Poster Sessions

Professor Lee Gray 

Distinguished Professor Jean-Claude Thill 

Associate Professor Kent Brintnall

The Summer Ventures Science and Mathematics (SVSM) program is a state-sponsored four week course for exceptional high school students in North Carolina to learn about a science or math topic and complete their own original research on a college campus. A Nanoscale Science course has been conducted in the summers of 2022 and 2023 at UNC Charlotte for SVSM. The course content covers the major concepts of nanoscale science such as structure of matter, forces and interactions, quantum effects, size-dependent properties, and self-assembly. Students experience hands-on lab activities as well as characterization tool demonstrations while being guided through the completion of their own research project. The course content was developed by graduate students in the Nanoscale Science Ph.D. program at UNC Charlotte. The course was designed to utilize active learning and student-centered learning approaches to instruction. The course curriculum was revised between the two iterations with the aim to improve student content knowledge and research design skills. To understand the impact of the course and the revisions, student final papers will be graded using a rubric that evaluates based on two main components; the incorporation of Nanoscale Science concepts from the course and the quality of the research practices and reporting. The scores from the five categories in the data sets from each year will be compared to analyze for shifts in student outcomes from the revisions to the course. The aims of this research are to analyze and evaluate the Nanoscale Science SVSM course to understand the impact it has had on its participants and offer informed suggestions for improvement and revision for future iterations. The goals of the Nanoscale Science SVSM course to build student research skills and scientific literacy in an interdisciplinary subject support the national goals of increasing the number of students who enter and are successful in STEM degree programs. 

Pathway-based analysis facilitates the interpretation of multi-omics data from a wide diversity of studies. These studies include gene expression analysis such as RNA-Seq data, genome wide sequencing that screens for a large number of variants from multiple individuals as well as proteomics and metabolomics data. Pathway-based analysis is an important visualization aid in the understanding of complex disease. In addition, looking at data at the pathway level instead of looking at individual markers is more sensitive and allows us to identify signals missed if we only looked at individual markers. Pathview package in R allows users to map both gene and compound level data onto relevant pathway visualizations. The package automatically downloads, integrates, and parses information to create pathway maps in commonly used and human understandable formats of KEGG and graphviz. Here we have developed an end-to-end pathway-based analysis workflow for genomic data in the open-source R programming language with the goal of simplifying data analysis for those with little to no programming experience or unfamiliar with some of these tools. The workflow uses existing R packages like VariantAnnotation and SeqArray as a part of BioConductor to read, manipulate and filter Variant Call Format (VCF) files produced from genome wide studies. Genes are then mapped to pathways using GAGE and visualized using the Pathview package. This work complements other work done in our lab creating a workflow for microarray and RNA-Seq data. These workflows bring us closer to the goal of opening up multi-omics data analysis and visualization to a wider audience with fewer bioinformatics skills and in understanding key mechanisms involved in complex diseases that involve hundreds of genes.

This study investigates the variations in the output signal of 3D-printed PVDF-TrFE single-layer sensors in shear mode owing to varied geometries and electrode materials, with applications in hydraulic systems, open channels, etc. Square (30mm x 30mm x 0.4mm) and rectangular (30mm x 15mm x 0.4mm) samples are printed with the Prusa i3 MK3 3D printer using an extruded PVDF-TrFE filament of 1.75 diameter. The samples are corona-poled with a voltage of 25 kV under room temperature and coated separately with 50 microns of gold, silver, and platinum, using the AJA sputter coater. Other samples of the two geometries with brush-painted silver electrodes are considered for further comparison. Flickering and deflection up to 5mm are performed as initial sensitivity tests for the sensors. For the signal output, a setup consisting of a sensor at a time connected with DS1052E digital oscilloscope temporarily sealed to the middle of an elevated acoustic tube of 50mm internal diameter sealed at one end and on the other connected with 2446J-16 ohms JBL speaker which in turn connected with a Model SR830 DSP lock-in amplifier. Shear stresses due to the transient airflow generated in the tube by the speaker via a directed series of frequencies at corresponding voltages from the amplifier are measured by the sensors laid along the airflow direction in the middle of the acoustic tube. It is hypothesized that the rectangular sensor sputter-coated with the silver electrode gives the highest signal response and output above 50 mV, succeeded by rectangular sensors with gold and platinum electrodes. Consequently, a rectangular PVDF-TrFE sensor with a sputter-coated silver electrode hypothetically proves to be the most suitable for measuring stress in shear mode. Experimental processes are ongoing to scrutinize the hypothesis and further investigations to verify scalability and versatility with a different flow material.

This paper explores the profound impact of burnout among healthcare professionals, focusing on female nurses in the US. It delves into the multifaceted challenges faced by these professionals, from excessive workloads to emotional strain arising from patient interactions and systemic issues within healthcare organizations. The study emphasizes the urgency of understanding emotional exhaustion as a critical dimension of burnout due to its pervasive effects on mental health, job satisfaction, patient care, and the healthcare system at large. The research highlights the need for tailored interventions to address emotional exhaustion and its consequences. We will apply a quantitative assessment approach utilizing data from the General Social Survey Quality of Work Life module employing linear regression to measure emotional exhaustion levels among female healthcare workers, exploring their lived experiences and identifying triggers and coping mechanisms. We expect to see a significant correlation between job-related factors and heightened emotional exhaustion. Triggers such as increased workloads and challenging patient interactions may also be identified. Ultimately, this paper advocates for a more comprehensive understanding of burnout's dimensions, particularly emotional exhaustion, to inform targeted interventions that can alleviate the plight of healthcare professionals, enhance patient care, and foster a healthier work environment within the US healthcare system.

Adolescents and young adults (AYA) living with inflammatory bowel disease (IBD) must navigate the transition of healthcare as they age into adulthood. Nearly 1 in 100 people in the United States are diagnosed with a form of IBD, with 25% diagnosed prior to the age of 18 years. The increasing prevalence of IBD in childhood and adolescence poses a concern for how individuals progress through the healthcare system. Transition of care is the dynamic, purposeful, and planned movement of AYA from pediatric to adult care. The multifaceted process poses a challenge for AYA as they must navigate the normative responsibilities of entering adulthood while living with a chronic disease. Unsuccessful transitions can leave harmful impacts on disease outcomes, quality of life, and psychosocial wellbeing. Identifying barriers and facilitators to transition may provide individuals and healthcare teams greater understanding of this complex period of change. The purpose of the review was to examine peer-reviewed literature focused on identifying factors that may hinder or facilitate the transition process among AYA with IBD. A literature search was conducted using PubMed, PsycInfo, Science Direct, and Google Scholar using the following keywords: inflammatory bowel disease, transition of care, and patient experience. The search populated ten relevant articles, identifying barriers (i.e. lack of disease knowledge, financial and insurance concerns) and facilitators (i.e. independence, trust in healthcare team). The search revealed that only one study examined psychosocial factors (i.e. social and emotional support, environment). Future suggestions include the amplification of patient voice to better understand how transitions impact social, emotional, and psychological wellbeing, opening the door for increased evidence-based interventions. Examining transition of care is critical for interdisciplinary teams to effectively collaborate and navigate the individual needs of patients.

Because the PCAOB had been unable to inspect the audits completed by Chinese accounting firms until recently, U.S. regulators introduced legislation on March 28, 2019, which became effective on December 18, 2020 (HFCAA), forcing U.S.-listed Chinese companies to delist if the PCAOB is unable to inspect the audits for three consecutive years. We investigate the economic cost to U.S. shareholders because of the potential delisting of U.S.-listed Chinese companies. We find that Chinese companies outperform other Asian firms for the pre-HFCAA period. In sharp contrast, Chinese companies underperform other Asian firms from the time the HFCAA bill is introduced until an agreement was reached on August 26, 2022 allowing inspections. For the post-Agreement period, Chinese stocks perform at par with other Asian firms. Between March 28, 2019 and December 31, 2022, based on the mean (median) value, a typical U.S. shareholder lost about 46% (76%) of wealth invested in Chinese stocks. Compared to other Asian companies, the stock underperformance of Chinese companies is even worse at around 61% (87%). 

The escalating global population has heightened concerns about food security, emphasizing the need for effective policy planning and accurate crop yield forecasting. Traditionally relying on statistical and mechanistic models, this field has progressed to machine learning and deep learning due to the complex, nonlinear interplay of variables influencing crop growth and yield. These variables include vegetation indices, weather, soil, and management data, each with unique spatial and temporal resolutions. However, the relative impact of these variables on yield prediction accuracy remains underexplored. Identifying key variables could simplify predictive models, reducing computational expense and training time. Focusing on select counties in North Carolina, this project has gathered data encompassing soil, vegetation indices, weather, and historical corn yields. This data will undergo preprocessing to ensure uniform spatial and temporal resolution. The study will utilize an ensemble learning model, merging a convolutional neural network (CNN) for soil data with a Long Short-Term Memory (LSTM) network for vegetation and weather data. Subsequently, feature importance analysis will be done to identify the most influential variables for yield prediction. Finally, it is expected that the results obtained from this study will provide evidence that some variables are more important than others for corn yield prediction. In addition, this will also facilitate crop yield prediction research in developing countries that are known to lack accurate and sufficient data on many variables that influence yield prediction.