Aniso - a dirty drug?

The say long-term learning depends on protein synthesis.  A recent J Neurosci article from Sharma et al. suggests, however, that the most common protein synthesis inhibitor (anisomycin) also blocks neural activity, greatly complicating the intepretation of studies using this drug.  This had me running to the lab to check if the finding applies to Aplysia as well (I happened to be in the midst of an experiment using it!).  Happy to say, that consistent with several reports from the Kandel lab (and others), Aniso did *not* decrease spontaneous activity in the nerves of the abdominal ganglion when applied at 10 and 20um.  So what gives?  Is it a vertebrate only effect or is something else going on?

Sharma, a. V., Nargang, F. E., & Dickson, C. T. (2012). Neurosilence: Profound Suppression of Neural Activity following Intracerebral Administration of the Protein Synthesis Inhibitor Anisomycin. Journal of Neuroscience, 32(7), 2377-2387. doi:10.1523/JNEUROSCI.3543-11.2012.  http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.3543-11.2012

Fragile X treatment on the horizon?

New promise for a treatment for fragile X?  Michalon et al. report promising results for treating a mouse model of fragileX syndrom (FXS) with a long-lasting mGlur5 inhibitor, CTEP.  FXS is due to repeated genetic elements that slience the FXMR protein, causing global elevations in translation and throghout the brain and other tissues.  Despite these widespread effects, many of the neural effects of FXS can be mimicked/blocked by activaiton/deactivation of mGLURs.  Several mGLUR antagonists are currently in clinical trials.  CTEP is showing remarkable promise, though, as it seems to be able to reverse symptoms of FXS in a mouse model even relatively late in development.  Mice also seem to tolerate it well (less weight gain, lower grip strength)  None of the other drugs in the development pipeline are expected to work so late in development.  Will it pan out?  From bench to clinic for a developmental disorder?  mGLUR only?  I wouldn't have bet on it, but.. (Neuron's preview also added).

Michalon, A., Sidorov, M., Ballard, T. M., Ozmen, L., Spooren, W., Wettstein, J. G., Jaeschke, G., et al. (2012). Chronic Pharmacological mGlu5 Inhibition Corrects Fragile X in Adult Mice. Neuron, 74(1), 49-56. Elsevier Inc. doi:10.1016/j.neuron.2012.03.009.  http://linkinghub.elsevier.com/retrieve/pii/S0896627312002723

Neuron's preview: Bhattacharya, A., & Klann, E. (2012). Fragile X Syndrome Therapeutics S(C)TEP through the Developmental Window. Neuron, 74(1), 1-3. Elsevier Inc. doi:10.1016/j.neuron.2012.03.014.  http://linkinghub.elsevier.com/retrieve/pii/S0896627312002772

Brain wiring to be cheap but effective...

Take several hot topics in neuro and blend; the result is this new paper by Sporns in NatRevNeuro, which integrates network analysis with economics and brain energetics. Sporns proposes that brains are subject to an economic comeptition of providing maximal adaptive value for the minimum cost. Costs are both fixed (wiring and synaptic connections) and dynamic (activity). Network analysis suggests that brain topology is sensitive but not fully determined by fixed costs, and economic analysis of networks correctly predicts some patterns of disruption that occur when brain energy is disrupted. Overall, a lot to chew over. I get worried that the network features discerned may be tautological or tangential to brian function, especially when Sporns mentions that new studies show that task demands can radically alter DTI-traced network topology within seconds! Also, his focus seems entirely on fixed demands (wiring)--I suspect dynamics costs (max information/spike) are equally important.

Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13(MAY), 336-349. doi:10.1038/nrn3214.  http://www.nature.com/doifinder/10.1038/nrn3214.

Alumni earning, college quality, and the case of the missing correlations

The Chronicle of Higher Education ran a commentary on 5/8/11 that really got me agitated: http://chronicle.com.proxy.cc.uic.edu/article/Want-to-Know-What-Grads-Make-/127421/ 

The commentary, by Richard Vedder, argues that the IRS should work with colleges and universities to compile reports on alumni earnings:

"Students could readily compare colleges not only in terms of costs (based on tuition charges, less expected financial assistance), but also potential future revenues. Companies, including those that rank colleges, could calculate estimated average rates of return on families' investments in college, taking into account such factors as average time to degree and dropout rates"  -- (Vedder, 2011)

I can think of several reasons why this is a bad idea.  Most prominent is that differences in alumni earnings are not driven solely by differences in school quality—there will also be a strong influence from the personal characteristics of the students a school can attract (SAT/ACT score, high-school GPA, SES, etc).  It would be ludicrous, then, to encourage students in the general public to make predictions of 'potential future revenues' that don't take into account their personal attributes.  More plainly, unless you are already 'Harvard material', attending Harvard will not magically confer upon you the earning potential of its other distinguished alums.  Similarly, a student already accepted into Harvard probably won't doom themselves to future poverty by slumming it at Dartmouth.

These considerations suggest an easy improvement to Vedder's proposal: simply take student characteristics into account.  Specifically, you could regress school-average student characteristics onto alumni earnings; the residuals would then reflect true differences in earning potential that are independent of initial differences in student characteristics. 

Although this sounds reasonable, it may not be so simple due to a statistical mystery still unfolding in higher-education studies: a curious lack of correlation between college characteristics and learning gains once student characteristics are accounted for.  As summarized in Pascarella & Terenzi’s masterful second volume of How College Effects Students (2005), there is a large body of empirical evidence examining how student’s basic academic skills improve over the course of college.  The main findings are: a) that gains are fairly modest, and b) that once student characteristics are accounted for, there are no strong and reliable effects of college characteristics.  Specifically, it doesn’t seem to matter if the schools are selective or not, 2-year of 4-year, female-only or co-ed, historically Black or integrated, etc.  Although a few college characteristics have been modestly associated with adjusted learning gains, Pascarella & Terenzi conclude that "between-college effects on the acquisition of subject matter knowledge and academic skills are generally inconsistent and quite small in magnitude" (p. 146).  Take a moment and let that sink in: students do learn in college, and the amount they learn is a function of the skills they bring with them to college, but the specific college they attend does not seem to matter, at least not in a way that we can reliably capture with empirical studies.

This curious case of the missing correlation suggests caution with using alumni earnings to guide college decision-making.  It would be wholly inappropriate to use raw alumni earnings without adjusting for student characteristics, but once these student qualities are factored out the value of alumni earnings may simply vanish.  As is the case with learning gains, the adjustment for student characteristics may leave no consistent or strong relationship between what alumni earn and what school they attended.  Wouldn’t that be a pickle?  Indeed this is exactly the claim made by a UofPhoenix funded and quite controversial report which claims to show that graduates of for-profit institutions end up earning comparable wages to graduates of selective non-profit schools within 10 years of graduation (CoHE summary here: http://chronicle.com/article/article-content/127480/; I haven’t actually read the report or the full article yet).

While I feel this is a compelling critique of the use of alumni income to guide college choices, it has forced me to return to How College Effects Students and contemplate again this statistical equivalent of the dog that didn’t bark in the night: how can it be that college characteristics don’t predict learning gains beyond student characteristics?  Does this mean that all schools are equally good (or bad) at teaching students?  Or, as some would argue, that no learning is occurring at all?  Or is it a methodological issue that is simply obscuring the real impact of college environment?  Perhaps this mystery has already been solved and I’m simply not aware (the CLA, for example, claims to be able to reliably measure ‘value added’ learning that is beyond what is predicted by ability at enrollment, but seems to be curiously mum about the year-to-year stability of scores).  Anyways: I don’t have any easy answers, but I’m going to keep digging.

References:
Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: Vol. 2. A third decade of research. San Francisco: Jossey-Bass

Information overload vs. Borges' library

I'm not alone in feeling overwhelmed by the volume of scientific output in my field.  I try to 'keep up' with just a couple of journals, but my RSS feeds are constantly stacking up with articles, and at best I skim some abstracts and star a few for later reading (which almost never happens).

I find this dispiriting--I know that I'm not really paying attention to most of the research out there, and it makes me suspect that no one else is either.  As I contemplate another day in the lab, I consider the strong probability that my next paper, like all my previous papers, will have the same impact and significance as a rain drop falling in the middle of the Atlantic.  It's at this point I often find myself walking out of the lab to fritter my day away on the internet.  Why bother?

This dour line of thought got me thinking--are we already approaching the completion of Borges' library?  Could we be so awash in science that we've actually produced some measurable fragment of all possible science articles?  I decided to crunch some numbers, hoping it would cheer me up.

First the depressing numbers.  In a fantastic article, Arif E. Jinha estimates that in 2009 we reached the mark of 50 million for scholarly journal articles in existence (Jinha, 2010).  Incredibly, this number is growing at an ever-increasing rate, with an estimated of 1.3 million articles published in 2006 (Bjork, Roos & Lauri, 2009), representing a 2.6% growth rate.  Combining these two studies, I'd estimate about 52 million journal articles in existence here in the middle of 2011. 

How well would these 52 million articles fill Borges' library?  I've been thinking of some different ways to answer this question, and figured I'd start by narrowing the library down to titles and abstracts.  The APA limits titles to 13 words and abstracts to between 150-250 words (the APA has a fetish for minutia like this; apparently this is what psychologists do instead of science).  This is extremely stingy, but seems to be a good starting point.

First, titles.  The OED estimates that there are at least 250,000 words in the English language.  So, the number of total possible (not to say grammatical) titles would be 250,000¹³ = 1.49x10⁷⁰.  The current crop of 52 million articles would thus represent 3.5x10^-63 of the problem space for titles.   In other words, the bulk of all the world's collective scientific wisdom represents only a ludicrously small fraction of all the possible scientific output allowed by the APA's 13-word title rule.

Obviously, the numbers for abstracts are even more impossible to fathom.  With the lower limit of 150 words, there would be 2.5x10⁷⁵⁰, a number MSExcel refuses to even contemplate.  The current crop of 52 million articles would thus represent 1.25x10⁷³⁹% of all possible abstracts.

There are all kinds of caveats to these numbers, most of which are pretty boring (problems with repeats in titles/abstracts, many scientific terms are not in the OED, yadda, yadda, yadda).   In turning this over in my mind, however, a couple of points seemed interesting to me:

1. Borges imagined, as I've calculated here, a combinatorial library, with every possible permutation of a language expressed.  How much smaller, however, would a grammatical library be?  That is, what % of the possible word space is actually sensible?  Moreover, how would you go about determining this number?  I've thought of two strategies, which I may try.  One would be actually randomly sampling some subset of the title space and reading the results.  The other is doing a part-of-speech analysis on current titles to see if I can constrain the set of likely titles by existing patterns in word-usage.  
2. When we've figured it all out, what will be the title of the final science paper (in APA-mandated 13-words or less)?  Will an ultimate theory of the universe be expressible in English (or any other existing language)?

I suppose I can find these reflections a bit motivating--there's certainly much more out there that *could* be said.  I'm not entirely convinced that it makes it worth it for me, or that many of the 1.3 million+ papers that will be foisted upon Pubmed this year will be worth even their digital ink. At least I know now that the science wing of Borges' library is not even remotely close to full. 

References:

Björk, B-C., Roos, A. & Lauri, M. (2009). "Scientific journal publishing: yearly volume and open access availability" Information Research, 14(1) paper 391. [Available from 12 January, 2009 at http://InformationR.net/ir/14-1/paper391.html]

Jinha, Arif E. Article 50 million: an estimate of the number of scholarly articles in existence. Learned Publishing 23(3): 58-263. http://openurl.ingenta.com/content/xref?genre=article&issn=0953-1513&volume=23&issue=3&spage=258.