Lifeboat Foundation

How do plants defend themselves against pathogenic microorganisms? This is a complex puzzle, of which a team of biologists from the University of Amsterdam has solved a new piece. The team, led by Harrold van den Burg, discovered that while the water pores (hydathodes) in leaves provide an entry point for bacteria, they are also an active part of the defense against these invaders. The team’s research has now been published in the journal Current Biology.

Anyone who is used to giving plants plenty of water might know the phenomenon: small droplets of plant sap that sometimes appear at the edge of the leaves, especially at nighttime. When plants take up more water via their roots than they lose through evaporation, they can use their water pores on the leaf margins to release excess water. The pores literally prevent root water pressure from becoming too high. This is an important mechanism, but at the same time, risky. Pathogenic microorganisms can enter the plant’s veins through these sap droplets to colonize the water pores.

Biologists have therefore been asking themselves for a long time: How do plants defend themselves against this wide-open entry point? Are those water pores, the hydathodes, defenseless glands that allow ample entry of harmful pests? Or have they evolved in such a way that they are part of the plant’s line of defense against pathogens?

A team of researchers from Illinois Institute of Technology and the University of Washington is trying to change the way that the field of biology understands how muscles contract.

In a paper published on January 25, 2023, in the Proceedings of the National Academy of Sciences titled “Structural OFF/ON Transition of Myosin in Related Porcine Myocardium Predict Calcium Activated Force,” Illinois Tech Research Assistant Professor Weikang Ma and Professor of Biology and Physics Thomas Irving—working in collaboration with Professor of Bioengineering Michael Regnier’s group at Washington—make the case for a second, newly discovered aspect to muscle contraction that could play a significant role in developing treatments for inherited cardiac conditions.

The consensus for how muscle contraction occurs has been that the relationship between the thin and thick filaments that comprise muscle tissue was a more straightforward process. When targets on thin filaments were activated, it was thought that the myosin motor proteins that make up the thick filaments would automatically find their way to those thin filaments to start generating force and contract the muscle.

Artificial intelligence has already shaved years off research into protein engineering. Now, for the first time, scientists have synthesized proteins predicted by an AI model in the lab, and found them to work just as well as their natural counterparts.

The first time a language model was used to synthesize human proteins.

Of late, AI models are really flexing their muscles. We have recently seen how ChatGPT has become a poster child for platforms that comprehend human languages. Now a team of researchers has tested a language model to create amino acid sequences, showcasing abilities to replicate human biology and evolution.

The language model, which is named ProGen, is capable of generating protein sequences with a certain degree of control. The result was achieved by training the model to learn the composition of proteins. The experiment marks the first time a language model was used to synthesize human proteins.

The man himself:

Blueprint is a public science experiment to determine whether it’s possible to stay the same biological age. This requires slowing down aging processes as much as possible and then reversing the aging that has happened. Currently my speed of aging is .76 (DunedinPACE). That means for every 365 days each year, I age 277 days. My goal is to remain the same age biologically for every 365 days that pass.

Continue reading “Why I Am Spending Millions To Be 18 Again” »

(http://www.evol-net.fr/index.php?option=com_tlpteam&view=team&id=2&Itemid=559) is a Research Director at the French National Centre for Scientific Research (CNRS), the French state research organization and the largest fundamental science agency in Europe.

Dr. Bapteste has both a Ph.D. in evolutionary biology from Pierre and Marie Curie University and a Ph.D. in the philosophy of biology from Pantheon-Sorbonne University.

Continue reading “Dr. Eric Bapteste, Ph.D. — CNRS/AIRE — Interactomics For Deeper Understanding Of Aging And Evolution” »

What led to the emergence of complex organisms on Earth? It’s a significant unanswered question in biology. Researchers from Christa Schleper’s team at the University of Vienna and Martin Pilhofer’s team at ETH Zurich have taken a step towards resolving it. The scientists succeeded in cultivating a special archaeon and characterizing it more precisely using microscopic methods.

This member of the Asgard archaea exhibits unique cellular characteristics and may represent an evolutionary “missing link” to more complex life forms such as animals and plants. The study was recently published in the journal Nature.

All life forms on earth are divided into three major domains: eukaryotes, bacteria and archaea. Eukaryotes include the groups of animals, plants and fungi. Their cells are usually much larger and, at first glance, more complex than the cells of bacteria and archaea. The genetic material of eukaryotes, for example, is packaged in a cell nucleus and the cells also have a large number of other compartments. Cell shape and transport within the eukaryotic cell are also based on an extensive cytoskeleton. But how did the evolutionary leap to such complex eukaryotic cells come about?

A model for information storage in the brain reveals how memories decay with age.

Theoretical constructs called attractor networks provide a model for memory in the brain. A new study of such networks traces the route by which memories are stored and ultimately forgotten [1]. The mathematical model and simulations show that, as they age, memories recorded in patterns of neural activity become chaotic—impossible to predict—before disintegrating into random noise. Whether this behavior occurs in real brains remains to be seen, but the researchers propose looking for it by monitoring how neural activity changes over time in memory-retrieval tasks.

Memories in both artificial and biological neural networks are stored and retrieved as patterns in the way signals are passed among many nodes (neurons) in a network. In an artificial neural network, each node’s output value at any time is determined by the inputs it receives from the other nodes to which it’s connected. Analogously, the likelihood of a biological neuron “firing” (sending out an electrical pulse), as well as the frequency of firing, depends on its inputs. In another analogy with neurons, the links between nodes, which represent synapses, have “weights” that can amplify or reduce the signals they transmit. The weight of a given link is determined by the degree of synchronization of the two nodes that it connects and may be altered as new memories are stored.

This video covers the timelapse of metaverse technologies from 2030 to 3000+. Watch this next video about the Future of Virtual Reality (2030 – 3000+): https://bit.ly/3zfjybO.
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SOURCES:
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• The Singularity Is Near: When Humans Transcend Biology (Ray Kurzweil): https://amzn.to/3ftOhXI

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