has there ever been a problem in science where the solution hasn't been "it's far more nuanced, complex, & context dependent than we thought" just once it would be nice for overwhelming complexity to suddenly simplify itself with additional understanding from a new discovery

I had a coworker bug the first author at aacr about not controlling for covid deaths in this work and he told my coworker they were in the process of working on it

one of my favorite parts of the essay. almost whimsical

kidney!

DON'T GOOGLE/CLAUDE/CHAT if you had to guess where adult fish make the majority of their blood cells (hematopoiesis) you'd guess

It pains me to say this but OpenEvidence AI is superior to UpToDate (and it’s free!) @EvidenceOpen

this paper came out almost five years ago and imo it should have been national news based on how many older couples are working with such a low n of embryos to begin with tl/dr: (~oversimplified, please read their work!) early aneuploidy is normal and development autocorrects

meanwhile in science

this is my favorite drug repurposing story where they took a breast cancer drug/PI3Ka inhibitor and used it to cure a rare disease (cloves syndrome) does cause diabetes so patients are on a strict keto diet and interestingly works for everything except osseous overgrowths

"AI isn't replacing radiologists" good article Expectation: rapid progress in image recognition AI will delete radiology jobs (e.g. as famously predicted by Geoff Hinton now almost a decade ago). Reality: radiology is doing great and is growing. There are a lot of imo naive predictions out there on the imminent impact of AI on the job market. E.g. a ~year ago, I was asked by someone who should know better if I think there will be any software engineers still today. (Spoiler: I think we're going to make it). This is happening too broadly. The post goes into detail on why it's not that simple, using the example of radiology: - the benchmarks are nowhere near broad enough to reflect actual, real scenarios. - the job is a lot more multifaceted than just image recognition. - deployment realities: regulatory, insurance and liability, diffusion and institutional inertia. - Jevons paradox: if radiologists are sped up via AI as a tool, a lot more demand shows up. I will say that radiology was imo not among the best examples to pick on in 2016 - it's too multi-faceted, too high risk, too regulated. When looking for jobs that will change a lot due to AI on shorter time scales, I'd look in other places - jobs that look like repetition of one rote task, each task being relatively independent, closed (not requiring too much context), short (in time), forgiving (the cost of mistake is low), and of course automatable giving current (and digital) capability. Even then, I'd expect to see AI adopted as a tool at first, where jobs change and refactor (e.g. more monitoring or supervising than manual doing, etc). Maybe coming up, we'll find better and broader set of examples of how this is all playing out across the industry. About 6 months ago, I was also asked to vote if we will have less or more software engineers in 5 years. Exercise left for the reader. Full post (the whole The Works in Progress Newsletter is quite good): worksinprogress.news/p/why-a…

how biologists with attention issues see the world

New paper on arousal in the brain! What controls the controller?

Here’s a new approach to treating diseases caused by mutated proteins, including the rare but lethal liver cancer fibrolamellar carcinoma (FLC). Many diseases are driven by mutations: sometimes a single change, sometimes two proteins fused together. (1/9)

I love that when RNA splicing was discovered the title of the paper was "An amazing sequence arrangement at 5′ ends of adenovirus 2 messenger RNA". I think every scientist should get 1 paper in their career where they can call their finding something like "amazing" in the title.

Part 2 of my postdoc work is out! We show that the ploidy-to-cell size ratio is a highly conserved, “intrinsic” determinant of cell physiology. Check out the paper for more insight into how and why. Link - nature.com/articles/s41594-0… Free PDF – rdcu.be/dONo3 Also, a fun🧵!

Exciting news! OpenTn5 is live on @biorXiv - we introduce our robust open-source protocol for producing multi-purpose Tn5 transposase. But what is Tn5? 🧵 1/11 biorxiv.org/content/10.1101/…