By contrast, a postdoc salary runs on the order of $60k-$120k annually, depending upon the field and experience. Can get 2-4 human PhD researchers for the price of a fake hallucinating one.

Indeed. And moreover, cochlear implants require constant communication (and power) via an implanted transceiver just behind the ear.

The cost-benefit analysis changes with the *purpose* of the plant, because it changes the benefits and who gets them even if the costs are unchanged.

When the plant is used to generate electricity for real life--dishwashers, lights, computers, and so on--then perhaps the local residents have decided there's enough benefit to justify the health and climate costs.

When the plant is repurposed for a few people to get rich mining a virtual currency (with arguable real-world utility), that balance changes. The local community gets all the cost, and none of the benefit.

Except the AmazonBasics USB cables and microwaves and car chargers that catch fire: https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fwww.cnn.com%2F2020%2F09%2F10...

Personally, I'd be interested if the fake personas drew real salaries. That could point to some nasty / illegal financials ..

You bet! Blew me away first time I saw it. Biology gets crazier the more you look. :-)

You can actually see mitochondria crawl inside cells.

Here's an example, imaged over about 10 minutes: https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Ftwitter.com%2FMAG2ART%2Fstatus%2F1087386722667761665.

Here's another gorgeous video I just found: https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3Dx5IxkI6tkn0

I'm guessing cloud cover. Which would tend to track with the underlying land ... Just like the clouds over much of southern Europe around 0:21 and 7:03 in the video.

Not to mention there's no guarantee it's actually ADT and not somebody casing houses and installing fake or compromised equipment. Not like it takes a lot to fake plolo shirts and a folder. :-) If you wanted their services, you'd call and schedule so you could have more assurance of identity.

Shared memory parallel codes (OpenMP) could benefit, though. Many (originally single-threaded) or homemade scientific applications run in this space: get some parallelism for relatively little work (insert pragmas, be careful to be thread safe, and test test test), without all the extra work of redesigning those simulations for efficient message passing.

You certainly find problems where it is much better bang for the buck to throw an expensive processor and OpenMP ( O($10^2 to $10^3) ) at a problem than to throw specialized MPI development effort ( O($10^4 to $10^6) ) at that problem. Especially when the first step to any hybrid OpenMP-MPI code is to work on single compute node performance with OpenMP, before connecting nodes with MPI.

Maybe they can call them General REpair Micro LocomotIve automatoNS. Everybody wants to see gremlins in the engines. :-)

We taught a little of this in some of our engineering courses this Spring. Suppose gene A down-regulates gene B, and gene B down-regulates gene A. Then if (A,B) is your network state, (1,0) and (0,1) are stable states, and all intermediate states go back to one of these. This is a bistable toggle. It's a way to write a bit of data to a cell.

Now, add two more genes: A promotes P which blocks A. B promotes Q which blocks B. This turns the system into a biological oscillator. Now you have a system click with tics (A up and B down) and tocs (A down and B up). Fun stuff.

You're exactly right.

I took a brief look through the paper. Table 3, glia (rightmost columns) seems to sum up this study nicely. Control group had 817 mice, 3 malignant brain tumors. Highest dose had 409 mice, 3 with malignant brain tumors. Not a significant difference in this entire table at any dose in any sub-population, even at p = 0.05 levels.

Table 2 focused on schwannomas, and they had to dig deep to male mice at highest exposure (n = 207) to get a significantly significant (at p = 0.05) difference. We're talking 3 / 207 male mice with malignant schwannomas at highest exposure. The control males had no cases (n = 412), but we're really in the weeds here where a stochastic variation of +/- 1 mouse makes a huge difference in their tallies. No other significant difference in any other dose in any other sub-population in any other table in this paper.

Kaplan-Meier survival curves (Figure 3 g-h) look just about identical for all doses: we're not seeing a big difference in survival times at any doses. And there's no effort to estimate error bars for those curves. That's a hint about (lack of) replicates.

From what I can see, there was exactly one replicate for each group / arm (e.g., mice exposed to a specific dose). This is not good, because technical and biological variability can cause flukes and false differences. 1 technical replicate per arm: if a technician had a bad day or screwed up a protocol when the exposing the mice to the highest dose, your one measurement set could be off. 1 biological replicate per arm: a weird batch of mice, or a batch of sick mice, etc., could throw off your one measurement set for the arm. Most cell line experiments we've worked with have at least 3 technical and biological replicates, in very controlled culture conditions. You'd be amazed at the variability, even in "identical" cells.

Oh, and read the neat Nature story (summary) where the sex of the scientist performing the experiments on mice can cause statistically significant differences. Because the male and female scents in our clothing can actually induce stress hormone changes in mice. Experiments are sensitive. Replicates are a good thing.

Thanks, Bruce. That was my first question at this story, and I appreciate hearing it from your expertise!!!!

No problem, and thanks for reading and replying.

I was confused on that, too, and neurobiology is pretty far from my regular work. Part of the problem is that there's a lot going on here: plasticity and differentiation (cell adaptations, transformations from one phenotype to another, differentiation, etc.), cell proliferation, tuning of connections, etc. It's a messy area with lots of new and sometimes contradictory data coming out.

From what I understand, there's a lot of plasticity in the brain as an adult: far more than was originally appreciated. I thought that we "knew" there were no new brain cells, and then we "knew" there were, and now we may "know" there aren't. That's the nature of evolving science, as others point out. And imprecise science communication--and imprecise English--doesn't help, either. Even biologists can get tripped up: we talk about tumor growth and cancer cell population growth, but we really primarily are talking about cell division there.

And there are all sorts of neat surprises. It was found that glial cells (a type of brain cell responsible for maintaining brain structure) can transdifferentiate into endothelial cells (which make blood vessels). See this PNAS paper and this one. This has all sorts of implications for gliomas and other brain cancers. And God only knows what other surprises are waiting to be found.

I suppose that's another reason they looked at the "new neuronal cell" marker: to see whether other cells could become new neuronal cells by transdifferentiation or other plastic processes. Biology is weird. :-)