Onward to the future...

One week ago from yesterday I successfully defended my dissertation, "Mesoscale light-matter interactions," which means that I am effectively finished with my graduate school career (woot woot!). I would offer my dissertation for viewing here on the web, but due to publication restraints associated with UCF, I have to wait one year before disseminating the work publicly. This is too bad, but the decision to do this was largely out of my control.

I'm excited to be moving on to a slightly new line of work. Later this year, I'll be moving to EPFL in Lausanne, Switzerland to work on STORM microscopes and problems in single molecule biophysics.

I'm particularly excited about this work because now I'll be able to apply my knowledge of optics to biology problems. Most of my PhD work was focused on designing new optics-based techniques and then looking for a problem to solve; I hope to become immersed enough in the biophysics community that I can identify unknowns in biology first and then design measurements to address these unknowns afterward. I think a problem-driven route is a more appropriate for the design of optical sensing techniques and I just can't wait to begin.

During my last year at graduate school, I've also begun several endeavors that I believe make me a much better researcher and contributor to science. Some of these new practices include:

1. Following a paradigm that makes my research as open as possible. This includes making easily reproducible code and sharing data openly. (I'm exploring the use of Figshare and following @openscience on Twitter. Looking at these resources are great starting points).
2. Related to the first point, I'm writing a Python package for processing dynamic light scattering data and simulating experiments that I intend to release freely and open sourced. I think that my expertise in this area would serve many others extremely well, since DLS is often treated like a "black box" technique by a lot of people who use it for macromolecular studies.
3. Though my writing has waned since I began serious work on my defense, I want to begin writing again as a means of exploring more topics in research and academia.
4. I'm structuring my own research around simple questions. I think simple questions, such as "how do cells respond to light" form great, long term questions in science. More complicated questions often lead to short term research goals.

I'll expound on this last point in a later post. Overall, though, I'm beginning to see how I can contribute to my field beyond just publishing.

I won't get started in my new position until November or December. In the mean time, my wife and I are going on a long climbing and hiking trip out West, so you may have to be patient if you're waiting for posts between September and November.

And if you're still working on your PhD or Masters degree, keep up the hard work. It will pay off :)

Making presentation slides flow

Today I gave the first practice talk for my defense presentation to my research group. Prior to today I had been dragging my feet with working on it because making a presentation is fairly boring. It also requires a lot of work, so yesterday I put together quite a few, rather dense slides without taking the time to ensure that the content on each side had a certain "flow," that is,  a logical spatial order to the information presented on them. Now, the slides made sense in the order that they came in, but the information on each individual slide was not so well ordered with respect to other text and figures on the same slide.

Of course, being the first practice, it was a bit rough. But one thing in particular struck me as insightful. I had attempted to tell a story for each slide based on the information that the slides contained. However, since the information was mixed up on any given slide, I often stumbled with the explanations because my attention would jump randomly from one region of the slide to the next.

In prior presentations I took the time to add animations such that graphs or illustrations would appear on a slide as I talked about them. This was a good thing since it automatically gave some nice order to each slide. More importantly, it kept my speech coherent because I wouldn't get confused about what point to talk about.

The downside to making slides like these is that it takes a lot more time. For example, if I have one plot with three curves on it and I want to make the curves appear one at a time as I click the mouse, then I have to in reality make three separate plots!

In the end, though, I think it's worth it. It keeps me talking coherently, it makes the slides aesthetically pleasing, and it enforces a flow with which information is communicated to the audience.

Why we should just say no

Well, it's certainly been a while since I last wrote a post. I have a good reason for it, though. My defense is in less than two weeks and life has been crazy. I'm a bit sorry that I haven't had the time to write lately since it's a good stress outlet, but my mental energies have been absolutely and continuously drained by other tasks.

It's perhaps ironic then that I'm writing this post because it was my lack of time to think about writing that inspired me to, well, write.

From the last few months I've learned the value of saying "no" to requests that people ask of me. It was never really necessary before and I was usually happy to oblige people who needed help with something.

These days, however, I must say "no" if I want to finish the things I need to graduate. And I've come to appreciate that saying "no" to things should apply to more people than just graduate students nearing the end of their work.

I think academics have a hard time with trying to limit the number of projects and tasks they take on. As a result, they and their lab members may become overworked and so attention to detail slips. This often leads to sloppy science, such as not checking hypotheses and assumptions, making conclusions on poorly measured data, etc. At the extreme, it might also be fatal to academic careers.

Unfortunately, I think sloppy science has become very common because, in part, people just take on too many things. I can think of a personal reason for why academics take on too much. I become excited at the start of a new project, but bored near the end, so I tend to start more than I can handle while letting others die off. I shouldn't do this, but I do.

Recognizing that this is an issue is the first step to fixing it. I am glad to see that other academics are slowly fighting back against the status quo and saying "no" to too many tasks. I realize it might be hard at times, but it is very necessary to stay happy and to do good science.

Challenging dogmatic science

I am back from Europe after a brief visit to a couple institutions at which I am applying for a post-doc position. The trip was brief, taxing, and very informative. I'll try to write about the experience when I have the time. Overall, I'm glad to be back and set on finishing my dissertation. I only hope I've succeeded in obtaining a job from this trip!

On another note, I wanted to highlight a very nice paragraph that sums up a concern many have with some scientific fields. It comes from an article entitled "Thinking outside the simulation box" and appears in this month's issue of Nature Physics. Here it is:

One would have naively expected scientific activity to be open-minded to critical questioning of its architectural design, but the reality is that conservatism prevails within the modern academic setting. Orthodoxy with respect to mainstream scientific dogmas does not lead to extreme atrocities such as burning at the stake for heresy but it propagates other collective punishments, such as an unfair presentation of an innovative idea at conferences, bullying and drying up of resources for creative thinkers.

The author, Abraham Loeb, is arguing that too many cosmologists are concerned with building support for an existing paradigm, rather than challenging these paradigms or building new ones. One reason, he thinks, is that it is very difficult to build a career as junior scientists by challenging beliefs that are held true by members of academic job selection committees.

However, for science to remain healthy, scientists must challenge the assumptions and beliefs of their paradigm. It is too bad, in my mind, that the architecture of an academic career is setup to encourage scientists to avoid this line of thinking.

Note: I also very much liked the following excerpt.

Conceptual work is often undervalued in the minds of those who work on the details. A common misconception is that the truth will inevitably be revealed by working out the particulars. But this highlights the biggest blunder in the history of science: that the accumulation of details can be accommodated in any prevailing paradigm by tweaking and complicating the model. A classic example is Ptolemaic cosmology — a theory of epicycles for the motion of the Sun and planets around the Earth — which survived empirical scrutiny for longer than it deserved.

Signalling in intrinsically disordered proteins

I take a small interest in biology and biophysics because of its complexity and the large number of unsolved but important problems. Lately I've noticed an increase in the number of popular articles on intrinsically disordered proteins (IDP's). These proteins are shaking up conventional wisdom on how proteins work and the importance of their structures.

This interesting News & Views article in Nature summarizes some recent work on disordered proteins and how they respond to activators and inhibitors. While I don't understand much of the jargon in the article, the overall message is exciting. I found the following excerpts of interest:

The observation of striking differences in the crystal structures of haemoglobin in the presence and absence of oxygen seemed to validate the idea that allostery [the link is my own] can be rationalized, and possibly even quantitatively accounted for, by examining the structural distortions that connect the different oxygen-binding sites... This structural view of allostery has largely guided the field ever since. However, the realization that more than 30% of the proteome — the complete set of proteins found in a cell — consists of intrinsically disordered proteins (IDPs), and that intrinsic disorder is hyper-abundant in allosteric signalling proteins such as transcription factors, raises the possibility that a well-defined structure is neither necessary nor sufficient for signal transmission.

The take-home message of Ferreon and colleagues' work, and the reason that a switch is possible, is that proteins should not be thought of as multiple copies of identical structures that respond uniformly to a signal. Instead, proteins — especially IDPs — exist as ensembles of sometimes radically different structural states. This structural heterogeneity can produce ensembles that are functionally 'pluripotent', a property that endows IDPs with a unique repertoire of regulatory strategies.

I absolutely love that IDP's are currently rewriting the dogmas of much of molecular biology.

Fourier transforms are not good for analyzing nonstationary signals

I'm currently thumbing through parts of "Image Processing and Data Analysis: The Multiscale Approach." In Chapter 1, I found this enlightening comment on the Fourier transform:

The Fourier transform is well suited only to the study of stationary signals where all frequencies have an infinite coherence time, or – otherwise expressed – the signal’s statistical properties do not change over time. Fourier analysis is based on global information which is not adequate for the study of compact or local patterns.

You can find a free pdf of this book here: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122.8536&rep=rep1&type=pdf

Understanding the static structure factor

The structure factor \(S(q)\) is an important quantity for characterizing disordered systems of particles, like colloids. Its significance comes from the fact that it can be directly measured in a light scattering experiment and is related to other quantities that characterize a system's microscopic arrangement and inter-particle interactions. However, it's difficult to learn about it the context of disorder because it's primarily used in crystallography. Crystals are far from being disordered.

In this post, I'll explore the nature of the static structure factor, which is something of an average structure factor over many microscopic configurations of a material. A dynamic structure factor describes the statistics of a material in time as well as space.

The structure factor of a disordered material can be measured by illuminating the material with a beam of some type of radiation (usually X-rays, neutrons, or light). The choice of radiation depends on the material. It is also important that the material should not scatter the incident beam too strongly because the structure function is typically found in singly-scattered radiation (see the first Born approximation for a discussion about a related concept). If the material is multiply scattering, the information about the material's structure, which is largely carried by the singly scattered light, is washed out.

In a measurement, the sample is usually placed at the center of rotation of a long rotating arm. A detector for the radiation is placed at the opposite end of the arm. The arm is rotated about this axis and the intensity of the scattered radiation as determined by the detector is recorded as a function of the angle. This data set essentially contains the structure factor, but must be transformed and corrected for, accordingly.

First, the structure factor is usefully represented as a function of the scattering wave number, \(q\), and not as a function of the scattering angle. In optics, \(q\) is usually given by

\[ q = \frac{4 \pi n}{\lambda} \sin( \theta / 2) \]

where \(n\) is the refractive index of the background material (usually a solvent like water) and \(\lambda\) is the wavelength of the light. \(\theta\) is the scattering angle.

Additionally, the structure factor must be corrected for a large number of confounding factors, such as scattering from the sample cell and radiation frequency-dependent detectors. A classic paper that details all these corrections to find \(S(q)\) in a neutron scattering experiment is given here.

Once the structure factor is found in an experiment, it may be Fourier transformed numerically to give the radial distribution function, \(g(r)\) (see Ziman for the proper conditions for which this applies) of particles. This function gives the probability of finding a particle at a radial distance from another particle in the system. Many important thermodynamic properties are related to \(g(r)\). Importantly, the pair-wise interaction potential between any two particles is related to \(g(r)\), and the pair-wise interaction determines many macroscopic system properties.

The structure factor as \(q\) (or equivalently the scattering angle) goes to zero is also an important quantity in itself. \(S(0)\) is equal to the macroscopic density fluctuations of particles in the medium (see Ziman, Section 4.4, p. 130). But density fluctuations can be calculated from thermodynamics and leads to the isothermal compressibility of a material.

In the language of optics, which I'll stick to for the rest of this post, the density fluctuations would correspond to large regions of refractive index variations across the sample.

This leads to an interesting problem, the resolution of which reminds me of the fallibility in taking some models too literally: for a homogeneous and non-scattering optical material, like a very nice piece of glass, the value of the density fluctuations in the refractive index are essentially zero (this is true because the disorder in a glass is at a length scale that is much smaller than the wavelength of light). This means that \(S(0) = 0\). At the same time, the scattered intensity in the type of experiment measured above is directly proportional to the structure factor:

\[I(q) \sim S(q).\]

So, if I illuminate a nice piece of glass with a laser beam, and I know that \(S(0)\) is equal to zero, the above expression means that there should be no scattered intensity in the forward direction. But this is a silly conclusion, because when I do this experiment in the lab I see the laser beam shining straight through the glass! In other words, \(I(0)\) is not zero.

The problem is that this expression is for the scattered intensity. In random media, we often talk about the scattered light and the ballistic light. The latter of these two is not scattered but directly transmitted through the material as if the material were not there. So, even though no light is scattered into the forward direction, there is still the ballistic, unscattered beam, that is passing straight through the sample.

Most small angle light scattering experiments measure as close as they can to \(q=0\) and extrapolate to the structure function's limiting value. \(S(0)\) can't actually be measured. But, it's determination is important for materials with significant long-range order, such as those near a phase transition, because the small angles correspond to large distances, due to their inverse Fourier relationship.

One can also engineer a material to not transmit any light into the forward direction. To do this, \(S(q)\) must be zero AND there must be no ballistic light passing through the material. This can be achieved with a crystal that diffracts all the light into directions other than the forward direction, such as a blazed grating.

On a final note, the structure factor can sometimes be related to important material properties beyond the radial distribution function. Ziman says in section 4.1, pg. 126 that the direct correlation function (which measures interactions between pairs of particles) can be derived directly from the structure factor. This correlation function is related to the Percus-Yevick model for liquids.