[FRIAM] How swarms of bees go from preferring one target to preferring another

[Marcus Daniels]

Yeah, I’ve noticed interesting stuff in SRA datasets.   I have a suspicion it is underutilized information, but I haven’t really investigated (the literature).   There are some CDC BAA’s out recently along these lines.  Like high-performance metagenomics tools that can characterize all the pathogen variants in a sample.

I suppose one could try to further regulate it, but some countries may actually sponsor this kind of research in their defense budgets.   And there are good reasons to understand the potential badness and diversity of viral / host (human) interactions.    And nature in its infinite spite can come up with this stuff itself, so it is good to be prepared.   Simply refusing to investigate or discuss scary topics is pointless.

From: Friam <friam-bounces at redfish.com> On Behalf Of David Eric Smith
Sent: Tuesday, May 25, 2021 5:17 PM
To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
Subject: [FRIAM] How swarms of bees go from preferring one target to preferring another

I assume you all have been following the following: (?)
https://thebulletin.org/2021/05/the-origin-of-covid-did-people-or-nature-open-pandoras-box-at-wuhan/

I had seen bits and pieces of the claims summarized above in other sources, but they were either technical work that I did not put time into trying to read and understand (and likely didn’t have expertise to weigh in on anyway), or they were from writers I didn’t trust not to mis-represent.  But the above is a decent concise summary of several things, and the source may be better.  Some of his links I am less sure of, but have not gone down the tree to judge.

Some weeks ago, a day or two after it came out, I got a pointer to this paper:
https://arxiv.org/abs/2104.01533
which suggests a piece of big-data forensics I would very much like to see done (if Google wanted, for instance, to make itself useful).  (Did I already send this link?  Or was it one of those abortive posts that went mercifully to /dev/null?

I didn’t know anything about what gets filed in gene registries, how much of raw short reads versus just assembled contigs etc.  But it sounds like a lot of stuff gets filed, from which you can tell if some other sample was run through the same machine as the reported sample, and may be in the data as a contaminant.  I guess sequence assemblers also quite frequently insert contaminant reads into what they think the real sample sequence was, so all kinds of crazy nonsense ends up here or there in “assembled” genomes from next-gen sequencers (which I guess are now this-get, almost to the exclusion of older (Sanger?) methods).  The above article mostly focuses on labs in an agricultural university in Wuhan, suggesting that WIV was farming out sequencing jobs (pun not intended) to labs without the BSL procedures in place to perform them.  The first article (the BAS editorial) adds a bit of clarity to what I knew before: apparently a lot of the coronavirus research is only listed as BSL2 to begin with.  So the error if it is an escape would be the same, but it would be a matter of institutional decision-making, rather than something read as a reflection of culture or customs across the society, which has ramifications for the response.

The big-data work I would like to be done would be a kind of ongoing scrub of gene repositories, to assemble a catalogue of who is working on what, whether reported or not reported.  It is one thing to do a targeted search after a pattern of interest is known, but that takes a lot oa manual tooling, and is only likely to be motivated too late to be helpful as a preventative.  I am thinking of something more in the vein of a surveillance method that can be part of a regulatory and oversight regime.  Of course, once the pipeline is written, governments and militaries will self-screen before reporting (like your students run their essays through plagiarism finders before they send them to you, to know which things won’t be caught), and the signal will get smaller.  But we have a lot of historical data that has not been sieved in the way the Zhang et al. paper does, and would be very pertinent to research still going on now.

There was a nice comment in the summary section of the BAS article, which would fit into several conversations on FRIAM lf late:

Professions that cannot regulate themselves deserve to get regulated by others, and this would seem to be the future that virologists are choosing for themselves.

If this does go the direction that it was gain-of-function work that agencies either should not have authorized, or should have been as much more diligent in limiting as they knew to do, whatever goodwill the medical profession has earned in a year of trying so hard to take care of people will be swept away in the backlash, since as we know resentment is a much stronger motivator than gratitude, even when spontaneous, and even more when manipulated.  I find it disappointing that people seem capable of so little complexity that they can’t experience both, and direct each where it belongs.  My guess would be that, rather than put serious commitment into the hard work of figuring out what is appropriate and designing a regulatory regime, it will be easier to kill it off.  We’ll see.

Eric


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