Matching Items (89)
Description
PD-L1 blockade has shown recent success in cancer therapy and cancer vaccine regimens. One approach for anti-PD-L1 antibodies has been their application as adjuvants for cancer vaccines. Given the disadvantages of such antibodies, including long half-life and adverse events related to their use, a novel strategy using synbodies in place of antibodies can be tested. Synbodies offer a variety of advantages, including shorter half-life, smaller size, and cheaper cost. Peptides that could bind PD-L1 were identified via peptide arrays and used to construct synbodies. These synbodies were tested with inhibition ELISA assays, SPR, and pull down assays. Additional flow cytometry analysis was done to determine the binding specificity of the synbodies to PD-L1 and the ability of those synbodies to inhibit the PD-L1/PD-1 interaction. Although analysis of permeabilized cells expressing PD-L1 indicated that the synbodies could successfully bind PD-L1, those results were not replicated in non-permeabilized cells. Further assays suggested that the binding of the synbodies was non-specific. Other tests were done to see if the synbodies could inhibit the PD-1/PD-L1 interaction. This assay did not yield any conclusive results and further experimentation is needed to determine the efficacy of the synbodies in inhibiting this interaction.
ContributorsMujahed, Tala (Author) / Johnston, Stephen (Thesis director) / Blattman, Joseph (Committee member) / Diehnelt, Chris (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The devastating 2014 Ebola virus outbreak in Western Africa demonstrated the lack of therapeutic approaches available for the virus. Although monoclonal antibodies (mAb) and other molecules have been developed that bind the virus, no therapeutic has shown the efficacy needed for FDA approval. Here, a library of 50 peptide based ligands that bind the glycoprotein of the Zaire Ebola virus (GP) were developed. Using whole virus screening of vesicular stomatitis virus pseudotyped with GP, low affinity peptides were identified for ligand construction. In depth analysis showed that two of the peptide based molecules bound the Zaire GP with <100 nM KD. One of these two ligands was blocked by a known neutralizing mAb, 2G4, and showed cross-reactivity to the Sudan GP. This work presents ligands with promise for therapeutic applications across multiple variants of the Ebola virus.
ContributorsRabinowitz, Joshua Avraam (Author) / Diehnelt, Chris (Thesis director) / Johnston, Stephen (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Both technological and scientific fields continue to revolutionize in a similar fashion; however, a major difference is that high-tech corporations have found models to continue progressions while still keeping product costs low. The main objective was to identify which, if any, components of certain technological models could be used with the vaccine and pharmaceutical markets to significantly lower their costs. Smartphones and computers were the two main items investigated while the two main items from the scientific standpoint were vaccines and pharmaceuticals. One concept had the ability to conceivably decrease the costs of vaccines and drugs and that was "market competition". If the United States were able to allow competition within the vaccine and drug companies, it would allow for the product prices to be best affected. It would only take a few small companies to generate generic versions of the drugs and decrease the prices. It would force the larger competition to most likely decrease their prices. Furthermore, the PC companies use a cumulative density function (CDF) to effectively divide their price setting in each product cycle. It was predicted that if this CDF model were applied to the vaccine and drug markets, the prices would no longer have to be extreme. The corporations would be able to set the highest price for the wealthiest consumers and then slowly begin to decrease the costs for the middle and lower class. Unfortunately, the problem within the vaccine and pharmaceutical markets was not the lack of innovation or business models. The problem lied with their liberty to choose product costs due to poor U.S. government regulations.
ContributorsCalderon, Gerardo (Author) / Johnston, Stephen (Thesis director) / Diehnelt, Chris (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.
ContributorsKumar, Pallav (Author) / Woodbury, Neal (Thesis advisor) / Allen, James (Committee member) / Johnston, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Improving the quality of Origin-Destination (OD) demand estimates increases the effectiveness of design, evaluation and implementation of traffic planning and management systems. The associated bilevel Sensor Location Flow-Estimation problem considers two important research questions: (1) how to compute the best estimates of the flows of interest by using anticipated data from given candidate sensors location; and (2) how to decide on the optimum subset of links where sensors should be located. In this dissertation, a decision framework is developed to optimally locate and obtain high quality OD volume estimates in vehicular traffic networks. The framework includes a traffic assignment model to load the OD traffic volumes on routes in a known choice set, a sensor location model to decide on which subset of links to locate counting sensors to observe traffic volumes, and an estimation model to obtain best estimates of OD or route flow volumes. The dissertation first addresses the deterministic route flow estimation problem given apriori knowledge of route flows and their uncertainties. Two procedures are developed to locate "perfect" and "noisy" sensors respectively. Next, it addresses a stochastic route flow estimation problem. A hierarchical linear Bayesian model is developed, where the real route flows are assumed to be generated from a Multivariate Normal distribution with two parameters: "mean" and "variance-covariance matrix". The prior knowledge for the "mean" parameter is described by a probability distribution. When assuming the "variance-covariance matrix" parameter is known, a Bayesian A-optimal design is developed. When the "variance-covariance matrix" parameter is unknown, Markov Chain Monte Carlo approach is used to estimate the aposteriori quantities. In all the sensor location model the objective is the maximization of the reduction in the variances of the distribution of the estimates of the OD volume. Developed models are compared with other available models in the literature. The comparison showed that the models developed performed better than available models.
ContributorsWang, Ning (Author) / Mirchandani, Pitu (Thesis advisor) / Murray, Alan (Committee member) / Pendyala, Ram (Committee member) / Runger, George C. (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges. Vulnerability assessment (VA) examines the potential consequences a system is likely to experience due to exposure to perturbation or stressors and lack of the capacity to adapt. Post-fire debris flow and heat represent particularly challenging problems for infrastructure and users in the arid U.S. West. Post-fire debris flow, which is manifested with heat and drought, produces powerful runoff threatening physical transportation infrastructures. And heat waves have devastating health effects on transportation infrastructure users, including increased mortality rates. VA anticipates the potential consequences of these perturbations and enables infrastructure stakeholders to improve the system's resilience. The current transportation climate VA—which only considers a single direct climate stressor on the infrastructure—falls short of addressing the wildfire and heat challenges. This work proposes advanced transportation climate VA methods to address the complex and multiple climate stressors and the vulnerability of infrastructure users. Two specific regions were chosen to carry out the progressive transportation climate VA: 1) the California transportation networks’ vulnerability to post-fire debris flows, and 2) the transportation infrastructure user’s vulnerability to heat exposure in Phoenix.
ContributorsLi, Rui (Author) / Chester, Mikhail V. (Thesis advisor) / Middel, Ariane (Committee member) / Hondula, David M. (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2022
Description
The emerging multimodal mobility as a service (MaaS) and connected and automated mobility (CAM) are expected to improve individual travel experience and entire transportation system performance in various aspects, such as convenience, safety, and reliability. There have been extensive efforts in the literature devoted to enhancing existing and developing new methodologies and tools to investigate the impacts and potentials of CAM systems. Due to the hierarchical nature of CAM systems and associated intrinsic correlated human factors and physical infrastructures from various resolutions, simply considering components across different levels into a single model may be practically infeasible and computationally prohibitive in operation and decision stages. One of the greatest challenges in existing studies is to construct a theoretically sound and computationally efficient architecture such that CAM system modeling can be performed in an inherently consistent cross-resolution manner. This research aims to contribute to the modeling of CAM systems on layered transportation networks, with a special focus on the following three aspects: (1) layered CAM system architecture with a tight network and modeling consistency, in which different levels of tasks can be efficiently performed at dedicated layers; (2) cross-resolution traffic state estimation in CAM systems using heterogeneous observations; and (3) integrated city logistics operation optimization in CAM for improving system performance.
ContributorsLu, Jiawei (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Xue, Guoliang (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2022
Description
Particulate Guanylyl Cyclase Receptor A (pGC-A) is an atrial natriuretic peptide receptor, which plays a vital role in controlling cardiovascular, renal, and endocrine functions. The extracellular domain of pGC-A interacts with natriuretic peptides and triggers the intracellular guanylyl cyclase domain to convert GTP to cGMP. To effectively develop a method that can regulate pGC-A, structural information regarding its intact form is necessary. Currently, only the extracellular domain structure of rat pGC-A has been determined. However, structural data regarding the transmembrane domain, as well as functional intracellular domain regions, need to be elucidated.This dissertation presents detailed information regarding pGC-A expression and optimization in the baculovirus expression vector system, along with the first purification method for purifying functional intact human pGC-A. The first in vitro evidence of a purified intact human pGC-A tetramer was detected in detergent micellar solution. Intact pGC-A is currently proposed to function as a homodimer. Upon analyzing my findings and acknowledging that dimer formation is required for pGC-A functionality, I proposed the first tetramer complex model composed of two functional subunits (homodimer). Forming tetramer complexes on the cell membrane increases pGC-A binding efficiency and ligand sensitivity.
Currently, a two-step mechanism has been proposed for ATP-dependent pGC-A signal transduction. Based on cGMP functional assay results, it can be suggested that the binding ligand also moderately activates pGC-A, and that ATP is not crucial for the activation of guanylyl cyclase. Instead, three modulators can regulate different activation levels in intact pGC-A.
Crystallization of purified intact pGC-A was performed to determine its structure. During the crystallization condition screening process, I successfully selected seven promising initial crystallization conditions for intact human pGC-A crystallization. One selected condition led to the formation of excellent needle-shaped crystals. During the serial crystallography diffraction experiment, five diffraction patterns were detected. The highest diffraction resolution spot reached 3 Å.
This work will allow the determination of the intact human pGC-A structure while also providing structural information on the protein signal transduction mechanism. Further structural knowledge may potentially lead to improved drug design. More precise mutation experiments could help verify the current pGC-A signal transduction and activation mechanism.
ContributorsZhang, Shangji (Author) / Fromme, Petra (Thesis advisor) / Johnston, Stephen (Committee member) / Mazor, Yuval (Committee member) / Arizona State University (Publisher)
Created2021
Description
Priced Managed Lanes (MLs) have been increasingly advocated as one of the effective ways to mitigating congestion in recent years. This study explores a new and innovative pricing strategy for MLs called Travel Time Refund (TTR). The proposed TTR provides an additional option to paying drivers that insures their travel time by issuing a refund to the toll cost if they do not reach their destination within specified travel times due to accidents or other unforeseen circumstances. Perceived benefits of TTR include raised public acceptance towards priced MLs, utilization increase of HOV/HOT lanes, overall congestion mitigation, and additional funding for relevant transportation agencies. To gauge travelers’ interests of TTR and to analyse its possible impacts, a stated preference (SP) survey was performed. An exploratory and statistical analysis of the survey responses revealed negative interest towards HOT and TTR option in accordance with common wisdom and previous studies. However, it is found that travelers are less negative about TTR than HOT alone; supporting the idea, that TTR could make HOT facilities more appealing. The impact of travel time reliability and latent variables representing psychological constructs on travelers’ choices in response to this new pricing strategy was also analysed. The results indicate that along with travel time and reliability, the decision maker’s attitudes and the level of comprehension of the concept of HOT and TTR play a significant role in their choice making. While the refund option may be theoretically and analytically feasible, the practical implementation issues cannot be ignored. This study also provides a discussion of the potential implementation considerations that include information provision to connected and non-connected vehicles, distinction between toll-only and refund customers, measurement of actual travel time, refund calculation and processing and safety and human factors issues. As the market availability of Connected and Automated Vehicles (CAVs) is prognosticated by 2020, the potential impact of such technologies on effective demand management, especially on MLs is worth investigating. Simulation analysis was performed to evaluate the system performance of a hypothetical road network at varying market penetration of CAVs. The results indicate that Connected Vehicles (CVs) could potentially encourage and enhance the use of MLs.
ContributorsVadlamani, Sravani (Author) / Lou, Yingyan (Thesis advisor) / Pendyala, Ram (Committee member) / Zhou, Xuesong (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation research is concerned with the study of two important traffic phenomena; merging and lane-specific traffic behavior. First, this research investigates merging traffic behavior through empirical analysis and evaluation of freeway merge ratios. Merges are important components of freeways and traffic behavior around them have a significant impact in the evolution and stability of congested traffic. At merges, drivers from conflicting traffic branches take turns to merge into a single stream at a rate referred to as the “merge ratio”. In this research, data from several freeway merges was used to evaluate existing macroscopic merge models and theoretical principles of merging behavior. Findings suggest that current merge ratio estimation methods can be insufficient to represent site-specific merge ratios, due to observed within-site variations and unaccounted effects of downstream merge geometry. To overcome these limitations, merge ratios were formulated based on their site-specific lane flow distribution (LFD), the proportion of flow in each freeway lane, for two types of merge geometries. Results demonstrate that the proposed methods are able to improve merge ratio estimates, reproduce within-site variations of merge ratio, and represent more effectively disproportionate redistribution of merging flow for merges where vehicles compete directly to merge due a downstream lane reduction.
Second, this research investigates lane-specific traffic behavior through empirical analysis and statistical modeling of lane flow distribution. Lane-specific traffic behavior is also an important component in evaluating freeway performance and has a significant impact in the mechanism of queue evolution, particularly around merges, and bottleneck discharge rate. In this research, site-specific linear LFD trends of three-lane congested freeways were investigated and modeled. A large-scale data collection process was implemented to systematically characterize the effects of several traffic and geometric features of freeways in the occurrence of between-site LFD variations. Also, an innovative three-stage modeling framework was used to model LFD behavior using multiple logistic regression to describe between-site LFD variations and Dirichlet regression to model recurrent combinations of linear LFD trends. This novel approach is able to represent both between and within site variations of LFD trends better, while accounting for the unit-sum constraint and distribution assumptions inherent of proportions data. Results revealed that proximity to freeway merges, a site’s level of congestion, and the presence of HOV lanes are significant factors that influence site-specific recurrent LFD behavior.
Findings from this work significantly improve the state-of-the-art knowledge on merging and lane-specific traffic behavior, which can help to improve traffic operations and reduce traffic congestion in freeways.
Second, this research investigates lane-specific traffic behavior through empirical analysis and statistical modeling of lane flow distribution. Lane-specific traffic behavior is also an important component in evaluating freeway performance and has a significant impact in the mechanism of queue evolution, particularly around merges, and bottleneck discharge rate. In this research, site-specific linear LFD trends of three-lane congested freeways were investigated and modeled. A large-scale data collection process was implemented to systematically characterize the effects of several traffic and geometric features of freeways in the occurrence of between-site LFD variations. Also, an innovative three-stage modeling framework was used to model LFD behavior using multiple logistic regression to describe between-site LFD variations and Dirichlet regression to model recurrent combinations of linear LFD trends. This novel approach is able to represent both between and within site variations of LFD trends better, while accounting for the unit-sum constraint and distribution assumptions inherent of proportions data. Results revealed that proximity to freeway merges, a site’s level of congestion, and the presence of HOV lanes are significant factors that influence site-specific recurrent LFD behavior.
Findings from this work significantly improve the state-of-the-art knowledge on merging and lane-specific traffic behavior, which can help to improve traffic operations and reduce traffic congestion in freeways.
ContributorsReina, Paulina (Author) / Ahn, Soyoung (Thesis advisor) / Pendyala, Ram (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2015