Engineering – Page 3 – UROP Spring Symposium 2021

Engineering

Residue Depth Computation Project

Proteins, a class of macromolecules essential to biological processes, are characterized by their structure which directly correlates to functionality. Residues are considered the building blocks of proteins and studying their positions has been crucial in understanding the role a protein plays in a biological system. This research project investigates different measures of residue depth through calculations of RSA, DPX, Residue Depth, HalfSpace Depth, and L1 depth. The L1 depth function surpasses other residue structure predictors in that it can obtain positions of residues buried under the protein surface, or nested in pockets. In addition, the function uses O(N) time complexity, making it much more efficient than the HalfSpace predictor. Values of depth have strong correlations with properties such as physiochemical propensities, flexibility and polarity. Data was obtained from CULLPDB, Phospho3D, and PLB datasets. After reading in necessary information into an IDE, residue depth of each protein in the datasets and the correlation coefficients between the means of depth values of amino acids and hydrophobicity index of amino acids were calculated through an algorithm written in C++. These results showed that the means of depth values were strongly relative with hydrophobicity of amino acids. In addition, the secondary structures of the residues are also associated with L1 depth values, as residues in sheets are deeper under the surface in comparison to residues in coils and helices.

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The importance of the interface in predict protein-protein interactions

Protein-protein interactions (PPIs) are physical contacts between two or more protein molecules. The field of structural homology-based PPI prediction, has generally accepted that the interface region of the complex structure matters more than the global structure of proteins in predicting PPIs, although our preliminary data suggests otherwise. A study was undertaken to investigate the influence of the protein interface in predicting PPIs, using a tweaked version of TMalign. This edited version of the algorithm allows a weighting factor to be applied to specific residues, the interface residues, in these experiments. This study consists of experiments using different weighting values, each of which will be done on three sets of query data. The three data sets are: 1. pairs of proteins that are known to have positive interactions, 2. pairs of proteins that are said to not interact with one another, and 3. a randomized data set. The latter two will allow us to confirm the results of non-interactions. Once each query protein is aligned to the structural homologs in the database, the score can be calculated and then all scores will be sorted to determine the cutoff for classifying predicted positive interactions. The results allow us to determine how impactful the interface is in predicting whether two given proteins will interact or not. Additionally, it will either confirm or deny the long standing assumption that only the interface matters.

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Development of Handheld Magnetometer Prototype

Equipment for detecting magnetic fields can often be a burden to carry, calibrate, and use effectively with ease. While some handheld magnetometers are commercially available, not all meet the desired ease of use, accuracy, or price. As such, with the help of the company PNI, I, through the assistance and guidance of the Michigan Moldwin Magnetics Lab, have been working towards developing a prototype for a handheld magnetometer with these desired qualities. In order to accomplish this, I spent the first months of my research developing 3D modeling skills with the Solidworks software, as well as developing understanding of the C++ coding language and the Arudino coding software. Afterwards I spent time reading over documentation for PNI magnetic sensors to extract data collected from them, and I’m currently in the process of writing code, along with other lab members, that will allow this magnetometer to function with accuracy and ease. The current problems are: converting data into a readable format and connecting multiple sensors with one single central processing unit. Currently I have developed a basic design for the sensor housing , as well as worked with a team towards designing the circuitry and code for this magnetometer that we expect to be completed by the end of the semester. While the prototype will currently be just that – a prototype, we hope that its use in the lab is extended to the greater scientific community that deals with such sensors, and perhaps even commercializing the design through PNI.

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Aerospace Engineering Outreach

With the COVID-19 pandemic forcing schools to close, effectively teaching students ages 11-18 has created two main challenges: (1) keeping the students engaged in a virtual environment and (2) adapting hands-on activities normally done in a classroom. This project aimed to tackle both of these issues by creating a video and hands-on activities on the core topics of aerospace and physics. Many high schools in Michigan have students who don’t know of any engineers in their lives and as a result these students have no interest in becoming an engineer themselves. In order to reach these students, this outreach project focused on making a video about aerospace engineering concepts related to American football to keep them interesting. Many students have an unbelievable passion for sports, and if only a fraction of that could be focused on an academic area then their futures would brighten significantly. The feedback we have received about this video shows that students were well engaged with the content created and found the hands-on activities to be simple enough to complete at home. The next step in the project is making an entire aerospace engineering course, which is normally taught to undergraduate upperclassmen, adapted to fit the needs of high school students. Ultimately we want to provide teachers with an engaging way to teach students, and to inspire students to follow a career path that elicits a similar passion in them that we have for aerospace engineering.

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Aerospace Engineering Outreach

With the COVID-19 pandemic forcing schools to close, effectively teaching students ages 11-18 has created two main challenges: (1) keeping the students engaged in a virtual environment and (2) adapting hands-on activities normally done in a classroom. This project aimed to tackle both of these issues by creating a video and hands-on activities on the core topics of aerospace and physics. Many high schools in Michigan have students who don’t know of any engineers in their lives and as a result these students have no interest in becoming an engineer themselves. In order to reach these students, this outreach project focused on making a video about aerospace engineering concepts related to American football to keep them interesting. Many students have an unbelievable passion for sports, and if only a fraction of that could be focused on an academic area then their futures would brighten significantly. The feedback we have received about this video shows that students were well engaged with the content created and found the hands-on activities to be simple enough to complete at home. The next step in the project is making an entire aerospace engineering course, which is normally taught to undergraduate upperclassmen, adapted to fit the needs of high school students. Ultimately we want to provide teachers with an engaging way to teach students, and to inspire students to follow a career path that elicits a similar passion in them that we have for aerospace engineering.

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Modeling Cas9 efficiency in cutting Long Interspersed Nuclear Elements 1 through Biopython

Transposable elements (TEs), DNA sequences that can change their position within the genome, can result in mutations associated with somatic and heritable diseases. TEs are repetitive in the genome and are therefore hard to map. In this study, we determined definitive mapping of one set of TEs, called Long Interspersed Nuclear Elements 1 (L1), through the utilization of recent technology, specifically CRISPR-Cas9 and nanopore sequencing. However, the cutting precision of Cas9 must be deduced for efficient mapping of these elements. This study aims to better understand the cutting preferences of Cas9 in the context of the L1 sequence by exploring how Python can map transposable elements in the genome. The biological analysis and mapping of these reads through the use of Biopython will result in a better understanding of the cutting preferences of Cas9 and its efficiency. Through an RNA guide, Cas9 targets and cuts at a specific region of DNA, where nanopore sequencing will then read the L1 retrotransposons. This data will be processed through the use of Biopython’s modules. A “for” loop was used to: extract individual reads contained within the input data files; perform a local alignment of each read to L1; and obtain the positions of the alignments with respect to L1. We also accounted for reads that align to the reverse complement of the L1 sequence by comparing the two scores obtained from the local alignments. Our data demonstrates that the median starting alignment position is consistent between alignments of the reads to L1 and its reverse complement. Thus, we conclude that Cas9 cutting occurs approximately 5900 bp downstream in L1. However, further analysis of the location of PAM sites and the RNA guide is necessary to confirm Cas9’s efficiency and function.

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Modeling Cas9 efficiency in cutting Long Interspersed Nuclear Elements 1 through Biopython

Transposable elements (TEs), DNA sequences that can change their position within the genome, can result in mutations associated with somatic and heritable diseases. TEs are repetitive in the genome and are therefore hard to map. In this study, we determined definitive mapping of one set of TEs, called Long Interspersed Nuclear Elements 1 (L1), through the utilization of recent technology, specifically CRISPR-Cas9 and nanopore sequencing. However, the cutting precision of Cas9 must be deduced for efficient mapping of these elements. This study aims to better understand the cutting preferences of Cas9 in the context of the L1 sequence by exploring how Python can map transposable elements in the genome. The biological analysis and mapping of these reads through the use of Biopython will result in a better understanding of the cutting preferences of Cas9 and its efficiency.

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Making Learning Visible in the Clinical Team-based Simulations

In the medical field, the technique of “breaking bad news” is incredibly important for future doctors and social workers to practice and receive meaningful feedback on. Our research team transcribed, analyzed, and interpreted over 150 medical simulation videos to analyze body language, tone of voice, and responses to see how students reacted to feedback from debriefers. A methodology was employed for multimodal sentiment analysis, which consists of gathering sentiments from available simulation videos by extracting audio, visual, and textual data features as sources of information. Then, this information can be used to predict and analyze a trainee’s emotional states when receiving feedback. As this study is still in process, conclusive findings cannot be stated but thus far the findings show a range of different reactions to the feedback ranging from negative deactivating to positive activating. Overall, the goal is to optimize the feedback given in order to fully prepare future medical professionals for the critical task of delivering bad news. This research is valuable to the future of medical education, as analyzing the quality of feedback given can help to optimize these patient simulations and better prepare medical students for real-life situations.

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Making Learning Visible in the Clinical Team-based Simulations

In the medical field, the concept of “breaking bad news” is incredibly important for future doctors and social workers to practice and receive meaningful feedback on. The moment a medical professional tells a family member bad news, that instance stays with the patient’s family for the rest of their life. Our team transcribed, analyzed, and interpreted over 150 medical simulation videos to analyze body language, tone of voice, and responses to see how they reacted to feedback from debriefers. Our goal is to optimize the feedback given in order to fully prepare future medical professionals for this critical task. This study was conducted on a sample of over 150 fifteen-minute videos of medical students debriefing with supervisors about their breaking bad news patient simulation.

Making Learning Visible in the Clinical Team-based Simulations Read More »

Making Learning Visible in the Clinical Team-based Simulations

In the medical field, the concept of “breaking bad news” is incredibly important for future doctors and social workers to practice and receive meaningful feedback on. The moment a medical professional tells a family member bad news, that instance stays with the patient’s family for the rest of their life. Our team transcribed, analyzed, and interpreted over 150 medical simulation videos to analyze body language, tone of voice, and responses to see how they reacted to feedback from debriefers. Our goal is to optimize the feedback given in order to fully prepare future medical professionals for this critical task. This study was conducted on a sample of over 150 fifteen-minute long videos of medical and social work students debriefing with supervisors about their breaking bad news standardized patient simulation.

Making Learning Visible in the Clinical Team-based Simulations Read More »

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