Category: College of Engineering and Computing

BURT: A Bug Reporting Chatbot

Author(s): Kristen Goebel, Jasmine Obas

Mentor(s): Kevin Moran, Computer Science

Many bug reports written by end users lack key information, such as the steps needed to reproduce the bug. Without this information, it can be difficult to reproduce and assess the reported bug, leading to more time spent working on resolving bugs as well as reported problems that cannot be fixed because they cannot be reproduced. Our work this summer focused on doing analysis for BURT, a bug reporting chatbot which aims to improve the quality of bug reports. We used various tool and metrics to assess the quality of existing bug reports and app reviews, creating a baseline for the current quality of reports and highlighting areas for improvement. In particular, we have made use of a tool which uses neural sentence classification and linear support vector machines to identify whether important information is included in a given bug report. Furthermore, we also analyze the readability of bug report prose through the calculation of three complementary metrics: spelling mistakes, grammar mistakes, and language regularity. Additionally, we have assisted with collecting data for and setting up a user study that will be used to evaluate BURT in comparison to existing bug reporting systems. Our research this summer lays the groundwork for illustrating the benefits that interactive bug reporting systems can have for both end-users and developers — leading to the creation of more informative bug reports for developers, with low effort required from end-users.

[0:01] My name is Kristen Goebel and I’m a senior at Clarkson University studying math and computer science. And my name is Jasmine Obas and I’m a senior at George Mason University studying Applied Computer Science with a Concentration in Computer Game Design. This summer we worked with Dr. Kevin Moran on BURT, a bug reporting chatbot. [0:20] To start, we’ll take a look at the current state of bug reporting. Ideally, end users and developers report issues with software by creating informative bug reports that allow developers to easily understand, recreate, and fix these issues. [0:35] Unfortunately, these bug reports are often lacking key information, especially in the steps to reproduce the bug. These steps are often missing, ambiguous, or incomplete, making it difficult to recreate the bug. [0:46] Lack of information is such an issue in bug reports that in a petition to GitHub, GitHub developers noted missing crucial information, like reproduction steps, as a major issue they feel needs to be addressed by the site. [1:00] As a result of these issues, many bug reports cannot be reproduced and go unfixed. More time may be needed to understand and assess the severity of the bug, and bugs may take longer to be resolved. [1:14] To help improve the quality of these bug reports, we propose to use BURT, a bug reporting chatbot. This chatbot allows users to interact with a messaging interface that guides them through reporting the necessary information to make their bug report more complete and more valuable to developers. The goal of the chatbot is to ensure reports contain, at minimum, the observed behavior, the expected behavior, and the complete set of steps to reproduce the bug. [1:39] BURT is able to help users report the complete set of steps to reproduce by using its app execution model. This graph contains possible steps that can be taken in the app based on the app’s current state. This allows the chatbot to suggest possible next steps to the user. This graph is obtained through automated exploration and manual traces of the app. [2:02] In our motivational study we have the BEE API Tool that was used to analyze two datasets in our study. Which was ARMiner and AndroR2 which include bug reports and app reviews from various Android applications. The tool would take in the text in json format and return the texts tagged with one or more of the three components SR, OB, and EB. We then took the data on our collected bug reports to create a chart of the results and give a visual of what users usually forget in the reports and the underlying problem that they usually are missing key information for a standard bug report. Being able to tag these texts with the api also shows the benefit of it and how we can use it to detect when these key components are missing and prompt the user to provide this information before submitting a ticket. [2:54] To give a closer look at the BEE Tool components we’ll go over what these tags mean. We have OB for Observed Behavior, meaning the user shared the issue they observed. EB for expected behavior, which meant the user shared what the behavior should’ve been. And lastly, SR which stands for steps to reproduce, meaning the user shared the steps to find the exact issue. These components help the developer better organize the reports received, as well as get a quicker understanding of what the bug reports are about. [3:27] The ARMiner, which is one of our data sets that I mentioned, is a dataset of app reviews on Android Applications. We processed this dataset in our BEE Tool and Language Tool. BEE tool tagged the components of the texts for meaning and the Language tool detected grammar errors in the texts. These were our results with that dataset and based on these charts we can see that grammar and information in bug reports is a huge barrier in bug report comprehension. We will be repeating this same process for AndroR2 which is a dataset with over 82,000 bug reports. [4:04] Our user study involves having participants record and document app traces in various android apps. They’d do this by using an Android Emulator on Android Studios, to get a virtual phone to download selected apps we provide. To collect an app trace the user would download the app and familiarize themselves with the layout first. Then when they’re ready to begin they would have to delete and reinstall the app for each trace they will make, which is usually around 5. To record their app traces they would use a capture tool developed by one of our researchers. Once this tool was started they could complete one app trace, and during this the tool would record the screen and log the components the user selects on the GUI. Once the user was done with their app trace they would end the capture tool and all the mentioned data would be saved to a file. The user would repeat these steps for each individual app trace. [5:01] To summarize, users often create low quality bug reports that can make it difficult for developers to fix these bugs. Our work this summer shows the current quality of bug reporting and will serve as a baseline for assessing the results from the future BURT user study. Our goal is for BURT to be used to help end users generate higher quality bug reports, improving the software development process.

College of Engineering and Computing Undergraduate Research Scholars Program (URSP) - OSCAR

Surface Degradation of Additively Manufactured ABS for Naval Applications

Author(s): Daniel Hernandez

Mentor(s): Ali Beheshti, Mechanical Engineering

Additive manufacturing (AM) has played a great part in the manufacturing process of several industries due to its rare shape-making ability that can optimize the creation of parts. Within the marine industry, the application of AM is confined due to the remaining questions regarding the degradation of polymers in a saline environment and whether AM is more viable than its traditional manufacture (TM) counterpart. In this work, the surface degradation of AM and TM ABS samples is analyzed in a simulated marine environment. The simulated environment is created by having a set temperature of 22°C, 27°C, and 105°C respectively where the samples are aged for a duration of 4, 8, and 16 days. The ABS material is molded and cut into rectangle samples for comparison with its 3D printed counterpart. Surface tests are conducted using a DektakXT profiler testing machine to measure the degradation of the surface roughness of each sample. Microindentaion tests will be done to show determine the average elastic modulus of the ABS samples. This research will help with the widespread adoption of AM ABS in the naval industry as well as provide information regarding the viability of AM in a corrosive environment.

Hello, my name is Daniel Hernandez. I am a rising junior mechanical engineering student, here at GMU. This summer I had the privilege of working with Ali Beheshti on the surface degradation of additively manufactured ABS for naval applications. Additive manufacturing is a disruptive technology that has become a forefront in many industries due to its ability to create virtually any geometrical 3D structure at exceptional quality. There are various forms of additive manufacturing and materials that can be used in the manufacturing of a product. A range of materials from metals to ceramics can be used in the additive manufacturing process, however, polymers such as ABS and PLA are the optimal choice given their excellent durability, strength, and thermal resistance. Many of the current additive manufacturing processes involve adding successive layers of material on top of each other vertically until the object is created. This creative form of production has opened the door for endless possibilities in many industries. As revealed by the medical industry additive manufacturing has been used to create more realistic and affordable casts and prosthetics. Where there has even been a success in research studies in additively producing organs such as a small-scale functioning heart. Currently, in terms of additive manufacturing in the naval application, many polymers have been used for surface-ship repairs and recreation of existing ship parts. Naval Sea Systems Control, a system that is responsible for the design, deliverance, and maintenance of ships for the US Navy, currently uses additive manufacturing for the replacement of equipment knobs and door bolts. However, for the use of additive manufactured parts for at sea application the usage is rather selective due to the significant design factors the corrosive environment, and high temperatures the ocean imposes on marine components. The questions that were aimed to be answered by this project are: To what degree does the surface of additively manufactured and traditionally manufactured ABS polymers degrade in a simulated saline environment? and how viable is additively manufactured ABS polymers in a saline environment in comparison to its traditionally manufactured counterpart? Studies have shown after extensive exposure to the marine environment the cross-section of the additively manufactured polymers stayed intact and showed little to no degradation rather the polymers were shown to chemically break down layer by layer starting at the surface due to water exposure. Resulting in the formation of pores, indents, and film lines. Based on a similar experiment done by the University of Alabama, an aging process was modeled to expose ABS samples to a simulated saline environment. Where sets of samples are to be submerged for 4,8, and 16 days in varying temperatures. Tanks, artificial seawater, and heating equipment were ordered in preparation for the submergence of the polymers. Rectangular ABS samples were modeled through the use of CAD inventor and were fabricated through the use of fusion deposition modeling. Following this traditionally manufactured ABS samples were ordered from a plastic manufacturer. Although we were unable to start the experimental or aging phase of the project this semester it is our hope to carry on the project in the spring of 2021 to observe the change in mechanical strength, wear, and friction of the surfaces of the ABS polymers. Based on the outcomes of the degradation of other polymers in previous studies it is anticipated that the traditionally manufactured polymers will perform significantly better. Thank you!

College of Engineering and Computing

Proof of Concept for Creating a Mixed Reality Application for AAC Users

Author(s): Adrienne Hembrick, Swati Rampalli

Mentor(s): Craig Yu, Computer Science

Augmentative and alternative communication (AAC) is a communication mechanism for those with complex communication needs, and existing AAC devices are forms of assistive technology containing hardware and software that can support or replace natural speech entirely. Augmented reality (AR), a user’s visual perception supplemented with additional computer-generated sensory modalities, is rising in its abilities to support assistive technology through rehabilitation therapies helping with motor and visual impairments. However, while immersive learning such as augmented and virtual reality have greatly supported individuals with disabilities, current AAC devices don’t carry the contextual intelligence to prompt appropriate conversation choices or phrases based on a user’s environment. This is particularly concerning for emergency situations where real-time communication is important for supporting AAC users who not only have to consider their decreased communication accommodations but navigate a crisis with heightened emotions. This prompts the need for an AI-driven AAC system aware of the environmental situation and demand. Major fields of computer science research, such as human computer interaction and computer vision are being leveraged to support context-aware, adaptive technologies to empower those with communication impairments. We discuss a proof of concept in creating a mixed reality application that will not limit AAC users’ vocabulary usage, by using object detection, conversation retrieval, and user interface display on the Microsoft HoloLens to create a context-based, self learning AAC system.

[introduction slide] My name is Adrienne and I am from Virginia Commonwealth University, and I am from Swati and I am from the University of Minnesota Twin-Cities. We are both working under the guidance of Dr. Craig Yu in creating a proof of concept for a mixed reality application that will help AAC users in everyday communication assistance.

[motivation]

In understanding the motivation behind this, augmentative and alternative communication (AAC) devices are forms of assistive technology being utilized to provide a communication mechanism for those with speech disabilities or other complex communication needs, containing hardware and software that can support or replace natural speech entirely. Augmented reality (AR), which is essentially overlaying virtual objects in the physical world, is being highly integrated into assistive technology, a prominent example being rehabilitation therapies for those with motor and visual impairments. However, while immersive learning is rising in its abilities to support those with disabilities, current AAC devices don’t carry the contextual intelligence to prompt appropriate conversation choices based on a user’s environment. This is especially important for emergency situations where real-time communication is important for supporting AAC users who not only have to consider their decreased communication accommodations but navigate a crisis with heightened emotions. This prompts the need for an AI-driven AAC system aware of the environment that can empower those with communication impairments so as not to limit their vocabulary in demanding situations.

[objective]

Our work aims to provide a proof of concept for creating a mixed reality application that can perform object detection within a grocery store environment to assist people with complex communication needs in everyday conversations. The principal device that will be used is the Microsoft Hololens, where context-based sentences will appear on its interface for users to select. The pipelined approach of this proof of concept involves the following: object recognition, conversation retrieval, user interface display, and text-to-speech generation. Based on the context of a grocery store, object recognition will detect objects commonly found within a grocery store (i.e. apple, milk, etc.). After retrieving the determined keywords, or object names, several sentences related to the keywords will be pulled from a sentence database and individual weights assigned such that sentences used more often will be ranked higher priority. The probability of a sentence being retrieved will be determined with a Naïve Bayes approach. The top three sentences with the highest probabilities will be sent to the user interface of the Microsoft Hololens for display, allowing the user to interact and click on the sentence they would like to be read out loud.

[object recognition]

Object recognition was performed with the expectation that our application would be able to accurately detect common grocery store items, such as apples, sugar, etc. Object recognition was carried out with three different approaches, or attempts, that each involved training the YOLOv3 model on a dataset consisting of grocery store items. YOLOv3 is a popular object detection model we chose specifically for its faster speed during real-time detection and limited spatial constraints in relation to our dataset. The initial two trials utilized a Python library called ImageAI, however yielded considerably low accuracy results. The first attempt specifically produced unpromising results and was left out of accuracy comparison. The last result replicated the results of the GrocerEye project, providing weights for a pretrained model yielding notably high accuracies for the classes of objects. The dataset used with this model was the Freiburg Grocery Item dataset, consisting of 25 different classes of commonly found supermarket items. Notable differences across each approach can be potentially attributed to the following: larger variation in dataset images (i.e. lighting, packaging, angle, etc.) was incompatible with the ImageAI library in that it demanded more time for training the model, which conflicted with GPU limitations provided by Google Colab. For this reason, ImageAI was deemed unfit for accommodating to the dataset size and variation that would make the model more robust.

[user interface]

On the front end is the user interface which consists of a menu with dialogue option that when selected can execute a text to speech task thread. The user interface was created using Unity 2018 and the MRTK provided by Microsoft. The user interface was tested with a combination of the unity game emulator as well as the Hololens Emulator. In order for the user interface to function, the hololens detects the user’s palm, thus bringing up the dialogue menu. The user would then use their other hand to select the more preferred dialogue option and the Hololens will speak the selected phrase out loud.

[discussion/future work]

Some future goals for us as we continue our research. Work on fulling connecting the back end or the object recognition to the front ended user interface. This would also involve creating a self learning AAC system for conversation retrieval based on user popularity and usage as well as adapting the user interface so that the dialogue options would change based on the items detected in the environment as well as making the design of the ui more accessible and user friendly.

College of Engineering and Computing Honors College Undergraduate Research Scholars Program (URSP) - OSCAR

Stability Studies of Lipid Nanoparticles Using Analytical Chemistry

Author(s): Lina Alkarmi

Mentor(s): Michael Buschmann, Bioengineering

Messenger RNA (mRNA) is the intermediary between protein encoding DNA and the proteins produced by ribosomes. mRNA sequences can be engineered to encode certain proteins that can be translated and expressed within cells. There are many applications of mRNA therapeutics such as the development of mRNA-based vaccines (Hogan, 2018). Although mRNA has become a promising alternative to traditional vaccine approaches, it is fragile and can degrade easily within cells. A delivery agent is needed to package the mRNA payload and efficiently deliver the mRNA in vivo. Lipid nanoparticles (LNPs) are commonly used as delivery agents to protect the payload and release it within the target cells. The four lipid components of LNPs are an ionizable cationic lipid such as Dlin-MC3-DMA (MC3), a neutral helper lipid, cholesterol, and a PEGylated lipid. This experiment tests the stability of the lipids in LNPs under different temperature and storage duration conditions. Lipid and mRNA samples were extracted from LNPs and stored for 4 weeks at 4°C, 25°C, and 37°C. The lipid samples stored at each temperature were evaluated for degradation using liquid chromatography mass spectrometry (LCMS) every week for three weeks after the initial storage period. Both high and low lipid concentrations were tested. LCMS was used to look for the presence of lipid components and their fragments in the organic lipid extract samples from LNPs. It was found that as the storage time increased, the signal intensity of both the cholesterol and MC3 compounds decreased. The inverse relationship between signal intensity and storage time shows that the longer the extractions are stored, the more the lipids will degrade. It was also noted that the samples stored at cooler temperatures generally had a higher intensity. Experimental results provide insight on the required storage conditions for LNPs and mRNA vaccines.

Hello everybody, my name is Lina Alkarmi, I’m a bioengineering undergraduate student, and today I will be giving an overview of my URSP project where I researched the stability of lipid nanoparticles using analytical chemistry techniques. With the recent Pfizer/BioNTech and Moderna Covid-19 vaccines, mRNA vaccines have been in the spotlight. mRNA is the intermediary between protein encoding DNA and the proteins produced by ribosomes. Lipid nanoparticles, or LNPs, are commonly used as delivery agents to protect the mRNA payload and release it within the target cells. Typically there are four components of LNPs, which are an ionizable cationic lipid, a neutral helper lipid, cholesterol, and a PEGylated lipid, which you can see the structures of on the right. MC3 is a commercial ionizable lipid commonly used in LNPs. LNPs are produced through microfluidic mixing, where the lipid components in ethanol are injected in one channel and the aqueous mRNA is injected in the other channel, so the cationic lipid will ionize and the particles will be formed. The stability of these nanoparticles is an important aspect of mRNA therapeutics because after the LNPs are formulated, they need to be kept stable for use so they can be effective. My research focuses on studying the stability of the lipids in LNPs under different conditions. My first research question focused on the effect of storage temperature on lipid stability, while my second research question focused on the effect of storage duration on lipid stability. For my experiment, after LNPs were formulated, the lipids and mRNA were extracted from the LNPs. I then stored these extractions for a period of a month at 4, 25, and 37°C, and tested their stability using LCMS over the course of 3 weeks to look for changes. I tested lipid samples with high and low concentration. Liquid chromatography mass spectrometry is an analytical chemistry technique that combines high performance liquid chromatography with mass spectrometry. HPLC is used for the separation of components in an organic mixture. It works by applying pressure to carry a sample through a column and detector. Depending on the polarity of a sample and the solvent system used, different compounds will elute off the column at different times. After coming off of the LC, the eluent will be hit with a large voltage which will turn it into an ion spray. The ions will be detected and measured according to their mass charge ratios. In the first quadrupole, Q1, the initial mass will be detected in the precursor scan. The second quadrupole is the collision cell, where the ion beam is hit with nitrogen gas that will interact with the molecule and break it apart into different products or daughter ions that will be filtered in Q3. For example, MC3 can be broken into fragmented products that can be observed. LCMS can also be used to look for degraded products. The process can be used to look for the presence of a compound in a solution. For my experiment, LCMS was used to look for the presence of lipid components and their fragments in the organic lipid extract samples from LNPs. For my samples, I looked for the presence of cholesterol and MC3 in the lipid extractions. I tested the lipid samples at the three temperatures 4 weeks, 5 weeks, and 6 weeks after extraction. In the chromatograms, taller peaks, or higher signal intensity, indicate a higher presence of compound with less degradation, and vice versa. Here on the left we have a chromatogram depicting the signal intensity versus retention time for the MC3 compound in the low concentration LNP samples. In the chromatogram, the tallest three peaks correspond to the most recently extracted lipids that were stored for 4 weeks. As the storage time increased, the intensity decreased, shown by the lower week 5 and week 6 peaks. At week 6, the MC3 compound was not detected at all, so it was completely degraded 6 weeks after extraction. The same trend was observed in the high concentration samples, where the intensity decreased as the storage time increased. The inverse relationship between signal intensity and storage time shows that the longer the extractions are stored, the more the lipids will degrade. The same trend was observed when looking for the presence of cholesterol in the high and low concentration samples. The lipid extracts stored for longer had a lower presence of cholesterol in them, shown by the lower signal intensity. It was also noted that the samples stored at cooler temperatures generally had a higher intensity. The LCMS data gathered for the samples indicates that after LNP extraction, the lipids began to degrade. It was found that a longer storage period resulted in less intensity and more degradation for the lipids, and the samples stored at higher temperatures generally had a lower signal intensity. Future steps would include testing fresh LNP extracts every week to monitor their degradation. Thank you to Dr. Buschmann, Dr. Alishetty, Zach Beaulac, and Manuel Carrasco for their guidance during this project. These are the sources referenced throughout this presentation, and thank you for watching!

College of Engineering and Computing Honors College

Technology Ethics: Analysis of Twitter Hashtags

Author(s): Ghaaliyah Brown, Brett Strosnider

Mentor(s): Aditya Johri, Department of Information Sciences and Technology

As artificial intelligence advances, discussions surrounding technology ethics are gaining prominence. Social media sites can provide valuable insight into these conversations. Over the period of one month, Twitter API was utilized to retrieve 6,230 original tweets using the keywords technology ethics, tech ethics, and ai ethics and the hashtags #aiethics, #ethicalai, and #responsibleai. Descriptive statistical analysis was performed in Python. Only 4% of the tweets originated from verified accounts. Although there was significant variation in the number of friends, followers, and favorited tweets associated with the source accounts, the majority had no friends, no followers, and no favorited tweets. The most common hashtags used in tandem with the search query terms were mostly composed of terms relating to policy and governance and methods through which artificial intelligence is implemented. Every fourth tweet of the sample was also selected for qualitative coding (N = 1,558). Five categories were established: ethical issues, industry/sector, content/media type, specific technologies discussed, and geographic location. The most common ethical issues discussed related to regulation, algorithm bias, and privacy. Ethical issues were most frequently discussed within the context of the technology industry, government entities, businesses, and healthcare. Social media platforms, robots, and biometric technologies were mentioned quite frequently, and the most popular geographic location mentioned was India. News articles comprised the majority of the media shared in tweets. Future investigations should consist of more rigorous statistical analysis as well as qualitative analysis of the contents of media shared in tweets.

*Voiceover for video is by Ghaaliyah Brown. Each paragraph represents one slide in the presentation. Hi everyone, my name is Ghaaliyah Brown and I worked with Brett on this project where we did an analysis of Twitter hashtags related to AI ethics to understand the stakeholders in the conversation and the topics of special interest and concern. We collected tweets between May 26 and June 30th using the hashtags, #aiethics, #ethicalai, and #responsibleai. And we used the keywords, technology ethics, tech ethics and AI ethics as search query terms as well. We retreated to 27,655 tweets and we ended up using 6230 of those for our analysis and those were the original tweets. The first part of our project was conducting statistical analysis, so this is where we worked with any numerical data that we collected. We used the Pandas and Seaborn libraries to analyze different quantitative metrics in Python. First, we chose to look at the distribution of verified accounts versus non verified accounts. And we found that the majority of these tweets were originating from non-verified accounts. We then looked at some of the different metrics related to the accounts from which the tweets originate, and some metrics relating to the tweets themselves. So in this table, you see that the friends count followers count in tweets, favorited, count all were very variable, so the standard deviation is extremely high, meaning that the data wasn’t closely distributed around the mean. Actually, when we made histograms for these different categories we found that most of the values were actually 0. In terms of retweet count and favorite count we actually weren’t able to properly adjust for some variables, so we chose not to discuss these very much in our report, and this is something that we’d look more into in a future project. We also looked at the frequency of tweets per day in our data collection period, and what we found is that there tended to be less activity on the weekends and there was more activity on the weekdays, but because our data collection was so short, there isn’t much more we can say beyond that. We looked at the most common hashtags used in the original tweets and something I want to point out here is that the most popular terms would either related to policy and governance. So you see governance, policy, and politics on this list or they’re related to methods through which AI is implemented. So machine learning algorithms, deep learning those are the kind of terms that tended to appear on this list. Now moving into the second part of our project, this is where we qualitatively coded the text of the tweets themselves. We chose to code every fourth tweet from that original sample size, so we coded a total of 1558 tweets and we did some using five different categories. The first category was geographic location. So the majority of these tweets actually didn’t mention specific geographic location, and so that’s what you see with N/A, but one of the tweets did mention a geographic location and they tended to mention India. India was by far the majority, and after that the European Union/UK, and the United States, those two category were also close behind. In the category ethical issues, we found that the most popular ethical issue is definitely law and regulation, and this follows with the trend that we noticed in the hashtags the most popular hashtags. It’s not surprising here and then that we expect the law and regulation as the most popular ethical issue. Bias was also another issue that came up a lot. Transparency, privacy, those were also issues that were quite popular. It is specific technology category the most popular code that we saw in the text, in the tweets excuse me, was definitely social media platforms and after that biometrics was also quite commonly mentioned, as were robots. We also got a category about the industry & sector and so at this category was describing is the context in which ethical issues are being discussed, and so by far as you’d expect most tweets were discussing ethical issues within the context of the tech industry, but a lot of them also discussed in the context of government, which again not fitting with this trend that we’re definitely seeing here, and business and healthcare. Our final category was immediate types shared and here we saw that definitely most of the tweets were sharing news articles, but we also saw a lot of tweets that were more of a discussion format, so they usually weren’t sharing any meaningful type of media. They were just part of an ongoing conversation or their tweets sharing blog posts or tweets of an advertising nature. In future research, we would definitely want to conduct a more rigorous analysis of quantitative metrics that we used in our statistical analysis. We would also want to look at actually analyzing the content of the media shared in tweets, or investigating pools of tweets pertaining to either specific pieces of regulation or specific technologies. Thank you very much. Feel free to reach out to us if you have any questions.

College of Engineering and Computing Honors College Undergraduate Research Scholars Program (URSP) - OSCAR

Multiplexed Antigen Attachment Using DNA Nanoparticles for Platform Vaccine Development

Author(s): Amanda Graf

Mentor(s): Remi Veneziano, Bioengineering

Due to the sensitive nature of my research this summer, I am unable to provide my full abstract at this time. I am happy to speak further about this or direct you to Dr. Veneziano with any further questions.

Vaccines have been used for over 200 years, starting when Edward Jenner first administered the smallpox vaccine in 1798. Despite the incredible positive impact that vaccines have had on public health for the past 2 centuries, if you had asked me or my colleagues a year ago, none of us would have ever guessed that vaccines would be the global hot topic they are now. There are many different ways to create a vaccine, but the novel approach that I have been working on this summer involves using brand new DNA nanoparticle technology as a biocompatible and versatile vector to safely deliver antigen and produce an immune response so the pathogen can be recognized by the body in the future. The process of creating the DNA nanoparticles that are used as a base for our vaccine technology begins with producing the scaffold for the desired 3 dimensional structure using asymmetric polymerase chain reaction (often referred to as aPCR). After adding all of the necessary primers, buffers, template, and DNA, I run the mixture through aPCR and it comes out as a mix of both double stranded DNA and the desired single stranded DNA. I then isolate the ssDNA by running it through gel electrophoresis in an agarose gel that I have prepared ahead of time. This process of gel electrophoresis separates out the ssDNA from the double stranded DNA. The one band shown at the start of the experiment turns into two separate strands, the top band being the dsDNA, and the bottom is the lighter ssDNA that travels further through the gel. I then manually cut out the ssDNA using a razor blade and begin the purification process. The purification process involves heating the gel in a liquid until it is completely liquified, and then running it through a centrifuge multiple times with a washing buffer and eventually an elution buffer. All of the ssDNA gets bound on a silica matrix that is made specifically for this process, and the elution buffer rinses the purified ssDNA that results from this process into a separate test tube so that I can use it in the folding process. At this point, our scaffold has been created and it is combined with the correct staple strand and put through a 12 hour folding process known as “annealing” which involves very specific slow decreases and drops in temperature to aid in the folding process. The last step is to attach the antigens we will use to the DNA nanoparticles using specific chemical attachment techniques, and then I run the particles through surface plasmon resonance which allows me to measure how well the nanoparticles bind based on antigen positioning and type. This technique is superior to other methods of antigen attachment because while current vaccine particles allow for attachment, they cannot control the precise location or stoichiometry of the attachment. What I have been doing in the lab allows for complete control over the stoichiometry and placement of the proteins, in addition to being biocompatible and biodegradable, and it has been proven to work under similar conditions (using the HIV virus). Prior to my research, this method has been tested once using one specific antigen on the surface of a DNA nanoparticle, but what the groundbreaking technology of DNA origami has to offer is still just beginning to be explored. It is my hope that the results of my research this summer will not only satisfy my own personal curiosity, but that it will open countless doors for vaccine development and the practical, worldwide use of DNA origami in the future.

College of Engineering and Computing Undergraduate Research Scholars Program (URSP) - OSCAR

Manufacturing Methods of PLA/HA Composite for 3D Printing Bone Scaffolds

Author(s): Zachary Turner

Mentor(s): Shayhayegh Bagheri, Mechanical Engineering

For my research I worked with Alex Assadi to develop four manufacturing methods of a Poly lactic Acid (PLA) Hydroxide apatite (HA) composite. We utilized lab equipment to produce filament from each of the four methods and tested sample for their mechanical properties. The knowledge gathered here will serve useful in the biomedical field where this composite can be used for bone implants as the polymer is biocompatible and HA adds the ability for the composite to be bio-regenerative.

Hello, my name is Zach Turner. I am a mechanical engineering student at George Mason University. My project, working along with Alex Assadi, Dr. Bagheri (Bageri), and Dr. Beheshti is to develop a manufacturing method for a composite polymer for use in 3D printing of bone scaffolds and over bone related replacements. Our composite is made of Poly lactic Acid PLA and Hydroxyapatite. PLA is a biocompatible polymer and hydroxide appetite is a mineral that will increase the composites bioresorbability. This composite will allow for bone scaffolds and implants to help both with recovery and regeneration. We have experimented with four manufacturing methods for synthesizing the composite filament. The first method involved dissolving PLA in methylene chloride and adding hydroxyapatite into the solution. The second method was similar, but we used a speed mixer to agitate the solution instead of a magnetic stirrer. The remaining two methods were the same as the first but not methylene chloride was added. Having created a composite, we pressed and extruded it into a filament for 3D printing. The ability to 3D prints the composite will allow for great range of applications. We now working towards 3D printing the samples to test for the composite’s elastic modulus. Thank you