Task relevant autoencoding enhances machine learning for human neuroscience.

In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects' behavior. However, state-of-the-art models typically require large datasets to train, and so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects' behavior rather than noise or other irrelevant factors.

Enrichment of Allelic Editing Outcomes by Prime Editing in Induced Pluripotent Stem Cells.

Gene editing in human induced pluripotent stem (iPS) cells with programmable nucleases facilitates reliable disease models, but methods using double-strand break repair often produce random on-target by-products. Prime editing (PE) combines Cas9 nickase with reverse transcriptase and PE guide RNA (pegRNA) encoding a repair template to reduce by-products. We implemented a GMP-compatible protocol for transfecting Cas9- or PE-2A-mCherry plasmids to track and fractionate human iPS cells based on PE expression level.

Modulating subjective pain perception with decoded Montreal Neurological Institute-space neurofeedback: a proof-of-concept study.

Pain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e.