The scientists are not armed with parkas and shovels. Instead, they have tapped into data from GPS satellite radiometers, capturing the sunlight bouncing off the Arctic. This data dive could be the key to cracking the Arctic amplification code.

“The uneven warming in the Arctic is both a scientific curiosity and a pressing concern, leading us to question why this landscape has been changing so dramatically,” said Erika Roesler, an atmospheric and climate scientist at Sandia.

Previous studies have suggested that sea-ice albedo feedbacks are likely driving Arctic amplification. These albedo feedbacks can be broken down into two main areas. First, there’s an overall reduction in sea ice, leading to more exposure of the dark ocean. This absorbs more sunlight than snow-covered ice and raises temperatures. The second factor is the reflectivity of the remaining sea ice, or local albedo, which includes ponding water on ice due to melting.

Sandia researchers aimed to gain a better understanding of the reduction in reflectivity in the Arctic. Senior scientist Phil Dreike collaborated with the U.S. Space Force to obtain permission for Sandia to analyze previously unpublished data from the radiometers on GPS satellites.

“New observational climate datasets are unique,” Roesler said. “To qualify as a climate dataset, observations must span a multitude of years. Small-scale science projects are typically not that long in duration, making this dataset particularly valuable.”

Amy Kaczmarowski, an engineer at Sandia, conducted an analysis of the data spanning from 2014 to 2019.

“There have been numerous local measurements and theoretical discussions regarding the effects of water puddling on ice albedo,” Kaczmarowski said. “This study represents one of the first comprehensive examinations of year-to-year effects in the Arctic region. Sandia’s data analysis revealed a 20% to 35% decrease in total reflectivity over the Arctic summer. According to microwave sea-ice extent measurements collected during the same period, one-third of this loss of reflectivity is attributed to fully melted ice.”

The other two-thirds of the loss in reflectivity is likely caused by the weathering of the remaining sea ice.

“The key discovery here is just how much the weathered ice is reducing reflectivity,” Kaczmarowski added. Weathered ice refers to the remaining sea ice, which can be thinner and may contain melt ponds.

The GPS satellites are expected to continue providing data through 2040. The Sandia team hopes other researchers will consider their findings, recently published in the journal Nature Scientific Reports, and incorporate them into their models for Arctic amplification. They plan to continue mining the GPS data and are enthusiastic about collaborating with other climate researchers for further analysis.

“We will continue to use this data to investigate various regions of the Earth for climate applications,” Kaczmarowski said.

The rules of statistical physics address the uncertainty about the state of a system that arises when that system interacts with its environment. But they’ve long missed another kind, say SFI Professor David Wolpert and Jan Korbel, a postdoctoral researcher at the Complexity Science Hub in Vienna, Austria. In a new paper published in Physical Review Research, the pair of physicists argue that uncertainty in the thermodynamic parameters themselves — built into equations that govern the energetic behavior of the system — may also influence the outcome of an experiment.

“At present, almost nothing is known about the thermodynamic consequences of this type of uncertainty despite its unavoidability,” says Wolpert. In the new paper, he and Korbel consider ways to modify the equations of stochastic thermodynamics to accommodate it.

When Korbel and Wolpert met at a 2019 workshop on information and thermodynamics, they began talking about this second kind of uncertainty in the context of non-equilibrium systems.

“We wondered, what happens if you don’t know the thermodynamic parameters governing your system exactly?” recalls Korbel. “And then we started playing around.” The equations that describe thermodynamic systems often include precisely defined terms for things like temperature and chemical potentials. “But as an experimenter or an observer you don’t necessarily know these values” to very large precision, says Korbel.

Even more vexing, they realized that it’s impossible to measure parameters like temperature, pressure, or volume precisely, both because of the limitations of measurement and the fact that these quantities change quickly. They recognized that uncertainty about those parameters not only influences information about the original state of the system, but also how it evolves.

It’s almost paradoxical, Korbel says. “In thermodynamics, you’re assuming uncertainty about your state so you describe it in a probabilistic way. And if you have quantum thermodynamics, you do this with quantum uncertainty,” he says. “But on the other hand, you’re assuming that all the parameters are known with exact precision.”

Korbel says the new work has implications for a range of natural and engineered systems. If a cell needs to sense the temperature to carry out some chemical reaction, for example, then it will be limited in its precision. The uncertainty in the temperature measurement could mean that the cell does more work — and uses more energy. “The cell has to pay this extra cost for not knowing the system,” he says.

Optical tweezers offer another example. These are high-energy laser beams configured to create a kind of trap for charged particles. Physicists use the term “stiffness” to describe the particle’s tendency to resist being moved by the trap. To determine the optimal configuration for the lasers they measure the stiffness as precisely as possible. They typically do this by taking repeated measurements, assuming that the uncertainty arises from the measurement itself.

But Korbel and Wolpert offer another possibility — that the uncertainty arises from the fact that the stiffness itself may be changing as the system evolves. If that’s the case, then repeated identical measurements won’t capture it, and finding the optimal configuration will remain elusive. “If you keep doing the same protocol, then the particle doesn’t end up in the same point, you may have to do a little push,” which means extra work that’s not described by the conventional equations.

This uncertainty could play out at all scales, Korbel says. What’s often interpreted as uncertainty in measurement may be uncertainty in the parameters in disguise. Maybe an experiment was done near a window where the sun was shining, and then repeated when it was cloudy. Or perhaps the air conditioner kicked on between multiple trials. In many situations, he says, “it’s relevant to look at this other type of uncertainty.”

The risks of cardiac conditions associated with the use of antipsychotics have been a concern for the last 30 years. Drugs have previously been either removed from the market or had their use restricted due to an unacceptably high risk of lethal ventricular arrhythmias. Drug-induced cardiac arrhythmias, however, remain an important clinical issue because there are drugs that increase the risk of SCD, but remain on the market because they serve an important clinical need and there are no safer alternatives.

Professor Jamie Vandenberg, PhD, MBBS, FHRS, of the Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia, co-author of the editorial accompanying the study, explains, “Of the 41 drugs on the market in the United States that are listed as having known risk of heart rhythm disorders, five are antipsychotic drugs, the mainstay of treatment for schizophrenia and psychosis. The use of antipsychotic drugs is associated with an approximately two-fold increased risk of sudden cardiac death. If we cannot eliminate this risk, then at the least, we need to minimize the risk by identifying those patients who are at highest risk and managing them more closely.”

Lead investigator of the study Shang-Hung Chang, MD, PhD, of the Cardiovascular Division, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan, adds, “The use of the antipsychotics quetiapine and haloperidol to treat mental disorders is widespread. In an effort to enhance patient safety and optimize the management of individuals receiving these medications, we have investigated the incidences, risk factors, and clinical outcomes of severe QT prolongation to provide valuable insights for healthcare professionals, patients, and caregivers.”

The research involved a retrospective analysis of electronic medical records of a large cohort of patients from a healthcare provider in Taiwan who received quetiapine or haloperidol therapy. Investigators evaluated the incidences, risk factors, and clinical correlates of severe QT prolongation (i.e., ventricular arrhythmias and sudden cardiac death) in these patients. The most significant results of the study were that more than 10% of patients developed severe QT prolongation during follow-up and the increased risk of ventricular arrhythmias and sudden cardiac death in quetiapine or haloperidol users who developed severe QT prolongation.

Co-author Chun-Li Wang, MD, of the Cardiovascular Division, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan, says the findings underscore the importance of closely monitoring patients receiving these medications and implementing appropriate risk mitigation strategies to ensure patient safety. “Clinicians should be aware of the potential risks associated with quetiapine use, particularly the risk of severe QT prolongation and its associated outcomes, including ventricular arrhythmias and sudden cardiac death.”

Professor Vandenberg comments, “It would be prudent to undertake an ECG before and after commencement of an antipsychotic drug. If it is an option, one could stop a drug causing QT prolongation and try a different antipsychotic. But if this is not practical, one should pay particular attention to reducing other risk factors, such as prescription of other drugs that may exacerbate QT prolongation and be vigilant for hypokalemia.”

The study found that, due in part to the challenges of extracting groundwater, an aquifer’s depletion can curb crop yields even when it appears saturated enough to continue meeting the demands of irrigation. Those agricultural losses escalate as an aquifer dwindles, the researchers reported, so that its depletion exerts a greater toll on corn and soybean yields when waning from, say, 100 feet thick to 50 than from 200 feet to 150.

That reality should encourage policymakers, resource managers and growers to reconsider the volume of crop-quenching groundwater they have at their disposal, the team said, especially in the face of fiercer, more frequent drought.

“As you draw down an aquifer to the point that it’s quite thin, very small changes in the aquifer thickness will then have progressively larger and larger impacts on your crop production and resilience,” said Nick Brozović, director of policy at the Daugherty Water for Food Global Institute. “And that’s a thing that we don’t predict well, because we tend to predict based on the past. So if we base what’s going to happen on our past experience, we’re always going to underpredict. We’re always going to be surprised by how bad things get.”

The team came to its conclusions after analyzing yields, weather and groundwater data from the High Plains Aquifer, which, as the largest in the United States, underlies portions of eight states — including nearly all of Nebraska. Some areas of the aquifer, especially those beneath Texas and Kansas but also the Cornhusker State, have diminished considerably over the past several decades, pumped for the sake of irrigating land that would otherwise stand little chance of sustaining crops.

“In terms of things that let you address food security under extreme conditions — in particular, drought and climate change — we really can’t do without irrigation,” said Brozović, professor of agricultural economics at the University of Nebraska-Lincoln. “If we want to feed the world with high-quality, nutritious food and a stable food supply, we need to irrigate.”

Brozović and Husker colleague Taro Mieno had already constructed plenty of models, and run plenty of simulations, on how the High Plains Aquifer responds to drought and dry conditions. But talking with farmers revealed that the models were not addressing their primary concern: well yield, or the amount of groundwater that growers can expect to continuously draw when trying to buffer their crops against drought.

“Everybody’s interested in how aquifer depletion affects the resiliency of irrigated agriculture in the region,” said Mieno, an associate professor of agricultural economics and lead author of the study, which was published in the journal Nature Water.

So the researchers consulted annual estimates of the High Plains Aquifer’s thickness, which date back to 1935, along with county-level yields of corn and soybean from 1985 through 2016. Meteorological data, meanwhile, allowed the team to calculate seasonal water deficits, or the difference between the water gained from precipitation and the amount that crops lost via evaporation and transpiration.

When the latter exceeds the former, farmers often turn to aquifers for help in making up the difference, the researchers knew. What they didn’t know: Under what conditions, and to what extent, would an aquifer’s depletion make pumping its water too difficult or expensive to undertake? And how much would the resulting decisions — to reduce the amount of irrigation per acre, to cease irrigating certain plots all together — influence corn and soybean yields?

Farmers fortunate enough to be growing corn and soybean above the most saturated swaths of the High Plains Aquifer — roughly 220 to 700 feet thick — continued to enjoy high irrigated yields even in times of extreme water deficits, the team found. By contrast, those depending on the least saturated areas — between 30 and 100 feet — saw their irrigated yields begin trending downward when water deficits reached just 400 millimeters, a common occurrence in Nebraska and other Midwestern states.

In years when the deficit approached or exceeded 700 millimeters, irrigated fields residing above the thickest groundwater yielded markedly more corn than those sitting above the thinnest. The results were starker during a 950-millimeter water deficit, which corresponds with extreme drought: Fields atop the least saturated stretches of aquifer yielded roughly 19.5 fewer bushels per acre.

“Because of the way that aquifers work, even if there’s a lot of water there, as they deplete, you actually lose the ability to meet those crop water needs during the driest periods, because well yield tends to decline as you deplete an aquifer,” Brozović said. “That has an economic consequence and a resilience consequence.”

The study captured another telling link between the water residing underground and that applied at the surface. When atop groundwater roughly 330 feet thick, farmers irrigated 89% of their acres dedicated to growing corn. Where the aquifer was a mere 30 feet thick? Just 70% of those acres received irrigation. That’s likely a result of lower well yield driving farmers to irrigate only some of their fields, Taro said, or even give up on irrigation.

To better understand how that reduced irrigation was contributing to agricultural losses amid dry conditions, the researchers then factored in yields from both irrigated and non-irrigated fields, the latter of which rely on precipitation alone. That analysis pegged yields as even more sensitive to even smaller water deficits, suggesting that the decline in irrigated land was compounding the losses endured on still-irrigated plots.

And it illustrated the runaway threat posed when an aquifer’s average thickness drops below certain thresholds. At a water deficit of 950 millimeters, reducing an aquifer’s thickness from roughly 330 to 230 feet was estimated to initiate an average loss of about 2.5 corn bushels per acre, what the authors called a “negligible difference.” The same absolute decrease, but from 230 to 130 feet, led to an estimated loss of 15 bushels per acre.

“As a consequence, your resilience to climate decreases rapidly,” Mieno said. “So when you’re operating on an aquifer that is very thick right now, you’re relatively safe. But you want to manage it in a way that you don’t go past that threshold, because from there, it’s all downhill.

“And the importance of aquifers is going to increase as climate change progresses in the future, for sure. As it gets hotter, you typically need more water. That means you need more irrigation, and you’re going to deplete the aquifer even faster, and things can get worse and worse.”

Nebraska is lucky, Brozović said, in that it sits above such a massive reservoir and has established a governance system designed to conserve it at a local scale. But most regulations focus on mandating how much and when groundwater gets pumped, not safeguarding the aquifer’s saturation level or the corresponding ability to extract water from it.

Brozović conceded that convincing policymakers to consider revising those parameters now, when much of the state still boasts sufficient groundwater, is “perhaps a tough sell.” He’s hopeful that the new study can at least help put that conversation on the table.

“Once you have a problem — once well yields are already declining and the aquifer’s really thin — even if you put in policies, you still get a lot of the (negative) impacts,” he said. “So the time to really put in meaningful policies is before things have gone off the cliff.

“First, you have to understand, you have to measure, you have to educate. You have to understand what you’re preserving, and why. The more you can provide the quantitative evidence for why it’s worth going to the trouble of doing all of this, and what’s at stake,” he said, “the easier that conversation is.”

Brozović and Mieno authored the Nature Water study with the University of Manchester’s Timothy Foster and the University of Minnesota’s Shunkei Kakimoto. The researchers received support in part from the U.S. Department of Agriculture.

Predicting how molecules will react is vital for the discovery and manufacture of new pharmaceuticals, but historically this has been a trial-and-error process, and the reactions often fail. To predict how molecules will react, chemists usually simulate electrons and atoms in simplified models, a process which is computationally expensive and often inaccurate.

Now, researchers from the University of Cambridge have developed a data-driven approach, inspired by genomics, where automated experiments are combined with machine learning to understand chemical reactivity, greatly speeding up the process. They’ve called their approach, which was validated on a dataset of more than 39,000 pharmaceutically relevant reactions, the chemical ‘reactome’.

Their results, reported in the journal Nature Chemistry, are the product of a collaboration between Cambridge and Pfizer.

“The reactome could change the way we think about organic chemistry,” said Dr Emma King-Smith from Cambridge’s Cavendish Laboratory, the paper’s first author. “A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster. But more fundamentally, the understanding we hope to generate will be beneficial to anyone who works with molecules.”

The reactome approach picks out relevant correlations between reactants, reagents, and performance of the reaction from the data, and points out gaps in the data itself. The data is generated from very fast, or high throughput, automated experiments.

“High throughput chemistry has been a game-changer, but we believed there was a way to uncover a deeper understanding of chemical reactions than what can be observed from the initial results of a high throughput experiment,” said King-Smith.

“Our approach uncovers the hidden relationships between reaction components and outcomes,” said Dr Alpha Lee, who led the research. “The dataset we trained the model on is massive — it will help bring the process of chemical discovery from trial-and-error to the age of big data.”

In a related paper, published in Nature Communications, the team developed a machine learning approach that enables chemists to introduce precise transformations to pre-specified regions of a molecule, enabling faster drug design.

The approach allows chemists to tweak complex molecules — like a last-minute design change — without having to make them from scratch. Making a molecule in the lab is typically a multi-step process, like building a house. If chemists want to vary the core of a molecule, the conventional way is to rebuild the molecule, like knocking the house down and rebuilding from scratch. However, core variations are important to medicine design.

A class of reactions, known as late-stage functionalisation reactions, attempts to directly introduce chemical transformations to the core, avoiding the need to start from scratch. However, it is challenging to make late-stage functionalisation selective and controlled — there are typically many regions of the molecules that can react, and it is difficult to predict the outcome.

“Late-stage functionalisations can yield unpredictable results and current methods of modelling, including our own expert intuition, isn’t perfect,” said King-Smith. “A more predictive model would give us the opportunity for better screening.”

The researchers developed a machine learning model that predicts where a molecule would react, and how the site of reaction vary as a function of different reaction conditions. This enables chemists to find ways to precisely tweak the core of a molecule.

“We pretrained the model on a large body of spectroscopic data — effectively teaching the model general chemistry — before fine-tuning it to predict these intricate transformations,” said King-Smith. This approach allowed the team to overcome the limitation of low data: there are relatively few late-stage functionalisation reactions reported in the scientific literature. The team experimentally validated the model on a diverse set of drug-like molecules and was able to accurately predict the sites of reactivity under different conditions.

“The application of machine learning to chemistry is often throttled by the problem that the amount of data is small compared to the vastness of chemical space,” said Lee. “Our approach — designing models that learn from large datasets that are similar but not the same as the problem we are trying to solve — resolve this fundamental low-data challenge and could unlock advances beyond late stage functionalisation.”

The research was supported in part by Pfizer and the Royal Society.

Many important reactions related to climate and environmental processes take place where water molecules interface with air. For example, the evaporation of ocean water plays an important role in atmospheric chemistry and climate science. Understanding these reactions is crucial to efforts to mitigate the human effect on our planet.

The distribution of ions at the interface of air and water can affect atmospheric processes. However, a precise understanding of the microscopic reactions at these important interfaces has so far been intensely debated.

In a paper published today in the journal Nature Chemistry, researchers from the University of Cambridge and the Max Planck Institute for Polymer Research in Germany show that ions and water molecules at the surface of most salt-water solutions, known as electrolyte solutions, are organised in a completely different way than traditionally understood. This could lead to better atmospheric chemistry models and other applications.

Technique

The researchers set out to study how water molecules are affected by the distribution of ions at the exact point where air and water meet. Traditionally, this has been done with a technique called vibrational sum-frequency generation (VSFG). With this laser radiation technique, it is possible to measure molecular vibrations directly at these key interfaces. However, although the strength of the signals can be measured, the technique does not measure whether the signals are positive or negative, which has made it difficult to interpret findings in the past. Additionally, using experimental data alone can give ambiguous results.

The team overcame these challenges by utilising a more sophisticated form of VSFG, called heterodyne-detected (HD)-VSFG, to study different electrolyte solutions. They then developed advanced computer models to simulate the interfaces in different scenarios.

The combined results showed that both positively charged ions, called cations, and negatively charged ions, called anions, are depleted from the water/air interface. The cations and anions of simple electrolytes orient water molecules in both up- and down-orientation. This is a reversal of textbook models, which teach that ions form an electrical double layer and orient water molecules in only one direction.

Co-first author Dr Yair Litman, from the Yusuf Hamied Department of Chemistry, said: “Our work demonstrates that the surface of simple electrolyte solutions has a different ion distribution than previously thought and that the ion-enriched subsurface determines how the interface is organised: at the very top there are a few layers of pure water, then an ion-rich layer, then finally the bulk salt solution.”

Co-first author Dr Kuo-Yang Chiang of the Max Planck Institute said: “This paper shows that combining high-level HD-VSFG with simulations is an invaluable tool that will contribute to the molecular-level understanding of liquid interfaces.”

Professor Mischa Bonn, who heads the Molecular Spectroscopy department of the Max Planck Institute, added: “These types of interfaces occur everywhere on the planet, so studying them not only helps our fundamental understanding but can also lead to better devices and technologies. We are applying these same methods to study solid/liquid interfaces, which could have potential applications in batteries and energy storage.”