While messing around with ideas for my talk at the Cheltenham Science Festival, I hit on the idea of combining a piece of acrylic that only passes infrared light, with a cheap Fresnel magnifying lens. Completely by accident, I managed to get the rather pretty effect to the right. You can see that the paper is burning where there is no visible light, in the infrared portion of the spectrum. Even better, it’s quite a cheap effect to achieve.
Gratifyingly, my technique correctly classifies drawings like this as pictures, not maps.
How effectively can a computer distinguish pictures or drawings of organisms from maps of their distribution? Based on my previous thoughts on recognising maps, a simple statistical technique allows a computer to correctly identify 99% of maps from within a training set of 1210 images (including 272 maps). Pleasingly, this classification has only a 0.5% false positive rate.
Pretty good, but in the case of images submitted to the Encyclopaedia of Life, we can do better. If we make a guess as to the original format of the image, and include this into the model, we can correctly separate all 272 maps from the 938 pictures and drawings in my particular dataset. If you want to try it out, the dataset is here, and the R code to perform the classification is near the end of this post.
Continue reading
How do you get a computer to distinguish pictures like the fallow deer at top from distribution maps (bottom)?
I’ve been writing some code to download freely usable images on the internet for large numbers of organisms – for example, for all species of mammal. The Encyclopedia of Life has done a lot of the hard work already – collecting images from Wikimedia Commons, Flickr, etc., encouraging experts to tag them as trusted, and providing an API for retrieving all the relevant data.
One problem is that a small percentage of the automatically harvested pictures are not pictures of the organism, but maps of its distribution, as seen in the lower picture on the right. Is there a way to automatically identify these as maps, or at least to flag up that they might need checking? Continue reading
Plot from wikipedia of male and female life expectancy at birth from CIA factbook data. The apparent 3D volumes of the bubbles are linearly proportional to their population
Last month I recorded an update to my Radio 4 programme on why women live longer than men (which you can still listen to online). A cut down version was broadcast on the BBC World Service. For this we wanted to include statistics for different countries. I was busy investigating official UN sources for these data, when I noticed that morning’s papers were reporting a relevant set of studies published that day in the Lancet (Das, Samarasekera, 2013). A couple of thoughts followed. Firstly, these new data indicate that in 2010, men had a higher life expectancy than women in only 2 countries: Afghanistan and Jordan (Wang, Dwyer-Lindgren, Lofgren, Rajaratnam, Marcus, Levin-Rector, Levitz, Lopez, Murray, 2013). That’s in contrast to the UN data, which finds this effect in a small number of African countries too – suggesting the UN data could be inaccurate in these cases. Secondly, the data are given with uncertainty intervals for the first time, which got me thinking about the best way to illustrate the data. In particular, I’ve been thinking about how to improve the plot above (taken from the wikipedia page listing the life expectancy of different countries).
There’s a fair bit of of publicly available data that can be used to answer questions about global trends. Previously, I’ve used data from the UN and the UK (often the Office for National Statistics), but I’ve only just discovered the commendable data service being provided by the British paper “The Guardian“. They encourage public access to (and visualization of) the data on which their articles are based. This is done by tidying up data and putting it in publicly accessible Google spreadsheets.
Plot reproducible from free online data using R
Dizygotic (non-identical) twin sisters, image from Wikimedia Commons, ©2006 Dustin M. Ramsey
In the light of the recent hospital admission of the Duchess of Cambridge for an condition loosely associated with female babies and multiple births, I’ve been asked by the Radio 4 programme “More or Less” to calculate the probability that she is pregnant with more than one embryo. I’m somewhat reluctant to contribute to what is already a topic of rampant media speculation, and the attendant intrusive journalism that often plagues issues like this (which, after I had written this post, led to a sad and particularly tragic outcome). Nevertheless, few media articles seem to give links to solid data sources, and some even give rather misleading information, so I’ve overcome my reluctance in order to put some solid statistical facts into the public domain. Simply put, compared to the average, the probability of a mother having twins given that she has this condition is not quite doubled. However, it’s still likely to be a very low number: something like an increase from about 1.5% to a 2.4% chance. For the gory details, read on. Continue reading
We’ll probably never see this plant again. Image from Wood (2012)
Many of the rarest and most endangered species we know of are plants. The sad story of Hibiscedelphus woodii, which I’ve only just read about, is not atypical. Of the 4 last known specimens, on a cliff in Hawaii,
three individuals of H. woodii were apparently crushed by a large fallen boulder and died between 1995 and 1998. on 17 August 2011, the last remaining H. woodii was observed dead. (Wood, 2012)
The unfortunate (but appropriate) choice of this as a endangered species at a recent event for the Society of Biology, prompted me to dig out the following, which I wrote in 2009 to answer the question “What is the world’s rarest plant”: Continue reading
I’m quite pleased that the Society of Biology has asked me to speak at the opening debate to promote Biology Week. Although the proliferation of commemorative days, weeks, and months risks stultifying many people, this seems an event worth supporting. The title is “Do we need pandas? Choosing which species to save”, and I thought I’d document my thoughts here.
Just how many words can you make using the element symbols. And what if you aren’t allowed to use the symbols more than once? An obvious job for a computer, especially if you have a text file of English words hanging around. So this morning I hunted for a decent list of chemical elements and symbols, and found 38163 valid words: 15% of a large English vocabulary… Continue reading
I’m quite pleased that the last “More or Less” question that’s been selected for me happens to be one of my pet topics (I’ve also written a cut-down reply for the magazine section of the BBC news website). The original email was rather long (as is, inevitably, this post), but essentially, the question is this
A listener, Jonathan Trigell has been looking at the Bible. In particular he’s interested in Jesus’ lineage. The Bible says Jesus was in the line of King David – which is important because the Messiah was expected to be of royal descent.
But it also has this to say about David’s son, Solomon
King Solomon, however, loved many foreign women besides Pharaoh’s daughter—Moabites, Ammonites, Edomites, Sidonians and Hittites. They were from nations about which the Lord had told the Israelites, “You must not intermarry with them, because they will surely turn your hearts after their gods.” Nevertheless, Solomon held fast to them in love. He had seven hundred wives of royal birth and three hundred concubines, and his wives led him astray.
Jonathan’s question is this: if Solomon had about a thousand wives and mistresses, assuming he fathered children with many of them, wouldn’t it be the case that by the time of Jesus – many generations later – pretty much everyone in Israel could claim to be a descendant of King David?
It might sound like a question for a pub quiz, rather than a science journal. But the fundamental mathematics behind family trees forms the basis of evolution and genetics, and so cuts to the heart of modern biology.