hirenj

Yes, that is exactly what I meant! Here’s an experiment to try: Frances Arnold got a nobel prize for work related to directed evolution. However, we know evolution is limited by the tools available to it as you mention. If we add random chaperones and co-factors to bacteria that we know other organisms use, can we push evolution outside of the known fold space? Is the limited fold space an absolute limit or the “accessible” limit?

I think there was a Twitter/Bluesky thread on the results from adding all the predicted folds from metagenomics too, and not ending up with many new clusters. If this continues to hold true as we keep looking at stuff, I will be relieved that at least natural protein folds and domains has a limited (tractable) solution space. All we need to do now is annotate the variation of these couple of thousands of fold variants. Challenging, but at least a bounded problem.

This approach is pretty much like the TED approach from a few years back. As far as I remember there wasn’t a ridiculous amount of fold diversity there either. It turns out evolution isn’t averse to a bit of liberal protein plagiarism.

https://www.science.org/doi/10.1126/science.adq4946

This looks very cool. Does the window picker for multi-window apps work with the pinning? So if I have a safari windows I want to have associated with a profile, and the option-tab will ignore all the other windows?

It is a constant pain when I cmd-tab in a space with safari, and it throws me out of the space to another one because the window that gets focus isn’t the “closest” one on the current space.

Even better, don’t ban it, but require companies to do age verification (above a certain age?) before displaying advertising. You get two wins in one: make the child market less attractive for algorithmic feeds, and you also can get a better product (no algorithmic feeds) without ads if you don’t age verify. Win-win situation!

Can you say what field that is? I hear this sometimes, but my feed there is significantly low signal to noise, and I have had to pollute my “connections” to the point where I accept everything, as I have been trying to advertise job openings using it too (which frankly has been pretty poor too).

It is a huge worry for me that unless we decouple the publishing “system” from the career pathways (i.e., rewards), we are going to lose access to both the careers (to robot-weilding bullshitters) and even worse, the shared space where scientific communication took place.

Does anyone know of any writing on the network effects of the publishing system? What would happen if the actual value of the journals (of the little they provide!) were to go away?

The death of scientific twitter, and the failure to establish any replacement makes me worry that we won’t be able to coalesce around a replacement system. Obviously preprints play a role, but we really need our scientific communities to engage with them in a more serious way.

For prosterity, the original report on the pilot program with the checklist including introducing names of participants (doi 10.1056/NEJMsa0810119).

Possibly popularised by Atul Gawande “The Checklist Manifesto”.

Meta-comment: LLMs continue to impress me in the capabilities with unearthing information from imprecise inputs/queries.

That is really insightful regarding the ritual improving outcomes through better communication - something I see reflected in many meetings I turn up to now which involve an introduction round between participants, and anecdotally improves participation in the meeting.

It would be amazing if someone had a link to a page with the MSF story, as that is a great reference to have! My google-fu hasn’t helped me in this case.

This is great - now I can get the authentic conference experience of a disengaged speaker reading out the slides in a monotone, without all the hassle of international travel and scheduling.

In all seriousness, there could be more utility in this if it helped explain the figures. I jumped ahead to one of the figures in the example video, and no real attention was given to it. In my experience, this is really where presentations live and die, in the clear presentation of datapoints, adding sufficient detail that you bring people along.

I was ruminating about how Atproto would be great for re-thinking the peer review system for scientific journals.

Imagine a world where a preprint is “published” onto the social web, from which you could aggregate reviews/comments. I eventually ended up thinking about exactly what you raise - it would be great to have some degree of access control on this so both comments and published things can be selectively shared (with an option to make everything public later on, maintaining all the links).

Arc institute probably.

It is a real shame that peer review reports were only first published relatively recently. These would have provided valuable training information as to what peer review performs. Unfortunately now, I fully expect the public peer review reports will be poorer in quality, and oftentimes superficial.

On this tool, I fully expect that it will not capture high level conceptual peer review, but could very much serve a role in identifying errors of omission from a manuscript as a checklist to improve quality (as long as this remains an author controlled process).

I will be interested to throw in some of my own published papers to see if it catches all the things I know I would have liked to improve in my papers.

Not my subject area, but at least one other group looked at ABCA1, and judging from this abstract, it has been linked via GWAS already, and furthermore concludes it doesn’t play a role (I haven’t looked at the data though).

I don’t know, but if we were to reframe this as some software to take a hit from a GWAS, look up the small molecule inhibitor/activator for it, and then do some RNA-seq on it, I doubt it would gain any interest.

https://iovs.arvojournals.org/article.aspx?articleid=2788418

I guess that whatever recognises the enhancers must be conserved ish in mice for us to be able to drop this region into the mouse genome. That might be interesting alone apart from the functions of the frizzleds. Always a curious question about how much new “machinery” we have in humans, vs us being a specific configuration of common machinery (i.e., if we were to swap out introns/regulatory regions from a mouse with human, and assuming we don’t screw up any checkpoints, how far would it get with looking/acting like a human?).

My innate immune system is here for chewing gum and binding viral glycans, and I’m all out of specific lectins.

Looks like this works by apparently binding complex N-glycans on the viral envelope. I can’t imagine this is particularly specific, but the good news is that in the mucosa, you should see relatively few N-glycans, so I guess it will hit the right stuff?

This is pretty interesting, also that they get it down to the amount of mannan in the fungal cell walls being somehow related to the β cell amounts. There's probably a nice project in here to figure out what the receptors are on the macrophages that are modulating this process. Obviously candidates would be some of the lectins they carry (mannose receptor?).

Ancient medicine solves that problem

https://news.ycombinator.com/item?id=43196488

I think what you mean is the MUC1-20 (ish, numbering is a bit sloppy but hopefully we can clean it up in near future) genes encode for mucins, that are synthesised on a ribosome, threaded into ER, shuffled off into the Golgi and then enzymatically modified by the enzymes encoded by the genes GALNT2, 7 & 10 (at least).

There are a further 20-30 ish enzymes that can modify the mucins as they are traversing the Golgi, and depending on the cell type, it can get packaged into vesicles (alongside calcium) for secretion.

So, they meet a lot of enzymes along the way, but harbour no catalytic activity themselves (as far as we know!).

Mucins aren't enzymes (as far as we know!), and the gut mucins are a bit different from those found on the endothelium, largely because they have different functions. The gut (and generally mucous epithelial) mucus is there for (amongst other things) clearing and maintaining a microbiome. Endothelial glycocalyx probably has some other mucin proteins, but also likely lots of mucin-like proteins, and the set of functions of these are much less clear.

Yes, something along these lines, and maybe the other pathways as the most interesting possibility. I don’t know if there any reports of sTn in older mice, but that would be pretty wild.

Catflap is a surprisingly apt analogy. In this case it is an endocytosis receptor, that selects cargo for uptake. Differs from virus to virus of course, but I could see changes in the sugars on these proteins altering behaviour. Mucus/mucins are basically proteins very heavily modified with sugars, so you have this common system that is adapted to do different things.

Fun fact: The AAVR (AAV receptor) is itself a glycoprotein (carrying the precise type of glycosylation they are trying to repair with the “gene therapy”). Haven’t read up what replacement gene dose ended up being, but entirely within realms of possibility that this could influence which cells get corrected.

We don’t know is the answer. However, if you were coral, then I would suggest increasing the amount of symbiotic algae, which stimulates mucin secretion (probably a nutrient boost).

A brain normally doesn’t receive enough sunlight for this strategy to work for humans.

This area is very much in my wheelhouse (both the biosynthetic process, and functions of mucins). They’re a pretty interesting biomolecule, present in all animals (slightly different molecules in other branches of life).

It kind of surprises me that such a low fold-change in core1 synthase yields such a huge change in glycocalyx. Everything we know about this enzyme says it is an absolute rocket on substrates, so I can’t really see this process being enzyme-limited. There might be other (mouse specific) things going on here that this is scratching the surface on.

First question I have is what kind of nutrient base conditions can we expect to start from? Should it be like Earth, or somewhere a bit more resource constrained (and how would it be constrained)?

I’d like to imagine solar reactors mimicking primordial goo to synthesise the essentials for these materials.

Colour me unsurprised - even not knowing data leakage had occurred, the hypothesis was underwhelming, as I mentioned in a comment on an earlier discussion. I sometimes despair for the state of thinking in science these days given how quickly people fawn over entirely pedestrian thinking and work.

https://news.ycombinator.com/item?id=43105759

I read the cf-PICI paper (abstract) and the hypothesis from the AI co-scientist. While the mechanism from the actual paper is pretty cool (if I'm understanding it correctly), I'm not particularly impressed with the hypothesis from the co-scientist.

It's quite a natural next step to take to consider the tails and binding partners to them, so much so that it's probably what I would have done and I have a background of about 20 minutes in this particular area. If the co-scientist had hypothesised the novel mechanism to start with, then I would be impressed at the intelligence of it. I would bet that there were enough hints towards these next steps in the discussion sections of the referenced papers anyway.

What's a bit suspicious is in the Supplementary Information, around where the hypothesis is laid out, it says "In addition, our own preliminary data indicate that cf-PICI capsids can indeed interact with tails from multiple phage types, providing further impetus for this research direction." (Page 35). A bit weird that it uses "our own preliminary data".

Absolutely agree that this is a fast-moving area, so we're not aiming to teach them specific details for anything. Instead, our goals are to demystify the ML and AI approaches, so that the students understand that rather than being oracles, these technologies are the result of a process.

We will explain the data landscape in medicine - what is available, good, bad and potentially useful, and then spend a lot of time going through examples of what people are doing right now, and what their experiences are. This includes things like ethics and data protection of patients.

Hopefully that's enough for them to approach new technologies as they are presented to them, knowing enough to ask about how it was put together. In an ideal world, we will inspire the students to think about engaging with these developments and be part of the solution in making it safe and effective.

This is the first time we're going to try running this course, so we'll find out very quickly if this is useful for students or not.

This is a great resource, thanks. We (myself, a bioinformatician, and my co-cordinators, clinicians) are currently designing a course to hopefully arm medical students with the required basic knowledge they need to navigate the changing world of medicine in light of the ML and LLM advances. Our goal is to not only demystify medical ML, but also give them a sense of the possibilities with these technologies, and maybe illustrate pathways for adoption, in the safest way possible.

Already in the process of putting this course together, it is scary how much stuff is being tried out right now, and is being treated like a magic box with correct answers.