CharonY

So you think that Ukraine is in a similar shape as Ukraine at that point? Can you substantiate that claim as well as those regarding perceived selectivity? How about some numbers?

So you are essentially saying that Germany and the rest of the EU should welcome more asylum applications?

While it was not undisputed a number of countries considered Ukraine as a safe country and in Germany there was a push from the conservative wings to do the same. Meanwhile there is significant migration from the Ukraine to EU member states, especially Poland (under a work visa). Significantly more than refugee status folks from Syria who arrived in Germany during the same time frame and the single largest group in 2017. So what is your reasoning then?

Yes there is a high prevalence of obese folks among the hospitalized folks. It is likely that obesity puts a strain on the cardiovascular system. However, there are also reports of many folks just being overweight, here it is a bit more difficult as in many countries there are a lot of overweight folks and it may be difficult to figure out whether the connection is spurious or not.

It is interesting that OP also does not consider the increasing development of countries which coincides with a reduction of birth rates. It is also weird that folks worrying about native birth rates at the same time hold up the spectre of unlimited immigration. And of course the weird assumption that if fewer natives are around that somehow there will be a cultural/scientific decline, without of a shred of evidence, of course.

More importantly, it was not the sole argument used against Kavanaugh although it took quite a bit of the limelight. There were other aspects that have been scrutinized, including his rulings on abortion and gay rights, for example. Another issue was his conduct (including display of partisanship unbefitting of SCOTUS, which has changed some of his supporters in the legal world to stop endorsing him). The whole package was toxic which makes it an easy case to engage in full partisanship. But even before that things have changed, and I think the most obvious point was when the Republican-led Senate blocked Garland. It was a relatively clear signal that SCOTUS nominations will now be partisan fights.

A dimer are complex consisting of two full (sub)-units. In this case we are talking about a homodimer, i.e. two copies of the the protein interacting with each other and the substrate. It is related to the concept of quarternary structure.

There is also the quite simple aspect that many restriction enzymes bind as dimers, and each half recognizes the sequence on the opposite strand resulting in a palindromic recognition site. There are thermodynamic aspects to it, too, in terms how the enzyme bends the strand but that has a bit more to do with how the enzymes bend the DNA while nicking (I am hazy on details though).

There is an interesting background to that, though. Throughout the years, certainly at least since Bush the response to outbreaks (I am talking about the US here, but one could exchange dates and it would somewhat also apply to many other countries) the common reaction is to have an outbreak, build up response capacities, then forget about them and slowly defund them up until the next one occurs. The playbook was one (possibly the most comprehensive) attempt to solidify these measures. But it is also true that when Obama came into office he initially disbanded an office for global health security (which was first disbanded by Bush but re-instated sometime after 9/11). So in a way Trump is doing the same mistakes as his predecessors (there is more nuance to it, but leave it a that). What seems to be massively different is that in this case there is no indication that there will be any lessons this time around. So basically the classic way is that we forget the lessons learned, get surprised by it blowing up, learn something from it and then over time forget again. This time we forget things, refuse to learn and double-down on ignorance. Can't wait to see what happens after that (ideally from the inside of my locked lab).

It has been employed against different viral diseases, incl. SARS, MERS Ebola and a few others. Don't recall respective efficacy, though. You are also correct that recovery time was the major effect.

To some degree. But unless you have quantitative coverage, you generally want to design your cohort so that they are representative in terms of your research questions (e.g. in terms of age, gender, area etc.).

Ehem: https://www.vox.com/2020/4/30/21243117/trump-blames-obama-coronavirus-broken-tests-jim-acosta

We cannot have one without the other. Test should not be random either. In the perfect scenario, everyone gets tested and has follow ups. If that is not possible one should prioritize likely cases as it is done in most areas, but it has to ramp up to capture those that may be unknown spreaders. Without that knowledge, containment plans have to be overkill to work, as you do not have a clear view of the spread if your test happens to be biased towards known cases. In turn that means that we need the ability to ramp up testing capacity as needed (the situation in the US is quite ridiculous, where states are competing with each other in a dystopian bidding war), have a centralized knowledge gathering and distribution system as well as means to conduct contact tracing. There are also specifics to this outbreak that need tweaks in methodology. For example, the worry about asymptomatic spread might need to be addressed via supplementary immunoassays, if direct testing for viral particles cannot be ramped up. Again, COVID-19 is one of many outbreak test runs we already had (in this century alone) and the common lesson is that a) we need to improve our response and b) periods of no outbreaks cannot allows us to reduce preparedness. Epidemics and pandemics will keep occurring, it is just a matter of when and where.

One issue is that Japan is severely undertested. I.e. the number of cases is probably not accurate.

I am not sure whether that is the right way to look at it. Pathogens do not need to overwhelm the immune system by force (though it is one strategy employed by some bacteria). It is sufficient that particles evade detection and start multiplying. The overwhelming part is then from the inside rather than the outside. So far it is not clear whether a higher initial dose would result in worse outcome under physiological conditions (there is only indirect evidence so far). And conversely it is unclear whether healthy patients can withstand higher doses than sick ones.

There are studies looking for viability- the data you might have seen where the number of infectious particles reduce under certain condition are conducted with in vitro testing. It is more difficult to try to recover under non-laboratory condition, of course and especially indirect contact is different to assess as there are a lot of variables going into it. That all being said, the overall pattern is consistent with mostly direct contact exposure, perhaps with additional indirect exposure when folks are not washing their hands, for example. I am not sure about that one. If you test positive, there are already detectable amount of viral particles in your blood stream at this point you are likely infected. You may be asymptomatic, but your body is already producing particles. If you are just exposed to a few thousand particles indirectly but are not actively producing it is unlikely to identify them (for reference the limit of detection of many kits is around 6 genomic copies of the virus in 1 ul of sample). While larger exposure is correlated with worse outcomes, it does not mean that only high exposure create serious outcomes. Especially in older folks as well as folks with comorbidities the prediction is worse. While it does not exclude a dose effect, we cannot conclude that it is indeed predominantly (or even significantly) does dependent.

That is done routinely, actually. A lot of products, not only insulin, but also lots of other compounds are being produced by bacteria. Some of these products are byproducts of bacterial fermentation and metabolism (such as alcohols or amino acids). Here the challenge is often developing (or finding) a strain that is more efficient in the production of these metabolites. Heterogeneous production of e.g. insulin, has the difficulty that products may not be produced accurately. Some require special folding or processing after production which bacteria do not do as they do not normally produce these proteins. There are ways to address them or one can use eukaryotic cell for production, for example.

It is independent of that, but most biosafety areas are under net negative pressure, as it is more important to make ensure that biohazards are less likely to escape. Conversely, a clean room (or operating theatre could run under positive pressure in order to keep contaminants out.

Yes, though for larger areas, maintaining true laminar flow can be difficult in terms of inlet-outlet configuration for the ducts to avoid turbulence. Other systems, such as clean benches do the opposite, they maintain horizontal (mostly) laminar flow, in order to move particles away from items to be protected, but that would be exactly what we would want to avoid here.

It would most likely damage lung tissue more. Remember, in the lung all the lining are living cells, unlike skin, which has a protective layer of dead cells. As such physical treatment has an immediate effect on tissue health and integrity. In case of cancer, the goal is to introduce tissue damage, with the hope that cancerous tissue is destroyed faster than the rest. In case of viral particles the issue is more whether you do more damage to the tissue than to the viral particles, which in turn might continue to proliferate and penetrate more as lung tissue degrades.

In biosafety facilities the workspace usually has a laminar air flow (top down) to minimize circulation of droplets or aerosols. Horizontal flows would in most cases result in broader distribution. I would think that a similar circulation (i.e. top down) could theoretically reduce spread, but are probably difficult to employ at scale.

The most common element is more extensive contact tracing and follow-ups with testing at the beginning of the infection cycle. I.e. getting ahead of the curve.

Technically, it would be the effective reproduction number (or R sometimes referred to as Rt) as R0 refers to the basic reproduction number which is absent of any intervention of disease transmission and where no immunity exists (though in press I see the terms used rather liberally). In case of SARS-CoV-2 it is even a bit more complicated as both numbers would be the same at the beginning of the infection process as there was no immunity and also no intervention. But aside the nitpick, the idea is indeed to surf the number close to one and hope to keep new cases low(ish). In some areas this does not seem feasible with still relatively high (or unknown) transmission rates.

OK, let's take a step back here a bit. Phage roughly speaking have two states. One is the lysogenic cycle, the other one is the lytic one. If lysogenic they remain inactive and are stably reproduced with the host genome. Once certain conditions are met, e.g. if the bacterium is exposed to stress, the phage can enter the lytic cycle, in which it reproduces and eventually destroys its host. Phages that have a lysogenic cycle are called temperate bacteriophages. In contrast, there are also agressive phages that predominantly (or solely) use a lytic cycle for reproduction. These are, unsurprisingly, called lytic phages and this is what the text refers to.

Perhaps it helps to highlight some of the basic ideas behind the models underlying many of the strategies currently in play. A simple, but frequently used one are SEIR models or their variations. These are simple differential equations calculating how the rate at which folks move from Susceptible to Exposed to Infected and finally to Recovery status. You can think it as a simple compartment model in which the population is part of either of these compartments. Values such as effective reproduction number (how many people an infected person infects), length for which a person is infectious, initial number of infectious people and so on then determine the movement of the population through these compartments. From there, one looks at the expected number of infected folks at any given time and often using age-adjusted models (as well as other info, if available) one can estimate how many folks at any given time might need hospitalization. As the capacities can vary significantly, especially in larger countries, usually more local modelling is conducted to understand the health impact of different infection rates. The major factor that measures such as social distancing and shutdowns are affecting are therefore the expected number of folks being infected in a given time frame (essentially by adjusting the effective reproduction number). This is pretty much an established effect by now, with different estimates for different measures (and areas). So basically the major strategies revolve around these measures, as well as increasing health care capacities, if possible. So one might allow the transmission to increase a little, provided that the health care facilities are free, but may want to shut down again, once they go up. Two critical things we need to know, but don't know yet is how efficient immunization is going to be (either via vaccine or via infection) as well as a how long potential immunity will last. There are models for that, which in the simplest case just has a flow back from Recovered populations back to Susceptible after a certain time.