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How swarms of bees go from preferring one target to preferring another

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David Eric Smith

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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Marcus G. Daniels

Re: How swarms of bees go from preferring one target to preferring another

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 <[hidden email]> On Behalf Of David Eric Smith
Sent: Tuesday, May 25, 2021 5:17 PM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
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/

Some weeks ago, a day or two after it came out, I got a pointer to this paper:

https://arxiv.org/abs/2104.01533

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.

Eric

David Eric Smith

Re: How swarms of bees go from preferring one target to preferring another

With your final sentence, I agree. That is why I consider taking up the problem of good regulatory design far preferable to killing outright the effort to understand a class of questions. I prefer it enough to be almost categorical, though even that question can be complicated.

If I worked in this field, or were a higher-up in NIAID, I would know more about how they have been trying to make these decisions, and what kinds of cost/benefit/controllability framings they use. These are not unsophisticated people. If I knew that, I could say more useful things about directions for change.

On May 26, 2021, at 12:46 PM, Marcus Daniels <[hidden email]> wrote:

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 <[hidden email]> On Behalf Of David Eric Smith
Sent: Tuesday, May 25, 2021 5:17 PM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
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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gepr

Re: How swarms of bees go from preferring one target to preferring another

This post was updated on .

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uǝʃƃ ⊥ glen

Marcus G. Daniels

Re: How swarms of bees go from preferring one target to preferring another

I noticed on the paper Eric shared that two of the authors were "Independent" researchers. I think that's great.

-----Original Message-----
From: Friam <[hidden email]> On Behalf Of u?l? ???
Sent: Wednesday, May 26, 2021 8:23 AM
To: [hidden email]
Subject: Re: [FRIAM] How swarms of bees go from preferring one target to preferring another

This reminds me of my (professional) biologist friends' reactions to my interest in DIY biology. It's a testament to Wade's objectivity that, here, he argues somewhat against the openness and speed of "natural" processes, whereas in his book (resoundingly "canceled" by the professionals) he argues for the openness and speed of "natural" processes. Still, his reasoning seems motivated, even if not as motivated as those who shout Conspiracy Theory.

But the larger regulation issue does intertwine nicely with the discussions we've been having on the goodness or badness of our biological trajectory. I'm as agnostic as I think Marcus is, though I'm willing to play Devil's Advocate for either more or less regulation, for or against our hyper-individualism, etc. But that's because I can't bring myself to believe in any of the -isms [⛧].

Where it is my job to care about the consequences of carelessness (e.g. small-scope privacy and security of data/code), I have to implement a broad spectrum smear of fine- and coarse-grained regulatory measures. So, for example, were the US to approach biohacking in the same way we approach nuclear proliferation, we'd have not only fine-grained regulation (like laws against cloning your children in your kitchen), but coarse-grained regulation (like sanctions for countries or labs that engage in cloning humans).

But as this gain-of-function in BSL[23] rated labs discussion indicates, is it even possible to regulate *all* the possible variables? At all possible scales? And, more importantly, if it's not, then we have to consider the higher order unintended consequences of *partial* regulation. Is inadequate regulation *worse* than no regulation in a controller-system where the controller exhibits less variation than the system being controlled?

What seems to me to be the ur-message, here, is our tendency to punish failure, whether it's about "canceling" some moron who misspeaks or accidentally allowing a pathogen out of your lab. Failure is ubiquitous and the most poignant path to new knowledge. Granted, our defense mechanisms (for both individual and nationalist machismo) are difficult to overcome. But if we could find ways to internalize failure and learn from it (again at any scale, any scope), it would be easier to smear the regulation across scales and scopes.

[⛧] It's not nihilism, but something more akin to "go with the flow", a fundamental inability to be conservative or progressive.

On 5/25/21 8:52 PM, David Eric Smith wrote:

> With your final sentence, I agree. That is why I consider taking up the problem of good regulatory design far preferable to killing outright the effort to understand a class of questions. I prefer it enough to be almost categorical, though even that question can be complicated.
>
> If I worked in this field, or were a higher-up in NIAID, I would know more about how they have been trying to make these decisions, and what kinds of cost/benefit/controllability framings they use. These are not unsophisticated people. If I knew that, I could say more useful things about directions for change.
>
>> On May 26, 2021, at 12:46 PM, Marcus Daniels <[hidden email] <mailto:[hidden email]>> wrote:
>>
>> 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 <[hidden email]
>> <mailto:[hidden email]>> *On Behalf Of *David Eric Smith
>> *Sent:* Tuesday, May 25, 2021 5:17 PM
>> *To:* The Friday Morning Applied Complexity Coffee Group
>> <[hidden email] <mailto:[hidden email]>>
>> *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-nat
>> ure-open-pandoras-box-at-wuhan/
>> <<a href="https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fthebulletin.org%">https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fthebulletin.org%
>> 2f2021%2f05%2fthe-origin-of-covid-did-people-or-nature-open-pandoras-
>> box-at-wuhan%2f&c=E,1,P6kXox553nNZ9am59NkxzTOCdiF6wmr49v3tEoJnczF-h6w
>> f2HI2SMSvVx47aaHjxAhK1ewYPO1o_DXXPgZEXGaiG5bxmxibJTx0PUdzN363Dg,,&typ
>> o=1>
>>
>> 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 <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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