The utterly unique Canadian Brass performing Rimsky-Korsakov’s “Flight of the Bumblebee”.
The utterly unique Canadian Brass performing Rimsky-Korsakov’s “Flight of the Bumblebee”.
Here is a letter (reposted with permission) from Michael Gottesman, Deputy Director for Intramural Research of the NIH, telling the story of how the NIH intramural research program was instrumental in helping Eric Betzig win this years Nobel Prize in Chemistry. I think it once again shows how great breakthroughs rarely occur in isolation.
The NIH intramural program has placed its mark on another Nobel Prize. You likely heard last week that Eric Betzig of HHMI’s Janelia Farm Research Campus will share the 2014 Nobel Prize in Chemistry “for the development of super-resolved fluorescence microscopy.” Eric’s key experiment came to life right here at the NIH, in the lab of Jennifer Lippincott-Schwartz.
In fact, Eric’s story is quite remarkable and highlights the key strengths of our intramural program: freedom to pursue high-risk research, opportunities to collaborate, and availability of funds to kick-start such a project.
Eric was “homeless” from a scientist’s viewpoint. He was unemployed and working out of a cottage in rural Michigan with no way of turning his theory into reality. He had a brilliant idea to isolate individual fluorescent molecules by a unique optical feature to overcome the diffraction limit of light microscopes, which is about 0.2 microns. He thought that if green fluorescent proteins (GFPs) could be switched on and off a few molecules at a time, it might be possible using Gaussian fitting to synthesize a series of images based on point localization that, when stacked, provide extraordinary resolution.
Eric chanced to meet Jennifer, who heads the NICHD’s Section on Organelle Biology. She and George Patterson, then a postdoc in Jennifer’s lab and now a PI in NIBIB, had developed a photoactivable version of GFP with these capabilities, which they were already applying to the study of organelles. Jennifer latched on to Eric’s idea immediately; she was among the first to understand its significance and saw that her laboratory had just the tool that Eric needed.
So, in mid-2005, Jennifer offered to host Eric and his friend and colleague, Harald Hess, to collaborate on building a super-resolution microscope based on the use of photoactivatable GFP. The two had constructed key elements of this microscope in Harald’s living room out of their personal funds.
Jennifer located a small space in her lab in Building 32. She and Juan Bonifacino, also in NICHD, then secured some centralized IATAP funds for microscope parts to supplement the resources that Eric and Harald brought to the lab. Owen Rennert, then the NICHD scientific director, provided matching funds. By October 2005, Eric and Harald became affiliated with HHMI, which also contributed funds to the project.
Eric and Harald quickly got to work with their new NICHD colleagues in their adopted NIH home. The end result was a fully operational microscope married to GFP technology capable of producing super-resolution images of intact cells for the first time. Called photoactivated localization microscopy (PALM), the new technique provided 10 times the resolution of conventional light microscopy.
Another postdoc in Jennifer’s lab, Rachid Sougrat, now at King Abdullah University of Science and Technology in Saudi Arabia, correlated the PALM images of cell organelles to electron micrographs to validate the new technique, yet another important contribution.
Upon hearing of Eric’s Nobel Prize, Jennifer told me: “We didn’t imagine at the time how quickly the point localization imaging would become such an amazing enabling technology; but it caught on like wildfire, expanding throughout many fields of biology.”
That it did! PALM and all its manifestations are at the heart of extraordinary discoveries. We think this is a quintessential intramural story. We see the elements of high-risk/high-reward research and the importance of collaboration and the freedom to pursue ideas, as well as NIH scientists with the vision to encourage and support this research.
Read the landmark 2006 Science article by Eric, Harald, and the NICHD team, “Imaging Intracellular Fluorescent Proteins at Nanometer Resolution,” at http://www.sciencemag.org/content/313/5793/1642.long.
The story of the origins of Eric Betzig’s Nobel Prize in Jennifer Lippincott-Schwartz’s lab is one that needs to be told. I feel proud to work for an organization that can attract such talent and enable such remarkable science to happen.
Kudos to Eric and to Jennifer and her crew.
Michael M. Gottesman
Deputy Director for Intramural Research
Many biology journals insist on receiving manuscripts in Microsoft Word prior to publication. Even though this probably violates some anti-trust law, I generally comply, to the point of painfully converting Latex manuscripts into Word on more than one occasion. Word is particularly unpleasant when writing papers with equations. Although the newer versions have a new equation editing system, I don’t use it because once in a past submission, a journal forced me to convert all the equations to the old equation editor system (the poor person’s version of MathType). It worked reasonably well in Word versions before 2008 but has now become very buggy in Word 2011. For instance, when I double-click on an equation to edit it, a blank equation editor window also pops up, which I have to close in order for the one I wanted to work. Additionally, when I reopen papers saved in the .docx format, the equations lose their alignment. Instead of the center of the equation aligned to a line, the base of the equation is aligned making inline equations float above the line. Finally, a big problem is equation numbering. Latex has a nice system where you give each equation a label and then it assigns numbers automatically when you compile. This way you can insert and delete equations without having to renumber them all. Is there a way you can do this in Word? Am I the only one with these problems? Are there work arounds?
People have been justly anguished by the recent gross mishandling of the Ebola patients in Texas and Spain and the risible lapse in security at the White House. The conventional wisdom is that these demonstrations of incompetence are a recent phenomenon signifying a breakdown in governmental competence. However, I think that incompetence has always been the norm; any semblance of competence in the past is due mostly to luck and the fact that people do not exploit incompetent governance because of a general tendency towards docile cooperativity (as well as incompetence of bad actors). In many ways, it is quite amazing at how reliably citizens of the US and other OECD members respect traffic laws, pay their bills and service their debts on time. This is a huge boon to an economy since excessive resources do not need to be spent on enforcing rules. This does not hold in some if not many developing nations where corruption is a major problem (c.f. this op-ed in the Times today). In fact, it is still an evolutionary puzzle as to why agents cooperate for the benefit of the group even though it is an advantage for an individual to defect. Cooperativity is also not likely to be all genetic since immigrants tend to follow the social norm of their adopted country, although there could be a self-selection effect here. However, the social pressure to cooperate could evaporate quickly if there is the perception of the lack of enforcement as evidenced by looting following natural disasters or the abundance of insider trading in the finance industry. Perhaps, as suggested by the work of Karl Sigmund and other evolutionary theorists, cooperativity is a transient phenomenon and will eventually be replaced by the evolutionarily more stable state of noncooperativity. In that sense, perceived incompetence could be rising but not because we are less able but because we are less cooperative.
Here is the iconic soprano Maria Callas singing Puccini’s aria “O mio babbino caro” from the opera Gianni Schicchi. It was also used in the 1985 film “A Room with a View.”
The Nobel Prize for Physiology or Medicine was awarded this morning to John O’Keefe and May-Brit Moser and Edward Moser for the discovery of place cells and grid cells, respectively. O’Keefe discovered in 1971 that there were cells in the hippocampus that fired when a rat was in a certain location. He called these place cells and a whole generation of scientists, including the Mosers, have been studying them ever since then. In 2005, the Mosers discovered grid cells in the entorhinal cortex, which feed into the hippocampus. Grid cells fire whenever rats pass through periodically spaced intervals in a given area such as a room, dividing the room into a triangular lattice. Different grid cells have different frequencies, phases and orientations.
For humans, the hippocampus is an area of the brain known to be associated with memory formation. Much of what we know about the hippocampus in humans was learned by studying Henry Molaison, known as H.M. in the scientific literature, who had both of his hippocampi removed as a young man because of severe epileptic fits. H.M. could carry on a conversation but could not remember any of it if he was distracted. He had to be re-introduced to the medical staff that treated and observed him every day. H.M. showed us that memory comes in at least three forms. There is very short term or working memory, necessary to carry a conversation or remember a phone number long enough to dial it. Then there is long term explicit or declarative memory for which the hippocampus is essential. This is the memory of episodic events in your life and random learned facts about the world. People without a hippocampus, as depicted in the film Memento, cannot form explicit memories. Finally, there is implicit long term memory, such as how to ride a bicycle or use a pencil. This type of memory does not seem to require the hippocampus as evidenced by the fact that H.M. could become more skilled at certain games that he was taught to play daily even though he professed to never having played the game each time. The implication of the hippocampus for spatial location for humans is more recent. There was the famous study that showed London cab drivers had an enlarged hippocampus compared to controls and neural imaging has now shown something akin to place fields in humans.
While the three new laureates are all excellent scientists and deserving of the prize, this is still another example of how the Nobel prize singles out individuals at the expense of other important contributors. O’Keefe’s coauthor on the 1971 paper, Jonathan Dovstrosky, was not awarded. I’ve also been told that my former colleague at the University of Pittsburgh, Bill Skaggs, was the one who pointed out to the Mosers that the patterns in their data corresponded to grid cells. Bill was one of the most brilliant scientists I have known but did not secure tenure and is not directly involved in academic research anymore as far as I know. The academic system should find a way to maximize the skills of people like Bill and Douglas Prasher.
Finally, the hype surrounding the prize announcement is that the research could be important for treating Alzheimer’s disease, which is associated with a loss of episodic memory and navigational ability. However, if we use the premise that there must be a neural correlate of anything an animal can do, then place cells must necessarily exist given that rats have the ability to discern spatial location. What we did not know was where these cells are and O’Keefe showed us that it is in the hippocampus but we could have also associated the hippocampus with the memory loss of Alzheimer’s disease from H.M. The existence of grid cells is perhaps less obvious since it is not inherently obvious that we can naturally divide a room into a triangular lattice. It is plausible that grid cells do the computation giving rise to place cells but we still need to understand the computation that gives rise to grid cells. It is not obvious to me that grid cells are easier to compute than place cells.
Here is a short snippet from an old BBC show featuring the great English guitarist Julian Bream playing two preludes from Brazilian composer Heitor Villa-Lobos from fifty years ago.
A linear system is one where the whole is precisely the sum of its parts. You can know how different parts will act together by simply knowing how they act in isolation. A nonlinear function lacks this nice property. For example, consider a linear function . It satisfies the property that . The function of the sum is the sum of the functions. One important point to note is that what is considered to be the paragon of linearity, namely a line on a graph, i.e. is not linear since . The y-intercept destroys the linearity of the line. A line is instead affine, which is to say a linear function shifted by a constant. A linear differential equation has the form
where can be in any dimension. Solutions of a linear differential equation can be multiplied by any constant and added together.
Linearity is thus essential for engineering. If you are designing a bridge then you simply add as many struts as you need to support the predicted load. Electronic circuit design is also linear in the sense that you combine as many logic circuits as you need to achieve your end. Imagine if bridge mechanics were completely nonlinear so that you had no way to predict how a bunch of struts would behave when assembled together. You would then have to test each combination to see how they work. Now, real bridges are not entirely linear but the deviations from pure linearity are mild enough that you can make predictions or have rules of thumb of what will work and what will not.
Chemistry is an example of a system that is highly nonlinear. You can’t know how a compound will act just based on the properties of its components. For example, you can’t simply mix glass and steel together to get a strong and hard transparent material. You need to be clever in coming up with something like gorilla glass used in iPhones. This is why engineering new drugs is so hard. Although organic chemistry is quite sophisticated in its ability to synthesize various compounds there is no systematic way to generate molecules of a given shape or potency. We really don’t know how molecules will behave until we create them. Hence, what is usually done in drug discovery is to screen a large number of molecules against specific targets and hope. I was at a computer-aided drug design Gordon conference a few years ago and you could cut the despair and angst with a knife.
That is not to say that engineering is completely hopeless for nonlinear systems. Most nonlinear systems act linearly if you perturb them gently enough. That is why linear regression is so useful and prevalent. Hence, even though the global climate system is a highly nonlinear system, it probably acts close to linear for small changes. Thus I feel confident that we can predict the increase in temperature for a 5% or 10% change in the concentration of greenhouse gases but much less confident in what will happen if we double or treble them. How linear a system will act depends on how close they are to a critical or bifurcation point. If the climate is very far from a bifurcation then it could act linearly over a large range but if we’re near a bifurcation then who knows what will happen if we cross it.
I think biology is an example of a nonlinear system with a wide linear range. Recent research has found that many complex traits and diseases like height and type 2 diabetes depend on a large number of linearly acting genes (see here). Their genetic effects are additive. Any nonlinear interactions they have with other genes (i.e. epistasis) are tiny. That is not to say that there are no nonlinear interactions between genes. It only suggests that common variations are mostly linear. This makes sense from an engineering and evolutionary perspective. It is hard to do either in a highly nonlinear regime. You need some predictability if you make a small change. If changing an allele had completely different effects depending on what other genes were present then natural selection would be hard pressed to act on it.
However, you also can’t have a perfectly linear system because you can’t make complex things. An exclusive OR logic circuit cannot be constructed without a threshold nonlinearity. Hence, biology and engineering must involve “the linear combination of nonlinear gadgets”. A bridge is the linear combination of highly nonlinear steel struts and cables. A computer is the linear combination of nonlinear logic gates. This occurs at all scales as well. In biology, you have nonlinear molecules forming a linear genetic code. Two nonlinear mitochondria may combine mostly linearly in a cell and two liver cells may combine mostly linearly in a liver. This effective linearity is why organisms can have a wide range of scales. A mouse liver is thousands of times smaller than a human one but their functions are mostly the same. You also don’t need very many nonlinear gadgets to have extreme complexity. The genes between organisms can be mostly conserved while the phenotypes are widely divergent.
Let’s bring in the fall with Vivaldi’s Autumn from the Four Seasons. Detroit is mostly known for cars and bankruptcy but it also has great culture. Here is the Detroit Symphony Orchestra with Scottish violinist Nicola Benedetti.
The Chromatic Fantasy and Fugue in D minor, BWV 903 by Johann Sebastian Bach played by Canadian pianist Angela Hewitt, perhaps the best Bach interpreter since the great Glenn Gould.
I’m totally committed to Julia now. It is blitzing fast and very similar to Matlab with some small differences (improvements). I particularly like the fact that when you declare an array with one argument, like x = zeros(10), it immediately gives you a vector of length 10 and not a 10 X 10 matrix. The broadcast function is also very useful. There are still many things I don’t yet know how to do in Julia like how to import and export data. Plotting is also not fully solved. I’ve been using PyCall to import matplotlib.pyplot, which works pretty well but not perfectly. There are things I miss in Matlab like context dependent history recall, i.e. I can recall an old command line by just typing the first few letters. If anyone knows how to do this in Julia please let me know. Right now, I’m hitting the uparrow button continuously until I find the line I want. I do worry that my abandoning Matlab means someone will lose their job, which is certainly not my intention. However, I am well ahead of schedule for zero Matlab.
I think the Meditation from Jules Massenet’s opera Thais is appropriate for the day after September 11. Here is violinist Sarah Chang.
Here is a true story. A young man is trained to hit people as hard as possible and to react immediately to any provocation with unhindered aggression. He signs a 5 year contract for 35 million dollars to do this 16 times a year or more if he and his colleagues are very successful at doing it. One day he gets upset with his fiancée and strikes her in the head so hard that she is knocked unconscious in a public place. This creates a minor stir so the employer mandates that he must apologize and is prohibited from smashing into people for 2 of the 16 times he is scheduled to do so. The fiancée-now-spouse also refuses to press charges because she doesn’t want to jeopardize the 27 million over the next 3 years owed to the man. However, a video showing the incident is made public creating a huge uproar so the employer abruptly fires the man and condemns him since he now is no longer financially useful to the employer. The public now feels vindicated that such a despicable man is no longer employed and that domestic violence now is given the attention it deserves. However, the spouse is very unhappy because her comfortable lifestyle has just been pulled from right under her. Now, other spouses who live with violent but rich men will be even more silent about abuse because they fear losing their livelihoods too. If we really cared about victims of domestic violence, we would force the employer to set up a fund to ensure that spouses that come forward are compensated financially. We would also force them to support institutions that help the many more victims of domestic abuse who are not married to rich and famous people. This young man is probably an upstanding citizen most of the time. Now he is unemployed and potentially even angrier. He should not be thrown out onto the street but given a chance to redeem himself. The employers and the system who trained and groomed these young men need to look at themselves.
The question in this week’s New York Times Ethicist column is whether it is wrong to watch football because of the inherent dangers to the players. The ethicist, Chuck Klosterman, says that it is ethical to watch football because the players made the decision to play freely with full knowledge of the risks. Although I think Klosterman has a valid point and I do not judge anyone who enjoys football, I have personally decided to forgo watching it. I simply could no longer stomach watching player after player going down with serious injuries each week. In Klosterman’s article, he goes on to say that even if football were the only livelihood the players had, we should still watch football so that they could have a livelihood. This is where I disagree. Aside from the fact that we shouldn’t have a society where the only chance to have a decent livelihood is through sports, football need not be that sport. If football did not exist, some other sport, including a modified safer football, would take its place. Soccer is the most popular sport in the rest of the world. Football exists in its current form because the fans support it. If that support moved to another sport, the players would move too.
I think we should start September off with Beethoven. Here is the first movement of the Sextet in E-flat op.81 b for two horns and strings performed by the Amici Ensemble Frankfurt. Note that they added a double bass, just to make it more special.
Chopin Nocturne No 8 Op 27 No 2 performed by Maurizio Pollini, whom I’ve had the fortune of seeing in Boston many years ago.
I’ve been asked in a comment to give a sample of pseudo code for an MCMC algorithm to fit a linear model to some data. See here for the original post on MCMC. With a linear model, you can write down the answer in closed form (see here), so it is a good model to test your algorithm and code. Here it is in pseudo-Julia code:
# initial guess for parameters a and b
# construct chi squared, where D is the data vector and x is the vector of the
# independent quantity
chi = norm(D - (a*x +b))^2;
for n = 1 : total;
# Make random guesses for new normally distributed a and b with mean old a and b
# and standard deviation asig and bsig
at = a + asig * randn()
bt = b + bsig * randn() chit = norm(D - (at*x + bt))^2;
# Take ratio of likelihoods, sigma is the data uncertainty
ratio=exp((-chit + chi)/(2*sigma^2));
# Compare the ratio to a uniform random number between 0 and 1,
# keep new parameters if ratio exceeds random number
if rand() < ratio a = at;
b = bt; chi = chit; end
# Keep running until convergence
George Gerschwin, although generally classified as jazz, was strongly influenced by classical music. Here is Summertime from his opera Porgy and Bess performed by soprano Renee Fleming.
I was given the sad news that J. Bryce McLeod died today in his home in England. Bryce was an extraordinary mathematician and an even better human being. I had the fortune of being his colleague in the math department at the University of Pittsburgh. I will always remember how gracious and welcoming he was when I started. One of the highlights of my career was being invited to a conference in his honour in Oxford in 2001. At the conference dinner, Bryce gave the most perfectly constructed speech I have ever heard. It was just like the way he did mathematics – elegantly and sublimely.
We’ve been using Julia for a little over a month now and I am quite pleased thus far. It is fast enough for us to get decent results for our MCMC runs; runs for which Python and Brian were too slow. Of course, we probably could have tried to optimize our other codes but Julia installs right out of the box and is very easy to program in. We still have issues for plotting but do have minimal plot functionality using PyCall and importing matplotlib.pyplot. One can also dump the results to a file and have either Python or some other software make the plots. I already find myself reaching for Julia when I want to write a quick code to solve a problem instead of relying on Matlab like I used to. While I would like to try PyDSTool, the lack of a trivial installation is holding us back. For you budding software developers out there, if it takes more than one click on a link to make it work on my computer, I am likely to go to something else. The reason I switched from Linux to Mac a decade ago was that I wanted Unix capability and the ability to print without having to consult with a sys admin.