Computers Math

Cells go through a life cycle that includes growing to the right size, being equipped to perform its functions, and finally dividing into two new cells. The cell cycle is critical because it ensures the perpetuation of the cell population and by extension of the greater structure they are a part of — for example a tissue in the body.
The cell cycle itself is tightly regulated by checkpoints, which prevent errors like mutations or DNA damage from being passed onto the next generation of cells. Each checkpoint acts as a kind of quality-control monitor (a biological “checklist”) that ensures the order, integrity, and fidelity of the cell cycle. But checkpoints themselves often fail or are overridden after a prolonged stop of the cell cycle. If this happens in the human body, the result could be unregulated cell growth and division, which is what happens in cancer.
“Checkpoints monitor cells or whole organisms and can stop either the cell cycle or the organism’s development when they detect problems,” says Sahand Jamal Rahi at EPFL’s School of Basic Sciences. “But if cells or organisms are stuck with an error for a very long time, in many cases, they just continue dividing or growing; they don’t stop forever. There is a real risk of dying if checkpoints do not stop at all, but also waiting forever is effectively equivalent to dying.”
The math of checkpoint override
The question is then, how does the cell balance risk and speed when dividing? Although critical, checkpoint override is not very well understood, neither theoretically nor experimentally. But in a new paper, Rahi and his colleagues put forward the first mathematical theory to describe the process of checkpoint override. “Many organisms have to predict what’s going to happen,” he says. “You have a problem and you have to assess how bad that problem could be because the consequences are not certain. You could survive this or you might not survive this. So, the cell makes a bet either way. And in this study, we analyze the odds of that bet.”
For a real-life model organism, the researchers looked at the budding yeast Saccharomyces cerevisiae, which has been used in winemaking, baking and brewing for centuries. “There are systems that monitor organisms, and among these systems, possibly the best studied is the DNA damage checkpoint in yeast,” says Rahi. “So, we thought, let’s look at that and see whether we can make sense of checkpoint overrides. We started with a mathematical analysis behind which was a very simple question: what if these organisms are balancing risk and speed because they have to predict the future?”
The risk-speed tradeoff More

An algorithm that can speed up by years the ability to identify from among thousands of possibilities, two or more drugs that work synergistically against a problem like cancer or a viral infection has been developed by bioinformatics experts.
The new algorithm enables investigators to use large existing databases with information about how one cancer drug changed the gene expression of a particular breast cancer cell line, and how well it killed the cell, then mathematically combine those results with the impact of another drug to see if they could work better together, says Dr. Richard McIndoe, director of the Center for Biotechnology and Genomic Medicine at the Medical College of Georgia.
While the algorithm does not immediately make available the kind of information that would set a clinical trial in motion, it does speed up the path to the trials, he says.
“The idea is we ultimately want to find these synergistic drug combinations that will hopefully help patients with cancer,” McIndoe says. “For researchers it becomes a particularly faster way to find those synergistic combinations, without having to screen one drug at a time, which is really not feasible.”
Drug combination therapies can improve drug efficiency, reduce drug dosage (and related toxicity) and overcome drug resistance in cancer treatments,” the investigators write in the journal PLOS ONE, and is becoming an important tool in cancer treatment.
“It’s not uncommon for the cancer to become resistant to chemotherapy drugs so one of the ways that clinicians try to get around that is using combinations, two chemotherapy drugs together,” McIndoe says. “The likelihood that you will develop resistance to both of them simultaneously is lower than if you had just one.”
But given the number of drugs and drug combinations available, there are not efficient, effective ways to identify the best combinations, the investigators say. More

Researchers have used a widespread species of blue-green algae to power a microprocessor continuously for a year — and counting — using nothing but ambient light and water. Their system has potential as a reliable and renewable way to power small devices.
The system, comparable in size to an AA battery, contains a type of non-toxic algae called Synechocystis that naturally harvests energy from the sun through photosynthesis. The tiny electrical current this generates then interacts with an aluminium electrode and is used to power a microprocessor.
The system is made of common, inexpensive and largely recyclable materials. This means it could easily be replicated hundreds of thousands of times to power large numbers of small devices as part of the Internet of Things. The researchers say it is likely to be most useful in off-grid situations or remote locations, where small amounts of power can be very beneficial.
“The growing Internet of Things needs an increasing amount of power, and we think this will have to come from systems that can generate energy, rather than simply store it like batteries,” said Professor Christopher Howe in the University of Cambridge’s Department of Biochemistry, joint senior author of the paper.
He added: “Our photosynthetic device doesn’t run down the way a battery does because it’s continually using light as the energy source.”
In the experiment, the device was used to power an Arm Cortex M0+, which is a microprocessor used widely in Internet of Things devices. It operated in a domestic environment and semi-outdoor conditions under natural light and associated temperature fluctuations, and after six months of continuous power production the results were submitted for publication.
The study is published today in the journal Energy & Environmental Science.
“We were impressed by how consistently the system worked over a long period of time — we thought it might stop after a few weeks but it just kept going,” said Dr Paolo Bombelli in the University of Cambridge’s Department of Biochemistry, first author of the paper.
The algae does not need feeding, because it creates its own food as it photosynthesises. And despite the fact that photosynthesis requires light, the device can even continue producing power during periods of darkness. The researchers think this is because the algae processes some of its food when there’s no light, and this continues to generate an electrical current.
The Internet of Things is a vast and growing network of electronic devices — each using only a small amount of power — that collect and share real-time data via the internet. Using low-cost computer chips and wireless networks, many billions of devices are part of this network — from smartwatches to temperature sensors in power stations. This figure is expected to grow to one trillion devices by 2035, requiring a vast number of portable energy sources.
The researchers say that powering trillions of Internet of Things devices using lithium-ion batteries would be impractical: it would need three times more lithium than is produced across the world annually. And traditional photovoltaic devices are made using hazardous materials that have adverse environmental effects.
The work was a collaboration between the University of Cambridge and Arm, a company leading the design of microprocessors. Arm Research developed the ultra-efficient Arm Cortex M0+ testchip, built the board, and set up the data-collection cloud interface presented in the experiments.
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The double-slit experiment is the most famous and probably the most important experiment in quantum physics: individual particles are shot at a wall with two openings, behind which a detector measures where the particles arrive. This shows that the particles do not move along a very specific path, as is known from classical objects, but along several paths simultaneously: each individual particle passes through both the left and the right opening.
Normally, however, this can only be proven by carrying out the experiment over and over again and evaluating the results of many particle detections at the end. At TU Wien, it has now been possible to develop a new variant of such a two-way interference experiment that can correct this flaw: A single neutron is measured at a specific position — and due to the sophisticated measurement setup, this single measurement proofs already that the particle moved along two different paths at the same time. It is even possible to determine the ratio in which the neutron was distributed between the two paths. Thus, the phenomenon of quantum superposition can be proven without having to resort to statistical arguments. The results have now been published in the journal “Physical Review Research.”
The double-slit experiment
“In the classical double-slit experiment, an interference pattern is created behind the double slit,” explains Stephan Sponar from the Atomic Institute at TU Wien. “The particles move as a wave through both openings at the same time, and the two partial waves then interfere with each other. In some places they reinforce each other, in other places they cancel each other out.”
The probability of measuring the particle behind the double slit at a very specific location depends on this interference pattern: where the quantum wave is amplified, the probability of measuring the particle is high. Where the quantum wave is cancelled out, the probability is low. Of course, this wave distribution cannot be seen by looking at a single particle. Only when the experiment is repeated many times does the wave pattern become increasingly recognisable point by point and particle by particle.
“So, the behaviour of individual particles is explained based on results that only become visible through the statistical investigation of many particles,” says Holger Hofmann from Hiroshima University, who developed the theory behind the experiment. “Of course, this is not entirely satisfactory. We have therefore considered how the phenomenon of two-way interference can be proven based on the detection of a single particle.”
Rotating the neutron More

A long-standing quest for science and technology has been to develop electronics and information processing that operate near the fastest timescales allowed by the laws of nature.
A promising way to achieve this goal involves using laser light to guide the motion of electrons in matter, and then using this control to develop electronic circuit elements — a concept known as lightwave electronics.
Remarkably, lasers currently allow us to generate bursts of electricity on femtosecond timescales — that is, in a millionth of a billionth of a second. Yet our ability to process information in these ultrafast timescales has remained elusive.
Now, researchers at the University of Rochester and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) have made a decisive step in this direction by demonstrating a logic gate — the building block of computation and information processing — that operates at femtosecond timescales. The feat, reported in the journal Nature, was accomplished by harnessing and independently controlling, for the first time, the real and virtual charge carriers that compose these ultrafast bursts of electricity.
The researchers’ advances have opened the door to information processing at the petahertz limit, where one quadrillion computational operations can be processed per second. That is almost a million times faster than today’s computers operating with gigahertz clock rates, where 1 petahertz is 1 million gigahertz.
“This is a great example of how fundamental science can lead to new technologies,” says Ignacio Franco, an associate professor of chemistry and physics at Rochester who, in collaboration with doctoral student Antonio José Garzón-Ramírez ’21 (PhD), performed the theoretical studies that lead to this discovery. More

Our electronics can no longer shrink and are on the verge of overheating. But in a new discovery from the University of Copenhagen, researchers have uncovered a fundamental property of magnetism, which may become relevant for the development of a new generation of more powerful and less hot computers.
The ongoing miniaturization of components for computers which have electrons as their vehicles for information transfer has become challenged. Instead, it could be possible to use magnetism and thereby keep up the development of both cheaper and more powerful computers. This is one of the perspectives as scientists from the Niels Bohr Institute (NBI), University of Copenhagen, today publish a new discovery in the journal Nature Communications.
“The function of a computer involves sending electric current through a microchip. While the amount is tiny, the current will not only transport information but also contribute to heating up the chip. When you have a huge number of components tightly packed, the heat becomes a problem. This is one of the reasons why we have reached the limit for how much you can shrink the components. A computer based on magnetism would avoid the problem of overheating,” says Professor Kim Lefmann, Condensed Matter Physics, NBI.
“Our discovery is not a direct recipe for making a computer based on magnetism. Rather we have disclosed a fundamental magnetic property which you need to control, if you want to design a such computer.”
Quantum mechanics halt acceleration
To grasp the discovery, one needs to know that magnetic materials are not necessarily uniformly oriented. In other words, areas with magnetic north and south poles may exist side by side. These areas are termed domains, and the border between a north and south pole domain is the domain wall. While the domain wall is not a physical object it nevertheless has several particle-like properties. Thereby, it is an example of what physicists refer to as quasi-particles, meaning virtual phenomena which resemble particles. More

A group of researchers from Pisa, Jyväskylä, San Sebastian and MIT have demonstrated how a heterostructure consisting of superconductors and magnets can be used to create uni-directional current like that found in semiconductor diodes.
These novel superconductor diodes, however, operate at much lower temperatures than their semiconductor counterparts and are therefore useful in quantum technologies.
Electronics for quantum technology
Most of our everyday electronic appliances, such as radios, logic components or solar panels, rely on diodes where current can flow primarily in one direction. Such diodes rely on the electronic properties of semiconductor systems which cease to work at the ultralow sub-Kelvin temperatures required in tomorrow’s quantum technology. Superconductors are metals whose electrical resistivity is usually zero but, when contacted with other metals, can exhibit high contact resistance.
This can be understood from the energy gap, which indicates a forbidden region for electronic excitations that form in superconductors. It resembles the energy gap in semiconductors but is typically much smaller. While the presence of such a gap has been known for decades, the diode-like feature has not been previously observed, because it requires breaking the usually robust symmetry of the contact’s current-voltage characteristics.
The new work demonstrates how this symmetry can be broken with the help of a ferromagnetic insulator suitably placed in the junction. Since a big part of today’s research on quantum technologies is based on superconducting materials operating at ultralow temperatures, this innovation is readily available for them.
Power of collaboration
The research finding was made as part of the SUPERTED project, which is being funded under the EU’s Future and Emerging Technologies (FET Open). This project aims at creating the world’s first superconducting thermoelectric detector of electromagnetic radiation, based on superconductor/magnet heterostructures.
“Actually, finding the diode functionality was a pleasant surprise, a consequence of the thorough characterization of SUPERTED samples,” explains Elia Strambini, from Istituto Nanoscienze — CNR and Scuola Normale Superiore (SNS) in Pisa, who made the initial discovery.
Francesco Giazotto, from Istituto Nanoscienze — CNR and SNS and who led the experimental efforts, says:
“I believe this finding is promising for several tasks in quantum technology, such as current rectification or current limiting.”
SUPERTED coordinator; Professor Tero Heikkilä, from the University of Jyväskylä, worked on the theory behind the effect: “This finding showed the power of collaboration between different types of researchers, from materials science to superconducting electronics and theory. Without European support such collaboration would not take place.” More

The best things in life are unlikely to occur. In many situations, taking at least moderate risks yields higher expected rewards. Yet many people struggle with taking such risks: they are overly cautious and forego high payoffs. “However, we are not alone in this struggle, but we can observe and learn from others,” says Wataru Toyokawa. “We therefore wanted to find out whether social learning can also rescue us from adverse risk aversion.” The answer is yes, as the authors from the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour showed in a just published study in the journal eLife.
Collective rescue occurs even among a biased collective
It is a long-established finding that collectives achieve better decisions by aggregating information or judgments, known as the wisdom of crowds. Individual errors cancel each other out, so that collectives do the right thing even if many individuals err. However, the wisdom of crowds does not work directly here, because the crowd is not wise; rather, the collective is biased towards undue risk aversion. “I wondered how social learning could still be beneficial in such a situation,” states Toyokawa. “Simply copying the majority would not help us at all, it would even yield more extreme risk aversion. So, if social learning helps at all, it must be by a different mechanism.”
To uncover these mechanisms, Toyokawa developed a dynamical mathematical model, which predicted that social learning can indeed promote favourable risk taking. He then proceeded to review the predictions from his model in large-scale online experiments with human subjects. Each participant played a browser-based game where they could choose between a variety of options — which might turn out good or bad, and with different probabilities. Toyokawa observed: “When the subjects played individually without any information from other participants, they predominantly preferred safe options with lower rewards. However, when social learning was possible, that is, when participants could see what others chose — but not know how successful others’ choices were -, it became more and more likely that they choose riskier options with higher expected rewards.” In other words, social learners made riskier choices that were more rewarding in the long run.
Occasionally copying others increases exploration and persistence
“By observing others’ choices, we could make smarter decisions, even though every single individual’s own decisions might be unduly risk averse,” Toyokawa summarizes. “Herewith, we identified a key mechanism underlying this counter-intuitive result: risk-aversion was mitigated not because the majority chose the risky option, nor were individuals simply attracted towards the majority. Rather, participants’ choices became risker even though the majority chose the safer alternative at the outset, by striking a right balance between what they experienced themselves and what they observed from others.”
Wolfgang Gaissmaier stresses that this is a striking demonstration of the power of social learning: “Under social influence, individuals became more explorative and more persistent in trying out the risky, more profitable option, even if that option might sometimes disappoint them in the short run. And once individual risk aversion was reduced, this process perpetuated itself, as there were more and more risk takers around to be copied.”
“The finding that adverse risk aversion is mitigated under social influence will help us better understand the evolution of learning under social interaction,” concludes Wataru Toyokawa. “The study suggests that social learning is advantageous in wider environmental conditions than previously assumed.”
The study was funded by the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour
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