Project acronym 3D Reloaded
Project 3D Reloaded: Novel Algorithms for 3D Shape Inference and Analysis
Researcher (PI) Daniel Cremers
Host Institution (HI) TECHNISCHE UNIVERSITAET MUENCHEN
Call Details Consolidator Grant (CoG), PE6, ERC-2014-CoG
Summary Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world.
In this project, we will focus on three lines of research:
A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion.
B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape).
C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times.
Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.
Summary
Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world.
In this project, we will focus on three lines of research:
A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion.
B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape).
C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times.
Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.
Max ERC Funding
2 000 000 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym 3D_Tryps
Project The role of three-dimensional genome architecture in antigenic variation
Researcher (PI) Tim Nicolai SIEGEL
Host Institution (HI) LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Call Details Starting Grant (StG), LS6, ERC-2016-STG
Summary Antigenic variation is a widely employed strategy to evade the host immune response. It has similar functional requirements even in evolutionarily divergent pathogens. These include the mutually exclusive expression of antigens and the periodic, nonrandom switching in the expression of different antigens during the course of an infection. Despite decades of research the mechanisms of antigenic variation are not fully understood in any organism.
The recent development of high-throughput sequencing-based assays to probe the 3D genome architecture (Hi-C) has revealed the importance of the spatial organization of DNA inside the nucleus. 3D genome architecture plays a critical role in the regulation of mutually exclusive gene expression and the frequency of translocation between different genomic loci in many eukaryotes. Thus, genome architecture may also be a key regulator of antigenic variation, yet the causal links between genome architecture and the expression of antigens have not been studied systematically. In addition, the development of CRISPR-Cas9-based approaches to perform nucleotide-specific genome editing has opened unprecedented opportunities to study the influence of DNA sequence elements on the spatial organization of DNA and how this impacts antigen expression.
I have adapted both Hi-C and CRISPR-Cas9 technology to the protozoan parasite Trypanosoma brucei, one of the most important model organisms to study antigenic variation. These techniques will enable me to bridge the field of antigenic variation research with that of genome architecture. I will perform the first systematic analysis of the role of genome architecture in the mutually exclusive and hierarchical expression of antigens in any pathogen.
The experiments outlined in this proposal will provide new insight, facilitating a new view of antigenic variation and may eventually help medical intervention in T. brucei and in other pathogens relying on antigenic variation for their survival.
Summary
Antigenic variation is a widely employed strategy to evade the host immune response. It has similar functional requirements even in evolutionarily divergent pathogens. These include the mutually exclusive expression of antigens and the periodic, nonrandom switching in the expression of different antigens during the course of an infection. Despite decades of research the mechanisms of antigenic variation are not fully understood in any organism.
The recent development of high-throughput sequencing-based assays to probe the 3D genome architecture (Hi-C) has revealed the importance of the spatial organization of DNA inside the nucleus. 3D genome architecture plays a critical role in the regulation of mutually exclusive gene expression and the frequency of translocation between different genomic loci in many eukaryotes. Thus, genome architecture may also be a key regulator of antigenic variation, yet the causal links between genome architecture and the expression of antigens have not been studied systematically. In addition, the development of CRISPR-Cas9-based approaches to perform nucleotide-specific genome editing has opened unprecedented opportunities to study the influence of DNA sequence elements on the spatial organization of DNA and how this impacts antigen expression.
I have adapted both Hi-C and CRISPR-Cas9 technology to the protozoan parasite Trypanosoma brucei, one of the most important model organisms to study antigenic variation. These techniques will enable me to bridge the field of antigenic variation research with that of genome architecture. I will perform the first systematic analysis of the role of genome architecture in the mutually exclusive and hierarchical expression of antigens in any pathogen.
The experiments outlined in this proposal will provide new insight, facilitating a new view of antigenic variation and may eventually help medical intervention in T. brucei and in other pathogens relying on antigenic variation for their survival.
Max ERC Funding
1 498 175 €
Duration
Start date: 2017-04-01, End date: 2022-03-31
Project acronym 4DRepLy
Project Closing the 4D Real World Reconstruction Loop
Researcher (PI) Christian THEOBALT
Host Institution (HI) MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
Call Details Consolidator Grant (CoG), PE6, ERC-2017-COG
Summary 4D reconstruction, the camera-based dense dynamic scene reconstruction, is a grand challenge in computer graphics and computer vision. Despite great progress, 4D capturing the complex, diverse real world outside a studio is still far from feasible. 4DRepLy builds a new generation of high-fidelity 4D reconstruction (4DRecon) methods. They will be the first to efficiently capture all types of deformable objects (humans and other types) in crowded real world scenes with a single color or depth camera. They capture space-time coherent deforming geometry, motion, high-frequency reflectance and illumination at unprecedented detail, and will be the first to handle difficult occlusions, topology changes and large groups of interacting objects. They automatically adapt to new scene types, yet deliver models with meaningful, interpretable parameters. This requires far reaching contributions: First, we develop groundbreaking new plasticity-enhanced model-based 4D reconstruction methods that automatically adapt to new scenes. Second, we develop radically new machine learning-based dense 4D reconstruction methods. Third, these model- and learning-based methods are combined in two revolutionary new classes of 4DRecon methods: 1) advanced fusion-based methods and 2) methods with deep architectural integration. Both, 1) and 2), are automatically designed in the 4D Real World Reconstruction Loop, a revolutionary new design paradigm in which 4DRecon methods refine and adapt themselves while continuously processing unlabeled real world input. This overcomes the previously unbreakable scalability barrier to real world scene diversity, complexity and generality. This paradigm shift opens up a new research direction in graphics and vision and has far reaching relevance across many scientific fields. It enables new applications of profound social pervasion and significant economic impact, e.g., for visual media and virtual/augmented reality, and for future autonomous and robotic systems.
Summary
4D reconstruction, the camera-based dense dynamic scene reconstruction, is a grand challenge in computer graphics and computer vision. Despite great progress, 4D capturing the complex, diverse real world outside a studio is still far from feasible. 4DRepLy builds a new generation of high-fidelity 4D reconstruction (4DRecon) methods. They will be the first to efficiently capture all types of deformable objects (humans and other types) in crowded real world scenes with a single color or depth camera. They capture space-time coherent deforming geometry, motion, high-frequency reflectance and illumination at unprecedented detail, and will be the first to handle difficult occlusions, topology changes and large groups of interacting objects. They automatically adapt to new scene types, yet deliver models with meaningful, interpretable parameters. This requires far reaching contributions: First, we develop groundbreaking new plasticity-enhanced model-based 4D reconstruction methods that automatically adapt to new scenes. Second, we develop radically new machine learning-based dense 4D reconstruction methods. Third, these model- and learning-based methods are combined in two revolutionary new classes of 4DRecon methods: 1) advanced fusion-based methods and 2) methods with deep architectural integration. Both, 1) and 2), are automatically designed in the 4D Real World Reconstruction Loop, a revolutionary new design paradigm in which 4DRecon methods refine and adapt themselves while continuously processing unlabeled real world input. This overcomes the previously unbreakable scalability barrier to real world scene diversity, complexity and generality. This paradigm shift opens up a new research direction in graphics and vision and has far reaching relevance across many scientific fields. It enables new applications of profound social pervasion and significant economic impact, e.g., for visual media and virtual/augmented reality, and for future autonomous and robotic systems.
Max ERC Funding
1 977 000 €
Duration
Start date: 2018-09-01, End date: 2023-08-31
Project acronym ACCORD
Project Algorithms for Complex Collective Decisions on Structured Domains
Researcher (PI) Edith Elkind
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Algorithms for Complex Collective Decisions on Structured Domains.
The aim of this proposal is to substantially advance the field of Computational Social Choice, by developing new tools and methodologies that can be used for making complex group decisions in rich and structured environments. We consider settings where each member of a decision-making body has preferences over a finite set of alternatives, and the goal is to synthesise a collective preference over these alternatives, which may take the form of a partial order over the set of alternatives with a predefined structure: examples include selecting a fixed-size set of alternatives, a ranking of the alternatives, a winner and up to two runner-ups, etc. We will formulate desiderata that apply to such preference aggregation procedures, design specific procedures that satisfy as many of these desiderata as possible, and develop efficient algorithms for computing them. As the latter step may be infeasible on general preference domains, we will focus on identifying the least restrictive domains that enable efficient computation, and use real-life preference data to verify whether the associated restrictions are likely to be satisfied in realistic preference aggregation scenarios. Also, we will determine whether our preference aggregation procedures are computationally resistant to malicious behavior. To lower the cognitive burden on the decision-makers, we will extend our procedures to accept partial rankings as inputs. Finally, to further contribute towards bridging the gap between theory and practice of collective decision making, we will provide open-source software implementations of our procedures, and reach out to the potential users to obtain feedback on their practical applicability.
Summary
Algorithms for Complex Collective Decisions on Structured Domains.
The aim of this proposal is to substantially advance the field of Computational Social Choice, by developing new tools and methodologies that can be used for making complex group decisions in rich and structured environments. We consider settings where each member of a decision-making body has preferences over a finite set of alternatives, and the goal is to synthesise a collective preference over these alternatives, which may take the form of a partial order over the set of alternatives with a predefined structure: examples include selecting a fixed-size set of alternatives, a ranking of the alternatives, a winner and up to two runner-ups, etc. We will formulate desiderata that apply to such preference aggregation procedures, design specific procedures that satisfy as many of these desiderata as possible, and develop efficient algorithms for computing them. As the latter step may be infeasible on general preference domains, we will focus on identifying the least restrictive domains that enable efficient computation, and use real-life preference data to verify whether the associated restrictions are likely to be satisfied in realistic preference aggregation scenarios. Also, we will determine whether our preference aggregation procedures are computationally resistant to malicious behavior. To lower the cognitive burden on the decision-makers, we will extend our procedures to accept partial rankings as inputs. Finally, to further contribute towards bridging the gap between theory and practice of collective decision making, we will provide open-source software implementations of our procedures, and reach out to the potential users to obtain feedback on their practical applicability.
Max ERC Funding
1 395 933 €
Duration
Start date: 2015-07-01, End date: 2020-06-30
Project acronym ACDC
Project Algorithms and Complexity of Highly Decentralized Computations
Researcher (PI) Fabian Daniel Kuhn
Host Institution (HI) ALBERT-LUDWIGS-UNIVERSITAET FREIBURG
Call Details Starting Grant (StG), PE6, ERC-2013-StG
Summary "Many of today's and tomorrow's computer systems are built on top of large-scale networks such as, e.g., the Internet, the world wide web, wireless ad hoc and sensor networks, or peer-to-peer networks. Driven by technological advances, new kinds of networks and applications have become possible and we can safely assume that this trend is going to continue. Often modern systems are envisioned to consist of a potentially large number of individual components that are organized in a completely decentralized way. There is no central authority that controls the topology of the network, how nodes join or leave the system, or in which way nodes communicate with each other. Also, many future distributed applications will be built using wireless devices that communicate via radio.
The general objective of the proposed project is to improve our understanding of the algorithmic and theoretical foundations of decentralized distributed systems. From an algorithmic point of view, decentralized networks and computations pose a number of fascinating and unique challenges that are not present in sequential or more standard distributed systems. As communication is limited and mostly between nearby nodes, each node of a large network can only maintain a very restricted view of the global state of the system. This is particularly true if the network can change dynamically, either by nodes joining or leaving the system or if the topology changes over time, e.g., because of the mobility of the devices in case of a wireless network. Nevertheless, the nodes of a network need to coordinate in order to achieve some global goal.
In particular, we plan to study algorithms and lower bounds for basic computation and information dissemination tasks in such systems. In addition, we are particularly interested in the complexity of distributed computations in dynamic and wireless networks."
Summary
"Many of today's and tomorrow's computer systems are built on top of large-scale networks such as, e.g., the Internet, the world wide web, wireless ad hoc and sensor networks, or peer-to-peer networks. Driven by technological advances, new kinds of networks and applications have become possible and we can safely assume that this trend is going to continue. Often modern systems are envisioned to consist of a potentially large number of individual components that are organized in a completely decentralized way. There is no central authority that controls the topology of the network, how nodes join or leave the system, or in which way nodes communicate with each other. Also, many future distributed applications will be built using wireless devices that communicate via radio.
The general objective of the proposed project is to improve our understanding of the algorithmic and theoretical foundations of decentralized distributed systems. From an algorithmic point of view, decentralized networks and computations pose a number of fascinating and unique challenges that are not present in sequential or more standard distributed systems. As communication is limited and mostly between nearby nodes, each node of a large network can only maintain a very restricted view of the global state of the system. This is particularly true if the network can change dynamically, either by nodes joining or leaving the system or if the topology changes over time, e.g., because of the mobility of the devices in case of a wireless network. Nevertheless, the nodes of a network need to coordinate in order to achieve some global goal.
In particular, we plan to study algorithms and lower bounds for basic computation and information dissemination tasks in such systems. In addition, we are particularly interested in the complexity of distributed computations in dynamic and wireless networks."
Max ERC Funding
1 148 000 €
Duration
Start date: 2013-11-01, End date: 2018-10-31
Project acronym ACROSS
Project 3D Reconstruction and Modeling across Different Levels of Abstraction
Researcher (PI) Leif Kobbelt
Host Institution (HI) RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary "Digital 3D models are gaining more and more importance in diverse application fields ranging from computer graphics, multimedia and simulation sciences to engineering, architecture, and medicine. Powerful technologies to digitize the 3D shape of real objects and scenes are becoming available even to consumers. However, the raw geometric data emerging from, e.g., 3D scanning or multi-view stereo often lacks a consistent structure and meta-information which are necessary for the effective deployment of such models in sophisticated down-stream applications like animation, simulation, or CAD/CAM that go beyond mere visualization. Our goal is to develop new fundamental algorithms which transform raw geometric input data into augmented 3D models that are equipped with structural meta information such as feature aligned meshes, patch segmentations, local and global geometric constraints, statistical shape variation data, or even procedural descriptions. Our methodological approach is inspired by the human perceptual system that integrates bottom-up (data-driven) and top-down (model-driven) mechanisms in its hierarchical processing. Similarly we combine algorithms operating on different levels of abstraction into reconstruction and modeling networks. Instead of developing an individual solution for each specific application scenario, we create an eco-system of algorithms for automatic processing and interactive design of highly complex 3D models. A key concept is the information flow across all levels of abstraction in a bottom-up as well as top-down fashion. We not only aim at optimizing geometric representations but in fact at bridging the gap between reconstruction and recognition of geometric objects. The results from this project will make it possible to bring 3D models of real world objects into many highly relevant applications in science, industry, and entertainment, greatly reducing the excessive manual effort that is still necessary today."
Summary
"Digital 3D models are gaining more and more importance in diverse application fields ranging from computer graphics, multimedia and simulation sciences to engineering, architecture, and medicine. Powerful technologies to digitize the 3D shape of real objects and scenes are becoming available even to consumers. However, the raw geometric data emerging from, e.g., 3D scanning or multi-view stereo often lacks a consistent structure and meta-information which are necessary for the effective deployment of such models in sophisticated down-stream applications like animation, simulation, or CAD/CAM that go beyond mere visualization. Our goal is to develop new fundamental algorithms which transform raw geometric input data into augmented 3D models that are equipped with structural meta information such as feature aligned meshes, patch segmentations, local and global geometric constraints, statistical shape variation data, or even procedural descriptions. Our methodological approach is inspired by the human perceptual system that integrates bottom-up (data-driven) and top-down (model-driven) mechanisms in its hierarchical processing. Similarly we combine algorithms operating on different levels of abstraction into reconstruction and modeling networks. Instead of developing an individual solution for each specific application scenario, we create an eco-system of algorithms for automatic processing and interactive design of highly complex 3D models. A key concept is the information flow across all levels of abstraction in a bottom-up as well as top-down fashion. We not only aim at optimizing geometric representations but in fact at bridging the gap between reconstruction and recognition of geometric objects. The results from this project will make it possible to bring 3D models of real world objects into many highly relevant applications in science, industry, and entertainment, greatly reducing the excessive manual effort that is still necessary today."
Max ERC Funding
2 482 000 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
Project acronym AHRIMMUNITY
Project The influence of Aryl hydrocarbon receptor ligands on protective and pathological immune responses
Researcher (PI) Brigitta Stockinger
Host Institution (HI) MEDICAL RESEARCH COUNCIL
Call Details Advanced Grant (AdG), LS6, ERC-2008-AdG
Summary The Aryl hydrocarbon receptor is an evolutionary conserved widely expressed transcription factor that mediates the toxicity of a substantial variety of exogenous toxins, but is also stimulated by endogenous physiological ligands. While it is known that this receptor mediates the toxicity of dioxin, this is unlikely to be its physiological function. We have recently identified selective expression of AhR in the Th17 subset of effector CD4 T cells. Ligation of AhR by a candidate endogenous ligand (FICZ) which is a UV metabolite of tryptophan causes expansion of Th17 cells and the induction of IL-22 production. As a consequence, AhR ligation will exacerbate autoimmune diseases such as experimental autoimmune encephalomyelitis. Little is known so far about the impact of AhR ligands on IL-17/IL-22 mediated immune defense functions. IL-22 is considered a pro-inflammatory Th17 cytokine, which is involved in the etiology of psoriasis, but it has also been shown to be a survival factor for epithelial cells. AhR is polymorphic and defined as high or low affinity receptor for dioxin leading to the classification of high and low responder mouse strains based on defined mutations. In humans similar polymorphisms exist and although on the whole human AhR is thought to be of low affinity in humans, there are identified mutations that confer high responder status. No correlations have been made with Th17 mediated immune responses in mice and humans. This study aims to investigate the role of AhR ligands and polymorphisms in autoimmunity as well as protective immune responses using both mouse models and human samples from normal controls as well as psoriasis patients.
Summary
The Aryl hydrocarbon receptor is an evolutionary conserved widely expressed transcription factor that mediates the toxicity of a substantial variety of exogenous toxins, but is also stimulated by endogenous physiological ligands. While it is known that this receptor mediates the toxicity of dioxin, this is unlikely to be its physiological function. We have recently identified selective expression of AhR in the Th17 subset of effector CD4 T cells. Ligation of AhR by a candidate endogenous ligand (FICZ) which is a UV metabolite of tryptophan causes expansion of Th17 cells and the induction of IL-22 production. As a consequence, AhR ligation will exacerbate autoimmune diseases such as experimental autoimmune encephalomyelitis. Little is known so far about the impact of AhR ligands on IL-17/IL-22 mediated immune defense functions. IL-22 is considered a pro-inflammatory Th17 cytokine, which is involved in the etiology of psoriasis, but it has also been shown to be a survival factor for epithelial cells. AhR is polymorphic and defined as high or low affinity receptor for dioxin leading to the classification of high and low responder mouse strains based on defined mutations. In humans similar polymorphisms exist and although on the whole human AhR is thought to be of low affinity in humans, there are identified mutations that confer high responder status. No correlations have been made with Th17 mediated immune responses in mice and humans. This study aims to investigate the role of AhR ligands and polymorphisms in autoimmunity as well as protective immune responses using both mouse models and human samples from normal controls as well as psoriasis patients.
Max ERC Funding
1 242 352 €
Duration
Start date: 2009-02-01, End date: 2014-01-31
Project acronym AIM2 INFLAMMASOME
Project Cytosolic recognition of foreign nucleic acids: Molecular and functional characterization of AIM2, a central player in DNA-triggered inflammasome activation
Researcher (PI) Veit Hornung
Host Institution (HI) UNIVERSITAETSKLINIKUM BONN
Call Details Starting Grant (StG), LS6, ERC-2009-StG
Summary Host cytokines, chemokines and type I IFNs are critical effectors of the innate immune response to viral and bacterial pathogens. Several classes of germ-line encoded pattern recognition receptors have been identified, which sense non-self nucleic acids and trigger these responses. Recently NLRP-3, a member of the NOD-like receptor (NLR) family, has been shown to sense endogenous danger signals, environmental insults and the DNA viruses adenovirus and HSV. Activation of NLRP-3 induces the formation of a large multiprotein complex in cells termed inflammasome , which controls the activity of pro-caspase-1 and the maturation of pro-IL-1² and pro-IL18 into their active forms. NLRP-3, however, does not regulate these responses to double stranded cytosolic DNA. We identified the cytosolic protein AIM2 as the missing receptor for cytosolic DNA. AIM2 contains a HIN200 domain, which binds to DNA and a pyrin domain, which associates with the adapter molecule ASC to activate both NF-ºB and caspase-1. Knock down of AIM2 down-regulates caspase-1-mediated IL-1² responses following DNA stimulation or vaccinia virus infection. Collectively, these observations demonstrate that AIM2 forms an inflammasome with the DNA ligand and ASC to activate caspase-1. Our underlying hypothesis for this proposal is that AIM2 plays a central role in host-defence to cytosolic microbial pathogens and also in DNA-triggered autoimmunity. The goals of this research proposal are to further characterize the DNA ligand for AIM2, to explore the molecular mechanisms of AIM2 activation, to define the contribution of AIM2 to host-defence against viral and bacterial pathogens and to assess its function in nucleic acid triggered autoimmune disease. The characterization of AIM2 and its role in innate immunity could open new avenues in the advancement of immunotherapy and treatment of autoimmune disease.
Summary
Host cytokines, chemokines and type I IFNs are critical effectors of the innate immune response to viral and bacterial pathogens. Several classes of germ-line encoded pattern recognition receptors have been identified, which sense non-self nucleic acids and trigger these responses. Recently NLRP-3, a member of the NOD-like receptor (NLR) family, has been shown to sense endogenous danger signals, environmental insults and the DNA viruses adenovirus and HSV. Activation of NLRP-3 induces the formation of a large multiprotein complex in cells termed inflammasome , which controls the activity of pro-caspase-1 and the maturation of pro-IL-1² and pro-IL18 into their active forms. NLRP-3, however, does not regulate these responses to double stranded cytosolic DNA. We identified the cytosolic protein AIM2 as the missing receptor for cytosolic DNA. AIM2 contains a HIN200 domain, which binds to DNA and a pyrin domain, which associates with the adapter molecule ASC to activate both NF-ºB and caspase-1. Knock down of AIM2 down-regulates caspase-1-mediated IL-1² responses following DNA stimulation or vaccinia virus infection. Collectively, these observations demonstrate that AIM2 forms an inflammasome with the DNA ligand and ASC to activate caspase-1. Our underlying hypothesis for this proposal is that AIM2 plays a central role in host-defence to cytosolic microbial pathogens and also in DNA-triggered autoimmunity. The goals of this research proposal are to further characterize the DNA ligand for AIM2, to explore the molecular mechanisms of AIM2 activation, to define the contribution of AIM2 to host-defence against viral and bacterial pathogens and to assess its function in nucleic acid triggered autoimmune disease. The characterization of AIM2 and its role in innate immunity could open new avenues in the advancement of immunotherapy and treatment of autoimmune disease.
Max ERC Funding
1 727 920 €
Duration
Start date: 2009-12-01, End date: 2014-11-30
Project acronym ALBUGON
Project Genomics and effectoromics to understand defence suppression and disease resistance in Arabidopsis-Albugo candida interactions
Researcher (PI) Jonathan Jones
Host Institution (HI) THE SAINSBURY LABORATORY
Call Details Advanced Grant (AdG), LS6, ERC-2008-AdG
Summary This project focuses on two questions about host/parasite interactions: how do biotrophic plant pathogens suppress host defence? and, what is the basis for pathogen specialization on specific host species? A broadly accepted model explains resistance and susceptibility to plant pathogens. First, pathogens make conserved molecules ( PAMPS ) such as flagellin, that plants detect via cell surface receptors, leading to PAMP-Triggered Immunity (PTI). Second, pathogens make effectors that suppress PTI. Third, plants carry 100s of Resistance (R) genes that detect an effector, and activate Effector-Triggered Immunity (ETI). One effector is sufficient to trigger resistance. Albugo candida (Ac) (white rust) strongly suppresses host defence; Ac-infected Arabidopsis are susceptible to pathogen races to which they are otherwise resistant. Ac is an oomycete, not a fungus. Arabidopsis is resistant to races of Ac that infect brassicas. The proposed project involves three programs. First ( genomics, transcriptomics and bioinformatics ), we will use next-generation sequencing (NGS) methods (Solexa and GS-Flex), and novel transcriptomics methods to define the genome sequence and effector set of three Ac strains, as well as carrying out >40- deep resequencing of 7 additional Ac strains. Second, ( effectoromics ), we will carry out functional assays using Effector Detector Vectors (Sohn Plant Cell 19:4077 [2007]), with the set of Ac effectors, screening for enhanced virulence, for suppression of defence, for effectors that are recognized by R genes in disease resistant Arabidopsis and for host effector targets. Third, ( resistance diversity ), we will characterize Arabidopsis germplasm for R genes to Ac, both for recognition of Arabidopsis strains of Ac, and for recognition in Arabidopsis of effectors from Ac strains that infect brassica. This proposal focuses on Ac, but will establish methods that could discover new R genes in non-hosts against many plant diseases.
Summary
This project focuses on two questions about host/parasite interactions: how do biotrophic plant pathogens suppress host defence? and, what is the basis for pathogen specialization on specific host species? A broadly accepted model explains resistance and susceptibility to plant pathogens. First, pathogens make conserved molecules ( PAMPS ) such as flagellin, that plants detect via cell surface receptors, leading to PAMP-Triggered Immunity (PTI). Second, pathogens make effectors that suppress PTI. Third, plants carry 100s of Resistance (R) genes that detect an effector, and activate Effector-Triggered Immunity (ETI). One effector is sufficient to trigger resistance. Albugo candida (Ac) (white rust) strongly suppresses host defence; Ac-infected Arabidopsis are susceptible to pathogen races to which they are otherwise resistant. Ac is an oomycete, not a fungus. Arabidopsis is resistant to races of Ac that infect brassicas. The proposed project involves three programs. First ( genomics, transcriptomics and bioinformatics ), we will use next-generation sequencing (NGS) methods (Solexa and GS-Flex), and novel transcriptomics methods to define the genome sequence and effector set of three Ac strains, as well as carrying out >40- deep resequencing of 7 additional Ac strains. Second, ( effectoromics ), we will carry out functional assays using Effector Detector Vectors (Sohn Plant Cell 19:4077 [2007]), with the set of Ac effectors, screening for enhanced virulence, for suppression of defence, for effectors that are recognized by R genes in disease resistant Arabidopsis and for host effector targets. Third, ( resistance diversity ), we will characterize Arabidopsis germplasm for R genes to Ac, both for recognition of Arabidopsis strains of Ac, and for recognition in Arabidopsis of effectors from Ac strains that infect brassica. This proposal focuses on Ac, but will establish methods that could discover new R genes in non-hosts against many plant diseases.
Max ERC Funding
2 498 923 €
Duration
Start date: 2009-01-01, End date: 2014-06-30
Project acronym ALEXANDRIA
Project "Foundations for Temporal Retrieval, Exploration and Analytics in Web Archives"
Researcher (PI) Wolfgang Nejdl
Host Institution (HI) GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary "Significant parts of our cultural heritage are produced on the Web, yet only insufficient opportunities exist for accessing and exploring the past of the Web. The ALEXANDRIA project aims to develop models, tools and techniques necessary to archive and index relevant parts of the Web, and to retrieve and explore this information in a meaningful way. While the easy accessibility to the current Web is a good baseline, optimal access to Web archives requires new models and algorithms for retrieval, exploration, and analytics which go far beyond what is needed to access the current state of the Web. This includes taking into account the unique temporal dimension of Web archives, structured semantic information already available on the Web, as well as social media and network information.
Within ALEXANDRIA, we will significantly advance semantic and time-based indexing for Web archives using human-compiled knowledge available on the Web, to efficiently index, retrieve and explore information about entities and events from the past. In doing so, we will focus on the concurrent evolution of this knowledge and the Web content to be indexed, and take into account diversity and incompleteness of this knowledge. We will further investigate mixed crowd- and machine-based Web analytics to support long- running and collaborative retrieval and analysis processes on Web archives. Usage of implicit human feedback will be essential to provide better indexing through insights during the analysis process and to better focus harvesting of content.
The ALEXANDRIA Testbed will provide an important context for research, exploration and evaluation of the concepts, methods and algorithms developed in this project, and will provide both relevant collections and algorithms that enable further research on and practical application of our research results to existing archives like the Internet Archive, the Internet Memory Foundation and Web archives maintained by European national libraries."
Summary
"Significant parts of our cultural heritage are produced on the Web, yet only insufficient opportunities exist for accessing and exploring the past of the Web. The ALEXANDRIA project aims to develop models, tools and techniques necessary to archive and index relevant parts of the Web, and to retrieve and explore this information in a meaningful way. While the easy accessibility to the current Web is a good baseline, optimal access to Web archives requires new models and algorithms for retrieval, exploration, and analytics which go far beyond what is needed to access the current state of the Web. This includes taking into account the unique temporal dimension of Web archives, structured semantic information already available on the Web, as well as social media and network information.
Within ALEXANDRIA, we will significantly advance semantic and time-based indexing for Web archives using human-compiled knowledge available on the Web, to efficiently index, retrieve and explore information about entities and events from the past. In doing so, we will focus on the concurrent evolution of this knowledge and the Web content to be indexed, and take into account diversity and incompleteness of this knowledge. We will further investigate mixed crowd- and machine-based Web analytics to support long- running and collaborative retrieval and analysis processes on Web archives. Usage of implicit human feedback will be essential to provide better indexing through insights during the analysis process and to better focus harvesting of content.
The ALEXANDRIA Testbed will provide an important context for research, exploration and evaluation of the concepts, methods and algorithms developed in this project, and will provide both relevant collections and algorithms that enable further research on and practical application of our research results to existing archives like the Internet Archive, the Internet Memory Foundation and Web archives maintained by European national libraries."
Max ERC Funding
2 493 600 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
