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Unlimited, at-home coronavirus testing for your organization

Unlimited, at-home coronavirus testing for your organization | Amazing Science |



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New mmWave radar sensor technology for intelligent multimodal traffic monitoring at intersections

New mmWave radar sensor technology for intelligent multimodal traffic monitoring at intersections | Amazing Science |

Intelligent Transportation Systems (ITS) need traffic data to run smoothly. At intersections, where there is the greatest potential for conflicts between road users, being able to reliably and intelligently monitor the different modes of traffic is crucial.


The Federal Highway Administration (FHWA) estimates that more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. For pedestrians the intersection is a particularly dangerous place: the City of Portland, Oregon identified that two-thirds of all crashes involving a pedestrian happen at intersections. And when darkness comes earlier in fall and winter, crashes increase dramatically. So knowing what's going on in low-visibility conditions is essential for mobility and safety of all road users.


Some agencies use cameras to monitor traffic modes, but cameras are limited in rainy, dark or foggy conditions. Some cities use radar instead of cameras, which works better in low-visibility but typically can't provide as rich a picture of what's going on. Conventional radar gives movement and position data for all approaching entities, but it's very hard to tell the difference between modes with any reliability.


In the latest report funded by the National Institute for Transportation and Communities (NITC), Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections, researchers Siyang Cao, Yao-jan Wu, Feng Jin and Xiaofeng Li of the University of Arizona have tackled the issue by developing a high-resolution radar sensor that can reliably distinguish between cars and pedestrians. This sensor also supplies the counts, speed, and direction of each moving target, no matter what the lighting and weather are like. In the future, they plan to further refine their model to interpret more complex data and be able to identify additional modes.


Why use mmWave radar?

The prototype device used a high-resolution millimeter-wave (mmWave) radar sensor that outperforms cameras in low-visibility conditions and beats conventional radar by providing a richer picture. "The mmWave radar is also different from other sensors in that it can provide relatively stable radial velocity, which is very helpful for us to identify the speed of vehicles," Cao said.


This gives the system an advantage over light-based sensors like LiDAR. LiDAR systems are able to "see" in great detail, making it easy to determine what an object is; but they have difficulty with movement and speed. MmWave radar can resolve the speed of a moving target much more reliably than LiDAR. 


"The key problem in multimodal traffic monitoring is finding the speed and volume of each mode. A sensor must be able to detect, track, classify, and measure the speed of an object, while also being low-cost and low power consumption. With real-time traffic statistics we hope to improve traffic efficiency and also reduce the incidence of crashes," Cao said.

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A scientific first: The CMS collaboration has observed three J/ψ particles emerging from a single collision between two protons

A scientific first: The CMS collaboration has observed three J/ψ particles emerging from a single collision between two protons | Amazing Science |

It’s a triple treat. By sifting through data from particle collisions at the Large Hadron Collider (LHC), the CMS collaboration has seen not one, not two but three J/ψ particles emerging from a single collision between two protons. In addition to being a first for particle physics, the observation opens a new window into how quarks and gluons are distributed inside the proton.


The J/ψ particle is a special particle. It was the first particle containing a charm quark to be discovered, winning Burton Richter and Samuel Ting a Nobel prize in physics and helping to establish the quark model of composite particles called hadrons. Experiments including ATLASCMS and LHCb at the LHC have previously seen one or two J/ψ particles coming out of a single particle collision, but never before have they seen the simultaneous production of three J/ψ particles – until the new CMS analysis.


The trick? Analyzing the vast amount of high-energy proton–proton collisions collected by the CMS detector during the second run of the LHC, and looking for the transformation of the J/ψ particles into pairs of muons, the heavier cousins of the electrons.

From this analysis, the CMS team identified five instances of single proton–proton collision events in which three J/ψ particles were produced simultaneously. The result has a statistical significance of more than five standard deviations – the threshold used to claim the observation of a particle or process in particle physics.


These three-J/ψ events are very rare. To get an idea, one-J/ψ events and two-J/ψ events are about 3.7 million and 1800 times more common, respectively. “But they are well worth investigating,” says CMS physicist Stefanos Leontsinis, “A larger sample of three-J/ψ events, which the LHC should be able to collect in the future, should allow us to improve our understanding of the internal structure of protons at small scales.”

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A Changing Ocean: Ocean State Report 5 shows eutrophication and oligotrophication

A Changing Ocean: Ocean State Report 5 shows eutrophication and oligotrophication | Amazing Science |

The Ocean State Report 5 Summary is now available online from the Copernicus Marine Service and Mercator Ocean International (see full report). This annual publication provides a comprehensive and state-of-the-art report on the current state, natural variations, and ongoing changes in the European regional seas and global ocean, particularly in 2019.


Available in a concise, illustrated, and easily accessible format, the Summary (available in English and French) is intended to act as a reference for the scientific community, policy-makers, and the general public to better understand the importance and impacts of a changing ocean.   The Summary is divided into four chapters, presenting the data of a changing ocean from several angles.


Chapters one, two, and three present the state and key observations of a changing ocean, examine the evolving impacts of these changes in line with climate change, and discuss the importance of sustainable ocean governance for managing impacts.


The Summary concludes with chapter four which highlights new tools developed using Copernicus Marine Service products and illustrates how accurate and timely information is key to monitoring, understanding, and adapting to a changing ocean. 

Nutrient enrichment in the Ocean from human activities and low nutrient waters can lead to eutrophication and oligotrophication affecting marine life for many years to come.
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Researchers shrink camera to the size of a salt grain

Researchers shrink camera to the size of a salt grain | Amazing Science |

Micro-sized cameras have great potential to spot problems in the human body and enable sensing for super-small robots, but past approaches captured fuzzy, distorted images with limited fields of view.


Now, researchers at Princeton University and the University of Washington have overcome these obstacles with an ultracompact camera the size of a coarse grain of salt. The new system can produce crisp, full-color images on par with a conventional compound camera lens 500,000 times larger in volume, the researchers reported in a paper published Nov. 29 in Nature Communications.


Enabled by a joint design of the camera's hardware and computational processing, the system could enable minimally invasive endoscopy with medical robots to diagnose and treat diseases, and improve imaging for other robots with size and weight constraints. Arrays of thousands of such cameras could be used for full-scene sensing, turning surfaces into cameras.


While a traditional camera uses a series of curved glass or plastic lenses to bend light rays into focus, the new optical system relies on a technology called a metasurface, which can be produced much like a computer chip. Just half a millimeter wide, the metasurface is studded with 1.6 million cylindrical posts, each roughly the size of the human immunodeficiency virus (HIV).


Each post has a unique geometry, and functions like an optical antenna. Varying the design of each post is necessary to correctly shape the entire optical wavefront. With the help of machine learning-based algorithms, the posts' interactions with light combine to produce the highest-quality images and widest field of view for a full-color metasurface camera developed to date.


A key innovation in the camera's creation was the integrated design of the optical surface and the signal processing algorithms that produce the image. This boosted the camera's performance in natural light conditions, in contrast to previous metasurface cameras that required the pure laser light of a laboratory or other ideal conditions to produce high-quality images, said Felix Heide, the study's senior author and an assistant professor of computer science at Princeton.

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DNA offers a new look at how Polynesia was settled

DNA offers a new look at how Polynesia was settled | Amazing Science |
Modern genetic evidence suggests that statue builders on islands such as Rapa Nui, also known as Easter Island, had a shared ancestry.


Polynesian voyagers settled islands across a vast expanse of the Pacific Ocean within about 500 years, leaving a genetic trail of the routes that the travelers took, scientists say. Comparisons of present-day Polynesians’ DNA indicate that sea journeys launched from Samoa in western Polynesia headed south and then east, reaching Rarotonga in the Cook Islands by around the year 830. From the mid-1100s to the mid-1300s, people who had traveled farther east to a string of small islands called the Tuamotus fanned out to settle Rapa Nui, also known as Easter Island, and several other islands separated by thousands of kilometers on Polynesia’s eastern edge. On each of those islands, the Tuamotu travelers built massive stone statues like the ones Easter Island is famed for.


That’s the scenario sketched out in a new study in the Sept. 23 2021 Nature by Stanford University computational biologist Alexander Ioannidis, population geneticist Andrés Moreno-Estrada of the National Laboratory of Genomics for Biodiversity in Irapuato, Mexico, and their colleagues. The new analysis generally aligns with archaeological estimates of human migrations across eastern Polynesia from roughly 900 to 1250. And the study offers an unprecedented look at settlement pathways that zigged and zagged over a distance of more than 5,000 kilometers, the researchers say.


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Lyme disease: mRNA vaccination induces tick resistance and prevents transmission of the Lyme disease-causing bacteria

Lyme disease: mRNA vaccination induces tick resistance and prevents transmission of the Lyme disease-causing bacteria | Amazing Science |

An mRNA vaccine that causes a red, itchy skin rash in response to bites by ticks may allow them to be removed before they transmit Lyme disease-causing bacteria


An mRNA vaccine designed to create an immune response to ticks so they can be removed before they transmit Lyme disease has been shown to be effective in guinea pigs. It is hoped the finding will pave the way for clinical trials in people.


Lyme disease is caused by a bacterium called Borrelia burgdorferi that is transmitted through tick bites. If left untreated, it can cause lifelong health problems like Lyme arthritis and nerve pain. Erol Fikrig at Yale University and his colleagues have developed a vaccine that trains the immune system to respond to tick bites, by exposing it to 19 proteins found in tick saliva.


The vaccine contains mRNA molecules that instruct cells to make these proteins, in the same way that mRNA covid-19 vaccines direct cells to make coronavirus proteins. Guinea pigs given the anti-tick vaccine developed red, itchy rashes when they were later bitten by ticks, suggesting their immune systems were responding. The ticks also tended to detach early without sucking as much blood as they normally would.


The researchers then placed ticks carrying Lyme disease-causing bacteria on vaccinated and unvaccinated guinea pigs. The ticks were removed from the vaccinated animals when their skin rashes emerged – usually in the first 18 hours – and none became infected with the bacteria. In contrast, half the unvaccinated animals became infected.


If the vaccine works the same way in people, it will enable us to “readily detect a tick bite early, due to redness at the bite site, and likely itching”, says Fikrig. This is important because tick bites are often painless and go unnoticed. The tick could then be pulled off before transmitting any Lyme disease-causing bacteria, which normally takes about 36 hours. Even without this deliberate tick removal, the immune response generated by the vaccine may encourage the tick to fall off naturally before transmitting the harmful bacteria, says Fikrig. The researchers will now test the vaccine in other animal models before beginning trials in people.


The anti-tick vaccine differs from other Lyme disease vaccines in development, which target the bacteria responsible rather than the tick carrier. Both approaches are promising, but one advantage of tick-targeted vaccines is that they may also protect against other tick-borne diseases like anaplasmosis and babesiosis, says Petr Kopáček at the Institute of Parasitology in the Czech Republic.


Fikrig hopes it will be possible to develop a vaccine that simultaneously targets the harmful bacteria and the ticks. “A combination of the two approaches might make a vaccine that is more effective than either one individually,” he says.


Other researchers are investigating whether Lyme disease could be eradicated in the wild by leaving out baits containing a chemical called hygromycin A that kills B. burgdorferi, but has little effect on most other bacteria and is harmless to animals.


Reference: Science Translational MedicineDOI: 10.1126/scitranslmed.abj9827

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Predicting novel drugs for SARS-CoV-2 (COVID-19) using machine learning from a >10 million chemical space

Predicting novel drugs for SARS-CoV-2 (COVID-19) using machine learning from a >10 million chemical space | Amazing Science |

There is an urgent need for the identification of effective therapeutics for COVID-19 and scientists have recently developed a machine learning drug discovery pipeline to identify several drug candidates. First, they collect assay data for 65 target human proteins known to interact with the SARS-CoV-2 proteins, including the ACE2 receptor. Next, they train machine learning models to predict inhibitory activity and use them to screen FDA registered chemicals and approved drugs (~100,000) and ~14 million purchasable chemicals. They then filter predictions according to estimated mammalian toxicity and vapor pressure. Prospective volatile candidates are proposed as novel inhaled therapeutics since the nasal cavity and respiratory tracts are early bottlenecks for infection. Additionally, the researchers identify candidates that act across multiple targets as promising for future analyses.


It is anticipated that this theoretical study can accelerate testing of two categories of therapeutics: repurposed drugs suited for short-term approval, and novel efficacious drugs suitable for a long-term follow up.

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Hyperalignment: Recognizing familiar faces relies on neural code that is shared across brains

Hyperalignment: Recognizing familiar faces relies on neural code that is shared across brains | Amazing Science |

The ability to recognize familiar faces is fundamental to social interaction. This process provides visual information and activates social and personal knowledge about a person who is familiar. But how the brain processes this information across participants has long been a question. Distinct information about familiar faces is encoded in a neural code that is shared across brains, according to a new Dartmouth study published in the Proceedings of the National Academy of Sciences.


“Within visual processing areas, we found that information about personally familiar and visually familiar faces is shared across the brains of people who have the same friends and acquaintances,” says first author Matteo Visconti di Oleggio Castello, Guarini ’18, who conducted this research as a graduate student in psychological and brain sciences at Dartmouth and is now a neuroscience post-doctoral scholar at the University of California, Berkeley.  “The surprising part of our findings was that the shared information about personally familiar faces also extends to areas that are non-visual and important for social processing, suggesting that there is shared social information across brains.”


For the study, the research team applied a method called hyperalignment, which creates a common representational space for understanding how brain activity is similar between participants. The team used data obtained from three fMRI tasks with 14 graduate students who had known each other for at least two years. In two of the tasks, participants were presented with images of four other personally familiar graduate students and four other visually familiar persons, who were previously unknown.


In the third task, participants watched parts of The Grand Budapest Hotel. The movie data, which is publicly available, was used to apply hyperalignment and align participants’ brain responses into a common representational space. This allowed the researchers to use machine learning classifiers to predict what stimuli a participant was looking at based on the brain activity of the other participants.


The results showed that the identity of visually familiar and personally familiar faces was decoded with accuracy across the brain in areas that are mostly involved in visual processing of faces. Outside of the visual areas however, there was not a lot of decoding. For visually familiar identities, participants only knew what the stimuli looked like; they did not know who these people were or have any other information about them. 

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Taking new aim at COVID-19 RNA

Taking new aim at COVID-19 RNA | Amazing Science |
We have vaccines to prevent COVID-19, but effective, easy-to-administer drugs to help people survive and recover once they’ve been infected remain limited. Duke University chemists are working on new ways to cure infection, using molecules that latch onto folds and loops in the virus’s RNA genome. A study in the journal Science Advances identifies the first chemical compounds that can bind to these 3D RNA structures and block the virus’s ability to replicate.


To the untrained eye, the loops, kinks and folds in the single strand of RNA that makes up the coronavirus genome look like a jumble of spaghetti or tangled yarn. But to researchers like Amanda Hargrove, a chemistry professor at Duke University, the complex shapes that RNA takes on as it folds upon itself could have untapped therapeutic potential in the fight against COVID-19.

In a study to appear Nov. 26 2021 in the journal Science Advances, Hargrove and colleagues have identified chemical compounds that can latch onto these 3D structures and block the virus’s ability to replicate. “These are the first molecules with antiviral activity that target the virus’s RNA specifically, so it's a totally new mechanism in that sense,” Hargrove said. Even more than 18 months into the pandemic, that’s good news. We have vaccines to prevent COVID-19, but effective, easy-to-administer drugs to help people survive and recover once they’ve been infected remain limited.


The virus is receding in some parts of the world, but cases are still surging in others where vaccines are in short supply. And even in regions with easy access to vaccines, COVID-19 vaccine hesitancy means many of the world’s eight billion people remain vulnerable to infection. To infect your cells, the coronavirus must break in, deliver its genetic instructions in the form of RNA, and hijack the body’s molecular machinery to build new copies of itself. The infected cell becomes a virus factory, reading the 30,000 nucleotide “letters” of the virus’s genetic code and churning out the proteins the virus needs to replicate and spread.


Most antivirals -- including remdesivir, molnupiravir and Paxlovid, the only antiviral drugs for COVID-19 that have been FDA-approved or are in line for approval -- work by binding to these proteins. But Hargrove and colleagues are taking a different approach. They’ve identified the first molecules that take aim at the viral genome itself -- and not just the linear sequence of A’s, C’s, G’s and U’s, but the complex three-dimensional structures the RNA strand folds into. When the first terrifying hints of the pandemic started to make headlines, the team including Hargrove, Blanton Tolbert from Case Western Reserve University and Gary Brewer and Mei-Ling Li from Rutgers were already investigating potential drug candidates to fight another RNA virus -- Enterovirus 71, a common cause of hand, foot and mouth disease in children. They had identified a class of small molecules called amilorides that can bind to hairpin-like folds in the virus’s genetic material and throw a wrench in the virus’s replication.


To see if the same compounds could work against coronaviruses too, first they tested 23 amiloride-based molecules against another, far less deadly coronavirus responsible for many common colds. They identified three compounds that, when added to infected monkey cells, reduced the amount of virus within 24 hours of infection without causing collateral damage to their host cells. They also showed greater effects at higher doses. The researchers got similar results when they tested the molecules on cells infected with SARS-CoV-2, the virus that causes COVID-19.


Further work showed that the molecules stopped the virus from building up by binding to a site in the first 800 letters of the viral genome. Most of this stretch of RNA doesn’t code for proteins itself but drives their production. The region folds in on itself to form multiple bulges and hairpin-like structures. Using computer modeling and a technique called nuclear magnetic resonance spectroscopy, the researchers were able to analyze these 3D RNA structures and pinpoint where the chemical compounds were binding. The researchers are still trying to figure out exactly how these compounds stop the virus from multiplying, once they’re bound to its genome.

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The WHO let Omicron skip over variant of interest, and go straight to variant of concern

The WHO let Omicron skip over variant of interest, and go straight to variant of concern | Amazing Science |

The WHO officially named a new version of the SARS-CoV-2 virus a variant of concern, and attached the Greek letter 'omicron' to the designation. The Omicron variant is notable for the sheer number of mutations in the spike protein of the virus and several other regions. While Omicron appears to have started spreading in Africa, it has already appeared in European countries like Belgium and the UK, which are working to limit its spread through surveillance and contact tracing. As of now, the data on Omicron is very limited; we don't currently know how readily it spreads compared to other variants, nor do we understand the degree of protection against Omicron offered by vaccines or past infections.

Many changes

While the Delta variant's version of spike has nine changes compared to the virus that started the pandemic, Omicron has 30 differences. While many of these haven't been identified previously, a number of these have been seen in other strains, where they have a variety of effects. These include increasing infectiveness of the virus, as a number of the changes increase the affinity between the spike protein and the protein on human cells that it targets when starting a new infection. Others changes in the spike occur in areas of the protein that are frequently targeted by antibodies that neutralize the virus. Changes here can mean that an immune response generated to vaccines or earlier versions of the virus are less able to target Omicron.


While many of these mutations are suggestive, understanding how they and the previously undescribed mutations in Omicron alter its behavior will depend on getting real-world data on its spread. Right now, however, we just don't have much of that. We are lucky in the sense that it's relatively easy to detect Omicron. According to the WHO, some of the large collection of mutations in the gene that encodes the spike protein interfere with the gene's recognition by common versions of PCR tests. Those tests continue to recognize the presence of the virus by also targeting other areas of the genome. So a PCR test that comes back spike-negative but virus-positive is suggestive of the presence of Omicron, which can then be confirmed by genome sequencing. These tests have shown that Omicron is spreading rapidly within a number of countries in southern Africa, although the total cases in Botswana and South Africa remain relatively low at the moment, so the significance of this spread is unclear. Vaccination rates in these countries also remain low, making it difficult to determine how much of a risk Omicron poses to those who have been immunized.


The cases identified outside of southern Africa so far have all been in travelers who spent time in this region. Public health authorities in those countries are currently engaged in contact tracing to try to limit the variant's spread outside of those already infected. and a number of countries (including the US) have already limited travel from countries in the region.


Testing and contact tracing are part of the now-familiar suite of public health measures that can limit the impact of Omicron while we're learning more about it. A statement from the CDC provides a reminder of the rest: social distance, mask when indoors, and get a vaccine if you are eligible. While we remain uncertain how much protection vaccines provide against Omicron, it's quite certain that their effectiveness against it is considerably greater than zero.

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10 times harder than steel: Newly-synthesized AM-III carbon is hardest and strongest amorphous material to date

10 times harder than steel: Newly-synthesized AM-III carbon is hardest and strongest amorphous material to date | Amazing Science |

A team of researchers affiliated with a host of institutions across the globe has synthesized an AM-III carbon that is the hardest and strongest amorphous material created to date. In their paper published in the journal National Science Review, the group describes the process they used to create their new material and suggest possible uses for it.


In this new effort, the researchers set out to create a new kind of glass that would be exceptionally strong. To that end, they subjected fullerenes to very high temperatures and enormous pressures and, in so doing, produced what they have called AM-III—a type of glass with crystals in it that measures higher on the Vickers hardness test than many diamonds.


When looking at a diamond under a microscope, the carbon atoms and molecules that make up its crystalline structure are lined up very neatly—glass on the other hand has very little order. This difference explains why diamonds are so hard and why glass is so easily shattered. Prior research has shown that diamonds can be made by exposing graphite to high temperatures and pressure—similar to the way they are created by nature. In this new work, the researchers instead used fullerenes—structures made of carbon in the form of hollow cages. They also slowed down the process, heating and squeezing their material for approximately 12 hours, a move to prevent the material from forming into diamond.


The resulting material, AM-III carbon, is yellowish, with no defined structure, and is very strong—it scored 113 giga-pascals on the Vickers hardness test, higher than some diamonds, which average just 100 giga-pascals. The researchers note that AM-III is approximately ten times as hard as steel and should be quite a bit better at stopping bullets than most vest technology. To prove its toughness, they used one sample to cut a deep scratch into a diamond. The researchers note that the toughness comes about from the material's makeup—it has micro-structures that are orderly like crystals, along with unordered glass, which makes it part glass and part crystal. It also makes the material a semiconductor with a bandgap range similar to silicon. Because of that, the researchers suggest their new material could prove useful in solar panel products.

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3D ink made of living cells allows the creation of "living structures"

3D ink made of living cells allows the creation of "living structures" | Amazing Science |
A team of researchers from Harvard University and Brigham and Women's Hospital, Harvard Medical School, has developed a type of living ink that can be used to print living materials. In their paper published in the journal Nature Communications, the group describes how they made their ink and possible uses for it.


For several years, microbial engineers have been working to develop a means to create living materials for use in a wide variety of applications such as medical devices. But getting such materials to conform to desired 3D structures has proven to be a daunting task. In this new effort, the researchers have taken a new approach to tackling the problem—engineering E. coli to produce a product that can be used as the basis for an ink for use in a 3D printer.


The work began by bioengineering the bacteria to produce living nanofibers. The researchers then bundled the fibers and added other ingredients to produce a type of living ink that could be used in a conventional 3D printer. Once they found the concept viable, the team bioengineered other microbes to produce other types of living fibers or materials and added them to the ink. They then used the ink to print 3D objects that had living components. One was a material that secreted azurin—an anticancer drug—when stimulated by certain chemicals. Another was a material that sequestered Bisphenol A (a toxin that has found its way into the environment) without assistance from other chemicals or devices.

The researchers believe that their concept suggests that producing such inks could be a self-creating proposition.


Engineering could be added to the microbes to push them to produce carbon copies of themselves—the ink could literally be grown in a jar. They also state that it appears possible that the technique could be used to print renewable building materials that would not only grow but could self heal—a possible approach to building self-sustaining homes here on Earth, or on the moon or on Mars.

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Sodium-based material yields stable alternative to lithium-ion batteries

Sodium-based material yields stable alternative to lithium-ion batteries | Amazing Science |

University of Texas at Austin researchers have created a new sodium-based battery material that is highly stable, capable of recharging as quickly as a traditional lithium-ion battery and able to pave the way toward delivering more energy than current battery technologies.


For about a decade, scientists and engineers have been developing sodium batteries, which replace both lithium and cobalt used in current lithium-ion batteries with cheaper, more environmentally friendly sodium. Unfortunately, in earlier sodium batteries, a component called the anode would tend to grow needle-like filaments called dendrites that can cause the battery to electrically short and even catch fire or explode.


In one of two recent sodium battery advances from UT Austin, the new material solves the dendrite problem and recharges as quickly as a lithium-ion battery. The team published their results in the journal Advanced Materials.


"We're essentially solving two problems at once," said David Mitlin, a professor in the Cockrell School of Engineering's Walker Department of Mechanical Engineering and Applied Research Laboratory who designed the new material. "Typically, the faster you charge, the more of these dendrites you grow. So if you suppress dendrite growth, you can charge and discharge faster, because all of a sudden it's safe."


Graeme Henkelman, a professor in the Department of Chemistry and the Oden Institute for Computational Engineering and Sciences, used a computer model to explain, from a theoretical perspective, why the material has the unique properties it does.

"This material is also exciting because the sodium metal anode theoretically has the highest energy density of any sodium anode," Henkelman said.

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If an exoplanet has a lot of methane in its atmosphere, life is the most likely the cause

If an exoplanet has a lot of methane in its atmosphere, life is the most likely the cause | Amazing Science |
The ultra-powerful James Webb Space Telescope will launch soon. Once it's deployed and in position at the Earth-Sun Lagrange Point 2, it'll begin work. One of its jobs is to examine the atmospheres of exoplanets and look for biosignatures. It should be simple, right? Just scan the atmosphere until you find oxygen, then close your laptop and head to the pub: Fanfare, confetti, Nobel prize.


Of course it's more complicated than that. In fact, the presence of oxygen is not necessarily reliable. It's methane that can send a stronger signal indicating the presence of life. Oxygen might seem like the obvious thing to look for in a planet's atmosphere when searching for signs of life, but that's not the case. Its presence or lack thereof is not a reliable indicator. Earth's history makes that clear.


Modern Earth's atmosphere contains about 21% oxygen, and we know that most of it comes from organisms in the planet's oceans. But there's a hitch: Once cyanobacteria on ancient Earth started producing oxygen as a byproduct of photosynthesis, it still took an awfully long time before the atmosphere became oxygenated, possibly a billion years.


What if we examined an exoplanet, found no oxygen, then moved on, not realizing that there was life down there, at the beginning of oxygenating that world? What if we were a billion years too early, and life hasn't oxygenated the exoplanet's atmosphere yet? Rocky planets have many oxygen sinks, and biologically produced oxygen wouldn't be found free in the atmosphere until those sinks were becoming saturated.


That's what happened on Earth, and that's what we expect might happen on other rocky worlds. On Earth, geological activity churns magma up from the mantle onto the crust. Much of the mantle material, like iron, for example, bonds with atmospheric oxygen, pulling it out of the atmosphere.


This is one reason that planetary scientists focus on other things, like methane (CH4). In a new paper, researchers examined the potential for methane to signal biological activity. They say that abundant methane in a planet's atmosphere is unlikely to come from volcanoes and most likely has a biological origin.


The paper's title is "Abundant Atmospheric Methane from Volcanism on Terrestrial Planets Is Unlikely and Strengthens the Case for Methane as a Biosignature." The lead author is Nicholas Wogan from the Dept. of Earth and Space Sciences, University of Washington, and from the Virtual Planetary Laboratory at the U of W. The paper is published in The Planetary Science Journal.

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Mantis shrimp have the world's best eyes – but why?

Mantis shrimp have the world's best eyes – but why? | Amazing Science |

As humans, we experience an amazing world of colors, but what can other animals see? Some see much more than us, but how they use this vision is largely unknown. 


The Mantis shrimp vision puts everything else to shame. These marine crustaceans may be well-known for their record breaking punch (the same acceleration as a .22 calibre bullet), but they also hold the world record for the most complex visual system.

They have up to 16 photoreceptors and can see UV, visible and polarised light. In fact, they are the only animals known to detect circularly polarised light, which is when the wave component of light rotates in a circular motion. They also can perceive depth with one eye and move each eye independently. It’s impossible to imagine what mantis shrimp see, but incredible to think about.


Mantis shrimps have compound eyes that are made up of tens of thousands of ommatidia (elements containing a cluster of photoreceptor cells, support cells and pigment cells) much like flies. In the species with spectacular vision, Gonodactylids and Lysiosquillids, the middle of the eye has six rows of modified ommatidia called the mid-band. This is where the magic happens.


Each row is specialized to detect either certain wavelengths of light or polarized light. The first four rows detect human visible light and UV light. In fact, each row contains a different receptor in the UV, giving mantis shrimp extremely good UV vision.

The ommatidia of the last two rows contain very precisely positioned, tiny hairs. This arrangement is most likely responsible for their polarization vision.


The overall structure of the eye is intriguing too. Three parts of each eye look at the same point in space. This results in about 70% of the eye focusing on a narrow strip in space, but also gives them the ability to perceive depth with just one eye. To create an image using this strip, mantis shrimp are constantly moving their eyes and scanning the environment. The ability to move each eye independently comes in useful here, and allows the mantis shrimp to have a large field of view.

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WHO has not seen any reports of deaths caused by Omicron COVID-19 variant, urges people not to panic as delta still dominant

WHO has not seen any reports of deaths caused by Omicron COVID-19 variant, urges people not to panic as delta still dominant | Amazing Science |

The World Health Organization said Friday it has not seen any reports of deaths caused by the new variant of the coronavirus that causes COVID-19 dubbed omicron, and again urged people not to panic as for now the delta strain remains dominant around the world.


The Delta variant currently accounts for 99% of all COVID infections, Soumya Swaminathan, WHO chief scientist, told Reuters, adding that it’s impossible to predict whether omicron will overtake it. The new strain, which was classified a “variant of concern” by the WHO last Friday, has so far spread fast in South Africa, where doctors first reported on it, and has been detected in at least two dozen countries. But further testing is required to determine whether it is more transmissible than other variants, including delta, more lethal or more resistant to vaccines and treatments.


WHO spokesman Christian Lindmeier told a U.N. briefing in Geneva that vaccine makers should get ready to tweak their products, and Ugur Sahin, head of Germany’s BioNTech said that company should be able to adapt the vaccine it co-developed with Pfizer relatively quickly. Sahin has said this week that the current vaccine should still offer protection against severe disease, even with the many mutations carried by omicron.

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NASA/ESA/Hubble: Dark Matter Visualized

NASA/ESA/Hubble: Dark Matter Visualized | Amazing Science |

Dark matter is a form of matter that cannot currently be observed directly, but surprisingly is thought to comprise about 85% of all matter in the Universe. In general, objects in our Universe are attracted to one another by gravity. The more mass an object has, the stronger its gravitational attraction. The effects of gravity can be observed across the entire Universe: planets orbit the Sun; exoplanets orbit other stars; galaxies interact and collide; and so on. If the mass of a celestial object is known, then it is possible to calculate how it will interact with other celestial objects via gravity.


However, calculations based on observations of many galaxies often do not work out in practice. The galaxies appear to lack sufficient mass to be held together by gravity alone — they should actually fly apart, or should never have formed at all, according to the laws of gravity. Therefore, many astronomers have theorized that there must be another form of matter that has not yet been observed, known as "dark matter", which is holding these galaxies together. For the calculations based on observations to make sense, dark matter must make up about 85% of all the matter in our Universe, and 27% of our Universe’s total mass-energy density. It is referred to as ‘dark’ because it does not appear to interact with the electromagnetic field, and therefore does not seem to emit, reflect or refract light.


Hubble carried out surveys and developed new methods to look for clues about dark matter. By analyzing the COSMOS survey — one of the largest surveys undertaken with Hubble — an international team of scientists assembled one of the most important results in cosmology: a three-dimensional map that offers a first look at the web-like large-scale distribution of dark matter in the Universe. This historic achievement in 2007 accurately confirmed standard theories of dark matter structure formation, and took astronomers from inference to a direct observation of dark matter's influence in the Universe. Mapping dark matter's distribution in space and time is fundamental to understanding how galaxies grew and clustered over billions of years. Tracing the growth of the clustering of dark matter may also eventually shed light on dark energy, a force which repels matter rather than attracts it as gravity does, which may have influenced how dark matter clumps.


In 2009, Hubble uncovered strong new evidence that galaxies are embedded in halos of dark matter. Peering into the tumultuous heart of the nearby Perseus galaxy cluster, Hubble discovered a large population of small galaxies that have remained intact while larger galaxies around them are being ripped apart by the gravitational tug of other galaxies. This provided further evidence that the undisturbed galaxies are enshrouded by a ‘cushion’ of dark matter that protects them from the rough-and-tumble in their neighborhood.


Hubble also solved the mystery of the galaxy NGC 1052-DF4 in 2020, which appeared to be missing most of its dark matter. Astronomers concluded that the gravitational forces of a neighboring galaxy stripped the dark matter from NGC 1052-DF4 and now the galaxy is being torn apart. The discovery not only solved an astronomical conundrum, but has also brought a sigh of relief to astronomers. Without it, scientists would be faced with having to revise our understanding of the laws of gravity.

Hubble and NASA’s Chandra X-ray Observatory observed a powerful collision between galaxy clusters, which revealed that the titanic collision has separated dark matter from ordinary matter. This 2008 discovery provided valuable and striking evidence for researchers in their quest to understand dark matter and its properties.


Further Hubble and Chandra collaboration continued in 2015 into the study of how dark matter in clusters of galaxies behaves when the clusters collide. The results showed that dark matter interacts with itself even less than previously thought, and narrows down the options for what this mysterious substance might be.

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Advancing mathematics by guiding human intuition with AI

Advancing mathematics by guiding human intuition with AI | Amazing Science |

The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems. Since the 1960s, mathematicians have used computers to assist in the discovery of patterns and formulation of conjectures1, most famously in the Birch and Swinnerton-Dyer conjecture2, a Millennium Prize Problem3.


Now, mathematical researchers provide examples of new fundamental results in pure mathematics that have been discovered with the assistance of machine learning—demonstrating a method by which machine learning can aid mathematicians in discovering new conjectures and theorems. They propose a process of using machine learning to discover potential patterns and relations between mathematical objects, understanding them with attribution techniques and using these observations to guide intuition and propose conjectures. In a recent paper, they outline this machine-learning-guided framework and demonstrate its successful application to current research questions in distinct areas of pure mathematics, in each case showing how it led to meaningful mathematical contributions on important open problems: a new connection between the algebraic and geometric structure of knots, and a candidate algorithm predicted by the combinatorial invariance conjecture for symmetric groups4.


This important work may serve as a model for collaboration between the fields of mathematics and artificial intelligence (AI) that can achieve surprising results by leveraging the respective strengths of mathematicians and machine learning.

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A Living Review of AI / Machine Learning Used for Particle Physics

A Living Review of AI / Machine Learning Used for Particle Physics | Amazing Science |

Modern machine learning techniques, including deep learning, is rapidly being applied, adapted, and developed for high energy physics. The goal of this review document is to provide a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions are most welcome.


The purpose of this site is to collect references for modern machine learning as applied to particle physics. A minimal number of categories is chosen in order to be as useful as possible. Note that papers may be referenced in more than one category. The fact that a paper is listed in this document does not endorse or validate its content - that is for the community (and for peer-review) to decide. Furthermore, the classification here is a best attempt and may have flaws - please let us know if (a) we have missed a paper you think should be included, (b) a paper has been misclassified, or (c) a citation for a paper is not correct or if the journal information is now available. In order to be as useful as possible, this document will continue to evolve so please check back before you write your next paper. If you find this review helpful, please consider citing it using {hepmllivingreview} in HEPML.bib.

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90% still to be named: AI could help biologists to classify the world's insects and other invertebrates

90% still to be named: AI could help biologists to classify the world's insects and other invertebrates | Amazing Science |

With biodiversity in decline around the world, researchers are desperate to catalog all of Earth's insects and other invertebrates, which represent 90% of the 9 million species yet to be named. To do so, scientists typically face long hours in the lab sorting through the specimens they collected.


Enter DiversityScanner. The approach involves a robot, which plucks individual insects and other small creatures one at a time from trays and photographs them. A computer then uses a type of artificial intelligence known as machine learning to compare each one's legs, antennae, and other features to known specimens. The technology then imposes a color code, or heat map, over the image (see above). The warmer the color, say, red, the more the computer program depended on that body part to make a call on the type of insect it was. This heat map makes it easier for researchers checking the identification to see what the program's "thought" process was.


The robot then moves each insect into a plate with 96 tiny wells, readying these specimens for DNA sequencing. The resulting species-identifying piece of sequence—a "DNA barcode"—is linked to the image in a database of all the cataloged specimens. Although not quite as good as a human expert, the approach accurately classifies insects 91% of the time, the designers of the technology report in a study posted to the preprint server bioRxiv. That accuracy will improve as more specimens are added to the database, they note.


The researchers have made the software and 3D printing plans for the technology openly available. And, as the scientists describe in a second preprint, they have simplified the sequencing steps and software so that developing countries and small organizations can take advantage of it—96 insects at a time.

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In a world first, South Africa grants a patent that has an artificial intelligence (AI) system as inventor

In a world first, South Africa grants a patent that has an artificial intelligence (AI) system as inventor | Amazing Science |

At first glance, a recently granted South African patent relating to a “food container based on fractal geometry” seems fairly mundane. The innovation in question involves interlocking food containers that are easy for robots to grasp and stack. On closer inspection, the patent is anything but mundane. That’s because the inventor is not a human being – it is an artificial intelligence (AI) system called DABUS.


DABUS (which stands for “device for the autonomous bootstrapping of unified sentience”) is an AI system created by Stephen Thaler, a pioneer in the field of AI and programming. The system simulates human brainstorming and creates new inventions. DABUS is a particular type of AI, often referred to as “creativity machines” because they are capable of independent and complex functioning. This differs from everyday AI like Siri, the “voice” of Apple’s iPhones. The patent application listing DABUS as the inventor was filed in patent offices around the world, including the US, Europe, Australia, and South Africa. But only South Africa granted the patent (Australia followed suit a few days later after a court judgment gave the go-ahead).


South Africa’s decision has received widespread backlash from intellectual property experts. Some have labelled it a mistake, or an oversight by the patent office. However, as a patent and AI scholar whose PhD aims to address the gaps in patent law – as I examine in this pre-print article – created by AI inventorship, I suggest that the decision is supported by the government’s policy environment in recent years. This has aimed to increase innovation, and views technology as a way to achieve this.


Creativity machines

Creativity machines can process and critically analyze data, learning from it. This process is known as machine learning. Once the machine learning phase has occurred, the machine is able to “autonomously” create without human intervention. As has been seen in the COVID pandemic, as just one example, AI is able to solve problems humans were unable to – and also much faster than people can.


Over the years there have been many kinds of creativity machines. Prior to DABUS, Thaler built another AI which created novel sheet music, and which he credited with inventing the cross-bristle toothbrush design. He filed a patent for the cross-bristle design, and it was granted – proving AI’s ability to generate truly novel inventions that meet the standards for patents. However, Thaler listed himself, rather than the AI, as the inventor at that time.

When it came to the food container invention by DABUS, Thaler, assisted by Ryan Abbott of the University of Surrey, decided instead to list DABUS as the rightful inventor, as the invention was entirely devised by the AI. This was the start of their push for AI to be recognized as inventors the world over.


The United States Patent and Trademark Office and the European Patent Office rejected these applications in the formal examination phase. They gave three reasons. First, their respective patent laws only provide for human inventors – not AI – as indicated by the use of pronouns such as “him” and “her” in their text. Second, ideas, for the purposes of patents, require the element of “mental conception” – something of which only a human mind is capable. Finally, inventorship comes with rights, which AI is not legally capable of possessing. Much to the surprise of the global community, South Africa’s patent office, the Companies and Intellectual Property Commission, granted the patent, recognizing DABUS as an inventor. It has not yet explained its reasons for doing so. This patent was published in July 2021 in the South African Patent Journal, with major news agencies including The Times reporting on the matter.

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AI and Large Bone Marrow Cell Data Set Help to Identify Blood Diseases

AI and Large Bone Marrow Cell Data Set Help to Identify Blood Diseases | Amazing Science |
A data set of more than 170,000 microscopic images allows for the training of neural networks for the identification of bone marrow cells with high accuracy.


Diagnosing blood disorders relies on a century-old method of using optical microscopes to analyze and classify samples of bone marrow cells. The method used to look for rare, but diagnostically important, cells is well-established, albeit laborious and time-consuming. Artificial intelligence (AI) has the potential to improve this method. However, training an AI algorithm requires a large amount of high-quality data. Now a team has used a data set of more than 170,000 microscopic images to train neural networks to identify bone marrow cells with high accuracy.


The Helmholtz Munich researchers developed the largest open-access database on microscopic images of bone marrow cells to date. The database consists of more than 170,000 single-cell images from over 900 patients with various blood diseases. It is the result of a collaboration from Helmholtz Munich with the LMU University Hospital Munich, the MLL Munich Leukemia Lab (one of the largest diagnostic providers in this field worldwide), and Fraunhofer Institute for Integrated Circuits.


The dataset, the researchers said, allowed them to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species “with high precision and recall.” Their convolutional neural networks outcompete previous feature-based approaches, they added, and provide a proof-of-concept for the classification problem of single bone marrow cells.


“On top of our database, we have developed a neural network that outperforms previous machine learning algorithms for cell classification in terms of accuracy, but also in terms of generalizability,” said Christian Matek, PhD, a postdoctoral researcher at the Helmholtz Zentrum München. The deep neural network is a machine learning concept specifically designed to process images. “The analysis of bone marrow cells has not yet been performed with such advanced neural networks,” Matek explained, “which is also due to the fact that high-quality, public datasets have not been available until now.”


The researchers aim to further expand their bone marrow cell database to capture a broader range of findings and to prospectively validate their model. “The database and the model are freely available for research and training purposes—to educate professionals or as a reference for further AI-based approaches, e.g., in blood cancer diagnostics,” said Carsten Marr, PhD, deputy head of institute and research group leader at the Institute of Computational Biology at Helmholtz Munich.


This work was published in Blood, in the paper, “Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.

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Several new studies suggest 2-aminoadenine-containing genomes are more widespread in bacteriophages than thought

Several new studies suggest 2-aminoadenine-containing genomes are more widespread in bacteriophages than thought | Amazing Science |

Three teams working independently have found evidence that suggests the Z-genome in bacteria-invading viruses is much more widespread than thought. All three of the groups have used a variety of genomic techniques to identify parts of the pathways that lead development of the Z-genome in bacteria-invading viruses known as bacteriophages. The first team was made up of researchers from several institutions in China and one in Singapore, the second with members from several institutions in France; the third was an international effort. All three teams have published their results in the journal Science. Michael Grome and Farren Isaacs with Yale University have also published a Perspectives piece in the same journal issue outlining the work of all three teams.


Four nucleobases. adenine (A), cytosine (C), guanine (G), and thymine (T), are usually thought to be invariable in DNA. In bacterial viruses, however, each of the DNA bases have variations that help them to escape degradation by bacterial restriction enzymes. In the genome of cyanophage S-2L, A is completely replaced by diaminopurine (Z), which forms three hydrogen bonds with T and thus creates non–Watson-Crick base pairing in the DNA of this virus. Zhou et al. and Sleiman et al. determined the biochemical pathway that produces Z, which revealed more Z genomes in viruses hosted in bacteria distributed widely in the environment and phylogeny. Pezo et al. identified a DNA polymerase that incorporates Z into DNA while rejecting A. These findings enrich our understanding of biodiversity and expand the genetic palette for synthetic biology. For details see Science p. 512, 516, 520; see also p. 460.

In general, cells have two purine pathways that synthesize adenine and guanine ribonucleotides from phosphoribose via inosylate. A chemical hybrid between adenine and guanine, 2-aminoadenine (Z), replaces adenine in the DNA of the cyanobacterial virus S-2L. A group of scientists was able to show now that S-2L and Vibrio phage PhiVC8 encode a third purine pathway catalyzed by PurZ, a distant paralog of succinoadenylate synthase (PurA), the enzyme condensing aspartate and inosylate in the adenine pathway. PurZ condenses aspartate with deoxyguanylate into dSMP (N6-succino-2-amino-2′-deoxyadenylate), which undergoes defumarylation and phosphorylation to give dZTP (2-amino-2′-deoxyadenosine-5′-triphosphate), a substrate for the phage DNA polymerase. Crystallography and phylogenetics analyses indicate a close relationship between phage PurZ and archaeal PurA enzymes. This important work elucidates the biocatalytic innovation that remodeled a DNA building block beyond canonical molecular biology.

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AI solves the who's who problem in NMR spectra of organic crystals

AI solves the who's who problem in NMR spectra of organic crystals | Amazing Science |
Solid-state nuclear magnetic resonance (NMR) spectroscopy—a technique that measures the frequencies emitted by the nuclei of some atoms exposed to radio waves in a strong magnetic field—can be used to determine chemical and 3D structures as well as the dynamics of molecules and materials.


A necessary initial step in the analysis is the so-called chemical shift assignment. This involves assigning each peak in the NMR spectrum to a given atom in the molecule or material under investigation. This can be a particularly complicated task. Assigning chemical shifts experimentally can be challenging and generally requires time-consuming multi-dimensional correlation experiments. Assignment by comparison to statistical analysis of experimental chemical shift databases would be an alternative solution, but there is no such database for molecular solids.


A team of researchers including EPFL professors Lyndon Emsley, head of the Laboratory of Magnetic Resonance, Michele Ceriotti, head of the Laboratory of Computational Science and Modeling and Ph.D. student Manuel Cordova decided to tackle this problem by developing a method of assigning NMR spectra of organic crystals probabilistically, directly from their 2D chemical structures. They started by creating their own database of chemical shifts for organic solids by combining the Cambridge Structural Database (CSD), a database of more than 200,000 three-dimensional organic structures, with ShiftML, a machine learning algorithm they had developed together previously that allows for the prediction of chemical shifts directly from the structure of molecular solids.


Initially described in a Nature Communications paper in 2018, ShiftML uses DFT calculations for training, but can then perform accurate predictions on new structures without performing additional quantum calculations. Though DFT accuracy is attained, the method can calculate chemical shifts for structures with ~100 atoms in seconds, reducing the computational cost by a factor of as much as 10,000 compared to current DFT chemical shift calculations. The accuracy of the method does not depend on the size of the structure examined and the prediction time is linear in the number of atoms. This sets the stage for calculating chemical shifts in situations where it would have been unfeasible before.


In the new Science Advances paper, the team used ShiftML to predict shifts on more than 200,000 compounds extracted from the CSD and then related the shifts obtained to topological representations of the molecular environments. This involved constructing a graph representing the covalent bonds between the atoms in the molecule, extending it a given number of bonds away from the central atoms. They then brought together all the identical instances of the graph in the database, allowing them to obtain statistical distributions of chemical shifts for each motif. The representation is a simplification of the covalent bonds around the atom in a molecule and doesn't contain any 3D structural features: this allowed them to obtain the probabilistic assignment of the NMR spectra of organic crystals directly from their two-dimensional chemical structures through a marginalization scheme that combined the distributions from all the atoms in the molecule.

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200,000 whole genomes made available for biomedical studies by U.K. effort

200,000 whole genomes made available for biomedical studies by U.K. effort | Amazing Science |
UK Biobank offers easy access to genomic database for researchers around the world


In the largest single release of whole genomes ever, the UK Biobank (UKBB) this week unveiled to scientists the entire genomes of 200,000 people who are part of a long-term British health study.


The trove of genomes, each linked to anonymized medical information, will allow biomedical scientists to scour the full 3 billion base pairs of human DNA for insights into the interplay of genes and health that could not be gleaned from partial sequences or scans of genome markers. “It is thrilling to see the release of this long-awaited resource,” says Stephen Glatt, a psychiatric geneticist at the State University of New York Upstate Medical University.


Other biobanks have also begun to compile vast numbers of whole genomes, 100,000 or more in some cases (see table, below). But UKBB stands out because it offers easy access to the genomic information, according to some of the more than 20,000 researchers in 90 countries who have signed up to use the data.


“In terms of availability and data quality, [UKBB] surpasses all others,” says physician and statistician Omar Yaxmehen Bello-Chavolla of the National Institute for Geriatrics in Mexico City.


A number of efforts are releasing many thousands of whole genomes, with varying degrees of access, to accelerate biomedical research.

BIOBANK Completed whole genomes Release information UK Biobank 200,000 300,000 more in early 2023 Trans-Omics for Precision Medicine 161,000 National Institutes of Health (NIH) requires project-specific consent Million Veteran Program 125,000 Non–Veterans Affairs researchers get access in 2022 Genomics England’s 100,000 Genomes 120,000 Researchers must join collaboration All of Us 90,000 NIH expects to release by early 2022


Having enrolled 500,000 middle-age and elderly participants of mostly European ancestry from 2006 to 2010, UKBB is one of the largest genetics research databases in the world. It proved its worth even before releasing whole genomes. Studies of specific DNA markers that vary among participants have revealed hundreds of new disease risk genes. Since 2019 researchers have also been probing participants’ exomes, the 2% of the whole genome sequence (WGS) that encodes proteins; the exomes from nearly all participants became available in the past 2 months. Exome studies are yielding risk genes that are very rare and can’t be found with genotyping data.


But whole genomes will make it possible to explore the influence of noncoding DNA, which controls when genes are turned off or on, and of gene rearrangements, as well as missing, repeated, or extra stretches of DNA in genes. Such changes play a role in diseases such as Huntington disease.

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