Author(s): Anna McElhinny
Mentor(s): David A. Luther, Shawn Heath Smith, Biology
AbstractAuthor(s): Anna McElhinny
Mentor(s): David A. Luther, Shawn Heath Smith, Biology
AbstractAuthor(s): Cameron Hunt, Emma Ealley, Isabella Roman, Skylar Leih
Mentor(s): Toni Farris, Honors College
AbstractAuthor(s): Gwendolyne Fields
Mentor(s): David Luther, College of Science
AbstractYou can join me inside to meet some of the elephants. Here we have Nhi Linh and Trong Nhi, mother and daughter, 19 years old and 9 years old. For my OSCAR Research project, I focused on them and their behaviour.
To tell you more about the elephants here at Smithsonian’s National Zoo, we have seven elephants. So we have six females and one male, Spike. Our other females along with Trong Nhi and Nhi Lhin are Kamala, Rani or Maharani, we also have Swarna and Bozie. These girls came to us in November and they are from Rotterdam Zoo in the Netherlands. And that’s why I focused on them because we do not know much about their behaviours and it was really important for us to better their care if we understood more about their behaviours themselves.
So to talk more about my research project, I ended up focusing on the stress behaviour that Trong Nhi was showing. She basically would raise her head up and do a big yawn or bob her head. So we wanted to look more into that and see why that was occurring and when it was happening. So we did a preliminary research portion where we looking at video footages for a day and split it between two hours within different parts of the day to see what time of day would she maybe show this behaviour.
So to give you an idea about what the day is like working with the animal keepers. We start off our day feeding the elephants at 6:30 in the morning and then we start cleaning the enclosures, meaning picking up poop and cleaning drinkers, as you can see in this video. The animal keepers do their health checks on the elephants to make sure there are no injuries and they all seem in good condition. Once we have our lunch break, we feed them again with bamboos and green vegetables and fill up on their hay. This is what I help with once a week, on top of doing my research project.
So as you can see here, this is a video of Trong Nhi and Nhi Lihn first meeting Spike. One of the reasons that the girls came to the Zoo here is to eventually get them to mate with Spike. Further along the research, the behaviours seen with other members of the herd, including Spike would be very beneficial.
The data of Trong Nhi and Nhi Lhin were collected through an ethogram, essentially a spreadsheet of the different behaviors that were observed for the elephants. When doing the video footage, I used the ZooMonitor Camera System and was able to view footages of the elephants for up to 2 months in the past.
With the time I had during the semester on top of going to the zoo once a week, I was only able to look through 15 hours of footage during two days. This obviously was not enough to establish a real connection between the stress behavior that Trong Nhi was demonstrating and what was causing it, but we did notice that Trong Nhi demonstrated her stress behavior during earlier parts of the day, before 12:00 pm.
Overall, we have a better idea of when we should maybe pay more attention to this stress behavior and have the team come up with a solution to better her care. Thank you for watching, I hope you guys enjoyed your day at the zoo with the Elephants at Smithsonian.
Author(s): Daniel Nephew
Mentor(s): Toni Farris, Honors College
Author(s): Nene Uwaomah
Mentor(s): Jhumka Gupta, College of Public Health
Some background from how we got to this topic question; endometriosis is an understudied disorder of the uterus. Endometriosis is a disorder in the female reproductive system that occurs when tissues that normally line the uterus, grow outside of the uterus. It is a chronically painful condition with poor health related outcomes for those diagnosed with it. Endometriosis impacts 1 in 10 women, and is a condition characterized by debilitating pelvic pain and activity impairment that can also lead to infertility. Other than the lack of research into the experiences of women in these populations with endometriosis, there are also disparities in the diagnostic outcomes. Studies have found that Black women were less likely to be diagnosed with endometriosis (OR 0.49, 95% CI 0.2 9 -0.8 3)4, or were more likely to have a delay in diagnosis (µ=2.6 years older (95% confidence interval [CI], 0.5-4.6), when compared to white women(Li, et al, 2021).
The methodology used in the study included both qualitative and quantitative formats. First was an IRB approved Qualtrics survey prepared by Endo-Served. A project developed for the study of endometriosis in women of color in the DC, Maryland, and Virginia metropolitan area. The study identified key similarities between volunteers and their experience with endometriosis and the healthcare system. From the survey, significant numbers showed that discrimination was a factor with the women’s diagnosis along with stigma. From the survey participants a few women volunteered to participate in individual meetings to discuss their diagnosis story and experience in healthcare. Authentication of participants was prioritized. Their identities remain anonymous. It was ensured that the participants were aware of and comfortable with the study design and purpose. The interviews allowed the participants shared their difficulties with their diagnosis, as well as answer questions pertaining to their experience. The questions included;
Tell me your diagnosis story
2. Where did you first hear the word “endometriosis”?
a. Probe for family/friends, social media vs health care provider
3. In our data with ENDO-Served (explain what Endo-served is if they are not familiar), we are finding that women who reported learning about endometriosis for the first time from a family member/friend also reported more experiences with racism in a healthcare setting. Why do you think this is?
a. How, if at all, does this relate to your experiences?
4. What do you think needs to change regarding racism in healthcare settings?
The interviews resulted in similar answers between the four participants. ¾ of the women had to receive second and third opinions as their concerns were not taken seriously enough by their first physician. All of the women experienced excruciating pain, heavy bleedings, nausea, extended fatigue and some experienced problems with childbearing. Each woman explained their difficulties as they did not feel heard by their medical professionals and were stereotyped into the stigma that their pain tolerance was higher as black women. They were made to feel isolated and crazy, resulting in trauma of life alterations. When the women were diagnosed, all had to undergo surgery in order to remove cysts, fibroids, and growths. At the point of diagnosis, the women were in moderate and severe stages of endometriosis.
Most women had just first heard of endometriosis when they were being diagnosed, and those that did not first hear it through diagnosis, had looked up their symptoms through non-formal sources and discovered endometriosis. The cause of endometriosis is unknown, but it is believed to be a genetic or hereditary disorder. There is no cure, but a hysterectomy is the medical recommendation to cease major pain.
The lack of representation in research and clinical studies, is of consequence for women of color in the diagnosis, management and treatment of endometriosis which may result in misunderstanding, misdiagnosis and lack of appropriate treatment options when engaging with healthcare providers. This prompts a need for further research and action on this topic as there are many more women suffering in pain due to insufficient knowledge on this topic. Continued work on this study will help to foster a deeper understanding of the perceptions and stigma of this condition and additionally provide evidence for developing programming related to reducing barriers as well as time of diagnosis, addressing racism, and stigma, and facilitating help-seeking among Black women and other women of color.
Thank you for listening and I would like to also thank my mentors and co-organizers at George Mason University; Dr. Jhumka Gupta, Dr. Anna Pollack, Julia Mandeville, and Lauren Kornegey, as well as all the volunteers for the interviews and survey participants. And of course, a big thanks to Dr. Karen Lee and the undergraduate research scholars program and OSCAR for funding my project this semester.
Author(s): Molly Izer
Mentor(s): Jennifer Victor, Schar School
Abstract
Audio Transcript
Author(s): Dhruv Dewan
Mentor(s): Blake Silver, Sociology and Anthropology
Abstract
Audio Transcript
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My project is a focus on the relationship regarding sense of belonging as someone moves to College from High School. So in other words, what affects the sense of belonging in college students and what affects the sense of belonging in high school students and is there a correlation? I focused on this topic because I realized that I had a decrease in my sense of belonging as I came to college but other people had an increase. Trying to see if there was a correlation, I realized that there was some research on college sense of belonging, less research done on high school sense of belonging, and almost no research done on correlating this information. As such, I wanted to be the first one to do such research.
[Next Slide]
So I began with gathering information on each survey participant and moved on to asking them questions related to their sense of belonging in college, sense of belonging in high school, and asking them to compare their sense of belonging between these 2 time periods. Creating a collection of responses based on varying factors found in the demographic questions. Sadly because I am still concluding the survey, there is little to no analysis done at the moment.
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Now, what can we do with this information? Hopefully later down the line this information can be transmitted into a test for High School students that will be able to inform students what college may be best for their sense of belonging. However, of course, this is if we can find a correlation and do a lot more research. I hope that this research can be done in multiple countries and we can compare if there is a difference among borders.
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This research project couldn’t have been completed without some help. So thank you to Dr. Blake Silver, Dr. Karen T Lee, URSP, and finally OSCAR for funding this project. Thank you for listening!
Author(s): Teagan Corpening
Mentor(s): Jennifer Salerno, Environmental Science and Policy
Abstract
Audio Transcript
Introduction:
Coral reefs are important ecosystems because of the many ecological services they provide, including coastal protection and providing habitat for a wide range of species. Protecting coral reef ecosystems is integral to protecting the biodiversity of marine ecosystems and preventing shoreline degradation. Understanding coral reefs at the microscale is important because of the connection between micro-communities of algae and other invertebrates that live on the sea floor and large-scale coral reef health. Coral recruitment and cover are heavily influenced by micro-communities. By better understanding the influences on coral recruitment, restoration methods can be improved.
I am working with Jordan Sims, who is a PhD student working on a larger settlement project in Roatan, Honduras.
My project seeks to better understand coral reef succession at the micro-scale by studying how micro-communities change through time and how environmental conditions, primarily light, drive community succession. My project seeks to answer the question: Do benthic micro-communities change over time, in light vs dark? This was done by observing community succession focusing on six different functional groups (CCA, fleshy macroalgae, filamentous algae, turf algae, other invertebrates, and biofilm). The presence of different functional groups is important to differentiate between because some groups, like turf algae, are coral recruitment inhibitors while other groups, like CCA, are coral recruitment facilitators.
Study Site:
This study was carried out in Roatan, an island off the coast of Honduras in Central Latin America. My study site is a reef on the northwestern coast of Roatan called White Hole.
Methods:
[1] Ceramic tiles were placed on three platforms across the White Hole reef site in Roatan Honduras. For this project, tile images at each platform were taken once per month between June and September of 2021. Images were taken of both the top and the bottom surfaces of each tile. The primary difference between the top and bottom surfaces of the tiles was the amount of light available to the communities. The tops of the tiles received more light than the bottoms of the tiles.
Here’s an image of one of our tile platforms once it has been secured to the reef. Settlement tiles were secured to the platform using zip ties.
[2] Here are representative unedited tile images after the tiles were removed from one of the platforms. On the left, we have the bottom of the tile and on the right, we have the top of the same tile.
[3] I cropped the unedited images to remove the unnecessary pieces including the collection bag and the sides of the tiles.
[4] These are the labelled tile images. I performed labeling by hand in ImageJ. I identified 85 different morphospecies and traced the boundaries of each morphospecies. The morphospecies were then combined into 6 different functional groups. After labeling, I used ImageJ to calculate the relative abundance of each functional group in each image.
functional groups:
These are examples of the six different functional groups that I combined the morphospecies into. These groups include Crustose Coraline Algae, filamentous algae, fleshy macroalgae, turf algae, biofilm, and other invertebrates.
Results:
[1] Differences in community composition over time and between tile surfaces were calculated using Bray-Curtis distances. To compare the micro-community composition on the tops and bottoms of the tiles and the micro-community composition through time, PERMANOVA tests with 999 permutations were performed. There was no significant change in community composition over time, but there were significantly different communities on the tops and bottoms of the tiles.
[2] On this graph, we can better visualize the drivers of the differences in functional group composition between tops and bottoms of the tiles. Bottom surfaces had a significantly higher percent cover of biofilm than top surfaces. We can also see a higher percent cover of other invertebrates on bottom surfaces. However, top surfaces had higher percent cover of turf algae and fleshy macroalgae.
Discussion:
The tops and the bottoms of the tiles were determined to have different community composition. This is most likely because of the differences in light. These results are supported by other studies that have also seen changes in benthic cover and functional group productivity influenced by the abundance of light (Vooren, 1981). The greater amount of light available to the communities on top surfaces is likely what is driving the observed increase in algal growth.
This study only used a portion of the tile images and data that were collected in the larger settlement project. There are four additional timepoints from this reef site and another full set of images from a replicate reef site. For the next steps of this project, I’ll continue to analyze more tile images so that more timepoints and replicates can be included in statistical analysis in the future. We expect to see a significant pattern of change emerge as more tile images are included in the project’s scope.
Acknowledgements:
Thank you to Dr. Salerno for being my mentor, to Jordan for helping me with the analyses, and to Jennifer Keck for taking the tile photos.
Author(s): Urooj Syeda, Lauren Tong
Mentor(s): Toni Farris, Honors College
Abstract
Audio Transcript
Author(s): Joselyn Castellon Almanza
Mentor(s): Blake Silver, Honors College
Our population is becoming more diverse with each generation. With many financial and housing options, this has resulted in a big portion of the college student population being first-generation students yearly. This is representative in the literature, especially regarding first-generation identity and housing status. A study has shown that the main difference between the first and continuing generations is cultural capital. Cultural capital is a form of recognition passed down through generations that certifies one’s cultural competence. Continuing generation students, who had more cultural capital, could understand the professors’ expectations (Collier and Morgan, 429). It went further to say that even with similar academic skills and learning environments for the first and continuing generation, there was still a difference in performance in the student role due to cultural capital (Collier and Morgan, 442). All in all, first-generation students struggle with a lack of information and unknown expectations as they face higher education with no reference point. These findings underscore the importance of considering the unique experiences of first-generation college students.
I then investigated housing status cause housing status as a topic has been explored as learning communities, residential dorms, and commuting from home. These living spaces provide different resources that could aid the college experience. One study looked into housing status on academic performance. It saw that commuters earned a higher academic standing than residential students (Simpson and Burnett, 2019). This was interesting because a lot of past studies stated the opposite. It even went further to say that students’ academic status is based on how much they invest their energy into the college experience instead of the actual living situation (Simpson and Burnett, 297). And since studies have investigated its impact on academic success and retention, it led to me thinking of housing status and how it could impact college belonging.
College belonging is about a sense of security, and its investigation has been seen to support academic success. Thus, focusing on this topic could bring about ways to increase the quality of the college experience. A study has seen that college belonging matters as it works with well-being, high persistence, and graduation. This discussion of belonging can help us see its importance, its complexity, and how it provides a feeling of security (Nunn, 48). Another study has shown that socioeconomic status informs how students experience belonging (Ostrove and Long 2007).
This leads to my research questions; What impact does being a “first-generation college student” have on someone? What impact does housing status, commuting or residential, have on someone? How do these identities influence college belonging?
With these questions, I will explore the connotations and feelings behind the statuses of “first-generation” and housing, with specifics to the terms “commuter” and “resident.” I will then see if it may influence a student’s sense of self and how they think others may view them, thus affecting their sense of belonging.
My investigation is based on hourly in-person interviews with first-year students that fit those identities. There will be 16 interviews with 16 people. Eight will particularly fit under the commuting identity, and eight will fall under the residential identity. With all being under the first-generation college student identity. The interviews will then be coded with Dedoose coding software. It will look at what words or phrases are used frequently. I will then analyze it to see the reoccurring theme and draw conclusions from it.
My interview questions are based on three topics: first-generation identity, housing status, and college belonging. And here are some of my example questions. For example: “What comes to mind when you think of a first-generation student?”, “How do you think others view you based on your housing status?” and “Do you feel like you belong in your college based on your definition?”
Through this, how the first-generation experience and housing status affect college belonging will be further investigated. In the future, I hope it leads to more efficient resources for first-generation college students and college housing. As well as reform programs to help those students that fit those identities.
Thank you. If you have any questions, please contact me at [email protected].
Author(s): Xander Boit
Mentor(s): Nathalia Peixoto, Neuroscience Program
Abstract
Audio Transcript
My mentor is Dr. Peixoto and my department is bioengineering.
This was completed in Fall 2022 as a URSP. Thank you OSCAR for providing funding and support for this project.
So existing literature; familiarity in music has just generally a positive correlation with how the brain responds.
There are a couple of sources I used.
The first one says that familiarity in music has been reported as an important factor in creating emotional and positive responses in the brain.
And then the second source says additionally, music can improve cognitive performance, especially when it is familiar.
And so, essentially what I concluded from all of these and other sources I found is that previous research shows that there are clear benefits associated with familiar music in relation to mental state.
Now what I was looking for is how does this actually apply to an action. Because a lot of this research is kind of stationary or is just a task. But with the VR equipment, we have this opportunity to have them record the EEG data while they are actually doing a task that involves full-body movement and music at the same time.
And so that’s why I kinda chose to do this project.
So the technology that I have for this project is in three categories: Virtual Reality Equipment, EEG Recording Equipment, and the Data Analyst Software.
The first part is the virtual reality equipment. The lab I am in has the HTC VIVE Pro 2 Headset.
These two pictures you can see are pictures I took in the lab of the VR headset. The top picture being the actual headset and the bottom being the two hand-held controllers that you use when using the headset.
I had a decent amount of issues getting the VR headset to work properly throughout the semester, and it kinda delayed my project a decent amount.
The first one was I had issues with audio output. The headset actually has two speakers on it, you can see off to the side of the top picture. The speakers are tilted outward currently. They would not play audio. And if they did play audio, if I got it to work, they were super scratchy and basically unbearable. Obviously, this is a huge issue for an audio based project, so I spent about several weeks figuring this out and eventually I just figured out that unplugging the cords and replugging them back in until it works is actually consistent for some reason, and I haven’t had many issues with it since, which surprises me to be honest. But, if it works, it works.
Then the other issue, probably the bigger issue, if video resolution. Now, we have a pretty good graphics card in the computer that is attached to this VR headset. But this VR headset is so powerful that it can outperform the top-of-the-line graphics cards. I decided to use my whole budget in one go and buy a new computer, which has a 3060 graphics card and top-of-the-line for lots of stuff. It was a pretty good deal since graphics cards are cheaper right now. I bought that, but with the whole new computer, it got delayed three to four times due to miscommunication, product delays, and whatnot. I will be getting it in the middle of December. I currently do not have it to actually use for this project, but I will have it for next semester.
The video game I am using is Beat Saber, which is probably the most popular VR game. If you have seen any VR game, it’s probably been Beat Saber. It’s essentially Guitar Hero, but VR. You have the two controllers and you have to slash things.
I have a video actually, it’s about thirty seconds long. I can show a little bit of what it looks like. This may be a little loud. I recorded this in the lab. You can see the two sabers I am holding. My left hand is the red one and my right hand is the blue one. You can see I am just slashing with the arrows on the cubes with the beat of the music. So this is how we are making it into a rhythm game recording thing.
I think the really cool thing about this is the EEG part of the project. We have the Muse 2 Headband in the lab, and that is what I am using; there is a picture of it to the right there.
This Muse headband records at four different locations: left ear, left forehead, right forehead, and right ear. So basically it’s just around the outside of the skull.
The cool thing about this is that when I initially designed the project, we planned on having a full-cap EEG on, which meant that we couldn’t use it during the VR section. But with this one, you can actually wear this underneath the VR headset. So we can record the EEG and have the VR going at the same time, so we don’t have to worry about any gap in time or just any of that error that could occur. This is very useful for getting what is actually going on in the brain while the activity is happening.
The third part of the technology aspect is MATLAB. You probably have heard about it before if you know anything about programming. It’s essentially a software that allows me to filter and analyze EEG data.
I went into it with two main goals. To separate my data into key frequency bands, like delta, gamma, beta, alpha, and theta. These are different frequency bands. Essentially, there is known information about what these bands indicate: calm, stressed, active. Just a bunch of different things, but I am not going to go into all of them, since they are kinda specific in some categories.
We eventually want to use these key frequencies to compare power levels of each band against familiar and unfamiliar samples, because this is a matched-pair design. Meaning that for each person, we are getting a familiar and unfamiliar sample. We are going to link those two samples together and look at the difference essentially of these power levels of each of the five frequency bands.
The progress so far; we haven’t made all the progress yet since I have not collected data yet. Generally, I think I got behind in this project, which is my own fault. I have the data, its loaded into MATLAB, and I can separate the frequency bands. I just need to figure out how to filter it exactly, which takes a decent amount of time going through literature and seeing what previous researchers have done. So that is where I am currently at with MATLAB.
Here is a picture I generated using my MATLAB code. This shows the RAW EEG data of the four locations that I talked about previously. The left ear, left forehead, right forehead, and right ear. You can see it’s a mess, this is why we need to filter and separate them. Because right now, this doesn’t mean anything.
This is an example kinda thing. This isn’t exactly how I want my data to look, since it is filtered incorrectly for what I am trying to do. You can see this separates the data into the five frequency bands. You can see the levels and how this is. This is kind of what I want, then we are gonna take the power level of these and compare them against each other for the familiar and unfamiliar.
For research logistics. The IRB approval process; I started way too late. I got distracted with troubleshooting the VR equipment and didn’t realize how long it would take. Right now I am at the final stage of approval for the IRB, so hopefully I get that done and can start next semester as soon as possible.
I mentioned it before, but computer purchasing delays. Had a lot of miscommunications, and it’s been pushed back to the middle of December now, when it is going to arrive.
Future plans. Get IRB approval, which I just mentioned. Gather the data, after I have the approval from IRB. Analyze and report the findings. And depending on how the results turn out, publish a research paper on the results.
This is my work cited. Thank you so much for watching this video and let me know if you have any questions.
Author(s): Gwendolyne Fields
Mentor(s): David Luther, Environmental Science and Policy
Abstract
Audio Transcript
So, a lot of our cats here are permanent residents. So, this means that a lot of them have come in before the age of 6 months and because of that, their mothers were not able to teach them the proper skills to actually survive in the wild. So, some skills including what prey to hunt, such as duikers, steenboks, springboks, kudus and oryx’s as well as what predators to avoid: lions, snakes and hyenas. And so, a lot of them have actually also been useful for research and understanding more about the species themselves and how to conserve them the best.
So here on our Namibian campus, we have 29 cheetahs and on Somaliland’s facility we have 86 cheetahs. So a lot of these cheetahs have come in at very different ages, so these siblings came to us at the age of four weeks. Others have come in at 5 days, 10 days, so very young. A lot of the cheetahs that have come in were rescued with different scenarios, so some farmers actually might have killed the mothers, some farmers might have captured them and in other cases people have kept the cheetahs as pets as well. So, like I mentioned before, having them here has been very useful for research and also their conservation.
Here are three of our cheetah cubs at CCF who arrived when they were 3 weeks old. Speaking of cheetah cubs for my research project, I have been working with Dr. Laurie marker, and other CCF staff members as well as my mentor Dr. David Luther where I am investigating the comparison of Somaliland and Namibian cheetah cubs growth rates. From data collected back from 2006 until most recently 2022, I have been gathering all this information on a new excel spreadsheet from individuals between the ages 0-6 months.
Other than spending time on my research project, I have been part of the cheetah team working to take care of the cheetahs by feeding them, giving their meds if needed, cleaning and managing their enclosures.
Moving on to the results for the project so far. I have created some graphs as well as produced some statistical analysis of the data through RStudio.
This first graph demonstrates the average weights of Somaliland and Namibian Cheetah cubs from the age of Day 1-14. As you can see there is a linear relationship between the weights and days of the cubs. The R-squared value is around 0.59 indicating a moderate goodness of fit of the model. We notice the Somaliland average weights are less than the Namibian weights. The second graph demonstrates the average weights of Somaliland and Namibian Cheetah cubs from the ages of Week 3-26. The R-squared value of 0.91 demonstrates a very strong linear relationship. Once again, the Namibian weights are larger than the Somaliland weights on average.
The third graph is a boxplot displaying the first two weeks of age. We notice the Namibian data appears to have more range and a higher average weight. The last graph is a boxplot displaying weeks 3-26 of age. We notice the Namibian data appears again to have more range and a higher average weight; however, it appears less robust than the previous graph. Running these last two graphs’ data through R, we found no significance in terms of their weights and comparing them between the two locations. Meaning the p-value was greater than 0.05.
Last but not least, the sum of the weights for all weeks did not appear to have significance with meat and milk consumption, yet the location of Somaliland was significant indicating their cubs are less likely to survive, and the males in general appear to have higher growth rates than females (indicating greater chance of survival).
We still have many questions to answer, such as how much the cheetah cubs grow in general, what month they grow the most and how exactly does diet affect their growth, especially relating between the two locations of cheetahs.
Thank you so much for watching and for everyone who has helped me on my project so far! Bye!