majos's comments

> Weird to single out HFT's. Isn't everyone working from home right now?

“That” in parent comment is being sequestered at work, so the opposite of working from home.

Slightly older (I don't think it mentions the exterior sugars) but more detailed and comprehensive Economist article about the coronavirus structure and how it motivates research into different solutions (e.g., why do we hope remdesivir will help?): https://www.economist.com/briefing/2020/03/12/understanding-...

Was also previously discussed here https://news.ycombinator.com/item?id=22572311

How does this “save big”? Isn’t supermarket ground beef about $5/lb?

Shouldn’t the title be “hoarding”?

Per the article, “increased 283-fold” means a specific variety of worm is now more common in the fish. It doesn’t mean parasites have become more common in people. It’s consistent with these results for sushi freezing to still kill the worms.

> their 3 biggest increases in new cases were the last 3 days

As raw numbers but not percentages. 13% each of the past 3 days is better than the 15-25% (mostly nearer 25%) of the past few weeks.

If it turns out that there’s a cheap, widely available, already-studied treatment that achieves something even close to the 100% cure rate observed in the paper, the timeline for total shutdown looks much shorter, no?

I really want this study to be legit, but there are a few more weird things:

1. the evidence-free claim of “full peer review”, 2. the fact that patients who refused or met “exclusion criteria” for the drug served as a portion of the control group, and were the only members of the control group from the same region as the experimental group, 3. the removal of people from the experiment group because they went to the ICU or died (only three of first and one of second, but a rather bizarre thing to happen before claiming 100% cure), 4. the lack of coverage from any major news source, despite this being reported about a day ago.

This reminds me of a pretty niche article I found a couple months ago. It was about the bones of a bunch of cave bears that a father and his young son found in Drachenloch Cave in Switzerland in the early 1900s. They called in naturalist Emil Bachler and he got to work finding more bones in the cave, organized in tantalizingly structured ways.

> In Drachenloch, however, not only were there [crude stone] walls, but behind these walls were found accumulations of bear bones — the long bones of the legs and more or less complete skulls. The pattern was very consistent. Where such walls were present, bones were present. Where they were absent, bones were rare along the cave walls.

These and other deliberate-looking accumulations of bones (including apparent crude “chests” of limestone slabs enclosing more bones) led Bachler to suggest the idea of a prehistoric cave bear worshipping cult. But a counterargument suggests this explanation is unnecessary. It might just be bears being bears, no cult required.

> When cave bears entered a cave to hibernate, they began by scratching nests into the cave fill. In the process, bones and small rocks were pushed aside, often falling into crevices among the fallen blocks. This had two effects. First, it helped to build up accumulations of bones in natural cavities among rocks or among piles of rocks. Second, it protected the bones that did enter such interstices from further trampling and, if they were buried there, from weathering and decay. It is perfectly natural, therefore, that modern excavators should find concentrations of bones in cavities surrounded by rock. Moreover, because further weathering of the cave roof naturally produced subsequent roof fall, it is perfectly normal that such cavities would be covered by slabs of greater or lesser size.

I wonder if something similar happened here. Obviously, the lack of a cave is an issue. But it’s a funny connection to something I read randomly a few months ago.

Cave bear stuff from https://www.penn.museum/sites/expedition/the-cult-of-the-cav... which is also a fun read if you like archaeologists sniping at each other.

Geez, I thought this might be an overblown piece about algorithms unwittingly optimizing for the wrong things, but the headline is pretty accurate. Discussing how moderators choose content to recommend to people in the “For You” section (not a user — I assume this is something highlighted to users):

> Under this policy, TikTok moderators were explicitly told to suppress uploads from users with flaws both congenital and inevitable. “Abnormal body shape,” “ugly facial looks,” dwarfism, and “obvious beer belly,” “too many wrinkles,” “eye disorders,” and many other “low quality” traits are all enough to keep uploads out of the algorithmic fire hose.

A TikTok spokesperson seems to confirm they are real guidelines, but won’t confirm how they were used.

> TikTok spokesperson Josh Gartner told The Intercept that “most of” the livestream guidelines reviewed by The Intercept “are either no longer in use, or in some cases appear to never have been in place,” but would not provide specifics.

The estimates in that first article seem oddly cavalier:

> “Around 40 million people died in 1918 Spanish flu outbreak," said Prof [of mathematical biology at ICL] Ferguson. "There are six times more people on the planet now so you could scale it up to around 200 million people probably."

As of 2014 the number of deaths is <500, although the Wikipedia article [1] is written as if bird flu is something that may yet turn much worse.

[1] https://en.m.wikipedia.org/wiki/Influenza_A_virus_subtype_H5...

The best source I know of for statistics like this is the Federal Reserve's most recent "Report on the Economic Well-Being of U.S. Households" [1]. It's a survey of about 10,000 people and, as far as I can tell, is the source for many claims like "40% of American adults cannot meet an unexpected $400 expense", which has been consistently true for several years, although it used to be more like 50% in 2013. Note that “cannot meet” here means anything other than “would pay the expense in full before next month”.

[1] https://www.federalreserve.gov/publications/files/2018-repor...

Not any kind of doctor, but the list of adverse effects on Wikipedia [1] is...not trivial. Seizures, deafness, retinopathy, and the bald assertion that “there is not enough evidence to determine whether chloroquine is safe to be given to people aged 65 and older.”

Granted, all these risks may be outweighed by those of covid, but it’s not a slam dunk choice yet.

There are trials for chloroquine starting at University of Minnesota [2] too.

[1] https://en.m.wikipedia.org/wiki/Chloroquine

[2] http://www.startribune.com/university-of-minnesota-to-test-t...

The “from orbit” part is a little misleading I think. You can also see individual cars, down to their passenger windows, from orbit in the referenced photos. The photos are pretty detailed.

For verifying the slides’ source? Not that I can think of, unfortunately. The slides themselves are at the featured link.

> This is NOT an estimate based on mathematical model, this is the estimate based on clinical data so far and virus's exponential growth rate.

I’m not saying you’re wrong, but an exponential growth rate is a mathematical model. We’re extrapolating the current curve into the future, that’s a model.

Just to check the source, these are the same slides (same Drive doc) tweeted by the listed author on the slides [1], who is a neurobiology and bioengineering professor at Stanford.

If you want an understandable, relatively short but info-dense writeup from a sober expert (there hasn’t been much of this on HN) this is nice.

[1] https://mobile.twitter.com/michaelzlin?lang=en

Confused — the article starts off with the reasonable point that we don’t know the true number of covid infections (likely only the most severe ones) so calculations of death rates are flawed. But then they address this by instead computing # deaths that day/# infected 14 days prior? How does this solve the initial problem?

Dumb question: does this mean normal citizens can take out loans at near-0% interest, or something close to that? I know credit card interest rates are typically tied to “the fed rate” but a but higher?

> From the figures, it looks like 10% of young are still severe pneumonia’s [0].

Sorry, where do you see that in [0]? Table 3 has a pneumonia diagnosis but isn’t broken down by age, Table 1 is broken down (somewhat) by age but only differentiates between nonsevere and severe “disease”?