Search results for: Kirsti Rouvinen-Watt

Authors: Don Buddika Oshadi Malaweera, Lora Harris, Bruce Rathgeber, Chibuike C. Udenigwe, Kirsti Rouvinen-Watt

Abstract:

Fatty liver disease is among the three most severe health concerns for mink and believed to occur through the same mechanism as nursing sickness. In North America, nursing sickness affects about 45% of mink farms and in Canada, approximately 50,000 mink females is affected annually. Orotic acid (OA) plays a critical role in lipid metabolism and can increase hepatic lipids by enhancing Sterol regulatory element binding protein-1c expression and decreasing Carnitine palmitoyl transferase I activity. This study was conducted to identify particular pathways and regulatory control points involved in fatty liver development, and evaluate the effectiveness of arginine and bioactive peptides for prevention and treatment of fatty liver disease in mink. A total of 45 mink were used in 9 treatments. The experimental diets consisted of 1% OA, 2% L-arginine and 5% of whey protein hydrolysates. At the end of 10 days of experimental period, the mink were anaesthetized, sampled for blood and euthanized, samples were obtained for histological, biochemical and molecular assays. The blood samples will be analyzed for clinical chemistry and triacylglycerol. The liver samples will be analyzed for total lipid content and analyzed for 6 genes of interest involved in adipogenic transformation, ER stress, and liver inflammation.

Keywords: fatty liver, L-arginine, mink, orotic acid, whey protein hydrolysates

Procedia PDF Downloads 279

Authors: Luanna B. Prevost, Kelli Carter, Margaurete Romero, Kirsti Martinez

Abstract:

Writing is an essential scientific practice, yet, in several countries, the increasing university science class-size limits the use of written assessments. Written assessments allow students to demonstrate their learning in their own words and permit the faculty to evaluate students’ understanding. However, the time and resources required to grade written assessments prohibit their use in large-enrollment science courses. This study examined the use of machine learning algorithms to automatically analyze student writing and provide timely feedback to the faculty about students' writing in biology. Written responses to questions about matter and energy transformation were collected from large-enrollment undergraduate introductory biology classrooms. Responses were analyzed using the LightSide text mining and classification software. Cohen’s Kappa was used to measure agreement between the LightSide models and human raters. Predictive models achieved agreement with human coding of 0.7 Cohen’s Kappa or greater. Models captured that when writing about matter-energy transformation at the ecosystem level, students focused on primarily on the concepts of heat loss, recycling of matter, and conservation of matter and energy. Models were also produced to capture writing about processes such as decomposition and biochemical cycling. The models created in this study can be used to provide automatic feedback about students understanding of these concepts to biology faculty who desire to use formative written assessments in larger enrollment biology classes, but do not have the time or personnel for manual grading.

Keywords: machine learning, written assessment, biology education, text mining

Procedia PDF Downloads 249