For centuries, scientists have explored the limits of biological jump height, and for decades, engineers have designed jumping machines that often mimicked or took inspiration from biological jumpers. Despite these efforts, general analyses are missing that compare the energetics of biological and engineered jumpers across scale. Here we show how biological and engineered jumpers have key differences in their jump energetics. The jump height of a biological jumper is limited by the work its linear motor (muscle) can produce in a single stroke. By contrast, the jump height of an engineered device can be far greater because its ratcheted or rotary motor can 'multiply work' during repeated strokes or rotations. As a consequence of these differences in energy production, biological and engineered jumpers should have divergent designs for maximizing jump height. Following these insights, we created a device that can jump over 30 metres high, to our knowledge far higher than previous engineered jumpers and over an order of magnitude higher than the best biological jumpers. Our work advances the understanding of jumping, shows a new level of performance, and underscores the importance of considering the differences between engineered and biological systems.
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http://dx.doi.org/10.1038/s41586-022-04606-3 | DOI Listing |
Knee Surg Relat Res
December 2024
Center for Shockwave Medicine and Tissue Engineering, Medical Research, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, 833, Taiwan.
Angew Chem Int Ed Engl
November 2024
Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17489, Greifswald, Germany.
The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures has advanced our understanding of how life's molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning-based protein structure prediction.
View Article and Find Full Text PDFNature
November 2024
Machine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
A critical challenge in glioma treatment is detecting tumour infiltration during surgery to achieve safe maximal resection. Unfortunately, safely resectable residual tumour is found in the majority of patients with glioma after surgery, causing early recurrence and decreased survival. Here we present FastGlioma, a visual foundation model for fast (<10 s) and accurate detection of glioma infiltration in fresh, unprocessed surgical tissue.
View Article and Find Full Text PDFCommun Biol
October 2024
School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.
The work by Hassabis and Jumper on protein structure prediction together with Baker’s supremacy in de novo protein design set the stage for a future where AI not only deciphers biology at the atomic level but also designs new molecules for biotechnology, medicine, and beyond. I provide an overview of the recent past, the present, and the future of AI in structural biology, from how it all started with the Critical Assessment of Structure Prediction (CASP) experiments and a protein engineering lab, to how the field could further evolve with AI models that eventually “understand” biology holistically.
View Article and Find Full Text PDFSci Robot
August 2024
State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
In contrast with jumping robots made from rigid materials, soft jumpers composed of compliant and elastically deformable materials exhibit superior impact resistance and mechanically robust functionality. However, recent efforts to create stimuli-responsive jumpers from soft materials were limited in their response speed, takeoff velocity, and travel distance. Here, we report a magnetic-driven, ultrafast bistable soft jumper that exhibits good jumping capability (jumping more than 108 body heights with a takeoff velocity of more than 2 meters per second) and fast response time (less than 15 milliseconds) compared with previous soft jumping robots.
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