Computers Math

A research team at the University of Basel and the SIB Swiss Institute of Bioinformatics uncovered a treasure trove of uncharacterised proteins. Embracing the recent deep learning revolution, they discovered hundreds of new protein families and even a novel predicted protein fold. The study has now been published in Nature.
In the past years, AlphaFold has revolutionised protein science. This Artificial Intelligence (AI) tool was trained on protein data collected by life scientists for over 50 years, and is able to predict the 3D shape of proteins with high accuracy. Its success prompted the modelling of an astounding 215 million proteins last year, providing insights into the shapes of almost any protein. This is particularly interesting for proteins that have not been studied experimentally, a complex and time-consuming process.
“There are now many sources of protein information, enclosing valuable insights into how proteins evolve and work” says Joana Pereira, the leader of the study. Nevertheless, research has long been faced with a data jungle. The research team led by Professor Torsten Schwede, group leader at the Biozentrum, University of Basel, and the Swiss Institute of Bioinformatics (SIB), has now succeeded in decrypting some of the concealed information.
A bird’s eye view reveals new protein families and folds
The researchers constructed an interactive network of 53 million proteins with high quality AlphaFold structures. “This network serves as a valuable source for theoretically predicting unknown protein families and their functions on a large scale,” underlines Dr. Janani Durairaj, the first author. The team was able to identify 290 new protein families and one new protein fold that resembles the shape of a flower.
Building on the expertise of the Schwede group in developing and maintaining the leading software SWISS-MODEL, they made the network available as an interactive web resource, termed the “Protein Universe Atlas.”
AI as a valuable tool in research
The team has employed Deep Learning-based tools for finding novelties in this network, paving the way to innovations in life sciences, from basic to applied research. “Understanding the structure and function of proteins is typically one of the first steps to develop a new drug, or modify their functions by protein engineering, for example,” says Pereira. The work was supported by a ‘kickstarter’ grant from SIB to encourage the adoption of AI in life science resources. It underscores the transformative potential of Deep Learning and intelligent algorithms in research.
With the Protein Universe Atlas, scientists can now learn more about proteins relevant to their research. “We hope this resource will help not only researchers and biocurators but also students and teachers by providing a new platform for learning about protein diversity, from structure, to function, to evolution,” says Janani Durairaj. More

In cloud computing, commercial providers make computing resources available on demand to their customers over the Internet. This service is partly offered “serverless,” that is, without servers. How can that work? Computing resources without a server, isn’t that like a restaurant without a kitchen?
“The term is misleading,” says computer science Professor Samuel Kounev from Julius-Maximilians-Universität (JMU) Würzburg in Bavaria, Germany. Because even serverless cloud services don’t get by without servers.
In classical cloud computing, for example, a web shop rents computing resources from a cloud provider in the form of virtual machines (VMs). However, the shop itself remains responsible for the management of “its” servers, that is, the VMs. It has to take care of security aspects as well as the avoidance of overload situations or the recovery from system failures.
The situation is different with serverless computing. Here, the cloud provider takes over responsibility for the complete server management. The cloud users can no longer even access the server, it remains hidden from them — hence the term “serverless.”
Research article in ACM’s “Communications of the ACM” magazine
“The basic idea of serverless computing has been around since the beginning of cloud computing. However, it has not become widely accepted,” explains Samuel Kounev, who heads the JMU Chair of Computer Science II (Software Engineering). But a shift can currently be observed in the industry and in science, the focus is increasingly moving towards serverless computing.
A recent article in the Communications of the ACM magazine of the Association for Computing Machinery (ACM) deals with the history, status and potential of serverless computing. Among the authors are Samuel Kounev and Dr. Nikolas Herbst, who heads the JMU research group “Data Analytics Clouds.” ACM has also produced a video with Professor Samuel Kounev to accompany the publication: https://vimeo.com/849237573
Experts define serverless computing inconsistently More

An upconversion organic light-emitting diode (OLED) based on a typical blue-fluorescence emitter achieves emission at an ultralow turn-on voltage of 1.47 V, as demonstrated by researchers from Tokyo Tech. Their technology circumvents the traditional high voltage requirement for blue OLEDs, leading to potential advancements in commercial smartphone and large screen displays.
Blue light is vital for light-emitting devices, lighting applications, as well as smartphone screens and large screen displays. However, it is challenging to develop efficient blue organic light-emitting diodes (OLEDs) owing to the high applied voltage required for their function. Conventional blue OLEDs typically require around 4 V for a luminance of 100 cd/m2; this is higher than the industrial target of 3.7 V — the voltage of lithium-ion batteries commonly used in smartphones. Therefore, there is an urgent need to develop novel blue OLEDs that can operate at lower voltages.
In this regard, Associate Professor Seiichiro Izawa from Tokyo Institute of Technology and Osaka University, collaborated with researchers from University of Toyama, Shizuoka University, and the Institute for Molecular Science has recently presented a novel OLED device with a remarkable ultralow turn-on voltage of 1.47 V for blue emission and a peak wavelength at 462 nm (2.68 eV). Their work will be published in Nature Communications.
The choice of materials used in this OLED significantly influences its turn-on voltage. The device utilizes NDI-HF (2,7-di(9H-fluoren-2-yl)benzo[lmn][3,8]-phenanthroline-1,3,6,8(2H,7H)-tetraone) as the acceptor, 1,2-ADN (9-(naphthalen-1-yl)-10-(naphthalen-2-yl)anthracene) as the donor, and TbPe (2,5,8,11-tetra-tert-butylperylene) as the fluorescent dopant. This OLED operates via a mechanism called upconversion (UC). Herein, holes and electrons are injected into donor (emitter) and acceptor (electron transport) layers, respectively. They recombine at the donor/acceptor (D/A) interface to form a charge transfer (CT) state. Dr. Izawa points out: “The intermolecular interactions at the D/A interface play a significant role in CT state formation, with stronger interactions yielding superior results.”
Subsequently, the energy of the CT state is selectively transferred to the low-energy first triplet excited states of the emitter, which results in blue light emission through the formation of a high-energy first singlet excited state by triplet-triplet annihilation (TTA). “As the energy of the CT state is much lower than the emitter’s bandgap energy, the UC mechanism with TTA significantly decreases the applied voltage required for exciting the emitter. As a result, this UC-OLED reaches a luminance of 100 cd/m2, equivalent to that of a commercial display, at just 1.97 V,” explains Dr. Izawa.
In effect, this study efficiently produces a novel OLED, with blue light emission at an ultralow turn-on voltage, using a typical fluorescent emitter widely utilized in commercial displays, thus marking a significant step toward meeting the commercial requirements for blue OLEDs. It emphasizes the importance of optimizing the design of the D/A interface for controlling excitonic processes and holds promise not only for OLEDs but also for organic photovoltaics and other organic electronic devices. More

Researchers have determined how to build reliable machine learning models that can understand complex equations in real-world situations while using far less training data than is normally expected.
The researchers, from the University of Cambridge and Cornell University, found that for partial differential equations — a class of physics equations that describe how things in the natural world evolve in space and time — machine learning models can produce reliable results even when they are provided with limited data.
Their results, reported in the Proceedings of the National Academy of Sciences, could be useful for constructing more time- and cost-efficient machine learning models for applications such as engineering and climate modelling.
Most machine learning models require large amounts of training data before they can begin returning accurate results. Traditionally, a human will annotate a large volume of data — such as a set of images, for example — to train the model.
“Using humans to train machine learning models is effective, but it’s also time-consuming and expensive,” said first author Dr Nicolas Boullé, from the Isaac Newton Institute for Mathematical Sciences. “We’re interested to know exactly how little data we actually need to train these models and still get reliable results.”
Other researchers have been able to train machine learning models with a small amount of data and get excellent results, but how this was achieved has not been well-explained. For their study, Boullé and his co-authors, Diana Halikias and Alex Townsend from Cornell University, focused on partial differential equations (PDEs).
“PDEs are like the building blocks of physics: they can help explain the physical laws of nature, such as how the steady state is held in a melting block of ice,” said Boullé, who is an INI-Simons Foundation Postdoctoral Fellow. “Since they are relatively simple models, we might be able to use them to make some generalisations about why these AI techniques have been so successful in physics.”
The researchers found that PDEs that model diffusion have a structure that is useful for designing AI models. “Using a simple model, you might be able to enforce some of the physics that you already know into the training data set to get better accuracy and performance,” said Boullé. More

Cornell researchers combined soft microactuators with high-energy-density chemical fuel to create an insect-scale quadrupedal robot that is powered by combustion and can outrace, outlift, outflex and outleap its electric-driven competitors.
The group’s paper, “Powerful, Soft Combustion Actuators for Insect-Scale Robots,” was published Sept. 14 in Science. The lead author is postdoctoral researcher Cameron Aubin, Ph.D. ’23.
The project was led by Rob Shepherd, associate professor of mechanical and aerospace engineering in Cornell Engineering, whose Organic Robotics Lab has previously used combustion to create a braille display for electronics.
As anyone who has witnessed an ant carry off food from a picnic knows, insects are far stronger than their puny size suggests. However, robots at that scale have yet to reach their full potential. One of the challenges is “motors and engines and pumps don’t really work when you shrink them down to this size,” Aubin said, so researchers have tried to compensate by creating bespoke mechanisms to perform such functions. So far, the majority of these robots have been tethered to their power sources — which usually means electricity.
“We thought using a high-energy-density chemical fuel, just like we would put in an automobile, would be one way that we could increase the onboard power and performance of these robots,” he said. “We’re not necessarily advocating for the return of fossil fuels on a large scale, obviously. But in this case, with these tiny, tiny robots, where a milliliter of fuel could lead to an hour of operation, instead of a battery that is too heavy for the robot to even lift, that’s kind of a no brainer.”
While the team has yet to create a fully untethered model — Aubin says they are halfway there — the current iteration “absolutely throttles the competition, in terms of their force output.”
The four-legged robot, which is just over an inch long and weighs the equivalent of one and a half paperclips, is 3D-printed with a flame-resistant resin. The body contains a pair of separated combustion chambers that lead to the four actuators, which serve as the feet. Each actuator/foot is a hollow cylinder capped with a piece of silicone rubber, like a drum skin, on the bottom. When offboard electronics are used to create a spark in the combustion chambers, premixed methane and oxygen are ignited, the combustion reaction inflates the drum skin, and the robot pops up into the air. More

The rapid advancements in flexible electronic technology have led to the emergence of innovative devices such as foldable displays, wearables, e-skin, and medical devices. These breakthroughs have created a growing demand for flexible adhesives that can quickly recover their shape while effectively connecting various components in these devices. However, conventional pressure-sensitive adhesives (PSAs) often face challenges in achieving a balance between recovery capabilities and adhesive strength. In an extraordinary study conducted at UNIST, researchers have successfully synthesized new types of urethane-based crosslinkers that address this critical challenge.
Led by Professor Dong Woog Lee from the School of Energy and Chemical Engineering at UNIST, the research team developed novel crosslinkers utilizing m-xylylene diisocyanate (XDI) or 1,3-bis(isocyanatomethyl)cyclohexane (H6XDI) as hard segments along with poly(ethylene glycol) (PEG) groups serving as soft segments. By incorporating these newly synthesized materials into pressure-sensitive adhesives, they achieved significantly improved recoverability compared to traditional methods.
The PSA formulated with H6XDI-PEG diacrylate (HPD) demonstrated exceptional recovery properties while maintaining high adhesion strength (~25.5 N 25 mm?1). Through extensive folding tests totaling 100k folds and multi-directional stretching tests spanning 10k cycles, the PSA crosslinked with HPD exhibited remarkable stability under repeated deformation — showcasing its potential for applications requiring both flexibility and recoverability.
Furthermore, even after subjecting the adhesive to strains up to 20%, it displayed high optical transmittance ( >90%), making it suitable for fields such as foldable displays that demand not only flexibility but also optical clarity.
“This breakthrough in adhesive technology offers promising possibilities for electronic products that require both high flexibility and rapid recovery characteristics,” said Professor Lee. “Our research addresses the long-standing challenge of balancing adhesion strength and resilience, opening up new avenues for the development of flexible electronic devices.”
Hyunok Park, a researcher involved in the study, emphasized the significance of this research by stating, “The introduction of this new crosslinking structure has led to an adhesive with exceptional adhesion and recovery properties. We believe it will drive future advancements in adhesive research while contributing to further developments in flexible electronics.”
The study findings have been published ahead of their official publication in the online version of Advanced Functional Materials on July 12, 2023. This work was supported through the 2023 Research Fund at UNIST and received additional support from organizations including the National Research Foundation (NRF) of Korea, Defense Acquisition Program Administration and Ministry of Trade. More

The semiconductor industry today is working to respond to a threefold mandate: increasing computing power, decreasing chip sizes and managing power in densely packed circuits.
To meet these demands, the industry must look beyond silicon to produce devices appropriate for the growing role of computing.
While unlikely to abandon the workhorse material anytime in the near or distant future, the technology sector will require creative enhancements in chip materials and architectures to produce devices appropriate for the growing role of computing.
One of the biggest shortcomings of silicon is that it can only be made so thin because its material properties are fundamentally limited to three dimensions [3D]. For this reason, two-dimensional [2D] semiconductors — so thin as to have almost no height — have become an object of interest to scientists, engineers and microelectronics manufacturers.
Thinner chip components would provide greater control and precision over the flow of electricity in a device, while lowering the amount of energy required to power it. A 2D semiconductor would also contribute to keeping the surface area of a chip to a minimum, lying in a thin film atop a supporting silicon device.
But until recently, attempts to create such a material have been unsuccessful.
Certain 2D semiconductors have performed well on their own, but required such high temperatures to deposit they destroyed the underlying silicon chip. Others could be deposited at silicon-compatible temperatures, but their electronic properties — energy usage, speed, precision — were lacking. Some fit the bill for temperature and performance but could not be grown to the requisite purity at industry-standard sizes. More

Rutgers researchers have developed a way of detecting the early onset of deadly infectious diseases using a test so ultrasensitive that it could someday revolutionize medical approaches to epidemics.
The test, described in Science Advances, is an electronic sensor contained within a computer chip. It employs nanoballs — microscopic spherical clumps made of tinier particles of genetic material, each of those with diameters 1,000 times smaller than the width of a human hair — and combines that technology with advanced electronics.
“During the COVID pandemic, one of the things that didn’t exist but could have stemmed the spread of the virus was a low-cost diagnostic that could flag people known as the ‘quiet infected’ — patients who don’t know they are infected because they are not exhibiting symptoms,” said Mehdi Javanmard, a professor in the Department of Electrical and Computer Engineering in the Rutgers School of Engineering and an author of the study. “In a pandemic, pinpointing an infection early with accuracy is the Holy Grail. Because once a person is showing symptoms — sneezing and coughing — it’s too late. That person has probably infected 20 people.”
For the past 20 years, Javanmard has been developing biosensors — devices that monitor and transmit information about a life process. During the COVID-19 pandemic, he became disheartened about the extent of infections and the extreme loss of life. He believed there had to be a way of using biosensors as a test to detect illness earlier.
Working with Muhammad Tayyab, a Rutgers doctoral student and co-author of the study, Javanmard and research colleagues at the Karolinska Institute in Sweden and Stanford and Yale universities started brainstorming.
“We thought: How is there a way where we can leverage our individual expertise to build something new?” Javanmard said.
The biosensor developed by the team works through a series of steps. First, it zeroes in on a virus’ characteristic sequence of nucleic acids — naturally occurring chemical compounds that serve as the primary information-carrying molecules in a cell. Next, because it amplifies any nucleic acid sequence found in the sample, it makes many more copies, as many as 10,000. Then, it clumps those thousands of specks of nucleic acids into nanoballs that are “large” enough to be detected. More