76 research outputs found
Occurrence of Pre- and Post-Harvest Mycotoxins and Other Secondary Metabolites in Danish Maize Silage
Maize silage is a widely used feed product for cattle worldwide, which may be contaminated with mycotoxins, pre- and post-harvest. This concerns both farmers and consumers. To assess the exposure of Danish cattle to mycotoxins from maize silage, 99 samples of whole-crop maize (ensiled and un-ensiled) were analyzed for their contents of 27 mycotoxins and other secondary fungal metabolites by liquid chromatography-tandem mass spectrometry. The method specifically targets the majority of common pre- and post-harvest fungi associated with maize silage in Denmark. Sixty-one samples contained one or more of the 27 analytes in detectable concentrations. The most common mycotoxins were zearalenone, enniatin B nivalenol and andrastin A, found in 34%, 28%, 16% and 15% of the samples, respectively. None of the samples contained mycotoxins above the EU recommended maximum concentrations for Fusarium toxins in cereal-based roughage. Thus, the present study does not indicate that Danish maize silage in general is a cause of acute single mycotoxin intoxications in cattle. However, 31 of the samples contained multiple analytes; two samples as much as seven different fungal metabolites. Feed rations with maize silage may therefore contain complex mixtures of fungal secondary metabolites with unknown biological activity. This emphasizes the need for a thorough examination of the effects of chronic exposure and possible synergistic effects
Dental and medical studentsâ self-directed learning and motivation: An evaluation of two multiple-choice questions systems using machine learning
This comparative case study reports a study investigating student evaluation of Multiple-Choice questions (MCQ) through machine learning as a means of learning. The focus is on self-directed learning and motivation. The study evaluates two systems developed at Aarhus University: "MED MCQ" used by medical students, and "MCQ anatomy" used by dental students. The study evaluates two surveys in SurveyXact with responses from 126 medical students and 70 dental students. We use topic modeling over free text responses. The machine learning model identifies two groups of students who, in different ways, experience interacting with the system as motivating and facilitating their learning process. The students' experience increases self-directed learning by being able to choose the form of presentation of questions and answer questions independently of the instructor. The article discusses how educators and developers can use MCQs to promote student learning and how to analyze open-ended questions with machine learning.This article reports a mixed methods study that uses machine learning and thematic analysis to investigate student evaluation of multiple-choice questions (MCQ). The focus is on medical and dental students' experience of self-regulated learning and motivation. We evaluate two systems developed at Aarhus University: "MED MCQ" used by medical students and "MCQ anatomy" used by dental students. We evaluate through two surveys in SurveyXact with responses from 126 medical students and 70 dental students. We use topic modelling over free text responses. The machine-learning model identifies two groups of students who, in different ways, both experience the system as motivating and facilitating their learning process. The students experience increased self-regulated learning by being able to choose the form of presentation of questions and answer questions independently of the instructor. The article discusses how educators and developers can use MCQs to promote student learning and how to analyze open-ended questions. We discuss the potential for using machine learning and integrating MCQ systems into teaching
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