Tyler O'Neal, Staff Editor CHEMISTRY

Bristol scientists demo new tool to tackle climate change at COP26

Leading atmospheric scientists are measuring emissions of the most dangerous greenhouse gases at COP26 and sharing them live online to highlight how rigorous measurement and detailed data reporting are essential in the fight against climate change. Measurements of carbon dioxide and methane are being made at COP26 from the Glasgow Science Centre Tower, as pictured, in central Glasgow. National Physical Laboratory

The initiative, by the University of Bristol, UK, and an international team of scientists deploy sophisticated instrumentation to measure the amount of carbon dioxide (CO2) and methane (CH4) – the main culprits in driving global warming – present in the air in Glasgow, where this year’s United Nations Climate Change Conference is being hosted.

The same technology is used in a wider Bristol-led project in partnership with the Met Office to gather this data from across the UK, called the Deriving Emissions linked to Climate Change (DECC) network. This globally unique system is used to check the accuracy of the UK government’s greenhouse gas emissions report to the United Nations.  

Experts from the team are involved in events and presentations at the 13-day summit to demonstrate how timely measurements present a vital but little-used tool to accurately monitor and effectively tackle climate change.

Prof. Matt Rigby, Professor of Atmospheric Chemistry at the university’s Cabot Institute for the Environment, said: “The UK is world-leading in its evaluation of emissions of greenhouse gases, including the two which pose the biggest threat to our planet. The aim of this work is to demonstrate the huge potential of this technology, and urge other countries to follow, especially the industrial powerhouses of China, the US, and India.”

A feed of the Glasgow data in real-time is available on an online dashboard and the statistics are being carefully analyzed by the team to identify possible trends and issues.

Professor Rigby said: “Advanced measurement and tightly-scrutinized evaluation of emissions is imperative to detect any irregularities which may warrant further investigation and to enable countries, including the UK, to meet crucial targets to curb emissions and limit global warming. This approach can help ensure accurate, consistent, and reliable emissions reporting to build and evidence progress.”

Government reporting of Greenhouse Gases (GHGs) is based on “bottom-up” or inventory accountancy methods, which calculate the national total contribution to climate change by estimating emissions from individual gas leaks, cars, cows, etc., and adding up the total number of each contributor. Although comprehensive, they can be subject to substantial inaccuracies. These reports do not generally consider arguably key information: measurements that can indicate the actual amount of greenhouse gas in the atmosphere.

By contrast, the team measures real-time levels of GHGs in the atmosphere across the UK and uses supercomputer models based on meteorological data to produce “top-down” emissions estimates. The UK was the first country to use this measurement-based approach to evaluate its GHG emission reports to the UN and is currently one of only three countries, including Switzerland and Australia, to do so.

Professor Alistair Manning, an atmospheric modeling expert at the Met Office, added: “As the UK government routinely cross-check with their figures against our top-down emissions estimates, the credibility of the UK reporting is greatly enhanced. By raising awareness of these methods at COP26 we hope that other countries will be encouraged to follow the UK’s lead, in turn improving the accuracy of the global emissions reporting system, which needs to become more consistent and robust.”

Hospital for Special Surgery sponsors the DREAM Challenge to develop better ML for assessment of damage in arthritis

Tyler O'Neal, Staff Editor CHEMISTRY

Crowdsourcing has become an increasingly popular way to develop machine learning algorithms to address many clinical problems in a variety of illnesses. Today at the American College of Rheumatology (ACR) annual meeting, a multicenter team led by an investigator from Hospital for Special Surgery (HSS) presented the results from the RA2-DREAM Challenge, a crowdsourced effort focused on developing better methods to quantify joint damage in people with rheumatoid arthritis (RA).

Damage in the joints of people with RA is currently measured by visual inspection and detailed scoring on radiographic images of small joints in the hands, wrists, and feet. This includes both joint space narrowing (which indicates cartilage loss) and bone erosions (which indicates damage from an invasion of the inflamed joint lining). The scoring system requires specially trained experts and is time-consuming and expensive. Finding an automated way to measure joint damage is important for both clinical research and for the care of patients, according to the study’s senior author, S. Louis Bridges, Jr., MD, Ph.D., physician-in-chief, and chair of the Department of Medicine at HSS.

“If a machine-learning approach could provide a quick, accurate quantitative score estimating the degree of joint damage in hands and feet, it would greatly help clinical research,” he said. “For example, researchers could analyze data from electronic health records and from genetic and other research assays to find biomarkers associated with progressive damage. Having to score all the images by visual inspection ourselves would be tedious, and outsourcing it is cost-prohibitive.”

“This approach could also aid rheumatologists by quickly assessing whether there is progression of damage over time, which would prompt a change in treatment to prevent further damage,” he added. “This is really important in geographic areas where expert musculoskeletal radiologists are not available.”

For the challenge, Dr. Bridges and his collaborators partnered with Sage Bionetworks, a nonprofit organization that helps investigators create DREAM (Dialogue on Reverse Engineering Assessment and Methods) Challenges. These competitions are focused on the development of innovative artificial intelligence-based tools in the life sciences. The investigators sent out a call for submissions, with grant money providing prizes for the winning teams. Competitors were from a variety of fields, including computer scientists, computational biologists, and physician-scientists; none were radiologists with expertise or training in reading radiographic images.

For the first part of the challenge, one set of images was provided to the teams, along with known scores that had been visually generated. These were used to train the algorithms. Additional sets of images were then provided so the competitors could test and refine the tools they had developed. The third set of images was given without scores in the final round, and competitors estimated the amount of joint space narrowing and erosions. Submissions were judged according to which most closely replicated the gold-standard visually generated scores. 26 teams submitted algorithms and 16 final submissions. In total, competitors were given 674 sets of images from 562 different RA patients, all of whom had participated in prior National Institutes of Health-funded research studies led by Dr. Bridges. In the end, four teams were named top performers.

For the DREAM Challenge organizers, it was important that any scoring system developed through the project be freely available rather than proprietary, so that it could be used by investigators and clinicians at no cost. “Part of the appeal of this collaboration was that everything is in the public domain,” Dr. Bridges said.

Dr. Bridges explained that additional research and development of computational methods are needed before the tools can be broadly used, but the current research demonstrates that this type of approach is feasible. “We still need to refine the algorithms, but we’re much closer to our goal than we were before the Challenge,” he concluded.

CCNY experts master defects in semiconductors

Tyler O'Neal, Staff Editor CHEMISTRY

Researchers at The City College of New York have discovered a novel way to manipulate defects in semiconductors. The study holds promising opportunities for novel forms of precision sensing, or the transfer of quantum information between physically separate qubits, as well as for improving the fundamental understanding of charge transport in semiconductors. Schematic representation of a mechanism of hole capture by a charged defect where a carrier gets weakly bound and eventually captured due to Coulombic attraction. Credit: Carlos Meriles

{module title="INSIDE STORY"}

Using laser optics and confocal microscopy, the researchers demonstrated that they could make one defect eject charges – holes – under laser illumination allowing the other defect several micrometers away to catch them. The charge state of the latter defect is then altered from a negative into a neutral one via a charge capture.

The study utilized a special type of point defect—nitrogen-vacancy center in diamond. These color centers possess spin—an inherent form of angular momentum carried by elementary particles—making them attractive for quantum sensing and quantum information processing. The researchers used a specific protocol to filter out the charges originating solely from the nitrogen-vacancy based on its spin projection.

“The key was isolating the source defect, with only the nitrogen-vacancy being present, which we achieved by making charge ejection conditional on the defect’s spin state,” said Artur Lozovoi, physics postdoctoral researcher in CCNY’s Division of Science and the paper’s lead author. “Another crucial aspect was having a “clean” diamond with as few defects as possible. Then, the long-range attractive Coulombic interaction between a defect and a hole substantially increases the probability of the charge going towards the target, which ultimately made our observations possible.”

The present study uncovered that in the clean material the charge transport efficiency is a thousand times higher than observed in previous experiments, a phenomenon characterized by the researchers as a “giant capture cross-section”. This discovery could pave the way towards establishing a quantum information bus between color center qubits in semiconductors.

“This process of a charge capture by an individual defect has only been described theoretically before,” added Lozovoi. “There is now an experimental platform that enables us to look into how these defects interact with free charges in crystals and how we can use it for quantum information processing.”