Aurelia Butler

In the form of DNA, nature shows how data can be stored in a space-saving and long-term manner. Bioinformatics specialists are developing DNA chips for computer technology. Researchers show how a combination of molecular biology, nanotechnology, novel polymers, electronics and automation, coupled with systematic development, could make DNA data storage useful for everyday use possible in a few years.
The hereditary molecule DNA can store a great deal of information over long periods of time in a very small space. For a good ten years, scientists have therefore been pursuing the goal of developing DNA chips for computer technology, for example for the long-term archiving of data. Such chips would be superior to conventional silicon-based chips in terms of storage density, longevity, and sustainability.
Four recurring basic building blocks are found in a DNA strand. A specific sequence of these blocks can be used to encode information, just as nature does. To build a DNA chip, the correspondingly coded DNA must be synthesised and stabilised. If this works well, the information is preserved for a very long time — researchers assume several thousand years. The information can be retrieved by automatically reading out and decoding the sequence of the four basic building blocks.
What challenges have to be overcome
“The fact that digital DNA data storage with high capacity and a long lifespan is feasible has been demonstrated several times in recent years,” says Professor Thomas Dandekar, head of the Chair of Bioinformatics at Julius-Maximilians-Universität (JMU) Würzburg. “But the storage costs are high, close to 400,000 US dollars per megabyte, and the information stored in the DNA can only be retrieved slowly. It takes hours to days, depending on the amount of data.”
These challenges must be overcome to make DNA data storage more applicable and marketable. Suitable tools for this are light-controlled enzymes and protein network design software. Thomas Dandekar and his chair team members Aman Akash and Elena Bencurova discuss this in a recent review in the journal Trends in Biotechnology.
Dandekar’s team is convinced that DNA has a future as a data store. In the journal, the JMU researchers show how a combination of molecular biology, nanotechnology, novel polymers, electronics and automation, coupled with systematic development, could make DNA data storage useful for everyday use possible in a few years. More

An innovative bimanual robot displays tactile sensitivity close to human-level dexterity using AI to inform its actions.
The new Bi-Touch system, designed by scientists at the University of Bristol and based at the Bristol Robotics Laboratory, allows robots to carry out manual tasks by sensing what to do from a digital helper.
The findings, published in IEEE Robotics and Automation Letters, show how an AI agent interprets its environment through tactile and proprioceptive feedback, and then control the robots’ behaviours, enabling precise sensing, gentle interaction, and effective object manipulation to accomplish robotic tasks.
This development could revolutionise industries such as fruit picking, domestic service, and eventually recreate touch in artificial limbs.
Lead author Yijiong Lin from the Faculty of Engineering, explained: “With our Bi-Touch system, we can easily train AI agents in a virtual world within a couple of hours to achieve bimanual tasks that are tailored towards the touch. And more importantly, we can directly apply these agents from the virtual world to the real world without further training.
“The tactile bimanual agent can solve tasks even under unexpected perturbations and manipulate delicate objects in a gentle way.”
Bimanual manipulation with tactile feedback will be key to human-level robot dexterity. However, this topic is less explored than single-arm settings, partly due to the availability of suitable hardware along with the complexity of designing effective controllers for tasks with relatively large state-action spaces. The team were able to develop a tactile dual-arm robotic system using recent advances in AI and robotic tactile sensing. More

Whenever the precision of a measurement approaches the uncertainty limit defined by quantum mechanics, the outcomes of the measurement depend on the dynamics of the interactions with the meter used to determine a physical property of the system. This finding may explain why quantum experiments often produce conflicting results and may contradict basic assumptions regarding physical reality.
Two quantum physicists from Hiroshima University recently analyzed the dynamics of a measurement interaction, where the value of a physical property is identified with a quantitative change in the meter state. This is a difficult problem, because quantum theory does not identify the value of a physical property unless the system is in a so-called “eigenstate” of that physical property, a very small set of special quantum states for which the physical property has a fixed value. The researchers solved this fundamental problem by combining information about the past of the system with information about its future in a description of the dynamics of the system during the measurement interaction, demonstrating that the observable values of a physical system depend on the dynamics of the measurement interaction by which they are observed.
The team published the results of their study on July 31 in Physical Review Research.
“There is much disagreement about the interpretation of quantum mechanics because different experimental results cannot be reconciled with the same physical reality,” said Holger Hofmann, professor in the Graduate School of Advanced Science and Engineering at Hiroshima University in Hiroshima, Japan.
“In this paper, we investigate how quantum superpositions in the dynamics of the measurement interaction shape the observable reality of a system seen in the response of a meter. This is a major step towards explaining the meaning of “superposition” in quantum mechanics,” said Hofmann.
In quantum mechanics, a superposition describes a situation in which two possible realities seem to co-exist, even though they can be distinguished clearly when an appropriate measurement is performed. The analysis of the team’s study suggests that superpositions describe different kinds of reality when different measurements are performed. The reality of an object depends on the object’s interactions with its surroundings.
“Our results show that the physical reality of an object cannot be separated from the context of all its interactions with the environment, past, present and future, providing strong evidence against the widespread belief that our world can be reduced to a mere configuration of material building blocks,” said Hofmann. More

Triplons are tricky little things. Experimentally, they’re exceedingly difficult to observe. And even then, researchers usually conduct the tests on macroscopic materials, in which measurements are expressed as an average across the whole sample.
That’s where designer quantum materials offer a unique advantage, says Academy Research Fellow Robert Drost, the first author of a paper published in Physical Review Letters on August 22. These designer quantum materials let researchers create phenomena not found in natural compounds, ultimately enabling the realization of exotic quantum excitations.
‘These materials are very complex. They give you very exciting physics, but the most exotic ones are also challenging to find and study. So, we are trying a different approach here by building an artificial material using individual components,’ says Professor Peter Liljeroth, head of the Atomic Scale physics research group at Aalto University.
Quantum materials are governed by the interactions between electrons at the microscopic level. These electronic correlations lead to unusual phenomena like high-temperature superconductivity or complex magnetic states, and quantum correlations give rise to new electronic states.
In the case of two electrons, there are two entangled states known as singlet and triplet states. Supplying energy to the electron system can excite it from the singlet to the triplet state. In some cases, this excitation can propagate through a material in an entanglement wave known as a triplon. These excitations are not present in conventional magnetic materials, and measuring them has remained an open challenge in quantum materials.
The team’s triplon experiments
In the new study, the team used small organic molecules to create an artificial quantum material with unusual magnetic properties. Each of the cobalt-phthalocyanine molecules used in the experiment contains two frontier electrons. More

With the rise in global demand for staple crop products projected to substantially increase by 2050 due to population growth, rising per capita income, and the growing use of biofuels, it is necessary to adopt sustainable agricultural intensification practices in existing croplands to meet this demand. However, estimation processes currently employed in the global South remain inadequate. Traditional methods like self-reporting and crop cutting have their limitations, and remote sensing technologies are not fully utilized in this context.
However, recent advancements in artificial intelligence and machine learning, particularly deep learning with convolutional neural networks (CNNs), offer promising solutions here. To explore the scope of this new technology, researchers from Japan conducted a study focusing on rice. They used ground-based digital images taken at harvesting stage of the crop, combined with CNNs, to estimate rice yield. Their study appeared online on 29 June 2023 and was published on 28 July 2023 in Volume 5 of Plant Phenomics.
“We started by conducting an extensive field campaign. We gathered rice canopy images and rough grain yield data from 20 locations in seven countries in order to create a comprehensive multinational database,” says Dr. Yu Tanaka, Associate Professor at the Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, who led the study.
The images were captured using digital cameras which could gather the required data from a distance of 0.8-0.9 meters, vertically downwards from the rice canopy. With Dr. Kazuki Saito of the International Rice Research Institute (formerly Africa Rice Center) and other collaborators, the team successfully created a database of 4,820 yield data of harvesting plots and 22,067 images, encompassing various rice cultivars, production systems, and crop management practices.
Next, a CNN model was developed to estimate the grain yield for each of the collected images. The team used a visual-occlusion method to visualize the additive effect of different regions in the rice canopy images. It involved masking specific parts of the images and observing how the model’s yield estimation changed in response to the masked regions. The insights gained from this method allowed the researchers to understand how the CNN model interpreted various features in the rice canopy images, influencing its accuracy and its ability to distinguish between yield-contributing components and non-contributing elements in the canopy.
The model performed well, explaining around 68%-69% of yield variation in the validation and test datasets. Study results highlighted the importance of panicles — loose-branching clusters of flowers — in yield estimation through occlusion-based visualization. The model could predict yield accurately during the ripening stage, recognizing mature panicles, and also detect cultivar and water management differences in yield in the prediction dataset. Its accuracy, however, decreased as image resolution decreased.
Nevertheless, the model proved robust, showing good accuracy at different shooting angles and times of day. “Overall, the developed CNN model demonstrated promising capabilities in estimating rough grain yield from rice canopy images across diverse environments and cultivars. Another appealing aspect is that it is highly cost effective and does not require labor-intensive crop cuts or complex remote-sensing technologies,” says Dr. Tanaka enthusiastically.
The study emphasizes the potential of CNN-based models for monitoring rice productivity at regional scales. However, the model’s accuracy may vary under different conditions, and further research should focus on adapting the model to low-yielding and rainy environments. The AI-based method has also been made available to farmers and researchers through a simple smartphone application, thus greatly improving accessibility of the technology and its real-life applications. The name of this application is ‘HOJO’, and it is already available on iOS and Android. The researchers hope that their work will lead to better management of rice fields and assist accelerated breeding programs, contributing positively to global food production and sustainability initiatives. More

In physics, a “disordered system” refers to a physical system whose components — e.g. its atoms — are not organized in any discernible way. Like a drawer full of random socks, a disordered system lacks a well-defined, ordered pattern due to various factors like impurities, defects, or interactions between components.
This randomness makes it difficult to predict the system’s behavior accurately. And given that disordered systems are found in anything from materials science to climate or social networks and beyond, this limitation can be a serious, real-life problem.
Now, a team of scientists led by Lenka Zdeborová at EPFL have developed a novel approach to understanding how things change and evolve in disordered systems, even when they are undergoing rapid changes, like a temperature change. The study was carried out by Freya Behrens at Zdeborová’s lab, and Barbora Hudcová visiting EPFL from the Charles University in Prague.
The approach is called the Backtracking Dynamical Cavity Method (BDCM) and it works by looking first at the end state of the system rather than the beginning; instead of studying the system’s trajectory forward from the start, it traces the steps backward from stable points.
But why “cavity”? The term comes from the “Cavity Method” in statistical physics and refers to isolating a particular component of a complex system to make it easier to study — putting it in a conceptual “hole” or “cavity” while ignoring all the other components.
In a similar way, the BDCM isolates a specific component of the disordered system, but working instead backwards to understand its evolution throughout time. This innovative twist provides valuable insights about the system’s dynamic properties, even when it is far from equilibrium, like how materials cool down or how opinions on a social network evolve, or even how our brains work.
“From our early results, we saw that it can be quite deceiving to only look at the number of attractors of the system,” says Freya Behrens, referring to stable states that a system settles into over time. “Just because there are many attractors of a given type, it does not mean your dynamics end up there. But we really did not expect that taking just a few steps back from the attractor into its basin would reveal so many details about the complete dynamics. It was quite surprising.”
Applying the BDCM to a random arrangement of magnets, the scientists found out what happens to their energy of when they rapidly cool down or what type of patterns they form when they start with different arrangements.
“What I like a lot about this work is that we obtained theoretical answers to basic yet open questions about the dynamics of the Ising model, among the most studied models in statistical physics,” says Lenka Zdeborová. “The method we developed is very versatile, indicating that it will find many applications in studies of the dynamics of complex interacting systems, for which the Ising model is one of the simplest examples. Some application areas I can foresee include social dynamics, learning in neural networks or, for instance, gene regulation. I am looking forward to seeing the follow-up work!” More

Parents should be taught how to better understand the increasingly volatile social media landscape that is deploying sophisticated algorithms, according to a new study from the University of Surrey.
The study investigated how parents interpret and navigate social media algorithms that are central to their children’s digital experiences.
The research found that a child’s age shaped their parent’s perspective on a platform’s algorithm. For example, the study revealed that parents of toddlers who watched YouTube, while concerned, often regarded their fears to be an issue for the future.
Researchers also found that parents’ own views on social media platforms often influence how they manage their children’s online activities. Even though parents think their own online data is different from their offspring’s, the study found a lot of overlap because of shared family information and data.
Professor Ranjana Das, lead investigator and Professor of Media and Communication at the University of Surrey, said:
“Parents engage with so many platforms in the course of their day-to-day parenting. We wanted to see how they make sense of and interact with the algorithms responsible for serving themselves and their children with the content on those platforms.”
Professor Das interviewed 30 parents who are raising children aged 0 to 18 across England. More