High-fidelity record of Earth's climate history puts current changes in context

 

View of Planet Earth

For the first time, climate scientists have compiled a continuous, high-fidelity record of variations in Earth's climate extending 66 million years into the past. The record reveals four distinctive climate states, which the researchers dubbed Hothouse, Warmhouse, Coolhouse, and Icehouse.

These major climate states persisted for millions and sometimes tens of millions of years, and within each one the climate shows rhythmic variations corresponding to changes in Earth's orbit around the sun. But each climate state has a distinctive response to orbital variations, which drive relatively small changes in global temperatures compared with the dramatic shifts between different climate states.

The new findings, published September 10 in Science, are the result of decades of work and a large international collaboration. The challenge was to determine past climate variations on a time scale fine enough to see the variability attributable to orbital variations (in the eccentricity of Earth's orbit around the sun and the precession and tilt of its rotational axis).

"We've known for a long time that the glacial-interglacial cycles are paced by changes in Earth's orbit, which alter the amount of solar energy reaching Earth's surface, and astronomers have been computing these orbital variations back in time," explained coauthor James Zachos, distinguished professor of Earth and planetary sciences and Ida Benson Lynn Professor of Ocean Health at UC Santa Cruz.

"As we reconstructed past climates, we could see long-term coarse changes quite well. We also knew there should be finer-scale rhythmic variability due to orbital variations, but for a long time it was considered impossible to recover that signal," Zachos said. "Now that we have succeeded in capturing the natural climate variability, we can see that the projected anthropogenic warming will be much greater than that."

For the past 3 million years, Earth's climate has been in an Icehouse state characterized by alternating glacial and interglacial periods. Modern humans evolved during this time, but greenhouse gas emissions and other human activities are now driving the planet toward the Warmhouse and Hothouse climate states not seen since the Eocene epoch, which ended about 34 million years ago. During the early Eocene, there were no polar ice caps, and average global temperatures were 9 to 14 degrees Celsius higher than today.

"The IPCC projections for 2300 in the 'business-as-usual' scenario will potentially bring global temperature to a level the planet has not seen in 50 million years," Zachos said.

Critical to compiling the new climate record was getting high-quality sediment cores from deep ocean basins through the international Ocean Drilling Program (ODP, later the Integrated Ocean Drilling Program, IODP, succeeded in 2013 by the International Ocean Discovery Program). Signatures of past climates are recorded in the shells of microscopic plankton (called foraminifera) preserved in the seafloor sediments. After analyzing the sediment cores, researchers then had to develop an "astrochronology" by matching the climate variations recorded in sediment layers with variations in Earth's orbit (known as Milankovitch cycles).

"The community figured out how to extend this strategy to older time intervals in the mid-1990s," said Zachos, who led a study published in 2001 in Science that showed the climate response to orbital variations for a 5-million-year period covering the transition from the Oligocene epoch to the Miocene, about 25 million years ago.

"That changed everything, because if we could do that, we knew we could go all the way back to maybe 66 million years ago and put these transient events and major transitions in Earth's climate in the context of orbital-scale variations," he said.

Zachos has collaborated for years with lead author Thomas Westerhold at the University of Bremen Center for Marine Environmental Sciences (MARUM) in Germany, which houses a vast repository of sediment cores. The Bremen lab along with Zachos's group at UCSC generated much of the new data for the older part of the record.

Westerhold oversaw a critical step, splicing together overlapping segments of the climate record obtained from sediment cores from different parts of the world. "It's a tedious process to assemble this long megasplice of climate records, and we also wanted to replicate the records with separate sediment cores to verify the signals, so this was a big effort of the international community working together," Zachos said.

Now that they have compiled a continuous, astronomically dated climate record of the past 66 million years, the researchers can see that the climate's response to orbital variations depends on factors such as greenhouse gas levels and the extent of polar ice sheets.

"In an extreme greenhouse world with no ice, there won't be any feedbacks involving the ice sheets, and that changes the dynamics of the climate," Zachos explained.

Most of the major climate transitions in the past 66 million years have been associated with changes in greenhouse gas levels. Zachos has done extensive research on the Paleocene-Eocene Thermal Maximum (PETM), for example, showing that this episode of rapid global warming, which drove the climate into a Hothouse state, was associated with a massive release of carbon into the atmosphere. Similarly, in the late Eocene, as atmospheric carbon dioxide levels were dropping, ice sheets began to form in Antarctica and the climate transitioned to a Coolhouse state.

"The climate can become unstable when it's nearing one of these transitions, and we see more deterministic responses to orbital forcing, so that's something we would like to better understand," Zachos said.

The new climate record provides a valuable framework for many areas of research, he added. It is not only useful for testing climate models, but also for geophysicists studying different aspects of Earth dynamics and paleontologists studying how changing environments drive the evolution of species.

Read more at Science Daily

New Hubble data suggests there is an ingredient missing from current dark matter theories

 

This Hubble Space Telescope image shows the massive galaxy cluster MACSJ 1206. Embedded within the cluster are the distorted images of distant background galaxies, seen as arcs and smeared features. These distortions are caused by the dark matter in the cluster, whose gravity bends and magnifies the light from faraway galaxies, an effect called gravitational lensing. This phenomenon allows astronomers to study remote galaxies that would otherwise be too faint to see. Astronomers measured the amount of gravitational lensing caused by this cluster to produce a detailed map of the distribution of dark matter in it. Dark matter is the invisible glue that keeps stars bound together inside a galaxy and makes up the bulk of the matter in the Universe. The Hubble image is a combination of visible- and infrared-light observations taken in 2011 by the Advanced Camera for Surveys and Wide Field Camera 3.

Observations by the NASA/ESA Hubble Space Telescope and the European Southern Observatory's Very Large Telescope (VLT) in Chile have found that something may be missing from the theories of how dark matter behaves. This missing ingredient may explain why researchers have uncovered an unexpected discrepancy between observations of the dark matter concentrations in a sample of massive galaxy clusters and theoretical computer simulations of how dark matter should be distributed in clusters. The new findings indicate that some small-scale concentrations of dark matter produce lensing effects that are 10 times stronger than expected.

Dark matter is the invisible glue that keeps stars, dust, and gas together in a galaxy. This mysterious substance makes up the bulk of a galaxy's mass and forms the foundation of our Universe's large-scale structure. Because dark matter does not emit, absorb, or reflect light, its presence is only known through its gravitational pull on visible matter in space. Astronomers and physicists are still trying to pin down what it is.

Galaxy clusters, the most massive and recently assembled structures in the Universe, are also the largest repositories of dark matter. Clusters are composed of individual member galaxies that are held together largely by the gravity of dark matter.

"Galaxy clusters are ideal laboratories in which to study whether the numerical simulations of the Universe that are currently available reproduce well what we can infer from gravitational lensing," said Massimo Meneghetti of the INAF-Observatory of Astrophysics and Space Science of Bologna in Italy, the study's lead author.

"We have done a lot of testing of the data in this study, and we are sure that this mismatch indicates that some physical ingredient is missing either from the simulations or from our understanding of the nature of dark matter," added Meneghetti.

"There's a feature of the real Universe that we are simply not capturing in our current theoretical models," added Priyamvada Natarajan of Yale University in Connecticut, USA, one of the senior theorists on the team. "This could signal a gap in our current understanding of the nature of dark matter and its properties, as these exquisite data have permitted us to probe the detailed distribution of dark matter on the smallest scales."

The distribution of dark matter in clusters is mapped by measuring the bending of light -- the gravitational lensing effect -- that they produce. The gravity of dark matter concentrated in clusters magnifies and warps light from distant background objects. This effect produces distortions in the shapes of background galaxies which appear in images of the clusters. Gravitational lensing can often also produce multiple images of the same distant galaxy.

The higher the concentration of dark matter in a cluster, the more dramatic its light-bending effect. The presence of smaller-scale clumps of dark matter associated with individual cluster galaxies enhances the level of distortions. In some sense, the galaxy cluster acts as a large-scale lens that has many smaller lenses embedded within it.

Hubble's crisp images were taken by the telescope's Wide Field Camera 3 and Advanced Camera for Surveys. Coupled with spectra from the European Southern Observatory's Very Large Telescope (VLT), the team produced an accurate, high-fidelity, dark-matter map. By measuring the lensing distortions astronomers could trace out the amount and distribution of dark matter. The three key galaxy clusters, MACS J1206.2-0847, MACS J0416.1-2403, and Abell S1063, were part of two Hubble surveys: The Frontier Fields and the Cluster Lensing And Supernova survey with Hubble (CLASH) programs.

To the team's surprise, in addition to the dramatic arcs and elongated features of distant galaxies produced by each cluster's gravitational lensing, the Hubble images also revealed an unexpected number of smaller-scale arcs and distorted images nested near each cluster's core, where the most massive galaxies reside. The researchers believe the nested lenses are produced by the gravity of dense concentrations of matter inside the individual cluster galaxies. Follow-up spectroscopic observations measured the velocity of the stars orbiting inside several of the cluster galaxies to therby pin down their masses.

"The data from Hubble and the VLT provided excellent synergy," shared team member Piero Rosati of the Università degli Studi di Ferrara in Italy, who led the spectroscopic campaign. "We were able to associate the galaxies with each cluster and estimate their distances."

"The speed of the stars gave us an estimate of each individual galaxy's mass, including the amount of dark matter," added team member Pietro Bergamini of the INAF-Observatory of Astrophysics and Space Science in Bologna, Italy.

By combining Hubble imaging and VLT spectroscopy, the astronomers were able to identify dozens of multiply imaged, lensed, background galaxies. This allowed them to assemble a well-calibrated, high-resolution map of the mass distribution of dark matter in each cluster.

The team compared the dark-matter maps with samples of simulated galaxy clusters with similar masses, located at roughly the same distances. The clusters in the computer model did not show any of the same level of dark-matter concentration on the smallest scales -- the scales associated with individual cluster galaxies.

"The results of these analyses further demonstrate how observations and numerical simulations go hand in hand," said team member Elena Rasia of the INAF-Astronomical Observatory of Trieste, Italy.

Read more at Science Daily

Giant particle accelerator in the sky

 The Earth's magnetic field is trapping high energy particles. When the first satellites were launched into space, scientists led by James Van Allen unexpectedly discovered the high energy particle radiation regions, which were later named after its discoverer Van Allen Radiation Belts. Visualized, these look like two donut-shaped regions encompassing our planet.

Now, a new study led by researchers from GFZ German Research Centre for Geosciences shows that electrons in the radiation belts can be accelerated to very high speeds locally. The study shows that magnetosphere works as a very efficient particle accelerator speeding up electrons to so-called ultra-relativistic energies. The study conducted by Hayley Allison, a postdoctoral scholar at GFZ Potsdam, and Yuri Shprits from GFZ and Professor at the University of Potsdam, is published in Nature Communications.

To better understand the origin of the Van Allen Belts, in 2012 NASA launched the Van Allen Probes twin spacecraft to traverse this most harsh environment and conduct detailed measurements in this hazardous region. The measurements included a full range of particles moving at different speeds and in different directions and plasma waves. Plasma waves are similar to the waves that we see on the water surface, but are in fact invisible to the naked eye. They can be compared to ripples in the electric and magnetic field.

Recent observations revealed that the energy of electrons in the belts can go up to so called ultra-relativistic energies. These electrons with temperatures above 100 Billion degrees Fahrenheit, move so fast that their energy of motion is much higher than their energy of rest given by Einstein's famous E=mc2 formula. They are so fast that the time flow significantly slows down for these particles.

Scientists were surprised to find these ultra-relativistic electrons and assumed that such high energies can be only reached by a combination of two processes: the inward transport of particles from the outer regions of the magnetosphere, which accelerates them; and a local acceleration of particles by plasma waves.

However, the new study shows that electrons reach such incredible energies locally, in the heart of the belts, by taking all this energy from plasma waves. This process turns out to be extremely efficient. The unexpected discovery of how acceleration of particles to ultra-relativistic energies operates in the near-Earth space, may help scientists understand the fundamental processes of acceleration on the Sun, near outer planets, and even in the distant corners of the universe where space probes cannot reach.

From Science Daily

Unique supernova explosion

 One-hundred million light years away from Earth, an unusual supernova is exploding.

That exploding star -- which is known as "supernova LSQ14fmg" -- was the faraway object discovered by a 37-member international research team led by Florida State University Assistant Professor of Physics Eric Hsiao. Their research, which was published in the Astrophysical Journal, helped uncover the origins of the group of supernovae this star belongs to.

This supernova's characteristics -- it gets brighter extremely slowly, and it is also one of the brightest explosions in its class -- are unlike any other.

"This was a truly unique and strange event, and our explanation for it is equally interesting," said Hsiao, the paper's lead author.

The exploding star is what is known as a Type Ia supernova, and more specifically, a member of the "super-Chandrasekhar" group.

Stars go through a sort of life cycle, and these supernovae are the exploding finale of some stars with low mass. They are so powerful that they shape the evolution of galaxies, and so bright that we can observe them from Earth even halfway across the observable universe.

An image of the "Blue Snowball" planetary nebula taken with the Florida State University Observatory. The supernova LSQ14fmg exploded in a system similar to this, with a central star losing a copious amount of mass through a stellar wind. When the mass loss abruptly stopped, it created a ring of material surrounding the star. Courtesy of Eric Hsiao

Type Ia supernovae were crucial tools for discovering what's known as dark energy, which is the name given to the unknown energy that causes the current accelerated expansion of the universe. Despite their importance, astronomers knew little about the origins of these supernova explosions, other than that they are the thermonuclear explosions of white dwarf stars.

But the research team knew that the light from a Type Ia supernova rises and falls over the course of weeks, powered by the radioactive decay of nickel produced in the explosion. A supernova of that type would get brighter as the nickel becomes more exposed, then fainter as the supernova cools and the nickel decays to cobalt and to iron.

After collecting data with telescopes in Chile and Spain, the research team saw that the supernova was hitting some material surrounding it, which caused more light to be released along with the light from the decaying nickel. They also saw evidence that carbon monoxide was being produced. Those observations led to their conclusion -- the supernova was exploding inside what had been an asymptotic giant branch (AGB) star on the way to becoming a planetary nebula.

"Seeing how the observation of this interesting event agrees with the theory is very exciting," said Jing Lu, an FSU doctoral candidate and a co-author of the paper.

They theorized that the explosion was triggered by the merger of the core of the AGB star and another white dwarf star orbiting within it. The central star was losing a copious amount of mass through a stellar wind before the mass loss was turned off abruptly and created a ring of material surrounding the star. Soon after the supernova exploded, it impacted a ring of material often seen in planetary nebulae and produced the extra light and the slow brightening observed.

Read more at Science Daily

Loss of a pet can potentially trigger mental health issues in children

 The death of a family pet can trigger a sense of grief in children that is profound and prolonged, and can potentially lead to subsequent mental health issues, according to a new study by researchers at Massachusetts General Hospital (MGH). In a paper appearing in European Child & Adolescent Psychiatry, the team found that the strong emotional attachment of youngsters to pets might result in measurable psychological distress that can serve as an indicator of depression in children and adolescents for as long as three years or more after the loss of a beloved pet.

"One of the first major losses a child will encounter is likely to be the death of a pet, and the impact can be traumatic, especially when that pet feels like a member of the family," says Katherine Crawford, CGC, previously with the Center for Genomic Medicine at MGH, and lead author of the study. "We found this experience of pet death is often associated with elevated mental health symptoms in children, and that parents and physicians need to recognize and take those symptoms seriously, not simply brush them off."

Roughly half of households in developed countries own at least one pet. And as the MGH investigators reported, the bonds that children form with pets can resemble secure human relationships in terms of providing affection, protection and reassurance. What's more, previous studies have shown that children often turn to pets for comfort and to voice their fears and emotional experiences. While the increased empathy, self-esteem and social competence that often flow from this interaction is clearly beneficial, the downside is the exposure of children to the death of a pet which, the MGH study found, occurs with 63 percent of children with pets during their first seven years of life.

Prior research has focused on the attachment of adults to pets and the consequences of an animal's death. The MGH team is the first to examine mental health responses in children. Their analysis is based on a sample of 6,260 children from the Avon Longitudinal Study of Parents and Children (ALSPAC), in Bristol, England. This population-based sample is replete with data collected from mothers and children that enabled researchers to track the experience of pet ownership and pet loss from a child's early age up to eight years.

"Thanks to this cohort, we were able to analyze the mental and emotional health of children after examining their experiences with pet death over an extended period," notes Erin Dunn, ScD, MPH, with the MGH Center for Genomic Medicine and Department of Psychiatry, and senior author of the study. "And we observed that the association between exposure to a pet's death and psychopathology symptoms in childhood occurred regardless of the child's socio-economic status or hardships they had already endured in their young lives."

Researchers also learned that the relationship between pet death and increased psychopathology was more pronounced in male than female children -- a finding that surprised them in light of prior research -- and that the strength of the association was independent of when the pet's death occurred during childhood, and how many times or how recently it occurred. According to Dunn, this latter finding speaks to "the durability of the bond with pets that is formed at a very early age, and how it can affect children across their development."

Read more at Science Daily

COVID-19 study links strict social distancing to much lower chance of infection

 

People social distancing concept

Using public transportation, visiting a place of worship, or otherwise traveling from the home is associated with a significantly higher likelihood of testing positive with the coronavirus SARS-CoV-2, while practicing strict social distancing is associated with a markedly lower likelihood, suggests a study from researchers at the Johns Hopkins Bloomberg School of Public Health.

For their analysis, the researchers surveyed a random sample of more than 1,000 people in the state of Maryland in late June, asking about their social distancing practices, use of public transportation, SARS-CoV-2 infection history, and other COVID-19-relevant behaviors. They found, for example, that those reporting frequent public transport use were more than four times as likely to report a history of testing positive for SARS-CoV-2 infection, while those who reported practicing strict outdoor social distancing were just a tenth as likely to report ever being SARS-CoV-2 positive.

The study is believed to be among the first large-scale evaluations of COVID-19-relevant behaviors that is based on individual-level survey data, as opposed to aggregated data from sources such as cellphone apps.

The results were published online on September 2 in Clinical Infectious Diseases.

"Our findings support the idea that if you're going out, you should practice social distancing to the extent possible because it does seem strongly associated with a lower chance of getting infected," says study senior author Sunil Solomon, MBBS, PhD, MPH, an associate professor in the Bloomberg School's Department of Epidemiology and an associate professor of medicine at Johns Hopkins School Medicine. "Studies like this are also relatively easy to do, so we think they have the potential to be useful tools for identification of places or population subgroups with higher vulnerability."

The novel coronavirus SARS-CoV-2 has infected nearly 27 million people around the world, of whom some 900,000 have died, according to the World Health Organization. In the absence of a vaccine, public health authorities have emphasized practices such as staying at home, and wearing masks and maintaining social distancing while in public. Yet there hasn't been a good way to monitor whether -- and among which groups -- such practices are being followed.

Solomon and colleagues, including first author Steven Clipman, a PhD candidate in the Bloomberg School's Department of International Health, quickly accessed willing survey participants via a company that maintains a large nationwide pool of potential participants as a commercial service for market research. The 1,030 people included in the study were all living in Maryland, which has logged more than 113,000 SARS-CoV-2 confirmed cases and nearly 3,700 confirmed deaths, according to the Maryland Department of Health.

The researchers asked the survey participants questions about recent travel outside the home, their use of masks, social distancing and related practices, and any confirmed infection with SARS-CoV-2 either recently or at all.

The results indicated that 55 (5.3 percent) of the 1,030 participants had tested positive for SARS-CoV-2 infection at any time, while 18 (1.7 percent) reported testing positive in the two weeks before they were surveyed.

The researchers found that when considering all the variables they could evaluate, spending more time in public places was strongly associated with having a history of SARS-CoV-2 infection. For example, an infection history was about 4.3 times more common among participants who stated that they had used public transportation more than three times in the prior two weeks, compared to participants who stated they had never used public transportation in the two-week period.

An infection history also was 16 times more common among those who reported having visited a place of worship three or more times in the prior two weeks, compared to those who reported visiting no place of worship during the period. The survey did not distinguish between visiting a place of worship for a religious service or other purposes, such as a meeting, summer camp or meal.

Conversely, those who reported practicing social distancing outdoors "always" were only 10 percent as likely to have a SARS-CoV-2 history, compared to those who reported "never" practicing social distancing.

An initial, relatively simple analysis linked many other variables to SARS-CoV-2 infection history, including being Black or Hispanic. But a more sophisticated, "multivariable" analysis suggested that many of these apparent links were largely due to differences in movement and social distancing.

"When we adjusted for other variables such as social distancing practices, a lot of those simple associations went away, which provides evidence that social distancing is an effective measure for reducing SARS-CoV-2 transmission," Clipman says.

The data indicated a greater adoption of social distancing practices among some groups who are especially vulnerable to serious COVID-19 illness, suggesting that they were relatively aware of their vulnerability. For example, 81 percent of over-65 participants reported always practicing social distancing at outdoor activities, while only 58 percent of 18-24 year olds did so.

The results are consistent with the general public health message that mask-wearing, social distancing, and limiting travel whenever possible reduce SARS-CoV-2 transmission. The researchers suggest, though, that studies such as these, employing similarly rapid surveys of targeted groups, could also become useful tools for predicting where and among which groups infectious diseases will spread most quickly.

"We did this study in Maryland in June, and it showed among other things that younger people in the state were less likely to reduce their infection risk with social distancing -- and a month later a large proportion of the SARS-CoV-2 infections detected in Maryland was among younger people," says Solomon. "So, it points to the possibility of using these quick, inexpensive surveys to predict where outbreaks are going to happen based on behaviors, and then mobilizing public health resources accordingly."

Read more at Science Daily

Atomistic modelling probes the behavior of matter at the center of Jupiter

 The hydrogen atom, with its single proton orbited by a single electron, is arguably the simplest material out there. Elemental hydrogen can nonetheless exhibit extremely complex behavior -- at megabar pressures, for example, it undergoes a transition from being an insulating fluid to being a metallic conductive fluid.

While the transition is fascinating simply from the point of view of condensed matter physics and materials science -- liquid-liquid phase transitions are rather unusual -- it also has significant implications for planetary science, since liquid hydrogen makes up the interior of giant planets such as Jupiter and Saturn as well as brown dwarf stars. Understanding the liquid-liquid transition is then a central part of accurately modelling the structure and evolution of such planets and standard models generally assume a sharp transition between the insulating molecular fluid and the conducting metallic fluid. This sharp transition is linked to a discontinuity in density and therefore a clear border between an inner metallic mantle and an outer insulating mantle in these planets.

While scientists have made considerable efforts to explore and characterize this transition as well as dense hydrogen's many unusual properties -- including rich and poorly understood solid polymorphism, anomalous melting line, and the possible transition to a superconducting state -- laboratory investigation is complicated because of the need to create a controllable high pressure and temperature environment as well as to confine hydrogen during measurements. Experimental research has then not yet reached a consensus on whether the transition is abrupt or smooth and different experiments have located the liquid-liquid transition at pressures that are as much as 100 gigapascals apart.

"The kind of experiment that you need to be able to do to be able to study a material in the same range of pressures that you find on Jupiter is highly non-trivial," Ceriotti said. "As a result of the constraints, many different experiments have been performed, with results that are very different from each other."

Though modelling techniques introduced in the last decade have allowed scientists to better understand the system, the huge computational expense involved in essentially solving the quantum mechanical problem for the behavior of hydrogen atoms has meant that these simulations were necessarily limited in time, to a scale of a few picoseconds, and to a scope of just a few hundred atoms. Results here have also been mixed.

In order to examine the problem more thoroughly, Ceriotti and colleagues Bingqing Chen at the University of Cambridge and Guglielmo Mazzola at IBM Research Zurich used an artificial neural network architecture to construct a machine learning potential. Based on a small number of very accurate (and time consuming) calculations of the electronic structure problem, the inexpensive machine-learning potential allowed for the investigation of hydrogen phase transitions for temperatures between 100 and 4000 K, and pressures between 25 and 400 gigapascals, with converged simulation size and time. The simulations, mostly run on EPFL computers at SCITAS, took just a few weeks compared with the 100s of millions of years in CPU time that it would have taken to run traditional simulations for solving the quantum mechanical problem.

The resulting theoretical study of the phase diagram of dense hydrogen allowed the team to reproduce the re-entrant melting behavior and the polymorphism of the solid phase. Simulations based on the machine learning potential showed, contrary to the common assumption that hydrogen undergoes a first-order phase transition, evidence of continuous metallization in the liquid. This in turn not only suggests a smooth transition between insulating and metallic layers in giant gas planets, it also reconciles existing discrepancies between both lab and modelling experiments.

"If high-pressure hydrogen is supercritical, as our simulations suggest, there is no sharp transition where all the properties of the fluid have a sudden jump," Ceriotti said. "Depending on the exact property you probe, and the way you define a threshold, you would find the transition to occur at a different temperature or pressure. This may reconcile a decade of controversial results from high pressure experiments. Different experiments have measured slightly different things and they haven't been able to identify the transition at the same point because there is no sharp transition."

In terms of reconciling their results with some earlier modelling that indeed identified a sharp transition, Ceriotti says that they could only observe a clear-cut jump in properties when performing small simulations, and that in those cases they could trace the jump to solidification, rather than to a liquid-liquid transition. The sharp transition observed should then rather be understood as an artifact of the limitations of using simulations based on traditional physics-based modelling. The machine learning approach has allowed the researchers to run simulations that are typically between 4 and 10 times larger and several 100s of times longer. This gives them a much better overview of the entire process.

While it was applied in this particular paper to an issue linked to planetary science, the same technology can be applied to any problem in materials science or chemistry, Ceriotti said.

Read more at Science Daily

Tool transforms world landmark photos into 4D experiences

 Using publicly available tourist photos of world landmarks such as the Trevi Fountain in Rome or Top of the Rock in New York City, Cornell University researchers have developed a method to create maneuverable 3D images that show changes in appearance over time.

The method, which employs deep learning to ingest and synthesize tens of thousands of mostly untagged and undated photos, solves a problem that has eluded experts in computer vision for six decades.

"It's a new way of modeling scenes that not only allows you to move your head and see, say, the fountain from different viewpoints, but also gives you controls for changing the time," said Noah Snavely, associate professor of computer science at Cornell Tech and senior author of "Crowdsampling the Plenoptic Function," presented at the European Conference on Computer Vision, held virtually Aug. 23-28.

"If you really went to the Trevi Fountain on your vacation, the way it would look would depend on what time you went -- at night, it would be lit up by floodlights from the bottom. In the afternoon, it would be sunlit, unless you went on a cloudy day," Snavely said. "We learned the whole range of appearances, based on time of day and weather, from these unorganized photo collections, such that you can explore the whole range and simultaneously move around the scene."

Representing a place in a photorealistic way is challenging for traditional computer vision, partly because of the sheer number of textures to be reproduced. "The real world is so diverse in its appearance and has different kinds of materials -- shiny things, water, thin structures," Snavely said.

Another problem is the inconsistency of the available data. Describing how something looks from every possible viewpoint in space and time -- known as the plenoptic function -- would be a manageable task with hundreds of webcams affixed around a scene, recording data day and night. But since this isn't practical, the researchers had to develop a way to compensate.

"There may not be a photo taken at 4 p.m. from this exact viewpoint in the data set. So we have to learn from a photo taken at 9 p.m. at one location, and a photo taken at 4:03 from another location," Snavely said. "And we don't know the granularity of when these photos were taken. But using deep learning allows us to infer what the scene would have looked like at any given time and place."

The researchers introduced a new scene representation called Deep Multiplane Images to interpolate appearance in four dimensions -- 3D, plus changes over time. Their method is inspired in part on a classic animation technique developed by the Walt Disney Company in the 1930s, which uses layers of transparencies to create a 3D effect without redrawing every aspect of a scene.

"We use the same idea invented for creating 3D effects in 2D animation to create 3D effects in real-world scenes, to create this deep multilayer image by fitting it to all these disparate measurements from the tourists' photos," Snavely said. "It's interesting that it kind of stems from this very old, classic technique used in animation."

In the study, they showed that this model could be trained to create a scene using around 50,000 publicly available images found on sites such as Flickr and Instagram. The method has implications for computer vision research, as well as virtual tourism -- particularly useful at a time when few can travel in person.

"You can get the sense of really being there," Snavely said. "It works surprisingly well for a range of scenes."

First author of the paper is Cornell Tech doctoral student Zhengqi Li. Abe Davis, assistant professor of computer science in the Faculty of Computing and Information Science, and Cornell Tech doctoral student Wenqi Xian also contributed.

Read more at Science Daily

People who were children when their parents divorced have less 'love hormone'

 People who were children when their parents were divorced showed lower levels of oxytocin -- the so-called "love hormone" -- when they were adults than those whose parents remained married, according to a study led by Baylor University. That lower level may play a role in having trouble forming attachments when they are grown.

Oxytocin -- secreted in the brain and released during bonding experiences such as delivery of a baby or sexual interaction or nursing, even being hugged by a romantic partner -- has been shown in previous research to be important for social behavior and emotional attachments in early life. The oxytocin system also has been linked to parenting, attachment and anxiety.

The new study, published in the Journal of Comparative Psychology, delves into an area that has not been well researched -- a link between oxytocin, early experience and adult outcomes.

"Since the rates of divorce in our society began to increase, there has been concern about the effects of divorce on the children," said lead author Maria Boccia, Ph.D., professor of child and family studies at Baylor University in the Robbins College of Health and Human Sciences. "Most research has focused on short-term effects, like academic performance, or longer-term outcomes like the impact on relationships. How divorce causes these effects, however, is unknown.

"Oxytocin is a neurohormone that is important in regulating these behaviors and is also sensitive to the impact of stressful life events in early life," she said. "This is a first step towards understanding what mechanisms might be involved."

Previous studies of children whose parents were divorced have found that the experience was associated with mood disorders and substance abuse -- behaviors found to be related to oxytocin, Boccia said. Additionally, such childhood experiences as divorce or death of a parent are associated with depression and anxiety in adolescents and adults, as well as with poorer parenting in adulthood, less parental sensitivity and warmth, overreaction and increased use of punishment.

Researchers in the Baylor study examined the effect of the experience of parental divorce in childhood on later adult oxytocin levels. They also asked participants to complete a set of questionnaires on attachment style and other measures.

"What we found was that oxytocin was substantially lower in people who experienced parental divorce compared to those who did not and correlated with responses on several measures of attachment," Boccia said. "These results suggest that oxytocin levels are adversely affected by parental divorce and may be related to other effects that have been documented in people who experience parental divorce."

Animal studies also suggest that one mechanism contributing to the negative effects of early parental separation may be suppression of oxytocin activity.

For the latest study, researchers recruited 128 individuals ages 18 to 62 at two institutions of higher learning in the Southeast United States. Of those, 27.3% indicated their parents were divorced. The average age for participants when their parents divorced was 9 years.

Upon arriving at the study site, participants were asked to empty their bladders, then given a 16-ounce bottle of water to drink before filling out questionnaires about their parents and peers during childhood, as well as their current social functioning. The questions addressed their parents' style, including affection, protection, indifference, over-control and abuse; and their own levels of confidence, discomfort with closeness, need for approval and their styles of relationships and caregiving.

After participants completed the questionnaires, urine samples were collected, and researchers analyzed oxytocin concentrations. The levels were substantially lower in individuals whose childhood experience included their parents' divorce.

Further analysis showed that those individuals rated their parents as less caring and more indifferent. They also rated their fathers as more abusive. Those who experienced parental divorce during childhood were less confident, more uncomfortable with closeness and less secure in relationships. They rated their own caregiving style as less sensitive and close than did the participants whose parents had not divorced.

"One of the first questions I am asked when presenting this research to other scientists is 'does how old the child is when the divorce occurs matter?' That is the most pressing question that we need to explore," Boccia said.

Read more at Science Daily

Unconscious learning underlies belief in God, study suggests

 

Hands raised to sunset, prayer concept

Individuals who can unconsciously predict complex patterns, an ability called implicit pattern learning, are likely to hold stronger beliefs that there is a god who creates patterns of events in the universe, according to neuroscientists at Georgetown University.

Their research, reported in the journal Nature Communications, is the first to use implicit pattern learning to investigate religious belief. The study spanned two very different cultural and religious groups, one in the U.S. and one in Afghanistan.

The goal was to test whether implicit pattern learning is a basis of belief and, if so, whether that connection holds across different faiths and cultures. The researchers indeed found that implicit pattern learning appears to offer a key to understanding a variety of religions.

"Belief in a god or gods who intervene in the world to create order is a core element of global religions," says the study's senior investigator, Adam Green, an associate professor in the Department of Psychology and Interdisciplinary Program in Neuroscience at Georgetown, and director of the Georgetown Laboratory for Relational Cognition.

"This is not a study about whether God exists, this is a study about why and how brains come to believe in gods. Our hypothesis is that people whose brains are good at subconsciously discerning patterns in their environment may ascribe those patterns to the hand of a higher power," he adds.

"A really interesting observation was what happened between childhood and adulthood," explains Green. The data suggest that if children are unconsciously picking up on patterns in the environment, their belief is more likely to increase as they grow up, even if they are in a nonreligious household. Likewise, if they are not unconsciously picking up on patterns around them, their belief is more likely to decrease as they grow up, even in a religious household.

The study used a well-established cognitive test to measure implicit pattern learning. Participants watched as a sequence of dots appeared and disappeared on a computer screen. They pressed a button for each dot. The dots moved quickly, but some participants -- the ones with the strongest implicit learning ability -- began to subconsciously learn patterns hidden in the sequence, and even press the correct button for the next dot before that dot actually appeared. However, even the best implicit learners did not know that the dots formed patterns, showing that the learning was happening at an unconscious level.

The U.S. section of the study enrolled a predominantly Christian group of 199 participants from Washington, D.C. The Afghanistan section of the study enrolled a group of 149 Muslim participants in Kabul. The study's lead author was Adam Weinberger, a postdoctoral researcher in Green's lab at Georgetown and at the University of Pennsylvania. Co-authors Zachery Warren and Fathali Moghaddam led a team of local Afghan researchers who collected data in Kabul.

"The most interesting aspect of this study, for me, and also for the Afghan research team, was seeing patterns in cognitive processes and beliefs replicated across these two cultures," says Warren. "Afghans and Americans may be more alike than different, at least in certain cognitive processes involved in religious belief and making meaning of the world around us. Irrespective of one's faith, the findings suggest exciting insights into the nature of belief."

"A brain that is more predisposed to implicit pattern learning may be more inclined to believe in a god no matter where in the world that brain happens to find itself, or in which religious context," Green adds, though he cautions that further research is necessary.

"Optimistically," Green concludes, "this evidence might provide some neuro-cognitive common ground at a basic human level between believers of disparate faiths."

Read more at Science Daily