Lifeboat Foundation

In the deep subsurface that plunges into the Earth for miles, microscopic organisms inhabit vast bedrock pores and veins. Belowground microorganisms, or microbes, comprise up to half of all living material on the planet and support the existence of all life forms up the food chain. They are essential for realizing an environmentally sustainable future and can change the chemical makeup of minerals, break down pollutants, and alter the composition of groundwater.

While the significance of bacteria and archaea is undeniable, the only evidence of their existence in the deep subsurface comes from traces of biological material that seep through mine walls, cave streams, and drill holes that tap into aquifers.

Many scientists have assumed that the composition of microbial communities in the deep subsurface is primarily shaped by local environmental pressures on microbial survival such as temperature, acidity, and oxygen concentration. This process, environmental selection, can take years to millennia to cause significant community-level changes in slow-growing communities like the subsurface.

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Sustainable cell cultured mollusk seafood products — nikita michelsen, founder & CEO, pearlita foods.

Nikita Michelsen, is Founder & CEO of Pearlita Foods (https://www.pearlitafoods.com/), the world’s first cell-based mollusk company, which is developing sustainably & ethically grown products, like oysters and abalone, that are contaminant free without compromising flavor or nutrition.

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For the function of many biomolecules, their three-dimensional structure is crucial. Researchers are therefore not only interested in the sequence of the individual building blocks of biomolecules, but also in their spatial structure. With the help of artificial intelligence (AI), bioinformaticians can already reliably predict the three-dimensional structure of a protein from its amino acid sequence. For RNA molecules, however, this technology is still in its infancy. Researchers at Ruhr-Universität Bochum (RUB) describe a way to use AI to reliably predict the structure of certain RNA molecules from their nucleotide sequence in the journal PLOS Computational Biology on July 7, 2022.

For the work, the teams led by Vivian Brandenburg and Professor Franz Narberhaus from the RUB Chair of Biology of Microorganisms cooperated with Professor Axel Mosig from the Bioinformatics Competence Area of the Bochum Center for Protein Diagnostics.

As you’re reading this sentence, the cells in your brain, called neurons, are sending rapid-fire electrical signals between each other, transmitting information. They’re doing so via tiny, specialized junctions between them called synapses.

There are many different types of synapses that form between neurons, including “excitatory” or “inhibitory,” and the exact mechanisms by which these structures are generated remain unclear to scientists. A Colorado State University biochemistry lab has uncovered a major insight into this question by showing that the types of chemicals released from synapses ultimately guide which kinds of synapses form between neurons.

Soham Chanda, assistant professor in the Department of Biochemistry and Molecular Biology, led the study published in Nature Communications that demonstrates the possibility of changing the identity of synapses between neurons, both in vitro and in vivo, through enzymatic means. The other senior scientists who contributed to the project were Thomas Südhof of Stanford University and Matthew Xu-Friedman of the University at Buffalo.

A microbial organism pulls electricity from water in the air.

Artificial Intelligence is outgrowing the current pace of Hardware Improvements and requires a new kind of technology to keep up and enable future AI Applications. Scientists seem to have found that creating artificial brains out of nanowire can mimic the human brain and power the biggest and smartest AI models ever made at relatively low energy consumption.

Today’s deep neural networks already mimic one aspect of the brain: its highly interconnected network of neurons. But artificial neurons behave very differently than biological ones, as they only carry out computations. In the brain, neurons are also able to remember their previous activity, which then influences their future behavior. This in-built memory is a crucial aspect of how the brain processes information, and a major strand in neuromorphic engineering focuses on trying to recreate this functionality. This has resulted in a wide range of designs for so-called “memristors”: electrical components whose response depends on the previous signals they have been exposed to.
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Exclusive interview for ageless partners®: augmented fasting; reverse engineering immortality.

I am so happy and intellectually fulfilled to share the following interview I had with Jason C. Mercurio, MFE about Aging and the conclusions I’ve reached after 12 years of intensive research.

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The fundamental rotation of micro and nano-objects is crucial for the functionality of micro and nanorobotics, as well as three-dimensional imaging and lab-on-a-chip systems. These optical rotation methods can function fuel-free and remotely, and are therefore better suited for experiments, while current methods require laser beams with designed intensity profiles or objects with sophisticated shapes. These requirements are challenging for simpler optical setups with light-driven rotation of a variety of objects, including biological cells.

In a new report now published in Science Advances, Hongru Ding and a research team in engineering and materials science at the University of Texas at Austin, U.S., developed a universal approach for the out-of-plane rotation of various objects based on an arbitrary low-power laser beam. The scientists positioned the laser source away from the objects to reduce optical damage from direct illumination and combined the rotation mechanism via optothermal coupling with rigorous experiments, coupled to multiscale simulations. The general applicability and biocompatibility of the universal light-driven rotation platform is instrumental for a range of engineering and scientific applications.