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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.
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The new answer to a 77-year-old problem in data analysis, published today in @naturemethods. Instead of significance tests, use estimation graphics. Our software suite DABEST makes it easy for everyone to visualize effect sizes.https://t.co/UzwXJ7EUC5 pic.twitter.com/VtxyY0xaRM
— Adam Claridge-Chang (@adamcchang) June 19, 2019
https://t.co/hm9NoaU4nr
Open letter to journal editors: dynamite plots must die. Dynamite plots, also known as bar and line graphs, hide important information. Editors should require authors to show readers the data and avoid these plots. https://t.co/0GNKEIUCJL pic.twitter.com/OS9ytEFRZN
— Rafael Irizarry (@rafalab) February 22, 2019
https://t.co/8fKDiKjSWc
Couldn't find D3 code for grouped horisontal box plots that show data points so I made this @mbostock @thisisalfie https://t.co/cQjDPhyZdw pic.twitter.com/y6RNmDB2p3
— Ulrik Lyngs (@ulyngs) June 28, 2017
https://t.co/jkaicC1F2x
made a pkg for pirate plots in ggplot: add any of points/means/bars/CIs/violins \u2013 better than ye olde bar/box plotshttps://t.co/Z2m2kW3hsl pic.twitter.com/npAirPQexM
— Mika Braginsky (@mbraginsky) September 28, 2017
https://t.co/PpxWT4Jef4
See the new #PowerBI visual awesomeness for data points & sources, box-&-whisker plots! https://t.co/dOmgoxWfDE pic.twitter.com/HAUOAMJEJW
— Microsoft Power BI (@MSPowerBI) February 1, 2016
I find it remarkable that a section of society not rejoicing that children very rarely ill with COVID compared to other viruses and much less infectious than adults
— Michael Absoud \U0001f499 (@MAbsoud) February 12, 2021
Instead trying prove the opposite!
Why??
2. @c_drosten has talked about this extensively and @dgurdasani1 and @DrZoeHyde have repeatedly pointed out flaws in the studies which have purported to show this. Now for the other assertion: children are very rarely ill with COVID19.
3. Children seem to suffer less with acute illness, but we have no idea of the long-term impact of infection. We do know #LongCovid affects some children. @LongCovidKids now speaks for 1,500 children struggling with a wide range of long-term symptoms.
4. 1,500 children whose parents found a small campaign group. How many more are out there? We don’t know. ONS data suggests there might be many, but the issue hasn’t been studied sufficiently well or long enough for a definitive answer.
5. Some people have talked about #COVID19 being this generation’s Polio. According to US CDC, Polio resulted in inapparent infection in more than 99% of people. Severe disease occurred in a tiny fraction of those infected. Source:
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.