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Noise/transcriptional noise thread

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Also, an alex https://dlmp.uw.edu/faculty/mendenhall


Non-biological/thermodynamic noise/ERROR-CORRECTION




Guillaume Verdon (02:14:12) Yeah, originally we weren’t going to announce last week, but I think with the doxing and disclosure, we got our hand forced. So we had to disclose roughly what we were doing. But really, Extropic was born from my dissatisfaction, and that of my colleagues, with the quantum computing roadmap. Quantum computing was sort of the first path to physics-based computing that was trying to commercially scale, and I was working on physics-based AI that runs on these physics-based computers. But ultimately, our greatest enemy was this noise, this pervasive problem of noise that, as I mentioned, you have to constantly pump out the noise out of the system to maintain this pristine environment where quantum mechanics can take effect. And that constraint was just too much. It’s too costly to do that.


Edited by InquilineKea
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this yielded 50 cytosines out of the 450K DNAme array, GSE42861 dataset, Figure 5B. Consistent with the hypothesis of biological relevance, all these cytosines turned out to be in vital genes known to be homeostatically regulated, Table 1 and Supplementary Data Excel, which shows genome loci annotations of the 50 noise detector cytosines. The biological age (Right Y axis) was then quantified based on the Median of the normalized by the young age sums of SDs of these 50 cytosines (Left Y axis). This unadjusted primary data had a good fit with polynomial curve, it displayed age-imposed increase in biological noise with a plateau at ~35-45 years of age, in healthy subjects, and a clear increase in the noise for the young and middle-aged patients with arthritis, Figure 5B (each dot represents the indicated age; the raw data with dots representing individuals per each age is shown in Supplementary Figure 4A).

An external file that holds a picture, illustration, etc. Object name is aging-15-205046-g005.jpg

Direct quantification of biological age from DNAme array data through noise barometer. (A) Representative cytosine that is on average invariable in its methylation throughout lifespan but becomes visibly noisier, showing higher absolute deviation from the mean in older individuals. Each dot is an individual. (B) Polynomial curve was fitted to the dot-plot of Median of normalized by healthy young sums of SDs of the 50 cytosines for healthy individuals, left; the same polynomial curve is overlayed with the dot plot of the Median of normalized by healthy young sums of SDs of the 50 cytosines for arthritis patients, right. Biological ages were mapped onto the right Y axis, as described in Methods. Each dot is age range: blue – healthy, red, arthritis. (C) Polynomial curve was fitted to the 460 Summed SDs (of the 460 cytosines) v. chronological age ranges, using the six combined 450K DNAme datasets of healthy controls. Right Y axis shows mapping of the summed SDs into biological ages and the compression of specific age ranges. (D) Scatter plots on the changes in beta values over age for representative cytosines that are least regulated in young, with SD=0.3 of absolute Mean of beta values, and all 8 most regulated in young cytosines with SD=0.015 of absolute Mean of beta values. Each dot is a sample. (E) Dot plots of the summed SDs of the 17 cytosines and of the 8 cytosines. Each dot is 49 samples on average. Black arrows indicate transitions from low to high noise. (F) Same as (D), but for cytosines that have SD=0.3 and 0.015 of the relative Mean of beta values. (G) Same as (E), but for the summed cytosines with SD=0.3 and 0.015 of the relative Mean of beta values. (H) Venn diagrams of the cytosines of the published clocks and the 460, 50 and 5, 8 – most regulated, 13, 17 least regulated cytosines of the noise barometer that quantifies biological age; the text is color-coded per each cytosine set.



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