HN user
This seems to be the original:
https://s3.amazonaws.com/b2icontent.irpass.cc/2660/rl168199....
(via CBS News: https://www.cbsnews.com/news/truth-api-trump-media/)
So it seems to be precisely as nefarious as BBC reported.
My group published a cell atlas of the developing human brain in 2023, giving gene expression in single cells from postconception week 5 to 13. It’s on github: https://github.com/linnarsson-lab/developing-human-brain
The NIH BRAIN initiative is working on the next generation of that, covering more timepoints and better spatial data.
He was not head of state when the crime was committed and he is not head of state now.
If, like me, you wanted to see the actual farthest distance photo, here it is: https://beyondrange.wordpress.com/
The average federal tax rate is 14%, and the USAID budget was about 0.8% of the federal budget, so you’ve been paying about a 0.1% tax to fund USAID.
A much more detailed and thoughtful (and peer reviewed) take on the same question from my colleague Jussi Taipale: https://www.embopress.org/doi/full/10.15252/embj.201696114
Cool! Has anything similar been attempted in tumor tissue, given the many claims of microbes in tumors? Especially tumors not in contact with the exterior.
And further down: ” Contributions
J.J. and D.H. led the research. J.J., R.E., A. Pritzel, M.F., O.R., R.B., A. Potapenko, S.A.A.K., B.R.-P., J.A., M.P., T. Berghammer and O.V. developed the neural network architecture and training. T.G., A.Ž., K.T., R.B., A.B., R.E., A.J.B., A.C., S.N., R.J., D.R., M.Z. and S.B. developed the data, analytics and inference systems. D.H., K.K., P.K., C.M. and E.C. managed the research. T.G. led the technical platform. P.K., A.W.S., K.K., O.V., D.S., S.P. and T. Back contributed technical advice and ideas. M.S. created the BFD genomics database and provided technical assistance on HHBlits. D.H., R.E., A.W.S. and K.K. conceived the AlphaFold project. J.J., R.E. and A.W.S. conceived the end-to-end approach. J.J., A. Pritzel, O.R., A. Potapenko, R.E., M.F., T.G., K.T., C.M. and D.H. wrote the paper.”
These cells won’t divide because they will fail to replicate their DNA due to the lack of thymidine. The use case is cell therapies, where you give the patient cells grown in the lab but you don’t want those cells to potentially divide and cause cancer. For example, CAR T therapy to treat cancer, or dopaminergic neuron replacement therapy for Parkinson’s disease.
TERT activation is one of the most common alterations causing cancer. In fact, the whole point of the normally very low TERT expression in somatic cells is likely to be cancer prevention. It’s the mechanism behind the Hayflick limit, which puts a bound on the max number of divisions a cell can go through, via telomere shortening. Without such a limit, you get cancer. I highly doubt it will make you live longer.
Freezing and thawing organoids is not new, it’s fairly routine. The frozen piece of brain from an epilepsy patient doesn’t retain ”normal function”. There is no evidence in the paper that it integrates into neuronal circuits (this was not even tested), or supports anything like normal neuronal firing. The cells are alive, yes, and likely highly abnormally perturbed.
”The Emperor of all Maladies” by Siddhartha Mukherjee is fantastic
Related: Dijkstra, ”Why numbering should start at zero”
https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/E...
No that would be amazing. But we don’t have the technology to map all the connections in large mammalian brains. It was done in the fruitfly just this year: https://www.science.org/doi/10.1126/science.add9330
None, that was done 100 years ago, e.g. by Ramon y Cajal (Nobel prize 1906). But microscopic detail does not give molecular detail. What these current studies add is data on gene expression (mRNA molecules), chromatin accessibility (related to gene regulation), electrophysiology (in some cases), etc. We need such detail to connect disease genes inferred from genetics to specific brain cell types, for example.
Papers are paywalled but most can be found on bioRxiv, e.g. https://www.biorxiv.org/content/10.1101/2022.10.12.511898v1
That’s not a terrible summary of the one paper, but there are 20 more papers. An overview by the Science editor: https://www.science.org/doi/full/10.1126/science.adl0913
The full collection of papers is linked here: https://news.ycombinator.com/item?id=37878935
They are paywalled, but most are available as preprints on bioRxiv, e.g. https://www.biorxiv.org/content/10.1101/2022.10.12.511898v1
Yes, but at the same time most inbred strains have genetic disorders precisely because they are inbred. E.g. the most commonly used strain C57BL/6 develops age-related hearing loss. To avoid this, there are other lab strains like CD-1, which are deliberately bred to maintain a controlled level of genetic heterogeneity.
Salmon and cod are both delicious.
Boston Globe did a long, thoughtful writeup of the whole affair a while ago, for those interested in more nuance.
https://apps.bostonglobe.com/metro/investigations/spotlight/...
John PA Ioannidis, one of the authors of that paper (which is from 2018 btw) has published an average of 60 papers per year for the last decade, and more than 1100 in total (https://pubmed.ncbi.nlm.nih.gov/?term=Ioannidis+JPA&filter=d...). I’m pretty sure he doesn’t even have time to read most of his own papers.
Some observations in support, by researchers at Yale: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4167193
I have this glass sphere model of the universe [1] showing galaxies as tiny bubbles. It’s beautiful and humbling (it represents about 1/100 of the observable universe).
[1] https://www.cinks-labs.de/products/the-universe-in-a-sphere?...
DNA: https://en.m.wikipedia.org/wiki/DNA_digital_data_storage
It will last 10k years if stored reasonably. Storing GBs is no problem. Won’t go obsolete - the technology has been around for nearly four billion years.
Or maybe we need the opposite: basically just state that you did this stupid stuff only to avoid the tax, so you should pay the tax anyway.
Assuming you have found a drug that is safe and binds the target in the intended way (which itself can take years), you then have to manufacture it according to GMP standards. That takes at least a year and $1 million. Next, you have to run toxicity and pharmacokinetics (how the drug is absorbed and distributed in the body) in two different animal models. That takes 6-12 months and $1-2 million. Then you apply for permission to start phase I trials, and wait for the green light.
One reason RNA drugs are so exciting is that their manufacture is simple and can be standardized, cutting the GMP manufacturing step to a few weeks instead of a year or more. Small-molecule drugs are ”bespoke” in that each needs its own, different, manufacturing process. In contrast, all RNA molecules are essentially chemically identical, differing only in the ordering of the nucleotides.
Leipzig has a nice zoo, and it’s one of the rare zoos that has Bonobos. They are amazing to watch: mothers happily walking upright holding their baby, lots of playing, just generally enjoying themselves. To me they seem much more human than chimpanzees do (they are equally distant in evolutionary terms).
The single-letter variable names and the lack of types (beyond some duck-typing using bold and uppercase) is what makes math so difficult to read (for me). There must be tons of errors in math papers that go undetected just because there’s no type checking.
Hey vanderZwan, we have a paper in the collection (there’s 17 brain atlas papers in this issue of Nature) on human, marmoset and mouse motor cortex (my group did the human). I think it’s cool that pretty much all the cell types could be aligned between those species, which diverged 90 million years ago.
Our full human brain cell type atlas is work in progress but hopefully we’ll post the preprint early next year.
(Edit) Direct link to the papers, which are all open access: https://www.nature.com/nature/volumes/598/issues/7879
The size of the tumor likely makes a big difference too: a centimeter-sized lump contains 1000x more cells than a millimeter-sized one. It will contain 1000x more drug-resistant cells (given identical mutation rates). For example, a human brain tumor is likely to already contain hundreds of cells carrying a mutation for every amino acid in every protein. Thus for any inhibitor drug you try, hundreds of cells will already be resistant, and they will grow back the tumor in just a few months. That’s much less likely to be the case in a mouse tumor.