burning_hamster
burning_hamster on Hacker News
All basal metazoan branches (ctenophora, porifera, placozoa, cnidaria) have species that are capable of whole body regeneration, so this trait was likely lost on the bilaterian branch (which includes chordates like ourselves). If anything, there are evolutionary pressures to loose this trait.
human nuclear genome size (haploid): 3.1 billion bp
mitochondrial genome size: 16 000 bp
1 human nuclear genome per egg -> 3.1 billion bp nuclear DNA
100 000 mitochondria, each with 1-10 genomes per mitochondrion [1] -> 1.6-16 billion bp mitochondrial DNA
So the ratio of mitochondrial to nuclear DNA in human eggs is on the order of 0.5 to 5.
> I think it would be better to describe this as an ‘organelle’ transplant as it would be easier for people to understand and discuss.
Unlike previous attempts, the donor mitochondria are not transferred into the mother egg. Instead the donor cell is denucleated, and the nucleus from a mother's egg is transferred into the denucleated donor cell. Consequently, there is a wide variety of donor specific material, which may influence the early stages of development and only "wash out" after a number of cell divisions.
> But calling it a 3 person baby is unhelpful and misleading as IMO mitochondria DNA is of a different category to chromosomal DNA.
How so? Arguably, mitochondrial genes are much more essential than most nuclear genes.
1. Mutations in any mitochondrial gene often have dire consequences, whereas variants in nuclear genes are much more frequent.
2. Mitochondrial DNA is the most expressed in pretty much any cell by a huge margin. Mitochondria express 13 (IIRC) protein coding genes and two dozen other RNAs. Those 30 odd genes often make up 1-5 % of a cell's whole transcriptome. Only genes coding for ribosomal RNA are more strongly expressed.
You finish your degree, and start your PhD. The first year, you are busy learning techniques and getting caught up with the relevant literature. You are far too concentrated on learning new things to get any thinking done.
In your second year of your PhD, you are getting better. You can do most things without thinking about them. This frees up your brain to think about other things. However, your grasp of the wider literature is still lacking, so you use that brainspace to optimise your current experiments (as you should).
In your third year of your PhD, you are starting to write things up: either your thesis, or your first (big) paper. You read a lot more, you know a lot more. The deep thinking can commence.
Your first postdoc is probably your most productive time: you know what you are doing; you know the state of the literature and which parts are reliable and which aren't; you have a clear idea of what problems need solving. You are starting to write your first grant applications, but you only need one for yourself and not several to cover the needs of a full lab. You don't have any kids at home. This is a good time to solve some big problems. It lasts about 2-3 years.
At the start of your second postdoc, you panic. The big problem was harder than you thought and you don't have enough high-impact papers to be competitive in job applications for a principal investigator (PI) role. You start churning out low-value fillers and collaborating with everyone and their hamster to get your name on as many papers as possible. The rest of the time is taken up by applying for grants and PI positions. You don't even make it to the interview stage. You start pondering about life outside of academia.
The big problems are forgotten.
A Dutch speaker can't necessarily read or understand German. However, a Dutch person nearly always does, and often flawlessly so.
> In retrospect, I deeply regret trying to run Distill with the expectations of a scientific journal, rather than the freedom of a blog, or wish I'd pushed back more on process. Not only did it occupy enormous amounts of time and energy, but it was just very de-energizing.
Scientific peer review pretty much always is incredibly draining, and (assuming the initial draft is worth publishing) it rarely adds more than a few percent to the quality of the article. However, newcomers are drowning in a sea of low quality SEO spam (if they bother to search & read blogs at all and don't go straight to their LLMs, which tend to regurgitate the same rubbish). The insistence on scientific peer review created a brand, which to this day allows me to blindly recommend Distill articles to people that I am training or teaching. So I, for one, am incredibly grateful that you went the extra-mile(s).
[1] Alawi et al. (2018) Magnesium and Human Health: Perspectives and Research Directions; https://pmc.ncbi.nlm.nih.gov/articles/PMC5926493/
IIRC, feedback alignment [1] approximates Gauss-Newton minimization. So there is an easier way, that is potentially biologically more plausible, though not necessarily a better way.
This is exactly the framing the author is criticizing. It assumes that the placebo effect is a constant that cannot be improved upon, and thus deserves no consideration, when designing the treatment. However, the placebo effect is malleable, and can be improved [1], In scientific studies, this is typically done through suggestions and conditioning [2]. However, this is not standard clinical practice (AFAIK).
Where the author is wrong, is that people that are designing drugs, aren't thinking about using the placebo effect more optimally. It is fairly well known, that the efficacy of drugs correlates with the severity of off-target side effects: say that you are taking an analgesic that acts by binding receptor A, but which also induces nausea by also binding an unrelated receptor B. During drug development, the structure of the drug is often tweaked to reduce or abolish binding to such off-target receptors, thus limiting side effects. However, these structural changes also often reduce efficacy, even if the affinity of the drug to the intended target isn't altered at all. My colleagues and I (working in pharmacology but in academia) have often wondered to what degree drug companies try to actively keep non-severe side effects as part of the response profile, given that they may be beneficial for the treatment outcome.
[1] https://www.nature.com/articles/npp201081
[2] https://www.sciencedirect.com/science/article/pii/S030439590...
There are dozens of us! Mostly because Nedergaard's lab(s) are like 50 people all by themselves.
I always find it interesting what people's perception of the size of academic subfields is. For most topics in the biological sciences (i.e. excluding cancer, HIV, malaria, AD, and other "whales") you can fit everyone that has directly worked on that topic in the last 5 years in a medium-sized auditorium. And many people work on multiple topics!
[1] https://en.wikipedia.org/wiki/Critical_period_hypothesis
This is probably the case because your NREM / slow-wave sleep need was low to begin with. If you go to bed relatively rested, it's easy to be fully rested when you wake up. REM is typically delayed when you are exhausted.
Citation needed.
You can be salty about excessive use of animations in modern UI all day long, but you have to respect that level of commitment.
[1] Four, to be exact, and that number includes my own.
I love it. Though FaceTokGram rolls off the tongue easier.
Historically, throwing some money at a lot of approaches & different groups of people tends to yield better results in the long run (see cancer & AIDS research that have received a lot of funding in recent decades).
> The most popular idea, dating back to Weismann in the 1880s, is wrong, as he himself was quick to recognise. Weismann originally proposed that ageing and death rid populations of old worn-out individuals, replacing them with racy new models replete with a new set of genes remixed by sex. The idea invests death with some sort of nobility and symmetry, in service of a greater cause, even if it can hardly aspire to the grandeur of a religious purpose. In this view the death of an individual benefits the species, just as the death of some cells benefits the organism. But the argument is circular, as Weismann’s critics pointed out: old individuals are only ‘worn out’ if they age in the first place, so Weismann presupposed exactly what he was trying to explain. The question remained, what makes individuals ‘wear out’ with age, even if death does benefit populations?