ASU Scholarship Showcase

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.`

The threat of West Nile virus (WNV) epidemics with increasingly severe neuroinvasive infections demands the development and licensing of effective vaccines. To date, vaccine candidates based on inactivated, live-attenuated, or chimeric virus, and viral DNA and WNV protein subunits have been developed. Some have been approved for veterinary use or are under clinical investigation, yet no vaccine has been licensed for human use. Reaching the milestone of a commercialized human vaccine, however, may largely depend on the economics of vaccine production. Analysis suggests that currently only novel low-cost production technologies would allow vaccination to outcompete the cost of surveillance and clinical treatment. Here, we review progress using plants to address the economic challenges of WNV vaccine production. The advantages of plants as hosts for vaccine production in cost, speed and scalability, especially those of viral vector-based transient expression systems, are discussed. The progress in developing WNV subunit vaccines in plants is reviewed within the context of their expression, characterization, downstream processing, and immunogenicity in animal models. The development of vaccines based on enveloped and non-enveloped virus-like particles is also discussed. These advancements suggest that plants may provide a production platform that offers potent, safe and affordable human vaccines against WNV.

Osteosarcoma is the most common bone cancer in children and adolescents. Although 70% of patients with localized disease are cured with chemotherapy and surgical resection, patients with metastatic osteosarcoma are typically refractory to treatment. Numerous lines of evidence suggest that cytotoxic T lymphocytes (CTLs) limit the development of metastatic osteosarcoma. We have investigated the role of PD-1, an inhibitory TNFR family protein expressed on CTLs, in limiting the efficacy of immune-mediated control of metastatic osteosarcoma. We show that human metastatic, but not primary, osteosarcoma tumors express a ligand for PD-1 (PD-L1) and that tumor-infiltrating CTLs express PD-1, suggesting this pathway may limit CTLs control of metastatic osteosarcoma in patients. PD-L1 is also expressed on the K7M2 osteosarcoma tumor cell line that establishes metastases in mice, and PD-1 is expressed on tumor-infiltrating CTLs during disease progression. Blockade of PD-1/PD-L1 interactions dramatically improves the function of osteosarcoma-reactive CTLs in vitro and in vivo, and results in decreased tumor burden and increased survival in the K7M2 mouse model of metastatic osteosarcoma. Our results suggest that blockade of PD-1/PD-L1 interactions in patients with metastatic osteosarcoma should be pursued as a therapeutic strategy.

Cervical cancer is the most common malignancy among women particularly in developing countries, with human papillomavirus (HPV) 16 causing 50% of invasive cervical cancers. A plant-based HPV vaccine is an alternative to the currently available virus-like particle (VLP) vaccines, and would be much less expensive. We optimized methods to express HPV16 L1 protein and purify VLPs from tobacco (Nicotiana benthamiana) leaves transfected with the magnICON deconstructed viral vector expression system. L1 proteins were extracted from agro-infiltrated leaves using a series of pH and salt mediated buffers. Expression levels of L1 proteins and VLPs were verified by immunoblot and ELISA, which confirmed the presence of sequential and conformational epitopes, respectively. Among three constructs tested (16L1d22, TPL1d22, and TPL1F), TPL1F, containing a full-length L1 and chloroplast transit peptide, was best. Extraction of HPV16 L1 from leaf tissue was most efficient (> 2.5% of total soluble protein) with a low-salt phosphate buffer. VLPs were purified using both cesium chloride (CsCl) density gradient and size exclusion chromatography. Electron microscopy studies confirmed the presence of assembled forms of HPV16 L1 VLPs. Collectively; our results indicated that chloroplast-targeted transient expression in tobacco plants is promising for the production of a cheap, efficacious HPV16 L1 VLP vaccine. Studies are underway to develop plant VLPs for the production of a cervical cancer vaccine.

The botanical, Astragalus membranaceus, is a therapeutic in traditional Chinese medicine. Limited literature exists on the overall in vivo effects of A. membranaceus on the human body. This study evaluates the physiological responses to A. membranaceus by measuring leukocyte, platelet, and cytokine responses as well as body temperature and blood pressure in healthy individuals after the in vivo administration of A. membranaceus. A dose-dependent increase in monocytes, neutrophils, and lymphocytes was measured 8–12 hours after administration and an increase in the number of circulating platelets was seen as early as 4 hours. A dynamic change in the levels of circulating cytokines was observed, especially in interferon-γ and tumor necrosis factor-α, IL-13, IL-6, and soluble IL-2R. Subjective symptoms reported by participants were similar to those typically experienced in viral type immune responses and included fatigue, malaise, and headache. Systolic and diastolic blood pressure were reduced within 4 hours after administration, while body temperature mildly increased within 8 hours after administration. In general, all responses returned to baseline values by 24 hours. Collectively, these results support the role of A. membranaceus in priming for a potential immune response as well as its effect on blood flow and wound healing.

We previously reported a recombinant protein production system based on a geminivirus replicon that yields high levels of vaccine antigens and monoclonal antibodies in plants. The bean yellow dwarf virus (BeYDV) replicon generates massive amounts of DNA copies, which engage the plant transcription machinery. However, we noticed a disparity between transcript level and protein production, suggesting that mRNAs could be more efficiently utilized. In this study, we systematically evaluated genetic elements from human, viral, and plant sources for their potential to improve the BeYDV system. The tobacco extensin terminator enhanced transcript accumulation and protein production compared to other commonly used terminators, indicating that efficient transcript processing plays an important role in recombinant protein production.

Evaluation of human-derived 5′ untranslated regions (UTRs) indicated that many provided high levels of protein production, supporting their cross-kingdom function. Among the viral 5′ UTRs tested, we found the greatest enhancement with the tobacco mosaic virus omega leader. An analysis of the 5′ UTRs from the Arabidopsis thaliana and Nicotinana benthamiana photosystem I K genes found that they were highly active when truncated to include only the near upstream region, providing a dramatic enhancement of transgene production that exceeded that of the tobacco mosaic virus omega leader. The tobacco Rb7 matrix attachment region inserted downstream from the gene of interest provided significant enhancement, which was correlated with a reduction in plant cell death. Evaluation of Agrobacterium strains found that EHA105 enhanced protein production and reduced cell death compared to LBA4301 and GV3101. We used these improvements to produce Norwalk virus capsid protein at >20% total soluble protein, corresponding to 1.8 mg/g leaf fresh weight, more than twice the highest level ever reported in a plant system. We also produced the monoclonal antibody rituximab at 1 mg/g leaf fresh weight.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.