Human astrocytes are larger and more complex than those of infraprimate mammals, suggesting that their role in neural processing has expanded with evolution. To assess the cell-autonomous and species-selective properties of human glia, we engrafted human glial progenitor cells (GPCs) into neonatal immunodeficient mice. Upon maturation, the recipient brains exhibited large numbers and high proportions of both human glial progenitors and astrocytes. The engrafted human glia were gap-junction-coupled to host astroglia, yet retained the size and pleomorphism of hominid astroglia, and propagated Ca2+ signals 3-fold faster than their hosts. Long-term potentiation (LTP) was sharply enhanced in the human glial chimeric mice, as was their learning, as assessed by Barnes maze navigation, object-location memory, and both contextual and tone fear conditioning. Mice allografted with murine GPCs showed no enhancement of either LTP or learning. These findings indicate that human glia differentially enhance both activity-dependent plasticity and learning in mice.
The team studied the long, jointed legs of the crane fly, an insect known for its slow, deliberate movements. These legs appear perfectly designed to absorb impact and ensure a gentle touchdown.
Inspired by this natural design, the engineers outfitted the RoboBee with its own set of long, double-jointed legs. These new appendages are designed to cushion the impact as the robot transitions from air to ground, protecting its delicate components.
AI-generated avatars are increasingly used to deliver science content on platforms like TikTok, raising questions about how their appearance affects viewer trust.
Most energy generators currently employed within the electronics industry are based on inorganic piezoelectric materials that are not bio-compatible and contribute to the pollution of the environment on Earth. In recent years, some electronics researchers and chemical engineers have thus been trying to develop alternative devices that can generate electricity for medical implants, wearable electronics, robots and other electronics harnessing organic materials that are safe, bio-compatible and non-toxic.
Researchers at the Materials Science Centre, Indian Institute of Technology Kharagpur recently introduced a new device based on seeds from the mimosa pudica plant, which can serve both as a bio-piezoelectric nanogenerator and a self-chargeable supercapacitor. Their proposed device, outlined in a paper published in the Chemical Engineering Journal, was found to achieve remarkable efficiencies, while also having a lesser adverse impact on the environment.
“This study was motivated by the need for biocompatible, self-sustaining energy systems to power implantable medical devices (e.g., pacemakers, neurostimulators) and wearable electronics,” Prof. Dr. Bhanu Bhusan Khatua, senior author of the paper, told Tech Xplore.
“Disembodied Brains: Understanding our Intuitions on Human-Animal Neuro-Chimeras and Human Brain Organoids” by John H. Evans Book Link: https://amzn.to/40SSifF “Introduction to Organoid Intelligence: Lecture Notes on Computer Science” by Daniel Szelogowski Book Link: https://amzn.to/3Eqzf4C “The Emerging Field of Human Neural Organoids, Transplants, and Chimeras: Science, Ethics, and Governance” by The National Academy of Sciences, Engineering and Medicine Book Link: https://amzn.to/4hLR1Oe (Affiliate links: If you use these links to buy something, I may earn a commission at no extra cost to you.) Playlist: • Two AI’s Discuss: The Quantum Physics… The hosts explore the ethical and scientific implications of brain organoids and synthetic biological intelligence (SBI). Several sources discuss the potential for consciousness and sentience in these systems, prompting debate on their moral status and the need for ethical guidelines in research. A key focus is determining at what point, if any, brain organoids or SBI merit moral consideration similar to that afforded to humans or animals, influencing research limitations and regulations. The texts also examine the use of brain organoids as a replacement for animal testing in research, highlighting the potential benefits and challenges of this approach. Finally, the development of “Organoid Intelligence” (OI), combining organoids with AI, is presented as a promising but ethically complex frontier in biocomputing. Our sources discuss several types of brain organoids, which are 3D tissue cultures derived from human pluripotent stem cells (hPSCs) that self-organize to model features of the developing human brain. Here’s a brief overview: • Cerebral Organoids: This term is often used interchangeably with “brain organoids”. They are designed to model the human neocortex and can exhibit complex brain activity. These organoids can replicate the development of the brain in-vitro up to the mid-fetal period. • Cortical Organoids: These are a type of brain organoid specifically intended to model the human neocortex. They are formed of a single type of tissue and represent one important brain region. They have been shown to develop nerve tracts with functional output. • Whole-brain Organoids: These organoids are not developed with a specific focus, like the forebrain or cerebellum. They show electrical activity very similar to that of preterm infant brains. • Region-specific Organoids: These are designed to model specific regions of the brain such as the forebrain, midbrain, or hypothalamus. For example, midbrain-specific organoids can contain functional dopaminergic and neuromelanin-producing neurons. • Optic Vesicle-containing Brain Organoids (OVB-organoids): These organoids develop bilateral optic vesicles, which are light sensitive, and contain cellular components of a developing optic vesicle, including primitive corneal epithelial and lens-like cells, retinal pigment epithelia, retinal progenitor cells, axon-like projections, and electrically active neuronal networks. • Brain Assembloids: These are created when organoids from different parts of the brain are placed next to each other, forming links. • Brainspheres/Cortical Spheroids: These are simpler models that primarily resemble the developing in-vivo human prenatal brain, and are particularly useful for studying the cortex. Unlike brain organoids, they do not typically represent multiple brain regions. • Mini-brains: This term has been debunked in favor of the more accurate “brain organoid”. These various types of brain organoids offer diverse models for studying brain development, function, and disease. Researchers are also working to improve these models by incorporating features like vascularization and sensory input. #BrainOrganoids #organoid #Bioethics #OrganoidIntelligence #WetwareComputing #Sentience #ArtificialConsciousness #Neuroethics #AI #Biocomputing #NeuralNetworks #ConsciousnessResearch #PrecautionaryPrinciple #AnimalTestingAlternatives #ResearchEthics #EmergingTechnology #skeptic #podcast #synopsis #books #bookreview #ai #artificialintelligence #booktube #aigenerated #documentary #alternativeviews #aideepdive #science #hiddenhistory #futurism #videoessay #ethics
Tesla’s Full Self-Driving (FSD) technology is rapidly advancing, impressing users and analysts alike, while navigating challenges in the auto industry and broader economic factors.
Questions to inspire discussion.
Tesla’s FSD Progress.
🚗 Q: How many unsupervised miles has Tesla’s FSD driven? A: Tesla’s FSD has driven over 50,000 unsupervised miles, demonstrating significant progress in autonomous driving capabilities.
🌐 Q: What indicates Tesla’s transition to software-defined earnings? A: FSD unsupervised miles and operating domain growth are key leading indicators of Tesla’s shift towards software-defined earnings.
🤖 Q: How does Tesla’s FSD showcase AI potential in driving? A: Tesla’s FSD unsupervised capabilities, demonstrated in complex driving scenarios, serve as a proof case for artificial intelligence’s potential in autonomous driving.
A research team from the Institute of Statistical Mathematics and Panasonic Holdings Corporation has developed a machine learning algorithm, ShotgunCSP, that enables fast and accurate prediction of crystal structures from material compositions. The algorithm achieved world-leading performance in crystal structure prediction benchmarks.
Crystal structure prediction seeks to identify the stable or metastable crystal structures for any given chemical compound adopted under specific conditions. Traditionally, this process relies on iterative energy evaluations using time-consuming first-principles calculations and solving energy minimization problems to find stable atomic configurations. This challenge has been a cornerstone of materials science since the early 20th century.
Recently, advancements in computational technology and generative AI have enabled new approaches in this field. However, for large-scale or complex molecular systems, the exhaustive exploration of vast phase spaces demands enormous computational resources, making it an unresolved issue in materials science.
Researchers at the National University of Singapore (NUS) have shown that a single, standard silicon transistor, the core component of microchips found in computers, smartphones, and nearly all modern electronics, can mimic the functions of both a biological neuron and synapse.
A synapse is a specialized junction between nerve cells that allows for the transfer of electrical or chemical signals, through the release of neurotransmitters by the presynaptic neuron and the binding of receptors on the postsynaptic neuron. It plays a key role in communication between neurons and in various physiological processes including perception, movement, and memory.
