Student Presentations

Hao Tian: Prediction of allosteric sites through ensemble learning

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Winner: Theoretical and Computational Chemistry (Graduate)

Co-author: Xi Jiang

https://youtu.be/qKg_rBE1UQM

Allostery is the process by which proteins transmit perturbations caused by the binding effect at one site to another distal site. Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and dynamics information. Here, we provide a novel ensembled model, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN) to predict allosteric sites. Our model can learn both physical properties and topology structure without any prior information and exhibited good performance under several indicators. Prediction results have shown that 84.9% of allosteric pockets in the testing proteins appeared in the top 3 positions. An interactive and quick-response server, PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), is provided to facilitate further analysis in drug discovery.

Hao Tian
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Peng Tao

Trisha Punamiya (U): Evaluating the Socioeconomic Impact of COVID-19 in internal migrant workers in Mumbai and its surrounding areas

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https://youtu.be/hRlzL4_vnOo

According to statistical research, Indian industries have had a large number of internal migrant workers having a sizeable impact on the economics of India. Rural to urban migrant workers mostly engage in unskilled work, characterized by low wages, job insecurity and economic vulnerability, which are peculiar characteristics of informal work environments. The Covid-19 pandemic has severely impacted the internal migrant workers in India, displacing and leaving them stranded. With factories and workplaces shutting down due to the 68 day lockdown imposed by the Indian government, the migrant worker community dealt with great uncertainty about job security, loss of income, displacement of homes and health. While this impact was greatly monitored by the Indian government and local media outlets, poor policy implications left the workers under great distress. Many were left with no of access to any form of transportation enabling them to return back to their homes, forcing them to cover the distance on foot. Most available research surrounding the migrant workers focuses on the impact of migration from villages to cities and other socio-political and economic implications of the move on their families and lives. However, there is little study of the impact of this pandemic on their lives and into exploring and comparing differences within the migrant worker community.

Trisha Punamiya
Majors: Economics and Statistics
Faculty mentor: Thomas Osang

Claire Trotter: Reduced resting beat-to-beat blood pressure variability in multiple sclerosis

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Co-authors: Alex D. Smith, Ben E. Young, Mu Huang, Dustin R. Allen, Paul J. Fadel and Scott L. Davis

https://youtu.be/db5yGxbEgRk

Multiple Sclerosis (MS) is an autoimmune disease associated with increased cardiovascular risk. Greater resting beat-to-beat blood pressure variability (BPV) is a known predictor of cardiovascular risk. Therefore, we hypothesized that resting beat-to-beat BPV is increased in individuals with MS compared to matched healthy controls. In 7 patients with relapsing-remitting MS (2 males) and 7 sex-, age-, and weight-matched healthy controls, beat-to-beat blood pressure (Finometer) was recorded during 10 minutes of quiet supine rest. Individuals with MS had similar resting mean blood pressure (BP) compared to healthy controls (P= 0.736), however the BP standard deviation (SD) and coefficient of variation (CV) was less in MS (BP SD; MS: 3.2 +/- 0.2 vs. CON: 4.0 +/- 0.2, P=0.022 and BP CV; MS: 3.8 +/- 0.3 vs. CON: 4.7 +/- 0.2, P=0.025 ). Similarly, mean resting systolic blood pressure (SBP) was not different between MS and healthy controls (P=0.207) but the SBP SD and CV was less in MS (SBP SD; MS: 4.7 +/- 0.4 vs. CON: 6.6 +/- 0.5, P=0.013 and SBP CV; MS: 4.3 +/- 0.4 vs. CON: 5.8 +/- 0.4, P=0.033). In contrast, there was no difference in the DBP SD or CV between the two groups (P= 0.321 and P=0.295; respectively). Contrary to our hypothesis, individuals with MS exhibited reduced resting beat-to-beat BPV compared to healthy controls which may be related to altered autonomic function.

Claire Trotter
Program: PhD in Education-Applied Physiology
Faculty mentor: Scott Davis

Francesco Trozzi: UMAP as Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules

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https://youtu.be/J3CfmaX6vXg

Proteins are the molecular machines of life. The multitude of possible conformations that proteins can adopt determines their free energy landscapes. However, the inherently high dimensionality of a protein free energy landscape poses a challenge on the rationalization of how proteins perform their functions. For this reason. dimensionality reduction (DR) is an active field of research for molecular biologists. The Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction method based on a fuzzy topological analysis of data. In the present study, the performance of UMAP is compared to other popular dimensionality reduction methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE), Principal Component Analysis (Analysis (PCA), and time-structure Independent Components Analysis (t-ICA) in context of analyzing Molecular Dynamics simulations of the circadian clock protein Vivid. A good dimensionality reduction method should accurately represent the data structure on the projected components. The comparison of the raw high-dimensional data with the projections obtained using different DR methods, showed the superiority of UMAP compared to linear reduction methods (PCA, t-ICA) and comparable performance with t-SNE, thus far the state-of-the-art method.

Francesco Trozzi
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Peng Tao

Thomas Truong (U): VVD and ENV Equivalent Mutant Characterization

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Co-authors: Nischal Karki, Brian Zoltowski

https://youtu.be/7wFxV3gHAoQ

Light-Oxygen-Voltage (LOV) domains, present in all kingdoms of life, facilitate regulation of light dependent events in organisms by transducing light input into physiological signals. Examination of plant and fungal circadian networks has revealed that signaling mechanisms differ even within homologs of closely related species. Structural and computational studies of LOV-allostery have identified key signaling “hot-spots” that allow modification of the direction and amplitude of signal output. One key regulatory site was identified in Plant LOV domain proteins ZTL and FKF1, in which the amino acid residue at position 46 differentiates ZTL and FKF1 signaling mechanisms. Notably, the residue at this position differentiates FKF1-based signaling in monocots and dicots (Ala and Ser, respectively). An analogous residue substitution is observed in the LOV domain photoreceptors of two closely related filamentous fungi, Neurospora crassa VIVID and Trichoderma reesei ENVOY, where divergent signaling mechanisms gate adaptation to oxidative stress. Herein, we sought to test whether residue identity at the position equivalent to ZTL G46 (VIVID A72 and ENVOY S99) alters signal transduction in VIVID and ENVOY. To propagate signal transduction of blue-light, wildtype VVD forms a rapidly dissociating dimer whereas wildtype ENV requires oxidative conditions to form an irreversible disulfide dimer. Size exclusion chromatography (SEC) characterization revealed VVD A72S exhibits reduced dimerization capability and dimerizes at high concentrations only. Furthermore, ENV S99A dimerized under reducing conditions with a similar concentration-dependent increase in dimeric fraction. This altered dimerization capacity demonstrates evolutionary selection of G46 equivalent residues in differentiating LOV-domain photoreceptors and their signal transduction mechanisms.

Thomas Truong
Majors: Biology, Management
Faculty Mentor: Brian Zoltowski

Menglin Wang: Advanced Capacitors for Future Power Conversion Systems

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https://youtu.be/11AOmLE9dpo

Capacitors are critical for voltage source converter functionality. DC-link capacitors are known to have reliability issues. Therefore, the overall goal of this program is to improve the understanding of high voltage breakdown in high dielectric constant inorganic ceramic capacitors. This year the focus has been on improving the understanding of resistance degradation in BaTiO3 at elevated temperature, using single crystal BaTiO3 as a model system to understand the impact and control of oxygen vacancy migration to maximize the long-term reliability of high voltage inorganic multi-layer ceramic capacitors.

Menglin Wang
Program: PhD in Electrical Engineering
Faculty mentor: Bruce Gnade

Min Wang: Instructional Technology to Support Students’ Mathematical Problem-Posing

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Co-author: Candace Walkington

https://youtu.be/aRPhgmNoeeE

Problem-posing is an instructional activity that has been suggested to be beneficial for students' mathematical learning. However, the gap between problem-posing research and classroom implementation remains. The purpose of this presentation is to demonstrate how instructional technology can be integrated in mathematics classrooms to support students' problem-posing in different contexts. In this project, students were instructed to create geometry proof problems using the Hidden Village motion capture game, Algebra word problems using the ASSISTments web-based platform, and general mathematical problems based on students' surroundings using the online walkSTEM Gameboard. In addition, this presentation discusses students' learning behaviors, problem-posing performances, and dispositions toward mathematics when participating in these problem-posing activities.

Min Wang
Program: PhD in Education
Faculty mentor: Candace Walkington

Hannah Webb (U): A Thematic Analysis of Conference Programs for Residential College Professional Associations

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Co-authors: Laura Bell, Nikita Kulkarni, Grant Stoehr

https://youtu.be/PGM3WjdFrfU

Many professional associations in higher education hold conferences and conventions on an annual or biannual basis, often with residential college stakeholders taking part. Using a qualitative historical research perspective, we derived themes from conference program schedules (e.g., program session titles, presenters, presenter affiliations) for two prominent associations related to residential colleges—The Collegiate Way International and the Residential College Society. Data were collected from 2014 to 2020, examined, analyzed by thematic content analysis, and then organized by association, year, and location. Findings revealed professional development topics relevant to residential college stakeholders: specifically, what constitutes a residential college model, the resident experience, and leadership experiences. Findings are compared between the associations, and recommendations are made for practice, including consistency in presentation count and insights into professional development topics that can advance the field. Finally, we identified key institutions within the residential college movement and discuss how to diversify the field.

Hannah Webb
Majors: Marketing and Public Policy; Minors: Law & Legal Reasoning and Economics
Faculty mentor: Dustin Grabsch

Sharyl Wee: Emerging Adults’ and Parents’ Perceptions of Supportive Parenting: Associations with Family and Parent-Child Relationship Quality

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Co-authors: Naomi Ekas, Chrystyna Kouros

https://youtu.be/Z7FWiSsmcC0

Parents' and children's reports of parenting are often incongruent, with parents reporting themselves more favorably. The extent to which these discrepancies in perceptions of parenting predict family relationship quality later in development has not been extensively studied. 79 emerging adults and their caregiver completed the Supportive and Unsupportive subscales of Coping with Children's Negative Emotions. EAs completed the Family Environment Scale and the Inventory of Parent and Peer Attachment Scale. Multiple regression models showed greater discrepancies between EAs' reports and parents' reports of parent' unsupportive responses predicted lower current family relationship quality b=-1.94,SE=0.76,p=.01. A main effect of EAs' perceptions of supportive parent responses to their negative emotions in childhood predicted higher levels of EA-reported parent-child relationship quality b=5.49,SE=1.52,p=.001. The results suggest that when EAs and parents agree that the parent was unsupportive of their child's negative emotions in childhood, EAs rated the family relationship quality as worst. Moreover, when EAs remembered their parents as responding supportively to their negative emotions in childhood, they rated their current relationship with their parents better. Results stress the implications of parents' and youths' perceptions of parenting on family relationship quality.

Sharyl Wee
Program: PhD in Psychology
Faculty mentor: Chrystyna Kouros

Ann Marie Wernick: Coaching in the time of coronavirus 2019: how simulations spark reflection

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Co-authors: Jillian Conry & Paige Ware

https://youtu.be/yCHaUUOkuSE

This study investigates how debrief conversations unfold during virtual coaching sessions that provide embedded opportunities to practice teaching within a mixed reality simulation (MRS). We examine how teacher and coach topical episodes function (agreeing, explaining, clarifying, probing, recapping, reflecting and suggesting) to activate reflection as part of virtual coaching. Grounded in Vygotsky's sociocultural theory and the belief that learning is collaborative and impacts how pre- and in-service teachers construct knowledge, this exploratory case study draws on insights from 15 graduate students (5 pre-service teachers (PSTs) and 10 in-service teachers (ISTs)) who participated in virtual coaching with embedded practice opportunities. Data sources were video recordings and transcripts of 15 virtual coaching sessions, and one-on-one post coaching interviews. Coding categories were determined through the constant comparative analysis method. Findings indicate that MRS provides an immediate context for reflection, which guided the debrief conversations. Additionally, functions occurred with varying frequency among PSTs and ISTs, and across both groups, probing questions often led directly to reflecting and recapping the shared simulation context. In times of remote teaching, like during coronavirus 2019 (COVID-19), opportunities to simulate clinical experiences become vital.

Ann Marie Wernick
Program: PhD in Education
Faculty mentor: Annie Wilhelm

Peter Wetherbee (U): Big Reputation: An Examination of Perception in Art Museum Development

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https://youtu.be/VdvExg9DgKo

The study explores the prominent conception of New York's reputation as the reigning hub of fundraising and community engagement for art museums. However do those museums truly exhibit practices that maximize effectiveness of resources and strengthen holistic community relationships, or does their success stem primarily from their high reputation? A comparative analysis between prominent art museums' fundraising methods and engagement of their local communities is important because it allows museums to understand how effective their efforts are and how to possibly capitalize on their reputations. The following looks at the effect of reputation on fundraising and engagement practices, focusing on efficiency and impact.

Peter Wetherbee
Majors: Human Rights and International Studies; Minor: History
Faculty mentor: Alicia Schortgen

Anderson Wey (U): Synthesis and Characterization of biodegradable plastics

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Co-author: Jamie Hall

A cross-linked degradable plastic network will be created by mixing and curing varying ratios of monomers derived from silicon. Properties that will be tested include biodegradability, thermal, and mechanical strength.

Anderson Wey
Major: Biochemistry
Faculty mentor: David Son

Doran Wood: The Value of a Multistage Dynamic Approach for Radiation Therapy Planning

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Co-authors: Sila Çetinkaya, Harsha Gangammanavar

https://youtu.be/9RUaZPAkGs0

The goal of intensity modulated radiation therapy is to distribute a prescribed dose of radiation to cancerous tumors while sparing the surrounding healthy tissue. Current approaches implement a radiation plan such that the prescribed tumor dosage is divided equally and delivered through several treatment sessions. This equally distributed (uniform) approach involves solving an optimization problem at each treatment session without consideration of the future sessions or tumor evolution. Herein, we develop a generalization of the uniform formulation that does not automatically assume equal session tumor prescriptions (nonuniform) and also takes future decisions into consideration. This nonuniform multistage framework allows for a natural connection between treatment sessions as well as consideration for sources of uncertainty due to tumor evolution. For the proposed formulation, a sequence of prostate cancer scans provide numerical results revealing drastic improvement in tumor delivery precision while using a total dosage no more than the current practice methods.

Doran Wood
Program: Ph.D in Operations Research
Faculty mentor: Sila Çetinkaya

Bin Xia: Rheology of Particulate Suspensions in 3-D Printing

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Co-authors: Paul Krueger

https://youtu.be/FTlzRrGvPBg

Multi-functional 3-D printing has been developing quickly and is playing an increasingly vital role in manufacturing technologies. To satisfy the requirements of different functionalities, particulate composites have been widely utilized in this area. These types of materials are usually formulated with different functional particles and matrix materials such as polymer melts and silicone. The materials are particulate suspensions during formulation and printing, and their rheology is a key factor for the processing. This work will concentrate on the suspension rheology in capillaries scaled appropriately for 3-D printing applications (around 1 mm ID). The impact of particle volume fraction and the ratio of the capillary ID to the particle size were analyzed both theoretically and experimentally. Using the results of this investigation, the 3-D printing process based on particulate composites can be optimized, defects can be avoided, its efficiency and quality can also be improved.

Bin Xia
Program: PhD in Mechanical Engineering
Faculty Mentor: Paul Krueger

Yongjia Xu (U): Computational Mathematics in Calculating Protein pKas

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https://youtu.be/KT5gavSMULM

A common approach to computing protein pKas uses a continuum dielectric model in which the protein is a low dielectric medium with embedded atomic point charges, the solvent is a high dielectric medium with a Boltzmann distribution of ionic charges, and the pKa is related to the electrostatic free energy which is obtained by solving the Poisson-Boltzmann equation. Starting from the model pKa for a titrating residue, the method obtains the intrinsic pKa and then computes the protonation probability for a given pH including site-site interactions. This approach assumes that acid dissociation does not affect protein conformation aside from adding or deleting charges at titratable sites. In this work we demonstrate our treecode-accelerated boundary integral (TABI) solver for the relevant electrostatic calculations. Our next step is to use machine learning to help use find patterns and better predict the pKa. We aim to make our algorithm efficient in that our protein data bank is usually very large. Careful data processing can help us filter irrelevant or less relevant features and focus more on what could potentially affect pKa values.

Yongjia Xu
Major: Mathematics
Faculty mentor: Weihua Geng

Xin Yang: Kernel Independent Treecode Accelerated Kernel Smoothing

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https://youtu.be/HIP3Wghr8vI

A kernel-independent treecode (KITC) is presented for fast summation of particle interactions. A regular treecode algorithm computes particle-cluster or cluster-cluster interactions instead of particle-particle interactions. The KITC uses barycentric Lagrange interpolation instead for the far-field approximation when particle and cluster are well-separated. It reduces the operation count and therefore it is more efficient.

Xin Yang
Program: PhD in Mathematics
Faculty mentor: Weihua Geng

Yuqui Ian Yang: A decentralized sparse fare splitting algorithm

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https://youtu.be/hAgmXErY-xI

A fare splitting algorithm that is guaranteed to remove all redundant transactions is proposed. We proved the equivalence of the non-existence of redundant transactions and the minimization of the total transaction amount in the system. We also showed that although the resulting transaction amount is unique, the final transaction graph is not. By selecting a basic feasible solution, however, we can achieve a sparse solution.

Yuqui Ian Yang
Program: PhD in Biostatistics
Faculty mentor: Daniel Heitjan

Mai Zaru: Storybook Reading Practices for Children from Low Income Families

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https://youtu.be/Yp5Pt_8EG5s

This literature review describes effective features of storybook reading practices captured across three decades. The selected studies consist of (a) randomized control trials and quasi-experimental studies, (b) participants from low socioeconomic backgrounds, and (c) an age range of 2 to 7 years old. The selection of studies began with an exploration of two education databases and later cross-referenced with a manual search of storybook reading interventions approved by the Institute of Educational Science (IES). In the seven well-cited studies was a collection of classics published as early as the 1990s, with a total of 735 students, researchers reported the use of similar video-training techniques across. While read aloud interventions were found to have profound impacts on students' achievement across different types of implementers (teacher, parents, and researchers), many students remained unresponsive to storybook reading interventions. Finally, the differences detected across this small scope of studies made it challenging to compare their methodological rigor; however, implications and directions for future research are described in greater depth.

Mai Zaru
Program: PhD in Education
Faculty mentor: Stephanie Al Otaiba

Wenzhong Zhang: FBSDE based deep neural network methods for solving high-dimensional quasilinear parabolic PDEs

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https://youtu.be/6VSy90uB3Xg

We propose forward and backward stochastic differential equations (FBSDEs) based deep neural network (DNN) learning algorithms for the solution of high dimensional quasilinear parabolic partial differential equations (PDEs). The algorithms relies on a learning process by minimizing the path-wise difference of two discrete stochastic processes, which are defined by the time discretization of the FBSDEs and the DNN representation of the PDE solutions, respectively.

Wenzhong Zhang
Program: PhD in Mathematics
Faculty mentor: Wei Cai

Student Presentations – Research Days 2021

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Student presentations appear below.

These are listed in alphabetical order by last name of the lead student author (click “Older posts” to keep scrolling). You can use the search function to find a particular person, or look under “Project categories” to browse by discipline.

Comments are allowed and encouraged!