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SUMMARY:Seeing and believing at super-resolution
DTSTART:20190213T110000
DTEND:20190213T120000
DTSTAMP:20260408T021238Z
UID:4c4ba4a7ec9a8dea126137e73616b6076c28ab3ec391134af3dbb7df
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Susan COX\nSuper-resolution microscopy is a powerful tool f
 or imaging structures at a lengthscale of tens of nm\, but its utility for
  live cell imaging is limited by the time it takes to acquire the data nee
 ded for an image. For localisation microscopy the acquisition time can be 
 cut by more than two orders of magnitude by using advanced algorithms whic
 h can analyse dense data\, trading off acquisition and processing time. In
 formation can be traded for resolution: for example\, the whole dataset ca
 n by modelled as arising from blinking and bleaching fluorophores (Bayesia
 n analysis of Blinking and Bleaching)\, although at a high computational c
 ost. However\, all these approaches will come with a risk of artefacts\, w
 hich can mean that the image does not resemble the underlying sample. We h
 ave recently developed Harr Wavelet Kernel Analysis\, a multi-timescale pr
 efiltering technique which enables high density imaging without artefacts.
  The results of benchmarking with other techniques reveal that at high act
 ivation densities many analysis approaches may achieve high apparent preci
 sion (very sharp images)\, but poor accuracy (the images don’t look like
  the sample). I will discuss the relationship between precision\, accuracy
  and information content in super-resolution microscopy images
LOCATION:BSP 407 https://plan.epfl.ch/?room==BSP%20407
STATUS:CONFIRMED
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