HN user
I'm a physicist; I develop new microscopy techniques. https://andrewgyork.github.io
I'm fully convinced that magnets do absolutely nothing to (normal) animal physiology.
What's cool about this work though, is we show that magnetic effects on ENGINEERED biological systems can be enormous:
Even more context here:
https://www.science.org/content/article/magnetically-sensiti...
Thanks!
We figured out how to turn protein function on and off, with magnetic fields from small handheld magnets.
More context here: https://bsky.app/profile/andrewgyork.bsky.social/post/3mcbrd...
I'm one of the authors. There's some discussion of these results on Twitter:
https://twitter.com/AndrewGYork/status/1679258525900640256
For more context, the idea that any biological protein responds to magnetic fields is (at least slightly) controversial. Our observation (a commonly used, well-known fluorescent protein gets slightly but unmistakably dimmer in response to a handheld magnet) is surprising, but it's straightforward to reproduce.
It's not an immediately useful result, but there's a long history of successfully engineering small effects into big effects in fluorescent proteins. If that's possible here (unproven), we could imagine noninvasive control of arbitrary biological machinery ("magnetogenetics").
Even if it's technologically useless, this observation also raises interesting questions about the effects of magnetic fields on biochemistry. Is this a unique edge case, or an instance of a more general phenomenon? What are the necessary/sufficient conditions for magnetic fields to affect life? Given that MRI is nonlethal, there are bounds on how strong these effects can be... but are they really zero?
One of the questions raised at the end of the article:
"Do we ever benefit from explicitly putting Fourier layers into our models?"
Has a simple, partial answer here:
https://docs.scipy.org/doc/scipy/reference/generated/scipy.l...
"...multiplying a vector by the matrix returned by dft is mathematically equivalent to (but much less efficient than) the calculation performed by scipy.fft.fft."
fft-as-a-matrix-multiply is much slower to compute than a standard fft, especially on large input.
That's a good example! I never bothered soliciting formal peer review for that article, but many of the principles we simulated there have since been demonstrated:
As opposed to the traditional publishing high-school popularity game? I'm partially joking, but traditional publishing is very much a popularity contest. You're free to ignore this, but I don't recommend it.
My personal experience (twelve years of traditional publishing followed by five years of DIY publishing) is that I spend substantially less of my time on publishing/dissemination, have higher impact, and produce higher quality work. You should give it a try!
Every since I got my own lab, I've been skipping "traditional" publication, for these reasons and more. I've had great success and satisfaction sharing my research via "DIY" publishing:
Advancing my field is my life's mission, and disseminating my research is too important to outsource.
Believe it or not, Twitter has been crucial to the process. It's not great for nuanced discussion, but it's AMAZING for advertising the existence of technical information. For example:
https://twitter.com/AndrewGYork/status/1138963271594020864
FPBase and Talley Lambert ( twitter.com/talleyjlambert ) are both awesome. I'm a physicist working with fluorescence microscopy, and I use tools that Talley developed or contributed to all the time.
Which reminds me, also check out napari.org for a nice viewing/annotation tool for N-dimensional numpy arrays.
I asked this on Twitter, but maybe folks here can answer better: how important is nonlinearity for deep neural networks? This method's output seems to be a linear function of its (complex) input. Does that put important bounds on performance? https://mobile.twitter.com/AndrewGYork/status/10228414045888... https://news.ycombinator.com/item?id=17698135
A different target audience, but in the same spirit, here's an open source microscopy project from our lab: https://andrewgyork.github.io/remote_refocus/
My friend André Maia Chagas is doing some inspiring work in open hardware too: https://mobile.twitter.com/chagas_am
I asked this on Twitter, but maybe folks here can answer better: how important is nonlinearity for deep neural networks? This method's output seems to be a linear function of its (complex) input. Does that put important bounds on performance? https://mobile.twitter.com/AndrewGYork/status/10228414045888...
Sounds like we're pulling in the same direction re:improving scientific publishing. At the risk of debating an ally, do you mean to imply that preprint servers are "more archival"? I'm guessing you're familiar with CERN/Zenodo; you trust bioArxiv's continued existence more than CERN's? I rate them as similar, arguably with an edge to CERN.
My experience with publishing is that discoverability is a stronger function of advertising and (especially) getting cited than the publication venue. I do agree that bioarxiv and arxiv are nice advertising venues, but there are lots of ways to skin that cat.
In the same spirit, our lab is experimenting with a new approach to scientific publishing via Github: https://twitter.com/AndrewGYork/status/965765089373515776
No paywall, no delay, straight to the web. Open data, open code, interactive/animated figures. Transparent, rolling peer review, version control, CC-BY license, citable DOIs. I was worried no one would read it, but it turns out science twitter is awesome. Very positive experience, so far.