Caltech prof shows how coastal ocean currents increase Antarctic ice shelf melt

A new model developed by Caltech and JPL researchers suggests that Antarctica's ice shelves may be melting at an accelerated rate, which could eventually contribute to a more rapid sea level rise. The model accounts for an often-overlooked narrow ocean current along the Antarctic coast and simulates how rapidly flowing freshwater, melted from the ice shelves, can trap dense warm ocean water at the base of the ice, causing it to warm and melt even more. 

The study was conducted in the laboratory of Andy Thompson, professor of environmental science and engineering, and appears in the journal Science Advances on August 12.

Ice shelves are outcroppings of the Antarctic ice sheet, found where the ice juts out from land and floats on top of the ocean. The shelves, which are each several hundred meters thick, act as a protective buffer for the mainland ice, keeping the whole ice sheet from flowing into the ocean (which would dramatically raise global sea levels). However, a warming atmosphere and warming oceans caused by climate change are increasing the speed at which these ice shelves are melting, threatening their ability to hold back the flow of the ice sheet into the ocean.  

"If this mechanism that we've been studying is active in the real world, it may mean that ice shelf melt rates are 20 to 40 percent higher than the predictions in global climate models, which typically cannot simulate these strong currents near the Antarctic coast," Thompson says.

In this study, led by senior research scientist Mar Flexas, the researchers focused on one area of Antarctica: the West Antarctic Peninsula (WAP). Antarctica is roughly shaped like a disk, except where the WAP protrudes out of the high polar latitudes and into lower, warmer latitudes. It is here that Antarctica sees the most dramatic changes due to climate change. The team has previously deployed autonomous vehicles in this region, and scientists have used data from instrumented elephant seals to measure temperature and salinity in the water and ice. 

The team's model takes into account the narrow Antarctic Coastal Current that runs counterclockwise around the entire Antarctic continent, a current which many climate models do not include because it is so small. 

"Large global climate models don't include this coastal current, because it's very narrow—only about 20 kilometers wide, while most climate models only capture currents that are 100 kilometers across or larger," Flexas explains. "So, there is a potential for those models to not represent future melt rates very accurately."

The model illustrates how freshwater that melts from ice at the WAP is carried by the coastal current and transported around the continent. The less-dense freshwater moves along quickly near the surface of the ocean and traps relatively warm ocean saltwater against the underside of the ice shelves. This then causes the ice shelves to melt from below. In this way, increased meltwater at the WAP can propagate climate warming via the Coastal Current, which in turn can also escalate melting even at West Antarctic ice shelves thousands of kilometers away from the peninsula. This remote warming mechanism may be part of the reason that the loss of volume from West Antarctic ice shelves has accelerated in recent decades.

"There are aspects of the climate system that we are still discovering," Thompson says. "As we've made progress in our ability to model interactions between the ocean, ice shelves, and atmosphere, we're able to make more accurate predictions with better constraints on uncertainty. We may need to revisit some of the predictions of sea level rise in the next decades or century—that's work that we'll do going forward."

The paper is titled "Antarctic Peninsula warming triggers enhanced basal melt rates throughout West Antarctica." In addition to Flexas and Thompson, additional coauthors are Michael Schodlok and Hong Zhang of JPL, and Kevin Speer of Florida State University. Funding was provided by the National Science Foundation, the NASA Physical Oceanography program and Cryospheric Sciences program, NASA's Internal Research and Technology Development program (Earth 2050 project), JPL, and Caltech. Caltech manages JPL for NASA.

Australian prof builds models to fight malaria in Africa

Researchers have created a mathematical model to predict genetic resistance to antimalarial drugs in Africa to manage one of the biggest threats to global malarial control.

Malaria is a life-threatening disease caused by parasites and spread to humans through infected mosquitos. It is preventable and curable, yet resistance to current antimalarial drugs is causing avoidable loss of life. The World Health Organisation estimated there were 241 million cases of malaria worldwide in 2020, with more than 600,000 deaths. Families receiving malaria bed nets. Ghana. Photo: © Arne Hoel / The World Bank

In research published today in PLOS Computational Biology, an international research team used data from the WorldWide Antimalarial Resistance Network (WWARN), a global, scientifically independent collaboration, to map the prevalence of genetic markers that indicate resistance to Plasmodium falciparum – the parasite that causes malaria.

Lead author Associate Professor Jennifer Flegg from the University of Melbourne said malaria has devastating impacts on lower-income countries and effective treatment is key to elimination.

“The antimalarial drug sulfadoxine-pyrimethamine (SP) is commonly used in various preventative malaria treatment programs in Africa, particularly for infants, young children, and during pregnancy. But we know its efficacy as a treatment is threatened in areas where resistance to SP is high,” Associate Professor Flegg said.

“The statistical mapping tool we have developed is critical for health organizations to understand the spread of antimalarial resistance. The model takes in the data that is available and fills in the gaps by making continuous predictions in space and time.

“Health agencies can use this tool to understand when and where SP is appropriate to use as part preventive malaria treatments and where other antimalarial methods may need to be explored.”

Professor Karen Barnes, Head of WWARN Pharmacology and Elimination, said there is a rapidly increasing need for malaria chemoprevention (drugs that prevent malaria infections), but there are limited treatment options available.

“This timely evidence of the extent of SP resistance across Africa will help to inform where SP preventive treatment, alone or in combination with other antimalarials, would be most likely to have the greatest impact,” Professor Barnes said.

Professor Feiko ter Kuile, Head of WWARN’s Malaria in Pregnancy Scientific Group, said the updated model of SP resistance in Africa was long overdue.

“A lot of the resistance mapping has understandably focused on the emerging resistance to the artemisinin-based antimalarials used for treating malaria. Increasing resistance of the malaria parasite to sulfadoxine-pyrimethamine in Africa has been a concern for several decades. However, easily accessible resistance data was lacking,” Professor ter Kuile said.

“This study combines all the available SP resistance data from the last two decades in a single model. It allows national malaria control programs and researchers to get much-needed data on the degree of resistance in a given area in a given year. This allows us to understand better the impact of sulfadoxine-pyrimethamine resistance on the effectiveness of these preventive interventions and determine if and when to consider alternative drugs for chemoprevention.”

Associate Professor Flegg said, “This research tool should help guide health policies that will bring the World Health Organisation's ambitious target of eliminating malaria by 2030 one step closer.” 

The team included researchers from the University of Melbourne, the University of Oxford, Johnson C. Smith University, the University of Cape Town, and the University of Witwatersrand.

The research received funding from the Bill & Melinda Gates Foundation, the Smith Institute for Applied Research, and the Australian Research Council.

UK researchers develop AI algo that detects brain abnormalities could help cure epilepsy

An artificial intelligence (AI) algorithm that can detect subtle brain abnormalities which cause epileptic seizures has been developed by researchers at UCL in London, the United Kingdom.

The Multicentre Epilepsy Lesion Detection project (MELD) used over 1,000 patient MRI scans from 22 global epilepsy centers to develop the algorithm, which provides reports of where abnormalities are in cases of drug-resistant focal cortical dysplasia (FCD) – a leading cause of epilepsy.

FCDs are areas of the brain that have developed abnormally and often cause drug-resistant epilepsy. It is typically treated with surgery, however identifying the lesions from an MRI is an ongoing challenge for clinicians, as MRI scans in FCDs can look normal.

To develop the algorithm, the team quantified cortical features from the MRI scans, such as how thick or folded the cortex/brain surface was and used around 300,000 locations across the brain.

Researchers then trained the algorithm on examples labeled by expert radiologists as either being a healthy brain or having FCD – dependent on their patterns and features.

The findings, published in Brain, found that overall the algorithm was able to detect the FCD in 67% of cases in the cohort (538 participants).

Previously, 178 of the participants had been considered MRI negative, which means that radiologists had been unable to find the abnormality – yet the MELD algorithm was able to identify the FCD in 63% of these cases.

This is particularly important as if doctors can find the abnormality in the brain scan, then surgery to remove it can provide a cure.

Co-first author, Mathilde Ripart (UCL Great Ormond Street Institute of Child Health) said: “We put an emphasis on creating an AI algorithm that was interpretable and could help doctors make decisions. Showing doctors how the MELD algorithm made its predictions was an essential part of that process.”

Co-senior author, Dr. Konrad Wagstyl (UCL Queen Square Institute of Neurology) added: "This algorithm could help to find more of these hidden lesions in children and adults with epilepsy, and enable more patients with epilepsy to be considered for brain surgery that could cure epilepsy and improve their cognitive development. Roughly 440 children per year could benefit from epilepsy surgery in England."

Around 1% of the world’s population has the serious neurological condition epilepsy, which is characterized by frequent seizures.

In the UK some 600,000 people are affected. While drug treatments are available for the majority of people with epilepsy, 20-30% do not respond to medications.

In children who have had surgery to control their epilepsy, FCD is the most common cause, and in adults, it is the third most common cause.

Additionally, of patients who have epilepsy that has an abnormality in the brain that cannot be found on MRI scans, FCD is the most common cause.

Co-first author, Dr. Hannah Spitzer (Helmholtz Munich) said: “Our algorithm automatically learns to detect lesions from thousands of MRI scans of patients. It can reliably detect lesions of different types, shapes and sizes, and even many of those lesions that were previously missed by radiologists.”

Co-senior author, Dr. Sophie Adler (UCL Great Ormond Street Institute of Child Health) added: “We hope that this technology will help to identify epilepsy-causing abnormalities that are currently being missed. Ultimately it could enable more people with epilepsy to have potentially curative brain surgery.”

This study on FCD detection uses the largest MRI cohort of FCDs to date, meaning it can detect all types of FCD.

The MELD FCD classifier tool can be run on any patient with a suspicion of having an FCD who is over the age of 3 years and has an MRI scan.

The MELD project is supported by the Rosetrees Trust.