Lifeboat Foundation

CIEQSFTTLFACQTAAEIWRAFGYTVKIMVDNGNCRLHVC: these forty letters are a set of instructions for building a sophisticated medical device designed to recognize the flu virus in your body. The device latches onto the virus and deactivates the part of it that breaks into your cells. It is impossibly tiny—smaller than the virus on which it operates—and it can be manufactured, in tremendous quantities, by your own cells. It’s a protein.

Proteins—molecular machines capable of building, transforming, and interacting with other molecules—do most of the work of life. Antibodies, which defend our cells against invaders, are proteins. So are hormones, which deliver messages within us; enzymes, which carry out the chemical reactions we need to generate energy; and the myosin in our muscles, which contract when we move. A protein is a large molecule built from smaller molecules called amino acids. Our bodies use twenty amino acids to create proteins; our cells chain them together, following instructions in our DNA. (Each letter in a protein’s formula represents an amino acid: the first two in the flu-targeting protein above are cysteine and isoleucine.) After they’re assembled, these long chains crumple up into what often look like random globs. But the seeming chaos in their collapse is actually highly choreographed. Identical strings of amino acids almost always “fold” into identical three-dimensional shapes. This reliability allows each cell to create, on demand, its own suite of purpose-built biological tools. “Proteins are the most sophisticated molecules in the known universe,” Neil King, a biochemist at the University of Washington’s Institute for Protein Design (I.P.D.), told me. In their efficiency, refinement, and subtlety, they surpass pretty much anything that human beings can build.

Today, biochemists engineer proteins to fight infections, produce biofuels, and improve food stability. Usually, they tweak formulas that nature has already discovered, often by evolving new versions of naturally occurring proteins in their labs. But “de novo” protein design—design from scratch—has been “the holy grail of protein science for many decades,” Sarel Fleishman, a biochemist at the Weizmann Institute of Science, in Israel, told me. Designer proteins could help us cure diseases; build new kinds of materials and electronics; clean up the environment; create and transform life itself. In 2018, Frances Arnold, a chemical engineer at the California Institute of Technology, shared the Nobel Prize in Chemistry for her work on protein design. In April, when the coronavirus pandemic was peaking on the coasts, we spoke over video chat. Arnold, framed by palm trees, sat outside her home, in sunny Southern California. I asked how she thought about the potential of protein design. “Well, I think you just have to look at the world behind me, right?” she said. “Nature, for billions of years, has figured out how to extract resources from the environment—sunlight, carbon dioxide—and convert those into remarkable, living, functioning machines. That’s what we want to do—and do it sustainably, right? Do it in a way that life can go on.”

This cloud-based quantum computing service includes verification and is now available to members of the IBM Q Network.

IBM and Cambridge Quantum Computing have built a random number generator that uses quantum computing with verification and plan to offer the new capability as a cloud service.

IBM and CQC announced the news Thursday at the final day of the IBM Q Summit. CQC developed the application, which generates true maximal randomness, or entropy.

The world is one step closer to having a totally secure internet and an answer to the growing threat of cyber-attacks, thanks to a team of international scientists who have created a unique prototype that could transform how we communicate online.

The invention led by the University of Bristol, revealed today in the journal Science Advances, has the potential to serve millions of users, is understood to be the largest-ever quantum network of its kind, and could be used to secure people’s online communication, particularly in these internet-led times accelerated by the COVID-19 pandemic.

Human limitations are part of the reason we conjured up meta-humans with DNA that enables them to do things we could never dream of doing, such as fly, turn invisible, and regenerate.

The West African lungfish sounds like a creature spawned from science fiction. It can regrow its tail and fins if hungry jaws snap a part of it off, much like a salamander. Its incredible regeneration abilities indicate that these particular traits came from a common vertebrate ancestor — and humans are also vertebrates. Now evolutionary biologist Igor Schneider and his research team are trying to understand the mechanism behind this almost paranormal power, and how it could apply to a human.

“Hyperdegeneracy” could be used in quantum computing.

Microscopic oil droplets held aloft with optical tweezers can contain more than 200 resonant optical modes of similar energies, creating “hyperdegeneracy” for the first time. That is the claim of researchers in Israel, Spain and the US, who say that their breakthrough could ultimately find application in high-speed optical communications, sensing, quantum data processing and even the creation of dynamic optical circuits.

When optical materials with a high refractive index are formed into certain symmetrical shapes — such as rings, cylinders or spheres —light can be repeatedly reflected around the inside of the material, much in the same way that sound waves pass around the inside edge of St Paul’s Cathedral’s famous “whispering gallery”. The circulating light undergoes constructive interference, forming discrete resonant modes – or so-called degenerate states – with similar energies.

Continue reading “Floating oil droplet contains hundreds of degenerate optical modes” »

Composite fermions are exotic quasi-particles found in interacting 2-D fermion systems at relatively large perpendicular magnetic fields. These quasi-particles, which are composed of an electron and two magnetic flux quanta, have often been used to describe a physical phenomenon known as the fractional quantum Hall effect.

Researchers at Princeton University and Pennsylvania State University recently used composite fermions to test a theory introduced by physicist Felix Bloch almost a century ago, suggesting that at very low densities, a paramagnetic Fermi “sea” of electrons should spontaneously transition to a fully magnetized state, which is now referred to as Bloch ferromagnetism. Their paper, published in Nature Physics, provides evidence of an abrupt transition to full magnetization that is closely aligned with the state theorized by Bloch.

“Composite fermions are truly remarkable,” Mansour Shayegan, professor of Electrical Engineering at Princeton University and one of the researchers who carried out the study, told Phys.org. “They are born of interaction and magnetic flux, and yet they map such a complex system to a simple collection of quasi-particles that to a large degree behave as non-interacting and also behave as if they don’t feel the large magnetic field. One of their most interesting properties is their spin polarization.”

The removal of one gene renders poxviruses—a lethal family of viral infections that are known to spread from animals to humans—harmless, a new study in the journal Science Advances reports.

During this ground-breaking study, scientists from the Spanish National Research Council and the University of Surrey investigated the immune response of cells to poxviruses. Poxviruses, such as cowpox and monkeypox, can spread to humans from infected animals, causing skin lesions, fever, swollen lymph nodes and even death.

Viruses contain genetic material which helps them outsmart host cells, enabling replication and the spread of the infection. Cells in the body are comprised of molecules that sense the presence of viruses, sometimes via the recognition of their genetic material, and alert the immune system of an upcoming infection. Poxviruses, unlike other viruses, are highly unusual in that they have large DNA genomes that are replicated exclusively in the cell cytosol, an area of the cell full of sensors. How poxviruses manage to stay undetectable has remained unknown.

Artificial intelligence (AI) experts at the University of Massachusetts Amherst and the Baylor College of Medicine report that they have successfully addressed what they call a “major, long-standing obstacle to increasing AI capabilities” by drawing inspiration from a human brain memory mechanism known as “replay.”

First author and postdoctoral researcher Gido van de Ven and principal investigator Andreas Tolias at Baylor, with Hava Siegelmann at UMass Amherst, write in Nature Communications that they have developed a new method to protect—” surprisingly efficiently”— deep neural networks from “catastrophic forgetting;” upon learning new lessons, the networks forget what they had learned before.

Siegelmann and colleagues point out that deep neural networks are the main drivers behind recent AI advances, but progress is held back by this forgetting.

Artificial intelligence researchers at North Carolina State University have improved the performance of deep neural networks by combining feature normalization and feature attention modules into a single module that they call attentive normalization (AN). The hybrid module improves the accuracy of the system significantly, while using negligible extra computational power.

“Feature normalization is a crucial element of training deep neural networks, and feature attention is equally important for helping networks highlight which features learned from raw data are most important for accomplishing a given task,” says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at NC State. “But they have mostly been treated separately. We found that combining them made them more efficient and effective.”

To test their AN module, the researchers plugged it into four of the most widely used neural network architectures: ResNets, DenseNets, MobileNetsV2 and AOGNets. They then tested the networks against two industry standard benchmarks: the ImageNet-1000 classification benchmark and the MS-COCO 2017 object detection and instance segmentation benchmark.