The study, which was funded by Leronlimab maker CytoDyn Inc. and conducted by researchers either employed by or serving as consultants to the company, will be published online April 22 in the peer reviewed journal Clinical Infectious Diseases.

“While this was a small pilot study, it does suggest that some people with long COVID may actually have under-active immune systems after recovering from COVID-19, which means that boosting immunity in those individuals could be a treatment,” said senior author Dr. Otto Yang, a professor of medicine, division of infectious diseases, and of microbiology, immunology and molecular genetics at the David Geffen School of Medicine at UCLA.

COVID-19 is known to be caused by hyperactive immune responses against SARS-CoV-2 resulting in damage to lungs and other organs, and sometimes what is known as a “cytokine storm” that overwhelms the individual, which could lead to severe illness and death.

In a subset of persons who recover from the initial illness, various symptoms persist, such as fatigue, mental haziness, and shortness of breath, which can be debilitating and last for months. This is generally classified as long COVID, although symptoms vary widely and this syndrome is probably not a single disease entity. Limited understanding of its causes, however, makes finding ways to treat the condition particularly difficult.

Many scientists have suggested that persistence of immune hyperactivity after COVID-19 is a major contributor. Working under this theory, the researchers conducted a small exploratory trial of Leronlimab — an antibody that attaches to an immune receptor called CCR5 that is involved in inflammation — on 55 people with the syndrome.

Participants were randomly assigned to receive weekly injections of the antibody or a saline placebo for eight weeks, over which time they tracked any changes in 24 symptoms associated with long COVID, which also included loss of smell and taste, muscle and joint pain, and brain fog.

The researchers originally thought that blocking CCR5 with the antibody would dampen the activity of an overactive immune system after COVID-19 infection.

“But we found just the opposite,” Yang said. “Patients who improved were those who started with low CCR5 on their T cells, suggesting their immune system was less active than normal, and levels of CCR5 actually increased in people who improved. This leads to the new hypothesis that long COVID in some persons is related to the immune system being suppressed and not hyperactive, and that while blocking its activity, the antibody can stabilize CCR5 expression on the cell surface leading to upregulation of other immune receptors or functions.”

The findings, the researchers write, “suggests a complex role for CCR5 in balancing inflammatory and anti-inflammatory effects, e.g. through T regulatory cells,” although the results need to be confirmed in a larger, more definitive study.

Study co-authors are Norman Gaylis of Arthritis & Rheumatic Disease Specialties in Aventura, Florida; Angela Ritter of the Center for Advanced Research & Education in Gainesville, Georgia; Scott Kelly, Nader Pourhassan, and Christopher Recknor of CytoDyn Inc. in Vancouver, Washington; and Meenakshi Tiwary, Jonah Sacha, and Scott Hansen of Oregon Health & Science University.

Sacha, Hansen, and Yang are paid consultants for CytoDyn. Gaylis is on CytoDyn’s scientific board with stock options.

In a study published today they quantified regional fossil fuel CO₂ emissions reductions during the Covid-19 lockdowns of 2020-2021, using atmospheric measurements of CO₂ and oxygen (O₂) from the Weybourne Atmospheric Observatory, on the north Norfolk coast in the UK.

The estimate uses a new method for separating CO₂ signals from land plants and fossil fuels in the atmosphere. Previously it has not been possible to quantify changes in regional-scale fossil fuel CO₂ emissions with high accuracy and in near real-time.

Existing atmospheric-based methods have largely been unsuccessful at separating fossil fuel CO₂ from large natural CO₂ variability, so that estimates of changes, such as those occurring in response to the lockdowns, must rely on indirect data sources, which can take months or years to compile.

The atmospheric O₂-based method, published in the journal Science Advances, is in good agreement with three lower frequency UK emissions estimates produced during the pandemic by the Department for Business, Energy and Industrial Strategy, the Global Carbon Budget and Carbon Monitor, which used different methods and combinations of data, for example those based on energy usage.

Crucially, as well as being completely independent of the other estimates, this approach can be calculated much more quickly.

The researchers are also able to detect changes in emissions with higher frequency, such as daily estimates, and can clearly see two periods of reductions associated with two UK lockdown periods, separated by a period of emissions recovery when Covid restrictions were eased, during the summer of 2020.

Researchers at UEA — home of the UK’s only high-precision atmospheric O₂ measurement laboratory — worked with colleagues at Wageningen University in the Netherlands and the Max Planck Institute for Biogeochemistry, Germany.

The study’s lead author, Dr Penelope Pickers, of UEA’s Centre for Ocean and Atmospheric Sciences, said: “If humans are to reduce our CO₂ emissions from fossil fuels and our impact on the climate, we first need to know how much emissions are changing.

“Our study is a major achievement in atmospheric science. Several others, based solely on CO₂ data, have been unsuccessful, owing to large emissions from land plants, which obscure fossil fuel CO₂ signals in the atmosphere.

“Using atmospheric O₂ combined with CO₂ to isolate fossil fuel CO₂ in the atmosphere has enabled us to detect and quantify these important signals using a ‘top-down’ approach for the first time. Our findings indicate that a network of continuous measurement sites has strong potential for providing this evaluation of fossil fuel CO₂ at regional levels.”

Currently, fossil fuel CO₂ emissions are officially reported with a ‘bottom-up’ approach, using accounting methods that combine emission factors with energy statistics to calculate emissions.

These are then compiled into national inventories of estimated greenhouse gas (GHG) emissions to the atmosphere from anthropogenic sources and activities, such as domestic buildings, vehicles, and industrial processes.

However, inventories can be inaccurate, especially in less developed countries, which makes it more difficult to meet climate targets.

It can also take years for the inventory assessments to be completed, and at the regional scale, or on a monthly or weekly basis, the uncertainties are much larger.

An alternative method of estimating GHG emissions is to use a ‘top-down’ approach, based on atmospheric measurements and modelling.

The UK emissions inventory is already successfully informed and supported by independent top-down assessments for some key GHGs, such as methane and nitrous oxide.

But for CO₂, the most important GHG for climate change, this has never before been feasible, because of the difficulties distinguishing between CO₂ emissions from fossil fuels and land plant sources in the atmosphere.

Dr Pickers said: “The time taken for inventories to be completed makes it hard to characterise changes in emissions that happen suddenly, such as the reductions associated with the Covid pandemic lockdowns.

“We need reliable fossil fuel CO₂ emissions estimates quickly and at finer scales, so that we can monitor and inform climate change policies to prevent reaching 2°C of global warming.

“Our O₂-based approach is cost-effective and provides high frequency information, with the potential to provide fossil fuel CO₂ estimates quickly and at finer spatial scales, such as for counties, states or cities.”

The team used 10 years of high-precision, hourly measurements of atmospheric O₂ and CO₂ from Weybourne Atmospheric Observatory, which are supported by the UK’s National Centre for Atmospheric Science. Having long-term measurements of these climatically important gases was crucial to the success of the study.

To detect a Covid signal, they had to first remove the effects of atmospheric transport on their O₂ and CO₂ datasets, using a machine learning model.

They trained the machine learning model on pre-pandemic data, to estimate the fossil fuel CO₂ they would have expected to observe at Weybourne if the pandemic had never occurred.

They then compared this estimate to the fossil fuel CO₂ that was actually observed during 2020-2021, which revealed the relative reduction in CO₂ emissions.

‘Novel quantification of regional fossil fuel CO₂ reductions during COVID-19 lockdowns using atmospheric oxygen measurements’, Penelope A. Pickers et al., is published in Science Advances on Friday, April 22, 2022.

“We now have a new understanding of how the computational machinery of the brain is working,” says Albright, the Conrad T. Prebys Chair in Vision Research and director of Salk’s Vision Center Laboratory. “The model helps explain how the brain’s underlying state can change, affecting people’s attention, focus, or ability to process information.”

Researchers have long known that waves of electrical activity exist in the brain, both during sleep and wakefulness. But the underlying theories as to how the brain processes information — particularly sensory information, like the sight of a light or the sound of a bell — have revolved around information being detected by specialized brain cells and then shuttled from one neuron to the next like a relay.

This traditional model of the brain, however, couldn’t explain how a single sensory cell can react so differently to the same thing under different conditions. A cell, for instance, might become activated in response to a quick flash of light when an animal is particularly alert, but will remain inactive in response to the same light if the animal’s attention is focused on something else.

Gepshtein likens the new understanding to wave-particle duality in physics and chemistry — the idea that light and matter have properties of both particles and waves. In some situations, light behaves as if it is a particle (also known as a photon). In other situations, it behaves as if it is a wave. Particles are confined to a specific location, and waves are distributed across many locations. Both views of light are needed to explain its complex behavior.

“The traditional view of brain function describes brain activity as an interaction of neurons. Since every neuron is confined to a specific location, this view is akin to the description of light as a particle,” says Gepshtein, director of Salk’s Collaboratory for Adaptive Sensory Technologies. “We’ve found that in some situations, brain activity is better described as interaction of waves, which is similar to the description of light as a wave. Both views are needed for understanding the brain.”

Some sensory cell properties observed in the past were not easy to explain given the “particle” approach to the brain. In the new study, the team observed the activity of 139 neurons in an animal model to better understand how the cells coordinated their response to visual information. In collaboration with physicist Sergey Savel’ev of Loughborough University, they created a mathematical framework to interpret the activity of neurons and to predict new phenomena.

The best way to explain how the neurons were behaving, they discovered, was through interaction of microscopic waves of activity rather than interaction of individual neurons. Rather than a flash of light activating specialized sensory cells, the researchers showed how it creates distributed patterns: waves of activity across many neighboring cells, with alternating peaks and troughs of activation — like ocean waves.

When these waves are being simultaneously generated in different places in the brain, they inevitably crash into one another. If two peaks of activity meet, they generate an even higher activity, while if a trough of low activity meets a peak, it might cancel it out. This process is called wave interference.

“When you’re out in the world, there are many, many inputs and so all these different waves are generated,” says Albright. “The net response of the brain to the world around you has to do with how all these waves interact.”

To test their mathematical model of how neural waves occur in the brain, the team designed an accompanying visual experiment. Two people were asked to detect a thin faint line (“probe”) located on a screen and flanked by other light patterns. How well the people performed this task, the researchers found, depended on where the probe was. The ability to detect the probe was elevated at some locations and depressed at other locations, forming a spatial wave predicted by the model.

“Your ability to see this probe at every location will depend on how neural waves superimpose at that location,” says Gepshtein, who is also a member of Salk’s Center for the Neurobiology of Vision. “And we’ve now proposed how the brain mediates that.”

The discovery of how neural waves interact is much more far-reaching than explaining this optical illusion. The researchers hypothesize that the same kinds of waves are being generated — and interacting with each other — in every part of the brain’s cortex, not just the part responsible for the analysis of visual information. That means waves generated by the brain itself, by subtle cues in the environment or internal moods, can change the waves generated by sensory inputs.

This may explain how the brain’s response to something can shift from day to day, the researchers say.

Additional co-authors of the paper include Ambarish Pawar of Salk and Sunwoo Kwon of the University of California, Berkeley.

The work was supported in part by the Salk Institute’s Sloan-Swartz Center for Theoretical Neurobiology, the Kavli Institute for Brain and Mind, the Conrad T. Prebys Foundation, the National Institutes of Health (R01-EY018613, R01-EY029117) and the Engineering and Physical Sciences Research Council (EP/S032843/1).

Working to change that is a group of former and current researchers from the Rowland Institute at Harvard.

First, here’s how 3D printing works: The printers lay down flat layers of resin, which will harden into plastic after being exposed to laser light, on top of each other, again and again from the bottom to the top. Eventually, the object, such as a skull, takes shape. But if a piece of the print overhangs, like a bridge or a wing of a plane, it requires some type of flat support structure to actually print, or the resin will fall apart.

The researchers present a method to help the printers live up to their names and deliver a “true” 3D form of printing. In a new paper in Nature, they describe a technique of volumetric 3D printing that goes beyond the bottom-up, layered approach. The process eliminates the need for support structures because the resin it creates is self-supporting.

“What we were wondering is, could we actually print entire volumes without needing to do all these complicated steps?” said Daniel N. Congreve, an assistant professor at Stanford and former fellow at the Rowland Institute, where the bulk of the research took place. “Our goal was to use simply a laser moving around to truly pattern in three dimensions and not be limited by this sort of layer-by-layer nature of things.”

The key component in their novel design is turning red light into blue light by adding what’s known as an upconversion process to the resin, the light reactive liquid used in 3D printers that hardens into plastic.

In 3D printing, resin hardens in a flat and straight line along the path of the light. Here, the researchers use nano capsules to add chemicals so that it only reacts to a certain kind of light — a blue light at the focal point of the laser that’s created by the upconversion process. This beam is scanned in three dimensions, so it prints that way without needing to be layered onto something. The resulting resin has a greater viscosity than in the traditional method, so it can stand support-free once it’s printed.

“We designed the resin, we designed the system so that the red light does nothing,” Congreve said. “But that little dot of blue light triggers a chemical reaction that makes the resin harden and turn into plastic. Basically, what that means is you have this laser passing all the way through the system and only at that little blue do you get the polymerization, [only there] do you get the printing happening. We just scan that blue dot around in three dimensions and anywhere that blue dot hits it polymerizes and you get your 3D printing.”

The researchers used their printer to produce a 3D Harvard logo, Stanford logo, and a small boat, a standard yet difficult test for 3D printers because of the boat’s small size and fine details like overhanging portholes and open cabin spaces.

The researchers, who included Christopher Stokes from the Rowland Institute, plan to continue developing the system for speed and to refine it to print even finer details. The potential of volumetric 3D printing is seen as a game changer, because it will eliminate the need for complex support structures and dramatically speed up the process when it reaches its full potential. Think of the “replicator” from “Star Trek” that materializes objects all at once.

But right now, the researchers know they have quite a ways to go.

“We’re really just starting to scratch the surface of what this new technique could do,” Congreve said.

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Materials provided by Harvard University. Original written by Juan Siliezar. Note: Content may be edited for style and length.

Reconnection converts the magnetic field energy to particle eruptions in astrophysical plasmas by snapping apart and explosively reconnecting the magnetic field lines — a process that occurs within what are called dissipation regions that are often enormously smaller than the regions they impact.

Stressed magnetic field

“Plasma doesn’t like reconnection,” said Hantao Ji, a PPPL physicist and Princeton University professor who is first author of a paper that details the roadmap in Nature Reviews Physics. “However, reconnection does happen when the magnetic field is sufficiently stressed,” he said.

“Dissipation scales are tiny whereas astrophysical scales are very large and can extend for millions of miles. Finding a way to bridge these scales through a multiscale mechanism is a key to solving the reconnection puzzle.”

The roadmap outlines the role of developing technologies with multiscale capabilities such as the Facility for Laboratory Reconnection Experiment (FLARE), a recently installed collaborative facility that is being upgraded and will probe facets of magnetic reconnection never before accessible to laboratory experiments. Complementing these experiments will be simulations on coming exascale supercomputers that will be 10 times faster than current computers. “The hope is for FLARE and exascale computing to go hand-in-hand,” Ji said.

The working theory the PPPL roadmap proposes is that multiple plasmoids, or magnetic islands, that arise from reconnection along lengthy plasma current sheets could bridge the vast range of scales. Such plasmoids would correspond more closely to the affected reconnection region, with multiscale laboratory experiments planned to provide the first tests of this theory and to evaluate competing hypotheses.

“Exascale will allow us to do more credible simulations based on high-fidelity FLARE experiments,” said PPPL physicist Jongsoo Yoo, a coauthor of the paper. The increased size and power of the new machine — its diameter will be twice that of the sports-utility-vehicle-sized Magnetic Reconnection Experiment (MRX), PPPL’s long-standing laboratory experiment — and will enable scientists to replicate reconnection in nature more faithfully.

“FLARE can access wider astrophysical regimes than MRX with multiple reconnection points and measure the field geometry during reconnection,” said William Daughton, a computational scientist at Los Alamos National Laboratory and a coauthor of the paper. “Understanding this physics is important for predicting how reconnection proceeds in solar flares,” he said.

Key challenge

A key challenge to the coming experiments will be innovating new high-resolution diagnostic systems free from restrictive assumptions. Once developed these systems will enable FLARE to build upon satellite sightings such as those produced by the Magnetospheric Multiscale mission, a fleet of four spacecraft launched in 2015 to study reconnection in the magnetosphere, the magnetic field that surrounds the Earth.

“Progress in understanding multiscale physics critically depends on innovation and efficient implementation of such diagnostics systems in the coming decade,” the paper said. The new findings will address open questions that include:

• How exactly does reconnection start?

• How are explosive plasma particles heated and accelerated?

• What role does reconnection play in related processes such as turbulence and space shocks?

Overall, “The paper lays out plans to provide the entire space physics and astrophysics communities with methods to solve the multiscale problem,” Yoo said. Such a solution would mark a major step toward a more complete understanding of magnetic reconnection in large systems throughout the universe.

Support for this work comes from the DOE Office of Science. Coauthors include Jonathan Jara-Almonte of PPPL and Ari Le and Adam Stanier of Los Alamos National Laboratory.

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Materials provided by DOE/Princeton Plasma Physics Laboratory. Original written by John Greenwald. Note: Content may be edited for style and length.

This new technique can also be applied to analysis of two-dimensional materials which have the potential to play a role in advanced energy systems, electronics and quantum computers.

Today in Science, the researchers in UCI’s Department of Physics & Astronomy and Department of Chemistry describe how they positioned two bound atoms of hydrogen in between the silver tip of the STM and a sample composed of a flat copper surface arrayed with small islands of copper nitride. With pulses of the laser lasting trillionths of a second, the scientists were able to excite the hydrogen molecule and detect changes in its quantum states at cryogenic temperatures and in the ultrahigh vacuum environment of the instrument, rendering atomic-scale, time-lapsed images of the sample.

“This project represents an advance in both the measurement technique and the scientific question the approach allowed us to explore,” said co-author Wilson Ho, Bren Professor of physics & astronomy and chemistry. “A quantum microscope that relies on probing the coherent superposition of states in a two-level system is much more sensitive than existing instruments that are not based on this quantum physics principle.”

Ho said the hydrogen molecule is an example of a two-level system because its orientation shifts between two positions, up and down and slightly horizontally tilted. Through a laser pulse, the scientists can coax the system to go from a ground state to an excited state in a cyclical fashion resulting in a superposition of the two states. The duration of the cyclic oscillations is vanishingly brief — lasting mere tens of picoseconds — but by measuring this “decoherence time” and the cyclic periods the scientists were able to see how the hydrogen molecule was interacting with its environment.

“The hydrogen molecule became part of the quantum microscope in the sense that wherever the microscope scanned, the hydrogen was there in between the tip and the sample,” said Ho. “It makes for an extremely sensitive probe, allowing us to see variations down to 0.1 angstrom. At this resolution, we could see how the charge distributions change on the sample.”

The space between the STM tip and the sample is almost unimaginably small, about six angstroms or 0.6 nanometers. The STM that Ho and his team assembled is equipped to detect minute electrical current flowing in this space and produce spectroscopic readings proving the presence of the hydrogen molecule and sample elements. Ho said this experiment represents the first demonstration of a chemically sensitive spectroscopy based on terahertz-induced rectification current through a single molecule.

The ability to characterize materials at this level of detail based on hydrogen’s quantum coherence can be of great use in the science and engineering of catalysts, since their functioning often depends on surface imperfections at the scale of single atoms, according to Ho.

“As long as hydrogen can be adsorbed onto a material, in principle, you can use hydrogen as a sensor to characterize the material itself through observations of their electrostatic field distribution,” said study lead author Likun Wang, UCI graduate student in physics & astronomy.

Joining Ho and Wang on this project, which was supported by the U.S. Department of Energy Office of Basic Energy Sciences, was Yunpeng Xia, UCI graduate student in physics & astronomy.

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