Lifeboat Foundation

Protons may have more “charm” than we thought, new research suggests.

A proton is one of the subatomic particles that make up the nucleus of an atom. As small as protons are, they are composed of even tinier elementary particles known as quarks, which come in a variety of “flavors,” or types: up, down, strange, charm, bottom, and top.

Typically, a proton is thought to be made of two up quarks and one down quark. But a new study finds it’s more complicated than that.

Biophysical Therapeutics, a drug discovery platform company that leverages computational biology, has emerged from stealth. The primary targets of the Delaware-based company are cancer, the diseases of aging (including Alzheimer’s disease) and – excitingly – aging itself.

Founded by Dr Michael Forrest, a Cambridge University biochemistry graduate with a PhD in computer science, Biophysical Therapeutics boasts renowned biotech entrepreneur Professor George Church (of Harvard Medical School) as an advisor to the company. Professor Bruno Conti of the Scripps Institute in La Jolla, California is also an advisor.

Longevity. Technology: Back in 2006, Conti and his team reported an exciting result in the prestigious journal Science. They showed (in female mice) that slightly reducing the metabolic rate by slightly reducing metabolic heat generation (decreasing body temperature by 0.34°C) increased lifespan by 20%.

HRL Laboratories, LLC, has published the first demonstration of universal control of encoded spin qubits. This newly emerging approach to quantum computation uses a novel silicon-based qubit device architecture, fabricated in HRL’s Malibu cleanroom, to trap single electrons in quantum dots. Spins of three such single electrons host energy-degenerate qubit states, which are controlled by nearest-neighbor contact interactions that partially swap spin states with those of their neighbors.

Summary: Researchers investigate brain region synchronization in order to assist control of brain-machine interfaces.

Source: UPF Barcelona.

Just a few decades ago, the possibility of connecting the brain with a computer to convert neural signals into concrete actions would have seemed like something from science fiction.

Even a couple of years ago, the idea that artificial intelligence might be conscious and capable of subjective experience seemed like pure science fiction. But in recent months, we’ve witnessed a dizzying flurry of developments in AI, including language models like ChatGPT and Bing Chat with remarkable skill at seemingly human conversation.

Given these rapid shifts and the flood of money and talent devoted to developing ever smarter, more humanlike systems, it will become increasingly plausible that AI systems could exhibit something like consciousness. But if we find ourselves seriously questioning whether they are capable of real emotions and suffering, we face a potentially catastrophic moral dilemma: either give those systems rights, or don’t.

Experts are already contemplating the possibility. In February 2022, Ilya Sutskever, chief scientist at OpenAI, publicly pondered whether “today’s large neural networks are slightly conscious.” A few months later, Google engineer Blake Lemoine made international headlines when he declared that the computer language model, or chatbot, LaMDA might have real emotions. Ordinary users of Replika, advertised as “the world’s best AI friend,” sometimes report falling in love with it.

Wake up and smell the cortex.

ABOVE: © ISTOCK.COM, CHRISTOPH BURGSTEDT

Artificial intelligence algorithms have had a meteoric impact on protein structure, such as when DeepMind’s AlphaFold2 predicted the structures of 200 million proteins. Now, David Baker and his team of biochemists at the University of Washington have taken protein-folding AI a step further. In a Nature publication from February 22, they outlined how they used AI to design tailor-made, functional proteins that they could synthesize and produce in live cells, creating new opportunities for protein engineering. Ali Madani, founder and CEO of Profluent, a company that uses other AI technology to design proteins, says this study “went the distance” in protein design and remarks that we’re now witnessing “the burgeoning of a new field.”

Proteins are made up of different combinations of amino acids linked together in folded chains, producing a boundless variety of 3D shapes. Predicting a protein’s 3D structure based on its sequence alone is an impossible task for the human mind, owing to numerous factors that govern protein folding, such as the sequence and length of the biomolecule’s amino acids, how it interacts with other molecules, and the sugars added to its surface. Instead, scientists have determined protein structure for decades using experimental techniques such as X-ray crystallography, which can resolve protein folds in atomic detail by diffracting X-rays through crystallized protein. But such methods are expensive, time-consuming, and depend on skillful execution. Still, scientists using these techniques have managed to resolve thousands of protein structures, creating a wealth of data that could then be used to train AI algorithms to determine the structures of other proteins. DeepMind famously demonstrated that machine learning could predict a protein’s structure from its amino acid sequence with the AlphaFold system and then improved its accuracy by training AlphaFold2 on 170,000 protein structures.

Hydrogen power!

The first flight of a hydrogen-powered airplane was a success this week, showing zero-emissions air travel could be on the horizon.

Researchers at Kyushu University, the National Institute of Advanced Industrial Science and Technology (AIST) and Osaka University in Japan have recently introduced a new strategy for synthesizing multi-layer hexagonal boron nitride (hBN), a material that could be used to integrate different 2D materials in electronic devices, while preserving their unique properties. Their proposed approach, outlined in a paper published in Nature Electronics, could facilitate the fabrication of new highly performing graphene-based devices.

“The atomically flat 2D insulator hBN is a key material for the integration of 2D materials into electronic devices,” Hiroki Ago, one of the researchers who carried out the study, told Tech Xplore. “For example, the highest carrier mobility in monolayer graphene is achieved only when it is sandwiched by multilayer hBN. Superconductivity observed in twisted bilayer graphene also needs multilayer hBN to isolate from environment.”

In addition to its value for fabricating graphene-based devices, hBN can also be used to integrate transition metal dichalcogenides (TMDs) in devices, achieving strong photoluminescence and high carrier mobility. It can also be valuable for conducting studies focusing on moiré physics.