me), establishing chromatin-based memory. Two variants of PRC2 are present in mammalian cells, PRC2-EZH2 which is predominantly present in differentiating cells, and PRC2-EZH1 that predominates in post-mitotic tissues. PRC2-EZH1α/β pathway is involved in the response of muscle cells to oxidative stress. Atrophied muscle cells respond to oxidative stress by enabling the nuclear translocation of EED and its assembly with EZH1α and SUZ12. Here we prove that the metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) long noncoding RNA (lncRNA) is required for the assembly of PRC2-EZH1 components. The absence of MALAT1 significantly decreased the association between EED and EZH1α proteins. Biochemical analysis shows that the presence of MALAT1 increases the enzymatic activity of PRC2-EZH1 in vitro. In addition, we show that the simultaneous expression of PRC2 core components is necessary for their solubility. The successful expression of PRC2 proteins enables the execution of several downstream experiments, which will further explain the nature of the interplay between MALAT1 and PRC2.
Design and Real-time Implementation of Model-free Control for Solar Collector(2019-08) [Thesis]
Advisor: Laleg-Kirati, Taous-Meriem
Committee members: Ahmed, Shehab; Kammoun,Alba; Diagne, MamadouThis work addresses the design and real-time implementation of adaptive control strategies on the parabolic solar collector to enhance the production efficiency under varying working conditions. For example, the unpredictable variations of the solar irradiance and thermal losses, these factors can be a major problem in the control design. The control objective is to force the outlet temperature of the collector fluid, to track a predefined reference temperature regardless of the environmental changes. In this work, two control strategies have been designed and analyzed. First, an intelligent proportional-integral feedback control, which combines the proportionalintegral feedback control with an ultra-local model is proposed. This strategy uses a transfer function model that has been derived and identified from real-time data and used to test the controller performance. Second, an adaptive nonlinear control using Lyapunov stability theory combined with the phenomenological representation of the system is introduced. This strategy uses a bilinear model derived from the heat transfer equation. Both control strategies showed good performance in the simulations with respect to the convergence time and tracking accuracy. Besides, the conventional proportional-integral controller has been successfully implemented in the real system.
Optimizing a Selective Whole Genome Amplification (SWGA) Strategy for Clinical Malaria Infections(2019-08) [Thesis]
Advisor: Pain, Arnab
Committee members: Habuchi, Satoshi; Blilou, IkramPlasmodium is a genus well known for causing malaria, a life-threatening infection for many people where malaria is endemic. The blood-borne disease is transmitted by the female Anopheles mosquito. Till date, eight parasite species have been reported to cause malaria in humans that include P. falciparum, P. vivax, P. malariae, P. ovale curtisi, P. ovale wallikeri, P. cynomolgi, P. knowlesi and more recently P. simium. Amongst them, the most genetically understood species is P. falciparum, causing most of the deaths in children from malaria. Understanding genome variation at the population level of all malaria species is of utmost importance, including clinical cases with very low parasitemia. To achieve this purpose, we need sufficient amounts of parasite DNA material from the pool of host DNA, which always is overrepresented in clinical infections. We utilized a strategy of selective whole genome amplification (SWGA) technology on P. malariae and P. ovale curtisi (two neglected human infecting malaria parasites that often cause mild yet clinically relevant infections with low parasitemia) to efficiently enrich their genomic DNA for high-quality whole genome sequencing. Previous studies on SWGA applied on P. falciparum and P. vivax showed that SWGA could efficiently enrich the amount of starting DNA material from inadequate amounts of parasites directly from clinical samples without separating the host DNA using specifically designed primer sets. We have successfully designed multiple sets of primers and tested the efficiency of five best primer sets using polymerase chain reaction to enrich the genomes of P. malariae and P. ovale curtisi. The efficiency of primers in enriching the genome was tested on two clinical samples for each of P. malariae and P. ovale curtisi. We were able to enrich the genome of P. malariae with an average of 19-fold (19X) enrichment across both samples. For P. ovale curtisi, we could achieve an enrichment of 3 folds only. Nevertheless, we still obtained a sufficient amount of gDNA to prepare Illumina sequencing libraries and call for SNPs and Indels in a biologically reproducible manner at genome-scale.
Leader-Follower Approach with an On-board Localization Scheme for Underwater Swarm Applications(2019-08) [Thesis]
Advisor: Shamma, Jeff S.
Committee members: Shamma, Jeff S.; Ooi, Boon S.; Jones, BurtonA striking feature of swarm robotics is its ability to solve complex tasks through simple local interactions between robots. Those interactions require a good infrastructure in communication and localization. However, in underwater environments, the severe attenuation of radio waves complicates communication and localization of di erent vehicles. Existing literature on underwater swarms use centralized network topology which require physical vicinity to the central node to ensure reliability. We are interested in building a decentralized underwater swarm with a decentralized network topology that only requires neighbour communication and self-localization. We develop a simple leader-follower interaction rule where the follower estimates the leader's position and acts upon that estimation. The leader shines a 450 nm di racted blue laser that the follower uses to continuously align its light sensors to the light source. Furthermore, the leader's laser can be modulated for explicit communication purposes. The proposed leader-follower approach produces satisfactory results in surge and sway axes, however, it is not robust against illumination changes in the environment. We then proceed to solve the self-localization problem, by fusing Inertial Measurement Unit (IMU) values with the thrust to estimate a robot's position. In an Ardusub Simulation in the loop (SITL), the particle lter showed a slightly better performance than the Extended Kalman Filter (EKF) in the surge axis. However, both lters are prone to drifting after a while. We have observed that IMU values need to be ltered properly or another reliable sensor must be used alternatively.
Monte-Carlo Based Laminar Flame Speed Correlation for Gasoline(2019-08) [Thesis]
Advisor: Farooq, Aamir
Committee members: Roberts, William Lafayette; Ombao,HernandoGasoline is a complex fuel containing hundreds of species, and, therefore, it is quite difficult to model all components present in gasoline. Alternatively, researchers tend to employ simpler surrogates that mimic targeted physical and chemical properties of gasoline. Two properties of gasoline, i.e., autoignition and laminar flame speed, play key role in the overall performance of spark-ignition and modern engines. For fuel-engine optimization, it is very important to have simple models which can accurately predict autoignition and laminar flame speed of gasoline. In this work, universal laminar flame speed correlation is proposed for typical gasolines. This correlation is based on Monte-Carlo simulations of randomly generated mixtures comprising of 21 gasoline-relevant molecules. Laminar flame speed of each molecule is numerically computed over a wide range of thermodynamic conditions using detailed chemical kinetic models, while flame speed of each mixture is estimated using a mixing rule. The proposed universal correlation is validated against experimentally-measured laminar flame speed of various gasoline fuels.
Variations in reef-associated fish communities in response to different benthic states in the east central Red Sea(2019-08) [Thesis]
Advisor: Jones, Burton
Committee members: Berumen, Michael; Carvalho, SusanaCoral reefs are priority habitats which are vulnerable to natural and anthropogenic disturbances. These can cause phase shifts from coral habitat to degraded algal-dominated states – and consequent changes in the distribution, abundance and activity of associated fish species. In the eastern Red Sea, human-induced reef degradation is likely to increase with planned development of the Saudi Arabian coast and the changing climate. The present study therefore investigates the ecological effects of coral-algal phase shifts in reef-associated fish communities, using naturally occurring within-reef benthic zones as proxies for levels of habitat health - with a focus on how these responses differ temporally. These zones were dominated by: hard coral (coral zone), coral and turf algae (transition zone), and macroalgal canopies (algal zone). Six inshore reef areas, were studied in periods with low and high densities of Sargassum in the algal zones (May and November respectively). Community composition was assessed via visual census and predation activity predicted using two proxies: in situ experiments and biomass of carnivores. In both periods, we observed distinct fish communities in each zone - with reduced species richness, Shannon-Wiener diversity and predation intensity, from the coral to the algal zones. Decreases in the abundance and biomass of fish also occurred from the coral to algal zones in May but a spike, as well as a shift in community composition, occurred in the algal zone in November. This shift is attributed to the vast increases in grazer biomass, predominantly Siganus luridus, associated with the November bloom of Sargassum canopies. The present study established, the composition and functioning of Red Sea fish communities is spatially and temporally affected by increased macroalgal dominance. This finding supports the need for herbivorous fish to be made a conservation priority in the management and conservation of reef systems in order to prevent phase shifts to algal dominated states. We conclude that if Red Sea reefs are allowed to shift to alternate states, depending on the density of macroalgal canopies, reefs may support high biomass and abundance of fish but the functioning of the fish community will be altered and the diversity lost.
Comparative metabolic modeling and analysis of human pathogens(2019-08) [Thesis]
Advisor: Gojobori, Takashi
Committee members: Gao, Xin; Al-Babili, Salim; Bajic, Vladimir; Lewis, NathanInfectious diseases continue to be major health concerns worldwide. Although major advances have led to accumulation of genomic data about human pathogens, there clearly exists a gap between genome information and studies aiming at identifying potential drug targets. Here, constraint-based modeling (CBM) was deployed to integrate disparate data types with genome-scale metabolic models (GEMs) to advance our understanding of the pathogenesis of infectious agents with respect to identifying and prioritizing drug targets. Specifically, genome-scale metabolic modeling of multiple stages and species of Plasmodium, the causative agent of malaria, was used to prioritize potential drug targets that could be used to simultaneously treat (anti-malarials) and block transmission of the parasite. In addition, species-specific metabolic models were used to guide translation of findings from non-human experimental disease models to human-infecting species. Further, comparative analysis of the essentiality of metabolic genes for V. cholerae, the causative agent of cholera, growth and survival in single and co-infections with other enteric pathogens led to prioritizing conditionally independent essential genes that would be potential drug targets in both single and co-infection scenarios. Taken together, our findings highlight the utility of using genome-scale metabolic models to prioritize druggable targets that would be of broader spectrum against human pathogens.
Evaluating the regulation of signaling pathways downstream of CD44 antibody treatment in AML(2019-08) [Thesis]
Advisor: Merzaban, Jasmeen
Committee members: Liberale, Carlo; Blilou, IkramAcute myeloid leukemia (AML) is a subset of leukemia that is characterized by the clonal expansion of cytogenetically and molecularly abnormal myeloid blasts. These blasts are highly proliferative accumulating in bone marrow and blood which leads to severe infections, anemia, and bone marrow failure. The poor prognosis of AML patients caused by the low tolerance to intensive chemotherapy has encouraged the pursuit of alternative therapeutic approaches. Differentiation therapy which involves the use of agents that can release the differentiation block in these leukemic blasts has emerged as a promising therapeutic approach. The use of All-trans retinoic acid (ATRA) represents a successful example of such an approach, nonetheless its efficacy is restricted to one subtype of AML. Efforts have been focused on finding differentiation agents which are effective for the other more common AML subtypes. Anti-CD44 targeted antibodies that activate the CD44 cell surface antigen are a promising candidate. Previous studies have shown that anti-CD44 treatment has been able to release the differentiation block in AML1 through AML5 subtypes. The exact mechanism by which anti-CD44 treatment is able to induce its effects has not been fully elucidated. Recent studies highlight the role that epigenetic mechanisms play during haematopoiesis and leukemogenesis and therefore, in this work we investigated the epigenetic mechanisms associated with anti-CD44 induced differentiation. Using AML cell lines from different subtypes, we demonstrated that anti-CD44-induced differentiation results in an extensive change of histone modification levels. We found that inhibiting enzymes responsible for the H3K9ac, H3K4me, H3K9me, and H3K27me modifications, attenuated the anti-proliferative and differentiation promoting effects of antic-CD44 treatment. Taken together, these data highlight the promising potential of using anti-CD44 as a therapeutic agent across multiple subtypes in AML
Detection of Pre-ignition Events using Deep Neural Networks(2019-08) [Thesis]
Advisor: Sarathy, Mani
Committee members: Castano, Pedro; Ghanem, BernardAbstract: Engine downsizing and boosting have been some of the most widely used strategies for improving engine efficiency in recent years. Several studies have offered significant departures from on-road pre-ignition to steady-state engine laboratory studies, necessitating more robust data-driven diagnostic tools that can identify pre-ignition events in real world environments. The goal of this study is to apply deep neural networks for pre-ignition (PI) detection, based on scientific data obtained from less expensive sensors (like lambda and low-resolution exhaust back pressure (EBP) data), as a replacement for high resolution in-cylinder pressure measurements. Two deep neural network (DNN) models are proposed and applied for classification of 221,728 combustion cycles from 18 experiments with varying EBP. DNNs combined convolutional neural networks (CNNs) for detection of repetitive patterns in array-structured data, and recurrent neural networks (RNNs) for modelling in a temporal domain. The first model was fed data from the principal component analysis (PCA); the second model eliminated this step and was focused on time series input. As a performance metric, the area under the curve (AUC) of the receiving operating curve (ROC) was used for comparison of the two models. The model’s accuracy was tested on 44,305 cycles. Based on the AUC-ROC metric, the former model was better able to differentiate between pre-ignition and normal combustion cycles.
Transcriptome of Mycobacterium riyadhense in an in vitro Infection Model(2019-08) [Thesis]
Advisor: Pain, Arnab
Committee members: Habuchi, Satoshi; Blilou, IkramMycobacteria is a genus characterized by its unique layer of mycomembrane, which enhances its pathogenicity causing notorious infections such as tuberculosis or leprosy in humans. Some pathogenic mycobacteria are part of the Mycobacterium tuberculosis complex (MTBC), while others are predominantly environmental and belong to the class of non-tuberculosis mycobacteria (NTM). Some of the NTMs are also opportunistic pathogens causing infections mostly in immunocompromised individuals. In this study, we focus on a recently discovered species of NTM known as M. riyadhense, originally isolated from a patient with TB-like symptoms in Riyadh. With prepublication access to the completely assembled and fully annotated genomes of M. riyadhense, we wanted to study the gene expression of M. riyadhense after establishing an infection model using a murine macrophage cell line. We performed transcriptomic analysis of M. riyadhense upon infection using RAW264.7 murine macrophages to determine the hallmarks of differentially expressed (DE) genes at early infection time points. Most DE genes observed belong to one of the crucial secretion systems known as ESX-1. Most genes were highly upregulated during 12-hour of infection, particularly esxA and esxB, which encode for ESAT-6 and CFP-10 secretion proteins. These substrates are essential for the virulence and pathogenicity of Mycobacterium tuberculosis (MTB). In addition, we observed downregulation of WhiB5, a transcriptional regulator that is a well-known controller of Mycobacterium tuberculosis virulence and reactivation, and regulates genes encoding the constituents of two type VII secretion systems, namely, ESX-2 and ESX-4. We have also identified other genes of yet unknown function that are highly upregulated during early infection needing functional characterization in future follow-up studies. Overall, we have established an in vitro cell infection model for M. riyadhense that can be used to study host pathogen cross talks during infection processes in tubercle bacilli.
Single-Crystal Halide Perovskites for High Efficiency Photovoltaics(2019-07-27) [Thesis]
Advisor: Bakr, Osman
Committee members: Alshareef, Husam N.; Mohammed, Omar F.Lead halide perovskite solar cells (PSCs) are considered the fastest growing photovoltaic technology, reaching an outstanding certified power conversion efficiency of 24.2% in just 10 years. The best performing PSCs are based on polycrystalline films, where the presence of grain boundaries and ultra-fast crystallization limit the further development of their performance by increasing the bulk and surface defects. Compared to their polycrystalline counterparts, single crystals of lead halide perovskites have been shown to possess much lower trap-state densities and diffusion lengths exceeding 100𝜇m. In this thesis, using a solution space-limited inverse temperature crystallization method, twenty-microns thick single crystals of MAPbI3 are grown directly on the charge selective contact to construct highly reproducible p-i-n inverted type solar cells with fill factors(FF) as high as 84.3% and power conversion efficiencies (PCEs) exceeding 21% under 1 sun illumination (AM 1.5G). A key requisite for high PCEs is avoiding surface hydration, in which moisture attacks the perovskite/transporting layer interface and causes a significant decrease in short-circuit current. These solar cells set a record for single crystal PSCs, and highlight the potential of single crystal PSCs in furthering perovskite photovoltaic technology.
Interactive Exploration of Objective Vortex Structures in Unsteady Flow using Observer Fields(2019-07) [Thesis]
Advisor: Hadwiger, Markus
Committee members: Hadwiger, Markus; Wonka, Peter; Moshkov, MikhailSuccessful characterization of vortex structures in unsteady flow fields depend crucially upon an adequate choice of a reference frame. Vortex detection approaches in flow visualization aspire to be objective, i.e., invariant under time-dependent rotations and translations of the input reference frame. However, objectivity by itself does not guarantee good results as different specific approaches lead to different results. Moreover, recent more generic approaches to objectivity still require parameters to be specified beforehand which can significantly influence the resulting vortex detection, depending on the complexity and characteristics of the input flow field. With the assumption that human intervention is unavoidable to some extent, we tackle the problem of specifying parameters for vortex detection from a human-centered perspective. In this work, we present a novel system that enables users to interactively explore the parameter space of a flexible objective method, while jointly computing and visualizing the resulting vortex structures. We build on the computation of an objective field of reference frames and enable users to interactively change computation parameters as well as choose different observers, compute vortex structures on-the-fly during exploration, and visualize the flow field from the viewpoint of the chosen observers. Overall, we illustrate that such an interactive approach can be of significant value to the user for analyzing vortex structures visually and understanding why a computational method has detected a specific structure as a vortex.
Simultaneous Ligwave Information and Power Transfer ht(SLIPT)(2019-07) [Thesis]
Advisor: Salama, Khaled N.
Committee members: Salama, Khaled N.; Alouini, Mohamed-Slim; Ooi, Boon S.; Baran, DeryaHarvesting energy became one of the most prominent research topics around the world, not only for research institutes and universities but also for technology companies as well. Mainly focused on internet of things (IoT) applications, harvesting energy is a crucial factor for reducing costs that come with the use of batteries and increasing the devices’ working time. Simultaneous lightwave information and power transfer is a technique that seeks to use wireless optical communication to achieve both fundamental objectives in modern communication systems. This work presents the main techniques that are used to achieve SLIPT, a novel circuit that improves the standard methods and applications employing this circuit.
Determining Signaling Pathways involved in Migration of Hematopoietic Stem Cells upon binding of E-selectin(2019-07) [Thesis]
Advisor: Merzaban, Jassmeen S.
Committee members: Hauser, Charlotte; Blilou, IkramE-selectin is a transmembrane endothelium adhesion protein involved in rolling, arrest and migration of leukocytes as well as in the metastasis of many cancer types. Previous reports suggested that the interactions between E-selectin and its ligands transduce signals into migrating leukocytes and in E-selectin expressing endothelial cells. This study investigates the signaling pathways involved in E-selectin binding to ligands on leukocytes. Using recombinant soluble E-selectin constructs, we simulated the binding of E-selectin to its ligand(s) to reveal important signaling pathways triggered upon these interactions in acute myeloid leukemia (AML) cells. Since phosphorylation is the major post-translational modification, we examined the changes in the phosphorylation profile in tyrosine residues. We found a time-dependent reduction in the phosphotyrosine levels upon E-selectin binding to the AML cell line, KG-1a. The results of this study revealed two tyrosine phosphatases with altered activity after E-selectin treatment. The first is a cytoplasmic, dual-specific, phosphatase known as PTEN which is involved in controlling cell survival and proliferation. The second is CD45, which is a major component of the leukocytes cell membrane responsible for antigen receptor signaling. A more global phosphoproteomics analysis in AML cells revealed large scale changes in the phosphorylation levels after E-selectin treatment. In particular, 2259 phosphorylated proteins were identified, 530 of which portray significant changes in their phosphorylation status. The majority of those proteins are related to nuclear functions and are involved in pathways crucial for the cell cycle. Knowing that E-selectin binding stimulates chemoresistance in cancer cells, the findings of this project can contribute to the identification of multiple pathways responsible for this phenomenon and help towards the development of drugs that may inhibit such pathways in controlling disease.
Swarm Localization and Control via On-board Sensing and Computation(2019-07) [Thesis]
Advisor: Shamma, Jeff S.
Committee members: Shamma, Jeff S.; Laleg-Kirati, Taous-Meriem; Ahmed, ShehabMulti-agent robotic system have been proved to be more superior in undertaking functionalities, arduous or even impossible when performed by single agents. The increased e ciency in multi agent systems is achieved by the execution of the task in cooperative manner. But to achieve cooperation in multi agent systems, a good localization system is an important prerequisite. Currently, most of the multi-agent system rely on the use of the GPS to provide global positioning information which su ers great deterioration in performance in indoor applications, and also all to all communication between the agents will be required which is not e cient especially when the number of agents is large. In this regard, a real-time localization scheme is introduced which makes use of the robot's on-board sensors and computational capabilities to determine the states of other agents in the multi agent system. This algorithm also takes the advantage of the swarming behaviour of the robots in the estimation of the states. This localization algorithm was found to produce more accurate agent state estimates as compared to a similar localization algorithm that does not take into account the swarming behaviour of the agents in simulations and real experiment involving two Unmanned Aerial Vehicles.