Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 4508

New foundation aims for scientific and rhetorical value – and to run the debug cycle for longevity research.

The Longevity Investors Conference is quickly turning into one of the highlights in the longevity calendar, and we were delighted to be able to interview some of the speakers in a few ‘backstage’ moments.

Held in the exclusive location of Gstaad in Switzerland, The Longevity Investors Conference (LIC) is the world’s leading and most private longevity-focused investors-only conference. Providing relevant insights into the fast-growing field of longevity, the conference also offers expert education and investment opportunities, as well as fostering excellent networking opportunities. Dr Aubrey de Grey was in Gstaad to address the conference on rejuvenation biotechnology as well as being part of a panel discussing where crypto meets longevity.

Visit Longevity. Technology — https://bit.ly/3PwtH8Y

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𝐂𝐨𝐧𝐬𝐜𝐢𝐨𝐮𝐬 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐈𝐬 𝐎𝐧𝐥𝐲 𝐚 𝐌𝐞𝐦𝐨𝐫𝐲 𝐨𝐟 𝐔𝐧𝐜𝐨𝐧𝐬𝐜𝐢𝐨𝐮𝐬 𝐀𝐜𝐭𝐢𝐨𝐧𝐬, 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭𝐬 𝐏𝐫𝐨𝐩𝐨𝐬𝐞 𝐈𝐧 𝐑𝐚𝐝𝐢𝐜𝐚𝐥 𝐍𝐞𝐰 𝐓𝐡𝐞𝐨𝐫𝐲

“𝙒𝙚 𝙥𝙚𝙧𝙘𝙚𝙞𝙫𝙚 𝙩𝙝𝙚 𝙬𝙤𝙧𝙡𝙙 𝙖𝙨 𝙖 𝙢𝙚𝙢𝙤𝙧𝙮,” 𝙩𝙝𝙚 𝙖𝙪𝙩𝙝𝙤𝙧𝙨 𝙤𝙛 𝙖 𝙧𝙚𝙘𝙚𝙣𝙩 𝙥𝙖𝙥𝙚𝙧 𝙬𝙧𝙤𝙩𝙚. “𝙄𝙣 𝙤𝙩𝙝𝙚𝙧 𝙬𝙤𝙧𝙙𝙨, 𝙩𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡𝙡𝙮, 𝙬𝙚 𝙖𝙧𝙚 𝙣𝙤𝙩 𝙘𝙤𝙣𝙨𝙘𝙞𝙤𝙪𝙨𝙡𝙮 𝙥𝙚𝙧𝙘𝙚𝙞𝙫𝙞𝙣𝙜 𝙖𝙣𝙮𝙩𝙝𝙞𝙣𝙜 𝙙𝙞𝙧𝙚𝙘𝙩𝙡𝙮.”

“We perceive the world as a memory,” the authors of a recent paper wrote. “In other words, technically, we are not consciously perceiving anything directly.”

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HBP researchers have trained a large-scale model of the primary visual cortex of the mouse to solve visual tasks in a highly robust way. The model provides the basis for a new generation of neural network models. Due to their versatility and energy-efficient processing, these models can contribute to advances in neuromorphic computing.

Modeling the brain can have a massive impact on artificial intelligence (AI): since the brain processes images in a much more energy-efficient way than artificial networks, scientists take inspiration from neuroscience to create neural networks that function similarly to the biological ones to significantly save energy.

In that sense, brain-inspired neural networks are likely to have an impact on future technology, by serving as blueprints for visual processing in more energy-efficient neuromorphic hardware. Now, a study by Human Brain Project (HBP) researchers from the Graz University of Technology (Austria) showed how a large data-based model can reproduce a number of the brain’s visual processing capabilities in a versatile and accurate way. The results were published in the journal Science Advances.

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You can’t move a pharmaceutical scientist from a lab to a kitchen and expect the same research output. Enzymes behave exactly the same: They are dependent upon a specific environment. But now, in a study recently published in ACS Synthetic Biology, researchers from Osaka University have imparted an analogous level of adaptability to enzymes, a goal that has remained elusive for over 30 years.

Enzymes perform impressive functions, enabled by the unique arrangement of their constituent amino acids, but usually only within a specific cellular environment. When you change the cellular environment, the enzyme rarely functions well—if at all. Thus, a long-standing research goal has been to retain or even improve upon the function of enzymes in different environments; for example, conditions that are favorable for biofuel production. Traditionally, such work has involved extensive experimental trial-and-error that might have little assurance of achieving an optimal result.

Artificial intelligence (a computer-based tool) can minimize this trial-and-error, but still relies on experimentally obtained crystal structures of enzymes—which can be unavailable or not especially useful. Thus, “the pertinent amino acids one should mutate in the enzyme might be only best-guesses,” says Teppei Niide, co-senior author. “To solve this problem, we devised a methodology of ranking amino acids that depends only on the widely available amino acid sequence of analogous enzymes from other living species.”

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Genetic engineering is a rapidly progressing scientific discipline, with tremendous current application and future potential. It’s a bit dizzying for a science communicator who is not directly involved in genetics research to keep up. I do have some graduate level training in genetics so at least I understand the language enough to try to translate the latest research for a general audience.

Many readers have by now heard of CRISPR – a powerful method of altering or silencing genes that brings down the cost and complexity so that almost any genetics lab can use this technique. CRISPR is actually just the latest of several powerful gene-altering techniques, such as TALEN. CRISPR is essentially a way to target a specific sequence of the DNA, and then deliver a package which does something, like splice the DNA. But you also need to target the correct cells. In a petri dish, this is simple. But in living organism, this is a huge challenge. We have developed several viral vectors that can be targeted to specific cell types in order to deliver the CRIPR (or TALEN), which then targets the specific DNA.

Now I would like to present a different technique I have not previously written about here – alternative splicing. A recent study presents what seems like a significant advance in this technology, so it’s a good time to review it. “Alternative splicing” refers to a natural phenomenon of genetics. Genes are composed of introns and exons. I always thought the nomenclature was counterintuitive, but the exons are actually the part of the gene that gets expressed into a protein. The introns are the part that is not expressed, so they are cut out of the gene when it is being converted into mRNA, and the exons are stitched together to form the sequence that is translated into a protein. Alternative splicing refers to the fact that the way in which the introns are removed and the exons stitched together can vary, creating alternative forms of the resulting protein.

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ARI gene groups (ARI downregulated genes, those highly expressed in BA17 and BA39/40 relative to other regions in controls; ARI upregulated genes, those expressed at low level in BA17 and BA39/40 relative to other regions in controls) were created through taking the union (without duplicates) across all ten identified ASD-attenuated regional comparisons, and sorting genes into the two groups based on gene-expression profiles across regions. The details of this process are described in the Supplementary Methods, along with functional annotation procedures.

Standard workflows using WGCNA17 were followed as previously described in Parikshak et al.5 and Gandal et al.1 (with minor modifications) to identify gene and transcript co-expression modules. Details regarding network formation, module identification, and module functional characterization are described in the Supplementary Methods.

Frozen brain samples were placed on dry ice in a dehydrated dissection chamber to reduce degradation effects from sample thawing and/or humidity. Approximately 50 mg of cortex was sectioned, ensuring specific grey matter–white matter boundary. The tissue section was homogenized in RNase-free conditions with a light detergent briefly on ice using a dounce homogenizer, filtered through a 40-μM filter and centrifuged at 1,000 g for 8 min at 4 °C. The pelleted nuclei were then filtered through a two-part micro gradient (30%/50%) for 20 min at 4 °C. Clean nuclei were pelleted away from debris. The nuclei were washed two more times with PBS/1%BSA/RNase and spun down at 500 g for 5 min. Cells were inspected for quality (shape, colour and membrane integrity) and counted on a Countess II instrument. They were then loaded onto the 10X Genomics platform to isolate single nuclei and generate libraries for RNA sequencing on the NovaS4 or NovaS2 Illumina machines.

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