Sunday, August 23, 2015

Thermodynamics for Biochemists: a YouTube textbook

As you may know I don't use textbooks in my courses anymore.  Instead I make my own video lectures and make the corresponding slides accessible.  Inspired by Engineering Mathematics: YouTube Workbook I have now organized the slides from one of my courses into a "YouTube textbook".

It's mostly in Danish but a few subsections are in English.  I am working on a fully English version where the bottleneck is re-recording the videos.  You can see the progress here.

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Tuesday, August 18, 2015

Writing an informed teaching statement for a university faculty position

In the US new open faculty positions are starting to be announced and ChemBark and Chemjobber are curating a list for chemistry this year.  Most of these positions will require a teaching statement. When I wrote my teaching statement back in 1996 I really didn't know what to write. You find a textbook, make some lecture notes, show up 3 hours a week and write on the blackboard, and assign some problems in the book. How do you fluff that up so it fills a page?

The main point of this blog post is that in 2015 the traditional lecture model is just one of many teaching styles you can choose and you should make an informed decision, i.e. even if you choose to lecture you now really have to argue why you choose that.  What follows is a very, very brief overview (CliffsNotes) that is mainly intended to introduce you to terms that you have not have heard of but that you really need to know in order to make an informed decision.

Alternatives to the lecture approach
The main argument against the lecture model is that students only real learn by actively doing, do the the most general umbrella term for these new approaches is active learning. One fairly popular variant of active learning is project based learning which can be combined with inquiry-based learning.  These approaches can be hard to implement for large-enrollment courses. Another, increasingly popular, variant of active learning is the flipped classroom approach. The flipped classroom is often equated with blended-learning and video lectures, but the flipped classroom approach can also be based on a textbook.

There are several variants of the flipped classroom that differ on how the "lecture" time is used.  The most basic implementation of flipped classroom is simply to use the lecture time as help sessions for homework. The flipped classroom approach can also be combined with inquiry based learning using the POGIL approach.  Perhaps the most popular variant of the flipped classroom approach is the peer instruction or "clicker" approach, which scales very nicely to very large courses.

Another interesting new idea in education (that can also be used with the standard lecture model) is specification grading which is part of a relatively new movement within higher education called competency-based learning.

Finally, here are two recent peer-reviewed studies that document improvements in learning compared to the traditional lecture approach: Active learning increases student performance in science, engineering, and mathematics and Improved Learning in a Large-Enrollment Physics Class

Some pedagogical terms and concepts
Here are some  pedagogical terms and concepts that should inform your teaching no matter which style you choose.
Cognitive load - you can learn up to 7 new things at a time
Spaced learning - it doesn’t stick until you’ve seen it 3-4 times over a period of time
Formative assessment - you learn by answering questions if you get immediate feedback
Just in case vs just in time teaching - “You’ll need to know this later” is not a good motivator

Video Lectures and Web clickers
Though not strictly required, many teachers who use the flipped classroom approach make video lectures. This can be done relatively cheaply and easily using screencasting software such as Camtasia or Screenflow together with Powerpoint. One can also make pencasts (handwritten video lectures) using, for example, iPad apps such as Explain Everything or the Livescribe pen but such pencasts often appear too slow when watched online.

Traditional clickers are increasingly being replaced by "web-clickers" such as Socrative or Poll Everywhere on smartphones and laptops

More information
Active learning: tools and tips
My flipped classroom: what I did and how I did it
Why Not Try A Scientific Approach To Science Education?
Psychological insights for improved physics teaching
Carl Wieman Science Education Initiative - Resources
Confessions of a converted lecturer (Youtube) (abbreviated version)

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Sunday, August 16, 2015

Finding the melting temperature by coexistence simulations

While reviewing a paper I came across the idea of finding melting temperatures by coexistence simulations.  The idea is very simple:

1. Run an $NVE$ MD simulation starting from a configuration where half the molecules are solid and the other half is liquid.
2. If the choice of $E$ is such that both phases exist after equilibrium then the average temperature will of the system will converge to the melting temperature.
3. In practice you determine $E$ by running a short $NVT$ MD simulation, where $T$ is reasonably close to the suspected melting temperature, and using the final position and velocities as initial conditions for the $NVE$ simulation.  It's probably best to use a range of temperatures.
4. If you want the melting temperature at constant $P$ run an $NPH$ MD simulation instead.

I traced the approach back as far as this paper, which also has a nice explanation of why this works:
A more direct approach (Ref) to finding the transition temperature is to avoid the nucleation problem altogether, i.e., by simulating coexisting phases and allowing the system to evolve to equilibrium. If the equilibrium system contains both solid and liquid phases, then the system will be at a melting point. This approach is suitable for both experimental and theoretical studies. Molecular dynamic (MD) techniques are particularly useful for this approach, due to the fact that total energy is conserved in conventional MD schemes. To understand how this helps the system evolve toward equilibrium, consider a system with a phase boundary. If the system as a whole is at a temperature slightly below the melting point, then some portion of the liquid phase will solidify, generating the appropriate latent heat. Because the system is closed, this heats up the system towards the melting point. Similarly, if the system is above the melting temperature, the latent heat required to melt the solid will cool the system. The pressure of the system will also tend to equilibrate; thus, the system will evolve toward an equilibrium phase. There is no difficulty in nucleating either the liquid or solid phases, as the interface assists in the nucleation for the melting or solidification process.
The paper references an earlier book chapter, which I didn't bother to get a hold of,* that might reference even earlier works.  (*yet another demonstration of why publishing original work as a book chapter is equivalent to burying it in your backyard).

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Saturday, August 15, 2015

New preprint: ProCS15: A DFT-based chemical shift predictor for backbone and Cβ atoms in proteins

2015.08.24 Update: the paper has now been submitted to PeerJ

Here is a new manuscript that just appeared on PeerJ Preprints today.  I plan to submit the paper in a week or so and am very interested in feedback.  There are several ways of providing feedback: here, comments below, on PeerJ, on Authorea (where I wrote the paper), and on PubPeer (which allows for anonymous comments).

I am thinking of submitting it to PeerJ but other ideas are also welcome.

Here's the abstract
We present ProCS15: A program that computes the isotropic chemical shielding values of backbone and Cβ atoms given a protein structure in less than a second. ProCS15 is based on around 2.35 million OPBE/6-31G(d,p)//PM6 calculations on tripeptides and small structural models of hydrogen-bonding. The ProCS15-predicted chemical shielding values are compared to experimentally measured chemical shifts for Ubiquitin and the third IgG-binding domain of Protein G through linear regression and yield RMSD values of up to 2.2, 0.7, and 4.8 ppm for carbon, hydrogen, and nitrogen atoms. These RMSD values are very similar to corresponding RMSD values computed using OPBE/6-31G(d,p) for the entire structure for each proteins. These maximum RMSD values can be reduced by using NMR-derived structural ensembles of Ubiquitin. For example, for the largest ensemble the largest RMSD values are 1.7, 0.5, and 3.5 ppm for carbon, hydrogen, and nitrogen. The corresponding RMSD values predicted by several empirical chemical shift predictors range between 0.7 - 1.1, 0.2 - 0.4, and 1.8 - 2.8 ppm for carbon, hydrogen, and nitrogen atoms, respectively.

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