Sieg heil, Herr Grammer!

So I don't proofread my Slashdot comments as well as my journal articles. Does anyone?

To your question - no, flu tends to lower libido, and no one cares about whether you are fertile. Most people can't think about sex when they have a major respiratory viral infection making them feel worse than they have ever felt...

Mostly right - it would be rare for 4 different strains to all infect the same cell, but two is pretty common. Since this virus was a mix of 4 swine viruses, it probably resulted from several dual-infection/random-packaging events (called reassortment, usually).

The easier question to answer is why did it have less effect in the elderly.

Flu crosses over from pigs to humans pretty often - there's a fairly well established theory that pigs are the link that lets flu get from birds (the major carrier) into human. This virus was a weird mix of 4 swine viruses (flu has 8 gene segments that can mix and match) that happened to infect both humans and pigs pretty well.

This is not the first time in recent memory that a virus has made the jump from pigs to humans, though. There was a pretty good H1N1-from-swine scare in 1976 - that virus turned out not to be a very big event, but it precipitated a major vaccination campaign that gave people in their 40s some small amount of immunity to the 2009 H1N1 strain. Additionally, the 1956 pandemic that introduced H2N2 into humans and the 1967 pandemic that introduced H3N2 into humans came from swine, too, so older people had some cross-immunity from those viruses. That cross immunity isn't very well understood, but it seems to lower the death rate quite a bit for the older generations.

What no one can really wrap their head around yet is this affected young adults more than very young children. It's a good question, and one that should be answered by research now in progress, but we just don't know yet. It wasn't that the young children didn't catch it, but instead that they didn't die from it. The only corollary is the 1918 virus, which killed a disproportionate number of young adults, too. That virus cause a massive overreaction of the host immune system, thereby killing people who had the most robust immune systems - young adults. Best working theory is that maybe the 2009 H1N1 strain did something like that, but was just a lot less fatal than the 1918 H1N1 virus was.

Quite a few less deaths than seasonal flu, seems to be the consensus. It was a pandemic in that it spread world-wide, but not a very deadly one. In fact, WHO seriously debated not calling it a pandemic to prevent panic, but eventually called it anyway because it met the definition.

This got a lot of press because it was a pandemic, and because it effected children instead of seniors. Didn't actually kill that many people, though.

The linked summary article is so much technobabble. Slashdot is full of smart people who can handle a link to an open access journal article...

Go to http://www.plospathogens.org/article/info%3Adoi%2F10.1371%2Fjournal.ppat.1001034 to find out that the lack of a Lysine (K627) in the PB2 gene would normally prohibit this virus from replicating in humans, but is compensated for by the presence of a Arginine (R591) residue. These are both basic amino acids, and are located near each other on the structure. So, just a standard compensatory mutation - the sort of thing flu does all the time.

This is a nice bit of science, but it hardly explains the cause of the whole pandemic (this was a Franken-virus cobbled together from 4 other viruses). More science, less sensationalism, please!

Two spaces at the end of sentences - a good typography setter will set the correct width for the end of the sentence, a crappy one will get it wrong no matter how many spaces you use, and for anything else two is the correct answer.

For code, four spaces with soft tab stops, allowing you to use the tab key to insert the correct number of spaces. Easier to read than two spaces (not enough difference in two spaces over more than 10 lines or so), takes up less horizontal space than the stupid 8 space tab stops. Setting up vim to do this is nearly required if you want to program Python, or make any other language look sane.

Were there any questions? If so, why? Try the experiment, realize that most of the world has worked it out already, and go with the flow. The real question was never the number of spaces - it was readability. To maximize that, two for sentences, four for code.

Because you wrote USD 29.000 instead of $29,000, you are probably not American, and probably also pay a crap-ton of taxes (~50%?). You can live comfortably on that if your government provides a lot of services.

To find a comfortable living wage in a medium sized, Midwest US city, double that number - $60k won't let you live like a rock star, and you'll have to save up to buy cool high tech toys, but you can afford a small house in a good neighborhood and a car, good food to eat, an occasional vacation. You don't get everything you want on that salary, but you don't have to compromise too much either.

Double it again if you want to live in New York. 100-120k will get you a decent apartment, maybe a car (but you need one less in NY than you do elsewhere). It's tough to live on much less than that - you need to live a long way outside the city, pick a worse neighborhood, or make some other compromise to get by on less.

You want to live big? 250k minimum in New York, probably more. Most programmers don't make that - hell, most people from any walk of life don't make that. If you want to do it as a programmer, you have to take on a high-risk, potential high-reward project. Do what these guys did and start your own company, do your old bosses job better than he ever could as a simple user of your software.

What people should really care about, though, is happiness. New Yorkers tend to be bitter and disillusioned - even the ones living on 250k+. Go back to the Midwest city, and the guy making 60k might wish it was 80k, but he's putting food on the table for his family, keeping his stress level lower, and is generally happier overall. If 30k allows you to live well in your part of the world, then it is enough. Just remember never to compare sizes with a New Yorker - his is always bigger, no matter what you are comparing.

I had a numerical analysis class taught by a fantastic prof that used this method, with a twist. We could bring a 3"x5" note card with anything we wanted to the exam, or we had an option to bring a 3ftx5ft 'note card' - nothing in between. Even when someone took him up on the 3ftx5ft version, those exams were *killer* - because many of the numerical techniques were too long to be done by hand on an exam, you were expected to understand the theory well enough to derive a simplified version of the technique, apply it, and get the correct answer during a standard timed exam period.

The note cards were nice - you didn't need to do any rote memorization. But no note card could save you on those exams if you hadn't been to class, participated in the discussion (the prof would randomly call on people, so you were forced to participate), read the book in detail, done all of the homework, etc.

You had to *actually know* the material - cheating just wasn't an option. Of course there were 20 people, and everyone except me was a math major.

On the flip side, I was head TA for freshmen Gen Chem. Two 300 person sections, scan-tron tests administered in the arena to have enough seating, electronic 'clickers' to check comprehension during lecture and record attendance, etc. The cheating was unreal.

Force the students to understand the material if they want to pass the class, and you will have no cheating problems.

You just 'proved' the whole point of the article, but not for the reason you think.

By naming the one known boy (Peter), you have turned the 'two child' problem into exactly the 'two child + Tuesday' problem being discussed. The point of this article is to point out that by supplying the extra info (day of week, or name, or anything else), the probability goes back to being close to (but less than) 1/2.

For your version - you forgot to account for the fact that both boys could be named Peter. If you think that never happens, just ask George Foreman. So the probability of two boys, given one boy named is named Peter, is approximately 0.5 - P(Peter, Peter)/2 - just less than 1/2.

If you don't name the one boy, you get back to four possible cases:

girl, girl
boy, girl
girl, boy
boy, boy

Since we are told that one child is a boy, P(girl, girl)=0 and P(otherboy)=1/3 - not 1/2.

The act of adding information to try to 'clarify' the problem (name=Peter, or day of birth=Tuesday, or 'likes ham sandwiches') actually changes the probability from 1/3 to very close to 1/2. Which is counter-intuitive to mathematicians, but brings the situation back much closer to everyday thinking, where the probabilities should be very nearly independent and therefore very close to 1/2.

You don't seem to understand the business side of science.

If you work do R&D for a big pharma company, your compensation package is mostly monetary, plus a few papers that look suspect to other scientists because you have a conflict of interest. The company makes money by selling the result of your work.

If you work in an academic or other 'pure research' setting, your monetary compensation is usually pretty low. The big-name prof on a huge grant may make decent money, but no one else does. The total compensation package is made up of a decent wage (but typically far below that made by people in other professions with similar amounts of schooling), and the lack of monetary compensation is expected to be offset by publishing big papers that *might* generate name recognition and lead to better opportunities for the researches long term - bigger grants, more prestigious positions, opportunities in industry, maybe patentable technology. The funding agencies take in money from taxes, and pay it out expecting that investing in fundamental science will lead to a better future for the nation on a long time horizon - most of the research they fund is not immediately commercializable.

If you work in a government science job, your first job is to be a public servent, and so you get typical government pay scales - decent money, but still *far* below people with similar amounts of schooling, and you are still expected to make up the difference by publishing, getting name recognition, etc. - just like an academic.

So, if you can make a ton more money in other professions, or non-academic settings, why does anyone, ever, agree to take less pay for doing fundamental research?

Pro Tip - if money is the only form of compensation you take, hard core science would not be a good career choice for you.

In both the government and academic cases you are beholden to some external funding agency to get your paycheck. They'll pay your wage today, giving the appearance of the scientist having a '9-5 job' where the compensation is totally monetary. But if you want to continue being paid tomorrow, you are expected to publish - or perish. The 'perish' part happens less in government, because you still have public services to maintain, but make no mistake - you are still expected to publish, demonstrating that you are spending public funds productively, wisely and deserve to be funded again next year.

In my world, shitty wait staff don't get tipped, and shitty scientists don't get their grant renewed.

On the other hand, exceptional service at a restaurant will get you a *much* more generous tip. The equivalent for a scientist is spending years hunched over in the lab, making that big breakthough, publishing a career-making paper, winning the respect of your peers, and thereby gaining access to better funding opportunities to continue your research. Very rarely does that translate into any kind vast personal wealth, though - financial secuturity, maybe, rock star lifestyle, no.

What does this have to do with releasing data? The same thing it did in my first comment. If you release your data too soon, you don't get the 'paid' for your work (regardless of the forms of compensation you accept). If you don't release your data soon enough, your work becomes suspect, especially if people are having a hard time reproducing it. Releasing the raw data in conjunction with that big paper is critically important for the long term success of science, and doesn't actually hurt you (unless you fabricated something...). Force the release of data too soon, though, and watch the quality of science plummet as it becomes a purely for-profit endevor. It is to the benefit of society to take the a long view of the benefits of science, which is why most scientists are beholden to a government funding agency, and do not report to you, the tax payer.

Are there cases of abuse of the current system? Yes. Should these be investigated and eliminated where possible? Yes. Is the best way to do that to force every bit of data to be public domain as soon it is generated? You'll come off looking like a raving lunatic if your say yes to this one - you will doom the fate of every single publicly-funded scientist to be decided in court.

I work for a government lab that produces DNA sequences. We are obligated to release our data into a public database as soon as it has been verified for any samples that come from the US, and we release most of our foreign data, too, unless the other country involved gets pissy.

Nothing good comes of that speed. We get crackpots thinking they've made major discoveries (not one real one yet), we get scooped for major papers (think Science), sometimes by our own collaborators using only our data and none of theirs, and we generally spend a lot of time, effort and *more money* on media spin control. There is such a thing as releasing the raw data too fast.

We get a *ton* of FoI requests, too - people think we are withholding the good data, or being stubborn by not providing them composite statistics in exactly the format they want to see. The truth is, up until I got involved, the data management technology was so far behind the current bog-standard capabilities of the rest of the world, we couldn't actually answer the questions that were being asked, barring Herculean effort.

Don't get me wrong, I think we *should* be releasing all of this data - delayed by just a bit. That way the people who generate it would have a better shot to get recognition/credit for their work, the crackpots would have less ammo for their rants, the press would be more likely to get the facts right the first time, and the scientific integrity of the whole process would be upheld, as everyone would get the raw data to review. It'd probably save a ton of money.

Collecting good data is hard work, and the payoff is big publications, which you need if you want to continue getting funded. Once you've got that big publication in your pocket, though, you'd better by coughing up that data set. Otherwise, everything you say is suspect. Kudos to the UK for getting this half-way right, but they'd better set some reasonable constraints on the timing of these required data releases, or face any number of frivolous lawsuits from conspiracy theorists and 'data analysis specialists' who don't want to do any of the hard work themselves...

I don't care if you are a ditch digger or a particle physicist. Doing all the hard work and getting none of the credit sucks regardless of what we are discussing or who is paying the bills. So put up or shut up. Would you be willing to do all of the grunt work in your job, but take none of the recognition? Most people wouldn't - those are the kinds of jobs that make people go 'Postal'. If you aren't doing it (and even if you are), do you really expect anyone else to?

That quote sounds almost like it is right out of this article...

http://www.theonion.com/articles/new-solar-system-discovered-four-feet-from-earth,1094/

I've had my name included on several 'hard science' papers that had horrible statistical assumptions. I fought, and lost, because my professor had a big grant to maintain, and nobody else understood the underlying assumptions (we used an absolute scaling function, guaranteeing that our distribution was not normal, then tried to assume that it was normal). The second half of my thesis refutes the math in the last three papers I was on. Not one single person who read it understood it, which is sad because it wasn't actually all that impressive.

The only reason I'm not completely ashamed to admit that is that the bad stats don't actually change the conclusions in this case. They do invalidate the confidence intervals, though...

The training in stats required for 'hard science' is essentially nil. Most of the hard science folks I know who are not into high-end mathematical modeling just assume a normal distribution for their data, do a bit of analysis, and publish. I was in an analytical chemistry lab, where that sort of thing normally works, and to a very high precision. However, we were working with sloppy biological assays, where being within a factor of two is a miracle. Under those conditions, you need to know a lot more statistics.

Basically, the people who know enough math are working on well defined systems and theories, and the medical and biological communities don't know much math at all, but are working on very sloppy systems that need a lot of math to analyze correctly. It is therefore easier to spot the mistakes in those communities, but don't assume they aren't there in the 'hard science' papers.

Apparently I really can't spell.

Photonetics.

Grr.

A perfectly good pun, ruined by a voiceless consonant in an uncommon english digraph.

I'm sure you wouldn't have noticed that extra letter if you were moving at 2/3 of the speed of light.

Hooked on phontonetics worked for me.