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Category: robotics/AI – Page 1513

Bioprinting in seconds.

Biofabrication technologies, including stereolithography and extrusion-based printing, are revolutionizing the creation of complex engineered tissues. The current paradigm in bioprinting relies on the additive layer-by-layer deposition and assembly of repetitive building blocks, typically cell-laden hydrogel fibers or voxels, single cells, or cellular aggregates. The scalability of these additive manufacturing technologies is limited by their printing velocity, as lengthy biofabrication processes impair cell functionality. Overcoming such limitations, the volumetric bioprinting of clinically relevant sized, anatomically shaped constructs, in a time frame ranging from seconds to tens of seconds is described. An optical-tomography-inspired printing approach, based on visible light projection, is developed to generate cell-laden tissue constructs with high viability (85%) from gelatin-based photoresponsive hydrogels. Free-form architectures, difficult to reproduce with conventional printing, are obtained, including anatomically correct trabecular bone models with embedded angiogenic sprouts and meniscal grafts. The latter undergoes maturation in vitro as the bioprinted chondroprogenitor cells synthesize neo-fibrocartilage matrix. Moreover, free-floating structures are generated, as demonstrated by printing functional hydrogel-based ball-and-cage fluidic valves. Volumetric bioprinting permits the creation of geometrically complex, centimeter-scale constructs at an unprecedented printing velocity, opening new avenues for upscaling the production of hydrogel-based constructs and for their application in tissue engineering, regenerative medicine, and soft robotics.

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DeepMind is using its AI prowess to accelerate scientific work.

AI research lab DeepMind has created the most comprehensive map of human proteins to date using artificial intelligence. The company, a subsidiary of Google-parent Alphabet, is releasing the data for free, with some scientists comparing the potential impact of the work to that of the Human Genome Project, an international effort to map every human gene.

Proteins are long, complex molecules that perform numerous tasks in the body, from building tissue to fighting disease. Their purpose is dictated by their structure, which folds like origami into complex and irregular shapes. Understanding how a protein folds helps explain its function, which in turn helps scientists with a range of tasks — from pursuing fundamental research on how the body works, to designing new medicines and treatments.

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Neuroscientists removed fear from rats by inactivating amygdala — brain region mediating fear.

#Neuroscience #Brain #YuriNeuro #Neurobiology #Amygdala.

Timecodes:
0:00-Introduction.
0:17-Amygdala role in fear regulation.
0:45-Difficulties in exploring prey-predator interaction.
1:02-Lego robot to simulate a predator. Robogator (LEGO Mindstorms robot)
1:53-Fear response before the amygdala inactivation.
2:33-Fear response aftert the amygdala inactivation.
3:59-Amygdala is one of the key regions of the fear regulation.
4:50 — Human-based experiments on the electrical stimulation of amygdala.
6:01-Future prospects. Optogenetics.
6:34-Share your ideas and emotions in the comments.

In this video I review a scientific neuroscience publication :“Amygdala regulates risk of predation in rats foraging in a dynamic fear environment” from University of Washington and Korea University, Seoul. The scientific paper addresses the mechanism of fear regulation in rats. Neuroscientists inactivated neurons of the brain region regulating fear — amygdala. In order to inactivate amygdala neurons neurobiologists applied GABAA receptor agonist muscimol. In this way neuroscientists made the rat fearless. Neurobiologists simulated fear enviroment by using lego robot — Robogator (LEGO Mindstorms robot) programmed to surge toward the animal as it emerges from the nesting area in search of food.

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Large space structures, such as telescopes and spacecraft, should ideally be assembled directly in space, as they are difficult or impossible to launch from Earth as a single piece. In several cases, however, assembling these technologies manually in space is either highly expensive or unfeasible.

In recent years, roboticists have thus been trying to develop systems that could be used to automatically assemble structures in . To simplify this , space structures could have a modular design, which essentially means that they are comprised of different building blocks or modules that can be shifted to create different shapes or forms.

Researchers at the German Aerospace Center (DLR) and Technische Universität München (TUM) have recently developed an autonomous planner that could be used to assemble reconfigurable structures directly in space. This system, introduced in a paper presented at the 2021 IEEE Aerospace Conference, could allow aerospace engineers and astronauts to assemble large structures in space and adapt them for specific use cases, reconfiguring them when necessary.

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There are many reasons for drones to be quick. The professional drone racing circuit aside, speed bodes well when you are searching for survivors on a disaster site, or delivering cargo, or even inspecting critical infrastructure. But how do you get something done in the shortest possible time with limited battery life when you have to navigate through obstacles, changing speeds, and altitude? You use an algorithm.

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DeepMind and EMBL release the most complete database of predicted 3D structures of human proteins.

Partners use AlphaFold, the AI system recognized last year as a solution to the protein structure prediction problem, to release more than 350000 protein structure predictions including the entire human proteome to the scientific community.

DeepMind today announced its partnership with the European Molecular Biology Laboratory (EMBL), Europe’s flagship laboratory for the life sciences, to make the most complete and accurate database yet of predicted protein structure models for the human proteome. This will cover all ~20000 proteins expressed by the human genome, and the data will be freely and openly available to the scientific community. The database and artificial intelligence system provide structural biologists with powerful new tools for examining a protein’s three-dimensional structure, and offer a treasure trove of data that could unlock future advances and herald a new era for AI-enabled biology.

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One of the holy grails of computer science is the development of an AI that can extrapolate from data. A team of researchers from the University of Southern California has announced the development of something profoundly new — a model for an AI with imagination.

Understanding “why” may be the key to unlocking an AI’s imagination.

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Scientists hunting for elusive gravitational waves across the universe may be able to supercharge their discoveries with a new tool: artificial intelligence.

Gravitational waves are ripples in spacetime, created when a massive object is accelerated or disturbed, such as when a black hole and a neutron star collide. Theorized by Albert Einstein, their existence was confirmed in 2015 with the first gravitational wave discovery by researchers using LIGO (the advanced Laser Interferometer Gravitational-Wave Observatory). Now, just six years later, there have been at least 50 gravitational wave events detected.

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