Like the currency of many African countries, the notes and coins of Mozambique feature images of some of the majestic animals of Africa: the elephant, the rhino, the lion. But one Mozambican coin features a rather more obscure animal, the coelacanth.
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Just over a year and a few months ago I moved to a small town in the northern provinces of Mozambique to support my wife in her work in community development and water and sanitation. I went with a willingness to help out where I could, but with no real background in development, what was there to do for a research scientist in rural Africa?
The UK General Election result in May surprised me; I did not expect a Conservative majority, nor did I expect UKIP to get so many votes. But why was I surprised?
Being in Mozambique, most of my UK news comes through the few news websites I visit (BBC News and The Guardian), Twitter and Facebook. In the lead up to the election, nearly all of these sources told me to expect a multi-party coalition, a form of government that we haven’t had for many years, and perhaps a farewell to the usefulness of first-past-the-post in the 21st century. I became convinced; the more I read, the more I expected this result. From the fallout and vocal complaints made by many left-leaning people, it seems like they too expected this result, and the Conservative majority shocked them into protest. The Guardian lead with headlines like “David Cameron wins surprise majority in general election”.
I asked myself, “How could this be?” Disbelief that my views are only shared by 30% of the population, that the rest didn’t respond to the social justice narrative I had consumed in my daily online news updates. A friend shares her similar experience on the election night, and my Facebook was filled with much of the same.
A new paper in Science sheds light on these ‘surprise’ results, using US data from Facebook (links shared and ideological affiliation) to explain the phenomena that has lead to people like me being so unprepared for the results of a national election.
To many people, the phenomenon known scientifically as the circadian rhythm is bleeding obvious. We sleep in the night and are awake during the day, long-haul flights like those from the UK to Australia gives you jetlag, and night shifts are a right pain in the bum. Detailed explanations involving transcription-translation feedback loops and phase response curves don’t change those facts, they’re a fact of life when we live on a rotating world. But many scientists, myself included, are fascinated in the details, and some scientists, like Céline Vetter and colleagues at the Institute of Medical Psychology at Ludwig-Maximilian-University in Munich, use this eye for detail to find out how we might best cope with our biological timing in a 24-hour society.
You can say all you like about lions or elephants being the coolest animals in Africa. They are awesome for sure but they’re not quite the top. I suggest that that title goes to the chameleon and the zebra.
Why? They are just so unique: one changes colour as much as a fashion model, and the other has a coat pattern unlike any other mammal. And thanks to two papers released this year, we’ve begun to understand a bit more about their unique skin.
Would you look at that! The story of mosquitos, cheese and body odour has taken another leap into scientific respectability with a paper being published in the pinnacle of journals, Nature. “Evolution of mosquito preference for humans linked to an odorant receptor” by McBride and colleagues was published towards the end of last year and looks at how the domestic form of the mosquito Aedes aegypti has evolved striking evolutionary adaptations that help it to find, bite, and spread disease to humans.
Two papers caught my eye recently that have taken advantage of the proliferation of whole genome sequencing techniques in recent years. With prices of sequencing whole genomes coming down and down, biologists are having access to vast amounts of data. The 1000 Genomes Project was one of the first to collect the vast amounts of human genome data into a story that told of human population origins. The two papers that I saw recently extend this story. One provides data from nearly 1500 people throughout the entire world to trace the genetic legacies of Ghengis Khan and Alexander the Great. The other delves deeper into the dawn of homo sapiens in Africa, over 300 whole genomes and nearly 1500 genotypes – the African Genome Project.
It was with great pleasure that I ‘virtually’ attended the second Planet Earth Institute #ScienceAfrica Unconference on the 18th November. From following on Twitter it seemed like an excellent day with good discussions and presentations. Since last year’s Unconference I have moved to Mozambique. In 9 months I have seen extreme rural areas and big cities and experienced the lives of Mozambicans that live there. It is with this experience that I came to the second Unconference – with thoughts on some of the unique challenges that a campaign like #ScienceAfrica faces.
Continue reading “#ScienceAfrica Unconference 2014 – reaching the whole spectrum of society”
When I talk about my career and my interest in evolutionary biology, I often get asked, “How do you actually get new species?”. It’s not a stupid question; for people without a background in biology it really is very hard to imagine how the diversity of life we see today has formed from the types of ancient creatures we find in the fossil record. I normally look to my favourite fish, the Mexican blind cavefish, or point out the variety that can be produced in the single species of dog, or mention horizontal gene transfer to confer antibacterial resistance in bacteria, to show how even small changes can result in quite big differences in a species. Add to that vast amounts of time and it becomes a little easier to imagine the “hedge” of life taking shape.
Publication metrics and success on the academic job market
David van Dijk, Ohad Manor, Lucas B. Carey
Current Biology 2014 Vol 24 No 11 R516
I caught this paper in a TOC email from Current Biology. “Predict who becomes a PI” they say. “Hmm,” I think, “This should be interesting.”
The abstract sets out the problem simply: “so far there has been no quantitative analysis of who becomes a principal investigator (PI). We here use a machine-learning approach to predict who becomes a PI, based on data from over 25,000 scientists in PubMed. We show that success in academia is predictable.”
Continue reading “Predicting your academic career”