Thursday, July 10, 2014

Undergrad class FAQ

Every semester, I get some questions from my undergrads, and in the interest of efficiency, I thought I'd post the answers here as an FAQ.  Feel free to modify for your own use as you see fit.

Q: When is the midterm?
A: Feb. 14th.

Q: I have a [job interview/wedding/film festival (seriously, I got this)] during the quiz, but I'll have some time Wednesday at 2pm. Can I make it up then?
A: No.

Q: Is this going to be on the exam?
A: Most of the material I cover in class and assign homework on will be fair game for the exam, with emphasis on homework problems. I will probably not ask you to produce derivations.

Q: Is this class graded on a curve?
A: Yes, I will take into account the class average and overall performance when assigning grades.

Q: What is my grade?
A: It's hard to say, given that I don't yet have the complete picture of the class performance.

Q: Is this going to be on the exam?
A: Material from class is fair game for the exam except when explicitly noted.

Q: Is this class graded on a curve?
A: Yes, I will grade on a curve.

Q: What is my grade?
A: I don't know yet.

Q: Is this going to be on the exam?
A: Yes.

Q: Is this class graded on a curve?
A: Yes.

Q: What is my grade?
A: B.

Q: Is this going to be on the exam?
A: Yes.

Q: Is this class graded on a curve?
A: Yes.

Q: What is my grade?
A: B.

Q: Is this going to be on the exam?
A: Yes.

Q: Is this class graded on a curve?
A: Yes.

Q: What is my grade?
A: C.

Saturday, July 5, 2014

The Fermi Paradox

I think almost every scientist has thought at one point or another about the possiblity of extra-terrestrial life. What I didn't appreciate was just how much thought some folks have put into the matter! I found this little article really summarized the various possibilities amazingly well. Reading it really gave me the willies, alternately filling me with joyous wonder, existential angst and primal dread.

One cool concept is that of the "Great Filter" that weeds out civilizations (explaining why we don't see them). Is this filter ahead or behind us? Hopefully behind, right? Better hope there's NOT life on Mars:
This is why Oxford University philosopher Nick Bostrom says that “no news is good news.” The discovery of even simple life on Mars would be devastating, because it would cut out a number of potential Great Filters behind us. And if we were to find fossilized complex life on Mars, Bostrom says “it would be by far the worst news ever printed on a newspaper cover,” because it would mean The Great Filter is almost definitely ahead of us—ultimately dooming the species. Bostrom believes that when it comes to The Fermi Paradox, “the silence of the night sky is golden.”

Friday, July 4, 2014

Smile, you've got a genetic disorder!

Check out this paper in eLife, in which the authors use machine learning applied to facial images to determine whether people have genetic disorders. So cool! From what I can gather, they use a training set of just under 3000 images of faces (1300 or so of them have a genetic disorder) and then use facial recognition software to quantify those images. Using that quantification, they can cluster different disorders based on these facial features–check out this cool animation showing the morphing of an average normal face to an average face of various disorders. Although they started with a training set of 8 syndromes, the resulting characteristics they used (the “Clinical Face Phenotype Space”) was sufficiently rich to distinguish 90 different syndromes with reasonable accuracy.

“Reasonable accuracy” being a key point. The authors are quick to point out that their accuracy (varies, can be around 95%) is not sufficient for diagnostic purposes, where you really want to know 100% (or as close to it as possible). Rather, it can assist the clinician by giving them some idea of what potential disorders might be. The advantage would be that pictures are so easy to take and share. With modern cell phone cameras having penetrated virtually every market in the world and their classification being computational, then a pretty big fraction of the world population could easily participate. I think this is one of the highlights of their work, because they note that previous approaches relied on 3D scans of people, which are obviously considerably harder to get your hands on.

This approach will have to compete with sequencing, which is both definitive for genetic disorders and getting cheaper and cheaper (woe to the imagers among us!). It doesn’t feel like a stretch to imagine sequencing a person for, say, $10 or $1 in the not so distant future, at which point sequencing’s advantages would be hard to beat.

That said, I feel like the approach in this paper has a lot of implications, even in a future where sequencing is much cheaper and more accessible. Firstly, there are diseases that are genetic but have no simple or readily discernible genetic basis, in which case sequencing may not reveal the answer (although as the number of genome sequences available increase, this may change).

Secondly, and perhaps more importantly, images are ubiquitous in ways that sequences are not. If you want someone’s sequence, you still have to get a physical sample. Not so for images, which are just a click away on Facebook. Will employers and insurers be able to discriminate based on a picture? Matchmakers? Can Facebook run the world’s largest genetic analyses? Will Facebook suggest friends with shared disorders? Can a family picture turn into a genetic pedigree? The authors even try to diagnose Abraham Lincoln with Marfan disorder from an old picture, and got a partial match. I’m sure a lot will depend on the ultimate limitations of image-based phenotyping, but still, this paper definitely got my mind whirring.

Wednesday, July 2, 2014

I think the Common Core is actually pretty good

Just read this article on NYTimes.com about people pushing back on the Common Core, which is about how a lot of parents and educators are pushing back against the "Common Core", which emphasizes problem solving skills and conceptual understanding over rote application of algorithms, i.e., plug and chug. I can't say that I'm super familiar with the details of the Common Core, but I can say that I now that I have taught the undergrads at Penn who are the products of the traditional algorithmic approach, it is clear to me that the old way of teaching math was just not cutting it. Endless repetitions of adding and subtracting progressively longer numbers is not a way to train people to think about math, as though the ability to keep numbers ordered is a proxy for conceptual understanding. I think many of the critiques of Common Core actually show many examples that seem to highlight just how much better the Common Core would be at teaching useful math.

Take as an example adding two digit numbers. I don't know anybody who does math as a part of their job who does the old "carry the 1" routine from elementary school. To me, a far better way to add numbers (and is the basis for the algorithm) is realize that 62+26 is 60+20 + 2+6. This is exactly the object of ridicule #7 in the previous link. I've been teaching my son how to add and subtract this way, and now he can pretty easily add and subtract numbers like these in his head (and no, he is not a math genius). From there, it was pretty easy to extend to other numbers and different situations as well, like, say, 640+42 and the such. I see absolutely no point in him even bothering to learn the old algorithms at this point. I think that those of us who developed mathematical skills in the old curriculum probably succeeded more despite the system than because of it.

The results of decades of algorithmic learning are students who have little conceptual understanding, and even worse, are frankly scared to think. I can't tell you how many students come to my office hours who essentially want me to spoon feed them how to solve a particular problem so that they can reproduce it correctly on a test. The result is people whose basic understanding is so weak and algorithmic that they are unable to deal with new situations. Consider this quote from the NYTimes article from a child complaining about the Common Core:
“Sometimes I had to draw 42 or 32 little dots, sometimes more,” she said, adding that being asked to provide multiple solutions to a problem could be confusing. “I wanted to know which way was right and which way was wrong.”
Even at this young age, already there is a "right way" and a "wrong way". Very dangerous!

I'm sure this Common Core thing has many faults. Beyond the obvious "Well, if it was good enough for me, grumble grumble harrumph!" reactions, I think there are probably some legitimate issues, and some of it probably stems from the fact that teaching math conceptually is a difficult thing to systematize and formalize. But from what I've seen, I think the Common Core is at least a big step in the right direction.

Saturday, June 28, 2014

Why bother studying molecular biology if the singularity is coming?

Perhaps I’m just being hopelessly optimistic, but I believe Ray Kurzweil’s singularity is going to happen, and while it may not happen on his particular timetable, I would not be surprised to see it in my lifetime. For those of you who haven’t heard of it, the singularity is when the power of artificial intelligence surpasses our own, at which point it becomes impossible to predict the future pace of change in technology. Sounds crazy, right? Well, I thought it was crazy to have a computer play Jeopardy, but not only did it play, but it crushed all human challengers. I think it’s a matter of when, not if, but reasonable people could disagree… :)

Anyway, that got me thinking: if artificial intelligence is the next version/successor of our species, and it’s coming within, say, 50 years, then what’s the point of studying molecular biology? If we consider a full understanding of the molecular basis of development to be a 50-100 year challenge, then what’s the point? Or cancer? Or any disease? What’s the point of studying an obsolete organism?

In fact, it’s unclear what the point is in studying anything other than how to bring about the super-intelligent machines. Because once we have them, then we can just sit back and have them figure everything else out. That spells doom for most biomedical research. You could make an argument for neuroscience, which may help hasten the onset of the machines, but otherwise, well, the writing’s on the wall. Or we can just do it for fun, which is the only reason we do anything anyway, I suppose…

Friday, June 27, 2014

Google Docs finally has "track changes"!

I love Google Docs! It's where we store tons of information in the lab–it is essentially indestructible, easy to use and easy to share. And, perhaps most important of all, it makes collaborating on documents SO much easier. No more endlessly sending around XYZ_manuscript_final3_actuallyfinal8.docx. The Word Doc sharing method is prone to errors and is slow, especially if you have many collaborators.

However, the one feature Google Docs was missing from Word was the infamous "track changes". Warts and all, this is an essential feature, since it allows editing while also showing you directly where the edits happened. Google Docs had great commenting features and had some version history feature, but it just was not as good as good-old track changes in Word, end of story. But now it exists! It's called editing in "suggest" mode, and you can change modes with the little pop-up menu towards the top right corner (it's in "edit" mode by default). So awesome! Now witness the firepower of this fully armed and operational Word replacement!

Thursday, June 26, 2014

I sincerely hope I never write about STAP again

Got a few negative comments on my last blog post about the STAP fiasco. Seems like some folks think I’m being overly apologetic or that I have no idea that she faked data. Haha, reminds me why I should just stick to blogging about Chipotle! Also, for the record, in case anyone cares (which I sincerely hope they don’t), I do not have, nor am qualified to give, any opinion as to whether the papers should be retracted or whether STAP cells are real. This is about the vilification of Obokata.

First of all, let me just say (in case it’s not obvious) that if someone has faked data, well, then they should be out of the game, permanently. I think everyone agrees that intentionally fabricating data is a capital offense.

Where, however, is the line between sloppy science and fabrication? Is it the intent to deceive? Are we sure Obokata had such intent? Let’s look at the evidence that I am aware of (and for those out there who think I was completely clueless about the context, I had considered most of this before writing my post).

Here is the RIKEN investigation’s report. At issue are two main problems: splicing of the lane of a gel and duplicating some teratoma figures from Obokata’s thesis. To the former, well, it was an inappropriate manipulation (scaled and spliced a positive control lane from one gel into another because it looked a bit better), but the original gel data was there and, while the manipulation was certainly not a good thing to do, it seems as though she wasn’t even aware that this was a bad thing to do. Moreover, her raw data doesn’t appear, from what I can find, to contradict what she portrayed. I’m not saying that this was a good thing to do, just that it seems like an honest mistake. The RIKEN report does not contradict this sentiment, by the way.

Then there is the more damning issue of the use of teratoma images duplicated from Obokata’s thesis. Yes, this is outright fabrication. Again, are we absolutely sure there was intent to deceive? Yes, I admit it does seem a bit weird that she would have forgotten what pictures came from where, especially from her thesis. That said, Obokata says that she has since provided what she claims to be correct images, although the data trail is very weak (which, by the way, is the case for imaging data in almost every biomedical research lab I’ve seen). Indeed, the RIKEN report itself says that she and Sasai provided the “correct" images just before the investigation began when notified of the issue, and so they didn’t even think they needed to provide further explanation. Obokata also maintains that she submitted a correction to Nature.

Certainly, this is beyond sloppy, probably worthy of retraction. But it also seems true, based on the report, that alternative images showing the effect exist that are not duplicates of her thesis (be these images legitimate or not). So, is she a fool or a knave? To the former, if we assume that she actually has the correct images and they show the right thing, as she maintains, then what other rational explanation is there for the images than an honest mistake? I mean, it beggars belief that she would purposefully show duplicated images when she had the right ones in hand. Now let’s assume she’s a knave and that she didn't actually have teratoma images and needed to manufacture evidence. Again, there is STILL absolutely no motivation for her to intentionally use images from her thesis when she had other non-duplicated fake-worthy images in hand. So she’s either a (yes, very) sloppy scientist, or an utterly incompetent faker.

I’ve never met this woman, and I’m not here to defend her honor, but personally, without seeing more of the evidence, I would feel reluctant to destroy a young woman’s life by branding her a liar and a fraud. I also wonder if we’d all be giving her the benefit of the doubt on all this if her results were immediately replicated by a dozen labs (and Wakayama did actually replicate the experiment, with coaching from Obokata). Would we then be asking for an erratum instead?

As I pointed out before, it’s absolutely nuts to deliberately fake results like this. What is the endgame? It’s obvious that people will attempt to replicate immediately, and so any “fame” would be fleeting at best, followed by probably the darkest period in your life. Whether she misinterpreted her data and those putative STAP cells are actually dying cells or contaminating cells or whatever, is a separate issue than whether she intentionally misled people in her work. Being (honestly) wrong in science isn’t the end of the world, nor should it be.

Or whatever, maybe I’m just hopelessly naive and she really is a faker and a cheat. I, like others, am working with limited information. If so, fine, I was wrong, and maybe she does deserve to be piled on. I’ve already written way more about this than I ever intended, and the whole thing has taken up way more of my brain space than I wanted. I suspect I'm not the only one to feel that way.