Aurelia Butler

Is it possible to make a femtosecond laser entirely out of glass? That’s the rabbit hole that Yves Bellouard, head of EPFL’s Galatea Laboratory, went down after years of spending hours — and hours — aligning femtosecond lasers for lab experiments.
The Galatea laboratory is at the crossroads between optics, mechanics and materials science, and femtosecond lasers is a crucial element of Bellouard’s work. Femtosecond lasers produce extremely short and regular bursts of laser light and have many applications such as laser eye surgery, non-linear microscopy, spectroscopy, laser material processing and recently, sustainable data storage. Commercial femtosecond lasers are made by putting optical components and their mounts on a substrate, typically optical breadboards, which requires fastidious alignment of the optics.
“We use femtosecond lasers for our research on the non-linear properties of materials and how materials can be modified in their volume,” explains Bellouard. “Going through the exercise of painful complex optical alignments makes you dream of simpler and more reliable ways to align complex optics.”
Bellouard and his team’s solution? Use a commercial femtosecond laser to make a femtosecond laser out of glass, no bigger than the size of a credit card, and with less alignment hassles. The results are published in the journal Optica.
How to make a femtosecond laser out of glass
To make a femtosecond laser using a glass substrate, the scientists start with a sheet of glass. “We want to make stable lasers, so we use glass because it has a lower thermal expansion than conventional substrates, it is a stable material and transparent for the laser light we use,” Bellouard explains.
Using a commercial femtosecond laser, the scientists etch out special grooves in the glass that allow for the precise placement of the essential components of their laser. Even at micron level precision fabrication, the grooves and the components are not sufficiently precise by themselves to reach laser quality alignment. In other words, the mirrors are not yet perfectly aligned, so at this stage, their glass device is not yet functional as a laser. More

Movement offers a window into how the brain operates and controls the body. From clipboard-and-pen observation to modern artificial intelligence-based techniques, tracking human and animal movement has come a long way. Current cutting-edge methods utilize artificial intelligence to automatically track parts of the body as they move. However, training these models is still time-intensive and limited by the need for researchers to manually mark each body part hundreds to thousands of times.
Now, Associate Professor Eiman Azim and team have created GlowTrack, a non-invasive movement tracking method that uses fluorescent dye markers to train artificial intelligence. GlowTrack is robust, time-efficient, and high definition — capable of tracking a single digit on a mouse’s paw or hundreds of landmarks on a human hand.
The technique, published in Nature Communications on September 26, 2023, has applications spanning from biology to robotics to medicine and beyond.
“Over the last several years, there has been a revolution in tracking behavior as powerful artificial intelligence tools have been brought into the laboratory,” says Azim, senior author and holder of the William Scandling Developmental Chair. “Our approach makes these tools more versatile, improving the ways we capture diverse movements in the laboratory. Better quantification of movement gives us better insight into how the brain controls behavior and could aid in the study of movement disorders like amyotrophic lateral sclerosis (ALS) and Parkinson’s disease.”
Current methods to capture animal movement often require researchers to manually and repeatedly mark body parts on a computer screen — a time-consuming process subject to human error and time constraints. Human annotation means that these methods can usually only be used in a narrow testing environment, since artificial intelligence models specialize to the limited amount of training data they receive. For example, if the light, orientation of the animal’s body, camera angle, or any number of other factors were to change, the model would no longer recognize the tracked body part.
To address these limitations, the researchers used fluorescent dye to label parts of the animal or human body. With these “invisible” fluorescent dye markers, an enormous amount of visually diverse data can be created quickly and fed into the artificial intelligence models without the need for human annotation. Once fed this robust data, these models can be used to track movements across a much more diverse set of environments and at a resolution that would be far more difficult to achieve with manual human labeling.
This opens the door for easier comparison of movement data between studies, as different laboratories can use the same models to track body movement across a variety of situations. According to Azim, comparison and reproducibility of experiments are essentialin the process of scientific discovery. More

One-hour training is enough for people to carry a task alone with their supernumerary robotic arms as effectively as with a partner, study finds.
A new study by researchers at Queen Mary University of London, Imperial College London and The University of Melbourne has found that people can learn to use supernumerary robotic arms as effectively as working with a partner in just one hour of training.
The study, published in the journal IEEE Open Journal of Engineering in Medicine and Biology, investigated the potential of supernumerary robotic arms to help people perform tasks that require more than two hands. The idea of human augmentation with additional artificial limbs has long been in science fiction, like in Doctor Octopus in The Amazing Spider-Man (1963).
“Many tasks in daily life, such as opening a door while carrying a big package, require more than two hands,” said Dr Ekaterina Ivanova, lead author of the study from Queen Mary University of London. “Supernumerary robotic arms have been proposed as a way to allow people to do these tasks more easily, but until now, it was not clear how easy they would be to use.”
The study involved 24 participants who were asked to perform a variety of tasks with a supernumerary robotic arm. The participants were either given one hour of training in how to use the arm, or they were asked to work with a partner.
The results showed that the participants who had received training on the supernumerary arm performed the tasks just as well as the participants who were working with a partner. This suggests that supernumerary robotic arms can be a viable alternative to working with a partner, and that they can be learned to use effectively in a relatively short amount of time.
“Our findings are promising for the development of supernumerary robotic arms,” said Dr Ivanova. “They suggest that these arms could be used to help people with a variety of tasks, such as surgery, industrial work, or rehabilitation.”
The study was funded by the EU H2020 NIMA (FETOPEN 899626), TRIMANUAL (MSCA 843408) and CONBOTS (ICT 871803) grants. More

In the future, quantum computers may be able to solve problems that are far too complex for today’s most powerful supercomputers. To realize this promise, quantum versions of error correction codes must be able to account for computational errors faster than they occur.
However, today’s quantum computers are not yet robust enough to realize such error correction at commercially relevant scales.
On the way to overcoming this roadblock, MIT researchers demonstrated a novel superconducting qubit architecture that can perform operations between qubits — the building blocks of a quantum computer — with much greater accuracy than scientists have previously been able to achieve.
They utilize a relatively new type of superconducting qubit, known as fluxonium, which can have a lifespan that is much longer than more commonly used superconducting qubits.
Their architecture involves a special coupling element between two fluxonium qubits that enables them to perform logical operations, known as gates, in a highly accurate manner. It suppresses a type of unwanted background interaction that can introduce errors into quantum operations.
This approach enabled two-qubit gates that exceeded 99.9 percent accuracy and single-qubit gates with 99.99 percent accuracy. In addition, the researchers implemented this architecture on a chip using an extensible fabrication process.
“Building a large-scale quantum computer starts with robust qubits and gates. We showed a highly promising two-qubit system and laid out its many advantages for scaling. Our next step is to increase the number of qubits,” says Leon Ding PhD ’23, who was a physics graduate student in the Engineering Quantum Systems (EQuS) group and is the lead author of a paper on this architecture. More

Scientists have discovered a simple and reliable test for signs of past or present life on other planets — “the holy grail of astrobiology.”
In the journal Proceedings of the National Academy of Sciences, a seven-member team, funded by the John Templeton Foundation and led by Jim Cleaves and Robert Hazen of the Carnegie Institution for Science, reports that, with 90% accuracy, their artificial intelligence-based method distinguished modern and ancient biological samples from those of abiotic origin.
“This routine analytical method has the potential to revolutionize the search for extraterrestrial life and deepen our understanding of both the origin and chemistry of the earliest life on Earth,” says Dr. Hazen. “It opens the way to using smart sensors on robotic spacecraft, landers and rovers to search for signs of life before the samples return to Earth.”
Most immediately, the new test could reveal the history of mysterious, ancient rocks on Earth, and possibly that of samples already collected by the Mars Curiosity rover’s Sample Analysis at Mars (SAM) instrument. The latter tests could be conducted using an onboard analytical instrument nicknamed “SAM” (for Sample Analysis at Mars.
“We’ll need to tweak our method to match SAM’s protocols, but it’s possible that we already have data in hand to determine if there are molecules on Mars from an organic Martian biosphere.”
“The search for extraterrestrial life remains one of the most tantalizing endeavors in modern science,” says lead author Jim Cleaves of the Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC.
“The implications of this new research are many, but there are three big takeaways: First, at some deep level, biochemistry differs from abiotic organic chemistry; second, we can look at Mars and ancient Earth samples to tell if they were once alive; and third, it is likely this new method could distinguish alternative biospheres from those of Earth, with significant implications for future astrobiology missions.”
The innovative analytical method does not rely simply on identifying a specific molecule or group of compounds in a sample. More

New research has used machine learning to find the properties of atomic pieces of geometry, in pioneering work that could drive the development of new results in mathematics.
Mathematicians from the University of Nottingham and Imperial College London have, for the first time, used Machine Learning to expand and accelerate work identifying ‘atomic shapes’ that form the basic pieces of geometry in higher dimensions. Their findings have been published in Nature Communications.
The research group started their work to create a Periodic Table for shapes several years ago. The atomic pieces are called Fano varieties. The team associate a sequence of numbers, called quantum periods, to each shape, giving a ‘barcode’ or ‘fingerprint’ that describes the shape. Their recent breakthrough uses a new machine learning methodology to sift very quickly through these barcodes, identifying shapes and their properties such as the dimension of each shape.
Alexander Kasprzyk is Associate Professor in Geometry in the School of Mathematical Sciences at the University of Nottingham and was one of the authors on the paper. He explains: “For mathematicians, the key step is working out what the pattern is in a given problem. This can be very difficult, and some mathematical theories can take years to discover.”
Professor Tom Coates from the Department of Mathematics at Imperial College London and co-author on the paper said, “This is where Artificial Intelligence could really revolutionise Mathematics as we have shown that machine learning is a powerful tool for spotting patterns in complex domains like algebra and geometry.”
Sara Veneziale, co-author and a PhD student in the team, continues: “We’re really excited about the fact that machine learning can be used in Pure Mathematics. This will accelerate new insights across the field.” More

Boosting virtual screening with machine learning allowed for a 10-fold time reductionin the processing of 1.56 billion drug-like molecules. Researchers from the University of Eastern Finland teamed up with industry and supercomputers to carry out one of the world’s largest virtual drug screens.
In their efforts to find novel drug molecules, researchers often rely on fast computer-aided screening of large compound libraries to identify agents that can block a drug target. Such a target can, for instance, be an enzyme that enables a bacterium to withstand antibiotics or a virus to infect its host. The size of these collections of small organic molecules has seen a massive surge over the past years. With libraries growing faster than the speed of the computers needed to process them, the screening of a modern billion-scale compound library against only a single drug target can take several months or years — even when using state-of-the-art supercomputers. Therefore, quite evidently, faster approaches are desperately needed.
In a recent study published in the Journal of Chemical Information and Modeling, Dr Ina Pöhner and colleagues from the University of Eastern Finland’s School of Pharmacy teamed up with the host organisation of Finland’s powerful supercomputers, CSC — IT Center for Science Ltd. — and industrial collaborators from Orion Pharma to study the prospect of machine learning in the speed-up of giga-scale virtual screens.
Before applying artificial intelligence to accelerate the screening, the researchers first established a baseline: In a virtual screening campaign of unprecedented size, 1.56 billion drug-like molecules were evaluated against two pharmacologically relevant targets over almost six months with the help of the supercomputers Mahti and Puhti, and molecular docking. Docking is a computational technique that fits the small molecules into a binding region of the target and computes a “docking score” to express how well they fit. This way, docking scores were first determined for all 1.56 billion molecules.
Next, the results were compared to a machine learning-boosted screen using HASTEN, a tool developed by Dr TuomoKalliokoski from Orion Pharma, a co-author of the study. “HASTEN uses machine learning to learn the properties of molecules and how those properties affect how well the compounds score. When presented with enough examples drawn from conventional docking, the machine learning model can predict docking scores for other compounds in the library much faster than the brute-force docking approach,” Kalliokoski explains.
Indeed, with only 1% of the whole library docked and used as training data, the tool correctly identified 90% of the best-scoring compounds within less than ten days.
The study represented the first rigorous comparison of a machine learning-boosted docking tool with a conventional docking baseline on the giga-scale. “We found the machine learning-boosted tool to reliably and repeatedly reproduce the majority of the top-scoring compounds identified by conventional docking in a significantly shortened time frame,” Pöhner says.
“This project is an excellent example of collaboration between academia and industry, and how CSC can offer one of the best computational resources in the world. By combining our ideas, resources and technology, it was possible to reach our ambitious goals,” continues Professor Antti Poso, who leads the computational drug discovery group within the University of Eastern Finland’s DrugTech Research Community.
Studies on a comparable scale remain elusive in most settings. Thus, the authors released large datasets generated as part of the study into the public domain: Their ready-to-use screening library for docking that enables others to speed up their respective screening efforts, and their entire 1.56 billion compound-docking results for two targets as benchmarking data. This data will encourage the future development of tools to save time and resources and will ultimately advance the field of computational drug discovery. More