88e282102ae2e5b

I've only looked into this a little bit, but I think one problem is that you're limited by either nitrates or phosphates, depending on whether the algae are being grown in ocean water or fresh water. Either way you have to supply some kind of nutrient, which has to be either mined or synthesized in some energy-intensive fashion.

I just keep the paper towel I used to dry my hands to cover the door handle as I open it, and only then throw it away.

Maybe this could finally bring the use of cryptographic signatures into the mainstream. An additional incentive might be that they could even be used to "cheat" when unfavorable news came out: an organization that routinely signs all of their press statements and internal documents could disavow any unsigned leaks, perhaps.

I legitimately don't see which guideline this violates and I thought it was a good question, even if it turns out that the case for paying off the debt is strong.

Though anecdotal, the one time I tried to restore my data from a duplicity backup (aside from when I initially started using it), I got a stack trace with an unhandled KeyError. I was never able to find out why the error happened or how to fix it, but thankfully I was just testing my ability to restore.

Anyway, I would recommend against using it - errors like that should be impossible when you're not doing anything exotic.

The viruses used to deliver CRISPR in the past are not able to replicate. They're so small that you have to remove all of the viral DNA in order to even fit your CRISPR-coding genes inside.

Cells need to be able to control how much cholesterol they have (cholesterol, notably, makes up about 25% of the cell membrane). They can either synthesize it themselves, or import it from the blood.

The system for importing cholesterol can't run continuously or else it might cram the cell full of cholesterol, so there needs to be a way of turning it off (or at least slowing it down) when levels are satisfactory. PCSK9, the gene that they targeted, does just that.

So it's not a "gene for high cholesterol" per se - but if you delete it, cells will import more cholesterol, thus lowering the levels in the blood. I really don't know much about this system, but I imagine you'd have to combine this modification with a low-cholesterol diet or else it might have an adverse effect on the modified cells.

It's not like CRISPR didn't pan out - it's just that biology takes a long time to understand. Cas9 was only characterized 5 years ago. It's now routinely used in all sorts of research applications and clinical trials for therapeutic uses are starting up - I can't imagine a faster timeline.

Personally, If I were picking the assumptions and hyperparameters, I'd end up with the golden age being seasons 3 through 8 :)

I'm definitely getting into hair-splitting territory, but I think it's important to not conflate modifying a protein with fusing two proteins. It would be easy to come away from the GP and think that scientists have the ability to easily change the enzymatic function of Cas9, when in reality they're effectively just connecting a protein to Cas9 and letting it do its thing.

So many reasons!

  * In order to get CRISPR to fit inside a virus you have to remove all the viral DNA, so it can't reproduce. 
  * Even if you could make such a virus, there would be enormous selective pressure to ditch the CRISPR proteins, and there's no pressure maintaining the CRISPR or guide RNA sequence, so it would be inactive pretty quickly
  * Race isn't well-defined genetically so it might not even be possible to target a group of interest
  * Bombs are cheap and a proven technology

> they modified the CRISPR associated DNA editing enzyme, cas9

This is incorrect. They modified a separate enzyme that is fused to cas9.

I've confronted it plenty of times, and for my use cases it's not really an issue. Many numpy functions release the GIL, so by keeping my performance-critical stuff in numpy it's just not an issue for me. I sometimes split tasks up with multiprocessing, which is a GIL workaround to be sure, but the memory footprint of extra interpreters is negligible compared to the data I work with.

I didn't mean to seem petty, it's just that going back and forth between zero-based and one-based languages just added extra mental overhead (admittedly this was with Fortran, so there may have been other issues).

I did read the post.

> but rather it's first-in-class domain-specific packages.

You'll note I didn't cite lack of libraries as a reason I'm reluctant to use Julia. I agree that a solid ecosystem is critical for adoption, but my point is that, for me, it's not sufficient to overcome the human/time/cost factors.

Is Julia really that much more performant than numpy?

It's great and all, but I can't justify switching languages for minor improvements over Python + numpy/scipy. I'd be abandoning:

  * My deep knowledge and experience with Python
  * My entire codebase
  * The ability to work on projects with colleagues who don't also switch
  * The certainty that when I leave my current job, someone will be able to pick up after me
  * Zero-based indexing

I've started to do some work in Rust when it makes sense, since there's occasionally a compelling case and it's substantially different than Python. Incremental advances in programming languages just aren't worth it, and knowing that other people are probably coming to the same conclusion means I can't expect a serious community to ever arise around Julia.

Genuine question: with an extremely strong password on the private key, what's wrong with just storing it on an external hard drive somewhere?

I would argue that Xubuntu is almost grandparent-ready.

The initial meeting with the IRB where they proposed ridiculous and irrational changes to the consent forms was strikingly reminiscent of every conversation I've had in a third world country where someone was asking me for a bribe, but couldn't say it directly.

I'm glad I did it, but it was definitely more work than I was expecting.

Well, you wouldn't want to modify a gene that isn't well-characterized, sure. But there are some things that are known with a great deal of certainty.

I don't think there's any need for a cui bono line of thinking. There are biologists who have nothing to gain who thought this paper was lousy. For example, every single biologist in my department that I spoke to, and every biologist I've found online who had something to say on the matter.

Perhaps that's too much of a selection bias for you, so I'll offer this critique: if you wanted to measure the off target effects of Cas9 in an organism, you would probably sequence the organism before you edited it, and then sequence it again after you edited it. But that's not what they did.

This paper was incredibly sloppy and has been widely dismissed by scientists in the field.

This whole concept seems like a combination of a desperate wish to be living in a better world and the need to seem interesting at parties. When there's a compelling reason to get such devices you won't have to ask people what they use them for, in the same way that you don't have to ask people why they have a cell phone.

It seems that for the glamor journals in biology it definitely is broken, at least for click-baity manuscripts like this. This paper was immediately shat upon by everyone in my department. There's a running joke often said when things like this crop up: 'just because it's in Cell/Nature/Science doesn't mean it's false".

Some of my colleagues speculated that this was not the intended study. Originally, they might have tried to delete a gene, but the expected phenotype didn't show up, which would have put an end to that line of research. But instead of completely abandoning the project they figured they could sequence the mice that were still around to see what the off-target effects were, and they thought they found something surprising and important.

All known CRISPR systems work only under very specific conditions. You could drink a vial of Cas9 and it wouldn't do anything to you because it couldn't enter your cells.

These differences appear quite small. It would really benefit from just having error bars, or any kind of significance test.

It conveys intent better. The function name is effectively a way of documenting what the result of np.mean should be interpreted as.

xval is a terrible name, but it does make it easier to see what's being passed into np.mean.