Here’s a very nice review on the molecular biology of endocytosis and endosomal trafficking. Found it very helpful for better understanding these pathways.
This brief overview of endocytic trafficking is written in honor of Renate Fuchs, who retires this year. In the mid-1980s, Renate pioneered studies on the ion-conducting properties of the recently discovered early and late endosomes and the mechanisms governing endosomal acidification. As described in this review, after uptake through one of many mechanistically distinct endocytic pathways, internalized proteins merge into a common early/sorting endosome. From there they again diverge along distinct sorting pathways, back to the cell surface, on to the trans-Golgi network or across polarized cells. Other transmembrane receptors are packaged into intraluminal vesicles of late endosomes/multivesicular bodies that eventually fuse with and deliver their content to lysosomes for degradation. Endosomal acidification, in part, determines sorting along this pathway.
Optogenetics, Biohybrid Implants And The Future Of Brain-Computer Interfaces — Dr. Alan Mardinly Ph.D. — CSO & Co-Founder, Science
What if we could restore vision, communicate directly with the brain, and even extend human life—not with machines alone, but with living, engineered biology?
Dr. Alan Mardinly, Ph.D. is the Chief Scientific Officer and Co-Founder of Science Corp. (https://science.xyz/), a neurotechnology company developing next-generation brain interfaces and biohybrid neural implants aimed at restoring human function.
Dr. Mardinly leads the company’s biohybrid program, focused on combining genetically engineered cells with advanced optical hardware to create optogenetic therapies for vision restoration and new types of brain-machine interfaces.
Dr. Mardinly has spent more than 15 years working at the intersection of neuroscience, genetics, and neural engineering.
Creating artificial systems that mimic the functioning of cells is one of the goals of what is known as synthetic biology. These models, known as synthetic or biomimetic cells, allow some of the basic processes of life to be reproduced in the laboratory to better understand how natural cells work and develop new technologies. In this context, a study involving a team of researchers from the Center for Research in Biological Chemistry and Molecular Materials (CiQUS) of the University of Santiago (USC) proposes a more flexible chemical strategy to create this type of system.
The objective, explain the researchers, is to design structures that mimic certain cellular functions and that can be used as small chemical reactors. The study is published in the Journal of the American Chemical Society.
“The idea is to try to replicate cellular functions at the level of encapsulation of communication enzymes,” explains researcher Lucas García, referring to artificial systems capable of recreating processes that in real cells allow, for example, different reactions to take place within the same compartment.
When people are talking, they can start to unconsciously mirror each other, for instance, in the words they use, their sentence structures and even hand gestures. This tendency to mirror others can lead to smoother conversations, while also fostering empathy and collaboration.
Past studies found that alignment can vary from person to person and that some individuals are more prone to mirroring others while they are interacting with them. However, earlier research did not conclusively establish whether alignment is a stable trait or if it varies based on situation-related factors.
Researchers at Aarhus University recently set out to address this unanswered question by analyzing large collections of recorded conversations used in linguistics research. The findings of their study, published in Proceedings of the Royal Society B: Biological Sciences, suggest that alignment is not a fixed trait, as the extent to which people mirror others varies greatly across different situations.
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What happens when human labor becomes mathematically obsolete? For thousands of years, the global economy has run on the biological engine of human workers. But a new era has arrived: The Physical Singularity.
In this video, we break down the brutal thermodynamics of the labor inversion, revealing how major AI companies are mass-producing humanoid robots that operate for just 57 cents an hour. We expose the massive industry shift from digital generation to “World Models,” and how China’s manufacturing miracle is driving hardware costs to zero. With 10 billion robots projected by the 2040s, experts like Geoffrey Hinton are warning of a hive-mind “alien intelligence.” The digital era is over. The physical agent era has begun.
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Sources & Research Links:
The 57¢ / Hour Labor Inversion Math: https://www.ark-invest.com/articles/valuation-models/ark-pub…oid-robots.
Unitree G1 Official $16,000 Pricing: https://www.unitree.com/g1/
China’s 2024 Robotics Dominance (IFR Report): https://ifr.org/ifr-press-releases/news/china-dominates-industrial-robot-market.
Elon Musk’s 10 Billion Robot Prediction: https://www.youtube.com/watch?v=ODsjGOGX_oM
Geoffrey Hinton on AI Hive Mind (“Immortality, but it’s not for us”): https://www.youtube.com/watch?v=qpoRO378qRY
Geordie Rose on Alien Intelligence (“The same way you don’t care about an ant”): https://www.youtube.com/watch?v=1pd4i2YlGmc.
DeepSeek AI Cost Efficiency Breakthroughs: https://www.deepseek.com/
Timestamps:
00;00 — The 57¢ Workforce & The Great Deception.
02;48 — The Math of the Labor Inversion.
05;01 — Why OpenAI Killed Sora (World Models)
09;16 — The Manufacturing Miracle: China’s Hardware Collapse.
12;53 — 10 Billion Robots & Alien Intelligence.
15;58 — How to Survive the Singularity.
Disclaimer:
The content in this video is for educational and informational purposes only and does not constitute financial or investment advice. The views and opinions expressed in this video are based on current research and industry trends, which are subject to rapid change. We do not guarantee the accuracy or completeness of the projections discussed. Copyright Disclaimer under section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, education, and research.
#PhysicalSingularity #HumanoidRobots #ArtificialIntelligence #OpenAI #FutureOfWork #TechTrends
We’re often seduced by the idea that the mind is a computer, and that consciousness is just a matter of running the right code. But philosopher Peter Godfrey-Smith, renowned for his work on octopus minds, disagrees. Fresh research into animal minds—from bees to jellyfish—suggests that consciousness arises not from software but from electrical oscillations moving rhythmically across cell membranes in living brains. And those oscillations, Godfrey-Smith argues, are unlikely to be reproducible in artificial hardware. Perhaps, then, only living brains can truly be conscious.
Late in the previous century, there seemed to be good reasons to think that the physical make-up of a system could not matter much to whether that system had a mind. The organization of the system is what matters, people thought, and physically different systems can be organized the same way. As a result, artificial minds making use of ordinary computer hardware should be possible. This whole discussion was hypothetical, because there weren’t any convincing possible cases of artificial minds to worry about.
Since then, two things have happened. From around 2022, we’ve been confronted with candidates for artificial minds that are disturbingly impressive. These are the LLM systems, such as ChatGPT. But reasons have emerged to doubt that the physical make-up of a system is irrelevant and minds are “substrate independent.” A view sometimes called biological naturalism holds that the biological details of nervous systems might make a difference to whether a physical system has a mind. (The term was coined, with this sense at least, by John Searle.) But if nervous systems and brains are special, what is it that makes them special?
In a recent study published in Nature Communications, researchers created a memristor that uses a built-in oxygen gradient to produce slow, stable conductance changes, enabling a reinforcement learning (RL) algorithm to learn faster and more stably than conventional approaches.
Reinforcement learning stands as one of the most promising ways to achieve continual learning in AI. The idea is to replicate how biological systems acquire and adapt knowledge slowly over time. The brain achieves this via ion gradients that regulate slow, directional signaling across cell membranes. Replicating this in hardware is a key goal of neuromorphic computing.
With their ability to mimic synaptic behavior, memristors have long been considered strong candidates for this. However, most existing devices suffer from unpredictable, abrupt conductance changes, making sustained and stable learning difficult.
Cycles in the growth and decay of Antarctica’s ice sheets once shaped marine biological productivity thousands of miles away in the subtropical ocean, according to new research led by scientists at the University of Wisconsin-Madison. The study, published in the Proceedings of the National Academy of Sciences, found that the obliquity cycle—a 40,000-year astronomical cycle tied to changes in Earth’s axial tilt—influenced ocean productivity in subtropical latitudes about 34 million years ago, when the Antarctic ice sheet was first expanding.
The finding surprised researchers because the 40,000-year cycle, while an important factor in the conditions at Earth’s poles, typically has a more limited influence on climate and ocean conditions near the equator.
“We generally expect other astronomical cycles to have a greater influence,” says Stephen Meyers, a professor of geoscience at UW-Madison and one of the study’s lead authors.