United States

Molsamp 2021

Professional Development

### Welcome to the 2023 Understanding Interventions/NSF CREST Annual Conference
Welcome to our annual conference, a gathering where the knowledge of our scientific communities share what they have researched since we last met. If it is your first time attending, then thank you for stepping into our world! If you are a returning guest, then thank you for coming back! Whether you are attending for the first or the fourteenth, we treasure your trust in what we have to offer. We promise to make it worth your while.

As in years past, we have put together an energetic collection of seminars, deeper dives, speeches, posters, and workshops to whet your appetite for learning. 

We are delighted that you have chosen to attend the 2023 Annual Understanding Interventions (UI) Conference, in partnership with the NSF Centers for Research Excellence in Science and Technology (CREST) Program. 

### About the Conference 
This conference was established to facilitate dissemination and exchange of hypothesis-based research on interventions and initiatives that broaden participation in science and engineering research careers. The conference is designed to create a dialogue among behavioral/social science and education researchers, evaluators, and faculty in STEM (science, technology, engineering, and mathematics) fields who participate in intervention programs. 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.

Thank you again, and welcome!<br><br>
**[Download Program Agenda](https://drive.google.com/file/d/1rTKTrWC2DOqnIF06po5xo4buAL3mPTqZ/view?usp=sharing)**<br><br><br><br>

15th Conference on Understanding Interventions 2023 

This conference was established to facilitate dissemination and exchange of hypothesis-based research on interventions and initiatives that broaden participation in science and engineering research careers. The conference is designed to create a dialogue among behavioral/social science and education researchers, evaluators, and faculty in STEM (science, technology, engineering, and mathematics) fields who participate in intervention programs.

live discussion

<figure class="video_container">
 <iframe width=1280 height=720 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/crfQFCVnYVB?ff=1&title=0&controls=1" allowfullscreen="true"></iframe>
</figure>


![](https://assets.underline.io/uploads/markdown_image/1/image/cc97440256914aab6b06659c769c9279.jpg)

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.

Downloads

Next from Molsamp 2021

Machine Learning Approaches to Allow Unbiased Analysis of Opioid Withdrawal.

Similar lecture

Postdoctoral Professional Development

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from Molsamp 2021

Machine Learning Approaches to Allow Unbiased Analysis of Opioid Withdrawal.

Similar lecture

Postdoctoral Professional Development

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads