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burning_hamster

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I was going to call BS on this one, but after crunching some numbers, if anything this is likely an underestimate.

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.

[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC4988970/

I disagree.

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.

Natural sciences: about 3 years at best.

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 read or understand German.

A Dutch speaker can't necessarily read or understand German. However, a Dutch person nearly always does, and often flawlessly so.

Huge fan of Distill here (and your personal blog).

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).

The Nobel Duel 1 year ago

I am a biochemist and neuroscientist and also thought it was fantastic read. It's rare that someone manages to cater to both audiences this well. Kudos!

Some animals get most if not all of their sleep through microsleep [1]. So whatever mechanism "refreshes" the brain, it can work on short time scales. The switch in brain firing dynamics from wake to sleep (NREM) is very fast -- on the order of one to a few seconds. People in team Nedergaard argue that it is the rhythmic neuronal activity during sleep that promotes fluid flow (though to be fair, some argue its arterial pressure). So their answer to your question would be yes, that should be enough time to enhance fluid flow (fluid is flowing all the time, the question debated by scientists is, whether is it being enhanced during sleep).

[1] https://www.science.org/doi/10.1126/science.adh0771

In theory, there is enough magnesium in your bones (~12 g) to cover your RDA (~400 mg) for 30 days [1]. However, only a third of that is available without strongly negatively affecting your health. So if your Mg stores are full (if!), then you have about 10 days worth "stored". However, your day-to-day Mg homeostasis is done by your kidneys, i.e. on a much, much shorter time scale. So your daily intake does matter, but sporadic deficiencies can be compensated for about 10 days.

[1] Alawi et al. (2018) Magnesium and Human Health: Perspectives and Research Directions; https://pmc.ncbi.nlm.nih.gov/articles/PMC5926493/

For example, let's say instead of gradient descent you want to do a Newton descent. Then maybe there's a better way to compute the needed weight updates besides backprop?

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.

[1] https://www.nature.com/articles/ncomms13276#Sec20

it always automatically happens

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...

sizeable camps on both sides

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!

You are referring to the critical period [1] of (second) language acquisition, which is generally thought to end with the onset of puberty [2]. Neurodevelopmentally, this period coincides with extensive synaptic and dendritic pruning and increased myelination (of axons) [3], which result in the loss of some connections and the strengthening and acceleration of others. Cell loss is not thought to be a major driver of brain maturation, nor is it thought to occur more frequently during this time window.

[1] https://en.wikipedia.org/wiki/Critical_period_hypothesis

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5857581/

[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982854/

Not to mention a fantastic writer. Already his PhD thesis was brilliant, and one of the few [1] I have ever read cover-to-cover. "Advanced data analysis from an elementary point of view" is one of my go-to recommendations for people (scientists) that have had some exposure to different concepts in data analysis (typically some regression and PCA), but want to acquire a more systematic understanding. His writing is charming as ever, his exposition exceptionally clean and straightforward, the math as simple as possible but not simpler, the advice practical. He manages to walk the very fine line in mathematical writing of being rigorous enough that the reader feels being taken seriously, while being engaging enough and maintaining a pace that allows one to actually finish the whole book without burning out.

[1] Four, to be exact, and that number includes my own.

This grug not big brain. This grug ate food and read article on big screen at the same time. Now food on screen, not in belly. Grug still hungry, but need to find rag now to clean screen. Otherwise no more work and no more shiny rocks for grug. But grug thanks other grug for article. Grug belly empty, but grug brain full now.

That's nonsense. Greek has prepositions, conjunctions, and cases that leave little room to interpretation. It is a rather precise language (much more so than English or even Latin).

Arguably, there have been a good number of wars (ostensibly) over books (in particular religious texts seem to do the trick), whereas we are yet to declare war over any form of web content.

Throwing a lot of money at one single approach doesn't validate that approach or make it magically successful. It gives one group of people the ability to try out more than just one idea (unlike typical grants that let you maybe work on 0.5-1.5 ideas at best). Sure, their second idea will be better than their first, but is their fourth idea still going to be better than their third?

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).

Indeed, it's the classical Weismann's fallacy. From Nick Lane's 'Life Ascending':

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?

Due to the blood-brain barrier, it is quite difficult to get substances injected into the blood into the brain (though some obviously do, otherwise there would be no psychoactive substances). Injecting into brain tissue directly is possible but probably not useful in an emergency: if you want to inject into cortex directly, you have to inject at ulta-low (yet positive) pressures (otherwise you destroy a lot of tissue just by injecting), and diffusion from the site of injection would be slow. In other words, you would have to drill a thousand holes into someones skull, and inject tiny volumes over the course of minutes each time. You could inject into the ventricles. That can be done quicker and with larger volumes without incurring too much damage, but again, lymphatic flow is very slow, so getting the drug into the tissue would take a long time (hours). So from first principles, you are fighting an uphill battle, and the incline is very steep.

The findings in the paper are interesting in the latter scenario, as they identify the deep cortical layers as the likely drivers of the wave of death (not a surprising finding for various reasons so quite likely true). Deep cortical layers are closer to the ventricles, so access would be a bit better than to the cortex in general.