Screening for Genetic Markers Captured in DNA to Determine Scrapie Resistance in Ovis aries

Overview

Scrapie is a neurological disease that is fatal to sheep. Since I raise and show sheep in 4-H, I wanted to make sure that my flock was resistant to scrapie and that when I am seeking potential sires/rams to breed my dams/ewes to that I am not infecting my flock, or breeding to a ram that could potentially pass the scrapie gene onto an offspring. Therefore, the problem being addressed in my experiment is Screening for Genetic Markers Captured in DNA to Determine Resistance in Ovis aries. My number one goal is managing a flock that is genetically most resistant to scrapie and/or that could need careful selection when used for breeding. To complete this goal, I collected DNA samples from each of my sheep and sent the samples off to Gene Check, Inc. for genotyping. The samples were taken using a Datamar Collector and Collection Tags. The results were emailed to me, and I was able to determine that one of my ewes in fact is susceptible to scrapie based on her Codon 171 results. In conclusion, I now have a decision to make regarding this one ewe. I am not going to rush to decide. I do plan to continue to collect DNA samples on all new sheep that enter my flock, so that I can make sure that I am accomplishing my main goal. Authorship Verification for Academic Dishonesty in the Era of AI Jun Jang Oxford High School, Oxford, MS The rise of artificial intelligence (AI) has both positive and negative effects on student performance in the classroom. While AI offers various learning benefits, studies suggest it can hinder creativity and critical thinking skills. One area where AI may do more harm than good is academic writing. This paper presents an authorship verification (AV) software designed to assist teachers in detecting writing dishonesty, a common form of academic dishonesty. Our proposed AV system extracts a novel set of ling uistic and structural features including vocabulary usage, sentence structure variation, readability, and predictability from texts to distinguish an author’s unique writing style. These features are then used to train machine learning (ML) classification models to determine authorship similarity. Using the Reuters dataset and real high school student essays, our proposed AV system demonstrates superior detection accuracy in comparison to the state - of-the-art AVs in the literature. Our study highlights the importance of feature development, preprocessing, robust feature engineering, and binary classification ML models as they significantly impact authorship verification performance.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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