Matching Items (83)
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
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis advisor) / Singharoy, Abhishek (Thesis advisor) / Woodbury, Neal (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2022
Description
Cities in the Global South face rapid urbanization challenges and often suffer an acute lack of infrastructure and governance capacities. Smart Cities Mission, in India, launched in 2015, aims to offer a novel approach for urban renewal of 100 cities following an area‐based development approach, where the use of ICT and digital technologies is particularly emphasized. This article presents a critical review of the design and implementation framework of this new urban renewal program across selected case‐study cities. The article examines the claims of the so‐called “smart cities” against actual urban transformation on‐ground and evaluates how “inclusive” and “sustainable” these developments are. We quantify the scale and coverage of the smart city urban renewal projects in the cities to highlight who the program includes and excludes. The article also presents a statistical analysis of the sectoral focus and budgetary allocations of the projects under the Smart Cities Mission to find an inherent bias in these smart city initiatives in terms of which types of development they promote and the ones it ignores. The findings indicate that a predominant emphasis on digital urban renewal of selected precincts and enclaves, branded as “smart cities,” leads to deepening social polarization and gentrification. The article offers crucial urban planning lessons for designing ICT‐driven urban renewal projects, while addressing critical questions around inclusion and sustainability in smart city ventures.`
ContributorsPraharaj, Sarbeswar (Author)
Created2021-05-07
Description
The fast pace of global urbanization makes cities the hotspots of population density and anthropogenic activities, leading to intensive emissions of heat and carbon dioxide (CO2), a primary greenhouse gas. Urban climate scientists have been actively seeking effective mitigation strategies over the past decades, aiming to improve the environmental quality for urban dwellers. Prior studies have identified the role of urban green spaces in the relief of urban heat stress. Yet little effort was devoted to quantify their contribution to local and regional CO2 budget. In fact, urban biogenic CO2 fluxes from photosynthesis and respiration are influenced by the microclimate in the built environment and are sensitive to anthropogenic disturbance. The high complexity of the urban ecosystem leads to an outstanding challenge for numerical urban models to disentangling and quantifying the interplay between heat and carbon dynamics.This dissertation aims to advance the simulation of thermal and carbon dynamics in urban land surface models, and to investigate the role of urban greening practices and urban system design in mitigating heat and CO2 emissions. The biogenic CO2 exchange in cities is parameterized by incorporating plant physiological functions into an advanced single-layer urban canopy model in the built environment. The simulation result replicates the microclimate and CO2 flux patterns measured from an eddy covariance system over a residential neighborhood in Phoenix, Arizona with satisfactory accuracy. Moreover, the model decomposes the total CO2 flux from observation and identifies the significant CO2 efflux from soil respiration. The model is then applied to quantify the impact of urban greening practices on heat and biogenic CO2 exchange over designed scenarios. The result shows the use of urban greenery is effective in mitigating both urban heat and carbon emissions, providing environmental co-benefit in cities. Furthermore, to seek the optimal urban system design in terms of thermal comfort and CO2 reduction, a multi-objective optimization algorithm is applied to the machine learning surrogates of the physical urban land surface model. There are manifest trade-offs among ameliorating diverse urban environmental indicators despite the co-benefit from urban greening. The findings of this dissertation, along with its implications on urban planning and landscaping management, would promote sustainable urban development strategies for achieving optimal environmental quality for policy makers, urban residents, and practitioners.
ContributorsLi, Peiyuan (Author) / Wang, Zhihua (Thesis advisor) / Vivoni, Enrique (Committee member) / Huang, Huei-Ping (Committee member) / Myint, Soe (Committee member) / Xu, Tianfang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Attitudes and habits are extremely resistant to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring long-term, massive societal changes. During the pandemic, people are being compelled to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. Going forward, a critical question is whether these experiences will result in changed behaviors and preferences in the long term. This paper presents initial findings on the likelihood of long-term changes in telework, daily travel, restaurant patronage, and air travel based on survey data collected from adults in the United States in Spring 2020. These data suggest that a sizable fraction of the increase in telework and decreases in both business air travel and restaurant patronage are likely here to stay. As for daily travel modes, public transit may not fully recover its pre-pandemic ridership levels, but many of our respondents are planning to bike and walk more than they used to. These data reflect the responses of a sample that is higher income and more highly educated than the US population. The response of these particular groups to the COVID-19 pandemic is perhaps especially important to understand, however, because their consumption patterns give them a large influence on many sectors of the economy.
ContributorsConway, Matthew Wigginton (Author) / Salon, Deborah (Author) / da Silva, Denise Capasso (Author) / Mirtich, Laura Christine (Author)
Created2020-09-03
Description
This work focuses on a novel approach to combine electrical current with cyanobacterial technology, called microbial electrophotosynthesis (MEPS). It involves using genetically modified PSII-less Synechocystis PCC 6803 cells to avoid photoinhibition, a problem that hinders green energy. In the work, a cathodic electron delivery system is employed for growth and synthesis. Photoinhibition leads to the dissipation energy and lower yield, and is a major obstacle to preventing green energy from competing with fossil fuels. However, the urgent need for alternative energy sources is driven by soaring energy consumption and rising atmospheric carbon dioxide levels. When developed, MEPS can contribute to a carbon capture technology while helping with energy demands. It is thought that if PSII electron flux can be replaced with an alternative source photosynthesis could be enhanced for more effective production. MEPS has the potential to address these challenges by serving as a carbon capture technology while meeting energy demands. The idea is to replace PSII electron flux with an alternative source, which can be enhanced for higher yields in light intensities not tolerated with PSII. This research specifically focuses on creating the initiation of electron flux between the cathode and the MEPS cells while controlling and measuring the system in real time.
The successful proof-of-concept work shows that MEPS can indeed generate high-light-dependent current at intensities up to 2050 µmol photons m^‒2 s^‒1, delivering 113 µmol electrons h^‒1 mg-chl^‒1. The results were further developed to characterize redox tuning for electron delivery of flux to the photosynthetic electron transport chain and redox-based kinetic analysis to model the limitations of the MEPS system.
ContributorsLewis, Christine Michelle (Author) / Torres, César I (Thesis advisor) / Fromme, Petra (Thesis advisor) / Woodbury, Neal (Committee member) / Hayes, Mark (Committee member) / Arizona State University (Publisher)
Created2023
Description
Concerns, such as global warming, greenhouse gas emissions, and changes in hydrological regimes, have been raised in response to the global ecosystem changes caused by humans. Understanding the ecosystem functions is crucial for assisting stakeholders in formulating viable plans to address the issues for a healthier planet. However, a systematic evaluation of recent environmental changes and current ecosystem status, focusing on terrestrial ecosystem carbon-water trade-off, in the Lower Mekong Basin (LMB) is lacking. This dissertation involves: (1) examining the long-term spatiotemporal patterns of ecosystem conditions in response to gains and losses of the forest; (2) evaluating the current consumptive water use variation across all biome and land use types with remotely sensed evapotranspiration (ET) products; (3) analyzing the trade-off between terrestrial carbon and water stress condition during the photosynthesis process in response to different climatic/ecosystem conditions, and (4) developing a spatial optimization model to effectively determine possible reforestation/afforestation options considering the balance between water conservation and carbon fluxes. These studies were conducted with many recently developed algorithms and satellite imagery. This dissertation makes significant contributions and expands the knowledge of the variation in water consumption and carbon assimilation within the ecosystem when different conditions are present. In addition, the spatial optimization model was applied to the entire region to formulate possible reforestation plans under different water-carbon tradeoff scenarios for the first time. The findings and results of this research can be used to provide constructive suggestions to policymakers, managers, planners, government officials, and any other stakeholders in LMB to formulate policies and guidelines for the environmentally responsible and sustainable development of LMB.
ContributorsLi, Yubin (Author) / Myint, Soe (Thesis advisor) / Tong, Daoqin (Thesis advisor) / Muenich, Rebecca (Committee member) / Schaffer-Smith, Danica (Committee member) / Arizona State University (Publisher)
Created2023
Description
Safe, readily available, and reliable sources of water are an essential component of any municipality’s infrastructure. Phoenix, Arizona, a southwestern city, has among the highest per capita water use in the United States, making it essential to carefully manage its reservoirs. Generally, municipal water bodies are monitored through field sampling. However, this approach is limited spatially and temporally in addition to being costly. In this study, the application of remotely sensed reflectance data from Landsat 7’s Enhanced Thematic Mapper Plus (ETM+) and Landsat 8’s Operational Land Imager (OLI) along with data generated through field-sampling is used to gain a better understanding of the seasonal development of algal communities and levels of suspended particulates in the three main terminal reservoirs supplying water to the Phoenix metro area: Bartlett Lake, Lake Pleasant, and Saguaro Lake. Algal abundances, particularly the abundance of filamentous cyanobacteria, increased with warmer temperatures in all three reservoirs and reached the highest comparative abundance in Bartlett Lake. Prymnesiophytes (the class of algae to which the toxin-producing golden algae belong) tended to peak between June and August, with one notable peak occurring in Saguaro Lake in August 2017 during which time a fish-kill was observed. In the cooler months algal abundance was comparatively lower in all three lakes, with a more even distribution of abundance across algae classes. In-situ data from March 2017 to March 2018 were compared with algal communities sampled approximately ten years ago in each reservoir to understand any possible long-term changes. The findings show that the algal communities in the reservoirs are relatively stable, particularly those of the filamentous cyanobacteria, chlorophytes, and prymnesiophytes with some notable exceptions, such as the abundance of diatoms, which increased in Bartlett Lake and Lake Pleasant. When in-situ data were compared with Landsat-derived reflectance data, two-band combinations were found to be the best-estimators of chlorophyll-a concentration (as a proxy for algal biomass) and total suspended sediment concentration. The ratio of the reflectance value of the red band and the blue band produced reasonable estimates for the in-situ parameters in Bartlett Lake. The ratio of the reflectance value of the green band and the blue band produced reasonable estimates for the in-situ parameters in Saguaro Lake. However, even the best performing two-band algorithm did not produce any significant correlation between reflectance and in-situ data in Lake Pleasant. Overall, remotely-sensed observations can significantly improve our understanding of the water quality as measured by algae abundance and particulate loading in Arizona Reservoirs, especially when applied over long timescales.
ContributorsRussell, Jazmine Barkley (Author) / Neuer, Susanne (Thesis advisor) / Fox, Peter (Committee member) / Myint, Soe (Committee member) / Arizona State University (Publisher)
Created2018
Description
The fundamental photophysics of fluorescent probes must be understood when the probes are used in biological applications. The photophysics of BODIPY dyes inside polymeric micelles and rhodamine dyes covalently linked to proteins were studied. Hydrophobic boron-dipyrromethene (BODIPY) dyes were noncovalently encapsulated inside polymeric micelles. Absorbance and fluorescence measurements were employed to study the photophysics of these BODIPY dyes in the micellar environments. Amphiphilic polymers with a hydrophobic character and low Critical Micelle Concentration (CMC) protected BODIPYS from the aqueous environment. Moderate dye loading conditions did not result in ground-state dimerization, and only fluorescence lifetimes and brightnesses were affected. However, amphiphilic polymers with a hydrophilic character and high CMC did not protect the BODIPYS from the aqueous environment with concomitant ground-state dimerization and quenching of the fluorescence intensity, lifetime, and brightnesses even at low dye loading conditions. At the doubly-labeled interfaces of Escherichia coli (E. coli) DNA processivity β clamps, the interchromophric interactions of four rhodamine dyes were studied: tetramethylrhodamine (TMR), TMR C6, Alexa Fluor 488, and Alexa Fluor 546. Absorbance and fluorescence measurements were performed on doubly-labeled β clamps with singly-labeled β clamps and free dyes as controls. The absorbance measurements revealed that both TMR and TMR C6 readily formed H-dimers (static quenching) at the doubly-labeled interfaces of the β clamps. However, the TMR with a longer linker (TMR C6) also displayed a degree of dynamic quenching. For Alexa Fluor 546 and Alexa Fluor 488, there were no clear signs of dimerization in the absorbance scans. However, the fluorescence properties (fluorescence intensity, lifetime, and anisotropy) of the Alexa Fluor dyes significantly changed when three methodologies were employed to disrupt the doubly-labeled interfaces: 1) the addition of sodium dodecyl sulfate (SDS) detergent to denature the proteins, 2) the addition of clamp loader (γ complex) to open one of the two interfaces, and 3) the use of subunit exchange to decrease the number of dyes per interface. These fluorescence measurements indicated that for the Alexa Fluor dyes, other interchromophoric interactions were present such as dynamic quenching and homo-Förster Resonance Energy Transfer (homo-FRET).
ContributorsDonaphon, Bryan Matthew (Author) / Levitus, Marcia (Thesis advisor) / Van Horn, Wade (Committee member) / Woodbury, Neal (Committee member) / Arizona State University (Publisher)
Created2018