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): Autumn Gray
Mentor(s): Klaus-Peter Kopefli, Smithsonian-Mason School of Conservation
AbstractSo the Saharan striped polecat and zorilla are both endemic to Africa with their range being found here. Here is where the, the yellow was where the zorilla is from and then the green is where the polecat can be found. They are lesser-known mesocarnivores and what that means is their primary diet is meat and flesh and they are less than 16 kilograms. They are the only two species to be found within Ictonyx. And they are listed as least concern on the IUCN Red List. And they don’t have much known about them.
So a few semesters ago, I did my research on the first complete mitochondrial genome of the Saharan striped polecat, so we sequenced, assembled, annotated, and analyzed that, um, mitogenome and placed it within the phylogenetic tree. It can be seen here, um, basically that study confirmed its placement within the phylogenetic tree and that it is in fact in Ictonychinae. It is also, ah, the polecat evolved before the zorilla, um, which is uh a species it is related to.
So this study, after that one, I was left with many questions, um, one of which is that there is only three complete mitochondrial genomes of these two species. So two being the zorilla and the one being the polecat I assembled, um, so I basically realized that their range is really large and I wanted to see how its genetic diversity differed across its range. Um, so we predict that both species will show genetic differentiation across their respective ranges just because of how large the their ranges are, along with them being very small species so their distribution ranges are a lot smaller than say megafauna, like an elephant.
So to do this we used the 23 frozen modern samples my mentor had and then we also seeked approval to use 13 historical samples provided by the Smithsonian Institution’s Museum Support Center. Um, so we will sequence those and those will be what is used for this study.
Now the historic samples, I want to give an idea of what they looked like. There was basically just a lot of skulls and skins, um. These photos are not from the Smithsonian, there from some other institution. Um, but on the left is a skull, um this is not what we would be looking for specifically for our samples. The samples we use have a lot more tissue on it since we scrap of the tissue off the skull and that’s what we are going to extract the DNA from. So the skull pictured here too clean for us to use. And then the skin we just use clippings a part of it, um, but we are going to try and use the skull instead of the skin.
So, we haven’t gotten this far, but basically after our DNA is collected or our samples are collected we are going to start sequencing. So we sent out the modern samples to Psomagen for DNA extraction, PCR, sequencing, and library preparation and we will do the same with the historic samples but we will do that in the historic DNA lab and then send them off. We then will get the samples back and I will run FastQC for quality checking. Um we will downsample, map to reference, and then annotate the consensus. And then we will do multiple sequence alignment and then a phylogeny network mapping. Um to see how different the different localities of the polecat and zorilla are. And then we will publish all of the annotated mitogenomes and put them on GenBank so other researchers can use it and we will publish our findings in a journal.
So we didn’t get too far this semester but we still have a lot of work to do. We have to sequence and analyze all of the samples, um and we are going to figure out if the species has a large genetic differentiation throughout its range, like predicted. Um.
So yeah, thank you to all of my mentors and supporters throughout this project. Um Dr. Koepfli, Dr. Figueiró, Dr. Edwards, Dr. Brito, Dr. Hawkins, Dr. Ferguson, Medhini, um, the Smithsonian Institution Museum Support Center, Dr. Lee, OSCAR, um College of Science, and everyone at SMSC.
So yeah, thank you for listening.
Author(s): Cynthia Davis
Mentor(s): Heather Green, Visual and Performing Arts
AbstractHow do we discern truth when we are lost in the labyrinths of our own realities?
How do we return to our roots when we have lost all hope?
The Office of Student Scholarship, Creative Activities, and Research at George Mason University granted me the Undergraduate Research Scholars Program grant, with my ambitious proposal of writing a book. In one semester.
Starting in January 2023, I began to scour through my artworks and writings from 2020 until present.
Every week, I took my laptop to the local coffee shop, got some coffee, and sat for a couple hours writing and writing.
My goal was to create an accessible narrative memoir of my experience with surviving abuse, how art and writing made an impact on helping me alleviate symptoms of trauma and post traumatic stress disorder, and craft a section with real research in the growing field of art therapy. I wanted to be part of the conversation in the intersection of art, psychology, and advocacy.
Within 4 months, I finished an 80 page book. With the help of my incredible mentor, professor Heather Green, I was able to find out how to self publish with the funding provided by the Undergraduate Research Scholars Program. The book will be available on Amazon and other platforms.
How do we discern truth when we are lost in the labyrinths of our own realities?
How do we return to our roots when we have lost all hope?
The Office of Student Scholarship, Creative Activities, and Research at George Mason University granted me the Undergraduate Research Scholars Program grant, with my ambitious proposal of writing a book. In one semester.
Starting in January 2023, I began to scour through my artworks and writings from 2020 until present.
Every week, I took my laptop to the local coffee shop, got some coffee, and sat for a couple hours writing and writing.
My goal was to create an accessible narrative memoir of my experience with surviving abuse, how art and writing made an impact on helping me alleviate symptoms of trauma and post traumatic stress disorder, and craft a section with real research in the growing field of art therapy. I wanted to be part of the conversation in the intersection of art, psychology, and advocacy.
Within 4 months, I finished an 80 page book. With the help of my incredible mentor, professor Heather Green, I was able to find out how to self publish with the funding provided by the Undergraduate Research Scholars Program. The book will be available on Amazon and other platforms.
Author(s): Delaney Soliday
Mentor(s): Laura Sauls, Global Affairs Program
AbstractSlide 2: In this study, I asked three primary research questions:
1. How have trends in terrorist attacks on vulnerable communities changed over time?
2. How have trends in terrorist attacks on civilians changed spatially?
3. How do patterns in terrorist attacks on detained populations compare with patterns of attacks on IDPs and refugees?
Slide 3: One of the key concepts I discuss in this study is what I call “strategic soft targeting.’ I define this term as “terrorist attacks on civilians with limited control over their own mobility.’ These types of vulnerable civilian communities include internally displaced persons, refugees, prisoners, and detained individuals.
Slide 4: These two maps compare SST activity in the Middle East and sub-Saharan Africa. On these heat maps, the warmer a color is, the denser SST activity is in that location. It is interesting to note that much of this activity is clustered around border regions and major cities in both regions, but attack distribution in the Lake Chad Basin is much denser than it is in the Middle East.
Slide 5: This map identifies statistically significant hot spots utilizing the Getis-Ord Gi* statistic. The test revealed that most SST activity conducted by the Islamic State and its affiliate groups between 2006 and 2022 is located in the border regions where the four Lake Chad Basin countries- Niger, Nigeria, Cameroon, and Chad- meet. That yellow bullseye we saw in the previous heat map is a statistically significant hot spot.
Slide 6: This map looks at the same cluster of four countries, but color codes local government areas or provinces with recorded SST activity by aggregate number of deaths. I found that each LGA saw an average of 32.5 deaths due to SST activity. A March 14, 2014, attack on the Giwa Military Barracks resulted in at least 622 deaths, though total casualties vary by source (BBC News 2014), which significantly skews the overall average deaths per LGA. That attack occurred in the local authority of Konduga, which is represented by the darkest shade of red on this map. When the Giwa attack is excluded, the average number of deaths drops to 18.67. This figure confirmed that the highest numbers of deaths are also occurring in the region with the highest numbers of SST attacks on civilian targets.
Slide 7: My analysis resulted in four primary findings.
• Terrorist attacks on civilians are, on average, three times deadlier in sub-Saharan Africa than they are in the Middle East and North Africa.
• Strategic soft targeting activity tends to center around 1) major population centers and 2) border regions in both the Middle East & North Africa and sub-Saharan Africa.
• All statistically significant hot spots of SST activity in sub-Saharan Africa are located in the border regions where the four Lake Chad Basin countries-Niger, Nigeria, Cameroon, and Chad-meet. Most attacks resulting in casualties occur in this region, as well.
• Attacks on detained persons and prisoners are primarily confined to Nigeria.
My findings demonstrate that security studies scholars can better understand trends in terrorist activity using GIS methodologies. My results also have potential applications in the policymaking sphere when it comes to determining U.S. counterterrorism priorities. We have historically placed a lot of emphasis on CT operations in the Middle East, but we may need to pivot our focus to sub-Saharan Africa, where groups have much more latitude to operate in ungoverned spaces.
Slide 8: I would like to thank my two project advisors, Dr. Sauls and Dr. Ashley, for their support throughout the research process, as well as the Global Affairs Department and the library’s Digital Scholarship Lab. Many thanks as well to OSCAR and the Undergraduate Research Scholars Program for providing not only funding for this project, but mentorship and community.
Author(s): Cameron Hunt, Emma Ealley, Isabella Roman, Skylar Leih
Mentor(s): Toni Farris, Honors College
AbstractAuthor(s): Elijah Pointer
Mentor(s): Dr. Shaghayegh Bagheri, Volgenau School of Engineering (Mechanical Engineering)
AbstractIn this video, I will first explain what PEEK/HA is and how it can be applied. Next, I will describe the filament extrusion process and different manufacturing methods of the material. Lastly, I will provide my results, difficulties, and progress since last semester.
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PEEK/HA is a composite material composed of polyether ether ketone and hydroxyapatite. In short, a composite material is a combination of different materials with the intent of combining or improving specific attributes.
PEEK is a high performance polymer with characteristics such as, but not limited to: biocompatibility, non-toxicity, radiolucency, and a bone-like elastic modulus. One of its few drawbacks is its inherent biological inertness. Supplementing it with an additive such as HA, however, reduces PEEK’s inertness and can encourage bone growth.
This in particular opens up the possibilities of utilizing PEEK/HA in orthopedics, and, given its plasticity, even 3D printing bone scaffolds and implants. This could be used to repair bones as you can see in the image on the left. A basic, but real 3D printed PEEK scaffold is shown on the right.
To use PEEK/HA material in 3D prints, the composite material is often made into pellets and drawn into filament using an extruder such as this one. Pellets are dropped into the funnel, where an auger pushes them further into the barrel, where they are melted down and pushed out of the nozzle as a thin filament.
The first manufacturing method I employed to create PEEK/HA material was dry mixing. This method involves mixing the PEEK pellets with HA powder one to two times at 3000 rpm for 30 seconds. I would then extrude the pellets into filament.
The second method, re-extrusion, is really just an extension of dry mixing. Basically, I would create the dry mixed filament but then cut it into pellet sized pieces and run them through the extruder one to two more times to better mix the composite material.
In situ was the final method I employed. Unlike dry mixing, the in situ method chemically forms PEEK/HA in one go. As such, it is the most complex and time consuming of the three. It involves adding various compounds into a heated mixture and gradually increasing the temperature until the solution is formed. It is then poured out into a tray and broken into extrudable pieces.
Each method aimed to improve the thoroughness of the PEEK/HA mixture at the cost of time and simplicity. The original goal of my research was to 3D print mechanically testable samples using each method, but given the inherent difficulty of extruding and printing with a high melting point thermoplastic, I spent most of this semester just trying to print dry mixed samples.
The image on the left details cylindrical PEEK compression samples which I printed for practice before using the composite material. This allowed me to get a better understanding of the intricacies of 3D printing with PEEK before beginning the process with PEEK/HA. The image on the right details my two closest attempts at creating rectangular prism PEEK/HA compression samples. The one on the left failed during printing due to a section of the extruded filament diameter being too large for the 3D printer to use. The one on the right experienced a similar problem in addition to gaps in the layers likely due to the problematic diameter again or uneven cooling.
Overall, practice with regular PEEK filament and utilization of better adhesive techniques brought me one step closer to printing a complete and testable sample compared to the previous semester. However, when I continue this project this coming summer, I might consider using an automated filament winder to obtain a more consistent diameter. I also want to investigate the effects of speed on the quality of the prints as well.
Thank you.
Author(s): Ewen Crunkhorn
Mentor(s): Caroline Hoemann, Department of Bioengineering
AbstractThe lab I did my experiment with was studying Lox-1 expression in Covid-19 immune response, specifically in immune suppressive cells. However, during experimentation different antibodies showed conflicting molecular weights, and upon further literature review, these molecular weights were supported in studies that used a specific antibody, but were still in conflict across antibodies. As such, we sought a way to show the specificity and sensitivity of an antibody to Lox-1. The slot blot was identified as a potential avenue for validation.
We chose a variety of factors as follows. Two versions of the extracellular domain, or ECD (one denatured to show potential conformational bindings) and one version of the intracellular domain, or ICD, at 3 concentrations to test for sensitivity. Bovine Serum Albumin (BSA) , a bovine protein, and cell extracts shown to have negligible amounts of Lox-1 were used as negative controls, while the antibody itself and cell extracts shown to contain high levels of full length Lox-1 were used as positive controls.
Here we have the ICD antibody results. The first antibody is from ThermoFisher, and only a very faint band of the antibody positive control is seen, so it would have to be retested at higher concentrations. The second is Abcam, and shows even binding to the cell extracts, but only faint binding to the ICD. This would also have to be retested as the positive control band is not visible, but does suggest that the antibody is more sensitive to other protein species. Finally the Biorbyt antibody showed very high recognition of the ICD peptide and positive control. It also bound to both cell extracts, though much fainter.
Here we have the two ECD antibodies. The Biotechne antibody shows only faint binding at the highest non-denatured ECD concentration, which suggest a conformational binding to Lox-1. The Sigma antibody only shows binding the negative control cell extracts, suggesting that it recognizes a species other than Lox-1. Both would need to be retested as the positive control bands are not visible.
In conclusion, the slot blot shows promise as a method to validate commercial antibodies for laboratory use, but needs refinement for publication data.
Author(s): Gabriel Yu
Mentor(s): Byunghwan Son, Global Affairs
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): Isaac Amouzou
Mentor(s): Ben Seiyon Lee, Department of Statistics
AbstractWhat are DBPs?
Disinfectant byproducts or DBPs for short are chemical compounds that form when disinfectants in water react with natural organic matter.
Chronic DBP exposure can cause significant negative health effects, such as bladder cancer, colon cancer, and pregnancy complications.
A person can be exposed to DBPs through highly disinfected water sources, for example, chlorine interacting with organic matter in water. To ensure drinking water safety, it is imperative that DBP levels in public water sources be properly monitored.
Unfortunately, DBP exposure is difficult to measure directly. Instead, epidemiologists have been using Trihalomethanes or THMs, which are easier to measure, as a surrogate for DBP exposure. This was because it was believed that THM concentrations are proportional to concentrations of other DBP classes.
A previous study examined the link between THMs and a DBP class Haloacetonitrile or HANs using over 9500 measurements from 248 public water systems. This study found that THMs could only explain 30% of the variance in HAN concentrations.
For the project, we wanted to create a statistical framework to model the concentrations of hard-to-measure unregulated DBPs that drive toxicity using a wide array of co-occurring DBPs.
We also wanted to take into account the public water system (PWS) of origin for the measurements.
The data used is from the Information Collection Request database from the environmental protection agency or EPA. The dataset has more than 13,000 measurements from 295 public water systems.
For models, Linear mixed models or LMMs were used. LMMs are useful for data with high variability between groups. And in this specific case, the variability between public water systems can be considered.
LMMs allow estimation of the fixed effects (ex: DBP concentration or categorical variables) that can be measured while accounting for the variability among groups (ex: Public Water System) with random effects.
For variable selection, LASSO regression was used, which allows us to select important variables using a penalization approach with a tuning parameter, lambda.
as you can see on the top graph here as we increase lambda the coefficients tend towards 0
and in the bottom graph you can see how we select lambda, by running LASSO regression with multiple lambdas and measuring the mean squared error, which is a metric used to evaluate prediction accuracy.
We select all DBPs that have nonzero coefficients.
Multiple model structures were tested against the selected model structure (Full LMM)
The metrics used to evaluate models included AIC and BIC which are for comparing Goodness of fit (lower is better)
Conditional R-squared which measures the proportion of variance of the response explained by the model. (higher is better)
RMSE which is for comparing prediction accuracy.
We found that for four different DBP classes (haloacetic acids-5 haloacetic acids-6, haloacetonitriles, and haloketones), we can model a significant amount of the variance with a unique group of DBPs when we take into account the water system and key categorical variables.
In the upcoming summer, we plan to conduct an in-depth missing analysis of data and prepare new models for better estimation of the concentrations using the information gained from this model. We also further plan to develop a methodology to classify at-risk water treatment systems.
This is my work cited and thank you for listening to my presentation.