Lauren Ammerman: Reversing Multidrug Resistance in Cancer through Iterative Identification of P-glycoprotein Inhibitors

Winner: Biological Sciences (Graduate)

co-authors: James McCormick (co-first author), Chanyang Park, Courtney Follit, Jesiska Lowe, Pia Vogel, and John Wise.

Multidrug resistance (MDR) describes the intrinsic or acquired resistance of cancers to diverse chemotherapeutics and is arguably one of most significant barriers to cancer treatment. As a mechanism of MDR, cancers commonly overexpress ATP-binding cassette transporters such as P-glycoprotein (P-gp). P-gp harnesses the power of ATP hydrolysis to efflux cytotoxic compounds across the cell membrane. Inhibition of P-gp can re-sensitize cancers to chemotherapeutics, but many P-gp inhibitors are also transport substrates of P-gp. Consequently, high compound dosages can be required to inhibit P-gp, and this can result in toxic off-target effects. To identify potential P-gp inhibitors that are not transport substrates, we iteratively screened millions of compounds against dynamic P-gp targets using massive parallel docking experiments. Hits from computational screens were then subjected to QSAR and purchased for testing. Compounds were assessed for their ability to reverse MDR using two sets of paired, human cancer cell lines – two chemotherapy resistant, P-gp overexpressing lines, and two chemotherapy-sensitive, non-P-gp overexpressing lines. Compounds were then tested for inherent toxicity against a non-cancerous human cell line. Lastly, we determined if our putative inhibitors are P-gp substrates using LC-MS/MS intracellular accumulation assays. We report a global hit rate of 15%.

Lauren Ammerman
Program: PhD in Biological Sciences
Faculty mentor: John Wise

Madison Arcemont: Computational Intestate Succession

Winner: Combined-Non-STEM (Graduate)

As new technologies develop, we continue our search for ways that this technology can improve our world, and particularly our legal system. One rarely thought of system with room for improvement is that of intestate succession, the court's management of the estates of individuals who die without a will. Computational law, the implementation of the law through computer code, is emerging as a field that can solve a wide range of issues in the law, including in the estate planning field. In particular, smart contracts, programs that carry out agreements procedurally, can be used to make the process of intestate succession more efficient, affordable, and accessible for the heirs of an individual who dies without a will. This paper investigates the necessity, feasibility, and challenges associated with implementing computational intestate succession and argues that states should implement such technology. While the field of estate planning has been slow to accept technological solutions, the trend towards acceptance shows that computational solutions to long-standing problems may be accepted in the near future. To demonstrate the feasibility of computational intestate succession, this article concludes with a sample program for the smart contract written in Lexon, a natural language program.

Madison Arcemont
Program: Juris Doctorate
Faculty mentor: Carla Reyes

Chelsea Carson: Broad Autism Phenotype and Relationship Satisfaction in Parents of a Child on the Autism Spectrum: The Role of Partner Discrepancy

Winner: Psychology (Graduate)

Co-authors: Naomi Ekas, Chrystyna Kouros

Previous research has linked poor relationship satisfaction with parenting a child with Autism Spectrum Disorder (ASD). Parents of children with ASD, however, also have higher levels of Broad Autism Phenotype (BAP) traits themselves—that is, they evidence subclinical levels of autism characteristics including communication difficulties, rigid personality traits, and emotional aloofness. Therefore, children’s ASD characteristics may not fully account for why these couples are at greater risk for marital discord. This study tested the extent to which BAP traits in parents of children with ASD, and discrepancy between partners in BAP, predicted their relationship satisfaction while controlling for their child’s ASD characteristics. Participants were 117 families with a child with ASD who were recruited to participate in a study about family dynamics. Couples completed questionnaires on their BAP traits, relationship satisfaction, and their child’s ASD characteristics. Husbands were higher in total BAP, aloofness, and pragmatic language. Husbands’ total BAP was associated with lower relationship satisfaction for husbands. Discrepancy between husbands and wives in total BAP and pragmatic language was associated with lower relationship satisfaction for husbands. These findings provide preliminary support for the relevance of partners’ discrepancy in BAP within romantic relationships.

Chelsea Carson
Program: PhD in Clinical Psychology
Faculty mentor: Chrystyna Kouros

Diane Chao: Effect of reward motivation on directed forgetting in younger and older adults

Winner: Psychology (Graduate)

Co-authors: Sara N. Gallant, Holly J. Bowen

An important feature of the memory system is the ability to forget, but aging is associated with declines in the ability to intentionally forget. Despite known cognitive deficits, sensitivity to affective manipulations are maintained in older age, for example, reward motivation can improve older adults' memory. Using a directed forgetting paradigm, we tested whether reward motivation could improve intentional forgetting in young and older adults. Participants were shown a sequence of words with instructions to remember (TBR) or forget (TBF) to earn a high ($.75) or low ($.01) reward. For older adults, there was no evidence that reward motivation improved cognitive control as high value reward anticipation did not improve directed forgetting. Instead, the findings are in line with hypotheses, that high value reward anticipation leads to better memory regardless of the TBR or TBF cue. Reward may bolster memory in an automatic fashion, overriding cognitive control of encoding processes.

Diane Chao
Program: PhD in Psychology
Faculty mentor: Holly Bowen

Bonnie Etter: Ceci n’est pas une pipe

Winner: Anthropology (Graduate)

Co-author: Molly Murphy Adams

I present an in depth artifact analysis of a pipe bowl, found in the Southern Methodist University Archaeological Research Collections

Bonnie Etter
Program: 
Anthropology
Faculty Mentor: Sunday Eiselt

Tyler Evans: Swelling as a stabilizing mechanism in irradiated thin films (the sequel)

Winner: Mathematics (Graduate)

Co-author: Scott Norris

The fields of nanoscale pattern formation and nanostructural engineering are still in their infancy (relative to many other scientific areas). Much research is still centered around identifying and quantifying the relevant nanoscale mechanisms responsible for experimentally-observed results, since the same physical forces operating at the nanoscale may look very different than at the macroscale. Here, we provide further results on a recently-identified candidate mechanism (swelling, or radiation damage) that could explain the observed angle-independent lack of nanostructuring in thin films amorphized at high energy. We present new analytical and numerical results, characterization of the mechanism in its full parameter space, and an unexpected, mathematically-interesting bifurcation.

Tyler Evans
Program: PhD in Mathematics
Faculty mentor: Scott Norris

Holly Grubbs: The Principal’s Leadership Impact utilizing Distributed Leadership Practices that drive School Improvement

Winner: Education: Ed.D. in Education Leadership (Graduate)

The role of the principal on the campus has shifted due to the significant workload of managing the school and the increased amount of accountability for teaching and learning. Through a solid vision and focused mission, the school's culture and student learning can achieve success. However, for a school principal to succeed in building the capacity of the teacher and reach the high expectations for student learning, a team of leaders must be in place. Developing an organization is not about delegating the work, but also about creating a team that is collaborative and able to work together through effective communication. While principals may struggle with the federal, state, and local accountability system, it is the success of the campus leadership team that establishes a focused mission for the day-to-day work impacting the teaching and learning for student success. For this reason, principals should look at distributed leadership and the focus of how to lead and inspire those who cross their path. In reviewing Bolden's (2011) synthesis of research on distributed leadership and how it can impact the leadership practice for educational leaders, I found it to be about the communication that takes place between the leaders and followers who are doing the work. The research sets a foundation for a distributed perspective for leading a school organization to success.

Holly Grubbs
Program: Ed.D in Educational Leadership
Faculty mentor: Dawson Orr

Shuang Jiang: BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies

Winner: Biostatistics (Graduate)

Co-authors: Quan Zhou, Xiaowei Zhan, Qiwei Li

The coronavirus disease of 2019 (COVID-19) is a pandemic. To characterize its disease transmissibility, we propose a Bayesian change point detection model using daily actively infectious cases. Our model builds on a Bayesian Poisson segmented regression model that 1) capture the epidemiological dynamics under the changing conditions caused by external or internal factors; 2) provide uncertainty estimates of both the number and locations of change points; and 3) has the potential to adjust for any time-varying covariate effects. Our model can be used to evaluate public health interventions, identify latent events associated with spreading rates, and yield better short-term forecasts.

Shuang Jiang
Program: PhD in Biostatistics
Faculty mentor: Xiaowei Zhan

Xiaoxian Jing: Parton distributions, nuclear deeply inelastic scattering, and electroweak precision measurements at the LHC

Winner: Combined-STEM (Graduate)

Parton distribution functions (PDFs) for quarks and gluons inside the proton are needed for predicting a variety of processes at the LHC, including Higgs boson production and searches for new physics beyond the Standard Model. PDF parametrizations are obtained by fitting a large number of cross-sections from many experiments at different (x,Q^2). In the global analyses of PDFs by CTEQ collaboration, we find that deeply inelastic scattering experiments on nuclear targets provide important constraints on combinations of PDFs relevant to the LHC electroweak precision measurements. What is the role of these experiments in the LHC era, and how influenced are they by nuclear effects? I explore this complex question using a new statistical indicator called "PDF sensitivity" for analyzing the impact and compatibility of experiments in a PDF fit.

Xiaoxian Jing
Program: PhD in Physics
Faculty mentor: Pavel Nadolsky

Konstantinos Kalfas: The dynamics of utility poles by considering soil-structure interaction (SSI)

Winner: Civil and Environmental Engineering (Graduate)

Co-author: Nicos Makris

Motivated from the large number of transmission and distribution utility poles that experience excessive vibrations during wind storms, this work sheds light to the eigenvalue analysis of a partially embedded flexural, prismatic column with embedded length, L, and exposed length, h, and accounts for the interaction with the soil of its embedded portion. The presentation will show that the dynamics of a partially embedded prismatic column depend solely on the embedment ratio, ε = L/h and a dimensionless stiffness that expresses the relative stiffness between the soil surrounding the embedded length (level of fixity) and the exposed portion of the flexural column. A partially embedded prismatic column exhibits a finite number of eigenmodes that are lower than its rigid-body mode; while, the associated eigenfrequencies are lower than the corresponding eigenfrequencies of the fixed-end cantilever. For a typical value of the embedment ratio ε = L/h = 0.15, the study uncovers that for any eigenmode n > 3, of the fixed-end cantilever, the partially embedded, prismatic column exhibits n + 1 eigenmodes. These rich dynamics result from soil-structure interaction and are associated with the way that the flexural patterns of the partially embedded column emerge from the ground.

Konstantinos Kalfas
Program: PhD in Civil and Environmental Engineering
Faculty mentor: Nicos Makris

Cameron Matson: Benefits of MIMO in 3D UAV-to-UAV topologies

Winner: Electrical Engineering (Graduate)

Co-authors: Syed Muhammad Hashir, Sicheng Song

Unmanned Aerial Vehicles (UAVs) often lack the size, weight, and power to support large antenna arrays or a large number of radio chains. Despite such limitations, emerging applications that require the use of swarms, where UAVs form a pattern and coordinate towards a common goal, must have the capability to transmit in any direction in 3D space from moment to moment. In this work, we design a measurement study to evaluate the role of antenna polarization diversity on single-antenna and multi-antenna UAV systems communicating in arbitrary 3D space. To do so, we construct flight patterns where one transmitting UAV is hovering at a high altitude (80 m) to focus on the impact of heterogeneous drone-based antenna polarization without multipath effects. Then, a receiving UAV hovers at 114 different positions that span an ellipsoid surrounding the transmitting UAV with a radius of approximately 20 m along equally-spaced elevation and azimuth angles. To understand the role of diverse antenna polarizations and multiple antennas, both UAVs have a dedicated radio chain to a horizontally-mounted antenna and a dedicated radio chain to a vertically-mounted antenna, creating four wireless channels. With this measurement campaign, we seek to understand how single-antenna systems could optimally select an antenna orientation or multi-antenna systems could have the greatest gains.

Cameron Matson
Program: Master’s in Electrical Engineering
Faculty mentor: Joe Camp

Christina McConville: Peritectic phase transition of benzene and acetonitrile into a cocrystal relevant to Titan, Saturn’s moon

Winner: Chemistry (Graduate)

Titan, Saturn’s largest moon, is the only body in the solar system known to have stable bodies of liquid—lakes, rivers, and seas—that undergo dynamic processes similar to Earth’s hydrological cycle. To study the potential formation of minerals on Titan, we use a combination of structural characterization methods including high-resolution synchrotron powder X-ray diffraction (PXRD) and differential scanning calorimetry (DSC) to analyze the constituents present on Titan's surface and evaluate their potential for cocrystal formation. Among the compounds detected on the surface of Titan are two common laboratory solvents: benzene and acetonitrile. Here we report the phase diagram of mixtures of acetonitrile and benzene, which features incongruent melting and a peritectic phase transition of solid benzene and liquid acetonitrile into a 1:3 acetonitrile:benzene cocrystal. The crystal structure of this cocrystal was solved and refined from in situ diffraction data using synchrotron radiation. Additionally, to mimic the environment on Titan more accurately, we tested the stability of the structure under liquid ethane. These results provide new insights into the structure and stability of potential extraterrestrial minerals and contribute to a better understanding of the surface composition of Titan.

Christina McConville
Program: PhD in Chemistry
Faculty Mentor: Tomče Runčevski

Samantha Navarro and team: How might we improve procedural justice for the Dallas Police Department?

Winner: Combined Non-STEM (Graduate)

Co-authors: Hope Anderson, Ramisa Faruque, Kaci McCartan

Master of Arts in Design and Innovation students, Hope Anderson, Ramisa Faruque, Kaci McCartan and Samantha Navarro are working directly with the Dallas Police Department over the spring 2021 semester to approach the design question: How might we improve procedural justice for the Dallas Police Department? Following seven-step Human-Centered Design process, we will ultimately test our hypotheses through a series of prototypes. Our design solution will be submitted to DPD and has the potential for real-world implementation. This semester presents a unique challenge of navigating sensitive social and political issues on the heels of a heightened Black Lives Matter Movement and push to defund the police across the nation.

Hope Anderson, Ramisa Faruque, Kaci McCartan, and Samantha Navarro
Program: Master of Arts in Design and Innovation (MADI)
Faculty mentor: Jessica Burnham

 

Denise Patton: Asset-oriented parent engagement for supporting the use of decontextualized language with preschoolers in LSES Latino families

Winner: Education: Ed.D. in Ed. Leadership

Parent participation in their preschoolers' language development helps build kindergarten readiness, particularly parents' use of decontextualized language (DL), language about abstract concepts or that does not reference the here and now. Thus, various parent training methods about DL have been developed. Most studies evaluating parent use of or training in DL focus on educated, middle-class, white families and/or parent training programs that are deficit-oriented in their design. This qualitative study, therefore, focused on Latino families of low socioeconomic status (LSES) and positioned them as assets in their preschoolers' language development. Accordingly, phase one of the study consisted of interviewing four LSES Latina parents regarding how they used language when talking with their preschoolers, as well as what more they preferred to learn about preschoolers' language development and how they preferred to learn it. Phase one findings suggest that LSES Latino families do use DL with their preschoolers and in similar ways; however, their interests in further training content and methods varied, except for preferring technological applications for learning more. The phase two interviews will garner the subjects' feedback regarding the resource's effectiveness in its dual aim of being asset-oriented and growing them as developers of their preschoolers' language skills.

Denise Patton
Program: Ed.D in Education Leadership
Faculty mentor: Alexandra Pavlakis

Robyn Pinilla: Creating a Bridge Between Research and Practice for Valid Assessment Use

Winner: Education (Graduate)

Co-author: Elizabeth Adams

While the educational measurement and assessment community asserts that tests themselves are not valid but that inferences made based on scores require validation (Cizek et al., 2008; Kane, 2013), this message has not been well translated to educators. A test's development, purpose, and use should align to interpret scores and make informed decisions properly. However, usage in practice does not always align with intended purposes. Researchers could help prepare teachers to use tests in valid ways, specifically with novel assessment formats necessitated by the COVID-19 pandemic. Traditionally, the measurement and assessment community placed the onus of valid use and interpretation on the end-user, the teacher. We herein propose streamlining a process in which researchers make validity evidences accessible and understandable to teachers. We examine what sources of validity evidence support classroom assessment use, how teachers can access this information in a meaningful way, and what sources of validity evidence seem superfluous or missing from the extant literature. This research proposes a method for researchers to facilitate valid use and interpretation of tests by gathering sources of validity evidence in a practitioner friendly format to put the end-users, teachers, at the forefront of the test validation process.

Robyn Pinilla
Program: PhD in Education
Faculty mentor: Leanne Ketterlin Geller

Tiffini Pruitt-Britton: Measuring High School Students’ Funds of Knowledge for Learning Mathematics

Winner: Education (Graduate)

Co-author: Candace Walkington

Mathematics experienced by students can be derived from the contextually situated "real world" experiences of the educator, which is typically White and middle class and not a reflection of the demographics of many classrooms in the United States. Activities where students find connections to their lives and interests have shown promise in enhancing student performance and experiences in mathematics classrooms. In this study, mathematics funds of knowledge are assessed in a novel survey instrument, reinforcing the salience of relating math experiences to students' lives and acknowledging skills and knowledge originating from experiences outside of the math classroom.

Tiffini Pruitt-Britton
Program: PhD in Education
Faculty mentor: Annie Wilhelm

Ishna Satyarth: Application of Neural Networks in Quantum Chemistry

Winner: Computer Science (Graduate)

Understanding the motion of electrons in a molecule is a big piece of the puzzle to understand the quantum world that these sub-atomic particles live in. This complexity increases several folds when correlation of pairs of electrons are interpreted in the wave function. Current methods of including pair correlation are too expensive to apply to large molecules. Our goal is to reduce the error in the Tensor Hypercontraction approximation, which can reduce the cost of such calculations. Artificial Intelligence and in particular the application of neural networks can prove to be a savior in such situations of many unknown parameters. A neural network works like a collection of brain cells where each neuron is responsible for processing a single piece of information and increasing the number of neurons allows adding complexity in a systematic way. Since there are more than 22 different input variables accounting for the energy error, we will be using a multilayer perceptron model. Using an Artificial Neural Network, we have tried to structure them into a systematic network and tried to decipher how each variable contributes towards the outcome of total energy or bond strength. We are currently working on collecting as many data points to train our Neural Network, so that we can get the most accurate results in the future.

Ishna Satyarth
Program: PhD in Computer Science
Faculty mentor: Devin Matthews (Chemistry)

Zilin Song: Quantifying Energy Contribution in Enzyme Catalysis using Machine-Learning based Regression Analysis

Winner: Theoretical and Computational Chemistry (Graduate)

The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non- linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.

Zilin Song
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Peng Tao

Siavash Tabrizian: A sampling-based branch and cut algorithm for two-stage stochastic mixed integer programs

Winner: Operations Research (Graduate)

Stochastic mixed-integer programs are among the most challenging class of optimization problems that finds many applications in practice. In this presentation, we describe a novel algorithmic framework for solving two-stage stochastic mixed-integer programs using internal sampling.

Siavash Tabrizian
Program: PhD in Operations Research
Faculty mentor: Harsha Gangammanavar

Hao Tian: Prediction of allosteric sites through ensemble learning

Winner: Theoretical and Computational Chemistry (Graduate)

Co-author: Xi Jiang

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