The semantic web tools never seemed to have their breakout moment and seem to run in parallel to "mainstream" history of relational and logic programming, but I've never really understood why.

Example gallery: https://view.qiime2.org/

I don't see why this is definitely doomed just because they discuss beta-amyloid plaques. Those exist and are real. They probably don't cause it any more than tombstones cause graveyards; very related, but not in the directly mechanistic way we wish.

> Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ) peptides in the brain.

This can be true and still not be the specific mechanism.

You can treat a specific waste product or you can repair the waste stream. The issue may be waste, but not a specific product, or the issue may not be the waste stream at all.

This work appears to demonstrate evidence of waste stream repair via a well-known waste-product. That doesn't mean that any specific waste product is or is not the problem or that this particular stream is definitely going to remove enough of the waste (if that was the problem).

Maybe there have been a lot of drugs which have similarly attempted waste-stream repair so there's good reason to doubt it on that alone. But I don't think that mentioning beta-amyloid plaque is enough to discard this out-of-hand.

https://thejenkinscomic.wordpress.com/2024/12/01/brady-bunch...

Just kidding, I don't see how the overhead of the directory entry is even remotely enough to warrant removal. Most of the magic can be left to efficient DEFLATE compatible blocks and removing entries not in the central directory in the first place (ZIP files can support concatenation of new data so long as you re-write the central directory at the end of the file).

https://coveryourtracks.eff.org/

https://amiunique.org/

I'm curious to know what you would rate as the most important features to make this work? It seems like calc+if do a lot of the heavy lifting, but the new function syntax is what makes instruction lookup tractable.

https://www.sciencedirect.com/science/article/pii/B978032399...

Once that's true it does seem there's a lot of host and microbiome interactions we've only begun to explore, but it shouldn't be surprising that co-evolution of the microbiome and host begins to take over as soon as you have one. One great example is short-chain-fatty-acid (SCFA) producing bacteria in the human gut. [1] These seem to be essential, and if there was a general takeaway to improve health, it would be to eat your roughage so they can do their job.

This is also why high alpha-diversity (community richness in particular) is such a dead-ringer for healthy vs diseased states. And frustratingly, is often exactly where the story ends for a lot of observational studies.

Also, in case you are curious, artificially sterile mice (gnotobiotic mice) tend to act differently than other mice, which is pretty odd to be honest, and why the gut-brain axis is a plausible mechanism to research further. [2]

[1]: https://pmc.ncbi.nlm.nih.gov/articles/PMC10180739/ [2]: https://www.sciencedirect.com/science/article/pii/S088915912...

"Is Human Behavior Just Running and Tumbling?": https://osf.io/preprints/psyarxiv/wzvn9_v1 (This used to be a blog post, but its down, so here's a essentially identical preprint.) A scale-invariant control-loop such as chemotaxis may still be the root algorithm we use, just adjusted for a dopamine gradient mediated by the prefrontal cortex.

"Give-up-itis: Neuropathology of extremis": https://www.sciencedirect.com/science/article/abs/pii/S03069... What happens when that dopamine gradient shuts down?

Usually Gibbs is used when there's no directly straight-forward gradient (or when you are interested in reproducing the distribution itself, rather than a point estimate), but you do have some marginal/conditional likelihoods which are simple to sample from.

Since each visible node depends on each hidden node and each hidden node effects all visible nodes, the gradient ends up being very messy, so its much simpler to use Gibbs sampling to adjust based on marginal likelihoods.

Ideas like fold and map where _never_ mentioned in lisp (to exaggerate, every function had to have the recursive implementation with 5 state variables and then a simpler form for the initial call), at no point did higher-order functions or closures make an appearance while rotating a list by 1 and then 2 positions.

The treatment of Prolog was somehow worse. Often the code only made any sense once you reversed what the lecturer was saying, realizing the arrow meant "X given Y" not "X implies Y", at which point, if you could imagine the variables ran "backwards" (unification was not explained) the outcome might start to seem _possible_. I expect the lecturer was as baffled by their presentation as we were.

In general, it left the rest of the class believing quite strongly that languages other than Java were impossible to use and generally a bad move. I may have been relatively bitter in the course evaluation by the end.

How would you do this otherwise? I find `conda list` to be terribly helpful.

As a tool developer for bioinformaticians, I can't imagine trying to work with OS package managers, so that would leave vendoring multiple languages and libraries in a home-grown scheme slightly worse and more brittle than conda.

I also don't think it's realistic to imagine that any single language (and thus language-specific build tools or pkg manager) is sufficient. Since we're still using fortran deep in the guts of many higher level libraries (recent tensor stuff is disrupting this a bit, but it's not like openBLAS isn't still there as a default backend).

That said, I don't think anyone who studies biology is particularly concerned with hard-line definitions, as nature tends to eschew them every chance it has.

I think Pando and corals being considered "modular bodyplans/habits" is perhaps a more useful concept than individual or clone.