Now showing items 21-40 of 2198

    • Face Image Editing using Generative Adversarial Networks

      Zhu, Peihao (2023-05) [Dissertation]
      Advisor: Wonka, Peter
      Committee members: Ghanem, Bernard; Hadwiger, Markus; Ritschel, Tobias
      This dissertation presents novel methods and applications for face image editing using Generative Adversarial Networks (GANs). GANs have significantly advanced the field of face image editing, and this work delves into the challenges and possibilities of this technology, with a focus on improving segmentationguided image editing, GAN embedding for real face editing, domain adaptation, and hairstyle transfer. The research introduces new techniques, including the Semantic Region-Adaptive Normalization (SEAN) block for GANs conditioned on segmentation masks, an improved GAN embedding algorithm, a single-shot domain adaptation method, and the Barbershop approach for hair transfer. The dissertation also proposes pose-invariant hairstyle transfer techniques for images with varying poses. These techniques are evaluated on multiple datasets, and results demonstrate significant improvements over existing state-of-the-art techniques. By providing a comprehensive understanding of GAN-based face image editing, this work contributes to the ongoing evolution of face image editing and GAN applications, paving the way for future research and development.
    • Artificial Intelligence Applications in Intrusion Detection Systems for Unmanned Aerial Vehicles

      Hamadi, Raby (2023-05) [Thesis]
      Advisor: Massoud, Yehia Mahmoud
      Committee members: Park, Shinkyu; Elatab, Nazek
      This master thesis focuses on the cutting-edge application of AI in developing intrusion detection systems (IDS) for unmanned aerial vehicles (UAVs) in smart cities. The objective is to address the escalating problem of UAV intrusions, which pose a significant risk to the safety and security of citizens and critical infrastructure. The thesis explores the current state of the art and provides a comprehensive understanding of recent advancements in the field, encompassing both physical and network attacks. The literature review examines various techniques and approaches employed in the development of AI-based IDS. This includes the utilization of machine learning algorithms, computer vision technologies, and edge computing. A proposed solution leveraging computer vision technologies is presented to detect and identify intruding UAVs in the sky effectively. The system employs machine learning algorithms to analyze video feeds from city-installed cameras, enabling real-time identification of potential intrusions. The proposed approach encompasses the detection of unauthorized drones, dangerous UAVs, and UAVs carrying suspicious payloads. Moreover, the thesis introduces a Cycle GAN network for image denoising that can translate noisy images to clean images without the need for paired training data. This approach employs two generators and two discriminators, incorporating a cycle consistency loss that ensures the generated images align with their corresponding input images. Furthermore, a distributed architecture is proposed for processing collected images using an edge-offloading approach within the UAV network. This architecture allows flying and ground cameras to leverage the computational capabilities of their IoT peers to process captured images. A hybrid neural network is developed to predict, based on input tasks, the potential edge computers capable of real-time processing. The edge-offloading approach reduces the computational burden on the centralized system and facilitates real-time analysis of network traffic, offering an efficient solution. In conclusion, the research outcomes of this thesis provide valuable insights into the development of secure and efficient IDS for UAVs in smart cities. The proposed solution contributes to the advancement of the UAV industry and enhances the safety and security of citizens and critical infrastructure within smart cities.
    • Optimization of UAV Locations in Multi-Cell VLC Networks

      Ibraiwish, Hussam (2023-05) [Thesis]
      Advisor: Alouini, Mohamed-Slim
      Committee members: Elatab, Nazek; Shihada, Basem
      Aerial base stations, which can be realized using unmanned aerial vehicles (UAVs) equipped with base station functionalities, have emerged as a promising solution to provide flexible and rapid network access in scenarios where conventional cellular networks face challenges with high traffic loads or insufficient coverage, especially in rural areas. Nevertheless, the increasing congestion of the radio frequency (RF) spectrum adversely affects the reliability and quality of service (QoS) of RF systems due to the elevated interference levels, which motivates the exploration of alternative electromagnetic spectrum bands. Recently, visible light communication (VLC), which uses light emitting diode (LED)--based luminaires to provide simultaneous illumination and communication, has been considered in a growing number of use cases, benefiting from the increasing deployment of LED-based lighting in a wide range of applications. This thesis investigates the optimal deployment of UAVs equipped with VLC technology to provide concurrent wireless service to multiple mobile users. We adopt a swarm intelligence approach and employ the particle swarm optimization (PSO) algorithm to design the trajectory of the UAVs; this enhances the data rate and fairness among the users while reducing the UAV power consumption due to their mobility. Following this, we also explore the potential of using VLC-enabled UAVs in precision agriculture systems, which can serve as dual-purpose devices that provide illumination and communication using LED-based grow lights. This can enhance the plants’ growth and support the wireless connectivity among various user nodes in the system, such as sensors, robots, and internet of things (IoT) devices. The advantages include achieving efficient resource utilization as grow lights simultaneously provide data communication and support plant growth and vegetation needs, especially in areas with limited sunlight. In addition, we highlight the impact of optimizing the UAV locations using simulations.
    • Towards Data-efficient Graph Learning

      Zhang, Qiannan (2023-05) [Dissertation]
      Advisors: Moshkov, Mikhail; Zhang, Xiangliang
      Committee members: Keyes, David E.; Elhoseiny, Mohamed; Liu, Huan
      Graphs are commonly employed to model complex data and discover latent patterns and relationships between entities in the real world. Canonical graph learning models have achieved remarkable progress in modeling and inference on graph-structured data that consists of nodes connected by edges. Generally, they leverage abundant labeled data for model training and thus inevitably suffer from the label scarcity issue due to the expense and hardship of data annotation in practice. Data-efficient graph learning attempts to address the prevailing data scarcity issue in graph mining problems, of which the key idea is to transfer knowledge from the related resources to obtain the models with good generalizability to the target graph-related tasks with mere annotations. However, the generalization of the models to data-scarce scenarios is faced with challenges including 1) dealing with graph structure and structural heterogeneity to extract transferable knowledge; 2) selecting beneficial and fine-grained knowledge for effective transfer; 3) addressing the divergence across different resources to promote knowledge transfer. Motivated by the aforementioned challenges, the dissertation mainly focuses on three perspectives, i.e., knowledge extraction with graph heterogeneity, knowledge selection, and knowledge transfer. The purposed models are applied to various node classification and graph classification tasks in the low-data regimes, evaluated on a variety of datasets, and have shown their effectiveness compared with the state-of-the-art baselines.
    • Hybrid Two-Dimensional Nanostructures For Battery Applications

      Bayhan, Zahra (2023-05) [Dissertation]
      Advisor: Alshareef, Husam N.
      Committee members: Ooi, Boon S.; Mohammed, Omar F.; Ho, Ghim Wei
      The increased deployment for renewable energy sources to mitigate the climate crisis has accelerated the need to develop efficient energy storage devices. Batteries are at the top of the list of the most in-demand devices in the current decade. Nowadays, research is in full swing to develop a battery that meets the needs of today’s renewable energy systems, which are intermittent by nature. Within the framework of improving the performance of batteries, there are parameters in the composition of the battery that play an important role in its performance: electrode materials, electrolytes, separators, and other factors. The key to battery development is the manufacture of electrode materials with optimal properties. Two-dimensional (2D) materials have led to advances in this field, firstly, using graphite as the anode in lithium-ion batteries (LIBs). However, when using the standard graphite as the anode for sodium-ion batteries (NIBs), the large ionic size and energetic instability of Na+ limit intercalation, resulting in a low storage capacity. Therefore, other 2D materials with large interlayer spacing need to be identified for use as electrodes. In this dissertation, our approach is focus on optimizing anode electrode materials by in situ conversion of 2D materials to obtain hybrid materials. These hybrids materials will synergistically improve the performance of LIBs and NIBs by combining the advantages of individual 2D materials. Starting with converted Ti0.87O2 nanosheets to the TiO2/TiS2 hybrid nanosheets. Then, taking advantage of the properties of MXene, we developed hybrid electrodes based on MXenes by converted V2CTx MXene into V2S3@C@V2S3 heterostructures. Finally, we boosted the redox kinetics and cycling stability of Mo2CTx MXene by using a laser scribing process to construct a multiple-scale Mo2CTx/Mo2C-carbon (LS-Mo2CTx) hybrid material.
    • Towards Understanding the Canonical Strigolactone Metabolism in Rice

      Chen, Guan-Ting Erica (2023-05) [Thesis]
      Advisor: Al-Babili, Salim
      Committee members: Blilou, Ikram; Gojobori, Takashi
      Strigolactones (SLs) exert various biological functions as a plant hormone, best known for regulating shoot architecture and inhibiting branching/tillering, and as rhizospheric signaling molecules released by plant roots to attract symbiotic arbuscular mycorrhizal fungi (AMF), particularly under phosphate starvation; however, released SLs are also sensed by seeds of root parasitic weeds, such as Striga hermonthica, which depend on and abuse them as a germination signal ensuring the presence of a suitable host in the close vicinity. There are around 35 natural SLs divided, based on their structure, into two groups: canonical and non-canonical SLs. Albeit the progress that has been recently made in SL biology, the question about the biological background of the structural diversity of SLs remained largely elusive. However, identifying particular function(s) of each class of SLs is very important, since it may allow engineering of specific traits, increasing mycorrhization efficiency and/or alleviating the infestation by root parasitic plants. To overcome the experimental difficulties due to the relative low concentration and instability of SLs, we organized a reproducible protocol that helps the identification and quantification of SLs. With the developed tools, we then further looked into the SL biosynthetic pathway in rice and demonstrated in vivo evidence showing 9-cis-β-apo-10ʹ-carotenal as the SL precursor. Moreover, we generated CRISPR-Cas9 rice mutants disrupted in the gene OsMAX1-1400, which encodes 4-deoxyorobanchol hydroxylase responsible for the conversion of the canonical 4-deoxyorobanchol into orobanchol, and Osmax1-1400/900 double mutant lacking canonical SL biosynthesis. Analysis of Osmax1-1400 mutants showed that they accumulate 4- deoxyorobanchol, among other changes in SL pattern. The mutants did not have the high-tillering expected for SL-deficient mutants, but showed unexpected phenotypes in root, shoot and panicle growth. Analysis of the Osmax1-900/1400 double mutant and exogenous application of 4-deoxyorobanchol suggested that these phenotypes are caused by an over-accumulation of 4- deoxyorobanchol, which negatively affected the symbiosis with AMF. Finally, we summarized and discussed recent advances in understanding the biological background of the structural diversity of the plant hormone SLs, drawing attention to important open questions for future research.
    • Producing and characterizing nanobodies for the detection of Zika and Dengue viruses

      Alqatari, Atheer (2023-05) [Thesis]
      Advisor: Arold, Stefan T.
      Committee members: Alsulaiman, Dana Z.; Inal, Sahika; Grunberg, Raik
      Early detection of illness is essential in preventing symptoms from escalating and infectious diseases from spreading. Electrochemical biosensors are a promis- ing tool in healthcare detection. Previously, the collaboration between the Arold and Inal labs has led to the design of organic electrochemical transistors (OECT) capable of rapidly detecting coronavirus in saliva by using nanobody constructs as biorecognition units. In this project, I aimed to prove the versatility of nanobody- functionalized OECT biosensors in detecting other relevant viruses, specifically, Zika and Dengue. Both viruses pose a risk to multiple populations around the world, including the Kingdom of Saudi Arabia. I designed and produced nanobod- ies that are reported to bind to the NS1 glycoprotein, which is released by Zika and Dengue into the blood of the patient. Then, I confirmed the binding of the nanobodies to their associated targets. I also developed a robotic liquid handling script to automate the biosensing operations. Ultimately, this project aims to support the design of a multiplex OECT biosensor for blood-borne pathogens.
    • Thermoset Matrices for Thermally Stable Organic Solar Cells through Green Solvent Process

      Wen, Yuanfan (2023-05) [Thesis]
      Advisor: Baran, Derya
      Committee members: Laquai, Frédéric; Heeney, Martin; Mohammed, Omar F.
      Organic solar cells (OSCs) have gained considerable attention from the scientific community in recent decades due to their remarkable power conversion efficiency (PCE), flexibility, and cost-effectiveness in producing large-area batteries. Despite the ongoing research efforts that have led to a PCE exceeding 19% for single-junction OSCs and surpassing 20% for multi-junction OSCs, the commercialization of these devices is hampered by their poor stability, reliance on specific additives, and the use of toxic solvents. To address these shortcomings, this study focuses on investigating the 3 * 3 thermosets matrix to facilitate the selection of precursors for in-situ crosslinking thermosets. Furthermore, in this study, we fabricated the devices using green solvents to narrow the gap between PCE and stability under environmentally friendly conditions. We utilized PTQ10: BTP-BO4Cl as the model system and employed tetrahydrofuran (THF) as an eco-friendly solvent. The research focused on examining the thermoset's glass transition temperature (Tg), modulus and morphology properties. The resulting cross-linked thermoset network has high-density hydrogen bonding and network grids, which helps to stabilize the morphology of the active layer. The findings indicated that selecting a thermoset with high Tg, high modulus (4-8 MPa), and good uniformity as an in-situ crosslinking additive would be beneficial. These results can guide the selection of universal in-situ crosslinking thermosets and aid in improving the stability of various organic electronic devices.
    • Engineering volatile isoprene production from the polyextremophilic red microalga Cyanidioschyzon merolae

      Abualsaud, Fatimah (2023-05) [Thesis]
      Advisor: Lauersen, Kyle J.
      Committee members: Saikaly, Pascal; Szekely, Gyorgy
      Isoprene is a 5-carbon volatile chemical monomer used in the production of various industrial products. Its current sourcing is primarily through petrochemical-based processes, but there is growing interest in developing more sustainable and bio-based production methods. This has led to the exploration of genetic engineering techniques to enable isoprene biosynthesis in photosynthetic microorganisms that convert carbon dioxide into their biomass and concomitantly, isoprene. This work focused on the use of the polyextremophilic red microalga, Cyanidioschyzon merolae strain 10D, as a candidate for heterologous isoprene synthesis. Through synthetic biology-enabled custom designed plasmid transformation and foreign transgene expression, a heterologous isoprene synthase (IspS) from sweet potato was successfully expressed from the nuclear genome of C. merolae and the translated protein was targeted to the algal plastid. In this work, transformant C. merolae strains were grown in a co-cultivation with related Galdieria sulphuraria, a mixotrophic Cyanidiophyceae, here fed with glucose to deliver respiratory CO2 to C. merolae when grown together in closed gas-chromatography vials. Through this strategy, the heterologous generation of isoprene could be quantified from transformants. It was determined that IspS-YFP fusions produced a higher isoprene yield compared to IspS-chloramphenicol selection marker fusions. The best isoprene-producing transformant was used to monitor the isoprene yield over 9-days of cultivation, reaching 231 mg/L culture on d9. This work represents a first-of-its-kind genetic engineering in red microalgae. The successful production of isoprene from this microalga could pave the way for the development of new and sustainable industrial applications for this commodity chemical.
    • 3D Room Layout Estimation from One Single RGB Panorama

      Lu, Jichen (2023-05) [Thesis]
      Advisor: Wonka, Peter
      Committee members: Hadwiger, Markus; Michels, Dominik L.
      3D Indoor room layout estimation refers to the reconstruction of a 3D layout of a room from a single RGB panoramic image. In this work, we focus on rooms that fit the Manhattan assumption; for each corner of the room, all three composed planes are orthogonal to each other. This paper proposes a new network for indoor room 3D layout estimation based on detection tasks and bin regression, utilizing the encoder-decoder architecture to embed feature and boundary query tokens separately. We also add the deformable attention mechanism to enable our network to extract information from muti-scale features, significantly increasing performance. Besides, several different loss terms have also been experimented with and compared with each other. The proposed network has been trained and tested on the Zillow dataset. Compared with previous SOTA work, our network has surpassed the previous 3D reconstruction accuracy with fewer parameters and fewer training epochs.
    • Molecular Bases of Salinity Resistance via Intrinsic Disordered Protein (IDP)

      Yuan, Xukun (2023-05) [Thesis]
      Advisor: Jaremko, Lukasz
      Committee members: Arold, Stefan T.; Tester, Mark A.
      Salt-affected soil is a prominent challenge in agriculture. Nowadays, more than 800 million hectares of land (about 6% of the world’s total land area) are induced with high salt concentrations, and thus, are unsuitable for growing typically salt-sensitive crop plants. The ongoing salinization of arable land exacerbates this limitation. To address this issue, the development of salinity-tolerant crop plants has gained considerable interest, with a protein identified by Prof. Mark Tester's group, named "SALTY2," offering promising potential. SALTY2 is overexpressed in response to NaCl treatment on Salicornia plants conferring salinity tolerance, and following the function of the SALTY2 protein from Salicornia and analogous proteins in Arabidopsis, yeast and in vitro, a universal mechanism in evolution is suggested. During my thesis, we analyzed the biophysical properties of SALTY2, and based on spectroscopic methods we confirmed it is an intrinsic disordered protein (IDP), which is consistent with previous studies claiming that IDPs play a vital role in stress response pathways. We have identified and characterized the loss-of-function "RG/RGG" to "KG/KGG" type mutation and a deltaSTM1 N-terminal mutation, and investigated the interaction of SALTY2 and other cellular components, including short fragment RNA, and 80S ribosome. Together with state-of-the-art high-resolution NMR and Cryo-EM methods we validated the direct interaction of SALTY2 with plant ribosomes, and 25nts random RNA, and determined the 3D structure of ribosome with the potential binding site of the SALTY2 protein. Combining biophysical, structural and functional analyses of the wild-type and loss-of-function mutants of SALTY2, we proposed a potential mechanism by which the IDP protein SALTY2 confers salinity tolerance in plants. These findings offer a deeper understanding of the molecular basis of salinity tolerance in plants via IDPs and contribute to the ongoing efforts to develop salinity-tolerant crop plants.
    • Learning 3D structures for protein function prediction

      Muttakin, Md Nurul (2023-05) [Thesis]
      Advisor: Hoehndorf, Robert
      Committee members: Ombao, Hernando; Elhoseiny, Mohamed
      Machine learning models such as AlphaFold can generate protein 3D conformation from primary sequence up to experimental accuracy, which gives rise to a bunch of research works to predict protein functions from 3D structures. Almost all of these works attempted to use graph neural networks (GNN) to learn 3D structures of proteins from 2D contact maps/graphs. Most of these works use rich 1D features such as ESM and LSTM embedding in addition to the contact graph. These rich 1D features essentially obfuscate the learning capability of GNNs. In this thesis, we evaluate the learning capabilities of GCNs from contact map graphs in the existing framework, where we attempt to incorporate distance information for better predictive performance. We found that GCNs fall far short with 1D-CNN without language models, even with distance information. Consequently, we further investigate the capabilities of GCNs to distinguish subgraph patterns corresponding to the InterPro domains. We found that GCNs perform better than highly rich sequence embedding with MLP in recognizing the structural patterns. Finally, we investigate the capability of GCNs to predict GO-terms (functions) individually. We found that GCNs perform almost on par in identifying GO-terms in the presence of only hard positive and hard negative examples. We also identified some GO-terms indistinguishable by GCNs and ESM2-based MLP models. This gives rise to new research questions to be investigated by future works.
    • Epigenetic transcriptional memory of thermal stress in the cnidarian model system Aiptasia

      Dix, Mascha (2023-05) [Thesis]
      Advisor: Aranda, Manuel
      Committee members: Orlando, Valerio; Cui, Guoxin
      Ocean warming is leading to increased occurrence of coral mass bleaching events, threatening the persistence of these ecosystems and the communities that rely on them. While reef recovery is possible, conservation approaches based purely on transplantation/coral-gardening will not suffice to maintain these ecosystems over the projected environmental changes. Assisted evolution approaches aim to boost acclimatization and adaptation processes. A potential approach could be to harness the naturally occurring mechanism of environmental memory that has been observed in corals and other organisms, where an organism remembers a priming stress event to allow a faster/stronger response when the stress re-occurs. In this thesis I aimed to investigate whether this mechanism exists and how it is regulated on a molecular level in the sea anemone Aiptasia. Aiptasia were primed to heat stress by exposing them to 32 °C water for several years, or for one week. After a recovery period of one week at 25 °C, a naïve and the primed treatments were exposed to lethal thermal stress at 34 °C for three days. Primed treatments performed better than the naïve treatment in survival, photosynthetic efficiency and symbiont density for two days, after which the priming advantage was lost. The difference between the primed treatments indicated that the priming dose may affect priming success. There were clear indications of an epigenetic transcriptional memory mechanism on a transcriptional level. I observed a pronounced difference between control and heat-stressed treatments, indicating that transcription returned to near baseline expression after cessation of the priming exposure. The functional categories of differentially expressed genes in heat stress relative to control were similar between naïve and primed treatments, with the main difference observed in a stronger up- and downregulation of stress response genes in the long-term primed treatment. I optimized a chromatin immunoprecipitation protocol for use with Aiptasia by adjusting fixation, sonication and immunoprecipitation conditions. The enrichment of H3K4me2/me3 and poised RNA Pol II in the promoters of stress response genes will be investigated next to elucidate the mechanism of the observed epigenetic transcriptional memory in Aiptasia, and to ultimately inform conservation strategies for coral reefs globally.
    • De novo assembly of the Haloxylon persicum genome as a part of the KSA Native Genome Project

      Bantan, Alamin M. (2023-05) [Thesis]
      Advisor: Wing, Rod Anthony
      Committee members: Tester, Mark A.; Aranda, Manuel
      Haloxylon persicum is a xerophytic desert tree that grows mostly in deserts in West and Central Asia. This tree is very tolerant to the harsh conditions of deserts, mainly drought and heat. As a part of the Kingdom of Saudi Arabia Native Genome Project, a voucher specimen was identified, and the genome of this plant was sequenced, assembled, and annotated. The chromosome level assembly was performed using the integration of PacBio Hifi reads and Bionano optical maps, resulting in 9 chromosome-sized molecules that only exhibit 3 gaps located in highly repetitive regions. The annotation of the transposable elements in the genome shows that more than 55% of the genome consists of transposable elements. Moreover, genes were predicted using Iso-seq and RNA-seq and annotated using publicly available protein databases, resulting in the identification of more than 45,000 predicted genes, of which ≈ 10,000 have RNA evidence. The genome assembly and annotation of Haloxylon persicum will: provide valuable insight on the evolutionary history of desert plants, aid in discovering the mechanisms developed by this species to cope with the extreme desert conditions and unveil the possibilities and opportunities of neo-domesticating this plant. Furthermore, this assembly can serve as a reference for assembling other plant species in the KSA Native genome project or any other project worldwide.
    • Targeted Protein Degradation by Nanobody-UBX Degraders (NUDs)

      Wang, Jun (2023-05) [Thesis]
      Advisor: Arold, Stefan T.
      Committee members: Habuchi, Satoshi; Lauersen, Kyle J.
      Targeted protein degradation refers to a technique that selectively eliminates specific proteins within cells. This can be achieved by recruiting E3 ligases to specific proteins, which label the target with ubiquitin and lead to their eventual degradation by the proteasome. Cdc48/p97, a highly conserved AAA+ ATPase, is an essential player in the ubiquitin-proteasome system. It works as an unfoldase and segregase, unfolding ubiquitinated proteins and driving their removal from protein complexes, chromatin, or membranes to be sent for proteasomal degradation. Cdc48/p97 cooperates with many UBX domain-containing adaptor proteins, which use their UBX domain to bind to Cdc48/p97 and enable it to perform various functions. To expand on previous targeted protein degradation techniques, we designed nanobody-UBX degraders (NUDs) that recruit Cdc48/p97 to unfold target proteins without requiring ubiquitination. By establishing in vitro assays, we have uncovered insights into the mechanisms of substrate unfolding by Cdc48 in conjunction with NUDs and its endogenous adaptors Ufd1-Npl4 and PUX proteins. Our findings suggest the potential for a novel targeted protein degradation strategy that leverages the unfolding mechanism of Cdc48/p97 and its interactions with adaptors.
    • Application of radar-based technologies in real-world scenarios

      Saifullin, Karim R. (2023-05) [Thesis]
      Advisor: Alouini, Mohamed-Slim
      Committee members: Al-Naffouri, Tareq Y.; Hadwiger, Markus
      The privacy issue is an important topic nowadays. With that, some real-world application requires working with such kind of data. For example, surveillance, or prayer tracking and assistance system. In this thesis, we develop a framework based on radar technologies, that can satisfy the privacy requirements, and provide enough capabilities to meet the system's demands. The usage of radar for some kind of surveillance is on the rise, but there is no research done for prayer tracking and assistance. Also, there is no full descriptive work that covered all aspects of the implementation of such a system. This thesis provides a tool to develop a prayer tracking and assistance system. This approach can be used for a broad variety of scenarios. The main pillar of the system is the classification algorithm. We developed and compared different algorithms and found that convolutional neural networks provide the best results with accuracy on completely unknown data up to 98.2 percent.
    • Enhancing the migration and engraftment of human and mouse long-term hematopoietic stem cells

      Al-Amoodi, Asma S. (2023-05) [Dissertation]
      Advisor: Merzaban, Jasmeen
      Committee members: Li, Mo; Blilou, Ikram; Dimitroff, Charles J.
      For over 50 years, bone marrow transplants have used CD34 to select stem cells. Recent research suggests that the most primitive hematopoietic stem cells (HSCs), long-term HSCs (LT-HSCs), are found in the CD34-negative portion of murine and human bone marrow cells. LT-HSCs are rare and cannot be isolated directly, making them difficult to study. During a bone marrow transplant, these stem cells must find their way to the bone marrow niche and engraft to become blood cells. Several cell adhesion molecules on the stem cell engage with their ligands on the endothelial cells lining the bone marrow vasculature to control this migration. Human LT-HSCs cells do not migrate and engraft well when infused in vivo, which may be due to a lack of adhesion molecules. Thus, the goal of this study was to determine whether this population of HSCs lacked adhesion systems (proteins and carbohydrate modifications) and, if so, to improve their migration and engraft ability by modifying key mechanistic steps in the adhesion cascade. Therefore, we investigated how distinct hematopoietic stem cell populations migrate to the bone marrow using adhesion mechanisms. This study represents the first direct analysis of adhesion molecules expression in LT-HSC and will potentially shed light on methods to optimally use these very valuable cells in the clinical bone marrow and cord blood transplants worldwide.
    • An Experimental Assessment of the Performance of Islanding Detection Techniques

      Alsabban, Maha (2023-05) [Thesis]
      Advisor: Ahmed, Shehab
      Committee members: Park, Shinkyu; Konstantinou, Charalambos
      The increase in solar energy installation capacity and the versatility of modern power inverters have enabled widespread penetration of distributed generation in modern power systems. Islanding detection techniques allow for fast identification and corrective action in the face of abnormal events. Current standards specify the operational limits for voltage, frequency, and detection time. Grid codes specify the procedures for disconnection to establish safe network maintenance conditions. Passive, active, and remote techniques require voltage, current, and frequency measurements and the definition of thresholds for detection. Operational parameters such as load mismatch and quality factors influence the detection capabilities. False-positive triggering due to grid transients can lead to unnecessary disconnection of distributed generation resources. Cybersecurity threats pose a critical challenge for power systems and can result in significant operational disruptions and security risks. In particular, when a power system initiates communication links between different nodes or ends, it becomes more vulnerable to various forms of cyber-attacks. As such, it is imperative to address the potential cybersecurity risks associated with communication links. Through a literature review, this work analyzes the performance of several islanding detection techniques and proposes a modified 9-bus benchmark system to verify the robustness of passive and active methods against false-positive detections upon severe grid-side transients. Furthermore, this thesis conducts a detailed analysis of cyber-attacks on the remote islanding detection technique, using a real-time simulator to assess the potential impact of such attacks on the technique's effectiveness by simulating various attack scenarios. The findings of this analysis can help power system operators to better protect their systems from cyber-attacks and ensure the reliable operation of their distributed generation resources. Moreover, it discusses a conceptual implementation of hardware-in-the-loop testing. The modeling of the systems is discussed. Guidelines and international standards are presented. Various setups for experimental work are suggested and implemented.
    • Novel Network Architectures for Under-Connected Environments

      Matracia, Maurilio (2023-05) [Dissertation]
      Advisor: Alouini, Mohamed-Slim
      Committee members: Shihada, Basem; Ahmed, Shehab; Dhillon, Harpreet S.
      During the last decade, the average mobile wireless data usage per person has tremendously increased. An even faster growth of the traffic demand is expected for the incoming years, due to several factors such as the increasing global population, the spread of the Internet of things (IoT), and the development of advanced technologies that require a higher amount of data. While mobile communication technologies have rapidly evolved to meet this need in the most usual situations, it is expected that the sixth generation (6G) of mobile connectivity will be the first one paying considerable attention to under-connected environments such as low-income, remote, or disaster-struck regions. Many specialized researchers and entrepreneurs are trying to design and implement alternative network architectures specifically meant for enhancing the performances of the current telecommunication (telecom) infrastructure. In particular, the use of aerial base stations (ABSs) has received considerable attention due to the main advantages of easy deployability and low-cost that are typical of unmanned aerial vehicles (UAVs), which are available in several fashions depending on the application; moreover, UAVs are also eligible to carry reflective intelligent surfaces (RISs), which represent a promising technology that allows to reflect signals towards specific directions. Another possibility that we have investigated consists in integrating the transceivers inside or atop existing rural wind turbine (WT) towers, in order to increase the coverage radius while avoiding the cost of building a separate telecom infrastructure. A powerful mathematical tool for evaluating the performance metrics of either terrestrial, aerial, or vertical heterogeneous wireless networks is stochastic geometry (SG), since it can be used to model the locations of the base stations (BSs) according to tractable spatial distributions (with either a fixed or a random cardinality) in order to imitate the typical deployments of the nodes made in realistic scenarios; in particular, in this work we focus on rural and post-disaster situations. SG makes use of point processes to model networks' topologies. The developed spatial models, in turn, allow us to analyze the quality of service (QoS) experienced by the typical user served by the proposed networks. To this extent, we creatively and efficiently studied our inhomogeneous systems by making use of what we call the indicator method, meaning that we do not subdivide the ground plane in multiple homogeneous sub-regions, but we use indicator functions to provide general expressions that are valid over the entire ground plane. To prove the effectiveness of the novel architectures, insightful comparisons with the conventional ones are presented.
    • Hypothalamic Control of Visual Processing

      Andejani, Noor (2023-05) [Thesis]
      Advisor: Ibrahim, Leena Ali
      Committee members: Magistretti, Pierre J.; Frøkjær-Jensen, Christian
      Sensory overload is the feeling of over-stimulation that may lead to increased anxiety and panic in individuals with psychiatric disorders such as autism, post traumatic stress disorder, etc. Understanding visual processing is crucial to enhancing our treatments for disorders where sensory overload is a symptom. How do changes in internal states such as stress or hunger alter visual processing? This project aims to explore how visual processing is affected by signaling in the hypothalamus, an area of the brain regulating changes in internal states and stress. Preliminary studies revealed there are a number of neurons projecting from the lateral area of the hypothalamus to the visual cortex. We want to understand the specific location, identity, and neural circuits of these neurons. Visual cortex neurons were retrogradely traced to identify which inputs originate from the hypothalamus, and the geographical location of these cells was mapped out. The molecular identities of these projection neurons was further explored using specific RNAScope probes to check if those cells are expressing any of four genes most commonly expressed in the hypothalamus: Gal, Crh, Hcrt, and Pmch. This exploration will help us understand the type of signals communicated from the hypothalamic nuclei to the visual cortex to modulate visual processing.