Lifeboat Foundation

There has been significant progress in the field of quantum computing. Big global players, such as Google and IBM, are already offering cloud-based quantum computing services. However, quantum computers cannot yet help with problems that occur when standard computers reach the limits of their capacities because the availability of qubits or quantum bits, i.e., the basic units of quantum information, is still insufficient.

One of the reasons for this is that bare qubits are not of immediate use for running a quantum algorithm. While the binary bits of customary computers store information in the form of fixed values of either 0 or 1, qubits can represent 0 and 1 at one and the same time, bringing probability as to their value into play. This is known as quantum superposition.

This makes them very susceptible to external influences, which means that the information they store can readily be lost. In order to ensure that quantum computers supply reliable results, it is necessary to generate a genuine entanglement to join together several physical qubits to form a logical qubit. Should one of these physical qubits fail, the other qubits will retain the information. However, one of the main difficulties preventing the development of functional quantum computers is the large number of physical qubits required.

ERS Genomics discusses how gene editing is transforming the future of food.

We are in the middle of a data-driven science boom. Huge, complex data sets, often with large numbers of individually measured and annotated ‘features’, are fodder for voracious artificial intelligence (AI) and machine-learning systems, with details of new applications being published almost daily.

But publication in itself is not synonymous with factuality. Just because a paper, method or data set is published does not mean that it is correct and free from mistakes. Without checking for accuracy and validity before using these resources, scientists will surely encounter errors. In fact, they already have.

In the past few months, members of our bioinformatics and systems-biology laboratory have reviewed state-of-the-art machine-learning methods for predicting the metabolic pathways that metabolites belong to, on the basis of the molecules’ chemical structures¹. We wanted to find, implement and potentially improve the best methods for identifying how metabolic pathways are perturbed under different conditions: for instance, in diseased versus normal tissues.

The young host galaxy, called GN-z11, glows from such an energetic black hole at its centre. Black holes cannot be directly observed, but instead they are detected by the tell-tale glow of a swirling accretion disc, which forms near the edges of a black hole. The gas in the accretion disc becomes extremely hot and starts to glow and radiate energy in the ultraviolet range. This strong glow is how astronomers are able to detect black holes.

GN-z11 is a compact galaxy, about one hundred times smaller than the Milky Way, but the black hole is likely harming its development. When black holes consume too much gas, it pushes the gas away like an ultra-fast wind. This ‘wind’ could stop the process of star formation, slowly killing the galaxy, but it will also kill the black hole itself, as it would also cut off the black hole’s source of ‘food’

Maiolino says that the gigantic leap forward provided by JWST makes this the most exciting time in his career. “It’s a new era: the giant leap in sensitivity, especially in the infrared, is like upgrading from Galileo’s telescope to a modern telescope overnight,” he said. “Before Webb came online, I thought maybe the universe isn’t so interesting when you go beyond what we could see with the Hubble Space Telescope. But that hasn’t been the case at all: the universe has been quite generous in what it’s showing us, and this is just the beginning.”

A mission more than a decade in the making, NASA’s Europa Clipper is slated to greatly expand our understanding of Jupiter’s icy moon, Europa, including whether it could support life. These findings will be conducted by a suite of powerful instruments contributed by a myriad of academic and research institutions across the United States. Recently, NASA JPL finished installing all these instruments on the pioneering spacecraft, bringing it one major step closer to its launch, which is currently scheduled for October of this year.

“The instruments work together hand in hand to answer our most pressing questions about Europa,” said Dr. Robert Pappalardo, who is the project scientist on Europa Clipper. “We will learn what makes Europa tick, from its core and rocky interior to its ocean and ice shell to its very thin atmosphere and the surrounding space environment.”

The nine instruments that will be responsible for accomplishing the fantastic science during the mission include the Europa Imaging System (EIS), Europa Thermal Emission Imaging System (E-THEMIS), Europa Ultraviolet Spectrograph (Europa-UVS), Mapping Imaging Spectrometer for Europa (MISE), Europa Clipper Magnetometer (ECM), Plasma Instrument for Magnetic Sounding (PIMS), Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON), MAss Spectrometer for Planetary EXploration/Europa (MASPEX), SUrface Dust Analyzer (SUDA).

A breakthrough plasmonic catalyst, stable in air, revolutionizes acetylene semi-hydrogenation, marking a significant advance in sustainable catalysis.

In a significant breakthrough, Prof. Polshettiwar’s group at TIFR, Mumbai has developed a novel “Plasmonic Reduction Catalyst Stable in Air,” defying the common instability of reduction catalysts in the presence of air. The catalyst merges platinum-doped ruthenium clusters, with ‘plasmonic black gold’. This black gold efficiently harvests visible light and generates numerous hot spots due to plasmonic coupling, enhancing its catalytic performance.

Superior Performance in Semi-Hydrogenation.

Window to the soul? Maybe, but the eyes are also a flashing neon sign for a new artificial intelligence-based system that can read them to predict what you’ll do next.

A University of Maryland researcher and two colleagues have used eye-tracking technology and a new deep-learning AI algorithm to predict study participants’ choices while they viewed a comparison website with rows and columns of products and their features.

The algorithm, known as RETINA (Raw Eye Tracking and Image Ncoder Architecture), could accurately zero in on selections before people had even made their decisions.

“Connectomes” among species provide chip-design inspiration.

New telescope detects more sources in six months than in the 60-year history of X-ray astronomy.