Categories Science
If you are into cryptography or reverse engineering, you should love this.
Thread:
DNA consists of four different 'bases', A, C, G and T. These bases have specific meaning within our biology. Specifically, within the 'coding part' of a gene, a triplet of bases encodes for an amino acid
Most DNA is stored redundantly, in two connected strands. Wherever there is an A on one strand, you'll find a T on the other one. And similarly for C and G:
T G T C A G T
A C A G T C A
(note how the other strand is upside down - this matters!)
If you take all the DNA of an organism (both strands), you will find equal numbers of A's and T's, as well as equal numbers of C's and G's. This is true by definition.
This is called Chargaff's 1st parity rule.
https://t.co/jD4cMt0PJ0
Strangely enough, this rule also holds per strand! So even if you take away the redundancy, there are 99% equal numbers of A/T and C/G * on each strand *. And we don't really know why.
This is called Chargaff's 2nd parity rule.
https://t.co/Xe5xFdtDfO
https://t.co/e3RBxj0ly3
1. Monkey Outrage!
— Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193 (@BillyBostickson) August 17, 2020
The worst treatment was kept for the monkeys. The macaques breed of monkeys are small, relatively light primates, which are often used for animal experiments at LPT. \u2018They are kept in cramped conditions in small cages. https://t.co/6D0yisjd9B
https://t.co/cJlCMqyP2v
11. Max Planck Monkey Photos (2) pic.twitter.com/0yE9D6iswp
— Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193 (@BillyBostickson) August 17, 2020
https://t.co/5n5TK67iKB
Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.
Feels like the next thing we're going to need is a ranking system for how concerning "variants of concern\u201d actually are.
— Kai Kupferschmidt (@kakape) January 15, 2021
A lot of constellations of mutations are concerning, but people are lumping together variants with vastly different levels of evidence that we need to worry.
You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.
The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.
In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.
The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.
Simulation: Riding in car for 120 min w/ infected passenger who seems fine other than a cough every few mins. (1) a lot of SARS-CoV-2 virus (in fine aerosol particles) accumulation in car cabin w/ windows closed; (2) cracking window open slightly = dramatic reduction. #COVID19 pic.twitter.com/bCmrmnLUPG
— Dr. Richard Corsi (@CorsIAQ) April 4, 2020
2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)
3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.
4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.
5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.
Basic Statistics Part 1: The Law of Large Numbers https://t.co/wUH8eAAIak
#Science #Statistics
Basic Statistics Part 2: Correlation vs. Causation
https://t.co/Azhyl8pDsX (2/7)
Basic Statistics Part 3: The Dangers of Large Data Sets: A Tale of P values, Error Rates, and Bonferroni Corrections
https://t.co/LetN6aEBRM (3/7)
Basic statistics part 4: understanding P values
https://t.co/K8MMMgTCOf (4/7)
Basic Statistics Part 5: Means vs Medians, Is the “Average”
Here's a (very disorganized and incomplete) handful of suggestions, which I may add to. Suggestions welcome, especially if you've dug into relevant literatures.
1. The already classic "Estimating the reproducibility of
psychological science" from the Open Science Collaboration of @BrianNosek et al. https://t.co/yjGczLZ6Je
(Look at that abstract, wow!)
Many people had pointed out problems with standard statistical methods, going back decades (what are the best refs?). But this paper was a sledgehammer, making it impossible to ignore the question: what, if anything, were we actually learning from all those statistical studies?
2. Dean Keith Simonton's book "Creativity in Science: Chance, Logic, Genius, and Zeitgeist". If an essentially scientometric book could be described as a fun romp through science & creativity, this would be it