Lifeboat Foundation

A team of researchers from QuTech in the Netherlands reports realization of the first multi-node quantum network, connecting three quantum processors. In addition, they achieved a proof-of-principle demonstration of key quantum network protocols. Their findings mark an important milestone toward the future quantum internet and have now been published in Science.

The power of the internet is that it allows any two computers on Earth to connect. Today, researchers in many labs around the world are working toward first versions of a quantum internet—a network that can connect any two quantum devices, such as quantum computers or sensors, over large distances. Whereas today’s internet distributes information in bits that can be either 0 or 1, a future quantum internet will make use of quantum bits that can be 0 and 1 at the same time.

“A quantum internet will open up a range of novel applications, from unhackable communication and cloud computing with complete user privacy to high-precision time-keeping,” says Matteo Pompili, Ph.D. student and a member of the research team. “And like with the internet 40 years ago, there are probably many applications we cannot foresee right now.”

A new generation of miniature recording probes can track the same neurons inside tiny mouse brains over weeks—and even months.

The new tools build on the success of the original Neuropixels probes released in 2017 and currently used in more than 400 labs. Neuropixels 2.0 are much smaller—about a third the size of their predecessors. They’re designed to record the electrical activity from more individual neurons and have the unique ability to track this activity over extended time periods. That makes them especially useful for studying long-term phenomena like learning and memory in small animals such as mice, says Tim Harris, a senior fellow at HHMI’s Janelia Research Campus who led the project. Harris and his colleagues describe the advance in a paper published online April 15 in the journal Science.

Neuropixels 2.0’s advances come from several key innovations, Harris says. Janelia scientists and engineers developed new ways to process the data. Strategic changes to the layout of the probes helped make them better suited to certain tasks. And engineers at imec, the non-profit nanoelectronics center that manufactures the probes, used imec’s proprietary technology to design, develop, and fabricate the probe.

The Lightmatter photonic computer is 10 times faster than the fastest NVIDIA artificial intelligence GPU while using far less energy. And it has a runway for boosting that massive advantage by a factor of 100, according to CEO Nicholas Harris.

In the process, it may just restart a moribund Moore’s Law.

Or completely blow it up.

The lab-grown meat product ‘incorporates muscle and fat similar to its slaughtered counterpart’, the firm says.

But the latest work has divided developmental biologists. Some question the need for such experiments using closely related primates — these animals are not likely to be used as model animals in the way that mice and rodents are. Nonhuman primates are protected by stricter research ethics rules than are rodents, and they worry such work is likely to stoke public opposition.

The chimaeras lived up to 19 days — but some scientists question the need for such research.

Local health authorities in several parts of the United States are monitoring dozens of travelers for Ebola after the Centers for Disease Control (CDC) ordered airlines to collect information on people who’d been in several western African countries, including Guinea or the Democratic Republic of Congo.

Washington state is monitoring at least 23 travelers. Another 45 are being watched in Ohio. Four people are also being monitored in Oregon.

Ebola is highly contagious and causes severe illness that often leads to death. Symptoms include fever, headache, pain and unexplained bleeding or bruising.

Spot was apparently being used for reconnaissance.

Pictures of the exercises were shared on Twitter by France’s foremost military school, the École Spéciale Militaire de Saint-Cyr. It described the tests as “raising students’ awareness of the challenges of tomorrow,” which include the “robotization of the battlefield.”

A report by French newspaper Ouest-France offers more detail, saying that Spot was one of a number of robots being tested by students from France’s École Militaire Interarmes (Combined Arms School), with the intention of assessing the usefulness of robots on future battlefields.

Continue reading “The French army is testing Boston Dynamics’ robot dog Spot in combat scenarios” »

Halloween is forever if you’re a Japanese black bear.

The Japanese city of Takikawa is using robot wolves to prevent bear attacks. Bear encounters have been on the rise as cities have grown and acorns — a key part of black bears’ pre-hibernation diet — have become harder to find. Since these robots were installed in Takikawa, there have been no attacks in the city’s surrounding area.

The tendency of many cellular proteins to form protein-rich biomolecular condensates underlies the formation of subcellular compartments and has been linked to various physiological functions. Understanding the molecular basis of this fundamental process and predicting protein phase behavior have therefore become important objectives. To develop a global understanding of how protein sequence determines its phase behavior, we constructed bespoke datasets of proteins of varying phase separation propensity and identified explicit biophysical and sequence-specific features common to phase-separating proteins. Moreover, by combining this insight with neural network-based sequence embeddings, we trained machine-learning classifiers that identified phase-separating sequences with high accuracy, including from independent external test data.

Intracellular phase separation of proteins into biomolecular condensates is increasingly recognized as a process with a key role in cellular compartmentalization and regulation. Different hypotheses about the parameters that determine the tendency of proteins to form condensates have been proposed, with some of them probed experimentally through the use of constructs generated by sequence alterations. To broaden the scope of these observations, we established an in silico strategy for understanding on a global level the associations between protein sequence and phase behavior and further constructed machine-learning models for predicting protein liquid–liquid phase separation (LLPS). Our analysis highlighted that LLPS-prone proteins are more disordered, less hydrophobic, and of lower Shannon entropy than sequences in the Protein Data Bank or the Swiss-Prot database and that they show a fine balance in their relative content of polar and hydrophobic residues.