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I'm forming a team to research AI and snake proteins for potential therapeutics. You can read more about our team's proposal on Discord.

https://discord.com/channels/1098625558780317766/12889513553...


The best writing I have ever done was in the acknowledgements section of my dissertation.

"...For all the experiments I’ve performed and the data I’ve collected, for none have I so unequivocally discovered truth as this: Love is eternal."

https://drum.lib.umd.edu/items/a0d83e35-0382-48b7-9183-c5aef...


It's hard to be a scientist and to be authentic in our current grant-funding race.


Being a dad is wonderful. Congratulations. My fatherhood (parent of four) has changed me in profound ways. The world glows brighter when we have a child with whom to share it.

I’m delighted that you’re searching for information about being the best dad possible. My advice, which has been supported by personal experience and research, is that the relationship with the mother matters. Marry the mother and stay married. More than any other variable, more than any other technique or quality of being a dad, a stable marriage (stable, enduring relationship) is the best quality we can give to our child. It is strong predictor of a child’s success and happiness. https://www.brookings.edu/articles/cohabiting-parents-differ...


This paper is a big deal. I appreciate the laborious efforts the authors underwent to calculate a more accurate infection fatality rate (IFR). The IFR is the number of deaths from a disease divided by the total number of cases. If 10 people die of the disease, and 500 actually have it, then the IFR is [10 / 500], or 2%.

The IFR is different for age-stratified groups. The IFR was calculated before COVID-19 vaccination became widely available.

It's a big deal because it reports a markedly lower COVID-19 IFR than previous studies. The panic may not have been warranted for non-elderly age groups. I read this to also mean that the risks to adolescents did not warrant school closures and for some ages vaccination, even though vaccination risks for this age group were minimal.

"...in many locations excessive hospitalizations may have been driven by irrationally high perceptions of IFR for non-elderly people and they may have caused unnecessary stress and damage to the health care system at large."


It is not a big deal. Ioannidis has been peddling basically the same study for years now. He put his reputation on the line in the early weeks of the pandemic by making some very badly thought out predictions. He believed the pandemic would kill a total of 10k people in the US, and basically ridiculed the people who thought it would be even as serious as the flu. That particular prediction was made when there were a hundred dead; his threshold of 10k was reached three weeks later.

But John wasn't discouraged! He then predicted that there would be at most 40k dead, and that it was imperative for there to be no measures that could hurt the economy. That number was exceeded 10 days after his prediction.

Since then, he has been trying to convince people that he really was right all along, and basically nobody died just like he predicted.


As someone who is not familiar with finding medical literature, I would appreciate if you could link to those earlier claims by the author.


They weren't in the medical literature. The 10k prediction was in STAT news (https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-a...) (note: it wasn't a hard prediction, but he seems to treat it as the best estimate based on the data at the time). The 40K prediction was quoted in the Washington Post (https://www.washingtonpost.com/opinions/without-mass-testing...). There's a good summary with additional links, quotes, and commentary here: https://sciencebasedmedicine.org/10000-deaths/


Thank you


Googling "Ioannadis 10000" gets you plenty of discussion within links on the first page.


> I read this to also mean that the risks to adolescents did not warrant school closures

I'm not saying there's an easy answer, but arguments to keep schools open because adolescents (the students) may be in a lower risk pool tend to gloss over the fact that teachers and administrators are often older adults, who are more at risk.


Every child is attached to a minimum of 1 adult on average. And they are very unlikely to be particularly adept at not acquiring whatever bug is going around let alone something as infectious as covid. So to me that was always the point. One infected kid leads to all kids being infected which leads to all parents getting infected, etc.

The R naught number was very high event for the early strains: https://abcnews.go.com/Health/r0-covid-19-virus-key-metric-o...


Seems wildly unfair to describe as irrational someone seeking treatment when they didn’t need to between March and June 2020.

We didn’t really have good numbers. The numbers we had were extremely alarming. It was entirely rational to seek treatment early given the information at the time.


Ioannidis published very suspect studies with misleading design and motivated reasoning during the pandemic. His credibility is low on this topic.


The paper does seem to address this:

"Several previous evaluations (Ioannidis, 2021a; Levin et al., 2020; O’Driscoll et al., 2021; Brazeau et al., 2020) have already synthesized information on age-stratified estimates of IFR. Most of those used data from early published studies, and these tended to have information from mostly hard hit countries, thus potentially with inflated IFR estimates. Moreover, several analytical and design choices for these reviews and data syntheses can be contested (Ioannidis, 2021b) and many more potentially informative seroprevalence studies have been published since then."


Aileen Neilsen did a 3 hr session discussing timeseries analysis SciPy 2016. Each chapter is a Python Notebook. The ARIMA model figures prominently in chapter 7 Forecasting. I find her session tremendously helpful when analyzing ts data especially her discussion of seasonality. Youtube walkthrough: https://www.youtube.com/watch?v=JNfxr4BQrLk Github data: https://github.com/AileenNielsen/TimeSeriesAnalysisWithPytho...


she also did it in Pycon 2017 https://www.youtube.com/watch?v=d3PtB5z9MyE

and she has an upcoming oreilly book on TS analysis. Preorder now! (no AF, just a fan of her) https://www.amazon.com/Practical-Time-Analysis-Prediction-St...


This seems like the one to watch. Not sure why but the 2016 version didn't much cover forecasting. Thanks!


Really happy for Solugen's success and what this means for other biotechs seeking to augment traditional petromethods with enzymatic ones.

Can you help us better understand how you use CRISPR/Cas9 to mutate the protein? Your enzyme is proprietary, sounds like human protein, but mutated in certain positions prob to increase rate, but how do you use Cas9? I assume like other biotechs you purify this single protein using either E. coli or yeast with protein on plasmid. So why use Cas9 instead of site-directed mutagenesis?


So, without going into too much detail the use of crispr/cas9 in our studies is even simpler than driving protein mutagenesis. We have been exploring CRISPRi has a mechanism to inhibit key promoter repressors of our gene of interest. We've found that we can affect affect specific protein abundances by changing the rates of both RNA synthesis and protein degradation, based on the two cross-kingdom control mechanisms CRISPRi and the N-end rule for protein stability.


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