In this episode of Discover ASAP, Yujie Fan, PhD, a postdoctoral fellow on the Collaborative Research Network (CRN)’s Team Gradinaru, joins us to discuss a programmable self-assembling system to model intracellular protein aggregation and decode neurodegenerative diseases.
Read the preprint
Showing 1-6 of 20
In this episode of Discover ASAP, Yujie Fan, PhD, a postdoctoral fellow on the Collaborative Research Network (CRN)’s Team Gradinaru, joins us to discuss a programmable self-assembling system to model intracellular protein aggregation and decode neurodegenerative diseases.
Check out the preprint.
In this episode of Discover ASAP, Armin Bayati, PhD, a research fellow on the Collaborative Research Network (CRN)’s Team Chen, joins us to discuss AutoMorphoTrack, a platform for quantitative analysis of organelle morphology, motility, and interactions at single-cell resolution.
Check out the preprint and Python package.
In this episode of Discover ASAP, Anastasia Filimontseva, a graduate student on the Collaborative Research Network (CRN)’s Team Vila, joins us to discuss a standardized, TruAI-based platform for automated quantification of intracellular neuromelanin granules in human brain tissue.
Check out the publication.
In this episode of Discover ASAP, Leonardo Parra-Rivas, PhD, a project scientist on the Collaborative Research Network (CRN)’s Team Gradinaru, joins us to discuss a protocol for CRISPR-based manipulation and visualization of endogenous α-synuclein in cultured mouse hippocampal neurons.
Check out the publication.
In this episode of Discover ASAP, Juan Li, PhD, a senior clinical research associate on the Collaborative Research Network (CRN)’s Team Schlossmacher, joins us to discuss the development of a simplified smell test to help distinguish patients with Parkinson’s disease from those with other neurological diseases.
Read the publication.
In this episode of Discover ASAP, Richard Gast, PhD, a postdoctoral fellow on the Collaborative Research Network (CRN) Team Surmeier, joins us to discuss PyRates, an open-source code-generation tool for modeling dynamical systems across languages.
Check out the publication and explore the tool on GitHub.
