Working under the auspices of the Oceans Melting Greenland mission for the past five years, the researchers used ships and aircraft to survey 226 glaciers in all sectors of one of Earth’s largest islands. They found that 74 glaciers situated in deep, steep-walled valleys accounted for nearly half of Greenland’s total ice loss between 1992 and 2017.

Such fjord-bound glaciers were discovered to be the most subject to undercutting, a process by which warm, salty water at the bottom of the canyons melts the ice from below, causing the masses to break apart more quickly than usual. In contrast, the team found that 51 glaciers positioned in shallower gullies experienced less undercutting and contributed only about 15 percent of the total ice loss.

“I was surprised by how lopsided it was. The biggest and deepest glaciers are undercut much faster than the smaller glaciers in shallow fjords,” said lead author Michael Wood, a post-doctorate researcher at NASA’s Jet Propulsion Laboratory in Southern California, who began this research as a doctoral student at UCI. “In other words, the biggest glaciers are the most sensitive to the warming waters and those are the ones really driving Greenland’s ice loss.”

The study highlighted the dynamic whereby deeper fjords allow the intrusion of warmer ocean water than shallow ones, hastening the process of undercutting with some of Greenland’s largest glaciers.

Greenland is home to one of Earth’s only two ice sheets, the largest being Antarctica’s. The ice in Greenland is more than two miles (three kilometers) thick in places. At the edges of the land mass, the vast glaciers extending from the ice sheet travel slowly down valleys like icy conveyor belts, which inch into the fjords and then melt or break off as icebergs. The ice is replenished by snowfall that is compressed over time into the ice pack.

If the ice sheet were in balance, the amount of snow accumulating on the top would roughly equal the ice lost from melt, evaporation and calving — chunks breaking free from anchored masses and floating off into the ocean.

But the ice sheet has been out of balance since the 1990s. Melt has accelerated and calving has increased, causing glaciers that extend into the sea to retreat back toward land. Together, these are resulting the ice sheet shrinkage.

According to the research team, the build-up of warm salty water at the bottom of fjords has been accelerated by increasing temperatures in the summer months, which heat the surfaces of glaciers, creating pools of meltwater. This liquid leaks through cracks in the ice to form subsurface freshwater rivers which flows into the sea where it interacts with salty water beneath fjords.

Glacier meltwater is free of salt, so it is lighter than seawater and rises to the surface as a plume, dragging up warm water and putting it in contact with the bottoms of glaciers. Fjord depth is a fairly immutable factor, but other factors such as seawater temperature and the amount of meltwater from glaciers surfaces are greatly impacted by climate warming. All three factors combine to cause accelerated deterioration of Greenland’s ice sheet, the researchers said.

As the water temperature around Greenland’s coastline is predicted to continue to increase in the future, these findings suggest that some climate models may underestimate glacial ice loss by at least a factor of two if they do not account for undercutting by a warm ocean.

The study also lends insight into why many of Greenland’s glaciers never recovered after an abrupt ocean warming between 1998 and 2007, which caused an increase in ocean temperature by nearly 2 degrees Celsius. Although ocean warming paused between 2008 and 2017, the glaciers had already experienced such extreme undercutting in the previous decade that they continued to retreat at an accelerated rate.

“We have known for well over a decade that the warmer ocean plays a major role in the evolution of Greenland glaciers,” said OMG deputy principal investigator Eric Rignot, also of JPL and UCI. “But for the first time, we have been able to quantify the undercutting effect and demonstrate its dominant impact on the glacier retreat over the past 20 years.”

Scientists have observed distinctive patterns in the electrical activity of neuron groups in healthy brains. Networks of neurons move through states of similar behavior (synchronization) and dissimilar behavior (desynchronization) in a process that is associated with memory and attention. But in a brain with a neurological disorder like epilepsy, synchronization can grow to a dangerous extent when a collection of brain cells begins to emit excess electricity. “Synchronization is thought to be important for information processing,” Jennifer Creaser of the University of Exeter said. “But too much synchronization — such as what occurs in epileptic seizures or Parkinson’s disease — is associated with disease states and can impair brain function.”

Measurements of epileptic seizures have revealed that desynchronization in brain networks often occurs before or during the early stages of a seizure. As the seizure progresses, networks become increasingly more synchronized as additional regions of the brain get involved, leading to high levels of synchronization towards the seizure’s end. Understanding the interactions between the increased electrical activity during a seizure and changes in synchronization is an important step towards improving the diagnosis and treatment of epilepsy.

Jennifer Creaser, Peter Ashwin (University of Exeter), and Krasimira Tsaneva-Atanasova (University of Exeter, Technical University of Munich, and Bulgarian Academy of Sciences) explored the mechanisms of synchronization that accompany seizure onset in a paper that published in December in the SIAM Journal on Applied Dynamical Systems. In their study — which took place at the Engineering and Physical Science Research Council’s Centre for Predictive Modelling in Healthcare at the University of Exeter and University of Birmingham — the researchers used mathematical modeling to explore the interplay between groups of neurons in the brain that leads to transitions in synchronization changes during seizure onset. “Although this is a theoretical study of an idealized model, it is inspired by challenges posed by understanding transitions between healthy and pathological activity in the brain,” Ashwin said.

The authors utilize an extended version of an existing mathematical model that represents the brain as a network connecting multiple nodes of neuron groups. The model network consists of bistable nodes, meaning that each node is able to switch between two stable states: resting (a quiescent state) and seizure (an active and oscillatory state). These nodes remain in their current state until they receive a stimulus that gives them a sufficient kick to escape to the other state. In the model, this stimulus comes from other connected nodes or appears in the form of “noise” — outside sources of neural activity, such as endocrine responses that are associated with an emotional state or physiological changes due to disease.

The influence between neighboring nodes is governed by a coupling function that represents the way in which the nodes in the network communicate with each other. The first of the two possible types of coupling is amplitude coupling, which is governed by the “loudness” of the neighboring nodes. The second is phase coupling, which is related to the speed at which the neighbors are firing. Although the researchers needed to utilize a simple formulation on a small network to even make their analysis possible — a more complex and realistic system would be too computationally taxing — they expected their model to exhibit the same types of behaviors that clinical recordings of real brain activity have revealed.

The nodes in the modeled system all begin in the healthy resting state. In previous research, the authors found that adding a small amount of noise to the system caused each node to transition to the active state — but the system’s geometry was such that returning to the resting state took much longer than leaving. Because of this, these escapes can spread sequentially as a “domino effect” when a number of nodes are connected. This leads to a cascade of escapes to the active state — much like a falling line of dominos — that spreads activity across the network.

Creaser, Ashwin, and Tsaneva-Atanasova’s new paper builds upon this previous research on the domino effect to explore the transitions into and out of synchrony that occur during cascades of escapes. The team used their model to identify the circumstances that bring about these changes in synchrony and investigate how the type of coupling in a network affects its behavior.

When the model incorporated only amplitude coupling, it exhibited a new phenomenon in which the domino effect could accelerate or decelerate. However, this effect had no bearing on synchronization changes in the network; all of the nodes started and remained synchronized. But when the model incorporated more general amplitude and phase coupling, the authors found that the nodes’ synchrony could change between consecutive escapes during the domino effect. They then determined which conditions would cause changes in synchrony under phase-amplitude coupling. This change in synchrony throughout the sequence of escapes was the study’s most novel result.

The results of this work could facilitate further studies on seizures and their management. “The mathematical modeling of seizure initiation and propagation can not only help to uncover seizures’ complex underlying mechanisms, but also provide a means for enabling in silico experiments to predict the outcome of manipulating the neural system,” Tsaneva-Atanasova said. Understanding the interplay between synchronized and desynchronized dynamics in brain networks could help identify clinically-relevant measures for seizure treatment. For example, Creaser and Tsaneva-Atanasova recently served as the lead and senior author, respectively, on a paper that utilized a simpler version of the model to classify patterns of seizure onset that were recorded in a clinical setting. In the future, these kinds of modeling studies may lead to the personalization of seizure identification and treatment for individuals with epilepsy.

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Materials provided by Society for Industrial and Applied Mathematics. Original written by Jillian Kunze. Note: Content may be edited for style and length.

“Over the years, many labs, including mine, have identified important roles for astrocytes in promoting neurological diseases,” said corresponding author Francisco Quintana, PhD, of the Ann Romney Center for Neurologic Diseases at the Brigham. “This is the first case in which we’re showing that at least a subset of these cells (astrocytes) can prevent inflammation. The reason we haven’t seen this before was because we were studying these cells as if they were uniform, or one single cell type. But now we have the resolution to see the differences between these cells.”

The researchers used refined gene- and protein-analysis tools to identify the novel astrocyte subset. The astrocyte population resides close to the meninges (the membrane enclosing the brain) and expresses a protein called LAMP1, along with a protein called TRAIL, which can induce the death of other cells. These features help the LAMP1+TRAIL+ astrocytes limit CNS inflammation by inducing cell death in T-cells that promote inflammation.

To determine what mechanism controls LAMP1+TRAIL+ astrocytes in the brain, the researchers performed a series of tests using the gene-editing tool CRISPR-Cas9. They found that a particular signaling molecule, called interferon-gamma, regulates TRAIL expression. Moreover, they found that the gut microbiome induces the expression of interferon-gamma in cells that circulate through the body and ultimately reach the meninges, where they can promote astrocyte anti-inflammatory activities.

Understanding the mechanisms driving the anti-inflammatory functions of LAMP1+TRAIL+ astrocytes could enable researchers to develop therapeutic approaches to combat neurological diseases, like multiple sclerosis. For example, they are exploring probiotic candidates that can be used to regulate the astrocytes’ anti-inflammatory activity. Additionally, the research team’s more recent data indicates that certain brain tumors exploit this pathway to evade the body’s immune response. The investigators are therefore developing cancer immunotherapies to retaliate against the tumors’ attacks.

“Finding microbiome-controlled anti-inflammatory subsets of astrocytes is an important advance in our understanding of CNS inflammation and its regulation,” Quintana said. “This is a very novel mechanism by which the gut controls inflammation in the brain. It guides new therapies for neurological diseases, and we believe that this mechanism could contribute to the pathogenesis of brain tumors.”

Quintana’s lab identified the only other subset of astrocyte known to be regulated by the gut microbiome in 2016, but the investigators believe that there are likely others. “It’s becoming clear that the gut flora are important in many diseases,” he said. “We’re lucky that we’ve been leading the charge to identify different subsets of astrocytes and the mechanisms that control them. We have a list of other populations of astrocytes, and we’re working to see how the gut flora may control them.”

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Materials provided by Brigham and Women’s Hospital. Note: Content may be edited for style and length.