Motion sickness as metaphor: engaging with diversity in STEM.

Science, technology, engineering, and math (STEM) continue to work to increase the diversity of the fields, yet there are still significant historical and societal hurdles to be overcome before we reach full representation throughout STEM. The concept of science identity has become a point of interest in this process; it has been suggested that development of one's identity as a scientist is critical to persistence in the field. Metaphors that are rooted in bodily experience can provide a starting point to understand abstract concepts, including science identity and how we as STEM educators respond to increasing diversity within our fields.

A predictive epilepsy index based on probabilistic classification of interictal spike waveforms.

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model.

Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice.

Visual scoring of murine EEG signals is time-consuming and subject to low inter-observer reproducibility. The Racine scale for behavioral seizure severity does not provide information about interictal or sub-clinical epileptiform activity. An automated algorithm for murine EEG analysis was developed using total signal variation and wavelet decomposition to identify spike, seizure, and other abnormal signal types in single-channel EEG collected from kainic acid-treated mice.