Current kidney organoid protocols that differentiate human induced pluripotent stem cells into kidney cell types are still unable to grow an entire set of kidney cell types nor functional kidney...Show moreCurrent kidney organoid protocols that differentiate human induced pluripotent stem cells into kidney cell types are still unable to grow an entire set of kidney cell types nor functional kidney structures. Protocols of Morizane[1] and Taguchi[2][3] were adapted to improve their results on one human induced pluripotent stem cell line. When we adjusted the Taguchi protocol we were unable to differentiate the cells beyond the metanephric mesenchyme stage. However, with the refined Morizane protocol, we observed podocyte-like cells which clustered together and formed small structures. Nevertheless, the cell line that we used did not form tubular structures as was expected from the Morizane paper. Presumably, different cell lines respond differently to the same protocol. Therefore, in addition to refining kidney organoid protocols, it is recommended to increase the docility of cell lines such that cells exhibit the same differentiation behaviour.Show less
With data-generation becoming increasingly complex and automatized as a result of technological developments, using computers to perform data-selection, preprocessing and data-analysis has become...Show moreWith data-generation becoming increasingly complex and automatized as a result of technological developments, using computers to perform data-selection, preprocessing and data-analysis has become indispensable in many fields of physics and astronomy. Hence, acquiring some basic knowledge of machine-learning techniques should be an essential part of the curriculum of these subjects. However, courses on the subject are mainly aimed at future computer-scientists. In this study, we explore the potential of using the Emotiv EPOC+, a consumer-grade EEG-device, as an educational tool in a hands-on machine learning course, tailor-made for physics and astronomy students. For this, we perform various experiments with a single subject, and use elementary neural networks to perform a binary classification to identify events in the self-produced EEG-data. We find that the Emotiv is capable of producing data containing sufficient consistency within a single recording to detect blinks and full-arm motion with more then 90% accuracy. However, these results are not reproducible with the same neural network once the head-set has been removed from the head between recordings. This means the networks have to be trained anew in order to classify events in new data. For the Emotiv to serve as an educational tool in a machine learning course a better understanding of this difference in noise between recordings is necessary, and a standardized preprocessing to reduce noise should be developed.Show less
