Aurelia Butler

Lancaster University researchers have come up with exciting and sophisticated new mapping technology enabling future generations to get involved in creating their own future built landscape.
They say, in their new research, that planners are missing a real trick when it comes to encouraging and involving the public to help shape their own towns, cities and counties for the future.
They also say that games platforms can be used to plan future cities and also help the public immerse themselves in these future worlds.
The researchers have modified Colossal Order’s game ‘Cities: Skylines’ where players control zones, public services and transportation.
Real-world buildings and models can be imported into the game to create realistic cities and inform planning.
Players can manage education, police and fire services, health and even set tax policies, amongst other realistic simulations. The game dashboard even measures how happy citizens are!
Players must add infrastructure, manage power, water and think carefully about what is needed for their community.

Given that, according to Royal Town Planning Institute statistics, only 20% of younger people are interested in planning, the use of digital games, say the researchers, enables the public to ‘play’ real-world planning policies based on a ‘real world’ place, which creates a dialogue with planners.
Dr Paul Cureton and Professor Paul Coulton, from ImaginationLancaster, Lancaster University’s design-led laboratory, share their research in an open access article ‘Game Based Worldbuilding: Planning, Models, Simulations and Digital Twins’ published in Acta Ludologica, the peer reviewed scientific journal in the field of games and digital games.
Their research, funded by the Digital Planning Programme, Department for Levelling Up, Housing and Communities (DLUHC), cites a lack of public interest in planning issues and a need for ‘urgent change’ given, what they say, is a general lack of public interest in planning processes.
Gaming technology has been used in 3D planning models and what is called City Information Models (CIMs), and Urban Digital Twins (UDTs). Urban digital twins are virtual replica systems of an environment that are connected to real-world sensors such as traffic or air quality to enhance public participation and engagement in the planning process and generate future scenarios.
But, say the researchers, while this is a good step forward, the use of gaming technology for real-world applications is ‘one-directional and misses opportunities’ to include game design and research, such as mechanics, dynamics, flow, and public participatory’ world-building’ for future scenarios.
They believe the technology can be used for higher levels of ‘citizen engagement’ by making the process more enjoyable.

The method, they add, is cost-effective and can be rolled out across the UK by any local authority.
They have already conducted gaming workshops playtesting alongside Lancaster City Council with 140 children to ‘play’ and plan Lancaster in the UK, in an area to be developed along with Lancashire County Council and national house builder Homes England, previously earmarked for development as a new garden village for 5,000 homes.
Digital games have a long tradition of providing simulations of various systems of human activities, such as politics, culture, society, environment, and war. Urban planning has been simulated through various city-building games such as the Summer Game (1964), EA Games, SimCity (1989), and Colossal Order’s Cities Skylines (2015, 2023), amongst many others.
While a range of future urban planning scenarios use gaming technology, they do not necessarily incorporate game design ideas such as mechanics and dynamics, levels, progress, flows and feedback as part of a game world, say the researchers.
And, they add, this needs to be more fully understood if such systems are to yield potential benefits in terms of citizen engagement more fully.
Dr Cureton and Professor Coulton created a reference tool for new planning models and ensuing case studies offer new insights into the opportunities for using game design and gaming technology in urban planning and digital transformation.
The article in Acta Ludologica develops an understanding of the role of worldbuilding games in urban planning, architecture, and design, developing a playable theoretical urban game continuum to illuminate both the various nuances of a range of precedents and scaffold future applications.
Cities and urban areas are complex systems, and games allow a player to explore the complexities of this landscape and simulate and model behaviour and realise scenarios.
Arguably, there is a restrictive incorporation of gaming technologies for real-world planning that misses opportunities to engage players in changing the rules of the system being replicated.
The researchers say this is much needed as new governments will look at what urgent change is required in planning if the shortfall in housing and stimulation of economic growth is to be addressed. To do this, planners will need support, skill development, and the tools to engage people.
Gaming technologies are intended for citizen participation and access, yet fundamental challenges remain unaddressed.
The Royal Town Planning Institute (RTPI) in the UK stated that : “Response rates to a typical pre-planning consultation are around 3% of those directly made aware of it. In Local Plan consultations, this figure can fall to less than 1% of a district.”
Professor Paul Coulton is Chair of Speculative and Game Design at ImaginationLancaster and is internationally recognised for his speculative design work.
He says: “Whilst games and game playing are often dismissed as trivial or problematic they can serve as powerful tools in delivering information and understanding of how systems operate in a manner that can the lead to real world engagement in processes which previously seemed opaque.”
Dr Paul Cureton is a Senior Lecturer in Design at ImaginationLancaster and a member of the Data Science Institute (DSI) whose work focuses on subjects in spatial planning, 3D GIS modelling and design futures.
He says: “Only 20% of 18-34-year-olds engaging in local plans, according to the Royal Town Planning Institute (2020). So few engage in how our spaces are being transformed, so there is space for gaming in this field to provide and help the public think like planners, play issues and use gaming tools for modelling future spaces.” More

Researchers at Karolinska Institutet in Sweden have developed nanorobots that kill cancer cells in mice. The robot’s weapon is hidden in a nanostructure and is exposed only in the tumour microenvironment, sparing healthy cells. The study is published in the journal Nature Nanotechnology.
The research group at Karolinska Institutet has previously developed structures that can organise so-called death receptors on the surface of cells, leading to cell death. The structures exhibit six peptides (amino acid chains) assembled in a hexagonal pattern.
“This hexagonal nanopattern of peptides becomes a lethal weapon,” explains Professor Björn Högberg at the Department of Medical Biochemistry and Biophysics, Karolinska Institutet, who led the study. “If you were to administer it as a drug, it would indiscriminately start killing cells in the body, which would not be good. To get around this problem, we have hidden the weapon inside a nanostructure built from DNA.”
Created a ‘kill switch’
The art of building nanoscale structures using DNA as a building material is called DNA origami and is something Björn Högberg’s research team has been working on for many years. Now they have used the technique to create a ‘kill switch’ that is activated under the right conditions.
“We have managed to hide the weapon in such a way that it can only be exposed in the environment found in and around a solid tumour,” he says. “This means that we have created a type of nanorobot that can specifically target and kill cancer cells.”
The key is the low pH, or acidic microenvironment that usually surrounds cancer cells, which activates the nanorobot’s weapon. In cell analyses in test tubes, the researchers were able to show that the peptide weapon is hidden inside the nanostructure at a normal pH of 7.4, but that it has a drastic cell-killing effect when the pH drops to 6.5.

Reduced tumour growth
They then tested injecting the nanorobot into mice with breast cancer tumours. This resulted in a 70 per cent reduction in tumour growth compared to mice given an inactive version of the nanorobot.
“We now need to investigate whether this works in more advanced cancer models that more closely resemble the real human disease,” says the study’s first author Yang Wang, a researcher at the Department of Medical Biochemistry and Biophysics, Karolinska Institutet. “We also need to find out what side effects the method has before it can be tested on humans.”
The researchers also plan to investigate whether it is possible to make the nanorobot more targeted by placing proteins or peptides on its surface that specifically bind to certain types of cancer. More

Researchers at the University of Gothenburg have developed an AI model that increases the potential for detecting cancer through sugar analyses. The AI model is faster and better at finding abnormalities than the current semi-manual method.
Glycans, or structures of sugar molecules in our cells, can be measured by mass spectrometry. One important use is that the structures can indicate different forms of cancer in the cells.
However, the data from the mass spectrometer measurement must be carefully analysed by humans to work out the structure from the glycan fragmentation. This process can take anywhere from hours to days for each sample and can only be carried out with high confidence by a small number of experts in the world, as it is essentially detective work learnt over many years.
Automating the detective work
The process is thus a bottleneck in the use of glycan analyses, for example for cancer detection, when there are many samples to be analysed.
Researchers at the University of Gothenburg have developed an AI model to automate this detective work. The AI model, named Candycrunch, solves the task in just a few seconds per test. The results are reported in a scientific article in the journal Nature Methods.
The AI model was trained using a database of over 500,000 examples of different fragmentations and associated structures of sugar molecules.

“The training has enabled Candycrunch to calculate the exact sugar structure in a sample in 90 per cent of cases,” says Daniel Bojar, Associate Senior Lecturer in Bioinformatics at the University of Gothenburg.
Can find new biomarkers
This means that the AI model could soon reach the same levels of accuracy as the sequencing of other biological sequences, such as DNA, RNA or proteins.
Because the AI model is so fast and accurate in its answers, it can accelerate the discovery of glycan-based biomarkers for both diagnosis and prognosis of the cancer.
“We believe that glycan analyses will become a bigger part of biological and clinical research now that we have automated the biggest bottleneck,” says Daniel Bojar.
The AI model Candycrunch is also able to identify structures that are often missed by human analyses due to their low concentrations. The model can therefore help researchers to find new glycan-based biomarkers. More

Aston University researchers are part of a team that has sent data at a record rate of 402 terabits per second using commercially available optical fibre.
This beats their previous record, announced in March 2024, of 301 terabits or 301,000,000 megabits per second using a single, standard optical fibre.
“If compared to the internet connection speed recommendations of Netflix, of 3 Mbit/s or higher, for a watching a HD movie, this speed is over 100 million times faster.
The speed was achieved by using a wider spectrum, using six bands rather than the previous four, which increased capacity for data sharing. Normally just one or two bands are used.
The international research team included Professor Wladek Forysiak and Dr Ian Philips who are members of the University’s Aston Institute of Photonic Technologies (AIPT). Led by the Photonic Network Laboratory of the National Institute of Information and Communications Technology (NICT) which is based in Tokyo, Japan it also including Nokia Bell labs of the USA.
Together they achieved the feat by constructing the first optical transmission system covering six wavelength bands (O,E,S,C,L and U) used in fibre optical communication. Aston University contributed specifically by building a set of U-band Raman amplifiers, the longest part of the combined wavelength spectrum, where conventional doped fibre amplifiers are not presently available from commercial sources.
Optical fibres are small tubular strands of glass that pass information using light unlike regular copper cables that can’t carry data at such speeds.

As well as increasing capacity by approximately a third, the technique uses so-called “standard fibre” that is already deployed in huge quantities worldwide, so there would be no need to install new specialist cables.
As demand for data from business and individuals increases this new discovery could help keep broadband prices stable despite an improvement in capacity and speed.
Aston University’s Dr Philips said: “This finding could help increase capacity on a single fibre so the world would have a higher performing system.
“The newly developed technology is expected to make a significant contribution to expand the communication capacity of the optical communication infrastructure as future data services rapidly increase demand.”
His colleague Professor Wladek Forysiak added: ‘This is a ‘hero experiment’ made possible by a multi-national team effort and very recent technical advances in telecommunications research laboratories from across the world’.”
The results of the experiment were accepted as a post-deadline paper at the 47th International Conference on Optical Fiber Communications (OFC 2024) in the USA on 28 March.
To help support some of its work in this area Aston University has received funding from EPSRC (UKRI), the Royal Society (RS Exchange grant with NICT) and the EU (European Training Network). More

For hundreds of years, the clarity and magnification of microscopes were ultimately limited by the physical properties of their optical lenses. Microscope makers pushed those boundaries by making increasingly complicated and expensive stacks of lens elements. Still, scientists had to decide between high resolution and a small field of view on the one hand or low resolution and a large field of view on the other.
In 2013, a team of Caltech engineers introduced a microscopy technique called FPM (for Fourier ptychographic microscopy). This technology marked the advent of computational microscopy, the use of techniques that wed the sensing of conventional microscopes with computer algorithms that process detected information in new ways to create deeper, sharper images covering larger areas. FPM has since been widely adopted for its ability to acquire high-resolution images of samples while maintaining a large field of view using relatively inexpensive equipment.
Now the same lab has developed a new method that can outperform FPM in its ability to obtain images free of blurriness or distortion, even while taking fewer measurements. The new technique, described in a paper that appeared in the journal Nature Communications, could lead to advances in such areas as biomedical imaging, digital pathology, and drug screening.
The new method, dubbed APIC (for Angular Ptychographic Imaging with Closed-form method), has all the advantages of FPM without what could be described as its biggest weakness — namely, that to arrive at a final image, the FPM algorithm relies on starting at one or several best guesses and then adjusting a bit at a time to arrive at its “optimal” solution, which may not always be true to the original image.
Under the leadership of Changhuei Yang, the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering and an investigator with the Heritage Medical Research Institute, the Caltech team realized that it was possible to eliminate this iterative nature of the algorithm.
Rather than relying on trial and error to try to home in on a solution, APIC solves a linear equation, yielding details of the aberrations, or distortions introduced by a microscope’s optical system. Once the aberrations are known, the system can correct for them, basically performing as though it is ideal, and yielding clear images covering large fields of view.
“We arrive at a solution of the high-resolution complex field in a closed-form fashion, as we now have a deeper understanding in what a microscope captures, what we already know, and what we need to truly figure out, so we don’t need any iteration,” says Ruizhi Cao (PhD ’24), co-lead author on the paper, a former graduate student in Yang’s lab, and now a postdoctoral scholar at UC Berkeley. “In this way, we can basically guarantee that we are seeing the true final details of a sample.”
As with FPM, the new method measures not only the intensity of the light seen through the microscope but also an important property of light called “phase,” which is related to the distance that light travels. This property goes undetected by human eyes but contains information that is very useful in terms of correcting aberrations. It was in solving for this phase information that FPM relied on a trial-and-error method, explains Cheng Shen (PhD ’23), co-lead author on the APIC paper, who also completed the work while in Yang’s lab and is now a computer vision algorithm engineer at Apple. “We have proven that our method gives you an analytical solution and in a much more straightforward way. It is faster, more accurate, and leverages some deep insights about the optical system.”

Beyond eliminating the iterative nature of the phase-solving algorithm, the new technique also allows researchers to gather clear images over a large field of view without repeatedly refocusing the microscope. With FPM, if the height of the sample varied even a few tens of microns from one section to another, the person using the microscope would have to refocus in order to make the algorithm work. Since these computational microscopy techniques frequently involve stitching together more than 100 lower-resolution images to piece together the larger field of view, that means APIC can make the process much faster and prevent the possible introduction of human error at many steps.
“We have developed a framework to correct for the aberrations and also to improve resolution,” says Cao. “Those two capabilities can be potentially fruitful for a broader range of imaging systems.”
Yang says the development of APIC is vital to the broader scope of work his lab is currently working on to optimize image data input for artificial intelligence (AI) applications. “Recently, my lab showed that AI can outperform expert pathologists at predicting metastatic progression from simple histopathology slides from lung cancer patients,” says Yang. “That prediction ability is exquisitely dependent on obtaining uniformly in-focus and high-quality microscopy images, something that APIC is highly suited for.” More

A team of researchers led by the University of California San Diego has developed a soft, stretchy electronic device capable of simulating the feeling of pressure or vibration when worn on the skin. This device, reported in a paper published in Science Robotics, represents a step towards creating haptic technologies that can reproduce a more varied and realistic range of touch sensations.
The device consists of a soft, stretchable electrode attached to a silicone patch. It can be worn like a sticker on either the fingertip or forearm. The electrode, in direct contact with the skin, is connected to an external power source via wires. By sending a mild electrical current through the skin, the device can produce sensations of either pressure or vibration depending on the signal’s frequency.
“Our goal is to create a wearable system that can deliver a wide gamut of touch sensations using electrical signals — without causing pain for the wearer,” said study co-first author Rachel Blau, a nano engineering postdoctoral researcher at the UC San Diego Jacobs School of Engineering.
Existing technologies that recreate a sense of touch through electrical stimulation often induce pain due to the use of rigid metal electrodes, which do not conform well to the skin. The air gaps between these electrodes and the skin can result in painful electrical currents.
To address these issues, Blau and a team of researchers led by Darren Lipomi, a professor in the Aiiso Yufeng Li Family Department of Chemical and Nano Engineering at UC San Diego, developed a soft, stretchy electrode that seamlessly conforms to the skin.
The electrode is made of a new polymer material constructed from the building blocks of two existing polymers: a conductive, rigid polymer known as PEDOT:PSS, and a soft, stretchy polymer known as PPEGMEA. “By optimizing the ratio of these [polymer building blocks], we molecularly engineered a material that is both conductive and stretchable,” said Blau.
The polymer electrode is laser-cut into a spring-shaped, concentric design and attached to a silicone substrate. “This design enhances the electrode’s stretchability and ensures that the electrical current targets a specific location on the skin, thus providing localized stimulation to prevent any pain,” said Abdulhameed Abdal, a Ph.D. student in the Department of Mechanical and Aerospace Engineering at UC San Diego and the study’s other co-first author. Abdal and Blau worked on the synthesis and fabrication of the electrode with UC San Diego nano engineering undergraduate students Yi Qie, Anthony Navarro and Jason Chin.

In tests, the electrode device was worn on the forearm by 10 participants. In collaboration with behavioral scientists and psychologists at the University of Amsterdam, the researchers first identified the lowest level of electrical current detectable. They then adjusted the frequency of the electrical stimulation, allowing participants to experience sensations categorized as either pressure or vibration.
“We found that by increasing the frequency, participants felt more vibration rather than pressure,” said Abdal. “This is interesting because biophysically, it was never known exactly how current is perceived by the skin.”
The new insights could pave the way for the development of advanced haptic devices for applications such as virtual reality, medical prosthetics and wearable technology.
This work was supported by the National Science Foundation Disability and Rehabilitation Engineering program (CBET-2223566). This work was performed in part at the San Diego Nanotechnology Infrastructure (SDNI) at UC San Diego, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the National Science Foundation (grant ECCS-1542148). More

Osteoporosis is so difficult to detect in early stage it’s called the “silent disease.” What if artificial intelligence could help predict a patient’s chances of having the bone-loss disease before ever stepping into a doctor’s office?
Tulane University researchers made progress toward that vision by developing a new deep learning algorithm that outperformed existing computer-based osteoporosis risk prediction methods, potentially leading to earlier diagnoses and better outcomes for patients with osteoporosis risk.
Their results were recently published in Frontiers in Artificial Intelligence.
Deep learning models have gained notice for their ability to mimic human neural networks and find trends within large datasets without being specifically programmed to do so. Researchers tested the deep neural network (DNN) model against four conventional machine learning algorithms and a traditional regression model, using data from over 8,000 participants aged 40 and older in the Louisiana Osteoporosis Study. The DNN achieved the best overall predictive performance, measured by scoring each model’s ability to identify true positives and avoid mistakes.
“The earlier osteoporosis risk is detected, the more time a patient has for preventative measures,” said lead author Chuan Qiu, a research assistant professor at the Tulane School of Medicine Center for Biomedical Informatics and Genomics. “We were pleased to see our DNN model outperform other models in accurately predicting the risk of osteoporosis in an aging population.”
In testing the algorithms using a large sample size of real-world health data, the researchers were also able to identify the 10 most important factors for predicting osteoporosis risk: weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, years of smoking, and income level.
Notably, the simplified DNN model using these top 10 risk factors performed nearly as well as the full model which included all risk factors.
While Qiu admitted that there is much more work to be done before an AI platform can be used by the public to predict an individual’s risk of osteoporosis, he said identifying the benefits of the deep learning model was a step in that direction.
“Our final aim is to allow people to enter their information and receive highly accurate osteoporosis risk scores to empower them to seek treatment to strengthen their bones and reduce any further damage,” Qiu said. More