This year marks the 50th anniversary of cisplatin’s accidental discovery as an anti-cancer drug. Despite its horrible side effects, and the ability of cancers to become resistant to it, the drug remains as relevant now as it was when it first reached the market.

And the good news is that the drug can, and is, being made better. New formulations are being designed to make it more effective and less toxic.

How should scientists interpret their data?

Emerging from their labs after days, weeks, months, even years spent measuring and recording, how do researchers draw conclusions about the results of their experiments?

Since they were pioneered by Robert Hooke 350 years ago, microscopes have been extending our vision. In the 21st century, scanning electron microscopy (SEM) and confocal microscopy, which uses a pinhole to remove out-of-focus light and allows 3D structures to be built from multiple images, have pushed the boundaries of resolution.

When there’s a report in the news about the latest science on climate change, the source is very often the United Nations Intergovernmental Panel on Climate Change.

This body plays a very important role in global climate change policy around the world. Its reports, five of which have been published since 1990, enjoy a degree of credibility that renders them influential for public opinion. And more important, the reports are accepted as the definitive source by international negotiators working under the United Nations Framework Convention on Climate Change (UNFCCC).

When I was an awkward teenager who did not fit in with the other kids, the logical Mr. Spock was a character I could really identify with. At this time, I did not know why I related to Spock because when I was a teenager, I did not know that my thinking process was different from that of most other people. I assumed that my logical picture-based thinking was the way that everybody thought.

Amid all the dire warnings that machines run by artificial intelligence (AI) will one day take over from humans we need to think more about how we program them in the first place.

The technology may be too far off to seriously entertain these worries – for now – but much of the distrust surrounding AI arises from misunderstandings in what it means to say a machine is “thinking”.

One of the current aims of AI research is to design machines, algorithms, input/output processes or mathematical functions that can mimic human thinking as much as possible.