Tag Archives: EPFL (Ecole polytechnique fédérale de Lausanne)

Nanosensors use AI to explore the biomolecular world

EPFL scientists have developed AI-powered nanosensors that let researchers track various kinds of biological molecules without disturbing them. Courtesy: École polytechnique fédérale de Lausanne (EPFL)

If you look at the big orange dot (representing the nanosensors?), you’ll see those purplish/fuschia objects resemble musical notes (biological molecules?). I think that brainlike object to the left and in light blue is the artificial intelligence (AI) component. (If anyone wants to correct my guesses or identify the bits I can’t, please feel free to add to the Comments for this blog.)

Getting back to my topic, keep the ‘musical notes’ in mind as you read about some of the latest research from l’École polytechnique fédérale de Lausanne (EPFL) in an April 7, 2021 news item on Nanowerk,

The tiny world of biomolecules is rich in fascinating interactions between a plethora of different agents such as intricate nanomachines (proteins), shape-shifting vessels (lipid complexes), chains of vital information (DNA) and energy fuel (carbohydrates). Yet the ways in which biomolecules meet and interact to define the symphony of life is exceedingly complex.

Scientists at the Bionanophotonic Systems Laboratory in EPFL’s School of Engineering have now developed a new biosensor that can be used to observe all major biomolecule classes of the nanoworld without disturbing them. Their innovative technique uses nanotechnology, metasurfaces, infrared light and artificial intelligence.

To each molecule its own melody

In this nano-sized symphony, perfect orchestration makes physiological wonders such as vision and taste possible, while slight dissonances can amplify into horrendous cacophonies leading to pathologies such as cancer and neurodegeneration.

An April 7, 2021 EPFL press release, which originated the news item, provides more detail,

“Tuning into this tiny world and being able to differentiate between proteins, lipids, nucleic acids and carbohydrates without disturbing their interactions is of fundamental importance for understanding life processes and disease mechanisms,” says Hatice Altug, the head of the Bionanophotonic Systems Laboratory. 

Light, and more specifically infrared light, is at the core of the biosensor developed by Altug’s team. Humans cannot see infrared light, which is beyond the visible light spectrum that ranges from blue to red. However, we can feel it in the form of heat in our bodies, as our molecules vibrate under the infrared light excitation.

Molecules consist of atoms bonded to each other and – depending on the mass of the atoms and the arrangement and stiffness of their bonds – vibrate at specific frequencies. This is similar to the strings on a musical instrument that vibrate at specific frequencies depending on their length. These resonant frequencies are molecule-specific, and they mostly occur in the infrared frequency range of the electromagnetic spectrum. 

“If you imagine audio frequencies instead of infrared frequencies, it’s as if each molecule has its own characteristic melody,” says Aurélian John-Herpin, a doctoral assistant at Altug’s lab and the first author of the publication. “However, tuning into these melodies is very challenging because without amplification, they are mere whispers in a sea of sounds. To make matters worse, their melodies can present very similar motifs making it hard to tell them apart.” 

Metasurfaces and artificial intelligence

The scientists solved these two issues using metasurfaces and AI. Metasurfaces are man-made materials with outstanding light manipulation capabilities at the nano scale, thereby enabling functions beyond what is otherwise seen in nature. Here, their precisely engineered meta-atoms made out of gold nanorods act like amplifiers of light-matter interactions by tapping into the plasmonic excitations resulting from the collective oscillations of free electrons in metals. “In our analogy, these enhanced interactions make the whispered molecule melodies more audible,” says John-Herpin.

AI is a powerful tool that can be fed with more data than humans can handle in the same amount of time and that can quickly develop the ability to recognize complex patterns from the data. John-Herpin explains, “AI can be imagined as a complete beginner musician who listens to the different amplified melodies and develops a perfect ear after just a few minutes and can tell the melodies apart, even when they are played together – like in an orchestra featuring many instruments simultaneously.” 

The first biosensor of its kind

When the scientists’ infrared metasurfaces are augmented with AI, the new sensor can be used to analyze biological assays featuring multiple analytes simultaneously from the major biomolecule classes and resolving their dynamic interactions. 

“We looked in particular at lipid vesicle-based nanoparticles and monitored their breakage through the insertion of a toxin peptide and the subsequent release of vesicle cargos of nucleotides and carbohydrates, as well as the formation of supported lipid bilayer patches on the metasurface,” says Altug.

This pioneering AI-powered, metasurface-based biosensor will open up exciting perspectives for studying and unraveling inherently complex biological processes, such as intercellular communication via exosomesand the interaction of nucleic acids and carbohydrates with proteins in gene regulation and neurodegeneration. 

“We imagine that our technology will have applications in the fields of biology, bioanalytics and pharmacology – from fundamental research and disease diagnostics to drug development,” says Altug. 

Here’s a link to and a citation for the paper,

Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules by Aurelian John‐Herpin, Deepthy Kavungal. Lea von Mücke, Hatice Altug. Advanced Materials Volume 33, Issue 14 April 8, 2021 2006054 DOI: https://doi.org/10.1002/adma.202006054 First published: 22 February 2021

This paper is open access.

Making carbon capture more efficient and cheaper with graphene filters

Years ago someone asked me if there was any nanotechnology research into carbon capture. I couldn’t answer the question at the time but since then I’ve been on the lookout for more on the topic. So, I’m happy to add this February 25, 2021 news item on Nanowerk to my growing number of carbon capture posts (Note: A link has been removed),

One of the main culprits of global warming is the vast amount of carbon dioxide pumped out into the atmosphere mostly from burning fossil fuels and the production of steel and cement. In response, scientists have been trying out a process that can sequester waste carbon dioxide, transporting it into a storage site, and then depositing it at a place where it cannot enter the atmosphere.

The problem is that capturing carbon from power plants and industrial emissions isn’t very cost-effective. The main reason is that waste carbon dioxide isn’t emitted pure, but is mixed with nitrogen and other gases, and extracting it from industrial emissions requires extra energy consumption – meaning a pricier bill.

Scientists have been trying to develop an energy-efficient carbon dioxide-filter. Referred to as a “membrane”, this technology can extract carbon dioxide out of the gas mix, which can then be either stored or converted into useful chemicals. “However, the performance of current carbon dioxide filters has been limited by the fundamental properties of currently available materials,” explains Professor Kumar Varoon Agrawal at EPFL’s School of Basic Sciences (EPFL Valais Wallis).

Now, Agrawal has led a team of chemical engineers to develop the world’s thinnest filter from graphene, the world-famous “wonder material” that won the Physics Nobel in 2010. But the graphene filter isn’t just the thinnest in the world, it can also separate carbon dioxide from a mix of gases such as those coming out of industrial emissions and do so with an efficiency and speed that surpasses most current filters.

A March 3, 2021 Ecole Polytechnique Fédérale de Lausanne (EPFL) press release (also on EurekAlert but published February 25, 2021), which originated the news item, delves further into the topic,

“Our approach was simple,” says Agrawal. “We made carbon dioxide-sized holes in graphene, which allowed carbon dioxide to flow through while blocking other gases such as nitrogen, which are larger than carbon dioxide.” The result is a record-high carbon dioxide-capture performance.

For comparison, current filters are required to exceed 1000 gas permeation units (GPUs), while their carbon-capturing specificity, referred to as their “carbon dioxide/nitrogen separation factor” must be above 20. The membranes that the EPFL scientists developed show more than ten-fold higher carbon dioxide permeance at 11,800 GPUs, while their separation factor stands at 22.5.

“We estimate that this technology will drop the cost of carbon capture close to $30 per ton of carbon dioxide, in contrast to commercial processes where the cost is two-to-four time higher,” says Agrawal. His team is now working on scaling up the process by developing a pilot plant demonstrator to capture 10 kg carbon dioxide per day, in a project funded by the Swiss government and Swiss industry.

Here’s a link to and a citation for the paper,

Millisecond lattice gasification for high-density CO2– and O2-sieving nanopores in single-layer graphene by Shiqi Huang, Shaoxian Li, Luis Francisco Villalobos, Mostapha Dakhchoune, Marina Micari, Deepu J. Babu, Mohammad Tohidi Vahdat, Mounir Mensi, Emad Oveisi and Kumar Varoon Agrawal. Science Advances 24 Feb 2021: Vol. 7, no. 9, eabf0116 DOI: 10.1126/sciadv.abf0116

This paper appears to be open access.

Exotic magnetism: a quantum simulation from D-Wave Sytems

Vancouver (Canada) area company, D-Wave Systems is trumpeting itself (with good reason) again. This 2021 ‘milestone’ achievement builds on work from 2018 (see my August 23, 2018 posting for the earlier work). For me, the big excitement was finding the best explanation for quantum annealing and D-Wave’s quantum computers that I’ve seen yet (that explanation and a link to more is at the end of this posting).

A February 18, 2021 news item on phys.org announces the latest achievement,

D-Wave Systems Inc. today [February 18, 2021] published a milestone study in collaboration with scientists at Google, demonstrating a computational performance advantage, increasing with both simulation size and problem hardness, to over 3 million times that of corresponding classical methods. Notably, this work was achieved on a practical application with real-world implications, simulating the topological phenomena behind the 2016 Nobel Prize in Physics. This performance advantage, exhibited in a complex quantum simulation of materials, is a meaningful step in the journey toward applications advantage in quantum computing.

A February 18, 2021 D-Wave Systems press release (also on EurekAlert), which originated the news item, describes the work in more detail,

The work by scientists at D-Wave and Google also demonstrates that quantum effects can be harnessed to provide a computational advantage in D-Wave processors, at problem scale that requires thousands of qubits. Recent experiments performed on multiple D-Wave processors represent by far the largest quantum simulations carried out by existing quantum computers to date.

The paper, entitled “Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets”, was published in the journal Nature Communications (DOI 10.1038/s41467-021-20901-5, February 18, 2021). D-Wave researchers programmed the D-Wave 2000Q™ system to model a two-dimensional frustrated quantum magnet using artificial spins. The behavior of the magnet was described by the Nobel-prize winning work of theoretical physicists Vadim Berezinskii, J. Michael Kosterlitz and David Thouless. They predicted a new state of matter in the 1970s characterized by nontrivial topological properties. This new research is a continuation of previous breakthrough work published by D-Wave’s team in a 2018 Nature paper entitled “Observation of topological phenomena in a programmable lattice of 1,800 qubits” (Vol. 560, Issue 7719, August 22, 2018). In this latest paper, researchers from D-Wave, alongside contributors from Google, utilize D-Wave’s lower noise processor to achieve superior performance and glean insights into the dynamics of the processor never observed before.

“This work is the clearest evidence yet that quantum effects provide a computational advantage in D-Wave processors,” said Dr. Andrew King, principal investigator for this work at D-Wave. “Tying the magnet up into a topological knot and watching it escape has given us the first detailed look at dynamics that are normally too fast to observe. What we see is a huge benefit in absolute terms, with the scaling advantage in temperature and size that we would hope for. This simulation is a real problem that scientists have already attacked using the algorithms we compared against, marking a significant milestone and an important foundation for future development. This wouldn’t have been possible today without D-Wave’s lower noise processor.”

“The search for quantum advantage in computations is becoming increasingly lively because there are special problems where genuine progress is being made. These problems may appear somewhat contrived even to physicists, but in this paper from a collaboration between D-Wave Systems, Google, and Simon Fraser University [SFU], it appears that there is an advantage for quantum annealing using a special purpose processor over classical simulations for the more ‘practical’ problem of finding the equilibrium state of a particular quantum magnet,” said Prof. Dr. Gabriel Aeppli, professor of physics at ETH Zürich and EPF Lausanne, and head of the Photon Science Division of the Paul Scherrer Institute. “This comes as a surprise given the belief of many that quantum annealing has no intrinsic advantage over path integral Monte Carlo programs implemented on classical processors.”

“Nascent quantum technologies mature into practical tools only when they leave classical counterparts in the dust in solving real-world problems,” said Hidetoshi Nishimori, Professor, Institute of Innovative Research, Tokyo Institute of Technology. “A key step in this direction has been achieved in this paper by providing clear evidence of a scaling advantage of the quantum annealer over an impregnable classical computing competitor in simulating dynamical properties of a complex material. I send sincere applause to the team.”

“Successfully demonstrating such complex phenomena is, on its own, further proof of the programmability and flexibility of D-Wave’s quantum computer,” said D-Wave CEO Alan Baratz. “But perhaps even more important is the fact that this was not demonstrated on a synthetic or ‘trick’ problem. This was achieved on a real problem in physics against an industry-standard tool for simulation–a demonstration of the practical value of the D-Wave processor. We must always be doing two things: furthering the science and increasing the performance of our systems and technologies to help customers develop applications with real-world business value. This kind of scientific breakthrough from our team is in line with that mission and speaks to the emerging value that it’s possible to derive from quantum computing today.”

The scientific achievements presented in Nature Communications further underpin D-Wave’s ongoing work with world-class customers to develop over 250 early quantum computing applications, with a number piloting in production applications, in diverse industries such as manufacturing, logistics, pharmaceutical, life sciences, retail and financial services. In September 2020, D-Wave brought its next-generation Advantage™ quantum system to market via the Leap™ quantum cloud service. The system includes more than 5,000 qubits and 15-way qubit connectivity, as well as an expanded hybrid solver service capable of running business problems with up to one million variables. The combination of Advantage’s computing power and scale with the hybrid solver service gives businesses the ability to run performant, real-world quantum applications for the first time.

That last paragraph seems more sales pitch than research oriented. It’s not unexpected in a company’s press release but I was surprised that the editors at EurekAlert didn’t remove it.

Here’s a link to and a citation for the latest paper,

Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets by Andrew D. King, Jack Raymond, Trevor Lanting, Sergei V. Isakov, Masoud Mohseni, Gabriel Poulin-Lamarre, Sara Ejtemaee, William Bernoudy, Isil Ozfidan, Anatoly Yu. Smirnov, Mauricio Reis, Fabio Altomare, Michael Babcock, Catia Baron, Andrew J. Berkley, Kelly Boothby, Paul I. Bunyk, Holly Christiani, Colin Enderud, Bram Evert, Richard Harris, Emile Hoskinson, Shuiyuan Huang, Kais Jooya, Ali Khodabandelou, Nicolas Ladizinsky, Ryan Li, P. Aaron Lott, Allison J. R. MacDonald, Danica Marsden, Gaelen Marsden, Teresa Medina, Reza Molavi, Richard Neufeld, Mana Norouzpour, Travis Oh, Igor Pavlov, Ilya Perminov, Thomas Prescott, Chris Rich, Yuki Sato, Benjamin Sheldan, George Sterling, Loren J. Swenson, Nicholas Tsai, Mark H. Volkmann, Jed D. Whittaker, Warren Wilkinson, Jason Yao, Hartmut Neven, Jeremy P. Hilton, Eric Ladizinsky, Mark W. Johnson, Mohammad H. Amin. Nature Communications volume 12, Article number: 1113 (2021) DOI: https://doi.org/10.1038/s41467-021-20901-5 Published: 18 February 2021

This paper is open access.

Quantum annealing and more

Dr. Andrew King, one of the D-Wave researchers, has written a February 18, 2021 article on Medium explaining some of the work. I’ve excerpted one of King’s points,

Insight #1: We observed what actually goes on under the hood in the processor for the first time

Quantum annealing — the approach adopted by D-Wave from the beginning — involves setting up a simple but purely quantum initial state, and gradually reducing the “quantumness” until the system is purely classical. This takes on the order of a microsecond. If you do it right, the classical system represents a hard (NP-complete) computational problem, and the state has evolved to an optimal, or at least near-optimal, solution to that problem.

What happens at the beginning and end of the computation are about as simple as quantum computing gets. But the action in the middle is hard to get a handle on, both theoretically and experimentally. That’s one reason these experiments are so important: they provide high-fidelity measurements of the physical processes at the core of quantum annealing. Our 2018 Nature article introduced the same simulation, but without measuring computation time. To benchmark the experiment this time around, we needed lower-noise hardware (in this case, we used the D-Wave 2000Q lower noise quantum computer), and we needed, strangely, to slow the simulation down. Since the quantum simulation happens so fast, we actually had to make things harder. And we had to find a way to slow down both quantum and classical simulation in an equitable way. The solution? Topological obstruction.

If you have time and the inclination, I encourage you to read King’s piece.

Wormlike communication at the nanoscale

These days I need a little joy and these two researchers seem happy to share,

Prof. Dirk Grundler and doctoral assistant Sho Watanabe with a broadband spin-wave spectroscopy set up. Credit: EPFL / Alain Herzog

A July 15, 2020 news item on phys.org announces the development that so delights these researchers,

Researchers at EPFL [École polytechnique fédérale de Lausanne; Switzerland] have shown that electromagnetic waves coupled to precisely engineered structures known as artificial ferromagnetic quasicrystals allow for more efficient information transmission and processing at the nanoscale. Their research also represents the first practical demonstration of Conway worms, a theoretical concept for the description of quasicrystals.

A July 15, 2020 EPFL press release, which originated the news item, explains further,

High-frequency electromagnetic waves are used to transmit and process information in microelectronic devices such as smartphones. It’s already appreciated that these waves can be compressed using magnetic oscillations known as spin waves or magnons. This compression could pave the way for the design of nanoscale, multifunctional microwave devices with a considerably reduced footprint. But first, scientists need to gain a better understanding of spin waves – or precisely how magnons behave and propagate in different structures.

Learning more about aperiodic structures

In a study conducted by the doctoral assistant Sho Watanabe, postdoctoral researcher Dr. Vinayak Bhat, and further team members, the scientists from EPFL’s Laboratory of Nanoscale Magnetic Materials and Magnonics (LMGN) examined how electromagnetic waves propagate, and how they could be manipulated, in precisely engineered nanostructures known as artificial ferromagnetic quasicrystals. The quasicrystals have a unique property: their structure is aperiodic, meaning that their constituent atoms or tailor-made elements do not follow a regular, repeating pattern but are still arranged deterministically. Although this characteristic makes materials especially useful for the design of everyday and high-tech devices, it remains poorly understood.

Faster, easier transmission of information

The LMGN team found that, under controlled conditions, a single electromagnetic wave coupled to an artificial quasicrystal splits into several spin waves, which then propagate within the structure. Each of these spin waves represents a different phase of the original electromagnetic wave, carrying different information. “It’s a very interesting discovery, because existing information-transmission methods follow the same principle,” says Dirk Grundler, an associate professor at EPFL’s School of Engineering (STI). “Except you need an extra device, a multiplexer, to split the input signal because – unlike in our study – it doesn’t divide on its own.”

Grundler also explains that, in conventional systems, the information contained in each wave can only be read at different frequencies – another inconvenience that the EPFL team overcame in their study. “In our two-dimensional quasicrystals, all the waves can be read at the same frequency,” he adds. The findings have been published in the journal Advanced Functional Materials.

Waves that spread like worms

The researchers also observed that, rather than propagating randomly, the waves often moved like so-called Conway worms, named after a well-known mathematician John Horton Conway who also developed a model to describe the behavior and feeding patterns of prehistoric worms. Conway discovered that, within two-dimensional quasicrystals, constituent elements arrange like meandering worms following a Fibonacci sequence. Thereby they form selected one-dimensional quasicrystals. “Our study represents the first practical demonstration of this theoretical concept, proving that the sequences induce interesting functional properties of waves in a quasicrystal,” says Grundler.

Take a look at that last paragraph. A mathematician develops a model for how prehistoric worms may have moved and applies it, theoretically, to 2D quasicrystals which these researchers believe they’ve observed in the laboratory and they believe this may have an impact on our future electronic devices. Sometimes I sit at home in wonder.

Here’s a link to and a citation for the paper,

Direct Observation of Worm‐Like Nanochannels and Emergent Magnon Motifs in Artificial Ferromagnetic Quasicrystals by Sho Watanabe, Vinayak S. Bhat, Korbinian Baumgaert, Dirk Grundler. Advanced Functional Materials DOI: https://doi.org/10.1002/adfm.202001388 First published: 15 July 2020

This is an open access paper.

The mention of quasicrystals reminded me of Daniel Schechtman who received the Nobel Prize for Chemistry in 2011 and who was mentioned in a December 24, 2013 posting here,

“I suggested earlier that this achievement has a fabulous quality and the Daniel Schechtman backstory is the reason. The winner of the 2011 Nobel Prize for Chemistry, Schechtman was reviled for years [emphasis mine] within his scientific community as Ian Sample notes in his Oct. 5, 2011 article on the announcement of Schechtman’s Nobel win written for the Guardian newspaper (Note: A link has been removed),

A scientist whose work was so controversial he was ridiculed and asked to leave his research group has won the Nobel Prize in Chemistry.

Daniel Shechtman, 70, a researcher at Technion-Israel Institute of Technology in Haifa, received the award for discovering seemingly impossible crystal structures in frozen gobbets of metal that resembled the beautiful patterns seen in Islamic mosaics.

Images of the metals showed their atoms were arranged in a way that broke well-establised rules of how crystals formed, a finding that fundamentally altered how chemists view solid matter.

You may want to click on the Guardian link to the full story about Schechtman and his quasicrystals. As for my December 24, 2013 posting, that features news of the creation of the first single-element quasicrystal in a laboratory along with an excerpt of the Schechtman story (scroll down about 50% of the way).

Feeling with a bionic finger

From what I understand one of the most difficult aspects of an amputation is the loss of touch, so, bravo to the engineers. From a March 8, 2016 news item on ScienceDaily,

An amputee was able to feel smoothness and roughness in real-time with an artificial fingertip that was surgically connected to nerves in his upper arm. Moreover, the nerves of non-amputees can also be stimulated to feel roughness, without the need of surgery, meaning that prosthetic touch for amputees can now be developed and safely tested on intact individuals.

The technology to deliver this sophisticated tactile information was developed by Silvestro Micera and his team at EPFL (Ecole polytechnique fédérale de Lausanne) and SSSA (Scuola Superiore Sant’Anna) together with Calogero Oddo and his team at SSSA. The results, published today in eLife, provide new and accelerated avenues for developing bionic prostheses, enhanced with sensory feedback.

A March 8, 2016 EPFL press release (also on EurekAlert), which originated the news item, provides more information about Sorenson’s experience and about the other tests the research team performed,

“The stimulation felt almost like what I would feel with my hand,” says amputee Dennis Aabo Sørensen about the artificial fingertip connected to his stump. He continues, “I still feel my missing hand, it is always clenched in a fist. I felt the texture sensations at the tip of the index finger of my phantom hand.”

Sørensen is the first person in the world to recognize texture using a bionic fingertip connected to electrodes that were surgically implanted above his stump.

Nerves in Sørensen’s arm were wired to an artificial fingertip equipped with sensors. A machine controlled the movement of the fingertip over different pieces of plastic engraved with different patterns, smooth or rough. As the fingertip moved across the textured plastic, the sensors generated an electrical signal. This signal was translated into a series of electrical spikes, imitating the language of the nervous system, then delivered to the nerves.

Sørensen could distinguish between rough and smooth surfaces 96% of the time.

In a previous study, Sorensen’s implants were connected to a sensory-enhanced prosthetic hand that allowed him to recognize shape and softness. In this new publication about texture in the journal eLife, the bionic fingertip attains a superior level of touch resolution.

Simulating touch in non-amputees

This same experiment testing coarseness was performed on non-amputees, without the need of surgery. The tactile information was delivered through fine, needles that were temporarily attached to the arm’s median nerve through the skin. The non-amputees were able to distinguish roughness in textures 77% of the time.

But does this information about touch from the bionic fingertip really resemble the feeling of touch from a real finger? The scientists tested this by comparing brain-wave activity of the non-amputees, once with the artificial fingertip and then with their own finger. The brain scans collected by an EEG cap on the subject’s head revealed that activated regions in the brain were analogous.

The research demonstrates that the needles relay the information about texture in much the same way as the implanted electrodes, giving scientists new protocols to accelerate for improving touch resolution in prosthetics.

“This study merges fundamental sciences and applied engineering: it provides additional evidence that research in neuroprosthetics can contribute to the neuroscience debate, specifically about the neuronal mechanisms of the human sense of touch,” says Calogero Oddo of the BioRobotics Institute of SSSA. “It will also be translated to other applications such as artificial touch in robotics for surgery, rescue, and manufacturing.”

Here’s a link to and a citation for the paper,

Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans by Calogero Maria Oddo, Stanisa Raspopovic, Fiorenzo Artoni, Alberto Mazzoni, Giacomo Spigler, Francesco Petrini, Federica Giambattistelli, Fabrizio Vecchio, Francesca Miraglia, Loredana Zollo, Giovanni Di Pino, Domenico Camboni, Maria Chiara Carrozza, Eugenio Guglielmelli, Paolo Maria Rossini, Ugo Faraguna, Silvestro Micera. eLife, 2016; 5 DOI: 10.7554/eLife.09148 Published March 8, 2016

This paper appears to be open access.