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Categories: Engineering: Robotics Research, Mathematics: Modeling

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A four-legged robot trained with machine learning has learned to avoid falls by spontaneously switching between walking, trotting, and pronking -- a milestone for roboticists as well as biologists interested in animal locomotion.

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Scientists have developed a smart, reusable adhesive more than ten times stronger than a gecko's feet adhesion, pointing the way for development of reusable superglue and grippers capable of holding heavy weights across rough and smooth surfaces. The research team found a way to maximize the adhesion of the smart adhesives by using shape-memory polymers, which can stick and detach easily when needed simply by heating them. This smart adhesive can support extremely heavy weights, opening new possibilities for robotic grippers that allow humans to scale walls effortlessly, or climbing robots that can cling onto ceilings for survey or repair applications.

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Researchers have demonstrated that ferromagnetism, an ordered state of atoms, can be induced by increasing particle motility and that repulsive forces between atoms are sufficient to maintain it. The discovery not only extends the concept of active matter to quantum systems but also contributes to the development of novel technologies that rely on the magnetic properties of particles, such as magnetic memory and quantum computing.

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Elucidating the relationship between the sequences of non-coding regulatory elements and their target genes is key to understanding gene regulation and its variation between plant species and ecotypes. Now, an international research team developed deep learning models that link gene sequence data with mRNA copy number for several plant species and predicted the regulatory effect of gene sequence variation.

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Researchers have developed tiny, flexible devices that can wrap around individual nerve fibers without damaging them. The researchers combined flexible electronics and soft robotics techniques to develop the devices, which could be used for the diagnosis and treatment of a range of disorders, including epilepsy and chronic pain, or the control of prosthetic limbs.

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For most human proteins, there are no small molecules known to bind them chemically (so called 'ligands'). Ligands frequently represent important starting points for drug development but this knowledge gap critically hampers the development of novel medicines. Researchers at CeMM, in a collaboration with Pfizer, have now leveraged and scaled a method to measure the binding activity of hundreds of small molecules against thousands of human proteins. This large-scale study revealed tens of thousands of ligand-protein interactions that can now be explored for the development of chemical tools and therapeutics. Moreover, powered by machine learning and artificial intelligence, it allows unbiased predictions of how small molecules interact with all proteins present in living human cells. These groundbreaking results have been published in the journal Science (DOI: 10.1126/science.adk5864), and all generated data and models are freely available for the scientific community.

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Cleveland Clinic researchers developed an artficial intelligence (AI) model that can determine the best combination and timeline to use when prescribing drugs to treat a bacterial infection, based solely on how quickly the bacteria grow given certain perturbations. PNAS recently published their findings.

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Robotics engineers have worked for decades and invested many millions of research dollars in attempts to create a robot that can walk or run as well as an animal. And yet, it remains the case that many animals are capable of feats that would be impossible for robots that exist today.

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Researchers have developed a method to use an image generation AI model to create realistic images of single cells, which are then used as 'synthetic data' to train an AI model to better carry out single-cell segmentation.

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A sensing technology that can assess the quality of components in fields such as aerospace could transform UK industry.

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Atrial fibrillation is the most common cardiac arrhythmia worldwide with around 59 million people concerned in 2019. This irregular heartbeat is associated with increased risks of heart failure, dementia and stroke. It constitutes a significant burden to healthcare systems, making its early detection and treatment a major goal. Researchers have recently developed a deep-learning model capable of predicting the transition from a normal cardiac rhythm to atrial fibrillation. It gives early warnings on average 30 minutes before onset, with an accuracy of around 80%. These results pave the way for integration into wearable technologies, allowing early interventions and better patient outcomes.

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The study highlights the rapid progress and transformative potential of AI in weather prediction.

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A new robotic suction cup which can grasp rough, curved and heavy stone, has been developed by scientists.

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A new typing model simulates the typing process instead of just predicting words.

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Contrary to previous assumptions, nerve cells in the human neocortex are wired differently than in mice. The study found that human neurons communicate in one direction, while in mice, signals tend to flow in loops. This increases the efficiency and capacity of the human brain to process information. These discoveries could further the development of artificial neural networks.

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Researchers are targeting the next generation of soft actuators and robots with an elastomer-based ink for 3D printing objects with locally changing mechanical properties, eliminating the need for cumbersome mechanical joints.

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Soft skin coverings and touch sensors have emerged as a promising feature for robots that are both safer and more intuitive for human interaction, but they are expensive and difficult to make. A recent study demonstrates that soft skin pads doubling as sensors made from thermoplastic urethane can be efficiently manufactured using 3D printers.

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More than five million central lines are placed in patients who need prolonged drug delivery, such as those undergoing cancer treatments, in the United States every year, yet the common procedure can lead to a bevy of complications in almost a million of those cases. Researchers developed a robotic simulation training program to provide trainee physicians with more practice on the procedure. A year after deploying the program the team found that all complication types -- mechanical issues, infections and blood clots -- were significantly lower.

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Star Trek's Holodeck is no longer just science fiction. Using AI, engineers have created a tool that can generate 3D environments, prompted by everyday language.

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A new technique can more effectively perform a safety check on an AI chatbot. Researchers enabled their model to prompt a chatbot to generate toxic responses, which are used to prevent the chatbot from giving hateful or harmful answers when deployed.