It just has RNA that can be transcribed directly by the host ribosome.

youtu.be/oCelMyMtRCk?t=167 mentions that they get their lipid layer from the Golgi complex of the host, where they replicate.

COVID happens in two stages:

This distinction is one of the reasons why separating the virus name (SARS-CoV-2) from the disease makes sense: the disease is much broader than the viral infection.

Why is it there such a clear separation of phases?

Why do people with mild symptoms go on to die? It is a great mystery.

Ciro Santilli's theory is that COVID is extremely effective at avoiding immune response. Then, in people where this is effective, things reach a point where there is so much virus, that the body notices and moves on to take a more drastic approach. This is compatible with the virus killing older people more, as they have weaker immunes systems. This is however incompatible with the fact that people don't seem to be contagious after the viral phase is over...

There are a few possibilities:

One promising way to find more of those would be with IP searches, since it was stated in the Reuters article that the CIA made the terrible mistake of using several contiguous IP blocks for those website. What a phenomenal OPSEC failure!!!

The easiest way would be if Wayback Machine itself had an IP search function, but we couldn't find one: Search Wayback Machine by IP.

viewdns.info was the first easily accessible website that Ciro Santilli could find that contained such information.

Our current results indicate that the typical IP range is about 30 IPs wide.

E.g. searching: viewdns.info/iphistory and considering only hits from 2011 or earlier we obtain:

Neither of these seem to be in the same ranges, the only common nearby hit amongst these ranges is the exact 68.178.232.100, and doing reverse IP search at viewdns.info/reverseip/?host=68.178.232.100&t=1 states that it has 2.5 million hostnames associated to it, so it must be some kind of Shared web hosting service, see also: superuser.com/questions/577070/is-it-possible-for-many-domain-names-to-share-one-ip-address, which makes search hard.

Ciro then tried some of the other IPs, and soon hit gold.

Initially, Ciro started by doing manual queries to viewdns.info/reversip until his IP was blocked. Then he created an account and used his 250 free queries with the following helper script: ../cia-2010-covert-communication-websites/viewdns-info.sh. The output of that script can be seen at: github.com/cirosantilli/media/blob/master/cia-2010-covert-communication-websites/viewdns-info.sh.

Ciro then found 2013 DNS Census which contained data highly disjoint form the viewdns-info one!

Summaries of the IP range exploration done so far follows, combined data from all databases above.

First sequenced variant: www.ncbi.nlm.nih.gov/genome/?term=86693

Genes at: www.ncbi.nlm.nih.gov/nuccore/MN908947.3 TODO protein list on a database?

30kbp, 10 genes, 29 proteins: cen.acs.org/biological-chemistry/infectious-disease/know-novel-coronaviruss-29-proteins/98/web/2020/04

50-200 nanometers in diameter.

Gene overview:

www.youtube.com/watch?v=6DxlkxA82FM COVID-19 Symposium: Entry of Coronavirus into Cells | Dr. Paul Bates

Interaction points:

Genes list: www.ncbi.nlm.nih.gov/nuccore/MN908947.3

Some are named after the encoded protein. Others that are not as clean are just orfXXX for open reading frame XXX.

Envelope.

As shown at pubmed.ncbi.nlm.nih.gov/16877062/#&gid=article-figures&pid=fig-3-uid-2 has transmembrane domain.

D'oh.

But to be serious. The Wayback Machine contains a very large proportion of all sites. It does happen sometime that a Wayback Machine archive is missing or broken and cqcounter has the screenshot. But the Wayback Machine is still the most complete database we have found so far. Some archives are very broken. But those are rare.

The only problem with the Wayback Machine is that there is no known efficient way to query its archives across domains. You have to have a domain in hand for CDX queries: Wayback Machine CDX scanning.

The Common Crawl project attempts in part to address this lack of querriability, but we haven't managed to extract any hits from it.

CDX + 2013 DNS Census + heuristics however has been fruitful however.

We have dumped all Wayback Machine archives of known websites to: github.com/cirosantilli/cia-2010-websites-dump using ../cia-2010-covert-communication-websites/download-websites.sh. This allows for better grepping and serves as a backup in case they ever go down.

Membrane.

As shown at pubmed.ncbi.nlm.nih.gov/16877062/#&gid=article-figures&pid=fig-3-uid-2 has transmembrane domain.

Accounts used so far: 6 (1500 reverse IP checks).

Their historic DNS and reverse DNS info was very valuable, and served as Ciro's the initial entry point to finding hits in the IP ranges given by Reuters.

Generic information about the website not specific on this project will be stored at: Section "viewdns.info".

Since this source is so scarce and valuable, we have been quite careful to note down all the domain and IP ranges that have been explored.

At news.ycombinator.com/item?id=38496244, the creator of the viewdns.info, "Hughesey", also stated that he'd able to give some free credits for public research projects such as this one. This would have saved up going to quite a few Cafes to get those sweet extra IPs! But it was more fun in hardmode, no doubt.

We do API access to IP ranges with this simple helper: ../cia-2010-covert-communication-websites/viewdns-info.sh, usage:e.g.:

For domain to IP queries from the API you should use "iphistory" viewdns.info/api/docs/ip-history.php:

Just beware of the viewdns.info reverse IP bug, that really sucks and led to us missing a ton of domains.

14 million images with more than 20k categories, typically denoting prominent objects in the image, either common daily objects, or a wild range of animals. About 1 million of them also have bounding boxes for the objects. The images have different sizes, they are not all standardized to a single size like MNIST^[ref].

Each image appears to have a single label associated to it. Care must have been taken somehow with categories, since some images contain severl possible objects, e.g. a person and some object.

In practice, the ILSVRC subset of ImageNet is the most commonly used dataset.

Official project page: www.image-net.org/

The data license is restrictive and forbids commercial usage: www.image-net.org/download.php. Also as a result you have to login to download the dataset. Super annoying.

How to visualize: datascience.stackexchange.com/questions/111756/where-can-i-view-the-imagenet-classes-as-a-hierarchy-on-wordnet

The categories are all part of WordNet, which means that there are several parent/child categories such as dog vs type of dog available. ImageNet1k only appears to have leaf nodes however (i.e. no "dog" label, just specific types of dog).

A major model that performed well on ImageNet starting on 2012 and became notable is AlexNet.

Bibliography: