Project acronym ACTIVIA
Project Visual Recognition of Function and Intention
Researcher (PI) Ivan Laptev
Host Institution (HI) INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE
Call Details Starting Grant (StG), PE6, ERC-2012-StG_20111012
Summary "Computer vision is concerned with the automated interpretation of images and video streams. Today's research is (mostly) aimed at answering queries such as ""Is this a picture of a dog?"", (classification) or sometimes ""Find the dog in this photo"" (detection). While categorisation and detection are useful for many tasks, inferring correct class labels is not the final answer to visual recognition. The categories and locations of objects do not provide direct understanding of their function i.e., how things work, what they can be used for, or how they can act and react. Such an understanding, however, would be highly desirable to answer currently unsolvable queries such as ""Am I in danger?"" or ""What can happen in this scene?"". Solving such queries is the aim of this proposal.
My goal is to uncover the functional properties of objects and the purpose of actions by addressing visual recognition from a different and yet unexplored perspective. The main novelty of this proposal is to leverage observations of people, i.e., their actions and interactions to automatically learn the use, the purpose and the function of objects and scenes from visual data. The project is timely as it builds upon the two key recent technological advances: (a) the immense progress in visual recognition of objects, scenes and human actions achieved in the last ten years, as well as (b) the emergence of a massive amount of public image and video data now available to train visual models.
ACTIVIA addresses fundamental research issues in automated interpretation of dynamic visual scenes, but its results are expected to serve as a basis for ground-breaking technological advances in practical applications. The recognition of functional properties and intentions as explored in this project will directly support high-impact applications such as detection of abnormal events, which are likely to revolutionise today's approaches to crime protection, hazard prevention, elderly care, and many others."
Summary
"Computer vision is concerned with the automated interpretation of images and video streams. Today's research is (mostly) aimed at answering queries such as ""Is this a picture of a dog?"", (classification) or sometimes ""Find the dog in this photo"" (detection). While categorisation and detection are useful for many tasks, inferring correct class labels is not the final answer to visual recognition. The categories and locations of objects do not provide direct understanding of their function i.e., how things work, what they can be used for, or how they can act and react. Such an understanding, however, would be highly desirable to answer currently unsolvable queries such as ""Am I in danger?"" or ""What can happen in this scene?"". Solving such queries is the aim of this proposal.
My goal is to uncover the functional properties of objects and the purpose of actions by addressing visual recognition from a different and yet unexplored perspective. The main novelty of this proposal is to leverage observations of people, i.e., their actions and interactions to automatically learn the use, the purpose and the function of objects and scenes from visual data. The project is timely as it builds upon the two key recent technological advances: (a) the immense progress in visual recognition of objects, scenes and human actions achieved in the last ten years, as well as (b) the emergence of a massive amount of public image and video data now available to train visual models.
ACTIVIA addresses fundamental research issues in automated interpretation of dynamic visual scenes, but its results are expected to serve as a basis for ground-breaking technological advances in practical applications. The recognition of functional properties and intentions as explored in this project will directly support high-impact applications such as detection of abnormal events, which are likely to revolutionise today's approaches to crime protection, hazard prevention, elderly care, and many others."
Max ERC Funding
1 497 420 €
Duration
Start date: 2013-01-01, End date: 2018-12-31
Project acronym ADAPT
Project Theory and Algorithms for Adaptive Particle Simulation
Researcher (PI) Stephane Redon
Host Institution (HI) INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE
Call Details Starting Grant (StG), PE6, ERC-2012-StG_20111012
Summary "During the twentieth century, the development of macroscopic engineering has been largely stimulated by progress in digital prototyping: cars, planes, boats, etc. are nowadays designed and tested on computers. Digital prototypes have progressively replaced actual ones, and effective computer-aided engineering tools have helped cut costs and reduce production cycles of these macroscopic systems.
The twenty-first century is most likely to see a similar development at the atomic scale. Indeed, the recent years have seen tremendous progress in nanotechnology - in particular in the ability to control matter at the atomic scale. Similar to what has happened with macroscopic engineering, powerful and generic computational tools will be needed to engineer complex nanosystems, through modeling and simulation. As a result, a major challenge is to develop efficient simulation methods and algorithms.
NANO-D, the INRIA research group I started in January 2008 in Grenoble, France, aims at developing
efficient computational methods for modeling and simulating complex nanosystems, both natural and artificial. In particular, NANO-D develops SAMSON, a software application which gathers all algorithms designed by the group and its collaborators (SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems).
In this project, I propose to develop a unified theory, and associated algorithms, for adaptive particle simulation. The proposed theory will avoid problems that plague current popular multi-scale or hybrid simulation approaches by simulating a single potential throughout the system, while allowing users to finely trade precision for computational speed.
I believe the full development of the adaptive particle simulation theory will have an important impact on current modeling and simulation practices, and will enable practical design of complex nanosystems on desktop computers, which should significantly boost the emergence of generic nano-engineering."
Summary
"During the twentieth century, the development of macroscopic engineering has been largely stimulated by progress in digital prototyping: cars, planes, boats, etc. are nowadays designed and tested on computers. Digital prototypes have progressively replaced actual ones, and effective computer-aided engineering tools have helped cut costs and reduce production cycles of these macroscopic systems.
The twenty-first century is most likely to see a similar development at the atomic scale. Indeed, the recent years have seen tremendous progress in nanotechnology - in particular in the ability to control matter at the atomic scale. Similar to what has happened with macroscopic engineering, powerful and generic computational tools will be needed to engineer complex nanosystems, through modeling and simulation. As a result, a major challenge is to develop efficient simulation methods and algorithms.
NANO-D, the INRIA research group I started in January 2008 in Grenoble, France, aims at developing
efficient computational methods for modeling and simulating complex nanosystems, both natural and artificial. In particular, NANO-D develops SAMSON, a software application which gathers all algorithms designed by the group and its collaborators (SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems).
In this project, I propose to develop a unified theory, and associated algorithms, for adaptive particle simulation. The proposed theory will avoid problems that plague current popular multi-scale or hybrid simulation approaches by simulating a single potential throughout the system, while allowing users to finely trade precision for computational speed.
I believe the full development of the adaptive particle simulation theory will have an important impact on current modeling and simulation practices, and will enable practical design of complex nanosystems on desktop computers, which should significantly boost the emergence of generic nano-engineering."
Max ERC Funding
1 476 882 €
Duration
Start date: 2012-09-01, End date: 2017-08-31
Project acronym AGENSI
Project A Genetic View into Past Sea Ice Variability in the Arctic
Researcher (PI) Stijn DE SCHEPPER
Host Institution (HI) NORCE NORWEGIAN RESEARCH CENTRE AS
Call Details Consolidator Grant (CoG), PE10, ERC-2018-COG
Summary Arctic sea ice decline is the exponent of the rapidly transforming Arctic climate. The ensuing local and global implications can be understood by studying past climate transitions, yet few methods are available to examine past Arctic sea ice cover, severely restricting our understanding of sea ice in the climate system. The decline in Arctic sea ice cover is a ‘canary in the coalmine’ for the state of our climate, and if greenhouse gas emissions remain unchecked, summer sea ice loss may pass a critical threshold that could drastically transform the Arctic. Because historical observations are limited, it is crucial to have reliable proxies for assessing natural sea ice variability, its stability and sensitivity to climate forcing on different time scales. Current proxies address aspects of sea ice variability, but are limited due to a selective fossil record, preservation effects, regional applicability, or being semi-quantitative. With such restraints on our knowledge about natural variations and drivers, major uncertainties about the future remain.
I propose to develop and apply a novel sea ice proxy that exploits genetic information stored in marine sediments, sedimentary ancient DNA (sedaDNA). This innovation uses the genetic signature of phytoplankton communities from surface waters and sea ice as it gets stored in sediments. This wealth of information has not been explored before for reconstructing sea ice conditions. Preliminary results from my cross-disciplinary team indicate that our unconventional approach can provide a detailed, qualitative account of past sea ice ecosystems and quantitative estimates of sea ice parameters. I will address fundamental questions about past Arctic sea ice variability on different timescales, information essential to provide a framework upon which to assess the ecological and socio-economic consequences of a changing Arctic. This new proxy is not limited to sea ice research and can transform the field of paleoceanography.
Summary
Arctic sea ice decline is the exponent of the rapidly transforming Arctic climate. The ensuing local and global implications can be understood by studying past climate transitions, yet few methods are available to examine past Arctic sea ice cover, severely restricting our understanding of sea ice in the climate system. The decline in Arctic sea ice cover is a ‘canary in the coalmine’ for the state of our climate, and if greenhouse gas emissions remain unchecked, summer sea ice loss may pass a critical threshold that could drastically transform the Arctic. Because historical observations are limited, it is crucial to have reliable proxies for assessing natural sea ice variability, its stability and sensitivity to climate forcing on different time scales. Current proxies address aspects of sea ice variability, but are limited due to a selective fossil record, preservation effects, regional applicability, or being semi-quantitative. With such restraints on our knowledge about natural variations and drivers, major uncertainties about the future remain.
I propose to develop and apply a novel sea ice proxy that exploits genetic information stored in marine sediments, sedimentary ancient DNA (sedaDNA). This innovation uses the genetic signature of phytoplankton communities from surface waters and sea ice as it gets stored in sediments. This wealth of information has not been explored before for reconstructing sea ice conditions. Preliminary results from my cross-disciplinary team indicate that our unconventional approach can provide a detailed, qualitative account of past sea ice ecosystems and quantitative estimates of sea ice parameters. I will address fundamental questions about past Arctic sea ice variability on different timescales, information essential to provide a framework upon which to assess the ecological and socio-economic consequences of a changing Arctic. This new proxy is not limited to sea ice research and can transform the field of paleoceanography.
Max ERC Funding
2 615 858 €
Duration
Start date: 2019-08-01, End date: 2024-07-31
Project acronym ALGAME
Project Algorithms, Games, Mechanisms, and the Price of Anarchy
Researcher (PI) Elias Koutsoupias
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Call Details Advanced Grant (AdG), PE6, ERC-2012-ADG_20120216
Summary The objective of this proposal is to bring together a local team of young researchers who will work closely with international collaborators to advance the state of the art of Algorithmic Game Theory and open new venues of research at the interface of Computer Science, Game Theory, and Economics. The proposal consists mainly of three intertwined research strands: algorithmic mechanism design, price of anarchy, and online algorithms.
Specifically, we will attempt to resolve some outstanding open problems in algorithmic mechanism design: characterizing the incentive compatible mechanisms for important domains, such as the domain of combinatorial auctions, and resolving the approximation ratio of mechanisms for scheduling unrelated machines. More generally, we will study centralized and distributed algorithms whose inputs are controlled by selfish agents that are interested in the outcome of the computation. We will investigate new notions of mechanisms with strong truthfulness and limited susceptibility to externalities that can facilitate modular design of mechanisms of complex domains.
We will expand the current research on the price of anarchy to time-dependent games where the players can select not only how to act but also when to act. We also plan to resolve outstanding questions on the price of stability and to build a robust approach to these questions, similar to smooth analysis. For repeated games, we will investigate convergence of simple strategies (e.g., fictitious play), online fairness, and strategic considerations (e.g., metagames). More generally, our aim is to find a productive formulation of playing unknown games by drawing on the fields of online algorithms and machine learning.
Summary
The objective of this proposal is to bring together a local team of young researchers who will work closely with international collaborators to advance the state of the art of Algorithmic Game Theory and open new venues of research at the interface of Computer Science, Game Theory, and Economics. The proposal consists mainly of three intertwined research strands: algorithmic mechanism design, price of anarchy, and online algorithms.
Specifically, we will attempt to resolve some outstanding open problems in algorithmic mechanism design: characterizing the incentive compatible mechanisms for important domains, such as the domain of combinatorial auctions, and resolving the approximation ratio of mechanisms for scheduling unrelated machines. More generally, we will study centralized and distributed algorithms whose inputs are controlled by selfish agents that are interested in the outcome of the computation. We will investigate new notions of mechanisms with strong truthfulness and limited susceptibility to externalities that can facilitate modular design of mechanisms of complex domains.
We will expand the current research on the price of anarchy to time-dependent games where the players can select not only how to act but also when to act. We also plan to resolve outstanding questions on the price of stability and to build a robust approach to these questions, similar to smooth analysis. For repeated games, we will investigate convergence of simple strategies (e.g., fictitious play), online fairness, and strategic considerations (e.g., metagames). More generally, our aim is to find a productive formulation of playing unknown games by drawing on the fields of online algorithms and machine learning.
Max ERC Funding
2 461 000 €
Duration
Start date: 2013-04-01, End date: 2019-03-31
Project acronym ALLEGRO
Project Active large-scale learning for visual recognition
Researcher (PI) Cordelia Schmid
Host Institution (HI) INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE
Call Details Advanced Grant (AdG), PE6, ERC-2012-ADG_20120216
Summary A massive and ever growing amount of digital image and video content
is available today, on sites such as
Flickr and YouTube, in audiovisual archives such as those of BBC and
INA, and in personal collections. In most cases, it comes with
additional information, such as text, audio or other metadata, that forms a
rather sparse and noisy, yet rich and diverse source of annotation,
ideally suited to emerging weakly supervised and active machine
learning technology. The ALLEGRO project will take visual recognition
to the next level by using this largely untapped source of data to
automatically learn visual models. The main research objective of
our project is the development of new algorithms and computer software
capable of autonomously exploring evolving data collections, selecting
the relevant information, and determining the visual models most
appropriate for different object, scene, and activity categories. An
emphasis will be put on learning visual models from video, a
particularly rich source of information, and on the representation of
human activities, one of today's most challenging problems in computer
vision. Although this project addresses fundamental research
issues, it is expected to result in significant advances in
high-impact applications that range from visual mining of the Web and
automated annotation and organization of family photo and video albums
to large-scale information retrieval in television archives.
Summary
A massive and ever growing amount of digital image and video content
is available today, on sites such as
Flickr and YouTube, in audiovisual archives such as those of BBC and
INA, and in personal collections. In most cases, it comes with
additional information, such as text, audio or other metadata, that forms a
rather sparse and noisy, yet rich and diverse source of annotation,
ideally suited to emerging weakly supervised and active machine
learning technology. The ALLEGRO project will take visual recognition
to the next level by using this largely untapped source of data to
automatically learn visual models. The main research objective of
our project is the development of new algorithms and computer software
capable of autonomously exploring evolving data collections, selecting
the relevant information, and determining the visual models most
appropriate for different object, scene, and activity categories. An
emphasis will be put on learning visual models from video, a
particularly rich source of information, and on the representation of
human activities, one of today's most challenging problems in computer
vision. Although this project addresses fundamental research
issues, it is expected to result in significant advances in
high-impact applications that range from visual mining of the Web and
automated annotation and organization of family photo and video albums
to large-scale information retrieval in television archives.
Max ERC Funding
2 493 322 €
Duration
Start date: 2013-04-01, End date: 2019-03-31
Project acronym AMYTOX
Project Amyloid fibril cytotoxicity: new insights from novel approaches
Researcher (PI) Sheena Radford
Host Institution (HI) UNIVERSITY OF LEEDS
Call Details Advanced Grant (AdG), LS1, ERC-2012-ADG_20120314
Summary Despite the discovery of amyloidosis more than a century ago, the molecular and cellular mechanisms of these devastating human disorders remain obscure. In addition to their involvement in disease, amyloid fibrils perform physiological functions, whilst others have potentials as biomaterials. To realise their use in nanotechnology and to enable the development of amyloid therapies, there is an urgent need to understand the molecular pathways of amyloid assembly and to determine how amyloid fibrils interact with cells and cellular components. The challenges lie in the transient nature and low population of aggregating species and the panoply of amyloid fibril structures. This molecular complexity renders identification of the culprits of amyloid disease impossible to achieve using traditional methods.
Here I propose a series of exciting experiments that aim to cast new light on the molecular and cellular mechanisms of amyloidosis by exploiting approaches capable of imaging individual protein molecules or single protein fibrils in vitro and in living cells. The proposal builds on new data from our laboratory that have shown that amyloid fibrils (disease-associated, functional and created from de novo designed sequences) kill cells by a mechanism that depends on fibril length and on cellular uptake. Specifically, I will (i) use single molecule fluorescence and non-covalent mass spectrometry and to determine why short fibril samples disrupt biological membranes more than their longer counterparts and electron tomography to determine, for the first time, the structural properties of cytotoxic fibril ends; (ii) develop single molecule force spectroscopy to probe the interactions between amyloid precursors, fibrils and cellular membranes; and (iii) develop cell biological assays to discover the biological mechanism(s) of amyloid-induced cell death and high resolution imaging and electron tomography to visualise amyloid fibrils in the act of killing living cells.
Summary
Despite the discovery of amyloidosis more than a century ago, the molecular and cellular mechanisms of these devastating human disorders remain obscure. In addition to their involvement in disease, amyloid fibrils perform physiological functions, whilst others have potentials as biomaterials. To realise their use in nanotechnology and to enable the development of amyloid therapies, there is an urgent need to understand the molecular pathways of amyloid assembly and to determine how amyloid fibrils interact with cells and cellular components. The challenges lie in the transient nature and low population of aggregating species and the panoply of amyloid fibril structures. This molecular complexity renders identification of the culprits of amyloid disease impossible to achieve using traditional methods.
Here I propose a series of exciting experiments that aim to cast new light on the molecular and cellular mechanisms of amyloidosis by exploiting approaches capable of imaging individual protein molecules or single protein fibrils in vitro and in living cells. The proposal builds on new data from our laboratory that have shown that amyloid fibrils (disease-associated, functional and created from de novo designed sequences) kill cells by a mechanism that depends on fibril length and on cellular uptake. Specifically, I will (i) use single molecule fluorescence and non-covalent mass spectrometry and to determine why short fibril samples disrupt biological membranes more than their longer counterparts and electron tomography to determine, for the first time, the structural properties of cytotoxic fibril ends; (ii) develop single molecule force spectroscopy to probe the interactions between amyloid precursors, fibrils and cellular membranes; and (iii) develop cell biological assays to discover the biological mechanism(s) of amyloid-induced cell death and high resolution imaging and electron tomography to visualise amyloid fibrils in the act of killing living cells.
Max ERC Funding
2 498 465 €
Duration
Start date: 2013-05-01, End date: 2019-04-30
Project acronym ANTICIPATE
Project Anticipatory Human-Computer Interaction
Researcher (PI) Andreas BULLING
Host Institution (HI) UNIVERSITAET STUTTGART
Call Details Starting Grant (StG), PE6, ERC-2018-STG
Summary Even after three decades of research on human-computer interaction (HCI), current general-purpose user interfaces (UI) still lack the ability to attribute mental states to their users, i.e. they fail to understand users' intentions and needs and to anticipate their actions. This drastically restricts their interactive capabilities.
ANTICIPATE aims to establish the scientific foundations for a new generation of user interfaces that pro-actively adapt to users' future input actions by monitoring their attention and predicting their interaction intentions - thereby significantly improving the naturalness, efficiency, and user experience of the interactions. Realising this vision of anticipatory human-computer interaction requires groundbreaking advances in everyday sensing of user attention from eye and brain activity. We will further pioneer methods to predict entangled user intentions and forecast interactive behaviour with fine temporal granularity during interactions in everyday stationary and mobile settings. Finally, we will develop fundamental interaction paradigms that enable anticipatory UIs to pro-actively adapt to users' attention and intentions in a mindful way. The new capabilities will be demonstrated in four challenging cases: 1) mobile information retrieval, 2) intelligent notification management, 3) Autism diagnosis and monitoring, and 4) computer-based training.
Anticipatory human-computer interaction offers a strong complement to existing UI paradigms that only react to user input post-hoc. If successful, ANTICIPATE will deliver the first important building blocks for implementing Theory of Mind in general-purpose UIs. As such, the project has the potential to drastically improve the billions of interactions we perform with computers every day, to trigger a wide range of follow-up research in HCI as well as adjacent areas within and outside computer science, and to act as a key technical enabler for new applications, e.g. in healthcare and education.
Summary
Even after three decades of research on human-computer interaction (HCI), current general-purpose user interfaces (UI) still lack the ability to attribute mental states to their users, i.e. they fail to understand users' intentions and needs and to anticipate their actions. This drastically restricts their interactive capabilities.
ANTICIPATE aims to establish the scientific foundations for a new generation of user interfaces that pro-actively adapt to users' future input actions by monitoring their attention and predicting their interaction intentions - thereby significantly improving the naturalness, efficiency, and user experience of the interactions. Realising this vision of anticipatory human-computer interaction requires groundbreaking advances in everyday sensing of user attention from eye and brain activity. We will further pioneer methods to predict entangled user intentions and forecast interactive behaviour with fine temporal granularity during interactions in everyday stationary and mobile settings. Finally, we will develop fundamental interaction paradigms that enable anticipatory UIs to pro-actively adapt to users' attention and intentions in a mindful way. The new capabilities will be demonstrated in four challenging cases: 1) mobile information retrieval, 2) intelligent notification management, 3) Autism diagnosis and monitoring, and 4) computer-based training.
Anticipatory human-computer interaction offers a strong complement to existing UI paradigms that only react to user input post-hoc. If successful, ANTICIPATE will deliver the first important building blocks for implementing Theory of Mind in general-purpose UIs. As such, the project has the potential to drastically improve the billions of interactions we perform with computers every day, to trigger a wide range of follow-up research in HCI as well as adjacent areas within and outside computer science, and to act as a key technical enabler for new applications, e.g. in healthcare and education.
Max ERC Funding
1 499 625 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym ARCHEIS
Project Understanding the onset and impact of Aquatic Resource Consumption in Human Evolution using novel Isotopic tracerS
Researcher (PI) Klervia Marie Madalen JAOUEN
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Call Details Starting Grant (StG), PE10, ERC-2018-STG
Summary The onset of the systematic consumption of marine resources is thought to mark a turning point for the hominin lineage. To date, this onset cannot be traced, since classic isotope markers are not preserved beyond 50 - 100 ky. Aquatic food products are essential in human nutrition as the main source of polyunsaturated fatty acids in hunter-gatherer diets. The exploitation of marine resources is also thought to have reduced human mobility and enhanced social and technological complexification. Systematic aquatic food consumption could well have been a distinctive feature of Homo sapiens species among his fellow hominins, and has been linked to the astonishing leap in human intelligence and conscience. Yet, this hypothesis is challenged by the existence of mollusk and marine mammal bone remains at Neanderthal archeological sites. Recent work demonstrated the sensitivity of Zn isotope composition in bioapatite, the mineral part of bones and teeth, to dietary Zn. By combining classic (C and C/N isotope analyses) and innovative techniques (compound specific C/N and bulk Zn isotope analyses), I will develop a suite of sensitive tracers for shellfish, fish and marine mammal consumption. Shellfish consumption will be investigated by comparing various South American and European prehistoric populations from the Atlantic coast associated to shell-midden and fish-mounds. Marine mammal consumption will be traced using an Inuit population of Arctic Canada and the Wairau Bar population of New Zealand. C/N/Zn isotope compositions of various aquatic products will also be assessed, as well as isotope fractionation during intestinal absorption. I will then use the fully calibrated isotope tools to detect and characterize the onset of marine food exploitation in human history, which will answer the question of its specificity to our species. Neanderthal, early modern humans and possibly other hominin remains from coastal and inland sites will be compared in that purpose.
Summary
The onset of the systematic consumption of marine resources is thought to mark a turning point for the hominin lineage. To date, this onset cannot be traced, since classic isotope markers are not preserved beyond 50 - 100 ky. Aquatic food products are essential in human nutrition as the main source of polyunsaturated fatty acids in hunter-gatherer diets. The exploitation of marine resources is also thought to have reduced human mobility and enhanced social and technological complexification. Systematic aquatic food consumption could well have been a distinctive feature of Homo sapiens species among his fellow hominins, and has been linked to the astonishing leap in human intelligence and conscience. Yet, this hypothesis is challenged by the existence of mollusk and marine mammal bone remains at Neanderthal archeological sites. Recent work demonstrated the sensitivity of Zn isotope composition in bioapatite, the mineral part of bones and teeth, to dietary Zn. By combining classic (C and C/N isotope analyses) and innovative techniques (compound specific C/N and bulk Zn isotope analyses), I will develop a suite of sensitive tracers for shellfish, fish and marine mammal consumption. Shellfish consumption will be investigated by comparing various South American and European prehistoric populations from the Atlantic coast associated to shell-midden and fish-mounds. Marine mammal consumption will be traced using an Inuit population of Arctic Canada and the Wairau Bar population of New Zealand. C/N/Zn isotope compositions of various aquatic products will also be assessed, as well as isotope fractionation during intestinal absorption. I will then use the fully calibrated isotope tools to detect and characterize the onset of marine food exploitation in human history, which will answer the question of its specificity to our species. Neanderthal, early modern humans and possibly other hominin remains from coastal and inland sites will be compared in that purpose.
Max ERC Funding
1 361 991 €
Duration
Start date: 2019-03-01, End date: 2024-02-29
Project acronym ARGO
Project The Quest of the Argonautes - from Myth to Reality
Researcher (PI) JOHN VAN DER OOST
Host Institution (HI) WAGENINGEN UNIVERSITY
Call Details Advanced Grant (AdG), LS1, ERC-2018-ADG
Summary Argonaute nucleases are key players of the eukaryotic RNA interference (RNAi) system. Using small RNA guides, these Argonaute (Ago) proteins specifically target complementary RNA molecules, resulting in regulation of a wide range of crucial processes, including chromosome organization, gene expression and anti-virus defence. Since 2010, my research team has studied closely-related prokaryotic Argonaute (pAgo) variants. This has revealed spectacular mechanistic variations: several thermophilic pAgos catalyse DNA-guided cleavage of double stranded DNA, but only at elevated temperatures. Interestingly, a recently discovered mesophilic Argonaute (CbAgo) can generate double strand DNA breaks at moderate temperatures, providing an excellent basis for this ARGO project. In addition, genome analysis has revealed many distantly-related Argonaute variants, often with unique domain architectures. Hence, the currently known Argonaute homologs are just the tip of the iceberg, and the stage is set for making a big leap in the exploration of the Argonaute family. Initially we will dissect the molecular basis of functional and mechanistic features of uncharacterized natural Argonaute variants, both in eukaryotes (the presence of an Ago-like subunit in the Mediator complex, strongly suggests a regulatory role of an elusive non-coding RNA ligand) and in prokaryotes (selected Ago variants possess distinct domains indicating novel functionalities). After their thorough biochemical characterization, I aim at engineering the functionality of the aforementioned CbAgo through an integrated rational & random approach, i.e. by tinkering of domains, and by an unprecedented in vitro laboratory evolution approach. Eventually, natural & synthetic Argonautes will be selected for their exploitation, and used for developing original genome editing applications (from silencing to base editing). Embarking on this ambitious ARGO expedition will lead us to many exciting discoveries.
Summary
Argonaute nucleases are key players of the eukaryotic RNA interference (RNAi) system. Using small RNA guides, these Argonaute (Ago) proteins specifically target complementary RNA molecules, resulting in regulation of a wide range of crucial processes, including chromosome organization, gene expression and anti-virus defence. Since 2010, my research team has studied closely-related prokaryotic Argonaute (pAgo) variants. This has revealed spectacular mechanistic variations: several thermophilic pAgos catalyse DNA-guided cleavage of double stranded DNA, but only at elevated temperatures. Interestingly, a recently discovered mesophilic Argonaute (CbAgo) can generate double strand DNA breaks at moderate temperatures, providing an excellent basis for this ARGO project. In addition, genome analysis has revealed many distantly-related Argonaute variants, often with unique domain architectures. Hence, the currently known Argonaute homologs are just the tip of the iceberg, and the stage is set for making a big leap in the exploration of the Argonaute family. Initially we will dissect the molecular basis of functional and mechanistic features of uncharacterized natural Argonaute variants, both in eukaryotes (the presence of an Ago-like subunit in the Mediator complex, strongly suggests a regulatory role of an elusive non-coding RNA ligand) and in prokaryotes (selected Ago variants possess distinct domains indicating novel functionalities). After their thorough biochemical characterization, I aim at engineering the functionality of the aforementioned CbAgo through an integrated rational & random approach, i.e. by tinkering of domains, and by an unprecedented in vitro laboratory evolution approach. Eventually, natural & synthetic Argonautes will be selected for their exploitation, and used for developing original genome editing applications (from silencing to base editing). Embarking on this ambitious ARGO expedition will lead us to many exciting discoveries.
Max ERC Funding
2 177 158 €
Duration
Start date: 2019-07-01, End date: 2024-06-30
Project acronym ATMMACHINE
Project Structural mechanism of recognition, signaling and resection of DNA double-strand breaks
Researcher (PI) Karl-Peter Hopfner
Host Institution (HI) LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Call Details Advanced Grant (AdG), LS1, ERC-2012-ADG_20120314
Summary DNA double-strand breaks are perhaps the most harmful DNA damages and result in carcinogenic chromosome aberrations. Cells protect their genome by activating a complex signaling and repair network, collectively denoted DNA damage response (DDR). A key initial step of the DDR is the activation of the 360 kDa checkpoint kinase ATM (ataxia telangiectasia mutated) by the multifunctional DSB repair factor Mre11-Rad50-Nbs1 (MRN). MRN senses and tethers DSBs, processes DSBs for further resection, and recruits and activates ATM to trigger the DDR. A mechanistic basis for the activities of the core DDR sensor MRN has not been established, despite intense research over the past decade. Our recent breakthroughs on structures of core Mre11-Rad50 and Mre11-Nbs1 complexes enable us now address three central questions to finally clarify the mechanism of MRN in the DDR:
- How does MRN interact with DNA or DNA ends in an ATP dependent manner?
- How do MRN and associated factors such as CtIP process blocked DNA ends?
- How do MRN and DNA activate ATM?
We will employ an innovative structural biology hybrid methods approach by combining X-ray crystallography, electron microscopy and small angle scattering with crosslink mass spectrometry and combine the structure-oriented techniques with validating in vitro and in vivo functional studies. The anticipated outcome will clarify the structural mechanism of one of the most important but enigmatic molecular machineries in maintaining genome stability and also help understand the molecular defects associated with several prominent cancer predisposition and neurodegenerative disorders.
Summary
DNA double-strand breaks are perhaps the most harmful DNA damages and result in carcinogenic chromosome aberrations. Cells protect their genome by activating a complex signaling and repair network, collectively denoted DNA damage response (DDR). A key initial step of the DDR is the activation of the 360 kDa checkpoint kinase ATM (ataxia telangiectasia mutated) by the multifunctional DSB repair factor Mre11-Rad50-Nbs1 (MRN). MRN senses and tethers DSBs, processes DSBs for further resection, and recruits and activates ATM to trigger the DDR. A mechanistic basis for the activities of the core DDR sensor MRN has not been established, despite intense research over the past decade. Our recent breakthroughs on structures of core Mre11-Rad50 and Mre11-Nbs1 complexes enable us now address three central questions to finally clarify the mechanism of MRN in the DDR:
- How does MRN interact with DNA or DNA ends in an ATP dependent manner?
- How do MRN and associated factors such as CtIP process blocked DNA ends?
- How do MRN and DNA activate ATM?
We will employ an innovative structural biology hybrid methods approach by combining X-ray crystallography, electron microscopy and small angle scattering with crosslink mass spectrometry and combine the structure-oriented techniques with validating in vitro and in vivo functional studies. The anticipated outcome will clarify the structural mechanism of one of the most important but enigmatic molecular machineries in maintaining genome stability and also help understand the molecular defects associated with several prominent cancer predisposition and neurodegenerative disorders.
Max ERC Funding
2 498 019 €
Duration
Start date: 2013-05-01, End date: 2018-04-30
