Project acronym DEEP PURPLE
Project DEEP PURPLE: darkening of the Greenland Ice Sheet
Researcher (PI) Martyn TRANTER, Alexandre Barbosa Anesio, Liane Benning
Host Institution (HI) AARHUS UNIVERSITET
Country Denmark
Call Details Synergy Grants (SyG), SyG, ERC-2019-SyG
Summary The stability of the Greenland Ice Sheet (GrIS) is a threat to coastal communities worldwide. The PIs have changed our understanding of why it darkens during the melt season, becoming increasingly deep purple due to pigmented ice algal blooms in the ice surface, producing more melt and accelerating the GrIS towards its tipping point, and increasing sea level. The next step jump in our understanding of biological darkening will be provided by DEEP PURPLE, which will establish the factors that control ice algal blooms. These factors are essential for modelling of future melting, which require a process-based understanding of blooming. DEEP PURPLE will quantify the synergies between the biology, chemistry and physics of ice algae micro-niches in rotting, melting ice, and examine the combination of factors which stabilise them. State-of-the-science analytical and observational methods will be employed to characterise the complex mosaic of wet ice habitats, dependent on factors such as the hydrology, nutrient status, particulate content and light fields within these continually evolving ice-water-particulate-microbe systems. We will quantitatively assess why and how the fine light mineral dust particulates contained within the melting ice amplify the growth of ice algae. The particulate content and composition of different layers in the GrIS is dependent on age, and so the algae that the melting ice can support may fundamentally change over time. We look back to understand if the ice biome has changed through the Anthropocene via analyse of fjord sediments. The first draft genome of ice algae will show their key adaptations to glacier surface habitats. DEEP PURPLE looks forward by providing the critical field data sets and conceptual models of ice algal growth that will facilitate the next generation of predictive models of sea level rise due to biologically enhanced melting of the GrIS.
Summary
The stability of the Greenland Ice Sheet (GrIS) is a threat to coastal communities worldwide. The PIs have changed our understanding of why it darkens during the melt season, becoming increasingly deep purple due to pigmented ice algal blooms in the ice surface, producing more melt and accelerating the GrIS towards its tipping point, and increasing sea level. The next step jump in our understanding of biological darkening will be provided by DEEP PURPLE, which will establish the factors that control ice algal blooms. These factors are essential for modelling of future melting, which require a process-based understanding of blooming. DEEP PURPLE will quantify the synergies between the biology, chemistry and physics of ice algae micro-niches in rotting, melting ice, and examine the combination of factors which stabilise them. State-of-the-science analytical and observational methods will be employed to characterise the complex mosaic of wet ice habitats, dependent on factors such as the hydrology, nutrient status, particulate content and light fields within these continually evolving ice-water-particulate-microbe systems. We will quantitatively assess why and how the fine light mineral dust particulates contained within the melting ice amplify the growth of ice algae. The particulate content and composition of different layers in the GrIS is dependent on age, and so the algae that the melting ice can support may fundamentally change over time. We look back to understand if the ice biome has changed through the Anthropocene via analyse of fjord sediments. The first draft genome of ice algae will show their key adaptations to glacier surface habitats. DEEP PURPLE looks forward by providing the critical field data sets and conceptual models of ice algal growth that will facilitate the next generation of predictive models of sea level rise due to biologically enhanced melting of the GrIS.
Max ERC Funding
11 007 344 €
Duration
Start date: 2020-01-01, End date: 2025-12-31
Project acronym RELEVANCE
Project How body relevance drives brain organization
Researcher (PI) Rufin Vogels, Bea de Gelder, Martin Giese
Host Institution (HI) KATHOLIEKE UNIVERSITEIT LEUVEN
Country Belgium
Call Details Synergy Grants (SyG), SyG, ERC-2019-SyG
Summary Social species, and specifically human and nonhuman primates, rely heavily on conspecifics for survival. Considerable time is spent watching each other’s behavior because this is often the most relevant source of information for preparing adaptive social responses. The project RELEVANCE aims to understand how the brain evolved special structures to process highly relevant social stimuli like bodies and to reveal how social vision sustains adaptive behaviour.
This requires a novel way of thinking about biological information processing, currently among the brains’ most distinctive and least understood characteristic that accounts for the biggest difference between brains and computers.
The project will develop a mechanistic and computational understanding of the visual processing of bodies and interactions and show how this processing sustains higher abilities such as understanding intention, action and emotion. Relevance will accomplish this by integrating advanced methods from multiple disciplines: psychophysics and high-field functional imaging in combination with virtual reality and neural stimulation in humans; electrophysiology with optogenetics and laminar recordings in monkeys.
Crosstalk between
human and monkey methods will establish homologies between the species, revealing cornerstones of the theory. In a radical departure from current practice, we will develop novel deep neural network models that unify the data. These models will not only capture detailed mechanisms of neural processing of complex social stimuli and its dynamics, but also reproduce the modulation of brain activity during active behavior.
RELEVANCE will reveal novel ways of understanding and diagnosing social communication deficits in neuropsychiatry, and suggest novel hypotheses about their genetic basis. It will motivate novel principles and architectures for processing of socially relevant information in computer and robotic systems.
Summary
Social species, and specifically human and nonhuman primates, rely heavily on conspecifics for survival. Considerable time is spent watching each other’s behavior because this is often the most relevant source of information for preparing adaptive social responses. The project RELEVANCE aims to understand how the brain evolved special structures to process highly relevant social stimuli like bodies and to reveal how social vision sustains adaptive behaviour.
This requires a novel way of thinking about biological information processing, currently among the brains’ most distinctive and least understood characteristic that accounts for the biggest difference between brains and computers.
The project will develop a mechanistic and computational understanding of the visual processing of bodies and interactions and show how this processing sustains higher abilities such as understanding intention, action and emotion. Relevance will accomplish this by integrating advanced methods from multiple disciplines: psychophysics and high-field functional imaging in combination with virtual reality and neural stimulation in humans; electrophysiology with optogenetics and laminar recordings in monkeys.
Crosstalk between
human and monkey methods will establish homologies between the species, revealing cornerstones of the theory. In a radical departure from current practice, we will develop novel deep neural network models that unify the data. These models will not only capture detailed mechanisms of neural processing of complex social stimuli and its dynamics, but also reproduce the modulation of brain activity during active behavior.
RELEVANCE will reveal novel ways of understanding and diagnosing social communication deficits in neuropsychiatry, and suggest novel hypotheses about their genetic basis. It will motivate novel principles and architectures for processing of socially relevant information in computer and robotic systems.
Max ERC Funding
8 309 114 €
Duration
Start date: 2020-07-01, End date: 2025-06-30
