A Year in The Life of a Computer

What does a year in the life of a computer look like?

Well, something like the map below! This is a map every bit of of mouse movement, every mouse click and every keyboard press I have done on my home and work computer over every day of a whole year.

2013-2014 [click for a bigger view]

To make it I wrote a little python script using pyHook to grab inputs in Windows, which I compiled to an .exe using py2exe. I set this up so that it starts recording the mouse movement, clicks, and keyboard presses after I log into my home or work computer. After 2 years it had collected nearly 10 Gb of data! This was far too much to look through by hand, so I wrote a second set of scripts to plot it to an image.

So what does it all mean? Well the map breaks down a bit like a normal calendar, with days of the week running from the top to the bottom of the map, and successive weeks running from left to right. The years and months are marked at the top of the map.

Within each day my computer activity is broken down by time. Time runs from the top to the bottom of each day, from midnight to midnight. Coloured speckles on the dark background indicate computer activity. It is easy to see that I use computers a lot, with a chunk of time from around midnight to 7 am when I am normally asleep, then smatterings of activity from around 8 am to midnight when I am at work or awake at home.

Different types of computer activity are shown in different colours.

The structure within each of the colours also contains information; distance in the horizontal direction corresponds to horizontal mouse position across my two screens (for mouse movement) which mouse button was clicked (for mouse clicks) and which key was pressed (for keyboard presses).

2012-2013 [click for a bigger view]

In these maps of usage some interesting structures jump out; you can spot the type of work I was doing with my computer based on the type of mouse and keyboard activity:

This is usage on a day where I was writing my PhD thesis. The keyboard (cyan) has loads of activity, while the mouse (magenta) did relatively little.

This is a day where I was mainly using Blender for 3D graphics. The mouse (magenta) has huge levels of activity, centred on just the left hand screen). The keyboard is hardly active except for the control and shift keys, which light up as a single column of bright cyan pixels.

It is quite scary how much information can be gleaned from these maps of computer activity. Without knowing which programs were open or which keyboard keys were being pressed it is still easy to work out where I have been, when I have been working, and the kind of things I was doing on my computer. Similar data can be collected remotely; particularly if an internet company tracks when and where you use the internet.

Stop for a second and think about the companies you interact with, and the data mining they can do. Think how much they can learn about you and your habits; Google and the websites you visit, your phone company and when and who you text and call, the supermarket you shop in and what you buy. These companies can work out what you are interested in, what you like and dislike, when you are awake and when you are asleep. This is big data, and it is valuable and it is powerful. Big data is how Target knew a man's teenage daughter was pregnant before he did!

Software used:

pyHook and py2exe: Data logging.

ImageJ: Data plotting.

Inkscape: Plot annotation.

Tree of Plants

Everyone knows what plants are like; they have leaves and roots, flowers and seeds. Or do they? All of these classic features of plants are actually relatively recent developments in plant evolution. Conifers don't have flowers, ferns don't have seeds or flowers and moss doesn't have leaves, roots, seeds or flowers! Leaves, roots, flowers and seeds are all features that evolved as plants adapted, starting at something like seaweed, to life on the land.

This term's issue of Phenotype has a bit of a focus on plants, and my research comic for this issue focuses on how plants evolved and adapted to land. You can download a pdf of this feature here, the full issue for the summer (Trinity) term will be available soon here.

While I was making this I started reconsidering just what the plant life cycle looks like, as a classic school education about how plants reproduce isn't very accurate! The classic teaching is that the pollen produced by a flower is like sperm in mammals (and humans), and the ovum in the flower is like the egg in mammals. In fact pollen and the developing seed are more like small haploid multicellular organisms, gametophytes, that used to be free living. If you go back through evolutionary time towards ferns then the gametophyte is a truly independent multicellular organism. Go back further still and the bryophytes spend most of their time as the gametophyte.

If you imagine the same evolutionary history for humans then it is easy to see how different this life cycle is to animals; if the ancestors of humans had a life cycle similar to ferns then, roughly speaking, ovaries and testicles would be free-living organisms that sprout a full grown human once fertilisation successfully occurs. I can't help but think that would have been a little strange!

Software used:
Autodesk Sketchbook Pro: Drawing the cells.
Inkscape: Page layout.

Cells and Worms - 2. The Shirt

Last post I talked about how seeing how many worms overlap if you drop them on a patch of ground, how (somehow) this was vaguely related to my scientific research, and that the simulation of this process even generates quite nice pictures. If you thought that was geeky, then this takes geekyness to a whole new level!

Part of my research has been into the shapes of trypanosome parasites. Trypanosomes that cause disease in people are fairly widely known (you might have heard of sleeping sickness, Chagas disease, or leishmaniasis) but trypanosomes don't just infect people. Trypanosome species have also been found infecting animals from sharks to penguins, crocodiles to elephants. There is even one species named after Steve Irwin (the crocodile hunter) that infects koalas!

A scanning electron microscope image of Trypanosoma brucei, the trypanosome which causes sleeping sickness.

In short, I did some research to test whether there were particular characteristic shapes of trypanosomes (length, width, etc.) that look like they might help the parasite survive in the bloodstream of different host animals. I made a big database of properties of trypanosome shape and, using the scripts I made to draw nicely tesselated trypanosome shapes I talked about in the last post, I put together a compelling summary of just how varied trypanosome shapes from different host species are are:

The science behind this picture suggests some interesting adaptation to help the parasites swim within their host bloodstream, but that's enough about the science. To me this pattern was just begging to be on a shirt, an abstract design with a biological twist!

Spoonflower is a fantastic online service where you can order customised fabric, wallpaper and other prints. So that is exactly what I did, and after some sewing (that I didn't do myself) I am now the proud owner of the world's only 100% scientifically accurate trypanosome shirt, featuring 27 different trypanosome species.

Scientists always say that research can take you down unexpected paths. This path from wriggly worms, through an image generating script, through research into trypanosome shape, to the world's only trypanosome shirt was quite an unexpected one!

Software used:
ImageJ: Automated trypanosome drawing.
Inkscape: Conversion to vector graphics for printing.

Cells and Worms - 1. The Theory

If you scatter 100 worms on a patch of soil 1 meter by 1 meter how many worms will fall on top of another worm? This might seem like a really pointless question, but it is surprisingly relevant to biological research using microscopes. It's also a surprisingly hard question to answer because worms are very wriggly! However, even this dry, theoretical, research problem provides the tools for making fun illustrations...

My work involves a lot of automated image analysis; taking a picture from a microscope and automatically analysing it to extract scientific data. To make sure an automated analysis is reliable you have to think about all the likely problems that might turn up, and with cells and microscopes a common problem is when two cells are lying on top of each other. The problems this causes are easy to imagine; if there are two cells with one nucleus lying on top of each other then it might look like one cell with two nuclei.

For some types of cells it is quite easy to work out how likely two are to touch or lie partly on top of each other when they are scattered randomly over a microscope slide. An example of an easy case is where all cells are circular and the same size; the approximate calculation is quite simple. Unfortunately the cells I work on are more worm-like in shape, about 17 microns long and 2 wide... if you scatter these cells over a slide how many will end up touching?

To work out the answer simulation is vital; the maths is just too complicated to do it analytically. A simulation of worm-like shapes proved to be quite simple:

1. Pick a random starting point, direction and curvature.
2. Start drawing a curved line from that point.
3. Occasionally re-randomise the curvature.
4. Stop once you have reached the length of the cell.
5. Draw the profile of the cell shape along that curve.

Following these simple rules and tweaking the parameters (e.g. the minimum and maximum curvature, frequency of randomising curvature, etc.) gives a simple algorithm for drawing a worm-like shape. With a bit of tweaking it could draw cells that look like trypanosomes. Using this drawing tool it was possible to measure the chance of a cell touching or lying on top of another cell already on the microscope slide. Just repeat the drawing process thousands of times and detect whether the newly drawn cell intersects with any previously drawn ones. Problem solved.

This process gave me the answer I needed, but it also provided a tool for drawing trypanosome-like shapes. Better than that, it was easy to adapt it to make sure no two cells overlapped and they fitted neatly together over the image... And just like that a dry, theoretical, research problem turned into a beautiful image:

This was also easy to adapt to other worm-like shapes, like earthworms:

Software used:

ImageJ

Cheeky

Human cheek cells are a classic subject of school microscopy. It is easy to collect some by gently scraping the inside of your cheek. This is a high resolution phase contrast image of one of my cheek cells, put together using focus stacking of a 4 by 4 montage of 57 focus slices using one of my ImageJ macros. The detail of the nuclear structure, the granular contents of the cytoplasm and the structured surface of the cell really jump out.

This cell is quite large for mammalian cells, about 75 μm across, and around 10 times larger than the single cell Leishmania parasite I currently do much of my research on. If you have sharp eyesight you can even see human cheek cells by eye (although only just) when they are spread on a slide.

Like most mammalian cells, cheek cells are essentially transparent. If you use a microscope in the most basic way, essentially as a giant magnifying glass, shining light straight through the sample towards your eye, you see something like this:

Bright field micrograph of a human cheek cell.

This picture has even had the contrast artificially enhanced. Practically it is tough to even find the cells on the slide and get them in focus!

For many years the best alternative was oblique or dark field microscopy. Here you deliberately avoid shining light straight through the sample, and instead make sure that only light scattered by structures in the sample can collected by the objective lens and get up to your eye.

Dark field micrograph of a human cheek cell.

Images by dark field microscopy can be hard to interpret, and are typically limited to fairly low resolution.

More complex methods based on interference of light travelling through the sample were developed in the 20th Century. These methods, phase contrast and differential interference contrast, were a revolution. They allowed completely new approaches for looking at the biology of cells, particularly live cells and dynamic processes like cell division. They were such a revolution that the inventor of phase contrast microscopy, Frits Zernik, was awarded the Nobel prize in Physics in 1953 for this work.

Phase contrast micrograph of a human cheek cell.

DIC micrograph of a human cheek cell.

It was not until the development of the famous green fluorescence protein, for fluorescence microscopy in live cells in the 1990s, that there was another discovery which improved the capacity for live cell microscopy to the same extent as phase contrast and DIC.

Software used:
ImageJ