Caitlin Moffett

Repurposing Exhaled CO2 for Spacecraft

Presented By Caitlin Moffett , University of Arizona (Graduate Student)

Life support is a vital component in any manned space mission, and some waste production is a small price to pay for keeping astronauts safe and healthy in space. Historically, life support on the ISS and other manned spacecraft have produced a methane by-product that is vented to space. Honeywell’s methane pyrolysis reactor now provides the opportunity for this waste methane to be reclaimed, transforming it into useful hydrogen and a solid carbon composite material that will grow in quantity with the progression of a space mission. If a viable application can be found for this carbon, it will greatly lower waste and raise the efficiency of the life support system as a whole. Our team, tasked with the challenge of finding such an application, has designed a storage box constructed out of the carbon material with little additional resources. It will be constructed out of 6 square carbon pieces adhered to a fabric base, which can be folded up to create a cube shaped container.

We have endeavored to create a design that succeeds on multiple levels: it not only employs the carbon material in an application that is appropriate to the spacecraft environment and can be used by astronauts on a trip home from Mars, but can also make use of other waste products such as astronaut clothing that has reached the end of its designated wearable lifetime. The box design is simple, collapsible for easy storage, and easily scalable depending on intended use. With this design, it is our hope that the first astronauts to walk on Mars will be able to store experimental samples, food, and other items in carbon boxes, produced while breathing into a life support system that is nearly zero-waste.

Subhadeep Dutta

Radiation-responsive Nanosensor Gel (RaNG): A New way to Monitor Cancer Radiotherapy Doses

Presented By Subhadeep Dutta, Arizona State University Graduate Student

Radiotherapy using ionizing radiation (e.g. X rays) still remains a mainstay of treatment modalities in clinic for different types of cancers. Overexposure to radiation can induce toxicity and damage to healthy tissue, whereas underdosing can lead to poor efficacies of tumor ablation. Robust radiation sensors are in critical demand to ensure effective delivery of radiation to patients undergoing radiotherapy. Currently existing sensors possess several inherent limitations in rapid radiation detection because of their sensitivity to light and heat, fragility, long processing times, dose dependence, complexity of application process and / or high costs. Commonly used NanodotsTM require separate read-out devices, do not conform to tissue morphologies and are expensive as well. We developed a novel easy-to-use Radiation-responsive Nanosensor Gel (RaNG)-technology, based on the formation of maroon-colored gold nanoparticles from their colorless precursor formulations, with exposure to different levels of ionizing radiation. By measuring the intensity of color developed with a simple bench-top absorbance spectrophotometer, we were able to precisely detect doses typically administered in fractional cancer radiotherapy (i.e. ranging from 0-5Gy) and beyond. Topographical distribution of delivered radiation doses was qualitatively and quantitatively determined and efficacy of the hydrogel sensor was validated using different ionizing radiation sources including protons, electrons, photons and radioactive isotopes. Translational capability of the nanosensor was demonstrated using anthropomorphic phantoms and in live canine patients undergoing clinical radiotherapy treatments (in collaboration with Banner MD Anderson Cancer Center). Our results manifest a new generation of low-cost nanosensor for colorimetric detection of radiation dose to improve patient safety in clinical radiotherapy and trauma care.

Ghena Krdi

Characterizing Primary Mesothelioma Cell Lines by Exome Sequencing

Presented by Ghena Krdi, University of Arizona (Graduate Student)

Malignant Pleural Mesothelioma is a rare and aggressive type of lung cancer. Currently, its main cause is asbestos exposure. It is usually discovered at an advanced stage which makes treatment harder as there is no cure. This is due to the lack of knowledge about early symptoms of the disease. Although tumor development takes a long time, there has not been enough research about reliable early disease biomarkers to look for in patients who are at risk. Along with that, mesothelioma in vitro research is usually conducted on commercial cell lines which do not yield accurate results due to the difference in the environment and metabolism of the cells. Primary cell lines are proven to yield more reliable in vitro results in mesothelioma research because the cells come from real patients. Although research is still conducted in vitro, primary cell lines are considered a better representation of mesothelioma because they show more accurate cell metabolic profiles and responses to drugs tested.

Besides using primary cell lines, DNA exome sequencing, a time efficient and inexpensive technique, is used for identifying specific DNA mutations which are important for identifying possible disease biomarkers including the expression of certain genes or transcription factors that are common for mesothelioma tumor cells. Computational analysis of exome sequencing data can be used to make conclusions about copy number variation and genes associated with tumor progression.

Isabele Ross

Green Infrastructure Impacts on Carbon Cycling: Evaluating Changes in Soil Microbial Composition and Function

Presented by Isabel Ross, Cienega High School, Vail, Arizona

Green Infrastructure (GI) redirects water into the soil, affecting soil microbial diversity, decomposition, and stabilization of carbon. Soil microbes drive decomposition and carbon stabilization, making them important for carbon cycling. Decomposition by soil microbes releases carbon dioxide, a greenhouse gas, into the soil and atmosphere which is important for climate change. This project focuses on how GI systems change soil microbial community composition and function to understand how GI affects the carbon cycle. We used a phylogenetic gene marker and FAPROTAX to identify bacteria taxa and function. We observed changes in microbial composition and key functions with rainwater harvesting techniques. Although decomposition also creates more nutrients for carbon-absorbing plants, increased decomposition observed in this study could lead to additional sources of greenhouse gases.

Suraj Puvvadi

Validation of Predictions From a Novel Small-Sample Statistic of Valley Fever and Dysregulated Pathways and Increasing Statistical Power of Metabolite Identifiers Through Biocuration

Presented by Suraj Puvvadi, BASIS Scottsdale

Precision medicine is an approach to patient care that optimizes the efficiency and personalizes treatments based on transcriptomic, genomic, and metabolomic profiling. Rigorous curation of high throughput data is the first step towards efficient precision medicine. The purpose of this research is to validate findings from a novel small-sample valley fever statistic and to increase the statistical power of a precision medicine framework.

Disseminated coccidioidomycosis (DCM) is a chronic and fungal form of valley fever that’s prevalent in the southwestern parts of the United States. Currently, no anti-fungal treatment exists for DCM which makes it a lethal disease. Through literature biocuration and non-deterministic ratings of associations between DCM and ontological medical conditions, significant dysregulated molecular pathways were unveiled. Patient associations were ranked based on types of limitations, experimental nuances, and environmental setup. Interrater agreement scores and hypothesis testing were used to authenticate association ratings. A 71.4% rate of the novelty of dysregulated pathways was calculated which indicated the specific pathway relationships that need to be studied to create individualized treatments for DCM.

Metabolomic profiling is part of the foundation of precision medicine. An existing N-of-1 pathways framework uses transcriptomic data to create patient profiles targeting molecular pathways that need to be treated. Creating a metabolomic layer to the N-of-1 framework increases the accuracy and clarity of a treatment profile. After the systematic biocuration of unique serum metabolites, these novel metabolite identifiers are linked to molecular pathways from high-throughput databases which increases the statistical power of dysregulated pathways used in the N-of-1 framework. Following the metabolite curation, a significant statistical boost was observed. These new metabolites will be further investigated through literature biocuration, as in the DCM component.

Overall, this project emphasizes the importance of biocuration techniques and is the first step in producing individualized treatments to heal humanity.

Katherine Wei

How Single Nucleotide Polymorphisms (SNPs) in Apolipoprotein (APOE) Impact Alzheimer’s Disease (AD) Pathology

Presented By Katherine Wei, BASIS High School, Chandler, Arizona

Alzheimer’s Disease (AD) is a neurodegenerative disease caused by beta-amyloid plaques that build up in the brain as well as tau protein which form tangles around brain cells. Currently, there is no cure for AD but there are some therapeutic treatments to slow down the process. Apolipoprotein E (APOE) is a protein that has been associated with Alzheimer’s and cardiovascular diseases because of its role in metabolism of fats in the body. This experiment will use several different databases in order to analyze the effects of single nucleotide polymorphisms (SNP) in the function of the APOE gene. Specifically, analysis of structure and function of different SNPs that may lead to changes in the gene will be conducted and comparisons between the expression of the APOE4 gene and cognitive impairment will be evaluated to determine their relationship. Using datasets that have the frequency of patients who suffer from AD as well as SNPs that are present in the patient’s genes, visualizations and graphs will be drawn to evaluate the relationship between SNPs and AD. Common SNPs in the APOE gene that may induce late-onset Alzheimer’s Disease (LOAD) are rs7412, rs429358, and rs769455. The purpose of this research is to understand how these SNPs in APOE correlate to Alzheimer’s Disease and how they may play a role in the production of beta-amyloid plaques. Understanding these concepts is fundamental in getting a better grasp of the functionality of the APOE4 gene in Alzheimer’s and possibilities in new therapeutic treatments.

Devin Bowes

SARS-CoV-2 Monitoring in Wastewater as an Early Warning Signal for COVID-19 Presence in Communities

Presented By Devin Bowes, Arizona State University

The COVID-19 global pandemic caused by the virus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has highlighted several challenges within our healthcare system, such as accessible testing and rapid reporting. Wastewater-based Epidemiology (WBE) has been proven to serve as an effective, near real-time tool to understand human health and behavior at population-scale by analyzing raw sewage for human biomarkers excreted via urine and/or feces. The study within showcases the first ever longitudinal investigation of monitoring SARS-CoV-2 in municipal wastewater at the neighborhood level to track trends throughout the COVID-19 global pandemic. Untreated wastewater samples were collected from within the sewer collection system throughout Tempe, Arizona and analyzed for SARS-CoV-2 using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Preliminary results indicate peak loadings of the virus observed in wastewater precede reported peaks of positive new clinical cases, COVID-related hospitalizations, and COVID-related deaths. This study holds great promise for the future of population-level diagnostics; acting as an early warning system for emerging infectious diseases and allowing more time for hospital preparation, resource deployment, and targeted educational interventions throughout communities.

Prasiddhi Gyawali

An R-Shiny app for trait data processing

Presented by Prasiddhi Gyawali, Sonoran Science Academy – Tucson

The Functional Trait Resource for Environmental Studies (FuTRES) project works with vertebrate trait data from individuals intending to make data more accessible and interoperable. Since data are collected by different people using different methods, it can be hard for everyone to understand exactly what the data mean. To solve this problem, FuTRES works to write programs that can be used to easily standardize datasets. Standardization is done by getting the data from the CyVerse Discovery Environment and transferring them into RStudio where functions are applied. Code is written for a specific dataset then generalized so that it functions with other datasets. Code can always be extended or further generalized to fit another need. An R-Shiny app contains the code, providing a user-friendly interface.

Eli Lefkowitz

Automated physiological analysis of Engineered Heart Tissues from Human Induced Pluripotent Stem Cells

Presented by Eli Lefkowitz, Catalina Foothills High School

Cardiovascular disease is responsible for one in every four deaths. To understand these deaths, cardiomyocytes can be generated from a patient’s blood by first converting blood into a stem cell state called induced pluripotent stem cells (iPSCs). Cardiomyocyte derived iPSCs (hiPSC-CM) can be assembled into 3D constructs that resemble the heart called engineered heart tissues (EHTs). While it is possible to perform cardiovascular disease modeling in a dish, the tools to analyze the function of hiPSC-CMs remains laborious. To meet this need, our lab developed a software (MATLAB based) to quantify cardiac physiological parameters (peak amplitude, peak duration, and faster rising rate) in an automated and highly accurate manner. Further deployment of our software will be used to understand how mutations lead to cardiovascular disease.

Shaun Karakkattu

Shaun Karakkattu, Basis High School, Mesa, AZ

Inhibiting the Proliferation of Patient-Derived Glioblastoma Multiforme (GBM) cells by activating Estrogen Receptor beta using estradiol and IGF-1


Glioblastoma multiforme (GBM) is the most aggressive and common type of primary brain tumor. Patients with GBM have a median survival rate between 12-14 months. There are sex-based differences that exist in the occurance of GBM with men having a 60% higher chance of developing a tumor indicating that differences in the function of Estrogen Receptor Beta (ERβ), a known tumor suppressor, may influence tumor progression in GBM. Considering ERβ function, it was hypothesized that 17β-estradiol and IGF-1, estrogen receptor agonists, may promote ERβ function and inhibit the proliferation of GBM cells in vitro. 17β-estradiol and IGF-1 were applied to a patient-derived GBM cell line and MDA-MB-453, breast cancer cell line, in-vitro. The concentrations tested ranged from 2 to 20 µg/mL of 17β-estradiol and IGF-1. After 48 hrs of exposure, GBM cell proliferation was analyzed through a trypan blue exclusion assay. The results revealed that cell proliferation was inhibited by the estrogen receptor agonists in the patient-derived GBM cells and promoted in the MDA-MB-453 cell line which served as a positive control. In conclusion, this study presents evidence that ERβ function may indeed inhibit GBM proliferation. This study presents a potential approach to the treatment of GBM. As this study continues, more steroid hormone receptors will be tested like the Androgen Receptor which may also cause the inhibition of cell proliferation in GBM.