Uppsala researchers have succeeded in designing a molecule that inhibits the replication of coronaviruses and that has great potential for development into a drug suitable for treating COVID-19. The molecule is effective against both the new variant and previously identified coronaviruses. The article has been published in the Journal of the American Chemical Society. 

The new coronavirus has caused more than five million deaths. Many lives could have been saved with antiviral drugs, but no treatment of this type has been available to the healthcare system. During the pandemic, researchers around the world have tried to find a pharmaceutical, but the development of new medications often takes a long time.

During the first months of the pandemic, researchers were able to determine the structure of the coronavirus and how it functions at the molecular level. One of the viral enzymes was identified as a promising target for a drug, which is a strategy that has been successful for other viral diseases, such as AIDS. The idea is to design a molecule with the ability to recognize and bind to the enzyme. This would block its activity and thereby prevent the virus from producing new virus particles, stopping the spread of the virus.

Used computer models

In 2020, researchers at Uppsala University, in collaboration with the Drug Discovery and Development platform at Scilifelab, began to screen for inhibitors of the enzyme. They used computer models to identify molecules that can inhibit the enzyme’s activity. This proved to be a fast way to discover starting points for the design of pharmaceuticals. Access to Swedish supercomputers has made it possible to evaluate several hundred million different molecules to find those that can bind to the enzyme. The molecules predicted by the models were then synthesized and tested in experiments. 

“The most promising molecule shows the same ability to inhibit the replication of the new coronavirus as the active substance in Paxlovid, a combination drug recently approved for treating COVID-19. Our molecule works well on its own, and we have shown that the molecule is also effective against previously identified variants of the coronavirus”, says Jens Carlsson, associate professor, and the article’s lead author.

Researchers at The University of Manchester may have cleared a significant hurdle on the path to quantum supercomputing, demonstrating step-change improvements in the spin transport characteristics of nanoscale graphene-based electronic devices. 

The team - comprising researchers from the National Graphene Institute (NGI) led by Dr. Ivan Vera Marun, alongside collaborators from Japan and including students internationally funded by Ecuador and Mexico - used monolayer graphene encapsulated by another 2D material (hexagonal boron nitride) in a so-called van der Waals heterostructure with one-dimensional contacts (main picture, above). This architecture was observed to deliver an extremely high-quality graphene channel, reducing the interference or electronic ‘doping’ by traditional 2D tunnel contacts.

‘Spintronic’ devices, as they are known, may offer higher energy efficiency and lower dissipation compared to conventional electronics, which rely on charge currents. In principle, phones and tablets operating with spin-based transistors and memories could be greatly improved in speed and storage capacity, exceeding Moore’s Law. 

As published in Nano Letters, the Manchester team measured electron mobility up to 130,000cm²/Vs at low temperatures (20K or -253^oC). For purposes of comparison, the only previously published efforts to fabricate a device with 1D contacts achieved mobility below 30,000cm²/Vs, and the 130k figure measured at the NGI is higher than recorded for any other previous graphene channel where spin transport was demonstrated.

The researchers also recorded spin diffusion lengths approaching 20μm. Where longer is better, most typical conducting materials (metals and semiconductors) have spin diffusion lengths <1μm. The value of spin diffusion length observed here is comparable to the best graphene spintronic devices demonstrated to date.

Lead author of the study Victor Guarochico said: “Our work is a contribution to the field of graphene spintronics. We have achieved the largest carrier mobility yet regarding spintronic devices based on graphene. Moreover, the spin information is conserved over distances comparable with the best reported in the literature. These aspects open up the possibility to explore logic architectures using lateral spintronic elements where long-distance spin transport is needed.”

Co-author Chris Anderson added: “This research work has provided exciting evidence for a significant and novel approach to controlling spin transport in graphene channels, thereby paving the way towards devices possessing comparable features to advanced contemporary charge-based devices. Building on this work, bilayer graphene devices boasting 1D contacts are now being characterized, where the presence of an electrostatically tunable bandgap enables an additional dimension to spin transport control.”

Discover more about our capabilities in graphene and 2D material research at the National Graphene Institute website.

There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or even failure of Moore's law makes it increasingly difficult to improve their performance and energy efficiency by relying on advanced semiconductor technology. Optical devices can have a super-large bandwidth and low power consumption. And light has an ultrahigh-frequency of up to 100 THz and multiple degrees of freedom in their quantum state, making optical computing one of the most competitive candidates for high-capacity and low-latency matrix information processing in the “More than Moore” era. In recent years, photonic matrix multiplication has been developed rapidly and widely used in photonic acceleration fields such as optical signal processing, artificial intelligence, and photonic neural network.  These applications based on matrix multiplication show the great potential and opportunities in the photonic accelerator. 

In a new review published in Light Science & Application, a team of scientists, led by Professor Jianji Dong from Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology in China and co-workers have introduced the methods of photonic matrix multiplication and summarized the developmental milestones of photonic matrix multiplication and the related applications. Then, their detailed advances in applications to optical signal processing and artificial neural networks in recent years were reviewed. Comments on the challenges and perspectives of photonic matrix multiplication and photonic acceleration were also discussed

The paper reviewed and discussed the progress of photonic accelerators from a unique viewpoint of photonic matrix multiplication. These scientists summarize the main content of this review:

“The methods for photonic matrix-vector multiplications (MVMs) mainly fall into three categories: the plane light conversion (PLC) method, Mach–Zehnder interferometer (MZI) method, and wavelength division multiplexing (WDM) method.”

“The photonic matrix multiplication network itself can be used as a general linear photonic loop for photonic signal processing. In recent years, MVM has been developed as a powerful tool for a variety of photonic signal processing methods.”

“AI technology has been widely used in various electronics industries, such as for deep-learning-based speech recognition and image processing. MVM, as the basic building block of ANNs, occupies most of the computing tasks, such as over 80% for GoogleNet and OverFeat models. Improving the MVM performance is one of the most effective means for ANN acceleration. Compared with electrical computing, optical computing is poor at data storage and flow control, and the low efficiency of optical nonlinearities limits the applications in nonlinear computation, such as activation functions. While it has significant advantages on massively parallel supercomputing through multiplexing strategies of wavelength, mode, and polarization, extremely high data modulation speeds up to 100 GHz. Hence, photonic networks are quite good at MVM. The combination of optical computing and AI is expected to realize intelligent photonic processors and photonic accelerators. In recent years, AI technology has also seen rapid developments in the field of optics.”

“In general, photonic computing has obvious advantages in terms of signal rate, latency, power consumption, and computing density, and its accuracy is generally lower than that of electrical computing.”

“Before the all-optical ANNs are mature, especially in optical nonlinear effect and optical cascade, optoelectronic-hybrid AI is a more practical and more competitive candidate for deep ANNs. Therefore, the development of a highly efficient and dedicated optoelectronic-hybrid AI hardware chip system is one of the core research routes of photonic AI.”

Mathematicians at the University of Leicester have developed a new mathematical model which could greatly increase the efficiency of pest control and hence significantly reduce the impact of pests on crops whilst minimizing the damage to the environment. 

A new study builds upon individual-based model (IBM) techniques to explain and predict the formation of high slug density patches in arable fields.

While existing models built around the Turing theory of pattern formation (named for AI pioneer Alan Turing) and its generalizations are shown to work well for patterns in plant distribution, these are rarely able to accurately predict the distribution of animals due to the complexity of behavioral responses.

Drawing on field data collected in a three-year project, computational modeling experts in the University of Leicester’s School of Computing and Mathematical Sciences, alongside colleagues from The University of Birmingham and Harper Adams University, applied mathematical concepts to building a new model which shows trends of distribution, accounting for the movements of individual creatures.

Their model could be used in creating more efficient methods of pest control – by targeting the use of pesticides and other techniques to protect crops – and could be adapted to better understand the collective behavior in other species, such as fish schools, bird flocks, and insect swarms.

Sergei Petrovskii is a Professor in Applied Mathematics at the University of Leicester and the lead author for the study. Professor Petrovskii said: “This study is an example of how a fundamental ecological concept, when applied to a real-world problem, can lead to breakthrough findings and ultimately helps to make agriculture more sustainable”

Keith Walters, Professor in Agriculture and Pest Control at Harper Adams University, said: “Understanding factors determining slug distribution in agricultural fields have been a long-standing problem. Using unique field techniques specifically developed to support modeling and simulations allowed progress that would hardly be possible with empirical tools alone.”

Dr. Natalia Petrovskaya, Senior Lecturer in Applied Mathematics at the University of Birmingham and corresponding author for the study, added: “Computer simulations helped us to reveal a hidden link between biological processes going on very different spatial scales, which was crucial for the success of this project.”