The Jacobi iterative method, a 169-year-old math strategy, may soon get a new lease on life.

When I was five years old I used to be sort of an attraction to relatives. One of my mother's brothers is an engineer, and he was amazed by my ability to do complex calculations by heart. But to me it was only amusing to observe their amazement for what I considered a triviality.

On one occasion - I remember it as it was yesterday - my uncle picked me up and while he kept me with his arms he asked me "Ok, let's see this. Tommaso, what is the square root of 5968?". Mind you, I do not remember the exact number; I only recall it was between 5000 and 7000. I watched up into the void for two seconds, and I replied "77.3". Uncle Ciccio put me down and ran for the pocket calculator - he did have one, although they were a real novelty those years.

Cell migration, which is involved in wound healing, cancer and tumor growth, and embryonic growth and development, has been a topic of interest to mathematicians and biologists for decades. 

It's often said that if we do make contact with Extra Terrestrials (ETs), e.g. detect a radio transmission from a distant galaxy through SETI, that maths would be one of the few things we would have in common with them. But - how similar would their maths actually be to ours?

Modern maths- with its many sizes of infinity and logical paradoxes, has lead to much debate and puzzlement over the last century or so. Would this be the same for ETs? And would it lead to many different ideas about maths and the philosophy of maths, as we have here, or would they find some other solution none of us have thought of?

What do you get when you mix theorists in computer science with evolutionary biologists? You get an algorithm to explain sex.

A fascinating mystery of evolution is how sexual recombination and natural selection produced the teeming diversity of life that exists today. The answer could lie in the game that genes play during sexual recombination, so computer scientists at the University of California, Berkeley, created an algorithm to describe the strategy used by these genes in this game.  

What did USC biomedical engineering assistant professor Megan McCain think when she first saw a real human heart, with all of those thin valves that have to open and close every second of our lives?

“Wow, there’s a lot of plaques of fat. I need to stop eating French fries.”

Nine years later, the “cardiac tissue engineer,” is trying to re-create the human heart on a chip.

I'd like to share some of the amazing range of rhythms you can find, linking music and maths, some discovered only in the last few years. These include: fibonacci gamelan patterns - highly structured yet the pattern of beats never repeats; the rhythm you get if two musicians each with perfectly steady rhythm play as out of time as possible; the rhythm of the famous "Cantor's set"; and the fairly recent discovery that many rhythms of music throughout the world are "Euclidean rhythms" - uneven beat patterns pleasing to the ear made with a surprisingly simple construction.

The modern world of Big Data increasingly requires knowledge of statistics and biologists are scrambling to master that along with all of the expertise needed to solve mysteries of nature.

A new statistical framework could help, according to a new paper, because it can substantially increase the power of genome-wide association studies (GWAS) to detect genetic influences on human disease.

Despite the proliferation of
genome-wide association studies
, the associations found so far have largely failed to account for the known effects of genes on complex disease — the problem of "missing heritability." Standard approaches also struggle to find combinations of multiple genes that affect disease risk in complex ways, known as genetic interactions.

What ties together Lissajous knots, the harmonic polyrhythms of Theremin's rhythmicon, and a way of adding sounds to Pendulum waves? And do they have healing properties? And what is the musical maths of sloth canon number sequences? 

It's been a decades-long experiment to throw darts at a dartboard and see if it outperforms the picks of experts. Darts win and lose at the same rate as the average of experts.

Some people clearly make money in financial markets but the old saying among brokers = 'we make money selling stocks, not buying them' - still applies.

Yet someone must know what they are doing. And backtesting is a good way to seem empirical. Example: Your financial advisor calls you up to suggest a new
investment scheme. Drawing on 20 years of data, he has set his
computer to work on this question: If you had invested according to
this scheme in the past, which portfolio would have been the best?
His model assembles thousands of such simulated portfolios and