Amazing Science

UK astronomers have discovered an asteroid with a spectral match to our moon lurking behind Mars, believing it could be a long-lost companion to our only natural satellite.

Important new studies of the evolution of birds and mammals relied on Progressive Cactus, a genome alignment tool developed at the UC Santa Cruz Genomics Institute.

Three papers published November 11 in Nature present major advances in understanding the evolution of birds and mammals, made possible by new methods for comparing the genomes of hundreds of species.

Comparative genomics uses genomic data to study the evolutionary relationships among species and to identify DNA sequences with essential functions conserved across many species. This approach requires an alignment of the genome sequences so that corresponding positions in different genomes can be compared, but that becomes increasingly difficult as the number of genomes grows.

Researchers at the UC Santa Cruz Genomics Institute developed a powerful new genome alignment method that has made the new studies possible, including the largest genome alignment ever achieved of more than 600 vertebrate genomes. The results provide a detailed view of how species are related to each other at the genetic level. "We're literally lining up the DNA sequences to see the corresponding positions in each genome, so you can look at individual elements of the genome and see in great detail what has changed and what's stayed the same over evolutionary time," explained Benedict Paten, associate professor of biomolecular engineering at UC Santa Cruz and a corresponding author of two of the new papers.

Identifying DNA sequences that are conserved, remaining unchanged over millions of years of evolution, enables scientists to pinpoint elements of the genome that control important functions across a wide range of species. "It tells you something is important there -- it hasn't changed because it can't -- and now we can see that with higher resolution than ever before," Paten explained.

The previous generation of alignment tools relied on comparing everything to a single reference genome, resulting in a problem called "reference bias." Paten and coauthor Glenn Hickey originally developed a reference-free alignment program called Cactus, which was state-of-the-art at the time, but worked only on a small scale. UCSC graduate student Joel Armstrong (now at Google) then extended it to create a powerful new program called Progressive Cactus, which can work for hundreds and even thousands of genomes.

"Most previous alignment methods were limited by reference bias, so if human is the reference, they could tell you a lot about the human genome's relationship to the mouse genome, and a lot about the human genome's relationship to the dog genome -- but not very much about the mouse genome's relationship to the dog genome," Armstrong explained. "What we've done with Progressive Cactus is work out how to avoid the reference-bias limitation while remaining efficient enough and accurate enough to handle the massive scale of today's genome sequencing projects."

An international consortium of researchers under the aegis of CMIP6 has calculated new estimates for the melting of Earth’s ice sheets due to greenhouse gas emissions and its impact on sea levels, showing that the ice sheets could together contribute more than 40 cm by the end of 2100.

Flying over the vast ice sheet of Greenland, on board an Iceland Air flight from Reykjavik, Iceland, to Seattle, USA. Notice the small “lakes” and rivers of melt water (CC BY 2.0, Bernt Rostad/Flickr)

One of the many effects of global warming is the increase of sea levels due to the melting and retreat of the ice sheets in the Arctic and the Antarctic. As the sea level rises, large areas of densely populated coastal land will become uninhabitable. It is vital that we understand the impact climate change interventions could have on the rate of melting and, consequently, changes in sea level.

The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6), consisting of more than 60 scientists from 36 institutions across the world – including Dr. Christopher Chambers and Professor Ralf Greve from the Institute of Low Temperature Science at Hokkaido University – has used the latest generation of models to estimate the impact of global warming on ice sheets. Their results were published in a special issue of the journal The Cryosphere on September 17.

The ISMIP6 team projected the changes of the Greenland and Antarctic Ice Sheets (GrIS/AIS) between 2015 and 2100 under global warming conditions predicted by climate modelling. The advantage of ISMIP6 was that it used fourteen different models to estimate the changes in the ice sheet under two greenhouse gas (GHG) emissions pathways: the pessimistic scenario, where there is no change in current trends, leading to a rapid increase in GHG emissions; and the optimistic scenario, where comprehensive steps are taken to reduce GHG emissions. Using different models to estimate the changes was vital to this endeavour, as they have different baselines and assumptions.

A "molecular volume knob" regulating electrical signals in the brain helps with learning and memory, according to a Dartmouth study. The molecular system controls the width of electrical signals that flow across synapses between neurons. The finding of the control mechanism, and the identification of the molecule that regulates it, could help researchers in their search for ways to manage neurological disorders, including Alzheimer's disease, Parkinson's disease and epilepsy.

On Jupiter's icy moon Europa, powerful eruptions may spew into space, raising questions among hopeful astrobiologists on Earth: What would blast out from miles-high plumes? Could they contain signs of extraterrestrial life? And where in Europa would they originate? A new explanation now points to a source closer to the frozen surface than might be expected.

Rather than originating from deep within Europa's oceans, some eruptions may originate from water pockets embedded in the icy shell itself, according to new evidence from researchers at Stanford University, the University of Arizona, the University of Texas and NASA's Jet Propulsion Laboratory.

Using images collected by the NASA spacecraft Galileo, the researchers developed a model to explain how a combination of freezing and pressurization could lead to a cryovolcanic eruption, or a burst of water. The results, published Nov. 10 in Geophysical Research Letters, have implications for the habitability of Europa's underlying ocean—and may explain eruptions on other icy bodies in the solar system.

Harbingers of life?

Scientists have speculated that the vast ocean hidden beneath Europa's icy crust could contain elements necessary to support life. But short of sending a submersible to the moon to explore, it's difficult to know for sure. That's one reason Europa's plumes have garnered so much interest: If the eruptions are coming from the subsurface ocean, the elements could be more easily detected by a spacecraft like the one planned for NASA's upcoming Europa Clipper mission.

But if the plumes originate in the moon's icy shell, they may be less hospitable to life, because it is more difficult to sustain the chemical energy to power life there. In this case, the chances of detecting habitability from space are diminished. "Understanding where these water plumes are coming from is very important for knowing whether future Europa explorers could have a chance to actually detect life from space without probing Europa's ocean," said lead author Gregor Steinbrügge, a postdoctoral researcher at Stanford's School of Earth, Energy & Environmental Sciences (Stanford Earth).

Time crystals sound like something out of science fiction, but they may be the next major leap in quantum network research. A team based in Japan has proposed a method to use time crystals to simulate massive networks with very little computing power. They published their results on October 16 Science Advances.

First theorized in 2012 and observed in 2017, time crystals are arrangements of matter that repeats in time. Normal crystals, such as diamonds or salt, repeat their atomic self-organization in space, but do not show any regularity in time. Time crystals self-organize and repeat their patterns in time, meaning their structure changes periodically as time progresses.

"The exploration of time crystals is a very active field of research and several varied experimental realizations have been achieved," said paper author Kae Nemoto, professor in the principles of informatics research division at the National Institute of Informatics. "Yet an intuitive and complete insight of the nature of time crystals and their characterization, as well as a set of proposed applications, is lacking. In this paper, we provide new tools based on graph theory and statistical mechanics to fill this gap."

Nemoto and her team specifically examined how the quantum nature of time crystals -- how they shift from moment to moment in a predictable, repeating pattern -- can be used to simulate large, specialized networks, such as communication systems or artificial intelligence. "In the classical world, this would be impossible as it would require a huge amount of computing resources," said Marta Estarellas, one of the first authors of the paper from the National Institute of Informatics. "We are not only bringing a new method to represent and understand quantum processes, but also a different way to look at quantum computers."

Quantum computers can store and manipulate multiple states of information, meaning they can process huge data sets with relatively little power and time by solving several potential outcomes at the same time, rather than one by one like classical computers. "Can we use this network representation and its tools to understand complex quantum systems and their phenomena, as well as identify applications?" Nemoto asked. "In this work, we show the answer is yes."

The researchers plan to explore different quantum systems using time crystals after their approach is experimentally tested. With this information, their goal is to propose real applications for embedding exponentially large complex networks in a few qubits, or quantum bits. "Using this method with several qubits, one could simulate a complex network the size of the entire worldwide internet," Nemoto said.

A new understanding of nanomaterials, sensor design and fabrication approaches could help advance stretchable, wearable gas sensors that monitor gaseous biomarkers in humans and toxic gas in an exposed environment, according to Penn State researchers.

Led by Huanyu "Larry" Cheng, Dorothy Quiggle Career Development Professor in the Penn State Department of Engineering Science and Mechanics, the research team recently published a review of the current state of gas-detecting stretchable sensors in Trends in Analytical Chemistry.

Recent developments in gas-sensing technologies have made it possible to detect gaseous biomarkers in humans by monitoring the metabolic process through exhaled breath or skin perspiration and detect harmful or toxic gasses in humans' surrounding environment. Human motions that significantly stretch the skin can degrade or deform the sensors, making it unable to detect gasses accurately. To make a more resilient sensor, Cheng and his team investigated the most effective sensor fabrication methods that could work for a variety of applications.

"With recent developments in breath analysis, we are starting to build momentum toward developing a gas sensor that could have a larger platform of applications," Cheng said. According to Cheng, the gas sensors can help provide an earlier medical diagnosis by detecting volatile organic compounds (VOCs) from human breath, which may indicate the presence of several diseases, including amoebic dysentery, intestinal bacterial infections and cancer.

Previous sensors could only monitor glucose and pH levels. "From human skin perspiration and the exhaled breath, we have about 2,600 biomarkers in the gas form," Cheng said. "This gives us vital information that we can leverage in the development of disease diagnostics." In addition to monitoring these biomarkers, the sensors can detect dangerous levels of toxic gasses that may be present in a human's surrounding environment. For example, the sensors could detect dangerous levels of methane in coal mines and potentially monitor the health and safety of coal miners. Current gas sensors exhibit similar characteristics to the versions the team is studying, but they have flaws, according to Cheng. For example, metal oxide-based gas sensors have high working temperatures, making them too hot for people to wear. By improving how current gas sensors are fabricated, Cheng said he plans to develop a more reliable and safer gas sensor.

The researchers are specifically interested in a novel platform that directly integrates laser-induced graphene (LIG) via a simple laser scribing process. According to Cheng, this is a cost-effective way to develop a more sensitive, more selective sensor capable of quickly detecting VOCs and harmful gas at ultra-low levels. LIG is highly porous and can be integrated with carbon-based or metal oxide nanomaterials, which are highly sensitive to gasses. Cheng's platform consists of LIG laser scribed on a film that is transferred to a soft substrate and coated with conductive metal to reduce its resistance. Because of the reduced resistance created through this method, the sensor can easily induce self-heating. The mixed metal oxide integrated with the novel LIG gas-sensing platform makes its workable temperatures significantly lower than the former metal oxide-based gas sensor.

Cheng and his colleagues are also studying how the shapes of composite materials comprising wearable, stretchable gas sensors can affect their environmental sensing performance.

Finding just the right model to study human development—from the early embryonic stage onward—has been a challenge for scientists over the last decade. Now, bioengineers at the University of California San Diego have homed in on an unusual candidate: teratomas.

Teratomas—which mean “monstrous tumors” in Greek—are tumors made up of different tissues such as bone, brain, hair and muscle. They form when a mass of stem cells differentiates uncontrollably, forming all types of tissues found in the body. Teratomas are generally considered an undesired byproduct of stem cell research, but UC San Diego researchers found an opportunity to study them as models for human development.

“We’ve been fascinated with the teratoma for quite a while,” said Prashant Mali, a professor of bioengineering at the UC San Diego Jacobs School of Engineering. “Not only is the teratoma an intriguing tumor to look at in terms of the diversity of cell types, but it also has regions of organized tissue-like structures. This prompted us to explore its utility in both cell science and cell engineering contexts.”

“There’s no other model like it. In just one tumor, you can study all of these different lineages, all of these different organs, at the same time,” said Daniella McDonald, an M.D/Ph.D. candidate in Mali’s lab and co-first author of the study. “Plus, it’s a vascularized model, it has a three-dimensional structure and it’s human-specific tissue, making it the ideal model for recreating the context in which human development happens.”

This work is published in a recent paper in Cell.

Extremely small antibodies, known as nanobodies, have been shown to neutralize SARS-CoV-2. But they're extracted from llamas and camels, which is problematic for a number of reasons. Now, scientists have identified a synthetic form of a nanobody, known as a sybody, which shows affinity to SARS-CoV-2 and neutralizing capabilities.

For SARS-CoV-2 to infect humans, the viral Spike (S) protein must interact with and bind to the angiotensin-converting enzyme 2 (ACE2) receptor on the surface of human cells. The S protein achieves this via the receptor binding domains (RBDs), and thus blocking the RBD using antibodies is just one therapeutic avenue that is being explored for the treatment of SARS-CoV-2. 

Back in May, Technology Networks covered the work of scientists exploring the potential utility of nanobodies against SARS-Cov-2. Nanobodies, also known as "single-domain antibodies" are extremely small antibodies that are found in llamas and camels.

Compared to conventional antibodies, nanobodies are advantageous for therapeutic use for several reasons: They can be constructed into multivalent forms, nebulized (a desirable property as it simplifies drug delivery) and possess higher thermal chemostability.

Several research teams have now demonstrated that nanobodies are able to inhibit the binding of the S protein to the ACE2 receptor, neutralizing SARS-CoV-2.

The fact that nanobodies are derived from animals raises some logistical challenges, namely; how can their production be scaled-up in a manner that is safe but also efficient in the context of a global pandemic?

Synthetic biology steps up to SARS-CoV-2

Scientists have turned to synthetic versions of nanobodies – known as sybodies – as a potential alternative. Sybodies are designed to mimic the natural shape and diversity of nanobodies, and due to their small size can target epitopes that are tricky for traditional antibodies to bind to. In 2018, the laboratory of Professor Markus Seeger at the University of Zurich published a protocol which provides researchers with the tools to rapidly select sybodies against specific membrane protein targets of interest and creating libraries of sybodies.1

A group of researchers led by Christian Löw at EMBL Hamburg have utilized this platform to search for existing sybodies that could block SARS-CoV-2. Their findings are published in Nature Communications.2 Discussing their rationale in the paper the research team say, "This methodology is an attempt to minimize the overwhelming impact that SARS-CoV-2 is having on the healthcare systems and to prepare for future pandemics. Thus, the R&D community can act in a prompter way for the development of efficient medication."

A new study of 24 adults with major depression finds that two doses of the psychedelic substance psilocybin, given with supportive psychotherapy, produced rapid and large reductions in depressive symptoms. Most of the participants showed improvement, and half achieved remission at the four-week follow-up.

Psilocybin is a compound found in so-called “magic mushrooms.” It produces visual and auditory hallucinations, and profound changes in consciousness, over a period of several hours.

The findings appeared on November 4 in JAMA Psychiatry. “The magnitude of the effect we saw was about four times larger than what clinical trials have shown for traditional antidepressants on the market,” said co-author Alan Davis of Johns Hopkins University.

As the paper explains, “the effect sizes reported in this study were approximately 2.5 times greater than the effect sizes found in psychotherapy, and more than 4 times greater than the effect sizes found in psychopharmacological depression treatment studies.”

Fossils of bizarre, armored amphibians known as albanerpetontids provide the oldest evidence of a slingshot-style tongue, a new Science study shows.

Despite having lizardlike claws, scales and tails, albanerpetontids – mercifully called “albies” for short – were amphibians, not reptiles. Their lineage was distinct from today’s frogs, salamanders and caecilians and dates back at least 165 million years, dying out only about 2 million years ago.

Now, a set of 99-million-year-old fossils redefines these tiny animals as sit-and-wait predators that snatched prey with a projectile firing of their tongue – and not underground burrowers, as once thought. The fossils, one previously misidentified as an early chameleon, are the first albies discovered in modern-day Myanmar and the only known examples in amber.

They also represent a new genus and species: Yaksha perettii, named after treasure-guarding spirits known as yakshas in Hindu literature and Adolf Peretti, the discoverer of two of the fossils. “This discovery adds a super-cool piece to the puzzle of this obscure group of weird little animals,” said study co-author Edward Stanley, director of the Florida Museum of Natural History’s Digital Discovery and Dissemination Laboratory. “Knowing they had this ballistic tongue gives us a whole new understanding of this entire lineage.”

Dr. Cheng and colleagues developed a network medicine strategy to predict disease manifestations associated with COVID-19 and find existing drugs with the potential to be effective COVID-19 treatments.

Results from a new Cleveland Clinic-led study suggest that melatonin, a hormone that regulates the sleep-wake cycle and is commonly used as an over-the-counter sleep aid, may be a viable treatment option for COVID-19. As COVID-19 continues to spread throughout the world, particularly with cases rising during what some have termed the “fall urge,” repurposing drugs already approved by the U.S. Food and Drug Administration for new therapeutic purposes continues to be the most efficient and cost-effective approach to treat or prevent the disease.

According to the findings published today in PLOS Biology, a novel artificial intelligence platform developed by Lerner Research Institute researchers to identify possible drugs for COVID-19 repurposing has revealed melatonin as a promising candidate. Analysis of patient data from Cleveland Clinic’s COVID-19 registry also revealed that melatonin usage was associated with a nearly 30 percent reduced likelihood of testing positive for SARS-CoV-2 (the virus that causes COVID-19) after adjusting for age, race, smoking history and various disease comorbidities. Notably, the reduced likelihood of testing positive for the virus increased from 30 to 52 percent for African Americans when adjusted for the same variables.

“It is very important to note these findings do not suggest people should start to take melatonin without consulting their physician,” said Feixiong Cheng, PhD, assistant staff in the Genomic Medicine Institute and lead author on the study. “Large-scale observational studies and randomized controlled trials are critical to validate the clinical benefit of melatonin for patients with COVID-19, but we are excited about the associations put forth in this study and the opportunity to further explore them.”

A very nearby supernova could wipe all life off the face of Earth. But even farther away events may still leave their mark on the planet, new research suggests.

Massive explosions of energy happening thousands of light-years from Earth may have left traces in our planet's biology and geology, according to new research by University of Colorado Boulder geoscientist Robert Brakenridge. The study, published this month in the International Journal of Astrobiology, probes the impacts of supernovas, some of the most violent events in the known universe. In the span of just a few months, a single one of these eruptions can release as much energy as the sun will during its entire lifetime. They're also bright -- really bright.

"We see supernovas in other galaxies all the time," said Brakenridge, a senior research associate at the Institute of Arctic and Alpine Research (INSTAAR) at CU Boulder. "Through a telescope, a galaxy is a little misty spot. Then, all of a sudden, a star appears and may be as bright as the rest of the galaxy." A very nearby supernova could be capable of wiping human civilization off the face of the Earth. But even from farther away, these explosions may still take a toll, Brakenridge said, bathing our planet in dangerous radiation and damaging its protective ozone layer.

As of 5 October 2020, there have been over seven million documented cases of SARS-CoV-2 in the United States, leading to more than 200,000 documented fatalities1. Despite large-scale efforts to suppress disease spread via lockdown orders and other non-pharmaceutical interventions including mask wearing2, there has been a resurgence of SARS-CoV-2 cases in the United States since late summer 2020; particularly in the south and west followed by a resurgence of cases in the midwest in early autumn 2020 (refs. 3,4). The rise of cases threatens public health, economic recovery and the re-opening of K-12 schools as well as colleges and universities5,6.

The basic reproduction number and large fraction of asymptomatic cases represent challenges for controlling SARS-CoV-27. Early estimates of the basic reproduction number of SARS-CoV-2 range from 2.1 to 4.58, with current best estimates from the Centers of Disease Control and Prevention (CDC) indicating a basic reproduction number of 2.59. Early studies found that approximately half of cases may be via pre-symptomatic, mild or asymptomatic transmission10,11. The absence of commonly associated symptoms (fever, cough, shortness of breath) may be more pronounced in younger individuals. In addition, effective isolation of symptomatic cases may increase the fraction of circulating cases that are mild or asymptomatic.

The strong and often undocumented spread of SARS-CoV-2 is exacerbated by large transmission incidents12, referred to as ‘super-spreading’ events13. Super-spreading of SARS-CoV-2 has been documented in multiple, indoor events or large gatherings in which a single infector is putatively associated with the infection of dozens (or more) of individuals14,15,16. Large gatherings pose particular challenges for preventing the spread of SARS-CoV-2.

First, the risk that one (or more) individuals is infected increases rapidly with group size; increasing the inherent risk of a potential exposure as groups increase in number. Second, the number of potential interactions increases with gathering size (up to the square of the number of individuals in small groups where all individuals might be in contact). Third, follow-up contact tracing is problematic given the potential unknown nature of identifying close interactions. Although the last two challenges can be hard to quantify due to logistical and privacy reasons, this first category of risk is quantifiable, presents a gateway to action taking and should be communicated to the public at large.

A scenario-driven approach has been developed to assess the risk that one (or more) individuals in a group was infected in groups of size 10, 100, 1,000, 10,000 and even 100,00017. The risk chart highlighted combinations of event size n and circulating cases in the United States that had equal risk. The visualized risk contours can be defined via a binomial statistical model as a set of values (p,n) such that risk is a constant r = 1 − (1 − p)n (the implications of these risk contours at the early stages of the SARS-CoV-2 outbreak in the United States are discussed in several references18,19,20.

Given a risk level r defined between 0 and 1, the per-capita probability along an equivalent risk contour scales as 1/n (converging rapidly to 0 when n is large). Hence, large events can potentially seed transmission even when the per-capita probability that an individual is infected remains low. With shelter-in-place orders now suspended in most of the United States, many businesses (from retail to sports), recreational facilities, daycare centers, schools (both K-12 and colleges/universities) are evaluating re-opening plans. These plans must also gauge the probable risk of transmission.

The COVID-19 Real-Time Event Risk Assessment website (https://covid19risk.biosci.gatech.edu/) uses a data-driven approach to connect circulating case reports with risk assessment by adapting a binomial model of risk to real-time estimates at the county level. The central purpose is to quantify and visualize the expected risk associated with gatherings of different sizes and to help guide action taking by policy makers and public health departments, as well as event planners and visitors. The interactive website has drawn over two million visitors since the launch of the county-level map on 7 July 2020, is updated daily and continues to provide real-time estimates of event-associated risk with extensions to risk assessment in select countries globally (released on 5 October 2020). The presented risk maps are intended to inform individuals on the need to take preventative steps to reduce new transmission, for example, by avoiding large gatherings and wearing masks when in close contact with others.

At any given moment in time, our brain is involved in various activities. For example, when typing on a keyboard, our brain not only dictates our finger movements but also how thirsty we feel at that time. As a result, brain signals contain dynamic neural patterns that reflect a combination of these activities simultaneously.

A standing challenge has been isolating those patterns in brain signals that relate to a specific behavior, such as finger movements. Further, developing brain-machine interfaces (BMIs) that help people with neurological and mental disorders requires the translation of brain signals into a specific behavior, a problem called decoding.

This decoding also depends on our ability to isolate neural patterns related to specific behaviors. These neural patterns can be masked by patterns related to other activities and can be missed by standard algorithms.

Led by Maryam Shanechi, Assistant Professor and Viterbi Early Career Chair in Electrical and Computer Engineering at the USC Viterbi School of Engineering, researchers have developed a machine learning algorithm that resolved the above challenge. The algorithm published in Nature Neuroscience uncovered neural patterns missed by other methods and enhanced the decoding of behaviors that originated from signals in the brain. This algorithm is a significant advance in modeling and decoding of complex brain activity which could both enable new neuroscience discoveries and enhance future brain-machine interfaces.

Standard algorithms, says Shanechi, can miss some neural patterns related to a given behavior that are masked by patterns related to other functions happening simultaneously. Shanechi and her PhD student Omid Sani developed a machine learning algorithm to resolve this challenge.

Shanechi, the paper’s lead senior author says, “We have developed an algorithm that, for the first time, can dissociate the dynamic patterns in brain signals that relate to specific behaviors one is interested in. Our algorithm was also much better at decoding these behaviors from the brain signals.”

Scientist Mac Schwager at Stanford University, an aeronautics and astronautics researcher, learned that biologists were seeking a better way to image a large penguin colony in Antarctica. Schwager saw an opportunity to collaborate, given his work with swarms of autonomous flying robots. That's how, more than 3 years later, Kunal Shah, a member of Schwager's team, found himself at Antarctica's McMurdo Station, ready for the first test flight of a new multi-drone imaging system that coordinates the flight of multiple autonomous drones.

"My hands were freezing," recalled Shah. "The drone batteries were too cold to work. The drone remote control was too cold. My phone was too cold and was flashing warnings. I thought, 'I'm down here for 2½ months and this is day one.'" Undeterred, Shah and his colleagues quickly adapted their system. That adaptation is the subject of a paper published in Science Robotics. With U.S. National Science Foundation support, they produced detailed visual surveys of approximately 300,000 nesting pairs of Adélie penguins over a 2-square-kilometer area at Cape Crozier -- roughly equivalent to the size of the country of Monaco -- and another smaller colony of about 3,000 nesting pairs at Cape Royds.

Previous human-piloted drone surveys of the Cape Crozier colony took two days. Each round of the new survey, however, was completed in about two-and-a-half hours, thanks to a route planning algorithm that coordinated two to four autonomous drones and prioritized efficient coverage of the colony. "Just moving all that equipment to a remote site and being able to prepare it, field it and deploy it with nothing other than tents and a small warming hut at your disposal, that's really phenomenal," said Schwager, who is the senior author of the paper but, to his disappointment, was not able to join the field team. "It really goes to show how practical autonomous robotic systems can be in remote environments."

Researchers at the University of Rochester and Cornell University have taken an important step toward developing a communications network that exchanges information across long distances by using photons, mass-less measures of light that are key elements of quantum computing and quantum communications systems.

The research team has designed a nanoscale node made out of magnetic and semiconducting materials that could interact with other nodes, using laser light to emit and accept photons. The development of such a quantum network—designed to take advantage of the physical properties of light and matter characterized by quantum mechanics—promises faster, more efficient ways to communicate, compute, and detect objects and materials as compared to networks currently used for computing and communications.

Described in the journal Nature Communications, the node consists of an array of pillars a mere 120 nanometers high. The pillars are part of a platform containing atomically thin layers of semiconductor and magnetic materials. The array is engineered so that each pillar serves as a location marker for a quantum state that can interact with photons and the associated photons can potentially interact with other locations across the device—and with similar arrays at other locations. This potential to connect quantum nodes across a remote network capitalizes on the concept of entanglement, a phenomenon of quantum mechanics that, at its very basic level, describes how the properties of particles are connected at the subatomic level.

"This is the beginnings of having a kind of register, if you like, where different spatial locations can store information and interact with photons," says Nick Vamivakas, professor of quantum optics and quantum physics at Rochester.

Functional materials are commonly employed in emerging technologies, such as green energy solutions and new electronic devices. These materials are typically blends of different organic and inorganic components and have many advantageous properties for novel applications. To achieve their full potential, we need precise knowledge on their atomic structure. State-of-the-art experimental tools, such as atomic force microscopy (AFM), can be used to investigate organic molecular adsorbates on metallic surfaces.

However, interpreting the actual structure from microscopy images is often difficult. Computational simulations can help to estimate the most probable structures, but with complex materials, accurate structure search is computationally intractable with conventional methods. Recently, the CEST group has developed new tools for automated structure prediction using machine-learning algorithms from computer science.

In this most recent work, researchers have demonstrated the accuracy and efficiency of the Bayesian Optimization Structure Search (BOSS) artificial intelligence method. BOSS identified the adsorbate configurations of a camphor molecule on a Cu(111) surface. This material has been previously studied with AFM, but inferring the structures from those images was inconclusive. Here, the researchers have shown that BOSS can successfully identify not only the most probable structure, but also eight stable adsorbate configurations that camphor can have on Cu(111).

They used these model structures to better interpret the AFM experiments and concluded that the images likely feature camphor chemically bound to the Cu surface via an oxygen atom. Analyzing single molecular adsorbates in this way is only the first step toward studying more complex assemblies of several molecules on surfaces and subsequently the formation of monolayers. The acquired insight on interface structures could help to optimize the functional properties of these materials.

One in 4,000 people is born without a corpus callosum, a brain structure consisting of neural fibers that are used to transfer information from one hemisphere to the other. A quarter of these individuals do not have any symptoms, while the remainder either have low intelligence quotients or suffer from severe cognitive disorders. In a study published in the journal Cerebral Cortex, neuroscientists from the University of Geneva (UNIGE) discovered that when the neuronal fibers that act as a bridge between the hemispheres are missing, the brain reorganizes itself and creates an impressive number of connections inside each hemisphere. These create more intra-hemispheric connections than in a healthy brain, indicating that plasticity mechanisms are involved. It is thought that these mechanisms enable the brain to compensate for the losses by recreating connections to other brain regions using alternative neural pathways.

The corpus callosum develops in utero between the tenth and twentieth week of gestation. Agenesis of the corpus callosum is a congenital brain malformation in which this brain structure fails to develop, resulting in one out of 4,000 babies born without a corpus callosum. When it is missing, nothing replaces this structure measuring about ten centimeters, with the exception of cerebrospinal fluid. This means that the information transmitted from one hemisphere to the other can no longer be conveyed by the neuronal projections from the corpus callosum. «Their role in a healthy brain,» begins Vanessa Siffredi, a researcher in UNIGE’s Faculty of Medicine, «is to ensure the functioning of various cognitive and sensorimotor functions». Surprisingly, 25% of people with this malformation have no visible signs; 50% have average intelligence quotients and learning difficulties; and the remaining 25% suffer from severe cognitive disorders. 

Mysterious fibers

The scientific literature shows that, in the absence of the corpus callosum, certain fibers designed to serve as a bridge between the hemispheres, known as Probst bundles, bypass the absent brain area and curl up inside each hemisphere. «The back-up zones vary from one individual to another. And we don’t understand their functions,» explains the neuroscientist. The UNIGE scientists – working in collaboration with their colleagues at the University of Melbourne – set out to understand this variability and to examine the role of the fibers. Using MRI brain imaging, they studied the anatomical and functional links between different brain regions of approximately 20 Australian children aged 8 to 17 suffering from agenesis of the corpus callosum. 

This approach first made it possible to observe the physical relationships between the different regions of the brain, i.e. their structural links. In children with corpus callosum agenesis, the neural fibers inside each hemisphere are greater in number and of higher quality than in healthy brains. Furthermore, the UNIGE scientists succeeded in determining the correlations between the activity of different brain regions and their functional links. «If two regions are active together, it means they are communicating with each other,» explains Dr Siffredi. The data shows that intra and inter-hemispheric functional connectivity of brains without the corpus callosum are comparable to those of healthy brains.

«Remarkably, communication between the two hemispheres is maintained. We think that plasticity mechanisms, such as the strengthening of structural bonds within each hemisphere, compensated for the lack of neuronal fibers between hemispheres. New connections are created and the signals can be re-routed so that communication is preserved between the two hemispheres.»

Five years ago today, NASA’s New Horizons spacecraft — designed, built and operated by the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland — made history. After a voyage of nearly 10 years and more than 3 billion miles, the intrepid piano-sized probe flew within 7,800 miles of Pluto. For the first time ever, we saw the surface of this distant world in spectacular, colored detail.

The encounter — which also included a detailed look at the largest of Pluto’s five moons, Charon — capped the initial reconnaissance of the planets started by NASA’s Mariner 2 mission more than ​50 years before. It revealed an icy world replete in magnificent landscapes and geology — towering mountains, giant ice sheets, pits, scarps, valleys and terrains seen nowhere else in the solar system.

And that was only the beginning. “New Horizons transformed Pluto from a fuzzy, telescopic dot into a living world with stunning diversity and surprising complexity,” said Hal Weaver, New Horizons project scientist at APL. “The Pluto encounter was exploration at its finest, a real tribute to the vision and persistence of the New Horizons team.”

In the five years since that groundbreaking flyby, nearly every conjecture about Pluto possibly being an inert ball of ice has been thrown out the window or flipped on its head.

“It’s clear to me that the solar system saved the best for last!” said Alan Stern, New Horizons principal investigator from the Southwest Research Institute, Boulder, Colorado. “We could not have explored a more fascinating or scientifically important planet at the edge of our solar system. The New Horizons team worked for 15 years to plan and execute this flyby and Pluto paid us back in spades!”

Scientists now know that, despite it being literally out in the cold, Pluto is an exciting, active and scientifically valuable world. Incredibly, it even holds some of the keys to better understand the other small planets in the far reaches of our solar system.

Here are 10 of the coolest, weirdest and most unexpected findings about the Pluto system that scientists have learned since 2015, thanks to data from New Horizons.

Nuclear clocks are a tick closer to reality thanks to experiments that measured the energy of the lowest excited state of a thorium-229 nucleus to the highest precision ever. A clock based on transitions between such nuclear states would be much more accurate than existing atomic clocks and would therefore place tighter constraints on the Standard Model of particle physics.

Atomic clocks “tick” at frequencies set by the regular transitions of electrons within atoms or ions, as measured by a laser kept in resonance with these transitions. Today’s best atomic clocks are accurate to within one part in 1018, which means they would slow down by less than one second if left running for 13 billion years (the age of the universe). However, a clock that relied on nuclear transitions would be more accurate still, because the small size of an atomic nucleus relative to an atom’s electron shell means that the behavior of the former is less affected by external electromagnetic fields.

Too low to be detected?
One promising candidate for a nuclear “clock” transition is the one between the ground state of the thorium-229 nucleus and its lowest nuclear excited state, thorium-229m. This state is sometimes called the isomer state due to its long lifetime, and it was discovered by the physicists L A Kroger and C W Reich in 1976 during an analysis of thorium’s nuclear level structure.

The energy difference between ground-state thorium-229 and excited thorium-229m is so small that Kroger and Reich were unable to observe separate spectral lines for the transition using the technology available to them at the time. Instead, they inferred the existence of thorium-229m from an anomaly in their measurements: one of the gamma-ray energies predicted by theory was missing in the measured signals.

Since then, physicists have learned that the excitation energy of thorium-229m (and thus the energy of the radiation given off when the thorium nucleus returns to its ground state) is the lowest out of all nuclear excited states, corresponding to a frequency in the upper UV rather than the gamma-ray region of the electromagnetic spectrum. According to Thorsten Schumm of Austria’s Vienna University of Technology, who initiated the new research effort, it is therefore possible to “promote” a thorium nucleus into its lowest excited state simply by shining UV light onto it with a table-top laser. Once this feat is achieved in a controlled way, a nuclear clock could then be realized by measuring the oscillation frequency of the thorium nucleus as it transitions between thorium-229 and thorium-229m.