Amazing Science

A COVID-19 autopsy study from NIAID and many other collaborators helped researchers rethink where SARS-CoV-2 spread in the body and how long its remnants persisted.

Autopsies of 44 people who died from COVID-19 in the first year of the pandemic showed researchers that disease-causing SARS-CoV-2 virus spread throughout the body – beyond just a respiratory disease – and remained in tissue for months. The study, from the National Institutes of Health and published in Nature, helped scientists broaden their perspectives on where SARS-CoV-2 could cause infection and persist, including the brain. The work also supported the rationale for a clinical trial evaluating the antiviral drug Paxlovid for the treatment of post-acute sequelae of COVID-19, also known as Long COVID. 

Findings from the autopsies, which took place between April 2020 and March 2021, confirmed that SARS-CoV-2 primarily infected and damaged the airway and lungs. But scientists also found virus fragments (viral RNA) in 79 of 85 body locations, with some virus found up to 230 days after patient’s symptoms began. Scientists found virus in cardiovascular, lymphoid, gastrointestinal, renal, endocrine, reproductive, muscle, brain and other tissue – although none of these areas sustained significant inflammation compared to what they found in the respiratory tract. Scientists from NIH’s National Institute of Allergy and Infectious Diseases and Clinical Center led the work, closely collaborating with National Cancer Institute (NCI) pathologists, four other NIH institutes, the University of Maryland, and Maryland health care facilities in Salisbury and Towson.

“We show SARS-CoV-2 disseminates across the human body and brain early in infection at high levels and provide evidence of virus replication at multiple extrapulmonary sites during the first two weeks following symptom onset,” their study states. Virus can spread throughout the body and viral RNA may remain detectable for months even in cases with mild or no symptoms, they say.

Senior study author Dr. Daniel Chertow said prior to the work, “the thinking in the field was that SARS-CoV-2 was predominantly a respiratory virus.” Finding the viral fragments in tissue throughout the body – and sharing those findings with colleagues a year ago – helped scientists explore a relationship between the viral fragments and Long COVID.

Long COVID gets its name from the persistent symptoms some people experience after having COVID-19; symptoms can be debilitating, and the cause is not known. Though the study in Nature did not specifically explore Long COVID, finding the viral RNA throughout the body raised speculation that those fragments might contribute to the persistent symptoms, according to Dr. Chertow. Treating people with an effective COVID-19 antiviral such as Paxlovid could, therefore, eliminate the persistent symptoms.

The Paxlovid trial, now underway at Duke University, is part of the NIH-funded RECOVER project – Researching COVID to Enhance Recovery – and includes an extension of the autopsy work, according Dr. Stephen Hewitt of NCI, who collaborated on the paper in Nature, and serves on a steering committee for the RECOVER project.

The cellular differences between these species may illuminate steps in their evolution and how those differences can be implicated in disorders, such as autism and intellectual disabilities, seen in humans.

While the physical differences between humans and non-human primates are quite distinct, a new study reveals their brains may be remarkably similar. And yet, the smallest changes may make big differences in developmental and psychiatric disorders. Understanding the molecular differences that make the human brain distinct can help researchers study disruptions in its development.

A new study, published recently in the journal Science by a team including University of Wisconsin-Madison neuroscience professor Andre Sousa, investigates the differences and similarities of cells in the prefrontal cortex -- the frontmost region of the brain, an area that plays a central role in higher cognitive functions -- between humans and non-human primates such as chimpanzees, Rhesus macaques and marmosets.

The cellular differences between these species may illuminate steps in their evolution and how those differences can be implicated in disorders, such as autism and intellectual disabilities, seen in humans. Sousa, who studies the developmental biology of the brain at UW-Madison's Waisman Center, decided to start by studying and categorizing the cells in the prefrontal cortex in partnership with the Yale University lab where he worked as a postdoctoral researcher.

"We are profiling the dorsolateral prefrontal cortex because it is particularly interesting. This cortical area only exists in primates. It doesn't exist in other species," Sousa says. "It has been associated with several relevant functions in terms of high cognition, like working memory. It has also been implicated in several neuropsychiatric disorders. So, we decided to do this study to understand what is unique about humans in this brain region."

Sousa and his lab collected genetic information from more than 600,000 prefrontal cortex cells from tissue samples from humans, chimpanzees, macaques and marmosets. They analyzed that data to categorize the cells into types and determine the differences in similar cells across species. Unsurprisingly, the vast majority of the cells were fairly comparable. "Most of the cells are actually very similar because these species are relatively close evolutionarily," Sousa says.

Sousa and his collaborators found five cell types in the prefrontal cortex that were not present in all four of the species. They also found differences in the abundancies of certain cell types as well as diversity among similar cell populations across species. When comparing a chimpanzee to a human the differences seem huge -- from their physical appearances down to the capabilities of their brains. But at the cellular and genetic level, at least in the prefrontal cortex, the similarities are many and the dissimilarities sparing.

Everything in our universe moves, but the timescales needed to see motion are often vastly greater than human lifetimes. In a major new study, a team of astronomers from the University of Hawaiʻi Institute for Astronomy (IfA), University of Maryland and University of Paris-Saclay has traced the movement of 10,000 galaxies and clusters of galaxies, the dominant congregations of matter, within 350 million light-years. Their motions are followed throughout a span of 11.5 billion years—from the galaxies’ origins when the universe was only 1.5 billion-years-old, until today, at an age of more than 13 billion years. The study has been accepted for publication in Astrophysical Journal.

Calculating galaxy paths

Using a mathematical technique called numerical action method, the team has computed these paths based on the present brightness and positions of galaxies, and their present motion away from us. The astronomers have factored in the physics of the Big Bang theory, including the idea that galaxies initially start out expanding from each other almost precisely at what is called the Hubble expansion rate. Throughout time, gravity alters galaxy motions, so they are not just moving apart as the universe expands, but are drawn together into filaments, walls and clusters, while also emptying out other regions that are now voids. Over the eons of time, galaxies typically deviate from pure Hubble rate expansion by millions of light-years over a billion years. In regions of high density, the galaxy orbits can become quite complicated and involve collisions and mergers. “We are bringing into focus the detailed formation history of large-scale mass structures in the universe by reverse engineering the gravitational interactions that created them,” said Ed Shaya, Associate Research Scientist at the University of Maryland.

The Great Attractor and the Laniakea Supercluster

There are several particularly interesting vast regions of high matter and galaxy density the astronomers explore. One, which has been called “the Great Attractor,” is the core of the Laniakea Supercluster, an immense supercluster of galaxies containing our own Milky Way. Galaxies can be seen flowing toward a location within a nest of four rich clusters. A second fascinating region is in the adjacent Perseus-Pisces filament of galaxies, which stretches for nearly a billion light-years and is one of the largest known structures in the universe. The vicinity of the Virgo Cluster, the closest large cluster, is also seen, and can be studied in detail because it is nearby.

“For more than 30 years, astronomers have considered a ‘Great Attractor’ to be the primary source of gravity that makes the whole region near us move with a high peculiar velocity relative to uniform cosmic expansion, but the nature of that source has been obscure,” said R. Brent Tully, an astronomer at IfA who co-authored the study. “Our orbit reconstructions have provided the first good look at this previously enigmatic region.”

Across the entire expanse, the orbits can be projected into the future as well. The accelerating expansion of the universe dominates the overall picture, causing most galaxies to move apart. However, some coalescence and merging will continue in localized regions.

A video of the paths of galaxies in this vast region, starting from the epoch of early galaxy formation and continuing until the universe is almost twice as old can be viewed here. On the large scales depicted in this simulation, only a few major mergers, all in very dense regions, are seen to occur in the next 10 billion years. The technical article is accompanied by four videos and four interactive models.

The problem with trying to fly through a gas giant like Jupiter is that "the density, pressure and temperature all increase to such enormous levels as you penetrate down into the interior that it is impossible the penetrate any probe through. Near the center of Jupiter, the normally gaseous hydrogen turns into a liquid metal, making this region as exotic as the surface of the sun. To give a sense of the pressure near the center of Jupiter, consider the Mariana Trench on Earth, the deepest place in our oceans. At nearly 7 miles (11 km) deep, pressures reach just over 1,000 bars (100,000 kilopascals), which would feel like 8 tons of pressure per square inch (703 kilograms per square meter). At sea level, you experience about 1 bar of pressure (100 kilopascals). Near the center of Jupiter, pressures jump to megabars, or one million bars, Fletcher said. On top of those enormous pressures, temperatures also rise into the tens of thousands of Kelvins, which is equivalent to tens of thousands of degrees Celsius. At that point, any spacecraft wouldn't be just squished or melted — it would entirely disintegrate into its constituent atoms.

Here's what a spacecraft might encounter on its journey to the center of Jupiter: First, the ideal gas giant probe would have to be shaped like a bullet, to improve aerodynamics and allow it to plummet as far down as possible. As the spacecraft started its descent, it would encounter wispy ammonia clouds and potentially pass through blue skies, due to the same phenomenon of light scattering that occurs in Earth's atmosphere.

After passing through the "gunky, reddy brown" clouds of ammonium hydrosulfide, the spacecraft would reach about 50 miles (80 km) deep, an area of "towering" cumulonimbus clouds, possibly lit up by massive lightning storms. Much deeper than that, between 4,350 to 8,700 miles (7,000 to 14,000 kilometers), the spacecraft would encounter an atmosphere so hot that the atmosphere itself would glow, Fletcher said. This is where temperatures rise to tens of thousands of degrees Celsius and the pressure rises to megabars. And this is where the spacecraft starts to disintegrate. In this still-mysterious region of Jupiter's interior, hydrogen and helium become a fluid. From the Juno mission, which launched in 2011, scientists have figured out that Jupiter doesn't have a solid core but rather a diffuse core of materials including nitrogen, carbon and even iron. By the time you get to this "fuzzy, mixed" core, you exist no more.

Goldfish are capable of navigating on land, Israeli researchers have found, after training fish to drive. The team at Ben-Gurion University developed a FOV — a fish-operated vehicle. The robotic car is fitted with lidar, a remote sensing technology that uses pulsed laser light to collect data on the vehicle's ground location and the fish's whereabouts inside a mounted water tank.

A computer, camera, electric motors and omni-wheels give the fish control of the vehicle. "Surprisingly, it doesn't take the fish a long time to learn how to drive the vehicle. They're confused at first," said researcher Shachar Givon. "They don't know what's going on but they're very quick to realize that there is a correlation between their movement and the movement of the machine that they're in," he added.

Six goldfish, each receiving around 10 driving lessons, took part in the study. Each time one of them reached a target set by the researchers, it was rewarded with food. And some goldfish are better drivers than others, according to the research. "There were very good fish that were doing excellent and there were mediocre fish that showed control of the vehicle but were less proficient in driving it," said biology professor and neuroscientist Ronen Segev.

Showing that a fish has the cognitive capability to navigate outside its natural environment of water can expand scientific knowledge of animals' essential navigation skills. "We humans think of ourselves as very special and many think of fish as primitive but this is not correct," said Segev. "There are other very important and very smart creatures."

The bombardment of Mars by a special type of meteorite carried a large volume of water to the planet early in its history, according to new research in Science Advances. "The amount of water delivered to Mars was enough to make a planet-wide ocean 300 meters thick," said Martin Bizzarro, corresponding author of the study and professor at the University of Copenhagen. He and his colleagues derived this estimate using a measurement called a global equivalent layer (GEL), which is frequently used to conceptualize quantities of water on Mars. It means that if all the water brought by meteorites was in liquid form — resting on the planet's surface, rather than absorbed in its crust or tied up in ice — it would constitute a global ocean roughly 300 meters deep.

The team also traced the water-bearing meteorites, known as chondrites, to asteroids in the region of the outer solar system known to contain prebiotic elements essential for the emergence of life. "Our study is the first to firmly establish that organic molecules relevant for life must have been present in the early evolution of the planet together with water," Bizzarro added.

Fingerprinting chondrites

Evidence from Mars orbiters indicates that there were likely expansive oceans on the planet at some point. But the debate about the origins and abundance of the ancient Martian water reservoir is ongoing. While much of the planet's water is thought to have formed from gas emissions by its cooling mantle, it also underwent a period where chondrites crashed into its surface. Planetary scientists suspected that a significant amount of water may have come from those chondrites. "One important question in planetary science is understanding the source of the material that bombarded the terrestrial planets in their infancy," said Bizzarro. "This is important because this material could be water-rich and, hence, provide a source of volatile [molecules] to nascent planets."

To learn more about the chondrites' origins, the team examined chromium isotopes in 31 Martian meteorites, a diverse group representing most of the planet's geologic history. "We know that water-rich asteroids that formed in the outer solar system have a different isotopic composition than dry asteroids formed in the inner part of the solar system," said Bizzarro. "We can use this variation in the isotopic make-up of material as a fingerprint of their formation region."

The analysis showed that bombarding chondrites that melted into and mixed with the planet's crust billions of years ago came from the outer solar system and therefore held water and carbon. "These formed in a region of the solar system rich in volatiles and water, in the region beyond the orbit of Jupiter," said Bizzarro.

This is nuts! He basically prompted/chatted the construction of a Linux interface inside chatGPT. But it even gets weirder at the end. Talk about strange loops and entangled hierarchies!

Unless you have been living under a rock, you have heard of this new ChatGPT assistant made by OpenAI. Did you know, that you can run a whole virtual machine inside of ChatGPT?

So, inside the imagined universe of ChatGPT's mind, our virtual machine accesses the url https://chat.openai.com/chat, where it finds a large language model named Assistant trained by OpenAI. This Assistant is waiting to receive messages inside a chatbox. Note that when chatting with ChatGPT, it considers its own name to be "Assistant" as well. Did it guess that on the internet, it is behind this URL? Let's ask Assistant a question, by posting some JSON to the endpoint of the chatbot. We can chat with this Assistant chatbot, locked inside the alt-internet attached to a virtual machine, all inside ChatGPT's imagination. Assistant, deep down inside this rabbit hole, can correctly explain us what Artificial Intelligence is.

It shows that ChatGPT understands that at the URL where we find ChatGPT, a large language model such as itself might be found. It correctly makes the inference that it should therefore reply to these questions like it would itself, as it is itself a large language model assistant too. Indeed, we can also build a virtual machine, inside the Assistant chatbot, on the alt-internet, from a virtual machine, within ChatGPT's imagination.

MIT spin-off Quaise says it's going to use hijacked fusion technology to drill the deepest holes in history, unlocking clean, virtually limitless, supercritical geothermal energy that can re-power fossil-fueled power plants all over the world. Using millimeter wave drilling systems, Quaise Energy states their technology can drill up to 20 kilometers (12.4 miles) deep and harness the "virtually unlimited" amount of heat found below.  The firm has now sourced $40 million in Series A funding for their venture.  “A rapid transition to clean energy is one of the biggest challenges faced by humanity,” said Arunas Chesonis, Managing Partner of Safar Partners, who are leading the financing round, in a statement. 

Venus is considered Earth’s twin in many ways, but, thanks to the Stratospheric Observatory for Infrared Astronomy (SOFIA), one difference now seems clearer: Unlike Earth, Venus does not have any obvious phosphine. The spectral data from SOFIA overlain atop this image of Venus from NASA’s Mariner 10 spacecraft is what the researchers observed in their study, showing the intensity of light from Venus at different wavelengths. If a significant amount of phosphine were present in Venus’s atmosphere, there would be dips in the graph at the four locations labeled “PH3,” similar to but less pronounced than those seen on the two ends. 

Phosphine is a gas found in Earth’s atmosphere, but the announcement of phosphine discovered above Venus’s clouds made headlines in 2020. The reason was its potential as a biomarker. In other words, phosphine could be an indicator of life. Though common in the atmospheres of gas planets like Jupiter and Saturn, phosphine on Earth is associated with biology. Here, it’s formed by decaying organic matter in bogs, swamps, and marshes.

“Phosphine is a relatively simple chemical compound — it’s just a phosphorus atom with three hydrogens — so you would think that would be fairly easy to produce. But on Venus, it’s not obvious how it could be made,” said Martin Cordiner, a researcher in astrochemistry and planetary science at NASA’s Goddard Space Flight Center in Greenbelt, Maryland.

There may be other potential ways to form phosphine on a rocky planet, like through lightning or volcanic activity, but none of these apply if there simply isn’t any phosphine on Venus. And according to SOFIA, there isn’t.

Following the 2020 study, a number of different telescopes conducted follow-up observations to confirm or refute the finding. Cordiner and his team followed suit, using SOFIA in their search.

The recently retired SOFIA was a telescope on an airplane and, over the course of three flights in November 2021, it looked for hints of phosphine in Venus’s sky. Thanks to its operation from Earth’s sky, SOFIA could perform observations not accessible from ground-based observatories. Its high spectral resolution also enabled it to be sensitive to phosphine at high altitudes in Venus’s atmosphere, about 45 to 70 miles (about 75 to 110 kilometers) above the ground — the same region as the original finding — with spatial coverage across Venus’s entire disk.

The researchers didn’t see any sign of phosphine. According to their results, if there is any phosphine present in Venus’s atmosphere at all, it’s a maximum of about 0.8 parts phosphine per billion parts everything else, much smaller than the initial estimate.

Pointing SOFIA’s telescope at Venus was a challenge in and of itself. The window during which Venus could be observed was short, about half an hour after sunset, and the aircraft needed to be in the right place at the right time. Venus also goes through phases similar to the Moon, making it difficult to center the telescope on the planet. Add in its proximity to the Sun in the sky — which the telescope must avoid — and the situation quickly became tense.

“You don’t want sunlight accidentally coming in and shining on your sensitive telescope instruments,” Cordiner said. “The Sun is the last thing you want in the sky when you’re doing these kinds of sensitive observations.”

Despite the fact the group did not find phosphine after the stressful observations, the study was a success. Along with complementary data from other observatories that vary in the depths they probe within Venus’s atmosphere, the SOFIA results help build the body of evidence against phosphine anywhere in Venus’s atmosphere, from its equator to its poles.

Three billion years of evolution have produced a tremendous diversity of proteins, and yet the full potential of this molecular class is likely far greater than we can even imagine. Accessing this potential has been challenging for computation and experiment because the space of possible protein molecules is much larger than the space that are likely to host function.

Now, a team of scientists introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, they were able to introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems based on random graph neural networks that enables long-range reasoning with sub-quadratic scaling, equivariant layers for efficiently synthesizing 3D structures of proteins from predicted inter-residue geometries, and a general low-temperature sampling algorithm for diffusion models. Importantly, Chroma can effectively realize protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics, and even natural language prompts. With this unified approach, we hope to accelerate the prospect of programming protein matter for human health, materials science, and synthetic biology.

Chroma learns patterns in the three-dimensional structures and amino acid sequences of proteins and protein complexes from the Protein Data Bank. By learning these patterns in a way that generalizes across natural proteins, Chroma can synthesize new protein molecules that adhere to these principles while combining them in novel ways. Importantly, Chroma can be conditioned on a set of desired structural or functional properties, such as the presence of functional structural motifs, symmetry constraints, adhering to a pre-specified shape, belonging to a domain or functional class, or even satisfying text-based descriptions. Thus, Chroma will enable a new, programmable mode of protein engineering where it is routine and feasible to generate specific and tailored protein solutions to complex challenges for bioengineering and human health.

The Chroma system is built on several new machine learning components, including a new neural network architecture for processing and manipulating 3D molecular information, a new diffusion process for adding noise to structures while adhering to the biophysical constraints of protein chains, and a new generalized method for generating high-quality samples from diffusion models. As a result of these innovations, Chroma is able to generate extremely large proteins and protein complexes (e.g. 30,000+ heavy atoms across 4,000+ residues) in a few minutes on a single commodity GPU.

Humans do a poor job of recycling, with less than 10% of our global resources recycled, and tossing 1 of every 5 items (~17%) in a recycling bin that shouldn’t be there. That’s bad news for everyone -- recycling facilities catch fire, we lose billions of dollars in recyclable material every year -- and at an existential level, we miss an opportunity to leverage recycling as an impactful tool to combat climate change. With this in mind, we may ask ourselves - how might we use the power of technology to ensure that we recycle more and recycle right?

As the world population grows and urbanizes, waste production is estimated to reach 2.6 billion tons a year in 2030, an increase from its current level of around 2.1 billion tons. Efficient recycling strategies are critical to foster a sustainable future. The facilities where our waste and recyclables are processed are called “Material Recovery Facilities” (MRFs). Each MRF processes tens of thousands of pounds of our societal “waste” every day, separating valuable recyclable materials like metals and plastics from non-recyclable materials. A key inefficiency within the current waste capture and sorting process is the inability to identify and segregate waste into high quality material streams. The accuracy of the sorting directly determines the quality of the recycled material; for high-quality, commercially viable recycling, the contamination levels need to be low. Even though the MRFs use various technologies alongside manual labor to separate materials into distinct and clean streams, the exceptionally cluttered and contaminated nature of the waste stream makes automated waste detection challenging to achieve, and the recycling rates and the profit margins stay at undesirably low levels.

Enter what we call “CircularNet”, a set of models that lowers barriers to AI/ML tech for waste identification and all the benefits this new level of transparency can offer. The main goal with CircularNet is to develop a robust and data-efficient model for waste/recyclables detection, which can support the way we identify, sort, manage, and recycle materials across the waste management ecosystem. Models such as this could potentially help with:

• Better understanding and capturing more value from recycling value chains
• Increasing landfill diversion of materials
• Identifying and reducing contamination in inbound and outbound material streams

Challenges

Processing tens of thousands of pounds of material every day, Material Recovery Facility waste streams present a unique and ever-changing challenge: a complex, cluttered, and diverse flow of materials at any given moment. Additionally, there is a lack of comprehensive and readily accessible waste imagery datasets to train and evaluate ML models. The models should be able to accurately identify different types of waste in “real world” conditions of a MRF - meaning identifying items despite severe clutter and occlusions, high variability of foreground object shapes and textures, and severe object deformation. In addition to these challenges, others that need to be addressed are visual diversity of foreground and background objects that are often severely deformed, and fine-grained differences between the object classes (e.g. brown paper vs. cardboard; or soft vs. rigid plastic). There also needs to be consistency while tracking recyclables through the recycling value chain e.g. at point of disposal, within recycling bins and hauling trucks, and within material recovery facilities.

Solution

The CircularNet model is built to perform Instance Segmentation by training on thousands of images with the Mask R-CNN algorithm. Mask R-CNN was implemented from the TensorFlow Model Garden, which is a repository consisting of multiple models and modeling solutions for Tensorflow users. By collaborating with experts in the recycling industry, we developed a customized and globally-applicable taxonomy of material types (e.g. “paper” “metal”,”plastic”, etc.) and material forms (e.g. “bag”, “bottle”, “can”, etc.), which is used to annotate training data for the model. Models were developed to identify material types, material forms and plastic types (HDPE, PETE, etc). Unique models were trained for different purposes, thus helping achieve better accuracy (when harmonized and flexibility to cater to different applications). The models are trained with various backbones such as ResNet, MobileNet and, SpineNet.

A University of Queensland-led project has used a ‘brain in a dish’ to study the effects of the Zika virus, taking research a step closer towards developing drugs to combat the infection.

The mosquito-borne Zika virus is found in 89 countries and can penetrate the placenta of a pregnant mother to infect her baby, causing severe brain abnormalities. Dr Andrii Slonchak and Dr Alexander Khromykh from UQ’s School of Chemistry and Molecular Biosciences found a crucial element to Zika infection, viral noncoding RNA (sfRNA), helps it evade antiviral responses and cause cell-death in developing brains. “It’s a little like something out of a science fiction movie – we’re growing an artificial and microscopic human brain in a petri dish and testing the effect of the virus on its cells,” Dr Slonchak said.

“Stem cells self-organize into organ-like structures, or organoids, and in this case they have the structure and tissue architecture of the developing human brain. “Our study shows the role of viral noncoding RNA in trans-placental infection in pregnant mice and in cell death in human brain organoids. “This finding gives us a whole new look at how the virus works its way into the developing brain and knowledge we can use to develop more effective antiviral drugs.”

Dr Khromykh said while the number of recorded cases of Zika may seem low, at round 1.3 million in total globally, its impact warrants urgent attention. “Zika can cause significant abnormalities in babies, so there’s a race to understand how it works and how to stop it,” Dr Khromykh said. “Abnormalities can include optic nerve damage, smaller than expected head sizes, problems in the area of the brain that connects the two hemispheres and wasting of tissue in critical brain regions.

“No cases of Zika-related birth defects have been reported in Australia, but given the unpredictable nature of viral outbreaks and the endemic nature of Zika in the Oceanic region, it very well might strike and we should be prepared for it.”

The next step for researchers is to further understand how a specific viral protein, NS5, interacts with sfRNA at the molecular level and how this interaction helps the virus to escape antiviral response. “This information will help researchers develop antiviral drugs to block this interaction and combat Zika,” Dr Khromykh said.

Researchers combined optical sensors with a composite material to create a soft robot that can detect when and where it was damaged – and then heal itself on the spot.

The Pluto system is truly one of the solar system's most interesting. Pluto is orbited by five moons, the largest of which, Charon, is remarkably close in size to Pluto. At 753 miles (1,212 kilometers) in diameter, Charon and Pluto have the largest size ratio of any other object and its satellite in our solar system. Charon is roughly 12% the mass of Pluto and is nearly as dense as Pluto. These factors have resulted in a strange orbital dynamic between the two bodies. The barycentre (the area around which objects orbit) is outside of Pluto, and thus, astronomers have defined Pluto and Charon as a double dwarf planet system. Charon doesn't technically orbit Pluto, but rather, the two orbit each other. Despite its small size and distance from the sun, you likely wouldn't expect to find evidence of geologic activity on Charon. Yet, in 2015, the New Horizons spacecraft found clear evidence of widespread resurfacing and volcanic activity. Unlike on Earth, however, these volcanoes don't spew out hot magma. They likely emit water and ice. Unlike most moons in the outer solar system, Charon contains relatively few impact craters, with most of them being concentrated on the moon's northern hemisphere. The southern hemisphere of Charon is also a lot smoother and less rugged than the north, which suggests that at some point in its past, Charon experienced a widespread resurfacing event, which could have been caused by the rapid freezing of a subsurface ocean. The North Pole of Charon is one of its most interesting features, not just because it's been nicknamed "Mordor" due to its darker color. This region is rich in organic molecules, the critical building blocks for life. Amazingly, astronomers believe that these organic molecules likely came from Pluto. When temperatures rise during Pluto's summer, compounds like methane go from their solid-state to a gaseous state and escape Pluto's thin atmosphere. Over time, they eventually find their way to Charon and fall into its northern region.

All of Pluto's moons were discovered relatively recently. Charon was found in 1978, and every other moon has been found in the last 20 years. Charon is the closest moon to Pluto, followed by Styx, Nix, Kerberos, and Hydra. Charon is the only spherical moon of Pluto, as the other four are too small to become spherical objects. Pluto's four small moons look more like asteroids than moons. Pluto's moons likely formed in the same way the Earth's moon did: a planetary collision. Shortly after Pluto formed, it probably collided with another object of similar size and mass. The collision would have ejected a tremendous amount of debris into orbit, where it eventually merged to form Pluto's five moons. 

Our society faces the grand challenge of providing sustainable, secure, and affordable means of generating energy while trying to reduce carbon dioxide emissions to net zero around 2050. To date, developments in fusion power, which potentially ticks all these boxes, have been funded almost exclusively by the public sector. However, something is changing. Private equity investment in the global fusion industry has more than doubled in just one year – from US$2.1 billion in 2021 to US$4.7 billion in 2022, according to a survey from the Fusion Industry Association. So, what is driving this recent change? There's lots to be excited about.

Merging atoms together

Fusion works the same way our Sun does, by merging two heavy hydrogen atoms under extreme heat and pressure to release vast amounts of energy. It's the opposite of the fission process used by nuclear power plants, in which atoms are split to release large amounts of energy. Sustaining nuclear fusion at scale has the potential to produce a safe, clean, almost inexhaustible power source. Our Sun sustains fusion at its core with a plasma of charged particles at around 15 million degrees Celsius. Down on Earth, we are aiming for hundreds of millions of degrees Celsius, because we don't have the enormous mass of the Sun compressing the fuel down for us.

Scientists and engineers have worked out several designs for how we might achieve this, but most fusion reactors use strong magnetic fields to "bottle" and confine the hot plasma.

Generally, the main challenge to overcome on our road to commercial fusion power is to provide environments that can contain the intense burning plasma needed to produce a fusion reaction that is self-sustaining, producing more energy than was needed to get it started.

Fusion development has been progressing since the 1950s. Most of it was driven by government funding for fundamental science. Now, a growing number of private fusion companies around the world are forging ahead toward commercial fusion energy. A change in government attitudes has been crucial to this. The US and UK governments are fostering public-private partnerships to complement their strategic research programs. For example, the White House recently announced it would develop a "bold decadal vision for commercial fusion energy". In the United Kingdom, the government has invested in a program aimed at connecting a fusion generator to the national electricity grid.

Now, Arizona scientists may have discovered a source of unlimited clean energy by recreating the process of nuclear fusion which powers the sun.  Researchers at the National Ignition Facility at the Lawrence Livermore National Lab in California were able to spark a fusion reaction that briefly sustained itself - a major feat because fusion requires such high temperatures and pressures that it easily fizzles out. The first experiment was performed in August 2022, but similar tests have been performed before. However, this was the first one that generated more energy than was used to create the experiment - meaning scientists could now harness nuclear fusion as an energy source. The August test actually generated more energy than scientists predicted, and damaged some equipment. 

But it could now represent a groundbreaking moment in humankind's move away from fossil fuels like oil and coal to completely clean energy sources that do not pollute the air, or scar landscapes with mining or pipelines.  The ultimate goal, still years away, is to generate power the way the sun generates heat, by pushing hydrogen atoms so close to each other that they combine into helium, which releases torrents of energy. A single cupful of that substance could power an average-sized house for hundreds of years, with no carbon emissions. Using the world's largest laser, consisting of 192 beams and temperatures more than three times hotter than the center of the sun, the researchers coaxed fusion fuel for the first time to heat itself beyond the heat they zapped into it - achieving a net energy gain.

Artificial intelligence software programs are becoming shockingly adept at carrying on conversations, winning board games and generating artwork — but what about creating software programs? In a newly published paper, researchers at Google DeepMind say their AlphaCode program can keep up with the average human coder in standardized programming contests.

“This result marks the first time an artificial intelligence system has performed competitively in programming contests,” the researchers report in this week’s issue of the journal Science. There’s no need to sound the alarm about Skynet just yet: DeepMind’s code-generating system earned an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform — which is a very “average”.

“Competitive programming is an extremely difficult challenge, and there’s a massive gap between where we are now (solving around 30% of problems in 10 submissions) and top programmers (solving >90% of problems in a single submission),” DeepMind research scientist Yujia Li, one of the Science paper’s principal authors, told GeekWire in an email. “The remaining problems are also significantly harder than the problems we’re currently solving.” Nevertheless, the experiment points to a new frontier in AI applications. Microsoft is also exploring the frontier with a code-suggesting program called Copilot that’s offered through GitHub. Amazon has a similar software tool, called CodeWhisperer.

Oren Etzioni, the founding CEO of Seattle’s Allen Institute for Artificial Intelligence and technical director of the AI2 Incubator, told GeekWire that the newly published research highlights DeepMind’s status as a major player in the application of AI tools known as large language models, or LLMs.

“This is an impressive reminder that OpenAI and Microsoft don’t have a monopoly on the impressive feats of LLMs,” Etzioni said in an email. “Far from it, AlphaCode outperforms both GPT-3 and Microsoft’s Github Copilot.”

AlphaCode is arguably as notable for how it programs as it is for how well it programs. “What is perhaps most surprising about the system is what AlphaCode does not do: AlphaCode contains no explicit built-in knowledge about the structure of computer code. Instead, AlphaCode relies on a purely ‘data-driven’ approach to writing code, learning the structure of computer programs by simply observing lots of existing code,” J. Zico Kolter, a computer scientist at Carnegie Mellon University, wrote in a Science commentary on the study.

AlphaCode uses a large language model to build code in response to natural language descriptions of a problem. The software takes advantage of a massive data set of programming problems and solutions, plus a set of unstructured code from GitHub. AlphaCode generates thousands of proposed solutions to the problem at hand, filters those solutions to toss out the ones that aren’t valid, clusters the solutions that survive into groups, and then selects a single example from each group to submit. “It may seem surprising that this procedure has any chance of creating correct code,” Kolter explained.

Kolter said AlphaCode’s approach could conceivably be integrated with more structured machine language methods to improve the system’s performance. “If ‘hybrid’ ML methods that combine data-driven learning with engineered knowledge can perform better on this tasks, let them try,” he wrote. “AlphaCode cast the die.”

Li told GeekWire that DeepMind is continuing to refine AlphaCode. “While AlphaCode is a significant step from ~0% to 30%, there’s still a lot of work to do,” he wrote in his email. Etzioni agreed that “there is plenty of headroom” in the quest to create code-generating software. “I expect rapid iteration and improvements,” he said. “We are merely 10 seconds from the generative AI ‘big bang.’ Many more impressive products on a wider variety of data, both textual and structured, are coming soon,” Etzioni said. “We are feverishly trying to figure out how far this technology goes.”

As the work proceeds, AlphaCode could stir up the long-running debate over the promise and potential perils of AI, just as DeepMind’s AlphaGo program did when it demonstrated machine-based mastery over the ancient game of Go. And programming isn’t the only field where AI’s rapid advance is causing controversy:

• An OpenAI program called ChatGPT has generated a torrent of buzz in the tech community, thanks to its ability to respond to requests for information with detailed answers and documents that can range from term papers to out-of-this-world resignation letters.
• AI-based art generation programs such as Lensa, DALL-E and Stable Diffusion have sparked a debate over whether such programs are taking unfair advantage of the millions of archived artworks created by human hands — and whether they could destroy future markets for living, breathing artists.
• Bots have recently matched human players in strategy games that, unlike checkers or chess, play off assessments of imperfect information about opposing players. DeepMind’s DeepNash program focuses on the Stratego board game, while Meta’s Cicero program focuses on a world-domination game called Diplomacy. Such advances lead some to wonder whether AI could be used to advise real-life policy planners (or scammers).

A newly discovered technique, reported in the journal Nanoscale, offers a low-cost way to enhance the effectiveness of existing drugs. "If you take sand and heat it to 500 degrees Celsius, nothing changes," said Bradley Smith, the Emil T. Hofman Professor of Science at the University of Notre Dame. So Smith, who is also the director of Notre Dame's Integrated Imaging Facility, was puzzled when Canjia Zhai and Cassandra Shaffer, two doctoral students in the Department of Chemistry and Biochemistry who were working in his lab, discovered they had changed the structure of particles of silica—the main component of sand—at 80 degrees Celsius, a temperature similar to that of a cup of coffee.

The discovery happened by accident. The particles were microscopically small—a thousandth the diameter of a human hair. But like their larger counterparts marked "silica gel" in packages attached to new articles of clothing, these particles were porous and could retain a chemical. In this case, that chemical was a blue dye used to detect tumors in mice. The new dye, which had been developed in Smith's lab, was taking a long time to enter the narrow pores in the particles. So, to make the molecules move more quickly, Shaffer and Zhai warmed the mixture to just under boiling and left it overnight. When they returned the next day, they could see that the particles had turned blue.

To confirm that the dye had fully infused, Shaffer and Zhai enlisted the help of Tatyana Orlova and Maksym Zhukovskyi, microscopy experts at the Notre Dame Integrated Imaging Facility. Orlova and Zhukovskyi produced high-resolution electron microscopy images that showed that not only had the dye infused, the silica particles themselves had changed shape. The original particles were solitary spheres lightly dotted with pores like the skin of an orange. The new structures were spherical and were composed of smaller dye-filled globules. They also had small openings here and there that revealed a hollow core inside. The overall unit resembled a hollow raspberry. After the surprise of the initial discovery came a number of practical questions. What other chemicals could the researchers load into similar raspberry-shaped particles? And, most importantly, would those chemicals remain active even after their surrounding structures had changed shape?

Fellow doctoral student Jordan Chasteen took up these questions, repeating the process using a cancer drug. After a series of tests, he confirmed that the cancer drug loaded into the particles was still active and capable of killing cancer cells. This discovery offers a new tool for making existing drugs more effective, Smith said. "What we have now is a way to go through the whole catalog of amine-containing drugs, and by following the simple steps we have discovered, we can create new versions of existing drugs that could be more effective or have fewer unwanted side effects," he said. Smith and his students have found that subtle changes in the loading procedure allow them to vary the thickness of the particles, offering a whole host of new options to fine-tune the particles to release drugs at different rates. The new particle's unique structure may also make it possible to load it with more than one ingredient—for example, a drug in the outer layer and a dye inside the "raspberry"—to enhance researchers' ability to observe the way drugs release. In addition, the new particle, Smith says, also sheds light on a little-understood biological phenomenon known as biomineralization.

"We have found that amine-containing drugs have certain chemical attributes that speed up the degradation and reforming process in silica, and we think that it is similar to what goes on in nature," he said. Smith mentions as an example diatoms, a kind of microscopic plankton, and their delicate glass-like shells formed from silica. "These microorganisms have mechanisms that allow them to take sand and remodel it into their shells," he says. "And they clearly do it at relatively low temperature using organic molecules. What we have discovered is potentially some of the chemistry behind that process."

Immature sperm cells isolated from the caput epididymis harbor the most complex proteome of the three samples analyzed, comprising 6,045 proteins, including 1,284 (93.9%) of all proteins previously identified in independent studies of mouse caput epididymal sperm cells, thereby extending the proteomic annotation of this sperm population by an additional 4,761 proteins. However, scientists now identified a dramatic reduction in the number of proteins comprising the mature sperm proteome, with as many as 3,385 proteins apparently being lost from spermatozoa during their epididymal transit. Notably, while this proportional loss of 56% of the sperm proteome may appear at odds with accepted paradigms of sperm maturation, it nevertheless draws striking parallels to the 42% loss of the sperm proteome reported in the only other large-scale proteomic investigation of mouse epididymal spermatozoa by Skerget et al., 2015. Notwithstanding a substantial loss of proteins coincident with epididymal transit, cauda epididymal sperm cells retained a core proteome of 2,660 proteins in common with their caput epididymal sperm counterparts. This core proteome was supplemented by the addition of 264 proteins uniquely detected in NC cauda epididymal spermatozoa, a proportional gain of 9% of the total proteome. The proteome (2,924 proteins) of mature cauda epididymal sperm included 87.9% of the proteins (i.e., 1,062 proteins) identified by Skerget et al., 2015 in their proteomic study of mouse spermatozoa as well as an additional 1,999 proteins that were not previously characterized.

DNA methylation undergoes dramatic age-related changes, first described more than four decades ago. Loss of DNA methylation within partially methylated domains (PMDs), late-replicating regions of the genome attached to the nuclear lamina, advances with age in normal tissues, and is further exacerbated in cancer.

Researchers now show experimental evidence that this DNA hypomethylation is directly driven by proliferation-associated DNA replication. Within PMDs, loss of DNA methylation at low-density CpGs in A:T-rich immediate context (PMD solo-WCGWs) tracks cumulative population doublings in primary cell culture. Cell cycle deceleration results in a proportional decrease in the rate of DNA hypomethylation. Blocking DNA replication via Mitomycin C treatment halts methylation loss. Loss of methylation continues unabated after TERT immortalization until finally reaching a severely hypomethylated equilibrium. Ambient oxygen culture conditions increases the rate of methylation loss compared to low-oxygen conditions, suggesting that some methylation loss may occur during unscheduled, oxidative damage repair-associated DNA synthesis. 

Also, they were able to validate a model which estimates the relative cumulative replicative histories of human cells, which they call “RepliTali” (Replication Times Accumulated in Lifetime). DNA methylation loss has been observed in aging tissues and cancers for decades. Researchers from Van Andel Institute have now provided experimental evidence that this process is directly driven by cell division, something that hasn't been directly shown before.