> Marijuana smokers.—Cases were identified by searching for the terms marijuana and cannabis in The Ottawa Hospital picture archiving and communications system, and results were filtered to include only those in which chest CT was performed. Charts were reviewed to assess the frequency and duration of marijuana use, as well as for concomitant tobacco use. A total of 56 marijuana smokers were identified with chest CT performed between October 2005 and July 2020. Patient ages were sorted into 5-year age blocks (15–19 years, 20–24 years, 25–30 years, etc), and the number of men and women in each age category was determined. Marijuana consumption was quantified using the conversion of 0.32 g of marijuana per joint, as described by Ridgeway et al (14).
> Our study had limitations. First, the small sample size precluded us from drawing strong conclusions. Second, the retrospective nature of the study had its own inherent limitations. Third, there was inconsistent quantification of patient marijuana use, due in part to the previous illegal nature of marijuana possession, which led to a lack of patient reporting. Accurate quantification is further complicated by the fact that users often share joints, use different inhalation techniques, and use marijuana of varying potency. Fourth, given that most marijuana smokers also smoke tobacco, the synergistic effects of these two substances cannot be effectively evaluated. Fifth, only a portion of patients could be age matched, since the tobacco-only cohort was taken from the lung cancer screening study and the patients were aged at least 50 years. Due to the age mismatch in the larger cohort, there are differences in the duration of smoking. Lastly, variable interobserver agreement limits our ability to draw strong conclusions about bronchial wall thickening and bronchiectasis.
So it reads like marijuana only smokers were not distinguished from marijuana + tobacco smokers. Aka the comparison was really between a) non-smokers b) tobacco only smokers c) marijuana only smokers d) marijuana and tobacco smokers. With c) and d) combined into the same group. Meaning there could be many interpretations like:
1) “People that smoke more have more adverse effects” - since it’s possible that some people in “marijuana smokers” group consumed the same amount of tobacco as did people in “tobacco-only” group in addition to also smoking marijuana
2) Marijuana when combined with tobacco results in a synergetic effect that causes more morbidity
Smaller sample size + not having a “marijuana only” group makes it difficult to attribute specific effects to marijuana, to tobacco or to combination thereof.
The only “definitive” conclusion is that not smoking at all is certainly better than being a smoker of any kind - which is not very surprising.
Overall I do agree with you, it does make reporting seem very “sponsored“ and disingenuous.
However, an argument could me made that “We have the first look at the new technology. We’re obviously very limited at what we can legally say or show, but nevertheless here’s a very limited sneak peak” is still genuine journalism. They’ve reported as much as they could and did not hide obvious Intel participation, etc.
The video was very underwhelming and cringey imho. But to informed audience it probably wasn’t misleading or false - that’s how I felt after watching it.
I would very much recommend Johann Gottlieb Fichte’s “Foundations of the Science of Knowledge”. It’s a dense read, nevertheless an interesting insight on consciousness.
Apologies in advance if my terminology is not entirely correct - no formal CS background.
I wonder what implications, if any, this method has on the way science is done.
Perhaps I’m out of date, but I have always thought that with machine learning, with have little insight into how the algorithm does exactly what it does. We know how to train it, how it works - in terms of expected output given a certain input. But is not knowing exactly how the result is achieved a problem?
For modelling airplane air resistance, etc - it may not matter much. For fundamental physics research, I am not so sure. If replicating and verifying experimental results is so important to the “scientific method” - how would one approach confirming the results obtained via ML? Does independently building another ML algorithm count?
Yep! Years ago while in school, I kindly asked Roche to send me the posters. These are great, both in terms of the form - as in room decoration, and in terms of function - great when studying biochem, etc.
Same sentiment here. I could come up with one "advantage" of OpenBSD over Linux - smaller user base. In a simplistic kind of a view that should make it less compelling to put in as much effort into developing exploits vs more common OS'. I couldn't back this up with any concrete data though.
Interesting for sure. When I've first heard reports of ibuprofen + CoV-19, I went to check out what Harvard Health was recommending [0].
Under "What treatments are available to treat coronavirus?":
Currently there is no specific antiviral treatment for COVID-19. However, similar to treatment of any viral infection, these measures can help...
...
Take acetaminophen, ibuprofen, or naproxen to reduce fever and ease aches and pains. Be sure to follow directions. If you are taking any combination cold or flu medicine, keep track of all the ingredients and the doses. For acetaminophen, the total daily dose from all products should not exceed 3,000 milligrams.
So it seems there is no widespread consensus on the matter for now. North American professionals, from what I've seen on social media, tend to disagree with their European colleagues on the matter.
Personally, I remain somewhat skeptical until there's more data available.
Well the thing is alerts are indeed for actionable events.
For example many remote locations have an on-site battery backup, which would supply power in an event of loosing commercial power. Those are actioned in terms of notifying field teams and deciding whether a specific location needs to be placed on a generator.
Imagine a hurricane disrupted commercial power grid and there are thousands of “site on battery” alerts; somewhere among them there is also an alert for OSPF down between two core switches.
Having a monitor with a large red warning saying “Link X at location Y is down!” - is a pretty effective way to not miss important notifications.
I mean playing devil’s advocate one might say “Then your alerts should have better filtering system with the important ones staying at the top of the page”... which is true. A lot of smart design features can render dashboards less relevant - however when there aren’t enough resources in a DevOps team to implement those solutions, a simple dashboard can go a long way!
I’ll chime in here to say we use both at work.
In a NOC at a medium-sized ISP, we are getting hammered with alerts 24/7. Some are not urgent, while others need to be actioned much faster - I mean 100G transit link down is no good.
We’d receive an automatic email about a large circuit going down, we’d also receive a ticket about it; sometimes people dont look at the tickets closely enough, other times people get distracted with other topics, issues, etc. Having a large screen with interface status monitoring has proven to be effective enough; for example, someone walks by the monitor and says “why is this thing red, is it supposed to be?... and we immediately know one of the larger interfaces is down.
In an ideal world, we would not need it because every ticket will be diligently dealt with.... however in a real world, having a big red part of the screen flashing had proved quite effective.
Couldn’t agree more! I also really enjoy shfmt, integrating both ShellCheck and shfmt had really helped me to save time and write safer, readable scripts.
I see what you mean. Specifically in the context of VPNs I’ve always interpreted the layer in terms of the payload that’s being encapsulated - which as far as I understand is IP (L3) packets in Wireguard’s case.
Maybe due to my own ignorance I misunderstood the meaning of derefr’s comment. Apologies if I sounded rude!
WireGuard operates at layer 3. The first sentence from the white paper by Jason A.: “ WireGuard is a secure network tunnel, operating at layer 3...”. [1]
Regardless of the layer, in a few words WireGuard is a simple encrypted tunnel over UDP. Since it’s UDP - there’s no guarantee all packets will be delivered, BUT - what WireGuard places emphasis on is all packets delivered from the WireGuard interface will be authenticated and encrypted. Similarity if packets are received from a particular peer, replies to that IP address will be guaranteed to go to that same peer.
The best feature of all imho is OpenSSH inspired authentication - makes configuring server/peers really straightforward.
Not an expert - just guessing. If entropy is a measure of overall degrees of freedom, then even though gas collapsing into stars and planets might seem more “ordered” - the total amount of degrees of freedom is increased. Hence no laws of thermodynamics are violated. Planets and starts are more “complex” systems compared to gas - so there must be much more entropy aka degrees of freedom contained within them.