Amazing Science

Machine learning can be used for more than violating your privacy for a social media challenge. For example, one fascinating application has been developed by Instrumental AI, which uses machine learning to detect defects and anomalies in photographs of parts during various stages of assembly, primarily in the electronics manufacturing industry.

Instrumental was founded by Anna Shedletsky, a former Apple engineer with two degrees from Stanford. A mechanical engineer by training, Shedletsky led system products design for the Apple Watch for six years. “I led the team that designed the physical product itself, as well as being responsible for the first production line. We had to prove mass production yields at mass production speeds on the first line,” said Shedletsky.

In this role at Apple, she noticed how very small defects on the line could cause huge delays and problems in the product lifecycle and profitability. So, she set out to build technology tools that could address these defects. “I had this kind of naïve, but correct insight that there aren't great tools for this. that we struggle by just throwing people and money at these problems. And if Apple didn't have it, it didn't exist.”

Shedletsky left Apple in 2015 to start Instrumental, a software company which has developed a toolkit for detecting manufacturing defects in digital photos taken on the manufacturing line. “We've been at it for four years. We work primarily with Fortune 500 companies in the consumer electronics and electronics space. We do have customers outside of electronics, but consumer electronics is kind of the hardest beast, because the volumes are pretty high,” she explained.  “The products themselves are high value, but they go through production super fast. So, some of our customers only run products in production for six months. It’s just these super fast cycles of development and production. So that's where we kind of specialize today. We build technology that helps accelerate the development process and then maintain control in production.”

This machine learning system analyzes 2D images of parts during assembly and identifies differences.

PySyft is a framework that enables secured, private computations in deep learning models. PySyft combines federated learning, secured multiple-party computations and differential privacy in a single programming model integrated into different deep learning frameworks such as PyTorch, Keras or TensorFlow. The principles of PySyft were originally outlined in a research paper and its first implementation was lead by OpenMind, one of the leading decentralized AI platforms.

The core component of PySyft is an abstraction called the SyftTensor. SyftTensors are meant to represent a state or transformation of the data and can be chained together. The chain structure always has at its head the PyTorch tensor, and the transformations or states embodied by the SyftTensors are accessed downward using the child attribute and upward using the parent attribute. Using PySyft is relatively simple and not very different than a standard PyTorch or Keras program.

PySyft represents one of the first attempts to enable robust privacy models in deep learning programs. As the space evolves, privacy is likely to become one of the foundational building blocks of the next generation of deep learning frameworks.

Researchers from China’s Peking University have developed a new gene-editing technology —  and they think it shows promise as a CRISPR alternative for fighting human diseases.

According to a paper published on Monday in the journal Nature Biotechnology, this new technology, LEAPER, which stands for “leveraging endogenous ADAR for programmable editing of RNA,” works similarly to CRISPR-Cas13, targeting RNA molecules as opposed to DNA like the well-known CRISPR-Cas9.

But while CRISPR-Cas13 relies on both a guide RNA and the Cas13 enzyme to make its edits to RNA, the LEAPER system needs just one component known as an arRNA. This makes the system “more easily deliverable and less likely to result in unwanted cellular immune responses,” the researchers told China-based media outlet Caixin.

As for the potential uses of their CRISPR alternative, Peking researcher Zhou Zhuo told Caixin that “there are clear prospects for using this technology in disease treatment.” In fact, in tests of cells taken from people with the debilitating genetic disorder Hurler syndrome, LEAPER was able to correct “sufficient amounts” of the cells’ mutated RNA, University of Queensland geneticist Ernst Wolvetang, who wasn’t involved in the research, told Caixin.

However, the research is still in the early stages and is only now moving into tests on animals — meaning we won’t know for some time if LEAPER will dethrone CRISPR as the preeminent gene-editing technology.

Results of a new Dartmouth study published this week in the journal eLIFE—which examined how environmental stressors put on fruit flies (Drosophilia melanogaster) can influence the phenotypes of their offspring—are adding some intriguing findings to the mix.

“While neuronally encoded behavior isn’t thought to be inherited across generations, we wanted to test the possibility that environmentally triggered modifications could allow ‘memory’ of parental experiences to be inherited,” explains Julianna “Lita” Bozler, a PhD candidate in the Bosco Lab at the Geisel School of Medicine, who served as lead author on the study.

When exposed to parasitoid wasps—which deposit their eggs into and kill the larvae of fruit flies—Drosophila melanogaster females are known to shift their preference to food containing ethanol as an egg laying substrate, which protects their larvae from wasp infection.

For the study, the fruit flies were co-habited with female wasps for four days before their eggs were collected. The embryos were separated into two cohorts—a wasp-exposed and unexposed (control) group—and developed to maturity without any contact with adult flies or wasps. One group was used to propagate the next generation and the other was analyzed for ethanol preference.

“We found that the original wasp-exposed flies laid about 94 percent of their eggs on ethanol food, and that this behavior persisted in their offspring, even though they’d never had direct interaction with wasps,” says Bozler.

The ethanol preference was less potent in the first-generation offspring, with 73 percent of their eggs laid on ethanol food. “But remarkably, this inherited ethanol preference persisted for five generations, gradually reverting back to a pre-wasp exposed level,” she says. “This tells us that inheritance of ethanol preference is not a permanent germline change, but rather a reversible trait.”

Has your steering wheel been too hot to touch this summer? A new thermoelectric material reported in the journal Science could offer relief. The widespread adoption of thermoelectric devices that can directly convert electricity into thermal energy for cooling and heating has been hindered, in part, by the lack of materials that are both inexpensive and highly efficient at room temperature.

Now researchers from the University of Houston and the Massachusetts Institute of Technology have reported the discovery of a new material that works efficiently at room temperature while requiring almost no costly tellurium, a major component of the current state-of-the-art material.

The work, described in a paper published online by Science Thursday, July 18, has potential applications for keeping electronic devices, vehicles and other components from overheating, said Zhifeng Ren, corresponding author on the work and director of the Texas Center for Superconductivity at UH, where he is also M.D. Anderson Professor of Physics.

“We have produced a new material, which is inexpensive but still performs almost as well as the traditional, more expensive material,” Ren said. The researchers say future work could close the slight performance gap between their new material and the traditional material, a bismuth-tellurium based alloy.

A team of physicists and astronomers from Northwestern University is poised to lead gravitational-wave astronomy into its next evolution. The W. M. Keck Foundation has awarded $1 million, which will be used to develop a prototype for a new kind of gravitational-wave detector that is small enough to fit on a tabletop and powerful enough to detect cosmic events that existing astronomical equipment cannot.

"This is the start of the next phase of gravitational-wave and multi-messenger astronomy," said Andrew Geraci, principal investigator on the project. "This tabletop sensor will be able to observe events we've never seen before, expanding our understanding of space and the universe."

Geraci is an associate professor of physics and astronomy in Northwestern's Weinberg College of Arts and Sciences and a faculty member of the Center for Fundamental Physics (CFP) and the Center for Interdisciplinary Exploration and Research for Astrophysics (CIERA) at Northwestern.

The Levitated Sensor Detector will extend the spectrum of detectable gravitational waves to higher frequencies, possibly opening a window to the types of events that are related to the mysterious dark matter in the universe. It will complement research being conducted at the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo observatories, which occupy several acres of land in the United States and Italy, by observing smaller cosmic events that produce waves at a frequency LIGO and Virgo cannot detect.

"If you think of gravitational waves like sound waves, the frequency we are trying to capture with levitated sensors is sort of like a dog whistle," said Vicky Kalogera, project co-investigator and CIERA director.

"Dogs are capable of hearing sound frequencies that the human ear cannot perceive. Similarly, levitated sensors will pick up frequencies that LIGO and Virgo cannot detect," said Kalogera, who also is the Daniel I. Linzer Distinguished University Professor of Physics and Astronomy and one of the foremost astrophysicists in the LIGO Scientific Collaboration (LSC).

Every year, robots get more and more life-like. Solar-powered bees fly on lithe wings, humanoids stick backflips, and teams of soccer bots strategize how to dribble, pass, and score. The more researchers discover about how living creatures move, the more machines can imitate them all the way down to their smallest molecules. “We have these amazing machines already in our bodies, and they work so well,” said Pallav Kosuri. “We just don’t know exactly how they work.”

For decades, researchers have chased ways to study how biological machines power living things. Every mechanical movement—from contracting a muscle to replicating DNA—relies on molecular motors that take tiny, near-undetectable steps.

Trying to see them move is like trying to watch a soccer game taking place on the moon.

Now, in a recent study published in Nature, a team of researchers including Xiaowei Zhuang, the David B. Arnold Professor of Science at Harvard University and a Howard Hughes Medical Institute Investigator, and Zhuang Lab postdoctoral scholar Pallav Kosuri and Benjamin Altheimer, a Ph.D. student in the Graduate School of Arts and Sciences, captured the first recorded rotational steps of a molecular motor as it moved from one DNA base pair to another.

In collaboration with Peng Yin, a professor at the Wyss Institute and Harvard Medical School, and his graduate student Mingjie Dai, the team combined DNA origami with high-precision single-molecule tracking, creating a new technique called ORBIT—origami-rotor-based imaging and tracking—to look at molecular machines in motion.

There's no known way to prove a three-dimensional "quantum spin liquid" exists, so Rice University physicists and their collaborators did the next best thing: They showed their single crystals of cerium zirconium pyrochlore had the right stuff to qualify as the first possible 3-D version of the long-sought state of matter. Despite the name, a quantum spin liquid is a solid material in which the weird property of quantum mechanics—entanglement—ensures a liquid-like magnetic state.

In a paper this week in Nature Physics, researchers offered a host of experimental evidence—including crucial neutron-scattering experiments at Oak Ridge National Laboratory (ORNL) and muon spin relaxation experiments at Switzerland's Paul Scherrer Institute (PSI)—to support their case that cerium zirconium pyrochlore, in its single-crystal form, is the first material that qualifies as a 3-D quantum spin liquid.

"A quantum spin liquid is something that scientists define based on what you don't see," said Rice's Pengcheng Dai, corresponding author of the study and a member of Rice's Center for Quantum Materials (RCQM). "You don't see long-range order in the arrangement of spins. You don't see disorder. And various other things. It's not this. It's not that. There's no conclusive positive identification."

The research team's samples are believed to be the first of their kind: Pyrochlores because of their 2-to-2-to-7 ratio of cerium, zirconium and oxygen, and single crystals because the atoms inside them are arranged in a continuous, unbroken lattice.

"We've done every experiment that we could think of on this compound," Dai said." Study co-author Emilia Morosan's group at Rice did heat capacity work to show that the material undergoes no phase transition down to 50 millikelvin. We did very careful crystallography to show there is no disorder in the crystal. We did muon spin relaxation experiments that demonstrated an absence of long-range magnetic order down to 20 millikelvin, and we did diffraction experiments that showed the sample has no oxygen vacancy or other known defects. Finally, we did inelastic neutron scattering that showed the presence of a spin-excitation continuum—which may be a quantum spin liquid hallmark—down to 35 millikelvin."

The photo album of baby planets may be emptier than astronomers thought.  New research is prompting debate about the theory that gaps in planet-forming disks around young stars mark spaces where planets are being created in real time. It turns out that the planets that, according to simulations, would grow up in those gaps don’t resemble the full-grown planets observed around more mature stars. 

So, maybe these broken-up disks don’t point to the formation of planets at all — or at least, not the most common kinds of planets that current telescopes can see. “The question we raised, and we put it boldly, is whether those gaps in rings are really caused by planets, or they are caused by other things,” says astrophysicist Nelson Ndugu of Mbarara University of Science and Technology in Uganda. “Right now, it is too early to take a side.” 

The basic planet formation recipe starts from a cloud of gas and dust, which collapses into a central star surrounded by a rotating disk. Gravity draws the gas and dust into pebble-sized clumps, which cluster into boulders. Eventually those boulders grow large and gravitationally powerful enough to start swallowing up the surrounding gas, ultimately growing into gas giants. Theoretical models suggested that whole process should take about 10 million years to build a Jupiter.

In 2014, astronomers got what looked like their first glimpse of this process in action. The Atacama Large Millimeter/submillimeter Array in Chile, or ALMA, snapped a shot of the young star HL Tau, about 450 light-years away in the constellation Taurus, that showed a disk interrupted by dark gaps.

Researchers initially attributed such gaps to newborn planets that scooped up disk gas as they grew. A similar process is thought to have shaped the rings and moons around Saturn. Then in 2018, astronomers reported a planet called PDS 70b, the first infant planet to have been spotted growing in such a disk gap. Later observations found a second planet orbiting the same star. 

In a world where energy consumption is on the rise, our only hope is the development of new energy-generation technologies. Although currently used renewable energy sources such as wind and solar energy have their merits, there is a gigantic, permanent, and untapped energy source quite literally under our noses: geothermal energy.

Generating electricity from geothermal energy requires devices that can somehow make use of the heat within the Earth's crust. Recently, a team of scientists at Tokyo Tech, led by Dr. Sachiko Matsushita, have made great progress in the understanding and development of sensitized thermal cells (STCs), a kind of battery that can generate electric power at 100 degrees C or less.

Several methods for converting heat into electric power exist, however, their large-scale application is not feasible. For example, hot-and-cold redox batteries and devices based on the Seebeck effect are not possible to simply bury them inside a heat source and exploit them.

Dr. Matsushita's team have previously reported the use of STCs as a new method for converting heat directly into electric power using dye-sensitized solar cells. They also replaced the dye with a semiconductor to allow the system to operate using heat instead of light. The graph above illustratively represents the STC, a battery that consists of three layers sandwiched between electrodes: an electron transport layer (ETM), a semiconductor layer (germanium), and a solid electrolyte layer (copper ions). In short, electrons go from a low-energy state to a high-energy state in the semiconductor by becoming thermally excited and then get transferred naturally to the ETM. Afterwards, they leave through the electrode, go through an external circuit, pass through the counter electrode, and then reach the electrolyte. Oxidation and reduction reactions involving copper ions take place at both interfaces of the electrolyte, resulting in low-energy electrons being transferred to the semiconductor layer so that the process can begin anew, thus completing an electric circuit.

However, it was not clear at that time whether such a battery could be used as a perpetual engine or if the current would stop at some point. After testing, the team observed that electricity indeed stopped flowing after a certain time and proposed a mechanism explaining this phenomenon. Basically, current stops because the redox reactions at the electrolyte layer stop owing to the relocation of the different types of copper ions. Most importantly, and also surprisingly, they found out that the battery can revert this situation itself in the presence of heat by simply opening the external circuit for some time; in other words, by using a simple switch. "With such a design, heat, usually regarded as low-quality energy, would become a great renewable energy source," states Matsushita.

DNA flows inside a cell's nucleus in a choreographed line dance,

DNA 'dances' in first explanation of how genetic material flows through a nucleus, new simulations reveal. The finding is the first large-scale explanation of genetic material moving within a working cell.

"Previous work mostly focused on what was going on at the microscale of DNA," says study co-author Michael Shelley, group leader for biophysical modeling at the Flatiron Institute's Center for Computational Biology in New York City and co-director of the Courant Institute's Applied Mathematics Laboratory at New York University. "People didn't really think about what was going on at the larger scale."

Shelley and colleagues simulated the motions of chromatin, the functional form of DNA inside the nucleus. Chromatin looks like beads on a string, with ball-like clusters of genetic material linked by strands of DNA. The researchers propose that molecular machines along the DNA cause segments of the chromatin to straighten and pull taut. This activity aligns neighboring strands to face the same direction. That alignment, in turn, results in a cascading waltz of genetic material shimmying across the nucleus.

The dancing DNA may play a role in gene expression, replication and remodeling, though the exact effects remain unclear, the researchers reported online October 22 in Proceedings of the National Academy of Sciences.

The findings help explain measurements reported in 2013 by scientists, including Alexandra Zidovska, at Harvard University. Besides previously known small-scale motions of individual genes, the scientists' experiment revealed large regions of chromatin that shifted in unison through a cell's nucleus at a rate of a fraction of a micron every few seconds. The scientists, though, couldn't identify the cause or details of the movement.

Nature has a way of finding optimal solutions to complex problems. For example, despite billions of ways for a single protein to fold, proteins always fold in a way that minimizes potential energy. Slime mold, a brainless organism, always finds the most efficient route to food, even when presented with an obstacle. A jump rope, when held on both ends, always winds up in the same shape, a curve known as catenary. That kind of optimization is explained by what’s known as a variational principle: any other variation of the shape found by the protein, mold or jump rope would require more energy.

Now NSF-funded researchers at Harvard University have found that some lasers use the same principle. The research is described in Physical Review Letters.

Frequency combs are widely-used, high-precision tools for measuring and detecting different frequencies -- colors -- of light. Unlike conventional lasers, which emit a single frequency, these lasers emit multiple frequencies in lockstep, evenly spaced to resemble the teeth of a comb.

When a laser produces a frequency comb, it emits light waves that repeat themselves periodically. Depending on the parameters of the comb, these waves can have a constant intensity while varying in color, or they can look like short pulses of light that build and drop in intensity.

Researchers know how combs produce pulses, but how so-called frequency-modulated lasers maintain a constant intensity in the face of changing frequencies has been a long-lasting puzzle. The researchers, led by Federico Capasso, reconstructed the waveform emitted by light sources known as quantum cascade lasers, widely used in spectroscopy and sensing. They found that the lasers choose to emit light waves that not only suppresses intensity fluctuations -- leading to a constant intensity in time -- but also maximizes power output.

The research is funded by NSF’s Directorate for Engineering. "Fundamental insights into laser technologies, like this frequency-modulated laser, promise ultra-broadband, high power, and precise light sources for us to understand our world and -- in the future -- advanced molecular spectroscopy, new technologies for atomic clocks, GPS, astronomy, and many other applications," said Ruyan Guo, NSF program director for Electronics, Photonics and Magnetic Devices.

To attain a better understanding of the cosmos, researchers successfully developed the first deep learning-based 3D simulation of the universe.  “We can run these simulations in a few milliseconds, while other ‘fast’ simulations take a couple of minutes,” says study co-author Shirley Ho, a group leader at the Flatiron Institute’s Center for Computational Astrophysics in New York City and an adjunct professor at Carnegie Mellon University. “Not only that, but we’re much more accurate.”

Working in collaboration with researchers from UC Berkeley, the Kavli Institute for the Physics and Mathematics of the Universe in Japan, University of British Columbia, and Carnegie Mellon University, the team developed a model called Deep Density Displacement Model (D3M). The tool accurately simulated how the universe looked and how it would look if certain parameters were tweaked. 

Using an NVIDIA V100 GPU, with the cuDNN-accelerated PyTorch deep learning framework, the team first trained a deep neural network by feeding it 8,000 different simulations.  By using the GPU for inference, the researchers reduced the time to generate 1,000 simulations from 115 seconds to 20. 

Once trained, the team ran simulations of a box-shaped universe 600 million light-years across, comparing the results to two models, a slow-but-accurate approach, a fast simulation, and the proposed method. The proposed model shattered previous records, outperforming them by hundreds of hours, and minutes, respectively, completing a simulation in just 30 milliseconds. 

In terms of accuracy, the model had a relative error or 2.8 percent, much better than the existing fast model that has an error of 9.3 percent. 

The principle behind the GAN was first proposed in 2014, and at its most basic level, it describes a system that pits two AI systems (neural networks) against each other to improve the quality of their results.

To understand how they work, imagine a blind forger trying to create copies of paintings by great masters. To start with, he has no idea what a painting should look like – but he happens to have a friend who has a photographic memory of every masterpiece that's ever been painted.

This friend – a detective – has to determine whether the paintings his friend is showing match the features of those created by the real great masters, or are obvious forgeries. This is the basic idea of how a GAN operates – only as they are AIs, both the forger and his friend are able to act at super-speed, making and detecting thousands of forgeries per second. Both of them then "learn" from the outcome to improve their future performance. As the detective becomes better at detecting forgeries, the forger must become better at creating them.

GANs have been the cause of a lot of excitement within the field of AI development in recent years, due to their ability to create “new” information following rules established by existing information. An example might be writing instruction manuals. By training a GAN on thousands of instruction manuals, it could one day be possible to create a system that could look at any tool, device or software and then create instructions on how to use it.

So, let’s look into how this works in a bit more depth. The “forger” network that creates fake data is termed the generative network, and its job is to read and understand the properties of the training data. It then attempts to replicate it by producing “candidate” datasets that follow the same rules.

The “detective” network tasked with determining whether the generative network is outputting false (artificially generated) data or real (training) data is known as the discriminative network. Because it competes against the generative network, the system as a whole is described as "adversarial."

For a great working example of a GAN in action, look no further than the popular demonstration This Person Does Not Exist. The network powering the website has learned to produce ultra-realistic images of human faces that, while they follow all of the rules regarding the way a human face should look, do not exist outside of the computer program.

While you might at first assume that the program builds images of faces by putting together pieces from a database of eyes, ears, mouths, and hair, this isn't the case. The "input" data for the generative network is simply a string of numbers – only the discriminative network sees the training data. The generative network improves its output based entirely on the output of the discriminative network.

As the only feedback the discriminative network gives is yes/no “guesses” at whether the generative output matches the training data, it takes many, many attempts before it starts to produce output that is acceptably close to the desired outputs – in this case, a realistic-looking image of a non-existent person. This example actually uses an updated model of the GAN known as proGAN which was developed by Nvidia last year, and works by gradually increasing the resolution of the image that the network generates, starting with a very low-resolution 4 pixel by 4 pixel image.

The data used for training an adversarial network does not have to be labelled, as the discriminative network can make judgments on the output of the generative network based entirely on features of the training data itself. This means GANs have applications in unsupervised learning as well as supervised (where the data is labelled) and reinforcement learning.Another useful feature of GANs is that they can be used to efficiently create training datasets for other AI applications. Most current AI techniques, in particular, Deep Learning, rely on access to large amounts of data for training purposes.

In recent years, the idea of life on other planets has become less far-fetched. NASA announced June 27, 2019,  that it will send a vehicle to Saturn’s icy moon Titan, a celestial body known to harbor surface lakes of methane and an ice-covered ocean of water, boosting its chance for supporting life.

On Earth, scientists are studying the most extreme environments to learn how life might exist under completely different settings, like on other planets. A University of Washington team has been studying the microbes found in “cryopegs,” trapped layers of sediment with water so salty that it remains liquid at below-freezing temperatures, which may be similar to environments on Mars or other planetary bodies farther from the sun.

At the recent AbSciCon meeting in Bellevue, Washington, researchers presented DNA sequencing and related results to show that brine samples from an Alaskan cryopeg isolated for tens of thousands of years contain thriving bacterial communities. The lifeforms are similar to those found in floating sea ice and in saltwater that flows from glaciers, but display some unique patterns.

“We study really old seawater trapped inside of permafrost for up to 50,000 years, to see how those bacterial communities have evolved over time,” said lead author Zachary Cooper, a UW doctoral student in oceanography.

Cryopegs were first discovered by geologists in Northern Alaska decades ago. This field site in Utqiaġvik, formerly known as Barrow, was excavated in the 1960s by the U.S. Army’s Cold Regions Research and Engineering Laboratory to explore large wedges of freshwater ice that occur in the permafrost there. Subsurface brine was eventually collected from the site in the 2000s.

For the first time ionized hydrogen has been detected at the lowest frequency ever towards the center of our Galaxy. The findings originate from a cloud that is both very cold (around -230 degrees Celsius) and also ionized, something that has never been detected before. This discovery may help to explain why stars don't form as quickly as they theoretically could.

Dr. Raymond Oonk (ASTRON/Leiden Observatory/SURFsara) led this study which is published today in MNRAS. He said: "The possible existence of cold ionized gas had been hinted at in previous work, but this is the first time we clearly see it."

Ionization is an energetic process that strips electrons away from atoms. The atom will become electrically charged and can then be called an ion. This typically happens in gas that is very hot (10000 degrees Celsius) and where atoms can easily lose their electrons. It was therefore puzzling to discover the ionized hydrogen from very cold gas in this cloud. Normal energy sources, such as photons from massive stars, would not cause this. More exotic energy forms, such as high energy particles created in supernova shockwaves and near black holes, are more likely to be responsible.

Dr. Oonk continues: "This discovery shows that the energy needed to ionize hydrogen atoms can penetrate deep into cold clouds. Such cold clouds are believed to be the fuel from which new stars are born. However, in our Galaxy we know that the stellar birth rate is very low, much lower than naively expected. Perhaps the energy observed here acts as a stabilizer for cold clouds, thereby preventing them from collapsing on to themselves and forming new stars."

Engineers have mimicked the human brain with an electronic chip that uses light to create and modify memories.

Researchers from RMIT University drew inspiration from an emerging tool in biotechnology -- optogenetics -- to develop a device that replicates the way the brain stores and loses information.

Optogenetics allows scientists to delve into the body's electrical system with incredible precision, using light to manipulate neurons so that they can be turned on or off. The new chip is based on an ultra-thin material that changes electrical resistance in response to different wavelengths of light, enabling it to mimic the way that neurons work to store and delete information in the brain.

Research team leader Dr Sumeet Walia said the technology moves us closer towards artificial intelligence (AI) that can harness the brain's full sophisticated functionality. "Our optogenetically-inspired chip imitates the fundamental biology of nature's best computer -- the human brain," Walia said.

"Being able to store, delete and process information is critical for computing, and the brain does this extremely efficiently. We're able to simulate the brain's neural approach simply by shining different colours onto our chip. This technology takes us further on the path towards fast, efficient and secure light-based computing. It also brings us an important step closer to the realization of a bionic brain -- a brain-on-a-chip that can learn from its environment just like humans do."

Dr Taimur Ahmed, lead author of the study published in Advanced Functional Materials, said being able to replicate neural behavior on an artificial chip offered exciting avenues for research across sectors. "This technology creates tremendous opportunities for researchers to better understand the brain and how it's affected by disorders that disrupt neural connections, like Alzheimer's disease and dementia," Ahmed said.

The researchers, from the Functional Materials and Microsystems Research Group at RMIT, have also demonstrated the chip can perform logic operations -- information processing -- ticking another box for brain-like functionality.

The EBRC Roadmap is a critical assessment of the current status and potential of engineering biology. It is intended to provide researchers and other stakeholders (including government funders) with a compelling set of technical challenges and opportunities in the near and long term. The ongoing roadmapping process was initiated in response to the recommendations put forth in the 2015 National Academies report, Industrialization of Biology, and was partially supported by the National Science Foundation. With this inaugural release of the Roadmap, EBRC endeavors to provide a go-to resource for engineering/synthetic biology research and related endeavors. Further details can be found in the overview section, including a brief discussion of societal and security considerations.

The EBRC Technical Roadmapping Working Group led the development of the roadmap scope and content. Collective insight and input was leveraged from more than 80 leading scientists and engineers, including academic, industry, and student members of EBRC and from the broader research community. Since mid-2018, the working group has held five workshops and countless teleconferences to develop the content and engage discussion around the roadmap. The result is a collaborative effort of the engineering biology research community and represents the community’s vision for the future of the field.

Earth has nearly a billion hectares suitable for new forests to start trapping carbon, a study finds.

A whopping new estimate of the power of planting trees could rearrange to-do lists for fighting climate change.  Planting trees on 0.9 billion hectares of land could trap about two-thirds the amount of carbon released by human activities since the start of the Industrial Revolution, a new study finds. The planet has that much tree-friendly land available for use. Without knocking down cities or taking over farms or natural grasslands, reforested pieces could add up to new tree cover totaling just about the area of the United States, researchers report in the July 5, 2019, Science.

The new calculation boosts tree planting to a top priority for gaining some time to fight climate change, says coauthor Tom Crowther, an ecologist at ETH Zurich. The study used satellite images to see how densely trees grow naturally in various ecosystems. Extrapolating from those images showed how much forest similar land could support. Plant a mix of native species, he urges. That will help preserve the birds, insects and other local creatures. The analysis revealed space to nourish enough trees to capture some 205 metric gigatons of carbon in about a century. That’s close to 10 times the savings expected from managing refrigerants, the top item on a list of climate-fighting strategies from the nonprofit Project Drawdown, a worldwide network of scientists, advocates and others proposing solutions to global warming.

The benefit of tree planting will shrivel if people wait, the researchers warn. Earth’s climate could change enough by 2050 to shrink the places trees can grow by some 223 million hectares if the world keeps emitting greenhouse gases as it does now, the analysis suggests.

Coral reefs are considered one of the most threatened ecosystems on the planet and are dying at alarming rates around the world. Scientists attribute coral bleaching and ultimately massive coral death to a number of environmental stressors, in particular, warming water temperatures due to climate change.

A new study published in the international journal Marine Biology, reveals what's really killing coral reefs. With 30 years of unique data from Looe Key Reef in the lower Florida Keys, researchers from Florida Atlantic University's Harbor Branch Oceanographic Institute and collaborators have discovered that the problem of coral bleaching is not just due to a warming planet, but also a planet that is simultaneously being enriched with reactive nitrogen from multiple sources.

Improperly treated sewage, fertilizers and top soil are elevating nitrogen levels, which are causing phosphorus starvation in the corals, reducing their temperature threshold for "bleaching." These coral reefs were dying off long before they were impacted by rising water temperatures. This study represents the longest record of reactive nutrients and algae concentrations for coral reefs anywhere in the world.

"Our results provide compelling evidence that nitrogen loading from the Florida Keys and greater Everglades ecosystem caused by humans, rather than warming temperatures, is the primary driver of coral reef degradation at Looe Key Sanctuary Preservation Area during our long-term study," said Brian Lapointe, Ph.D., senior author and a research professor at FAU's Harbor Branch.

A key finding from the study is that land-based nutrient runoff has increased the nitrogen:phosphorus ratio (N:P) in reef algae, which indicates an increasing degree of phosphorus limitation known to cause metabolic stress and eventually starvation in corals.

Concentrations of reactive nitrogen are above critical ecosystem threshold levels previously established for the Florida Keys as are phytoplankton levels for offshore reefs as evidenced by the presence of macroalgae and other harmful algal blooms due to excessive levels of nutrients.

Boosted by a historic heat wave in Europe and unusually warm conditions across the Arctic and Eurasia, the average temperature of the planet soared to its highest level ever recorded in June.

According to data released Monday by NASA, the global average temperature was 1.7 degrees Fahrenheit (0.93 Celsius) above the June norm (based on a 1951-to-1980 baseline), easily breaking the previous June record of 1.5 degrees Fahrenheit (0.82 Celsius), set in 2016, above the average.

The month was punctuated by a severe heat wave that struck Western Europe in particular during the last week, with numerous all-time-hottest-temperature records falling in countries with centuries-old data sets.

Notably, 13 locations in France surpassed their highest temperature ever recorded. The heat wave’s highest temperature of 114.6 degrees Fahrenheit (45.9 Celsius), posted in Gallargues-le-Montueux, was 3.2 degrees above the old record, set during an infamous heat wave in July and August 2003.

NASA is the second institution to confirm that it was Earth’s hottest June, as the Copernicus Climate Change Service had already determined that June 2019 was the warmest such month on record for Europe and globally. According to NASA GISTEMP, June 2019 was the warmest June on record 1880-present). Of the 11,756 grid cells with data for every year since 1900, 716 had their warmest June on record. 0 had their coolest June (or 2nd, 3rd, 4th, or 5th coolest).

June featured unusually mild conditions in the Arctic, particularly in Greenland, where the melt season got off to an early start. July is picking up right where June left off. Zeke Hausfather, a climate scientist based in Berkeley, Calif., tweeted that the month so far ranks as the hottest on record narrowly ahead of 2017, the previous record holder.

“If this July turns out to be the warmest July (it has a good shot at it), it will be the warmest month we have measured on Earth!” tweeted Michael Mann, a climate scientist at Pennsylvania State University.

Einstein’s theory of General Relativity is world famous – but it might not be the only way to explain how gravity works and how galaxies form. Physicists at Durham University created huge supercomputer simulations of the universe to test an alternative theory.

Our research found that f(R)-gravity – a so-called Chameleon Theory – could also explain the formation of structures in the cosmos. The simulations show that disc spiral galaxies like our Milky Way still form, even when a different law of gravity is applied.

Scientists already know that f(R)-gravity can reproduce the success of General Relativity in demonstrating the evolution of our solar system. But our new research now shows that this theory can also be applied to galaxy formation on very large cosmological scales.

This doesn’t mean that Einstein was wrong, though! Instead, it shows that there might be more than one way to explain gravity’s role in the evolution of the universe.

General Relativity was developed by Einstein in the early 1900s to explain the gravitational effect of large objects in space. It also has consequences for understanding the accelerating expansion of the universe, which it’s thought is driven by a mysterious substance called dark energy.

However, models like f(R)-gravity can also explain the universe’s rapid growth and our researchers say their work could also be small step towards identifying what dark energy is. Our astronomers and cosmologists are among the best in the world and are constantly shedding new light on the science of the universe.

In the past year we’ve also shown how supermassive black holes can help to measure the expansion of the early universe; how our Milky Way is on a catastrophic collision course with a neighboring galaxy; and why Uranus is tilted on its side. We’re also helping to build the Extremely Large Telescope – the biggest telescope in the world. Discover more about our Cosmology and Astronomy research.

The f(R)-gravity research is published in Nature Astronomy. It was led by Dr Christian Arnold and Professor Baojiu Li in our Institute for Computational Cosmology and postgraduate student Matteo Leo in our Institute for Particle Physics Phenomenology.