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SUMMARY:From Starlight to Synapses: How Cosmological Bayesian Inference Re
 veals the Brain's Molecular Structure
DTSTART;VALUE=DATE-TIME:20261028T182100Z
DTEND;VALUE=DATE-TIME:20261028T182200Z
DTSTAMP;VALUE=DATE-TIME:20261025T124214Z
UID:indico-contribution-3671@nyx.physics.mcgill.ca
DESCRIPTION:Speakers: Manveer Sidhu (Ontario Tech University\, Dunlap Inst
 itute for Astronomy and Astrophysics)\nSuper-resolution microscopy is a ra
 pidly evolving field\, and analytical tools are struggling to keep up with
  novel imaging techniques. 'MINFLUX' (minimal fluorescence photon fluxes) 
 is one such technique\; it uses targeted emission to track molecules down 
 to the nanometer scale. However\, 'MINFLUX' outputs localizations of these
  molecules rather than viewing the protein superstructure directly\, leavi
 ng a gap for a tool that can make that leap. 'AstroPhot' is a Bayesian fra
 mework traditionally used to analyze and deblend crowded/transient cosmolo
 gical data by separating objects and fitting parameters individually. With
  some unifying adjustments\, 'AstroPhot' can be calibrated to ingest MINFL
 UX data and fit shared parameters across objects to fit greater protein su
 perstructure based on molecular localizations. This approach is tested aga
 inst 'MINFLUX' data from neural inhibitory synapses\, where the hexagonal 
 lattice structure of the gephyrin protein is analyzed. Gephyrin is crucial
  in the regular function of the brain\; mutations and abnormalities in gep
 hyrin are linked to epilepsy\, Alzheimer's\, and schizophrenia. The natura
 l superstructure formed by gephyrin in the brain is debated throughout the
  literature. Thus\, understanding gephyrin structure and applying the appr
 oach used to other proteins is key to tackling some of the longstanding ne
 urological disorders plaguing humanity.\n\nhttps://nyx.physics.mcgill.ca/e
 vent/587/contributions/3671/
LOCATION:Online 
URL:https://nyx.physics.mcgill.ca/event/587/contributions/3671/
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