Pretty cool bioinformatics algorithm to speed up what was traditionally a dynamic Burrows-Wheeler Transform. Interested to see where this gets implemented outside of benchmarking in the next few years!
It's somewhat interesting, but the authors' conclusions are a bit odd given their data.
They acknowledge that fame is potentially confounding:
Risk factors (impulsivity, substance use, etc.) -> Fame achievement |
Risk factors -> Early mortality
The authors also appear to conclude that fame is semi-causal of the mortality risk. If, taking a causal statistical approach, the authors conditioned on the collider:
Risk factors (substance use, personality traits, mental health vulnerabilities) -> Becoming/staying a professional singer <- Talent/drive toward fame
I do applaud them for preregistering the study, but I think this paper needed a little more rigor in peer review.
This particular design more or less can't tell us whether fame in and of itself is a risk factor. We'd need to look at a cross-section of professions, not just musicians. Do marquee leading actors die younger than character actors? Do national politicians die younger than local? Do best-selling authors die younger than struggling authors who publish but never sell anything? Do professional athletes in popular sports die younger than athletes in less popular sports?
Mechanistically, it seems pretty obvious that fame can't cause a physical health outcome. I think the authors know this and they mention that it isn't really fame per se; it's the anxiety caused by public scrutiny and high expectations, often coped with by using illegal drugs to self-medicate.
That isn't a worthless finding, but what are we supposed to take from this? I would imagine drug-using hard-partying rock stars know their lifestyle in unhealthy and dangerous, just as I am fairly certain you'd be able to produce a retrospective study showing wingsuit divers die younger than big wall rock climbers, and big wall rock climbers die younger than trail runners. Anyone doing these things knows the risk and does it anyway. It seems the effect they found is famous musicians die 4.6 years younger on average than comparable unknown musicians. If you told me I could be a rock star but I'd die at 81 instead of 85, I think I'm probably taking that. Of course, we know it doesn't actually work that way, more that a few die in their 20s, far more in their 40s and 50s, and anyone making it past that is probably dying about the same time as anyone else, but whatever the risk is, if that's the life you want, so be it.
This is a non-issue with Polars dataframes to_pandas() method. You get all the performance of Polars for cleaning large datasets, and to_pandas() gives you backwards compatibility with other libraries. However, plotnine is completely compatible with Polars dataframe objects.
> Life's two most fundamental properties are homeostasis and reproduction.
> The loss of these two combined with its parasitic nature makes this cell a form on non-life.
This is a decidedly Eukaryote-centric take. Homeostasis in higher mammals is a complex network of genes -> RNA -> proteins -> metabolic pathways
Reproduction is also far more simple in organisms with binary fission cellular division.
A more appropriate scientific term would be obligate commensalism vs. "parasitic". That actually encapsulates their need for metabolic precursors from the host, but allows for tRNA, rRNA, origin of replication, etc...present in the organism's genome.
For all the folks saying, "Isn't this just a virus?"
The actual paper states that the genome encodes transfer RNA's and ribosomal RNA's. I think that's a really important biological distinction missing from the popular press junket. The primary source material is well written and elucidates a lot more than the Quanta article. https://www.biorxiv.org/content/10.1101/2025.05.02.651781v1
Yeah I think this is definitely the future. Recently, I too have spent considerable time on probabilistic hyper-graph models in certain domains of science. Maybe it _is_ the next big thing.
Countable is a relative term in microbiology. I like that the author stuck to the phrase "countable colonies", since colony forming units are not really "countable as cells".
Ah, brings me back to the countless nights I spent counting plate after plate of HEK293 cells using a Haemocytometer [0], a light microscope, and a mechanical counter [1].
At least with HEK293 cells you could mostly tell if they were dead through the microscope (dead cells are darker).
I've made the same transition and feel the same way. I miss the bench, but I don't miss the late hours, vending machine dinners, and Christmas day cell culture maintenance.
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