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I don't think any of that seriously counts as real work.

Casey Muratori addressed this years ago with the windows terminal drama, https://github.com/cmuratori/refterm

found this video about the author/origin: https://www.youtube.com/watch?v=Rofmr7_xc7A

I think you're spot on, it is easier to prove (or prosecutors have more experience prosecuting) financial crimes. To give them credit-- they at least tried to get Holmes for defrauding patients too, but she was acquitted of those charges [0]. Her partner, Sunny Balwani, was convicted on all counts, including defrauding patients [1].

[0] https://en.wikipedia.org/wiki/Elizabeth_Holmes#U.S._v._Holme....

[1] https://en.wikipedia.org/wiki/Sunny_Balwani#United_States_v....

I'll always admire clojure. Loved the simplicity and philosophy, and I wrote a few toy projects. Unfortunately I felt like I could never really take advantage of the power of clojure or do real work in it because I didn't know or have a history with Java. It always felt like clojure was for enlightened Java or JS programmers, and I didn't want to learn Java and clojure at once so I was stuck in beginner land.

You're not alone, I use noscript on firefox for exactly that. It makes some websites unusable, but for normal browsing, that's what I use. If the website is unusable after allowing a few scripts, then I don't want to be there. It is horrifying when you see how much JS some websites try to pull in.

Not exactly what you are looking for, but Open Secrets gets you almost there: https://www.opensecrets.org/federal-lobbying

Their API leaves a lot to be desired, but the data is much easier to access and analyze than the raw data from the US House or Senate websites.

This is an important point. What have the journals done? Raised their prices and business as usual.

Scientific editors do nothing for data validation. There is no accountability, even after retractions.

Scientific journal editors are glorified gatekeepers for "high impact" work (read: flashy), and then use free reviewer labor to cover themselves so they can call it 'peer reviewed'. In the rare cases when journals do require supporting data, they explicitly ask for excel spreadsheets :(

Excel is used as a database/storage/interchange format, especially after the initial analysis by someone who uses python or R. Bioinformatician does the analysis, then the PI wants to see it so they can Ctrl-F for genes they are interested in, so out comes an excel document.

And really, even if you know python or R, are you really going to fire up a jupyter notebook, load the data, and run pandas queries every time someone in lab meeting or after a talk asks you about this gene or that gene in your data?

I think the important question is why is date conversion a default? Would it really break backwards compatibility for MS Excel users if date conversions were explicit instead of automatic? Turning that off by default would fix a lot of this.

It's definitely possible, but I guess what I am saying is that research in this area hasn't really been explored in the context of humans.

In the lab, we use targeted genetic manipulations such as optogenetics [1] or chemogenetics (see DREADDS [2]) to achieve precise circuit manipulations that can (maybe/kinda) change emotional state (see [3] and [4] for manipulation of fear in mice, sorry may be pay-walled check sci-hub). But these are impractical in humans at the moment because they require specific genetic backgrounds (a CRISPR modified mouse expressing a specific artificial DNA sequence in certain types of neurons from birth), viral injections to add other genetic constructs that interact with the from-birth one, and implanting lights or adding drugs directly to the brain where the cells are. Precise electrical manipulation is not really done, even in animal labs because it is not precise or controllable for these types of things.

Again, I have no doubt that we will get there, maybe in a few decades too. But the techniques are much further from human use than the "reading" technology demonstrated here.

[1] https://en.wikipedia.org/wiki/Optogenetics [2] https://en.wikipedia.org/wiki/Receptor_activated_solely_by_a... [3] https://pubmed.ncbi.nlm.nih.gov/28288126/ [4] https://www.nature.com/articles/npp2015276/

I don't know, I think that is a big jump and definitely not trivial.

"Reading" neural activity is much different than "writing", and modifying the circuits/neural activity precisely enough to modify emotions.

These devices are typically cortical surface level electrode meshes, placed over the motor region of the cortex, while emotions are thought to come from various deep brain structures. Not saying it won't happen, but we are much, much, further from the latter than the former.

I totally agree and had a very similar experience in graduate school. Writing about my experiences and things I had learned (technical and project management) had a huge impact on my ability to demonstrate my knowledge and is without a doubt why I quickly received two job offers before defending my phd (biology/neuroscience). I think papers are a really poor way to demonstrate the huge amounts of work you've done unless you stay in academia (and probably not even then).

I am not qualified to get too in the weeds on the physics, but 'Resolution' is... complicated. Usually, when we talk about resolution we are talking about the ability to distinguish two points.

The 'resolution limit' (Abbe diffraction limit [1]) is related to a few things, but practically by the wavelength of the excitation light and the numerical aperture (NA) of the lens (d = wavelength/2NA). When we (physicists/biologists) say 'super resolution', we mean resolving things smaller than what was previously possible based on the Abbe diffraction limit. So rather than only being able to resolve two points separated by a minimum of 174nm with a 488nm laser and a 1.4NA objective, we can resolve particles separated by as little as 40-70nm with STED (but it varies in practice).

STED does not accomplish this by estimating PSFs and fitting Gaussians, it uses a doughnut shaped depleting laser to force surrounding fluorescence sources to a 'depleted' state, and an excitation laser to excite a much smaller point in the middle of the depletion (see the doughnut in the STED wikipedia page, Stephen Hell and Thomas Klar won the Nobel Prize in Chemistry for this in 1999 [2].

I know PALM/STORM uses statistics, blinking fluorescence point sources, and long imaging times to build up a super resolution image based on the point sources and computational reconstruction.

Not as familiar with that one or SIM, but I know the "Pure physics/optics" folks I work with regard STED as the most pure physics based one that doesn't rely on fitting, deconvolution, or tricks (not that any of that is bad or wrong!).

[1] https://en.wikipedia.org/wiki/Diffraction-limited_system#The... [2] https://en.wikipedia.org/wiki/STED_microscopy

unfortunately not, for real superresolution (i.e. resolving below the diffraction limit of light) all the current methods require expensive (and very dangerous) lasers and microscopes with all sorts of optics widgets, mirrors and computers. Lots of 'high resolution' imaging things are available for cameras, as well as some AI systems that will make up data for you so it looks better too!

I am least familiar with SIM, but you can do live-cell SIM imaging for sure. The processing is a bit computationally intensive but not so bad (and can always be done post-hoc).

The big thing you have to look out for is the light intensity killing the cells or bleaching your signals. One of our collaborators is actively working on on-the-fly SIM processing for live cell imaging.

A quick glance at pubmed it looks like 11Hz was do-able several years ago https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2895555/ and this (sorry paywalled, I've heard sci-hub has the paper...) https://pubmed.ncbi.nlm.nih.gov/30478322/

STED is promising for live imaging too. Lots of beautiful pictures out there!

Most are, but in biology/physics STED[1] and STORM are physics based methods for overcoming the diffraction limit[2]. STED is pure physics, no math/deconvolution/AI tricks.

[1] https://en.wikipedia.org/wiki/STED_microscopy

[2] https://en.wikipedia.org/wiki/Super-resolution_microscopy

For anyone interested in 'real' super resolution, we use these techniques to overcome the diffraction limit in microscopy (my field is neuroscience):

- https://en.wikipedia.org/wiki/Super-resolution_microscopy

- Stimulated emission depletion microscopy (STED): https://en.wikipedia.org/wiki/STED_microscopy

- stochastic optical reconstruction (PALM/STORM)

- structured illumination microscopy (SIM)

Here is one of my favorite STED imaging papers, looking at the skeleton of neurons: https://www.sciencedirect.com/science/article/pii/S221112471...

I'm a neuroscience PhD candidate finishing up my degree and transitioning to gov/'industry' in ~ a month. I've had a great experience with great mentors, but there are MANY problems with the current system (at least in bioscience/medicine).

1. Academia is a feudal system.

To get in, you need a glowing letter from 'Someone Important We Know' from 'Big Name University'. Sure there are some admits with letters from less important people/schools, but if we are being honest, we know that is a big minority (I think this also accounts for the lack of diversity in science, but that is a different topic). To advance in your career (get a postdoc/faculty job), guess what is also the most important thing? 'Glowing Letters From Important People We Know'.

The next most important thing is 'Big Paper From Journal We Know (Cell/Nature/Science (CNS))'. The dirty secret of biomedical science is that getting 'big' papers very often depends on who you work for. Anectdata, but I've seen many garbage papers in CNS, where I can't believe it is in this journal, only to see ohhh it is because 'Big Name Lab at Stanford/Harvard/JHU' with a track record submitted it... I see. Glam journals like glam authors, if you don't believe that you are unusually optimistic or uninformed.

2. There is little or no opportunity to get validate-able credit for any of your work.

The only way you get document-able credit for work is your (maybe 1 or 2) publications and any fellowship/small token grant you managed to get (there are very few). I mentioned this in a comment below, but I have spent months of my life creating data and figures for grant applications (that were won or not), and I practically get 0 credit or recognition for that work. My name is not on the grant, despite me doing virtually ALL of the work for it (ideas and experiments) because I am phd candidate and cannot actually receive the funding from an NIH R01.

The vast majority of my work will go completely un-credited (both inside and outside of academia) unless someone inside academia that I might want to work with happened to see my mentors talk where they gave me a shout-out on a slide.

If I leave academia, I have no 'proof of work' for anything outside my paper and thesis (no one will read it). I can't claim authorship on the very important $500K+ grants that I practically wrote and won myself. Those don't go on my CV/resume, and if they did then people looking could look up the grant and see I am not in fact listed as an author or contact.

So in the end, what is a student or post doc in a bad situation going to do? You can leave after 3/4 years as a PhD student with no savings, and very few marketable skills, and start over in another lab or try to find a non-academic job that values half a phd? It is worse for postdocs, who have 'invested' 10+ years and either have to suck it up to get that letter, or leave with nothing (also no savings).

I think a lot of it boils down to the feudal system of letters+publications = value

[edit] wording

I totally agree, recognition is a big deal, but also very ephemeral and kind of an empty gesture. My mentors always credit me in talks they give (they are excellent and I am happy with them), but I have spent months of my life creating data and figures for grant applications that were won or not, and I practically get 0 credit or recognition for that work. My name is not on the grant, despite me doing ALL of the work for it because I am phd candidate and cannot actually get the funding from an NIH R01.

The vast majority of my work will go completely uncredited (both inside and outside of academia) unless someone inside academia that I might want to work with happened to see my mentors talk. If I leave academia, I have no 'proof of work' for anything outside my paper and thesis (no one will read it). I can't claim authorship on very important 500K+ grants that I practically wrote and won myself, but others take credit for it. Those don't go on my CV/resume, and if they did then people looking could look up the grant and see I am not in fact listed as an author or contact. I've come to realize that this is a huge problem.

Prineas in 2001 showed that MS progression may occur in the absence of immune cells and inflammation

MS researcher here. MS as an autoimmune attack is largely settled in mainstream neuroscience. The paper you cited does not show what you said it does. From the paper: > restricted largely to short segments of disrupted myelin located within linear aggregates of microglial cells

The paper is interesting because it shows that demyelination is happening in lesions that we thought were inactive, but actually do contain microglia and macrophages (immune cells) eating myelin (i.e. an autoimmune --immune system attacking self-- attack). The paper is paywalled but I but you can read it on sci-hub (https://sci-hub.scihubtw.tw/10.1002/ana.1255) Although just reading the abstract says the opposite of what you are claiming, the entire thing is about microglia and macrophages contributing to lesions that the researchers thought were inactive.