[Bnc-faculty-all-list] FW: BME distinguished lecture series, Dr. Susan Cox (King's College London)

[Turner, Jaime J]

Purdue University
Weldon School of Biomedical Engineering
Distinguished Lecture Series ​

Wednesday, November 28, 2018
9:30-10:20am
MJIS 1001
*and via Zoom meeting at IUPUI

Seeing and believing at super-resolution

Susan Cox, Ph.D.
Randall Division of Cell and Molecular Biophysics
Guy’s Campus, King’s College London
London, UK, SE1 1UL

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Super-resolution microscopy is a powerful tool for 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 needed for an image. For localisation microscopy the acquisition time can be cut by more than two orders of magnitude by using advanced algorithms which can analyse dense data, trading off acquisition and processing time. Information can be traded for resolution: for example, the whole dataset can by modelled as arising from blinking and bleaching fluorophores (Bayesian analysis of Blinking and Bleaching), although at a high computational cost. However, all these approaches will come with a risk of artefacts, which can mean that the image does not resemble the underlying sample. We have recently developed Harr Wavelet Kernel Analysis, a multi-timescale prefiltering technique which enables high density imaging without artefacts. The results of benchmarking with other techniques reveal that at high activation densities many analysis approaches may achieve high apparent precision (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.

~BME Faculty Host: Fang Huang~
***Coffee and juice will be provided at West Lafayette***


--
Fang Huang
Assistant Professor
Weldon School of Biomedical Engineering
College of Engineering, Purdue University
West Lafayette, IN  47907

office:
206 S Martin Jischke Dr
West Lafayette, IN 47907
Room # 2025
Phone: +1 765 494 6216

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