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For the last few decades, battery research has largely focused on rechargeable lithium-ion batteries, which are used in everything from electric cars to portable electronics and have improved dramatically in terms of affordability and capacity. But nonrechargeable batteries have seen little improvement during that time, despite their crucial role in many important uses such as implantable medical devices like pacemakers.

Now, researchers at MIT have come up with a way to improve the energy density of these nonrechargeable, or “primary,” batteries. They say it could enable up to a 50% increase in useful lifetime, or a corresponding decrease in size and weight for a given amount of power or energy capacity, while also improving safety, with little or no increase in cost.

The new findings, which involve substituting the conventionally inactive battery electrolyte with a material that is active for energy delivery, are reported today in the journal Proceedings of the National Academy of Sciences, in a paper by MIT Kavanaugh Postdoctoral Fellow Haining Gao, graduate student Alejandro Sevilla, associate professor of mechanical engineering Betar Gallant, and four others at MIT and Caltech.

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

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.”

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.

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.”