United States

Molsamp 2021

Keynote Speaker - Kayunta Johnson-Winters

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The annual UI conference has grown from a meeting to a community of more than 6,000 researchers, program officers, evaluators, program directors, training faculty, and others within STEM, and now operates with a mission to mutually inform scholarship and practice, develop and enhance the community interested in educational interventions, and provide professional development for all STEM persons. UI held its first Virtual conference in December of 2020 in response to Covid-19 restrictions that forced the cancellation of the original March 2020 in person meeting. Born out of necessity, the virtual conference introduced a format that provided pre conference availability of symposia, Deeper Dives and Poster presentations. The 13th Annual Understanding Interventions Conference will continue with that design and we foresee future conferences adopting aspects of that format in reaching a virtual audience as well as those who attend the conferences in-person.

Attendees will experience plenaries, workshops, symposia, and poster presentations on hypothesis-based research on interventions and initiatives that broaden participation in STEM careers. The conference will continue to facilitate the dialogue among education, behavioral, and social science researchers, evaluators, and faculty in STEM fields who participate in intervention programs. Graduate students in these fields are strongly encouraged to attend. Workshops, sessions, and posters will communicate effective strategies from successful STEM intervention programs, present results from empirical research studies, and synthesize the translation of research findings into practice on STEM learning, undergraduate research, graduate education, and student retention.

The cost to attend the conference is $150. There are special rates for post-doctoral fellows and graduate students, as well as conference grants that can be provided for those with need. Registration for the conference is open, with additional information to come.

13th Annual Conference on Understanding Interventions

The annual UI conference has grown from a meeting to a community of more than 6,000 researchers, program officers, evaluators, program directors, training faculty, and others within STEM, and now operates with a mission to mutually inform scholarship and practice, develop and enhance the community interested in educational interventions, and provide professional development for all STEM persons. UI held its first Virtual conference in December of 2020 in response to Covid-19 restrictions that forced the cancellation of the original March 2020 in person meeting. Born out of necessity, the virtual conference introduced a format that provided pre conference availability of symposia, Deeper Dives and Poster presentations. The 13th Annual Understanding Interventions Conference will continue with that design and we foresee future conferences adopting aspects of that format in reaching a virtual audience as well as those who attend the conferences in-person.

keynote

https://www.dropbox.com/s/uy7tgnb198uiz0o/Abstract_Hamdan_FinalDraft.docx?dl=0

Machine Learning Approaches to Allow Unbiased Analysis of Opioid Withdrawal.

In the Milky Way galaxy, there exists a mysterious source of antimatter subatomic particles called positrons or antielectrons, the origins of which are currently unknown. The 511 keV photons emitted from positron-electron annihilations are the clues to understanding the origin of these positrons. The instrument responsible for the current, most-sensitive mapping of galactic 511 keV emissions is the International Gamma-Ray Astrophysics Laboratory (INTEGRAL). I am working on simulating high-purity germanium (HPGe) spectrometers using Geant4-based software to characterize the performance of the HPGe modules that will be placed in CubeSAT and SmallSAT payloads. These detectors will be the successor to INTEGRAL. In addition to the HPGe crystal providing the current highest resolution in mapping the 511 keV line, the plans to increase the active mass of the HPGe crystal while minimizing overall spacecraft mass will allow for higher detector efficiency while reducing noise caused by cosmic rays producing local positrons. These are some aspects of our detector that will provide a more sensitive mapping of the 511 keV line than INTEGRAL and allow us to further study the origin of these galactic positrons.

Simulating High-Purity Germanium Spectrometers Using Geant4-Based Software to Characterize Detector Performance

Back pain can originate from Intervertebral Disc (IVD) degeneration (IVDD), and inflammatory stimulation and degradative enzyme activity to contribute to the development and progression of IVDD. IVDD often develops through changes in nucleus pulposus (NP) structure compromising the function of the IVD. However, the metabolic effects of injury and localized inflammation and degradative enzyme activity on the NP is poorly understood. Therefore, this study was designed to determine the effects of NP stimulation with inflammatory cytokines (IL-1β, RANTES) and a degradative enzyme (MMP-1), shown to be produced by tissues during IVDD. It was hypothesized that localized stimulation of the NP within an IVD would result in significant increases in the pro-inflammatory and degradative metabolism, and decreased physical properties, of the tissue compared to controls. With ACUC approval, rat tail IVD explants were created, and assigned to the MMP-1 (Injury+injection), IL-1β (Injury+injection), RANTES (Injury+injection), PBS (Injury+injection), injury only, or uninjured control groups. A 25g needle was used to create an injury and inject 10ul of solution based on group. Explants were cultured for 6 days, media was collected for biomarker analysis, and IVDs were tested biomechanically and histologically. The IL-1β injection produced significantly higher levels of MIP-1𝛼, IL-6, MCP-1, GRO, IL-1β, and TIMP-1 compared to the PBS, RANTES, and MMP-1 groups on day 3 of culture, and MIP-1𝛼, IL-6, MCP-1, IL-1β, and TIMP-1 on day 6 of culture. The injury only group produced significantly higher levels of MIP-1𝛼, IL-6, MCP-1, and TIMP-1 compared to the RANTES and MMP group on day 3 of culture, and MIP-1𝛼, MCP-1, and TIMP-1 on day 6 of culture. The creep modulus in the IL-1β, RANTES, and MMP-1 groups were significantly lower than uninjured control on day 6 of culture. Average histology scores were higher for the MMP-1 and IL-1β groups compared to the uninjured controls. The data from this study indicates the IL-1β group was the most inflammatory of the treatments applied to the IVD, and produced significantly higher biomarker levels compared to other injection groups. Injury alone was more inflammatory than injury plus injection as the PBS, RANTES, and MMP-1 groups produced significantly lower inflammatory biomarkers compared to the injury only control. The IL-1β, RANTES, and MMP-1 resulted in physical changes to the IVD based on decreased creep modulus and higher histology scores. In conclusion, IL-1β elicited an inflammatory metabolic response, contributing to pain and inflammation in IVD degeneration and physical changes within the IVD, while RANTES and MMP-1 elicit physical changes in IVD degeneration but not a metabolic inflammatory response.

Investigation of the Effect of IL-1B, RANTES, and MMP-1 Injection into Nucleus Pulposus (NP) of Intervertebral Disc (IVD)

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.

Using Cerebellar Data to Predict Cortical Changes in GBMs

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.

Keynote Speaker - Kayunta Johnson-Winters

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Keynote Speaker - Kayunta Johnson-Winters

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Machine Learning Approaches to Allow Unbiased Analysis of Opioid Withdrawal.

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How to transform course in biology adn chemistry using high impact practices

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