United States

Glioblastoma (GBM) is one of the most aggressive malignant brain tumors, with a 5-year patient survival rate of 5% post diagnosis. Typically, magnetic resonance imaging (MRI) is used to identify tumor information to aid presurgical planning. Functional MRI (fMRI) is used in presurgical planning to identify eloquent areas of the cortex to avoid during surgery. Typical imaging captures the local tumor but fails to identify its global effects on cortical function, and we propose that fMRI can identify these effects. fMRI has identified resting state networks (RSN) throughout the brain relating to these global effects. Using fMRI data to guide GBM patient treatment, however, is difficult because GBM tumor mass distorts the cortex, making it difficult to know which RSNs are affected. However, unlike the cortex, the cerebellum is mostly unaffected in GBM and given its connections to the cortex, may provide a structurally-homogeneous framework to compare RSN changes across patients. Thus, we propose that by tracking changes in cerebellar networks, we can predict functional cortical effects by the tumor, allowing physicians to make more informed decisions on patient treatment. We organized the resting state fMRI data of 1000 healthy controls and 81 GBM patients into 280 regions of interest and assigned each region an RSN. We found consistent relationships between the within-network interhemispheric connectivity of the cortex and its cerebellar counterparts in controls, but this was altered in GBM patients. Network analysis has been used to investigate RSNs which revealed lower cerebellar global efficiency, modularity, and betweenness centrality in GBM patients. Our data suggest that GBM patients exhibit altered network architecture in the cerebellum and that GBMs disrupt functional connectivity between the cortex and cerebellum. Further analysis of cerebellar data in GBM patients could help localize the effects of cortical tumors on functional networks and improve pre-surgical planning.

Molsamp 2021

Using Cerebellar Data to Predict Cortical Changes in GBMs

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Proteins transport molecules, catalyze biochemical reactions, and serve other vital functions in organisms. Recently, researchers discovered a previously unstudied class of protein called microproteins. Contrary to normal proteins, which typically consist of 100-600 amino acids, microproteins contain fewer than 100 amino acids. The vast majority of microproteins currently have no known function. The goal of this project is to use computational sequence analysis to characterize microproteins in order to gain insight into their possible cellular and molecular functions. Microproteins lack features of folded proteins, such as having fewer amino acids. However, some proteins, called intrinsically disordered proteins (IDPs), do not fold into folded structures. Therefore, I hypothesize that many microproteins are IDPs. If this is true, we may be able to apply our growing understanding of how IDPs work to clarify the functional role of microproteins in cells. The set of microprotein sequences that we are analyzing was obtained through ribosome profiling and mass spectrometry. To analyze this set of sequences, I developed and applied Python-based sequence analysis tools to assess the amino acid composition and disorder tendency of the microprotein sequences. Through this analysis, I found that microproteins are somewhat enriched in a few disorder-promoting amino acids, notably in arginine, proline and glycine. Furthermore, microproteins were predicted to be much more disordered than normal human proteins, where shorter microproteins in the set (~<25 sequence length) have an even higher fraction of disorder than longer proteins. These predictions strongly suggest that at least a subset of microproteins can be classified as Intrinsically Disordered Proteins. Interestingly, IDPs enriched in the residues arginine and glycine are known to bind RNA, suggesting microproteins may somehow influence cellular RNA function. These results are the first steps in allowing us to understand and predict the possible roles of microproteins in human cells.

A Systematic Computational Analysis of Human Microproteins

Eastern black Walnut (Juglans nigra), endemic to the Eastern US is highly sought after for its timber products and uniquely flavored nuts. The University of Missouri houses a significant Black Walnut repository at the Horticulture and Agroforestry Research Center in New Franklin, MO. The cultivars in this repository are used to support a breeding program focused on economic traits related to nut production (nutmeat mass, overall nut quality, precociousness, and yield). In 1996, four ramets of 67 cultivars were each grafted and subsequently planted in an experimental orchard for further evaluation. Beginning in 2001 and ending in 2015, each tree was evaluated for eleven phenological and tens nut quality/yield characteristics. The economically relevant traits nut weight, percent kernel, and precociousness (total nuts produced age 6 to 10) ranged from 8.8 to 43.0 g, 5.3 to 39.3 percent, and 16 to 1522 total nut, respectively. Nut weight and percent kernel were negatively correlated (r=-0.60), while precociousness correlated positively (r=0.38) with percent kernel and negatively (r=-0.32) with nut weight. Preconsciousness also correlated negatively with first (r=-0.39) and last (r=-0.30) pistillate emergence. Furthermore, principal components and biplot analyses revealed high variation of the evaluated cultivars leading to the prioritization of certain breeding parents over others.

Elias Bunting is currently an undergraduate senior in the College of Agriculture Food and Natural Resources at the University of Missouri. Elias’ research interest lies in determining the drivers of genotypic and phenotypic traits in eastern American black walnut. In his research, he has worked to determine how to leverage these traits for improved walnut breeding programs.

As the world tries to shift to cleaner energy, it is important to keep track of how each country manages to meet their energy needs while reducing their impacts on the planet. Denmark is one of the leading countries in the world in wind power penetration, reporting that 47% of their electricity consumption came from wind in 2019 (Reuters). My research looks at how they have achieved this statistic, what exactly it means, and how it may be misleading for other countries or societies seeking to emulate Denmark’s high wind integration. The intermittency of wind energy is a big problem because the intermittency destabilizes the energy grid when combined with reliable, dispatchable energy sources like fossil fuels. My conclusions regarding how Denmark has been able to integrate wind energy and maintain high grid stability indicate that Denmark’s success is quite unique because of a) geographical proximity to the naturally high-wind areas of the North and Baltic seas, b) geographical proximity and strong import/export relationships to energy-intensive neighbors like Germany, which produces a lot of energy for its own domestic needs, and neighbors like Norway and Sweden, who possess vast amounts of hydropower reserves; c) Denmark consumes relatively little energy compared to its neighbors, and d) Denmark has enjoyed enough financial wealth and political stability to invest in diversified interconnectors to its neighbors as well as grow its own wind industry. All of these factors play important roles in how Denmark has been able to integrate wind into its electric grid, but perhaps the most important factor is the robust import/export system, which acts as a stabilizing “functional battery” for Denmark’s electricity system. More quantitative research is needed to establish a clearer idea of wind energy’s role in Denmark’s electric grid and understand how that energy is distributed.

Analyzing the Outlook for Renewable Energy: Denmark Case Study

COVID-19, or SARS-CoV-2, is a novel strain of coronavirus that has greatly affected societies world-wide. To combat this virus, Vitamin D3, or 1, 25-dihydroxy vitamin D3, is a promising factor to help manage the expression of COVID-19 in patients. The goal of this study is to understand the relationship between COVID-19 and Vitamin D with respect to various people, regardless of ethnic background. Patients selected will be from the St. Louis area and are within the age range of 18-50 years old. While Vitamin D3 is the overarching factor, there are other factors that modulate and enhance its capability, including: T-cells, memory B-cells and interleukin-10. Vitamin D will be analyzed using a 25(OH) Vitamin D ELISA kit. SARS-CoV-2 concentration will be determined by using RT-PCR. Flow cytometry will be used to determine levels of T-cell subset Th2. A memory B-cell isolation kit will be used to isolate memory B-cells by using antibody complexes. A Human IL-10 ELISA kit will be used to analyze IL-10 serum levels within patients. Furthermore, we plan to expand this study for the future by conducting this study in other countries and cities around the United States. In addition, access to healthcare and receiving blood work in marginalized communities will be explored in relation to Vitamin D will be explored for future potential application.

The Impact of Vitamin D on the Immune Response of COVID-19 Positive Patients

The disproportionate increase in cases of SARS-CoV-2 virus causing COVID-19 in communities of color across the U.S has sparked conversations on the effect that the deadly virus has on communities of color and the lingering question of why. COVID-19 was first reported in Wuhan, China in December 2019 and has subsequently spread worldwide thus, called a pandemic by the World Health Organization. Current data shows that communities of color have been impacted at a much higher rate than other ethnicities. This cohort study discusses why higher rates of diabetes among communities of color cause disproportionately higher rates of transmission and mortality as a result of COVID-19 and examines the mechanisms of diabetes that cause disproportionately higher rates of transmission and mortality as a result of COVID-19. By evaluating scientific evidence and physical data, our research findings show that the transmission and mortality as a result of COVID-19 among communities of color occur at a higher rate compared to other demographic groups due to underlying health conditions. In 2018, it was reported by the CDC that 11.7 percent of African Americans and 12.5 percent of Latinx people were diagnosed with diabetes. This research also indicates that people of color, specifically Latinx and African Americans, are disproportionately affected at higher rates by COVID-19 due to higher rates of diabetes compounded by medication that is commonly taken by diabetes patients, such as ACE inhibitors and ARBs. Both of these types of medications can potentially upregulate the ACE-2 receptor, which is the entryway for SARS-CoV-2 into the body. As a result, patients with diabetes have a higher susceptibility to severe SARS-CoV-2 infection, resulting in worse outcomes in communities of color. This is, however, contradicting speculation that these types of medications can be a potential treatment for COVID-19. Further, this research verifies the impact that socioeconomic factors have on minoritized people and how this may predispose them to diabetes and other underlying health conditions. In Dekalb County, Georgia, African Americans’ median household income was $50,901 in 2018; while their white peers' income was $95,450. The research study reflects the importance of good overall health and wellness management amongst communities of color, while also highlighting the importance of knowledge of and understanding about medical prescriptions, medications, and the effects that it has on the body.

The Effects of COVID-19 on Communities of Color Due to Diabetes Mellitus and Other Underlying Health Issues

Neurotransmitters are vital for understanding the relationship between biological interactions in the brain and their subsequent effects on cognition and behavior. Neuromodulators are neurotransmitters with the ability to regulate other cells, most commonly functioning by activating G-protein-coupled receptors (GPCRs). Protein kinase A (PKA) integrates input from GPCRs and triggers different cellular responses dependent on the timing of neuromodulatory inputs and the location of the subcellular compartment in which it is activated. PKA has previously been found to modulate several important functions in the brain, including synaptic transmission, plasticity, learning and memory. We have targeted a PKA activity reporter to different subcellular compartments. The goal of this project is to evaluate how accurately the subcellular reporting targeting is. I created short programs (macros) to automate the colocalization analysis of confocal images to find co-occurring or “colocalized” regions of interest between compartmentally targeted PKA reporters with synaptic terminal markers. These macros will help streamline colocalization analysis into a more objective and efficient process, as performing the analysis manually is a subjective and time-consuming procedure. I found that the macros successfully identified colocalized regions of interest between confocal images of PKA reporters and synaptic terminal markers in an automated manner, with some manual intervention required. Conducting colocalization analysis in an automated manner allows us to evaluate how efficiently we have targeted the PKA reporters to different subcellular compartments. Learning more about how and where GPCRs activate PKA, as well as the resultant behaviors, will help us to better understand the dynamics of neuromodulator action, and the various neurodegenerative and psychiatric disorders in which PKA plays a considerable role.

Automating colocalization analysis of confocal microscopy images

Interactions of aqueous solutions of K2PtCl4 with solutions of three nitroso-naphtolates at room temperature results in formation of precipitates of PtL2·Solv composition (Solv = acetonitrile CH3CN, or dimethylformamide DMF). Compounds will be isolated via filtration, washed with water, and then dried under vacuum in a desiccator charged with H2SO4(c). Characterization of these new compounds will be carried out using conventional physical-chemical techniques such as melting points determination, elemental analysis on C,H,N content, as well as using spectroscopic methods. These will be the IR-, UV-visible spectroscopy and fluorescence measurements (both in solid state and in solution). Particularly interesting will be investigations of the NIR region for the emission. Crystal growth for the X-ray analysis will be done by using the vapor diffusion method in two-vials closed system and selected single crystals will undergo structural characterization. All synthesized compounds are planned for biological in vitro cell culture studies against human cancers.

Synthesis and Characterization of Oximates of Pt-group Metals

The Springfield Plateau groundwater province is an aquifer that runs through southwestern Missouri and parts of Oklahoma and Arkansas. It overlies the Ozark confining unit and is mainly recharged by precipitation. This karst aquifer is unconfined, which makes it vulnerable to waste contaminants from animals or other surface activities, that can percolate through the soil and fractured limestone. Little research has been reported on this aquifer since it is not used for drinking supply or irrigation. There are karst features that have form, such as springs that feed into streams that sustain the nearby biota The objective of this study is to evaluate the ground water quality through analysis of the nitrates and selected major ions in samples collected from springs and streams in Polk county Missouri. I will be collecting about 40 samples and will analyze for pH, alkalinity, turbidity, sulfate, chloride, and nitrate. The results will show the extent of contamination of groundwater (springs) and their impact to receiving streams. Also, since I will be collecting samples at intervals of 1-2 months, I will determine seasonal changes in water quality.​ ​

Water Quality Analysis of the Springfield Plateau Aquifer

My research will primarily deal with synthesizing and characterizing various mesoporous nanomaterials and their composites. When synthesizing these materials, we aim to make them tunable in terms of their uniformity, size, and mesporosity to improve the nanomaterial’s nucleic acid binding capabilities for gene therapy, RNA stabilization, vaccine adjuvants, and other biological applications. I will be conducting wetlab research and synthesizing ZnO nanoparticles and their composite colloid via a template method. The precursor will be dissolved, heated in an autoclave, and the resultant powder will be centrifuged and washed. Eventually, the nanoparticles and colloid mixture will be ground and sintered, followed by calcination at 300-450 C. For this semester, my focus will be on synthesizing enough tunable mesoporous nanomaterials for extensive characterization and biological testing.

Synthesis of Tunable Mesoporous Zinc Oxide Nanoparticles for Biological Applications

Our planet’s energy crisis and environmental pollutions, caused partly by our continuous use of nonrenewable energy sources, have led us to explore new technologies that can harvest energy from the environment. Vibrational energy harvesters, that rely on mechanical energy, have proven themselves to be a useful way of converting ambient energy into electricity. Piezoelectric materials are used as transducers for electromechanical energy harvesting systems. This research will investigate optimal piezoelectric energy harvesting designs.

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A Systematic Computational Analysis of Human Microproteins

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