Fold it, suck it, blast it, show it

Public engagement isn't to my mind so much about educating the public, though that is of course one of the aims, but of enthusing folk to educate themselves. One key way to do this is with hands-on discovery - when you find something with your own hands, or see it with your own eyes it has so much more of an impact.

I have been impressed with the work of Cristina Cipriano (@crizipri‏ on Twitter) who is keen to bring low cost equipment to the general public. She has produced a 3D printed laser pen microscope which is pretty cool, but 3D printing isn't really my thing, and the pieces are very specific in size. A different brand of laser or syringe could lead to small alignment issues and not work as desired.

I put together a design to hold the laser pen and a syringe in a rigid folded cardboard that can be cut from a single A4 sheet. (plans here). The holes for the pen and syringe act as springs which hold them in place and can be tweaked to get perfect alignment.

How this works: The drop acts like a lens and will project whatever is inside as a image onto the surface behind the drop. I used a sample of dirty water from the bottom of the water butt in my garden.

I used single layer corrugated card. With careful attention to the laser cutter settings you can cut through just one wall at the folds making a very smart hinge.

And now for the health and safety. Lasers are dangerous. Laser pens can cause damage so treat them with respect. Look at what the laser is pointing at, not the laser itself.

Parts list:

1 A4 sheet of single wall corrugated card about 1.5mm thick

1 laser pen (about £1.50 on eBay)

1 10ml syringe with a luer tip (not luer lock or it won't fit down the hole.)
You will need a laser cutter or be very good at cutting out with a craft knife.

The first cut out. This used cardboard from an old shoe box. 

Ready to go. The cut cardboard. 

Folding the cardboard is easy with the part through cuts along the fold lines.

Before assembling the box, push the laser pen (abotu £1.50 on eBay) through the pen holder holes. The inner one has the cuts 'backward' so ensure you push it through in the right direction. Having a star cut allows for some 'wriggle' to get the laser nicely aligned.

One side folded. The tabs slot through then bend to lock in place. It is quite robust. I've made some slight adjustments to the design shown here to make it easier to insert the tabs(rounded corners etc.)

A 10 ml syringe fits nicely. If you want to use a needle, this can just pierce the cardboard directly. The design includes a hole for the tapered tip.

In action. The peg is keeping the laser pen on and there is a scrap of cardboard rolled up to hold it in the right place (just off the bottom of the photo).

Please do not look in this end. This was viewed with the camera, not my eye. There are windows both sides for aligning the drop and the laser. I will repeat 

DO NOT LOOK INTO THE LASER WITH YOUR REMAINING EYE.

And here is a picture - sludge from the bottom of the garden water butt. There is some diffraction, but movement of the particles can be clearly seen.  

This will happily project onto a wall or large piece of card. Remember to not look into the laser.

Acknowledgements: All cutting was carried out at the Dundee Makerspace.

It's the little things that grind you down.

I am a big fan of rubrics. They help to structure the assessments and direct the activity towards the learning outcome to be assessed.  We, as are many Higher Education Institutes, are big users of TurnItIn, the online assessment workbench. This has embedded rubrics and a suite of rubric management tools. We use rubrics to ensure clear marking and assessment guidelines, and consistency between different markers.

Unfortunately the rubric management tools are not the most user friendly, and many of my colleagues (and I) prefer to use a spreadsheet to edit and update out rubrics. Doing this online through the Rubric Manager is tedious and often frustrating when it times out and loses all your work.

TurnItIn has spreadsheet upload facilities but not download. The export from the rubric manager is in a JSON-like format and not particularly accessible for the lay person to amend.

I have a need to edit and share rubrics so I have put together a simple Python script that converts the .rbc file that you export from TurnItIn to a .csv that can be viewed/edited in Excel. Please note that this is not quite the format that is used for spreadsheet uploading, but amending the spreadsheet to the required format is quite straightforward.

The script is a Jupyter notebook (runnable on any of our uni computers through the Jupyter notebook app) and can be found here. Sufficient instructions are included. edit: We have noticed some issues with the TK library on some computers where the code hangs.

Jupyter notebooks can be run through the Anaconda Python distribution downloadable from http://anaconda.org

Let me know if you find it useful.

Oh no, not again!

It is the summer break in academia and we are either taking our well earned holidays or preparing material for the next session. A publisher has sent us a bunch of text books to look at - they hope we like them and would recommend them to the students for our courses.
Disclaimer: Whilst we are provided with the textbooks for no personal cost, there are no inducements to promote a particular book or publisher. Each book stands (or falls) on it's own merits.

I have an interest in improving the numeracy and basic data literacy amongst our students, so one of the titles appealed to me. It covered basic arithmetic though various applications in the lab. And one of those specific applications is Enzyme Kinetics.

The Michaelis-Menten equation is one of the foundations of kinetic analysis and the results can be represented in various ways. One of the classical method was developed by Lineweaver and Burk in 1934. It is superficially attractive in that the data is transformed from a hyperbolic curve to a straight line but suffers from reciprocal scaling of the errors. As it is a double reciprocal plot (plotting the inverse of one value against the inverse of the other) small errors in measurement at small values in the test tube become huge errors at huge values in the analysis. The plot has many uses for visualising the data though - identification of mechanism of inhibition is one of the classical examples given in introductory undergraduate lectures.

The problem comes when the plot is used for the determination of the constants (Vmax and Km) rather than their visualisation. Because of the reciprocal scaling of the errors, the estimates can be extremely unsafe (how much so we will get to in a moment). This was recognised by Lineweaver and Burk but this is typically left as an unquantified warning to students to be careful about their errors.

Since 1934 there have been other representations of the Michaelis-Menten equation that transform the data to linear forms. The Eadie-Hoftzee and Haynes-Woolf plots both provide linear transformations though interpretation and extraction of the parameters is marginally more complex than a simple reciprocal of the value of the intercepts on the x and y axes.

More recently the advent of computational methods of direct fitting to the equation removes the issue of error scaling completely and there is no real excuse for not using the direct method in any serious experimental study.

I wanted to see just how well each method would be in reconstructing correctly the correct parameters so constructed a small simulation. Taking a fixed Km and Vmax I can calculate the expected values for V at given substrate concentrations. Needless to say with ideal data every method retrieves the expected values perfectly.
In the lab, however, the world is not perfect.  I therfore add fixed errors corresponding to measurement errors as one might find on reading a typical UV/Vis spectrometer, and proportional errors such as might be expected from pipetting. I repeat each experiment 10 times and calculate mean/SD for the parameters. The calculations for the Lineweaver-Burk plot are performed with no error weighting, such as might be done in a typical undergrad classroom.

The results are clear. Lineweaver-Burk is giving extremely poor results - so much so that one would never consider it for any analysis.

The simulation can be found here (PDF) (R Source [latex]) if you want to work through it yourself. I recommend the RStudio IDE and you will need appropriate software for Sweave/Latex.

So back to the book. They provide a suitable treatment of the Michaelis-Menten equation but the only experimental treatment for determining the kinetic constants is the Lineweaver-Burk plot, with a cursory half sentence hinting that it may not be suitable. There is no consideration of alternative approaches. It is a pretty damning effort - the book is otherwise quite approachable and could otherwise be recommended, but serious errors where we have to correct the textbooks render it unacceptable. I'm not going to name the book or publisher, but will be contacting the authors directly.

Using Map data in R

Maps are useful things and R is a useful thing, so it would seem a natural useful thing to get them to work together.  However, maps are far more complex that we might think and dealing with them in R can be a bit interesting.

I started looking for map data to follow up a discussion on the correlation between votes for a far-right wing party (UKIP) and lack of exposure to immigrants. The recent UK elections have provided a wealth of data, and figures like these suggest that there may be a correlation.

Data for voting patterns in May 2013 can be obtained from the electoral commission at http://www.electoralcommission.org.uk/our-work/our-research/electoral-data
Data for the recent census can be retrieved from http://www.nomisweb.co.uk/census/2011/KS204EW which gives the country of birth at a local authority level.

The Electoral data is an Excel file that is nicely split and formatted in a way that makes it difficult to read directly into R. However, by spending a few minutes editing out the subheadings and copying the regional names to a new column, I have a tab-delimited file that gives me the electoral votes by local authority.

origins<-read.csv('bulk.csv',stringsAsFactors=F)
origins$geography <- toupper(origins$geography)
names(origins)[c(5:16)]<-c('All','UK','England','Northern.Ireland','Scotland','Wales','UK.Other','Ireland','EU','EU2001','EU.New','Other')

The Nomis data gives a good CSV file with appropriate headings, though these needed some rewriting to be more friendly in R. All the Local Authority names (origins$geography) have been capitalised to match the electoral data.

votes<-read.table('EPE-2014-Electoral-data.txt', header=T, sep='\t')
votes$Local.Authority<-toupper(votes$Local.Authority)
regions<-merge(votes,origins, by.x='Local.Authority', by.y='geography')

So this gives us the votes data and this can then be merged with the regional census data.
A plot is straightforward, as is a regression analysis.

UKIPvote<-100*regions$UKIP/regions[,4]
NonUK<-(regions$All.y-regions$UK)*100/regions$All.y
plot(UKIPvote~NonUK, xlim=c(0,60), ylim=c(0,60), xlab='% non-UK born',
       ylab='UKIP vote (%)', main='Anti-immigrant vote vs population composition')
abline(lm(UKIPvote~NonUK), col=2)

summary(lm(UKIPvote~NonUK))

Call:
lm(formula = UKIPvote ~ NonUK)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.0187  -3.6868  -0.1728   3.3297  14.5318 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 34.17057    0.67626   50.53   <2e-16 ***
NonUK       -0.49421    0.03117  -15.86   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.956 on 116 degrees of freedom
Multiple R-squared:  0.6843, Adjusted R-squared:  0.6816 
F-statistic: 251.4 on 1 and 116 DF,  p-value: < 0.00000000000000022

Looks good to me - fear of strangers leads to reactionary politics.
Now to recreate the maps. This is where it starts to get interesting.

Map data for the UK at local authority boundary level is not readily available. Datasets are at ward level or higher level regions. So I will need to read in the ward data and merge the local authorities so I can plot the electoral data. The frame of reference is latitude and longitude for the polygon coordinates.

ONS census ward (merged wards) data is available from http://www.ons.gov.uk/ons/guide-method/geography/products/census/spatial/2011/index.html. This downloads as a zip archive that unpacks into a folder containing multiple files with the same root filename. These are ARCGIS SHAPEfile format. Get the full resolution file as the generalised one fails to merge wards properly.

The R package maptools is used to read them. Whilst you are at it also install the libraries rgeos, rgdal and gpclib to process the data. Set your session to the directory containing the data files and read them in.

library(maptools)
library(rgeos)
library(rgdal)
library(gpclib)

GPClib is proprietary and licensed for non-commercial use. To enable it type

gpclibPermit()

wards <-readShapePoly("CMWD_2011_EW_BGC")

This gives the ward boundaries and these can be plotted readily using the shapefile objects plot command.

plot(wards) #this could take a while so try plotting a selection
plot(wards[1:20])

This datafile contains the local authority name. However, we need to merge the datafiles to local authority. That can be done by unifying individual polygons (dissolving in shapespeak) to anew boundary list where the id is the local authority name.

lamap <-unionShapePoly(wards, wards$LAD11NM)

But this gives an error.
So I process the data local authority by local authority until I find the error
for(x in levels(wards$LAD11NM)){
  try( {
    cat(x);
    lamap<-unionSpatialPolygons(wards[wards$LAD11NM==x,],    
        wards$LAD11NM[wards$LAD11NM==x]);
  })
}

This lists all the LA names as we go and the errors so it is clear to see which ones are providing problems. It does take a long time to process Wales - the coastline is quite challenging.

In total there were 5 local authorities who showed errors. I now have two options:

1. Fix the errors
2. Work round them by plotting everything except the problem ones and plotting them as wards.

I give up with 1.
With 2 I can use the R subset command to select the good wards and merge those. That leaves the bad wards to plot on top of them afterwards.

goodwards<-subset(wards, !(wards$LAD11NM== 'Bridgend'| wards$LAD11NM=='Gwynedd'| wards$LAD11NM=='Isle of Anglesey'| wards$LAD11NM=='Newcastle-under-Lyme'| wards$LAD11NM=='Oxford'))
badwards<-subset(wards, wards$LAD11NM== 'Bridgend'| wards$LAD11NM=='Gwynedd'| wards$LAD11NM=='Isle of Anglesey'| wards$LAD11NM=='Newcastle-under-Lyme'| wards$LAD11NM=='Oxford')

Now I can merge the good ones.

lamap<-unionSpatialPolygons(goodwards, goodwards$LAD11NM)

And plot

plot(lamap)
plot(badwards, add=T)

Now we'll work on colouring them by the data.

And I should update this for the EU referendum..

Moving House

This blog has moved from inside the Univoersity of Dundee VLE to outside as accessing it was proving to be, shall we say, suboptimal.  Now the whole world can see.

I will move most of the posts over in due course.