Here is another very enjoyable conversation, with @Pandata19’s scientific advisory board member, Dr Jay Bhattacharya. Key ideas in this thread.
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I think we have to expand our thinking about the toroidal sphere even more. When looking at maps, I noticed the da Vinci map, from 1514, which uses the Reuleaux Triangle. This triangle is formed from 3 intersecting circles, and is in the center of a trefoil.
The trefoil is the focal point in many gothic structures, repeatedly and prominently shown. It is represented in many ways. ‘Going down the rabbit hole’ now makes sense, if you understand the center point of the ears is the center of the torus, with the rabbit trefoil.
The trefoil can be found within the toroidal field. Here is a fun site, where you can manipulate it yourself. https://t.co/FCMcybuuFC
The wiki page makes it seem like there isn’t much of importance with the Reuleaux Triangle, besides being used for coinage, or some stupid bike. But the Wankel engine is an interesting engine, using this geometric design https://t.co/ayPOgkAqGN
https://t.co/m9EaWwF796
The trefoil is the focal point in many gothic structures, repeatedly and prominently shown. It is represented in many ways. ‘Going down the rabbit hole’ now makes sense, if you understand the center point of the ears is the center of the torus, with the rabbit trefoil.
The trefoil can be found within the toroidal field. Here is a fun site, where you can manipulate it yourself. https://t.co/FCMcybuuFC
The wiki page makes it seem like there isn’t much of importance with the Reuleaux Triangle, besides being used for coinage, or some stupid bike. But the Wankel engine is an interesting engine, using this geometric design https://t.co/ayPOgkAqGN
https://t.co/m9EaWwF796
@mugecevik is an excellent scientist and a responsible professional. She likely read the paper more carefully than most. She grasped some of its strengths and weaknesses that are not apparent from a cursory glance. Below, I will mention a few points some may have missed.
1/
The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
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The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/
The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/
The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
1/
I've recently come across a disinformation around evidence relating to school closures and community transmission that's been platformed prominently. This arises from flawed understanding of the data that underlies this evidence, and the methodologies used in these studies. pic.twitter.com/VM7cVKghgj
— Deepti Gurdasani (@dgurdasani1) February 1, 2021
The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/
The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/
The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/
The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
Localized Surface Plasmon Resonance - an overview | ScienceDirect Topics
https://t.co/mzS7vVSREJ
https://t.co/353PdAX2fa
https://t.co/3yBImjOdd4
In some cases, almost 100% of the light energy can be converted to the second harmonic frequency. These cases typically involve intense pulsed laser beams passing through large crystals, and careful alignment to obtain phase matching.
https://t.co/mzS7vVSREJ
https://t.co/353PdAX2fa
https://t.co/3yBImjOdd4
In some cases, almost 100% of the light energy can be converted to the second harmonic frequency. These cases typically involve intense pulsed laser beams passing through large crystals, and careful alignment to obtain phase matching.
Hard agree. And if this is useful, let me share something that often gets omitted (not by @kakape).
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.
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.
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.
