ABSTRACT TIPS

STUDENT RESEARCH CONFERENCE ABSTRACT TIPS

An abstract is a concise, independent summary of your research. By reading the abstract, a reader should learn the research question, the approach used to answer that question, and relevant findings.

General Guidelines

• The abstract must be written as one paragraph 
• Presentation titles, name of author(s), advisor(s), and institution should be included in the abstract
• The abstract should not contain formal citations to published work or literature 
• Footnotes should not be included, although a funding acknowledgement may be included at the end of the abstract
• While technical terms and scientific formulas are appropriate, avoid using abbreviations in the abstract unless the abbreviation is clearly explained
• A good abstract contains the following elements:

1. Clear research statement/hypothesis
2. Brief statement of research methodologies
3. Brief statement of research conclusions
4. Clear sense of how the research fits into the bigger picture

These posters won BEST posters at the 13th Annual QU Sigma Xi Conference (2024)

Seasonal Determination of Pharmaceuticals and Personal Care Products (PPCPs) in the Quinnipiac River using Solid Phase Extraction and GC-MS By Zimber, Isabelle, Student status: Quinnipiac University Undergraduate student, Department: Quinnipiac University Department of Chemistry and Physical Sciences, Mentor: Joanna Kinsey

Pharmaceuticals and personal care products (PPCPs) are a group of chemicals that include drugs, cosmetics as well as cleaning products. PPCPs primarily enter surface water from wastewater treatment plants, runoff, and septic systems. These emerging contaminants can be found in low (ng/L-μg/L) concentrations and can potentially cause adverse health effects to the local wildlife and humans. Water samples were collected at seven different sites along the Quinnipiac River, spanning from New Haven, CT to Meriden, CT. Samples were taken above and below water treatment plants. Seasonality was compared by sampling five times between June and August of 2023 and three times between November 2023 and January 2024. At each location, pH, salinity, and temperature of the water was measured.  Collected water samples were analyzed for dissolved organic carbon, absorbance and fluorescence, nitrate and nitrite concentrations, and fecal coliform bacteria counts. In order to determine the presence of PPCPs in the water, approximately 400-600 mL of water was concentrated using solid phase  extraction (SPE). The eluent was analyzed on a gas chromatograph-mass spectrometer (GC-MS). The results showed measurable amounts of several PPCPs, as well as fertilizers, hydrocarbons, and precursors to various plastics.

Expression and Characterization of the Copper Oxidase found in Staphylococcus aureus By Taylor, Nicholas Student status: Quinnipiac University Graduate student, Department: Quinnipiac University Department of Chemistry and Physical Sciences, Mentor: Dr. Robert Collins

A rising concern in modern medicine is the development of antibiotic-resistant bacteria, one example being methicillin-resistant Staphylococcus aureus, or MRSA. Within S. aureus, there is a widely understudied multicopper oxidase, denoted as SaMCO, and is believed to be involved in copper homeostasis by oxidizing Cu(I) to the less toxic Cu(II), which has been observed and investigated in other bacterium. Inhibition of this enzyme may be one strategy for new treatment approaches against MRSA, but further investigation is needed. To better characterize SaMCO, investigation of the oxidative potential, optimal activity conditions, potential substrates, along with protein domain analysis will allow for better understanding of the structure and function of this widely understudied protein. From studies conducted so far, an optimized expression procedure in protein in E. coli was determined, oxidative activity is dependent on the protein’s N-terminal region, and the protein’s ability to oxidize different substrates such as metals was exemplified.

Microscale Quantification of Iron in Iron Supplements using Black and Green Tea Extracts By Fassett, Alexandra Quinnipiac University Undergraduate student, Department: Quinnipiac University Department of Chemistry and Physical Sciences, Mentor: Dr. Robert Hansen

Iron deficiency is a significant global health concern, affecting billions and causing anemia, fatigue, and impaired cognitive function. Precise determination of iron concentration in supplements is crucial  for ensuring proper dosing and preventing adverse effects. In the context of chemistry education, the 

determination of iron in supplements is also a common experiment for laboratory courses in analytical 

chemistry. Most methods to measure iron concentrations either involve specialized instrumentation or 

hazardous reagents. These limitations preclude the inclusion of iron determination experiments in 

schools with resource constraints. To overcome these limitations, there has been recent interest in the 

use of polyphenols as reagents for the colorimetric determination of iron. Most of the experiments 

reported use large volumes (10-1000 mL) of reagents. In this research, we adapted an iron 

determination experiment using black tea as a reagent to use microscale techniques. The use of 

microscale techniques can minimize the volumes of reagents used and waste generated while 

maintaining adequate levels of precision and accuracy. We also optimized the reaction conditions to 

simplify the procedure of this iron determination experiment. We will present the results of this 

redevelopment and report on its implementation in an analytical chemistry course. We will also discuss 

future directions for this work. By establishing a sustainable and educational lab experiment, this 

research contributes to an analytical chemistry curriculum that incorporates principles of green 

chemistry. Findings from this work could also lead to a cost-effective and resource-efficient method for 

iron determination that can be used in areas with limited resources.

From the Sigam Xi website, Abstract examples

Foraging Efficiency and Learning in Capuchin Monkeys (Cebus capucinus)  

Student name, co-researchers names, mentor name (s) 

Primates have relatively large brain to body ratios and spend a substantial period of their life in the juvenile development stage. Large brains suggest long juvenile stage, hypothesizing that primates require long juvenile periods to learn complicated foraging techniques. Critics of this hypotheses argue that foraging efficiency increases primarily as a function of increased muscle mass, not learning. We set out to determine if the juvenile period is in fact used to learn complicated foraging techniques by examining food preferencing behavior in white faced capuchin monkeys (Cebus capuchinus). Individuals of varying ages were observed as they selected fruits from attalea palm trees (attalea butyracea). Learning was tested by counting the number of times each individual touched, bit, or dropped individual fruits before eating them. We found that individuals tested fruits less as they aged, indicating that individuals learned how to distinguish a desirable fruit from a non-desirable fruit over time. These results support the previously stated hypothesis and justify the long juvenile period in primates, offering insight to the evolutionary drivers of primate ecology. 

Creating a Statistically Characterized Reference Data Set to Test 2D Image Registration Algorithms for Testing Automated Portal Alignment for Patient Set-Up  

Student name, co-researchers names, mentor name (s) 

Create and characterize a reference data set for testing image registration algorithms that transform megavoltage portal image (MVPI) to digitally reconstructed radiograph (DRR), which will be used in future, studies to test automated portal alignment for patient set-up. Six orthogonal images set anterior/posterior (AP) and lateral (LAT) of head and neck, abdomen and pelvis were selected. Computer assisted manual point selection tool (CAMPST), devoted software created in-house, was used to manually select landmark point pairs by an expert. 58 anatomic landmark points were manually paired between the six images for AP and 52 for the LAT. Approximation of inter- and -intra observer variation was determined by repeat measurement on both images by three other readers as a 2D Euclidean distance. The hypothesis that the mean difference between intra and inter observer registration error equal some critical value between 1mm and 7mm using the test statistic for paired data was tested. The registration error was generally high for the MVPI than the DRR due to the inherent poor quality of images acquired using megavoltage energies. Also, the inter observer error was higher than the intra-observer error which is to be expected, as it is more likely for an individual to repeat their own point rather than someone else. The lower limit of the 95% confidence level was higher than 1mm and the upper limit higher 7mm.  Our results agree with what has been reported in literature that the accuracies of 2D and 3D registration method fall between 1mm to 7mm. 

Identifying Optimal Panobinostat Treatment Regimens Utilizing Reverse-Engineered Concentration-Time Curves 

Student name, co-researchers names, mentor name (s) 

The Ex Vivo Mathematical Malignancy Advisor Model (EMMA) is a support tool for treating Multiple Myeloma. A biopsy is taken, and patient plasma cells are cultured in plates to which chemotherapies are applied. These plates are imaged, and an algorithm produces a cell viability curve for each plate. EMMA is fit to these curves and parameterizes patient-specific models of chemosensitivity to each tested chemotherapy. For EMMA to predict patient response to a specific chemotherapy, the model must incorporate that chemotherapy’s concentration-time curves (CTCs). These describe the average temporal variation in concentration doses of a specified chemotherapy will undergo in humans. Because CTCs aren’t readily accessible to the public, a novel mathematical model was formulated to reconstruct the CTCs of orally-administered Panobinostat. Model parameters were fit by minimizing the residual between the 20mg model curve’s cmax, tmax, and AUCinf metrics, from those publicly provided about Panobinostat’s 20mg CTC. For the reconstructed CTCs of different doses, the model was solved using a different dosage value, and cmax, tmax, and AUCinf were checked to ensure they fell within the reported range. Model CTCs were coupled to create alternative treatment schedules. Using each logged patient’s chemosensitivity model, alternative treatment schedules were substituted, and EMMA was run to produce best response: the predicted largest percent reduction in tumor volume that patient will experience. For 51.4% of patients, treatment scheduling produced best response metrics varied such that they were not all >50% or <5%. This indicates for half of patients, Panobinostat scheduling can be optimized.