Jan Becker

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Jan Becker

Jan Becker

@RunningPhoton

I am a runner & data scientist at ZOMP (https://t.co/lRvQQEWC4X). Previously postdoc with @KukuraLab (@KavliOxford) and PhD with @RainerHeintzmann (@Leibniz_IPHT).

Cambridge, England Katılım Kasım 2019
261 Takip Edilen243 Takipçiler
Jan Becker retweetledi
Liuba Dvinskikh
Liuba Dvinskikh@LD_light_·
We demonstrate our method on biological samples with a range of RIs, including a whole fly brain 🧠 (imaged in < 3 min⚡) and > 1 cm long mouse bone 🐭🦴 (🧵 5/6).
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Jan Becker
Jan Becker@RunningPhoton·
@JamesDManton Maybe someone has some cool data from astronomy and wants to give it a try?
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Jan Becker
Jan Becker@RunningPhoton·
@JamesDManton Here just an example of the spokes target that I used in the original tweet many years ago. Notice how the stopping is SNR dependent: High = 23 iter. | Medium = 22 iter. | Low = 17 iter.
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James Manton
James Manton@JamesDManton·
Following a poke from @RunningPhoton, I've fixed a bug in my KL divergence calculation function which means our parameter-free deconvolution approach now *really* has no hand-tuned parameters at all: beryl.mrc-lmb.cam.ac.uk/rlgc_notebook/ Feedback always welcome. x.com/JamesDManton/s…
James Manton@JamesDManton

We (@AndrewGYork, @RunningPhoton, @microRussell and I) are still working on the manuscript describing our automatic parameter-free deconvolution approach, but we've made a Google Colab notebook where you can try it out without needing to install anything: beryl.mrc-lmb.cam.ac.uk/rlgc_notebook/

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Jan Becker
Jan Becker@RunningPhoton·
@JamesDManton It also depends on the object though! Objects containing a wide range of spatial frequencies tend to require larger number of iterations to be deconvolved.
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fabrice senger
fabrice senger@fabrice_senger·
@christlet Fact that it can be used as stop criterion for deconvolution is very interesting
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Jan Becker
Jan Becker@RunningPhoton·
A yellow photon running through the forrest ... moving at a speed v << c 😅
Jan Becker tweet media
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Jan Becker retweetledi
Liuba Dvinskikh
Liuba Dvinskikh@LD_light_·
Enjoying day 2 of #FOM2023🔬with lots of light-sheet talks. Visit poster P1-G/3 to learn more about dual-view oblique plane microscopy (dOPM) and its application to high-content imaging of 3D cancer organoid models as part of the @MACH3Cancer CRUK Accelerator.
Liuba Dvinskikh tweet media
Oporto, Portugal 🇵🇹 English
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Jan Becker
Jan Becker@RunningPhoton·
This study involves the simulation of the interferometric nature of the detected signal, a quantification of the effects of glass roughness and protein shape/orientation ... and many more. (2/2)
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