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. 2021 Oct 11;1:727066. doi: 10.3389/fbinf.2021.727066

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Copyright © 2021 Daetwyler, Mazloom-Farsibaf, Danuser and Craig.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Internship projects and student characteristics. (A1-A4) The internship projects spanned topics as diverse as microscopy, 3D visualization, surgical skill platform analysis and image restoration. (A1) A student applied compressed sensing algorithms (Rani, Dhok, and Deshmukh 2018) to reconstruct an original image of mouse brain neurons (white) from sparsely sampled points (brown) in a 3D volume obtained by simulating a random-access microscope. (A2) A student analyzed molecular signals on a 3D cell surface of a melanoma cell (Driscoll et al., 2019) and visualized the resulting data. (A3) A student established a full pipeline to perform automated analysis to score the performance of different surgeons on a novel surgical skill platform (Battaglia et al., 2021). (A4) A student explored artificial neuronal networks such as CARE (Weigert et al., 2018), to obtain higher resolution images (right) from raw data (left). (B) Applications (blue, circle size scaled for number of applications) for the U-Hack Gap Year were received from all over the US. Consequently, participating students (pink, circle size scaled for number of interns) were working remotely from all over the US, while the mentors were working at UT Southwestern in Dallas (orange). (C) Source of information that made participant aware of the U-Hack Med Internship program included advertisement through our curated college email list (74.7%), Handshake app (6.8%), no response (6.8%), word of mouth/friend (4.8%), search engine and social media (3.8%), U-Hack Med former participant mailing list (2.9%). (D) The knowledge background of the participating students was highly diverse ranging from freshman to graduate student knowledge. (E) The internship participants were six male and 4 female students.