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You can do it! Using published undergraduate research on Hydra mouth opening to train undergraduates

Hackler, S.; Goel, T.; Pacis, J.; Collins, E.-M.

2024-12-07 scientific communication and education
10.1101/2024.12.05.627060 bioRxiv
Show abstract

Biophysical research is both exciting and challenging. It is exciting because physical approaches to biology can provide novel insights, and it is challenging because it requires knowledge and skills from multiple disciplines. We have developed an undergraduate biophysical laboratory module that is accessible to both biology and physics majors, teaches fundamental skills such as time-lapse microscopy, image analysis, programming, critical reading of scientific literature, and basics of scientific writing and peer-review. This module uses published research on the biomechanics of Hydra mouth opening as its framework because this work was co-first authored by an undergraduate student and featured in the public press, thus providing two anchors that make this research accessible and exciting to undergraduates. Students start with a critical reading and discussion of the publication. Then they execute some of the experiments and analysis from the paper, thereby learning fluorescence time-lapse microscopy and image analysis using ImageJ and/or MATLAB/Python, and compare their data to the literature. The module culminates in the students writing a short paper about their results following the Micropublication journal style, a blinded peer-review, and final paper submission. Here, we describe one possible implementation of this module with the necessary resources to reproduce it, and summarize student feedback from a pilot run. We also provide suggestions for more advanced exercises. Several students expressed that repeating a published study done by an undergraduate student inspired and motivated them, thus creating buy-in and assurance that they "can do it", which we expect to help with confidence and retention.

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50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.