Amazing Science

Plastic waste affects environmental quality and ecosystem health. In 2010, an estimated 5 to 13 million metric tons (Mt) of plastic waste entered the ocean from both developing countries with insufficient solid waste infrastructure and high-income countries with very high waste generation. Scientists now demonstrate that, in 2016, the United States generated the largest amount of plastic waste of any country in the world (42.0 Mt). Between 0.14 and 0.41 Mt of this waste was illegally dumped in the United States, and 0.15 to 0.99 Mt was inadequately managed in countries that imported materials collected in the United States for recycling. Accounting for these contributions, the amount of plastic waste generated in the United States estimated to enter the coastal environment in 2016 was up to five times larger than that estimated for 2010, rendering the United States’ contribution among the highest in the world.

Scientists are tracking small differences in DNA to explain why the disease has different effects.

A study involving more than 5,000 COVID-19 patients in Houston finds that the virus that causes the disease is accumulating genetic mutations, one of which may have made it more contagious. According to the paper published in the peer-reviewed journal mBIO, that mutation, called D614G, is located in the spike protein that pries open our cells for viral entry. It’s the largest peer-reviewed study of SARS-CoV-2 genome sequences in one metropolitan region of the U.S. to date.

The paper shows “the virus is mutating due to a combination of neutral drift — which just means random genetic changes that don’t help or hurt the virus — and pressure from our immune systems,” said Ilya Finkelstein, associate professor of molecular biosciences at The University of Texas at Austin and co-author of the study. The study was carried out by scientists at Houston Methodist Hospital, UT Austin and elsewhere.

During the initial wave of the pandemic, 71% of the novel coronaviruses identified in patients in Houston had this mutation. When the second wave of the outbreak hit Houston during the summer, this variant had leaped to 99.9% prevalence. This mirrors a trend observed around the world. A study published in July based on more than 28,000 genome sequences found that variants carrying the D614G mutation became the globally dominant form of SARS-CoV-2 in about a month. SARS-CoV-2 is the coronavirus that causes COVID-19. So why did strains containing this mutation outcompete those that didn’t have it?

Perhaps they’re more contagious. A study of more than 25,000 genome sequences in the U.K. found that viruses with the mutation tended to transmit slightly faster than those without it and caused larger clusters of infections. Natural selection would favor strains of the virus that transmit more easily. But not all scientists are convinced. Some have suggested another explanation, called “founder’s effects.” In that scenario, the D614G mutation might have been more common in the first viruses to arrive in Europe and North America, essentially giving them a head start on other strains.

An MIT study finds waves that ripple across a newly fertilized egg are similar to the waves in seemingly unrelated systems, from ocean and atmospheric circulations to quantum fluids.

When an egg cell of almost any sexually reproducing species is fertilized, it sets off a series of waves that ripple across the egg’s surface. These waves are produced by billions of activated proteins that surge through the egg’s membrane like streams of tiny burrowing sentinels, signaling the egg to start dividing, folding, and dividing again, to form the first cellular seeds of an organism.

Now MIT scientists have taken a detailed look at the pattern of these waves, produced on the surface of starfish eggs. These eggs are large and therefore easy to observe, and scientists consider starfish eggs to be representative of the eggs of many other animal species.

In each egg, the team introduced a protein to mimic the onset of fertilization, and recorded the pattern of waves that rippled across their surfaces in response. They observed that each wave emerged in a spiral pattern, and that multiple spirals whirled across an egg’s surface at a time. Some spirals spontaneously appeared and swirled away in opposite directions, while others collided head-on and immediately disappeared.

The behavior of these swirling waves, the researchers realized, is similar to the waves generated in other, seemingly unrelated systems, such as the vortices in quantum fluids, the circulations in the atmosphere and oceans, and the electrical signals that propagate through the heart and brain.

“Not much was known about the dynamics of these surface waves in eggs, and after we started analyzing and modeling these waves, we found these same patterns show up in all these other systems,” says physicist Nikta Fakhri, the Thomas D. and Virginia W. Cabot Assistant Professor at MIT. “It’s a manifestation of this very universal wave pattern.”

As progress in traditional computing slows, new forms of computing are coming to the forefront. At Penn State, a team of engineers is attempting to pioneer a type of computing that mimics the efficiency of the brain’s neural networks while exploiting the brain’s analog nature.

Modern computing is digital, made up of two states, on-off or one and zero. An analog computer, like the brain, has many possible states. It is the difference between flipping a light switch on or off and turning a dimmer switch to varying amounts of lighting.

Neuromorphic or brain-inspired computing has been studied for more than 40 years, according to Saptarshi Das, the team leader and Penn State assistant professor of engineering science and mechanics. What’s new is that as the limits of digital computing have been reached, the need for high-speed image processing, for instance for self-driving cars, has grown. The rise of big data, which requires types of pattern recognition for which the brain architecture is particularly well suited, is another driver in the pursuit of neuromorphic computing.

“We have powerful computers, no doubt about that, the problem is you have to store the memory in one place and do the computing somewhere else,” Das said. The shuttling of this data from memory to logic and back again takes a lot of energy and slows the speed of computing. In addition, this computer architecture requires a lot of space. If the computation and memory storage could be located in the same space, this bottleneck could be eliminated.

“We are creating artificial neural networks, which seek to emulate the energy and area efficiencies of the brain,” explained Thomas Shranghamer, a doctoral student in the Das group and first author on a paper recently published in Nature Communications. “The brain is so compact it can fit on top of your shoulders, whereas a modern supercomputer takes up a space the size of two or three tennis courts.”

Like synapses connecting the neurons in the brain that can be reconfigured, the artificial neural networks the team is building can be reconfigured by applying a brief electric field to a sheet of graphene, the one-atomic-thick layer of carbon atoms. In this work they show at least 16 possible memory states, as opposed to the two in most oxide-based memristors, or memory resistors. “What we have shown is that we can control a large number of memory states with precision using simple graphene field effect transistors,” Das said.

The team thinks that ramping up this technology to a commercial scale is feasible. With many of the largest semiconductor companies actively pursuing neuromorphic computing, Das believes they will find this work of interest. In addition to Das and Shranghamer, the additional author on the paper, titled “Graphene Memristive Synapses for High Precision Neuromorphic Computing,” is Aaryan Oberoi, doctoral student in engineering science and mechanics.

While the ongoing coronavirus pandemic continues to threaten millions of lives around the world, the first half of 2020 saw an unprecedented decline in CO2 emissions -- larger than during the financial crisis of 2008, the oil crisis of the 1979, or even World War II. An international team of researchers has found that in the first six months of this year, 8.8 percent less carbon dioxide was emitted than in the same period in 2019 -- a total decrease of 1551 million tons. The groundbreaking study not only offers a much more precise look at COVID-19's impact on global energy consumption than previous analyses. It also suggests what fundamental steps could be taken to stabilize the global climate in the aftermath of the pandemic.

"What makes our study unique is the analysis of meticulously collected near-real-time data," explains lead author Zhu Liu from the Department of Earth System Science at Tsinghua University in Beijing. "By looking at the daily figures compiled by the Carbon Monitor research initiative we were able to get a much faster and more accurate overview, including timelines that show how emissions decreases have corresponded to lockdown measures in each country. In April, at the height of the first wave of Corona infections, when most major countries shut down their public life and parts of their economy, emissions even declined by 16.9 %. Overall, the various outbreaks resulted in emission drops that we normally see only on a short-term basis on holidays such as Christmas or the Chinese Spring Festival."

The study, published in the latest issue of Nature Communications, shows which parts of the global economy were most impacted. "The greatest reduction of emissions was observed in the ground transportation sector," explains Daniel Kammen, professor and Chair of the Energy and Resources Group and also professor in the Goldman School of Public Policy, University of California, Berkeley. "Largely because of working from home restrictions, transport CO2 emissions decreased by 40 % worldwide. In contrast, the power and industry sectors contributed less to the decline, with -22 % and -17 %, respectively, as did the aviation and shipping sectors. Surprisingly, even the residential sector saw a small emissions drop of 3 %: largely because of an abnormally warm winter in the northern hemisphere, heating energy consumption decreased with most people staying at home all day during lockdown periods."

By studying impact marks on the surface of asteroid Bennu – the target of NASA's OSIRIS-REx mission – a team of researchers led by the University of Arizona has uncovered the asteroid's past and revealed that despite forming hundreds of millions of years ago, Bennu wandered into Earth's neighborhood only very recently.

The study, published in the journal Nature, provides a new benchmark for understanding the evolution of asteroids, offers insights into a poorly understood population of space debris hazardous to spacecraft, and enhances scientists' understanding of the solar system.

The researchers used images and laser-based measurements taken during a two-year surveying phase in which the OSIRIS-REx spacecraft, about the size of a 15-passenger van, orbited Bennu and broke the record for the smallest spacecraft to orbit a small body.       

Presented at the opening day of the American Astronomical Society's Division for Planetary Sciences meeting on Oct. 26, the paper details the first observations and measurements of impact craters on individual boulders on an airless planetary surface since the Apollo missions to the moon 50 years ago, according to the authors.

The publication comes just a few days after a major milestone for NASA's University of Arizona-led OSIRIS-REx mission. On Oct. 20, the spacecraft successfully descended to asteroid Bennu to grab a sample from its boulder-scattered surface – a first for NASA. The sample has now been successfully stowed and will be returned to Earth for study in 2023, where it could give scientists insight into the earliest stages of the formation of our solar system.

Impact Craters on Rocks Tell the Story

Although Earth is being pelted with more than 100 tons of space debris each day, it is virtually impossible to find a rockface pitted by impacts from small objects at high velocities. Courtesy of our atmosphere, we get to enjoy any object smaller than a few meters as a shooting star rather than having to fear being struck by what essentially amounts to a bullet from outer space. 

Planetary bodies lacking such a protective layer, however, bear the full brunt of a perpetual cosmic barrage, and they have the scars to show for it. High-resolution images taken by the OSIRIS-REx spacecraft during its two-year survey campaign allowed researchers to study even tiny craters, with diameters ranging from a centimeter to a meter, on Bennu's boulders.

The team found boulders of 1 meter or larger to be scarred, on average, by anywhere from one to 60 pits – impacted by space debris ranging in size from a few millimeters to tens of centimeters. "I was surprised to see these features on the surface of Bennu," said the paper's lead author, Ronald Ballouz, a postdoctoral researcher in the UArizona Lunar and Planetary Laboratory and a scientist with the OSIRIS-REx regolith development working group. "The rocks tell their history through the craters they accumulated over time. We haven't observed anything like this since astronauts walked on the moon."

There's a long-standing question in planetary science about the origin of water on Earth, Mars and other large bodies such as the moon. One hypothesis says that it came from asteroids and comets post-formation. But some planetary researchers think that water might just be one of many substances that occur naturally during the formation of planets.

A new analysis of an ancient Martian meteorite adds support for this second hypothesis. Several years ago, a pair of dark meteorites were discovered in the Sahara Desert. They were dubbed NWA 7034 and NWA 7533, where NWA stands for North West Africa and the number is the order in which meteorites are officially approved by the Meteoritical Society, an international planetary science organization. Analysis showed these meteorites are new types of Martian meteorites and are mixtures of different rock fragments.

The earliest fragments formed on Mars 4.4 billion years ago, making them the oldest known Martian meteorites. Rocks like this are rare and can fetch up to $10,000 per gram. But recently 50 grams of NWA 7533 was acquired for analysis by the international team in which Professor Takashi Mikouchi at the University of Tokyo was participating.

"I study minerals in Martian meteorites to understand how Mars formed and its crust and mantle evolved. This is the first time I have investigated this particular meteorite, nicknamed Black Beauty for its dark color," said Mikouchi. "Our samples of NWA 7533 were subjected to four different kinds of spectroscopic analysis, ways of detecting chemical fingerprints. The results led our team to draw some exciting conclusions."

It's well known to planetary scientists that there has been water on Mars for at least 3.7 billion years. But from the mineral composition of the meteorite, Mikouchi and his team deduced it's likely there was water present much earlier, at around 4.4 billion years ago. "Igneous clasts, or fragmented rock, in the meteorite are formed from magma and are commonly caused by impacts and oxidation," said Mikouchi. "This oxidation could have occurred if there was water present on or in the Martian crust 4.4 billion years ago during an impact that melted part of the crust. Our analysis also suggests such an impact would have released a lot of hydrogen, which would have contributed to planetary warming at a time when Mars already had a thick insulating atmosphere of carbon dioxide."

Particle accelerators generate high-energy beams of electrons, protons and ions for a wide range of applications, including particle colliders that shed light on nature's subatomic components, X-ray lasers that film atoms and molecules during chemical reactions and medical devices for treating cancer.

As a rule of thumb, the longer the accelerator, the more powerful it is. Now, a team led by scientists at the Department of Energy's SLAC National Accelerator Laboratory has invented a new type of accelerator structure that delivers a 10 times larger energy gain over a given distance than conventional ones. This could make accelerators used for a given application 10 times shorter. The key idea behind the technology, described in a recent article in Applied Physics Letters, is to use terahertz radiation to boost particle energies.

In today's accelerators, particles draw energy from a radio-frequency (RF) field fed into specifically shaped accelerator structures, or cavities. Each cavity can deliver only a limited energy boost over a given distance, so very long strings of cavities are needed to produce high-energy beams.

Terahertz and radio waves are both electromagnetic radiation; they differ in their respective wavelengths. Because terahertz waves are 10 times shorter than radio waves, cavities in a terahertz accelerator can also be much smaller. In fact, the one invented in this study was only 0.2 inches long.

One major challenge to building these tiny cavity structures is to machine them very precisely. Over the past few years, SLAC teams developed a way to do just that. Instead of using the traditional process of stacking many layers of copper on top of each other, they built the minute structure by machining two halves and bonding them together.

The new structure also produces particle pulses a thousand times shorter than those coming out of conventional copper structures, which could be used to produce beams that pulse at a higher rate and unleash more power over a given time period.

Next, the researchers are planning to turn the invention into an electron gun—a device that could produce incredibly bright beams of electrons for discovery science, including next-generation X-ray lasers and electron microscopes that would allow us to see in real time how nature works on the atomic level. These beams could also be used for cancer treatment.

Delivering on this potential also requires further development of sources of terahertz radiation and their integration with advanced accelerators, such as the one described in this study. Because terahertz radiation has such a short wavelength, its sources are particularly challenging to develop, and there is little technology available at present. SLAC researchers are pursuing both electron beam and laser-based terahertz generation to provide the high peak powers needed to turn their accelerator research into future real-world applications.

A global study of ancient dog DNA, led by scientists at the Francis Crick Institute, University of Oxford, University of Vienna and archaeologists from more than 10 countries, presents evidence that there were different types of dogs more than 11,000 years ago in the period immediately following the Ice Age.

In their study, published in Science today, the research team sequenced ancient DNA from 27 dogs, some of which lived up to nearly 11,000 years ago, across Europe, the Near East and Siberia. They found that by this point in history, just after the Ice Age and before any other animal had been domesticated, there were already at least five different types of dog with distinct genetic ancestries.

This finding reveals that the diversity observed between dogs in different parts of the world today originated when all humans were still hunters and gatherers. Pontus Skoglund, author and group leader of the Crick's Ancient Genomics laboratory, says: "Some of the variation you see between dogs walking down the street today originated in the Ice Age. By the end of this period, dogs were already widespread across the northern hemisphere."

This study of ancient genomics involves extracting and analysing DNA from skeletal material. It provides a window into the past, allowing researchers to uncover evolutionary changes that occurred many thousands of years ago. The team showed that over the last 10,000 years, these early dog lineages mixed and moved to give rise to the dogs we know today. For example, early European dogs were initially diverse, appearing to originate from two highly distinct populations, one related to Near Eastern dogs and another to Siberian dogs. However, at some point this diversity was lost, as it is not present in European dogs today.

Anders Bergström, lead author and post-doctoral researcher in the Ancient Genomics laboratory at the Crick, says: "If we look back more than four or five thousand years ago, we can see that Europe was a very diverse place when it came to dogs. Although the European dogs we see today come in such an extraordinary array of shapes and forms, genetically they derive from only a very narrow subset of the diversity that used to exist."

The researchers also compared the evolution in dog history to changes in human evolution, lifestyles and migrations. In many cases comparable changes took place, likely reflecting how humans would bring their dogs with them as they migrated across the world.

Researchers from Sorbonne University in Paris have achieved a highly efficient transfer of quantum entanglement into and out of two quantum memory devices. This achievement brings a key ingredient for the scalability of a future quantum internet.

A quantum internet that connects multiple locations is a key step in quantum technology roadmaps worldwide. In this context, the European Quantum Flagship Programme launched the Quantum Internet Alliance in 2018. This consortium coordinated by Stephanie Wehner (QuTech-Delft) consists of 12 leading research groups at universities from eight European countries, in close cooperation with over 20 companies and institutes. They combined their resources and areas of expertise to develop a blueprint for a future quantum internet and the required technologies.

A quantum internet uses an intriguing quantum phenomenon to connect different nodes in a network together. In a normal network connection, nodes exchange information by sending electrons or photons back and forth, making them vulnerable to eavesdropping.

In a quantum network, the nodes are connected by entanglement, Einstein's famous "spooky action at a distance." These non-classical correlations at large distances would allow not only secure communications beyond direct transmission but also distributed quantum computing or enhanced sensing.

However, one major challenge in building large-scale quantum networks is the ability to generate such correlations between distant nodes. In principle, this challenge can be overcome if entanglement is reliably stored in quantum memory devices. By splitting the long distance into several shorter segments, it is possible to create entanglement between the ends of these elementary links, and then connect them until both initial nodes are entangled. The quantum memory devices store the entanglement, ensuring that entanglement has been created over all the segments before performing the connections. This protocol is known as a quantum repeater.

A critical parameter is the efficiency of the quantum memory devices: If one device fails in recording or retrieving the entangled light, the quantum repeater cannot function properly. For instance, an increase in storage-and-retrieval efficiency from 60% to 90% drastically decreases the average time for entanglement distribution over a distance of 600 kilometers, typically by two orders of magnitude. One of the goals of the QIA consortium is to lay the groundwork for quantum repeater technology by building highly efficient memory devices for entanglement using different physical platforms.

In the October 2020 online issue of Optica, Prof. Julien Laurat and his team at Kastler Brossel Laboratory (Sorbonne Université, CNRS, ENS-Université PSL, Collège de France) reported a long-awaited step for this endeavor. They have demonstrated the storage and retrieval of entangled light beams into two quantum memory devices, with an overall efficiency as high as 85%. This value constitutes more than a three-fold increase relative to prior works in the field.

"This achievement is the result of 10 years of experimental developments in our lab. It now opens the path to further investigation as many potential network architectures assume such efficiency value for scalability," says Félix Hoffet, a Ph.D. student at LKB and one of the leading authors of the paper.

While NASA set their sights on the Moon and outer planets, the Soviets spent some 30 years investigating the hellish inner planet Venus.

The Pioneer and Voyager probes the United States sent to explore the outer planets in the 1970s are often, and accurately, lauded as historic interplanetary achievements. That’s partly because, equipped with the Pioneer Plaque and the Voyager Golden Record, these objects are ostensibly meant to be found by aliens someday, helping them easily burrow into public consciousness. Similarly, robotic explorers to Mars, including the Viking landers and the Sojourner, Spirit, Opportunity, and Curiosity rovers, take innumerable headlines, and they’re often even given anthropomorphized personalities.

Almost forgotten, however, are the ambitious Soviet exploration missions of Venus. Beginning at the dawn of the Space Age in the late 1950s, the Soviets worked to design and construct a series of Venus probes. And for almost 30 years, they built and flew the interplanetary spacecraft as part of the Venera program — carrying out rather impressive feats, even by today’s standards.

In the early 1960s, the Cold War was in full swing and the Space Race was on. The Soviets were eager to notch as many “firsts” as they could in all realms of spaceflight. At that time, they had a better heavy-lifting capability than the United States. This allowed them to build and launch larger spacecraft, both manned and unmanned. And by using four-stage rockets and an advanced telemetry system, the Soviets could also mount missions to the difficult-to-reach inner planets.

Like a key, SARS-CoV-2 – the virus that causes coronavirus disease 2019 (COVID-19) – attaches to specific molecules on the host cell surface, opening gateways into the cell interior. Viral entry into host cells triggers a prodigious immune response. Much of this battle is waged within the lungs, which explains why many patients hospitalized with COVID-19 have severe respiratory symptoms. 

Respiratory symptoms, however, are only part of the story. Increasing evidence points toward blood vessel inflammation as having a crucial impact on the severity of COVID-19. In addition, anywhere from 30 to 80 percent of patients experience neurological symptoms, including dizziness, headache, nausea, and loss of concentration. These symptoms suggest that SARS-CoV-2 also affects cells of the central nervous system.

While there is no evidence yet that the virus invades the brain, new work by scientists at the Lewis Katz School of Medicine at Temple University shows that the spike proteins that extrude from SARS-CoV-2 promote inflammatory responses on the endothelial cells that form the blood-brain barrier. The study, published in the December print issue of the journal Neurobiology of Disease, is the first to show that SARS-CoV-2 spike proteins can cause this barrier to become “leaky,” potentially disrupting the delicate neural networks within the brain. 

“Previous studies have shown that SARS-CoV-2 infects host cells by using its spike proteins to bind to the angiotensin converting enzyme 2 (ACE2) on the host cell surface,” explained Servio H. Ramirez, PhD, Professor of Pathology and Laboratory Medicine at the Lewis Katz School of Medicine at Temple University and principal investigator on the new study. 

ACE2 is expressed on endothelial cells, which form the inner lining of blood vessels, and serves a central role in mediating different functions of the cardiovascular system. According to Dr. Ramirez, “since ACE2 is a major binding target for SARS-CoV-2 in the lungs and vasculature of other organs in the body, tissues that are behind the vasculature, that receive blood from affected vessels, are at risk of damage from the virus.”

It has been unclear, however, whether ACE2 is also present in the brain vasculature or whether its expression changes in health conditions that worsen COVID-19, such as high blood pressure (hypertension). To find out, the team began by examining postmortem human brain tissue for vascular ACE2 expression, using tissues from individuals without underlying health conditions and from individuals in whom hypertension and dementia had been established. Analyses showed that ACE2 is in fact expressed throughout blood vessels in the frontal cortex of the brain and is significantly increased in the brain vasculature of persons with a history of hypertension or dementia.

The researchers then investigated the effects of the SARS-CoV-2 spike protein on brain endothelial cells in cell culture models. Introduction of the spike protein, particularly a portion designated subunit 1, produced substantial changes in endothelial barrier function that led to declines in barrier integrity. The researchers also uncovered evidence that subunit 2 of the SARS-CoV-2 spike protein can directly impact blood-brain barrier function. “This is of importance because unlike subunit 1, subunit 2 of the spike protein doesn’t bind to ACE2, meaning that a breach to the blood-brain barrier could occur in a manner that is independent of ACE2,” explained postdoctoral fellow and first author on the new report Tetyana P. Buzhdygan, PhD. 

MIT engineers designed a tiny “brain-on-a-chip” from tens of thousands of artificial brain synapses known as memristors — silicon-based components that mimic the information-transmitting synapses in the human brain.

The researchers borrowed from principles of metallurgy to fabricate each memristor from alloys of silver and copper, along with silicon. When they ran the chip through several visual tasks, the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper and cleaner compared with existing memristor designs made with unalloyed elements.

Their results, published today in the journal Nature Nanotechnology, demonstrate a promising new memristor design for neuromorphic devices — electronics that are based on a new type of circuit that processes information in a way that mimics the brain’s neural architecture. Such brain-inspired circuits could be built into small, portable devices, and would carry out complex computational tasks that only today’s supercomputers can handle.

“So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognize lights and objects and make a decision immediately, without having to connect to the internet. We hope to use energy-efficient memristors to do those tasks on-site, in real-time.”

The scientists who re-engineered the plastic-eating enzyme PETase have now created an enzyme ‘cocktail’ which can digest plastic up to six times faster. A second enzyme, found in the same rubbish dwelling bacterium that lives on a diet of plastic bottles, has been combined with PETase to speed up the breakdown of plastic.

PETase breaks down polyethylene terephthalate (PET) back into its building blocks, creating an opportunity to recycle plastic infinitely and reduce plastic pollution and the greenhouse gases driving climate change.  PET is the most common thermoplastic, used to make single-use drinks bottles, clothing and carpets and it takes hundreds of years to break down in the environment, but PETase can shorten this time to days.

The initial discovery set up the prospect of a revolution in plastic recycling, creating a potential low-energy solution to tackle plastic waste. The team engineered the natural PETase enzyme in the laboratory to be around 20 percent faster at breaking down PET. Now, the same trans-Atlantic team have combined PETase and its ‘partner’, a second enzyme called MHETase, to generate much bigger improvements: simply mixing PETase with MHETase doubled the speed of PET breakdown, and engineering a connection between the two enzymes to create a ‘super-enzyme’, increased this activity by a further three times.

The study is published in the journal Proceedings of the National Academy of Sciences of the United States of America. 

Examining data from the first six weeks of their landmark DETECT study, a team of scientists from the Scripps Research Translational Institute sees encouraging signs that wearable fitness devices can improve public health efforts to control COVID-19.

The DETECT study, launched on March 25 2020, uses a mobile app to collect smartwatch and activity tracker data from consenting participants, and also gathers their self-reported symptoms and diagnostic test results. Any adult living in the United States is eligible to participate in the study by downloading the research app, MyDataHelps.

In a study published October 29, 2020 in Nature Medicine, the Scripps Research team reports that wearable devices like Fitbit are capable of identifying cases of COVID-19 by evaluating changes in heart rate, sleep and activity levels, along with self-reported symptom data—and can identify cases with greater success than looking at symptoms alone. “What’s exciting here is that we now have a validated digital signal for COVID-19. The next step is to use this to prevent emerging outbreaks from spreading,” says Eric Topol, MD, director and founder of the Scripps Research Translational Institute and executive vice president of Scripps Research.

“Roughly 100 million Americans already have a wearable tracker or smartwatch and can help us; all we need is a tiny fraction of them—just 1 percent or 2 percent—to use the app.” With data from the app, researchers can see when participants fall out of their normal range for sleep, activity level or resting heart rate; deviations from individual norms are a sign of viral illness or infection.

But how do they know if the illness causing those changes is COVID-19? To answer that question, the team reviewed data from those who reported developing symptoms and were tested for the novel coronavirus. Knowing the test results enabled them to pinpoint specific changes indicative of COVID-19 versus other illnesses.

“One of the greatest challenges in stopping COVID-19 from spreading is the ability to quickly identify, trace and isolate infected individuals,” says Giorgio Quer, PhD, director of artificial intelligence at Scripps Research Translational Institute and first author of the study. “Early identification of those who are pre-symptomatic or even asymptomatic would be especially valuable, as people may potentially be even more infectious during this period. That’s the ultimate goal.”

When atoms get extremely close, they develop intriguing interactions that could be harnessed to create new generations of computing and other technologies. These interactions in the realm of quantum physics have proven difficult to study experimentally due the basic limitations of optical microscopes.

Now a team of Princeton researchers, led by Jeff Thompson, an assistant professor of electrical engineering, have developed a new way to control and measure atoms that are so close together no optical lens can distinguish them.

Described in an article published Oct. 30 in the journal Science, their method excites closely-spaced erbium atoms in a crystal using a finely tuned laser in a nanometer-scale optical circuit.  The researchers take advantage of the fact that each atom responds to slightly different frequencies, or colors, of laser light, allowing the researchers to resolve and control multiple atoms, without relying on their spatial information.

In a conventional microscope, the space between two atoms effectively disappears when their separation is below a key distance called the diffraction limit, which is roughly equal to the light's wavelength. This is analogous to two distant stars that appear as a single point of light in the night sky. However, this is also the scale at which atoms start to interact and give rise to rich and interesting quantum mechanical behavior.

"We always wonder, at the most fundamental level — inside solids, inside crystals — what do atoms actually do? How do they interact?" said physicist Andrei Faraon, a professor at the California Institute of Technology who was not involved in the research. "This paper]opens the window to study atoms that are in very, very close proximity."

Studying atoms and their interactions at tiny distances allows scientists to explore and control a quantum property known as spin. As a form of momentum, spin is usually described as being either up or down (or both, but that's another story). When the distance between two atoms grows vanishingly small — mere billionths of a meter — the spin of one exerts influence over the spin of the other, and vice versa. As spins interact in this realm, they can become entangled, a term scientists use to describe two or more particles that are inextricably linked. Entangled particles behave as if they share one existence, no matter how far apart they later become. Entanglement is the essential phenomenon that separates quantum mechanics from the classical world, and it's at the center of the vision for quantum technologies. The new Princeton device is a stepping stone for scientists to study these spin interactions with unprecedented clarity.

Titan is an ocean world, with a sea of water and ammonia under its crust. And as scientists look for pathways for organic material to travel from the surface to the ocean underneath, impact craters offer a unique window into the subsurface.

The new research also found that one impact site, called Selk Crater, is completely covered with organics and untouched by the rain process that cleans the surface of other craters. Selk is in fact a target of NASA's Dragonfly mission, set to launch in 2027; the rotorcraft-lander will investigate key astrobiology questions as it searches for biologically important chemistry similar to early Earth before life emerged.

NASA got its first close-up encounter with Titan some 40 years ago, on Nov. 12, 1980, when the agency's Voyager 1 spacecraft flew by at a range of just 2,500 miles (4,000 kilometers). Voyager images showed a thick, opaque atmosphere, and data revealed that liquid might be present on the surface (it was - in the form of liquid methane and ethane), and indicated that prebiotic chemical reactions might be possible on Titan.

Managed by NASA's Jet Propulsion Laboratory in Southern California, Cassini was an orbiter that observed Saturn for more than 13 years before exhausting its fuel supply. The mission plunged it into the planet's atmosphere in September 2017, in part to protect moons that have the potential of holding conditions suitable for life.

The Cassini-Huygens mission is a cooperative project of NASA, ESA, and the Italian Space Agency. JPL, a division of Caltech in Pasadena, manages the mission for NASA's Science Mission Directorate in Washington. JPL designed, developed, and assembled the Cassini orbiter.

In a new international cross disciplinary study, researchers have used artificial intelligence to analyze large amounts of historical photos from WW2. Among other things, the study shows that artificial intelligence can recognize the identity of photographers based on the content of photos taken by them.

Artificial Intelligence (AI) is finally able to identify photographers based on the content of images they've taken. This is the conclusion of a new study at AU Engineering, Aarhus University, where, in collaboration with Tampere University and the Finnish Environment Institute, researchers have used state-of-the-art artificial intelligence to trawl through photographs taken by 23 well-known Finnish photographers during the Second World War.

The photographs used in the study are part of the publicly available Finnish Wartime Photograph Archive containing around 160,000 photographs from Finnish Winter, Continuation, and Lapland Wars captured in 1939-1945.

The study was published in the scientific journal IEEE Access, and the researchers wanted to demonstrate the advantages of using modern neural networks to analyse images to such an extent that the machine could automatically detect people and objects in a variety of scenes and even distinguish photographers on the basis of characteristics in the image - all much faster than any manual reviewing process.

Such an automatic analysis can serve as a tool for providing content-based textual descriptions of public photographic archives like the Photo Archives of the Museum of Danish Resistance, as it is now required at the European Union level. The EU Accessibility Directive (DIRECTIVE (EU) 2016/2102) came into effect in September 2020 and requires textual descriptions of image contents to be added to all public images on the Web.

The LIGO Scientific Collaboration and Virgo Collaboration released a catalog of results from the first half of its third observing run (O3a), and scientists have detected more than three times as many gravitational waves than the first two runs combined. Gravitational waves were first detected in 2015 and are ripples in time and space produced by merging black holes and/or neutron stars. Several researchers from Rochester Institute of Technology's Center for Computational Relativity and Gravitation (CCRG) were heavily involved in analyzing the gravitational waves and understanding their significance.

The catalog details 39 new gravitational wave events detected during O3a, bringing the total to 50, and several of the newly detected binaries have unique properties that expand our understanding of binary black hole formation. O3a uncovered the largest and smallest binary black holes to date, ranging from 150 times the size of our sun to just 3 times larger. O3a also detected the first binary black hole confidently formed from highly asymmetrical black holes as well as several binary black holes with unique spin properties.

Jacob Lange '18 MS (astrophysical sciences and technology), '20 Ph.D. (astrophysical sciences and technology) worked on the parameter estimation part of the analysis, which identifies important characteristics about each gravitational wave event, including the masses of the black holes or neutron stars involved, their spin, distance from Earth and position in the sky. While he was a Ph.D. student at RIT, he helped develop parameter estimation algorithms that were faster than conventional methods and used for many of the events released in the catalog.

Lange, who is now a postdoctoral researcher at Brown University's Institute for Computational and Experimental Research in Mathematics, said that improvements to the sensors and parameter estimation techniques have yielded increasingly unique findings that challenge our understanding of the universe. "We are seeing much more complex events where nature's really showing us its fascinating side," said Lange. "We'll be able to learn much more interesting physics and astrophysics from these detections. The more we build up this catalog of events, the more we can start making statements about the overall population."