Lifeboat Foundation

During development, cells seem to decode their fate through optimal information processing, which could hint at a more general principle of life.

Within a week, many world leaders went from downplaying the seriousness of coronavirus to declaring a state of emergency. Even the most efficacious of nations seem to be simultaneously confused and exasperated, with delayed responses revealing incompetence and inefficiency the world over.

So this begs the question: why is it so difficult for us to comprehend the scale of what an unmitigated global pandemic could do? The answer likely relates to how we process abstract concepts like exponential growth. Part of the reason we’ve struggled so much applying basic math to our practical environment is because humans think linearly. But like much of technology, biological systems such as viruses can grow exponentially.

As we scramble to contain and fight the pandemic, we’ve turned to technology as our saving grace. In doing so, we’ve effectively hit a “fast-forward” button on many tech trends that were already in place. From remote work and virtual events to virus-monitoring big data, technologies that were perhaps only familiar to a fringe tech community are now entering center stage—and as tends to be the case with wartime responses, these changes are likely here to stay.

A landmark proof in computer science has also solved an important problem called the Connes embedding conjecture. Mathematicians are working to understand it.

The laws of physics imply that the passage of time is an illusion. To avoid this conclusion, we might have to rethink the reality of infinitely precise numbers.

Mathematicians from the California Institute of Technology have solved an old problem related to a mathematical process called a random walk.

The team, which also worked with a colleague from Israel’s Ben-Gurion University, solved the problem in a rush after having a realization one evening. Lead author Omer Tamuz studies both economics and mathematics, using probability theory and ergodic theory as the link—a progressive and blended approach that this year’s Abel Prize-winning mathematicians helped to trailblaze.

The Royal Society is to create a network of disease modelling groups amid academic concern about the nation’s reliance on a single group of epidemiologists at Imperial College London whose predictions have dominated government policy, including the current lockdown.

It is to bring in modelling experts from fields as diverse as banking, astrophysics and the Met Office to build new mathematical representations of how the coronavirus epidemic is likely to spread across the UK — and how the lockdown can be ended.

The first public signs of academic tensions over Imperial’s domination of the debate came when Sunetra Gupta, professor of theoretical epidemiology at Oxford University, published a paper suggesting that some of Imperial’s key assumptions could be wrong.

Governments across the world are relying on mathematical projections to help guide decisions in this pandemic. Computer simulations account for only a fraction of the data analyses that modelling teams have performed in the crisis, Ferguson notes, but they are an increasingly important part of policymaking. But, as he and other modellers warn, much information about how SARS-CoV-2 spreads is still unknown and must be estimated or assumed — and that limits the precision of forecasts. An earlier version of the Imperial model, for instance, estimated that SARS-CoV-2 would be about as severe as influenza in necessitating the hospitalization of those infected. That turned out to be incorrect.

How epidemiologists rushed to model the coronavirus pandemic.

Using the same technology that allows high-frequency signals to travel on regular phone lines, researchers tested sending extremely high-frequency, 200 GHz signals through a pair of copper wires. The result is a link that can move data at rates of terabits per second, significantly faster than currently available channels.

While the technology to disentangle multiple, parallel signals moving through a channel already exists, thanks to signal processing methods developed by John Cioffi, the inventor of digital subscriber lines, or DSL, questions remained related to the effectiveness of implementing these ideas at higher frequencies.

To test the transmission of data at higher frequencies, authors of a paper published this week in Applied Physics Letters used experimental measurements and mathematical modeling to characterize the input and output signals in a waveguide.

Our body’s ability to detect disease, foreign material, and the location of food sources and toxins is all determined by a cocktail of chemicals that surround our cells, as well as our cells’ ability to ‘read’ these chemicals. Cells are highly sensitive. In fact, our immune system can be triggered by the presence of just one foreign molecule or ion. Yet researchers don’t know how cells achieve this level of sensitivity.

Now, scientists at the Biological Physics Theory Unit at Okinawa Institute of Science and Technology Graduate University (OIST) and collaborators at City University of New York have created a simple model that is providing some answers. They have used this model to determine which techniques a cell might employ to increase its sensitivity in different circumstances, shedding light on how the biochemical networks in our bodies operate.

“This model takes a complex biological system and abstracts it into a simple, understandable mathematical framework,” said Dr. Vudtiwat Ngampruetikorn, former postdoctoral researcher at OIST and the first author of the research paper, which was published in Nature Communications. “We can use it to tease apart how cells might choose to spend their energy budget, depending on the world around them and other cells they might be talking to.”

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