Lifeboat Foundation

Earth’s potassium arrived by meteoritic delivery service finds new research led by Carnegie’s Nicole Nie and Da Wang. Their work, published in Science, shows that some primitive meteorites contain a different mix of potassium isotopes than those found in other, more-chemically processed meteorites. These results can help elucidate the processes that shaped our solar system and determined the composition of its planets.

“The extreme conditions found in stellar interiors enable stars to manufacture elements using nuclear fusion,” explained Nie, a former Carnegie postdoc now at Caltech. “Each stellar generation seeds the raw material from which subsequent generations are born and we can trace the history of this material across time.”

Some of the material produced in the interiors of stars can be ejected out into space, where it accumulates as a cloud of gas and dust. More than 4.5 billion years ago, one such cloud collapsed in on itself to form our sun.

An international team of scientists have demonstrated a leap in preserving the quantum coherence of quantum dot spin qubits as part of the global push for practical quantum networks and quantum computers.

These technologies will be transformative to a broad range of industries and research efforts: from the security of information transfer, through the search for materials and chemicals with novel properties, to measurements of fundamental physical phenomena requiring precise time synchronization among the sensors.

Spin-photon interfaces are elementary building blocks for quantum networks that allow converting stationary quantum information (such as the quantum state of an ion or a solid-state spin qubit) into light, namely photons, that can be distributed over large distances. A major challenge is to find an interface that is both good at storing quantum information and efficient at converting it into light.

Cancer cells can shrink or super-size themselves to survive drug treatment or other challenges within their environment, researchers have discovered.

Scientists at The Institute of Cancer Research, London, combined biochemical profiling technologies with mathematical analyses to reveal how genetic changes lead to differences in the size of cancer cells—and how these changes could be exploited by new treatments.

The researchers believe smaller cells could be more vulnerable to DNA-damaging agents like chemotherapy combined with targeted drugs, while larger cancer cells might respond better to immunotherapy.

The AI, called ProGen, works in a similar way to AIs that can generate text. ProGen learned how to generate new proteins by learning the grammar of how amino acids combine to form 280 million existing proteins. Instead of the researchers choosing a topic for the AI to write about, they could specify a group of similar proteins for it to focus on. In this case, they chose a group of proteins with antimicrobial activity.

The researchers programmed checks into the AI’s process so it wouldn’t produce amino acid “gibberish”, but they also tested a sample of the AI-proposed molecules in real cells. Of the 100 molecules they physically created, 66 participated in chemical reactions similar to those of natural proteins that destroy bacteria in egg whites and saliva. This suggested that these new proteins could also kill bacteria.

The researchers selected the five proteins with the most intense reactions and added them to a sample of Escherichia coli bacteria. Two of the proteins destroyed the bacteria.

Biological and chemical weapons have the potential to pose a national security threat to the U.S. that the country is not equipped to handle, a panel of lawmakers and a military leader told an audience at the Aspen Security Forum.

The ability to see invisible structures in our bodies, like the inner workings of cells, or the aggregation of proteins, depends on the quality of one’s microscope. Ever since the first optical microscopes were invented in the 17th century, scientists have pushed for new ways to see more things more clearly, at smaller scales and deeper depths.

Randy Bartels, professor in the Department of Electrical Engineering at Colorado State University, is one of those scientists. He and a team of researchers at CSU and Colorado School of Mines are on a quest to invent some of the world’s most powerful light microscopes—ones that can resolve large swaths of biological material in unimaginable detail.

The name of the game is super–resolution microscopy, which is any optical imaging technique that can resolve things smaller than half the wavelength of light. The discipline was the subject of the 2014 Nobel Prize in Chemistry, and Bartels and others are in a race to keep circumventing that diffraction limit to illuminate biologically important structures inside the body.

Recent experiments conducted in Boston labs have shown reverse aging results among mice and could show similar results in people.

The combined experiments — which were conducted during a span of 13 years — published Thursday (January 12) in the scientific journal Cell reported that old, blind mice regained eyesight, developed smarter brains and built healthier muscle and kidney tissue, challenging the theory that DNA was the only cause of aging, as it proved that chemical and structural changes to chromatin played a factor without altering genetic code.

The research showed that a breakdown in epigenetic information caused the mice to age and the restoration of the epigenome reversed aging effects.

Researchers have developed a robot that brings speed, agility and reproducibility to laboratory-scale coin cell batteries.

Until now, laboratories studying battery technology have had to choose between the freedom to iterate and optimise battery chemistry by manually assembling each individual cell, and the reproducibility and speed of large-scale production. AutoBass (Automated battery assembly system), the first laboratory-scale coin cell assembly robot of its kind, is designed to bridge this gap.

Developed by a team from Helmholtz Institute Ulm and Karlsruhe Institute of Technology in Germany, AutoBass promises to improve characterisation of coin cell batteries and promote reproducibility by photographing each individual cell at key points in the assembly process. It produces batches of 64 cells a day.

The brain is often regarded as a soft-matter chemical computer, but the way it processes information is very different to that of conventional silicon circuits. Three groups now describe chemical systems capable of storing information in a manner that resembles the way that neurons communicate with one another at synaptic junctions. Such ‘neuromorphic’ devices could provide very-low-power computation and act as interfaces between conventional electronics and ‘wet’ chemical systems, potentially including neurons and other living cells themselves.

At a synapse, the electrical pulse or action potential that travels along a neuron triggers the release of neurotransmitter molecules that bridge the junction to the next neuron, altering the state of the second neuron by making it more or less likely to fire its own action potential. If one neuron repeatedly influences another, the connection between them may become strengthened. This is how information is thought to become imprinted as a memory, a process called Hebbian learning. The ability of synapses to adjust their connectivity in response to input signals is called plasticity, and in neural networks it typically happens on two timescales. Short-term plasticity (STP) creates connectivity patterns that fade quite fast and are used to filter and process sensory signals, while long-term plasticity (LTP, also called long-term potentiation) imprints more long-lived memories. Both biological processes are still imperfectly understood.

Neuromorphic circuits that display such learning behaviour have been developed previously using solid-state electronic devices called memristors, two-terminal devices in which the relationship between the current that passes through and the voltage applied depends on the charge that passed through previously. Memristors may retain this memory even when no power is applied – they are ‘non-volatile’ – meaning that neuromorphic circuits can potentially process information with very low power consumption, a feature crucial to the way our brains can function without overheating. Typically, memristor behaviour manifests as a current–voltage relationship on a loop, and the response varies depending on whether the voltage is increasing or decreasing: a property called hysteresis, which itself represents a kind of memory as the device behaviour is contingent on its history.