The Environmental Protection Agency estimates that the transportation sector accounts for approximately 27% of all greenhouse gas emissions annually in the United States, and emissions from road transportation — driven by carbon-creating internal combustion vehicles — account for a large majority of that.

For years, researchers have tried to measure these emissions more closely, but existing inventories are often outdated, incomplete, and limited. 

Now, scientists at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have leveraged artificial intelligence and machine learning (ML) to produce accurate estimates for road transportation emissions of the top 500 emitting cities worldwide. 

“In particular, we were able to estimate the average annual daily traffic on individual road segments in urban areas and combined this with localized estimates of vehicle emissions factors to produce a total emissions estimate,” explained Marisa Hughes, the assistant program manager for Environmental Resilience in APL’s Research and Exploratory Development Mission Area.

“The ability to calculate emissions per road segment provides an unprecedented level of detail and global coverage,” said Hughes, who helps manage the Laboratory’s climate change efforts. “The combination of ML-predicted road activity along with region-specific emissions factors data creates automated, accurate, global, timely and actionable road transportation greenhouse gas emissions estimates.”

Actionable is the keyword, Hughes said. “You can’t change what you can’t measure, and that’s a big challenge when it comes to tackling climate change. We need to know where the greenhouse gas emissions are coming from, but that means accounting for a vast number of minuscule contributions across time and geography.”

MAKING THE INVISIBLE VISIBLE

There are an estimated 1.4 billion motor vehicles in the world. An APL team of experts in remote sensing and computer vision thought they could measure the greenhouse gases from these vehicles by pulling signatures of emissions from visual satellite data, essentially making the invisible visible. They set about looking at existing emissions data, correlating it with those images, and teasing out anything they could with neural networks.

Then the team realized there was another way to address the challenge, Hughes said. APL had experience in mapping patterns of life for the Intelligence Community on a project called Functional Map of the World, where experts mapped out land use and structures, and used satellite data from different times of day to understand and differentiate between similar-looking structures, like office buildings versus apartment complexes.

“We thought, what if we took this same ‘pattern of life’ analysis and, instead of focusing on buildings, we looked at networks of roads that were connecting them,” recalled Derek Rollend, a senior ML researcher at APL. “We built a deep neural network that would take inputs that were available globally: satellite imagery, road network data, and population.”

Because some areas had fine-grained emissions and vehicle count data, “we were able to train our machine learning models with this ground-truth data on vehicle activity there, and then apply the model predictions first at the country level, and most recently for the top 500 cities globally,” he continued.

This work contributes a major piece to a larger effort to monitor greenhouse gas emissions on a global scale, conducted by Climate TRACE [Tracking Real-time Atmospheric Carbon Emissions], an international coalition created to make meaningful climate action faster and easier by independently tracking greenhouse gas emissions with unprecedented detail and speed. The coalition — which includes APL — counts former Vice President Al Gore among its chief funders and supporters, and Time magazine honored the effort as one of the top 100 inventions of 2020.

This work represents how APL is bringing its unique expertise to make sustainable contributions to the global challenges of climate change.

“It is a great example of how our unique skills and capabilities across machine learning, satellite imaging, and systems modeling can be applied to understand and one day solve complex problems,” said Bobby Armiger, who along with Hughes co-leads the Labwide focus on climate change research.

On Nov. 9, Climate TRACE released the most detailed inventory of greenhouse gas emissions ever compiled, providing asset-level emissions data for 81,087 individual sources worldwide — and APL’s transportation data was a part of that release. In addition to releasing facility-level information, Climate TRACE updated its independent inventory of emissions from every country to include data from 2021, providing a full view of annual greenhouse gas emissions since 2015 — the year of the Paris Agreement, an international treaty on climate change.

BRINGING RADICAL TRANSPARENCY TO GLOBAL EMISSIONS

Members of the Climate TRACE coalition have rallied around a goal they call radical transparency.

“It’s about knowing where all the greenhouse gases are, where the emissions are coming from, and, at the same time, being very open about how we are calculating those emissions,” Hughes said. “That’s a really big challenge. But if you’ve got that radical transparency, then you can dive in and start to do some analysis and some comparisons to figure out what’s working and what’s not in terms of mitigating emissions.”

One of the coalition’s goals for the coming year is to understand how to bring differently sourced, overlapping data sets and emissions inventories together to create a new best estimate of what’s going on.

“That dream of radical transparency and knowing where all of the emissions are coming from in real-time with every new satellite image is still a ways ahead of us,” Hughes said. “But now it feels within reach.”

A supernova is a stellar explosion, which occurs when the lives of some really massive stars come to an end. In this violent epilogue, the star expels the material from its outer layers by means of a shock wave, allowing us to see the various elements it was composed of.

An international team with the participation of Tom Broadhurst, Ikerbasque Research Professor of the University of the Basque Country (UPV/EHU) and Associate of the Donostia International Physics Center (DIPC), has obtained three images, each in a different color, of the same supernova in the distant universe, thanks to the magnifying power of a galaxy located in the foreground.

The research team developed a model of the gravitational field of the galaxy that acted as a lens, and that way it was possible to determine that the light from these three images traveled along three different paths, differing in distance by a few days. This accounts for the three colors obtained in the images because a variation in the color emitted takes place as the gas in the supernova expands and cools. The higher the temperature is, the bluer the light emitted will be, and as the temperature falls, the light emitted tends toward red. So the blue image is a photograph of the supernova a few hours after the stellar explosion, while the green and red images correspond to 2 and 8 days, respectively, after the explosion.

This information enabled the radius of the star that exploded to be determined; it was a red super-giant with a radius equal to 500 times that of the Sun, and exploded 11.5 billion years ago, long before the Earth was born, specifically at the moment when our Galaxy is thought to have formed. The images of this supernova captured by the Hubble Space Telescope are highly magnified by the gravitational field of a nearby galaxy that acts as a lens and allows us to see much further in distance and in time than all local supernovae in nearby galaxies.

The study of the explosions of these red super-giant stars tallies with the current understanding of how the heavier atomic elements were created inside stars and during supernova explosions: elements forged inside stars are released in these supernova explosions to become the next generation of gas and material from which solar systems and life as we know it are created. Without these explosions, the gas in today's galaxies would only include the hydrogen and helium that formed during the Big Bang and would not support complex life that requires other heavier chemical elements. Moreover, this supernova observed through a gravitational lens demonstrates that an event taking place in the distant Universe can be witnessed several times, so in principle, we could focus our instruments in advance to get a detailed view of the eruption of a star turning into a supernova.

Institute for Systems Biology (ISB) researchers show which blood metabolites are associated with the gut microbiome, genetics, or the interplay between both – findings have promising implications for guiding targeted therapies

The nearly 200-year-old phrase “you are what you eat” has some new evidence. ISB researchers have found that the gut microbiome, including what we feed it, is largely responsible for the variation in circulating blood metabolites across people. This knowledge will help guide targeted interventions designed to alter the composition of the human blood metabolome. The findings will be published in Nature Metabolism on Thursday, November 10 at 8 a.m. PT.

“We know that person-to-person variation in the blood metabolome – the small molecules found in the bloodstream that can interact with all the systems of our body – can tell us a lot about health and disease status. Figuring out what governs this variation is a necessary step that gets us closer to precision approaches to healthcare,” said Dr. Sean Gibbons, an ISB faculty member and co-corresponding author of the paper.

The research team examined 930 blood metabolites that were present in more than 1,500 individuals. Over 60 percent of the detected metabolites were significantly associated with either host genetics or the gut microbiome. “Notably, 69 percent of these associations were driven solely by the microbiome, with 15 percent driven solely by genetics and 16 percent were under hybrid genetic-microbiome control,” said ISB Senior Research Scientist Dr. Christian Diener, lead author of the study. Diener and co-lead author Chengzhen Dai analyzed the de-identified metabolomic, genomic, and microbiome data from consenting patients in a consumer scientific wellness program. 

They found that the blood metabolite variation explained by the microbiome was largely independent of the variation explained by the genome, even for hybrid metabolites that were significantly associated with both genetics and microbes. Additionally, certain metabolite-microbe associations were only significant in individuals with specific genetic backgrounds, indicating a nuanced interplay between the microbiome and host genetics in shaping the blood metabolome.

These new findings are promising for a couple of reasons. First, the high number of microbiome-specific metabolites suggests that much of our blood metabolome could be modified through dietary, probiotic, and other lifestyle interventions. Second, metabolites that are under stricter genetic control may not be responsive to lifestyle modification, making them targets for pharmacological interventions that directly target host pathways. 

“A deeper understanding of the determinants of the blood metabolome will provide us with a window into how these circulating metabolite levels can be engineered and optimized for health,” said Dr. Andrew Magis, co-corresponding author of the paper. “Understanding which circulating small molecules fall predominantly under host versus microbiome control will help guide interventions designed to prevent and/or treat a range of diseases.”

We breathe to survive. But a breath of fresh air does more than fill our lungs. New research from Aarhus University the second largest and second oldest university in Denmark, indicates that breathing impacts our emotions, attention and how we can process the outside world.

“Breathe in… Breathe out…” Or: “take a deep breath and count to ten.” The calming effect of breathing in stressful situations, is a concept most of us have met before. Now Professor Micah Allen from the Department of Clinical Medicine at Aarhus University has come to a step closer to understanding how the very act of breathing shapes our brain.

The researchers synthesized results from more than a dozen studies with rodent, monkey, and human brain imaging, and used it to propose a new computational model that explains how our breathing influences the brain’s expectations.

“What we found is that, across many different types of tasks and animals, brain rhythms are closely tied to the rhythm of our breath. We are more sensitive to the outside world when we are breathing in, whereas the brain tunes out more when we breathe out. This also aligns with how some extreme sports use breathing, for example, professional marksmen are trained to pull the trigger at the end of exhalation,” explains Professor Micah Allen.

The study suggests that breathing is more than just something we do to stay alive, explains Micah Allen.

“It suggests that the brain and breathing are closely intertwined in a way that goes far beyond survival, to actually impact our emotions, our attention, and how we process the outside world. Our model suggests there is a common mechanism in the brain which links the rhythm of breathing to these events.”

Breathing can affect our mental health

Understanding how breathing shapes our brain, and by extension, our mood, thoughts, and behaviors,  is an important goal in order to better prevent and treat mental illness.

“Difficulty breathing is associated with a very large increase in the risk for mood disorders such as anxiety and depression. We know that respiration, respiratory illness, and psychiatric disorders are closely linked. Our study raises the possibility that the next treatments for these disorders might be found in the development of new ways to realign the rhythms of the brain and body, rather than treating either in isolation,” explains Micah Allen.

Stabilizing our mind through breathing is a well-known and used tactic in many traditions such as yoga and meditation. The new study sheds light on how the brain makes it possible. It suggests that there are three pathways in the brain that control this interaction between breathing and brain activity. It also suggests that our pattern of breathing makes the brain more “excitable”, meaning neurons are more likely to fire during certain times of breathing

New research to come

The new study gives researchers a new target for future studies for example persons with respiratory or mood disorders, and Micah Allen and his group already have already started new projects based on the study.

“We have a variety of ongoing projects that are both building on and testing various parts of the model we have proposed. Ph.D. Student Malthe Brændholt is conducting innovative brain imaging studies in humans, to try and understand how different kinds of emotional and visual perception are influenced by breathing in the brain,” says Micah Allen.

The team is also collaborating with the Pulmonology team at Aarhus University Hospital, where tools developed in the lab are used to understand whether a person suffering from long-covid may have disruptions in the breath-brain alignment. And there are more projects coming, says Micah Allen.

”We will be using a combination of human and animal neuroimaging to better understand how breathing influences the brain, and also utilizing exploring how different drugs influence respiratory-brain interaction. We would also like to someday study how lifestyle factors like stress, sleep, and even things like winter swimming influence breath-brain interaction. We are very excited to continue this research,” says Micah Allen.