Subscribing to Specific Labels

I'm sure most of you reading this like to read everything I write, regardless of whether it's about politics, mad science, or food. However, some of you may only want to keep up on one aspect of my blogging. If so, here's how to subscribe to a specific label (aka tag) from my blog.

1. Figure out which label you want (they're over there on the right), and note it. Make sure you have the spelling right. Click the label to see if there are any special characters you need (for example, "mad science" is actually "mad%20science", because the space has to be encoded as %20 so browsers can understand it; %20 is currently the only special encoding you need for any of my labels, but that could theoretically change in the future).
2. Add that label to the end of this url: http://jonthegeek.blogspot.com/feeds/posts/default/-/ (for example, http://jonthegeek.blogspot.com/feeds/posts/default/-/mad%20science).
3. Add that url to your favorite RSS reader

That's all it takes. Enjoy!

Note: The same trick works for any blog here on blogger, just replace "jonthegeek" with the url of the blog you want.

Mad Science Monday, 7/6/2009

It's Monday again (already!), so that means it's time for some mad science. It might not be immediately obvious how this week's article fits the theme, but I have one mad science stereotype stuck in my head now about this one, so hopefully I can get you there, too.

Mad Observations: Despite portrayals in media, scientists are human beings. Sometimes decisions made by human beings are clouded by emotion.

Mad Reference: "Large-Scale Assessment of the Effect of Popularity on the Reliability of Research." Thomas Pfeiffer, Robert Hoffman. PLoS ONE 4(6); e5996. 2009 June 24.

Mad Hypothesis: Research is not impacted by the trendiness of the subject of that research. Yes, I know; this is one of those hypotheses that is pretty much obviously untrue once you say it, but it's something nobody had said scientifically (and followed up with experimentation), and thus it was tacitly accepted as truth.

Mad Experiment: This is what's known as a meta-analysis paper. The researchers didn't perform experiments, per se. Instead, they analyzed over 60,000 published statements about 30,000 unique interactions between yeast proteins. This data set was drawn from papers focusing on specific interactions; each paper from which the 60,000 statements were drawn focuses on one or a few interactions, investigated using small-scale, focused experiments. They evaluated the "popularity" of the proteins involved in these interactions by how many times those proteins were mentioned (ie, more mentions = more popular).

They then compared that first data set to a second data set, gathered using high-throughput, mostly automated techniques. These high-throughput techniques don't focus on one or a few interactions, but instead test pretty much everything simultaneously. In other words, these techniques don't focus on anything in particular, so they don't "care" whether the interactions they're looking at are "popular" or "interesting."

They All Laughed, But: The reason this research seems mad sciency to me is that I keep imagining these researchers giving their speech about the popular researchers laughing at them. Well, who's laughing now?? It turns out, when you compare the results from the specific data with the results from the high-throughput data, popular proteins seem to get by on their looks. Specifically, interactions involving unpopular proteins tend to agree in the data sets more often than interactions involving popular proteins. Popular proteins have a higher proportion of likely incorrect interactions published than do unpopular proteins.

When I first read the summary of the research, I thought this might be an example of damned lies; I figured it wasn't necessarily that the unpopular protein research was correct more often, it was just that nobody bothered disproving statements about those losers. But the methodology here seems sound; it looks like the popular proteins really are getting treated differently. This points out a possible large flaw in current research, and a need to put more safeguards in place to prevent this stuff from getting through. Strong work, mad scientists. You have successfully exposed the flaws in the work of your enemies.

Mad Science Monday, 6/29/2009

I'm a little late today (which is to say, I didn't write this over the weekend and schedule it to release at a seemingly random time during the day), so, to make up for it, I'm offering a twofer; one experiment that tested two hypotheses. They even threw in a little mad engineering to spice things up even more.

I first mentioned this story back in April, but it deserves a closer look. I really think it's going to end up being the biggest science story of the year.

Mad Observations: With things like the Human Genome Project and the other genome projects that preceded and followed it, we are gathering reams of data, more than we'll be able to fully investigate any time soon. At least, more than we can investigate by hand. But hey, computers are pretty advanced these days...

Mad Reference: "The automation of science." King RD, Rowland J, Oliver SG, Young M, Aubrey W, Byrne E, Liakata M, Markham M, Pir P, Soldatova LN, Sparkes A, Whelan KE, Clare A. Science. 2009 Apr 3; 324(5923): 85-9. I also recommend the excellent write-up on the research in Wired.

Mad Hypotheses: The first hypothesis was chosen by the researchers (who were bordering a bit on mad engineering, so the hypothesis is pretty close to "Let's see if we can do this"). It's along the lines of "It is possible for a properly programmed robot to investigate data, make a hypothesis, and test that hypothesis."

But then we get into the first experiment, and get the cooler hypothesis. The researchers programmed a robot, named Adam, to perform science. Without help, just looking at the data from the Saccharomyces cerevisiae (brewer's yeast) genome project and other genetic databases (plus a model of S. cerevisiae metabolism), Adam hypothesized that certain genes in the yeast genome coded for an enzyme that had a certain function in metabolism. These genes were there in the data, but had not yet been characterized. So Adam set out to characterize those genes, hypothesizing that they would produce an enzyme that would catalyze a certain reaction in yeast metabolism.

Mad Experiment: Unfortunately, I don't have full access to the article, and both the abstract and the Wired article are sketchy on the details here. I'll lay out a couple possibilities, though, for people interested in how a researcher (including a robotic researcher) might figure something like this out.

Put simply (but close enough to give the idea), Adam knew that yeast used an enzyme to turn compound A into compound B, and another to turn B into C, and yet another to turn C into D, etc. He just didn't know for sure what those enzymes were. Let's say he was hypothesizing that the enzyme he was looking at turns A into B.

One way to figure out if he's right would be to create a yeast cell that lacked the genes he was looking at (likely one at a time plus all three); the yeast cell would be exactly like a normal yeast cell, just missing the one gene he was looking at. If he fed normal yeast cells A, they'd grow and produce B, C, D, etc. If he fed his modified yeast cells A, if he was right, they wouldn't produce B, C, D, etc. He could then feed his modified cells B, and they'd then be able to produce C, D, etc. If any step of that didn't work as expected, his hypothesis would be false.

The other possibility would be that he directly characterized the genes, creating copies of the genes he was looking at in a test tube, supplying them with the components necessary to translate those genes into proteins, and seeing what happened when he put A into those test tubes. That sort of research is less reliable, though (if it doesn't work, it could be because you're missing some factor necessary to make the protein, not because the proteins are important in what you're looking at), so I think it's more likely that he used the first approach.

All this time, all the researchers did was supplied him with the chemicals he needed, and emptied out wastes. He did all the rest, designing and performing over a thousand new experiments a day.

They all laughed, but: Since I'm writing about it here, you've probably already figured out that it worked. He was able to identify that three previously uncharacterized genes in the yeast genome code for an enzyme that catalyzes a certain step in yeast metabolism.

The particular discovery made by Adam wasn't particularly Earth-shattering; he found something that would have otherwise have been assumed to be true, but he verified it. The next step is the cool part. Robots like Adam can now dig through the genomes that we've sequenced, making similar hypotheses and performing similar experiments. Now that we know that they work, the interesting part comes when they fail to verify what they're looking at. On top of verifying and adding to the body of science, they would then find something for us to look at more closely.

Mad Engineering Applications: Since this all started with a dose of mad engineering—specifically, making robots to analyze data, make hypotheses, perform experiments, and analyze their results—there isn't much left in this particular area for mad engineers to do. For a while now, this one's pure mad science. Hopefully they'll make us some more robots capable of performing other experiments, but, once they give us our army, it's scientists that will utilize that army. And hopefully more scientists (mad or not) will come up with more ways to apply this research, potentially dramatically increasing the rate of increase of the sum of human knowledge. "What we know" already increases dramatically every year, as does the rate of discovery of new information (so if it doubled last year, it's likely to more than double this year). If robot science catches on, the rate of increase is likely to go way, way up.

Have any ideas for what tasks we should set our army of robot scientists on? Let me know in the comments.

Mad Science Monday, 6/22/2009

Boo!

Are you scared? I hope so. Apparently that will help you grasp the broad details of Mad Science Monday #3!

Mad Observations: Studies had shown that emotion (particularly fear) makes people see better, but the details of this sensitization were not investigated.

Mad Reference: "Emotion Improves and Impairs Early Vision." Bocanegra and Zeelenberg. Psychological Science Volume 20 Issue 6, Pages 707-713 (5 May 2009).

Mad Hypothesis: The emotional benefit to vision is limited in scope; some things will be easier to make out while in a state of enhanced emotion, other things will be harder to make out.

Mad Experiment: I had high hopes going into this one that the experiment might be truly mad, but it wasn't nearly as bad as it might have been. The researchers briefly showed people pictures of "fearful" faces and "neutral" faces, and then showed those people other images for the people to evaluate. This methodology relies on our mirror neurons causing us to feel a little bit of the fear we observe being experienced by a fellow human, without requiring that the test subjects actually get scared themselves. Again, I'm very disappointed; this was so close to really being mad.

They all laughed, but: The researchers found that the "scared" group had higher sensitivity to the orientation of "low-spatial-frequency stimuli" (ie, figuring out whether thick stripes were vertical or slightly tilted), but lower sensitivity to the orientation of "high-spatial-frequency stimuli" (figuring out whether thin stripes were vertical or slightly tilted).

The team's interpretation for this makes sense. When we're scared, we can notice details about coarse-grained features (things like movement of large objects), but noticing the exact texture of those large objects, for example, or the color of their eyes... that's less important.

Mad Engineering Applications: I could see a mad engineer using this to create some sort of invisibility-to-people-who-are-afraid device, although it'd probably only "work" in a TV movie vaguely referencing this research.

If you have any other ideas for mad engineering applications, let me know in the comments.

Shared Google Reader Items, 6/20/2009

It's that time again: let's take a look at my favorite shared items from Google Reader for this week.

Car-Powered Supermarket Check Outs [Popular Science]

High-flying kites could power New York [Mongabay, via Slashdot; the original article won't load for me in Chrome, which is almost bad enough form for me to not link to it]

I love ideas about "free" energy--not the crazy, fake kind, but the kind where we find way to use energy that's already there to be harvested. These two stories definitely fall in that camp. The first one presumably steals a little gas to get the power, but, since the customers would be slowing down already, it's probably gas that would be used anyway. The second would take a lot of work to set up, but I love the image I get in my head trying to envision a city powered by kites.

With Iran Protests in Mind, State Department Blocks Twitter's Maintenance Outage [Popular Science]

Twitter had scheduled downtime for maintenance this week. That downtime was going to be during the day in Iran. Iranian protestors are using Twitter (among other things) to organize, so the US State Department asked Twitter to move the maintenance to the middle of the Iranian night. Mostly what I love about this is the chance (however slim) that Twitter (and other Web 2.0 sites) could help end the Islamic Revolution in Iran. It was interesting, for example, to hear a discussion last night (I think on Rachel Maddow) about how when Iran cracked down way back in 1999, they could cut protestors off from the world, but not so much in the 21st century. It's a strange world when things can be this different this quickly.

Network of Wi-Fi-Enabled Cyborg Insects Hunts Down WMDs [Popular Science]

I had read about insect detectors several years ago. In short, insects have amazingly good senses of smell (way better than dogs), and can be trained to react to the presence of certain smells (the example I read about was sarin gas, the stuff used in the 1995 Tokyo subway attack). But in the example I read, the insects were put into boxes, and their movements would set off the detectors. This article is about taking that process way to the next level.

In the new scheme, the twitching associated with the insects finding their target scent is detected by a chip mounted on the insect, and information about this can then be sent to other insects. Combined with systems that have already been developed by DARPA (is there any surprise that all of this is funded by DARPA?), the insects in the cohort could even be remote-controlled to help map whatever chemical they're being used to detect (for example, to find a perimeter around a gas release, and/or find the source if it's a chemical that doesn't affect insects).

Other than pissing off PETA, I can't come up with a down side of this research. I love this stuff.

US cities may have to be bulldozed in order to survive [Daily Telegraph, via Slashdot]

It's sad that it has to happen, but I loved the idea of shrinking Flint when I first heard about it. Basically, Flint is bigger than it needs to be anymore. The factors that led to Flint's growth (primarily the large number of GM plants that were once there) are gone, so the city is now larger than its industry can support. Many houses are empty, and that means garbage, buses, and police have to travel through a lot of empty areas to get to residents. The idea is to move the people in the outlying areas closer to the center of the city, and turn those empty areas into parks and such. It's a big change, but, since it will reduce crime and presumably increase property values, residents seem to support it.

Dan Kildee, the treasurer of Genesee County (which includes Flint), came up with the idea, and outlined it to Barack Obama while Obama was campaigning. Kildee has now been approached byt he Federal government to apply the idea to other cities that have lost the support to remain as large as they are.

Construction Begins on Spaceport America [Popular Science]

Virgin Galactic has broken ground in construction of the spaceport they'll use to launch commercial space flights. Construction has begun on the world's first spaceport. When this thing is done, it is officially The Future. Glee!

University of Colorado team finds definitive evidence for ancient lake on Mars [Eurekalert, via Slashdot]

A team at UC Boulder found the shoreline of a 3-billion-year-old lake on Mars, which was once 80 square miles and 1500 feet deep (the article says that's roughly equivalent to Lake Champlain, but Champlain is more than 5 times that area; Champlain isn't as deep, though, so I guess the total volume might be equivalent). More interestingly, they found deltas surrounding the basin, indicating that the lake was probably long-lived. And if there was water for a long time depositing material into deltas, we may have just found a very good place to look for evidence of life on Mars.

That's it for this week. As always, leave any comments on these or any of my other shared items below.