Project acronym ABC
Project Targeting Multidrug Resistant Cancer
Researcher (PI) Gergely Szakacs
Host Institution (HI) MAGYAR TUDOMANYOS AKADEMIA TERMESZETTUDOMANYI KUTATOKOZPONT
Call Details Starting Grant (StG), LS7, ERC-2010-StG_20091118
Summary Despite considerable advances in drug discovery, resistance to anticancer chemotherapy confounds the effective treatment of patients. Cancer cells can acquire broad cross-resistance to mechanistically and structurally unrelated drugs. P-glycoprotein (Pgp) actively extrudes many types of drugs from cancer cells, thereby conferring resistance to those agents. The central tenet of my work is that Pgp, a universally accepted biomarker of drug resistance, should in addition be considered as a molecular target of multidrug-resistant (MDR) cancer cells. Successful targeting of MDR cells would reduce the tumor burden and would also enable the elimination of ABC transporter-overexpressing cancer stem cells that are responsible for the replenishment of tumors. The proposed project is based on the following observations:
- First, by using a pharmacogenomic approach, I have revealed the hidden vulnerability of MDRcells (Szakács et al. 2004, Cancer Cell 6, 129-37);
- Second, I have identified a series of MDR-selective compounds with increased toxicity toPgp-expressing cells
(Turk et al.,Cancer Res, 2009. 69(21));
- Third, I have shown that MDR-selective compounds can be used to prevent theemergence of MDR (Ludwig, Szakács et al. 2006, Cancer Res 66, 4808-15);
- Fourth, we have generated initial pharmacophore models for cytotoxicity and MDR-selectivity (Hall et al. 2009, J Med Chem 52, 3191-3204).
I propose a comprehensive series of studies that will address thefollowing critical questions:
- First, what is the scope of MDR-selective compounds?
- Second, what is their mechanism of action?
- Third, what is the optimal therapeutic modality?
Extensive biological, pharmacological and bioinformatic analyses will be utilized to address four major specific aims. These aims address basic questions concerning the physiology of MDR ABC transporters in determining the mechanism of action of MDR-selective compounds, setting the stage for a fresh therapeutic approach that may eventually translate into improved patient care.
Summary
Despite considerable advances in drug discovery, resistance to anticancer chemotherapy confounds the effective treatment of patients. Cancer cells can acquire broad cross-resistance to mechanistically and structurally unrelated drugs. P-glycoprotein (Pgp) actively extrudes many types of drugs from cancer cells, thereby conferring resistance to those agents. The central tenet of my work is that Pgp, a universally accepted biomarker of drug resistance, should in addition be considered as a molecular target of multidrug-resistant (MDR) cancer cells. Successful targeting of MDR cells would reduce the tumor burden and would also enable the elimination of ABC transporter-overexpressing cancer stem cells that are responsible for the replenishment of tumors. The proposed project is based on the following observations:
- First, by using a pharmacogenomic approach, I have revealed the hidden vulnerability of MDRcells (Szakács et al. 2004, Cancer Cell 6, 129-37);
- Second, I have identified a series of MDR-selective compounds with increased toxicity toPgp-expressing cells
(Turk et al.,Cancer Res, 2009. 69(21));
- Third, I have shown that MDR-selective compounds can be used to prevent theemergence of MDR (Ludwig, Szakács et al. 2006, Cancer Res 66, 4808-15);
- Fourth, we have generated initial pharmacophore models for cytotoxicity and MDR-selectivity (Hall et al. 2009, J Med Chem 52, 3191-3204).
I propose a comprehensive series of studies that will address thefollowing critical questions:
- First, what is the scope of MDR-selective compounds?
- Second, what is their mechanism of action?
- Third, what is the optimal therapeutic modality?
Extensive biological, pharmacological and bioinformatic analyses will be utilized to address four major specific aims. These aims address basic questions concerning the physiology of MDR ABC transporters in determining the mechanism of action of MDR-selective compounds, setting the stage for a fresh therapeutic approach that may eventually translate into improved patient care.
Max ERC Funding
1 499 640 €
Duration
Start date: 2012-01-01, End date: 2016-12-31
Project acronym ABEP
Project Asset Bubbles and Economic Policy
Researcher (PI) Jaume Ventura Fontanet
Host Institution (HI) Centre de Recerca en Economia Internacional (CREI)
Call Details Advanced Grant (AdG), SH1, ERC-2009-AdG
Summary Advanced capitalist economies experience large and persistent movements in asset prices that are difficult to justify with economic fundamentals. The internet bubble of the 1990s and the real state market bubble of the 2000s are two recent examples. The predominant view is that these bubbles are a market failure, and are caused by some form of individual irrationality on the part of market participants. This project is based instead on the view that market participants are individually rational, although this does not preclude sometimes collectively sub-optimal outcomes. Bubbles are thus not a source of market failure by themselves but instead arise as a result of a pre-existing market failure, namely, the existence of pockets of dynamically inefficient investments. Under some conditions, bubbles partly solve this problem, increasing market efficiency and welfare. It is also possible however that bubbles do not solve the underlying problem and, in addition, create negative side-effects. The main objective of this project is to develop this view of asset bubbles, and produce an empirically-relevant macroeconomic framework that allows us to address the following questions: (i) What is the relationship between bubbles and financial market frictions? Special emphasis is given to how the globalization of financial markets and the development of new financial products affect the size and effects of bubbles. (ii) What is the relationship between bubbles, economic growth and unemployment? The theory suggests the presence of virtuous and vicious cycles, as economic growth creates the conditions for bubbles to pop up, while bubbles create incentives for economic growth to happen. (iii) What is the optimal policy to manage bubbles? We need to develop the tools that allow policy makers to sustain those bubbles that have positive effects and burst those that have negative effects.
Summary
Advanced capitalist economies experience large and persistent movements in asset prices that are difficult to justify with economic fundamentals. The internet bubble of the 1990s and the real state market bubble of the 2000s are two recent examples. The predominant view is that these bubbles are a market failure, and are caused by some form of individual irrationality on the part of market participants. This project is based instead on the view that market participants are individually rational, although this does not preclude sometimes collectively sub-optimal outcomes. Bubbles are thus not a source of market failure by themselves but instead arise as a result of a pre-existing market failure, namely, the existence of pockets of dynamically inefficient investments. Under some conditions, bubbles partly solve this problem, increasing market efficiency and welfare. It is also possible however that bubbles do not solve the underlying problem and, in addition, create negative side-effects. The main objective of this project is to develop this view of asset bubbles, and produce an empirically-relevant macroeconomic framework that allows us to address the following questions: (i) What is the relationship between bubbles and financial market frictions? Special emphasis is given to how the globalization of financial markets and the development of new financial products affect the size and effects of bubbles. (ii) What is the relationship between bubbles, economic growth and unemployment? The theory suggests the presence of virtuous and vicious cycles, as economic growth creates the conditions for bubbles to pop up, while bubbles create incentives for economic growth to happen. (iii) What is the optimal policy to manage bubbles? We need to develop the tools that allow policy makers to sustain those bubbles that have positive effects and burst those that have negative effects.
Max ERC Funding
1 000 000 €
Duration
Start date: 2010-04-01, End date: 2015-03-31
Project acronym ACCENT
Project Unravelling the architecture and the cartography of the human centriole
Researcher (PI) Paul, Philippe, Desiré GUICHARD
Host Institution (HI) UNIVERSITE DE GENEVE
Call Details Starting Grant (StG), LS1, ERC-2016-STG
Summary The centriole is the largest evolutionary conserved macromolecular structure responsible for building centrosomes and cilia or flagella in many eukaryotes. Centrioles are critical for the proper execution of important biological processes ranging from cell division to cell signaling. Moreover, centriolar defects have been associated to several human pathologies including ciliopathies and cancer. This state of facts emphasizes the importance of understanding centriole biogenesis. The study of centriole formation is a deep-rooted question, however our current knowledge on its molecular organization at high resolution remains fragmented and limited. In particular, exquisite details of the overall molecular architecture of the human centriole and in particular of its central core region are lacking to understand the basis of centriole organization and function. Resolving this important question represents a challenge that needs to be undertaken and will undoubtedly lead to groundbreaking advances. Another important question to tackle next is to develop innovative methods to enable the nanometric molecular mapping of centriolar proteins within distinct architectural elements of the centriole. This missing information will be key to unravel the molecular mechanisms behind centriolar organization.
This research proposal aims at building a cartography of the human centriole by elucidating its molecular composition and architecture. To this end, we will combine the use of innovative and multidisciplinary techniques encompassing spatial proteomics, cryo-electron tomography, state-of-the-art microscopy and in vitro assays and to achieve a comprehensive molecular and structural view of the human centriole. All together, we expect that these advances will help understand basic principles underlying centriole and cilia formation as well as might have further relevance for human health.
Summary
The centriole is the largest evolutionary conserved macromolecular structure responsible for building centrosomes and cilia or flagella in many eukaryotes. Centrioles are critical for the proper execution of important biological processes ranging from cell division to cell signaling. Moreover, centriolar defects have been associated to several human pathologies including ciliopathies and cancer. This state of facts emphasizes the importance of understanding centriole biogenesis. The study of centriole formation is a deep-rooted question, however our current knowledge on its molecular organization at high resolution remains fragmented and limited. In particular, exquisite details of the overall molecular architecture of the human centriole and in particular of its central core region are lacking to understand the basis of centriole organization and function. Resolving this important question represents a challenge that needs to be undertaken and will undoubtedly lead to groundbreaking advances. Another important question to tackle next is to develop innovative methods to enable the nanometric molecular mapping of centriolar proteins within distinct architectural elements of the centriole. This missing information will be key to unravel the molecular mechanisms behind centriolar organization.
This research proposal aims at building a cartography of the human centriole by elucidating its molecular composition and architecture. To this end, we will combine the use of innovative and multidisciplinary techniques encompassing spatial proteomics, cryo-electron tomography, state-of-the-art microscopy and in vitro assays and to achieve a comprehensive molecular and structural view of the human centriole. All together, we expect that these advances will help understand basic principles underlying centriole and cilia formation as well as might have further relevance for human health.
Max ERC Funding
1 498 965 €
Duration
Start date: 2017-01-01, End date: 2021-12-31
Project acronym ACQDIV
Project Acquisition processes in maximally diverse languages: Min(d)ing the ambient language
Researcher (PI) Sabine Erika Stoll
Host Institution (HI) UNIVERSITAT ZURICH
Call Details Consolidator Grant (CoG), SH4, ERC-2013-CoG
Summary "Children learn any language that they grow up with, adapting to any of the ca. 7000 languages of the world, no matter how divergent or complex their structures are. What cognitive processes make this extreme flexibility possible? This is one of the most burning questions in cognitive science and the ACQDIV project aims at answering it by testing and refining the following leading hypothesis: Language acquisition is flexible and adaptive to any kind of language because it relies on a small set of universal cognitive processes that variably target different structures at different times during acquisition in every language. The project aims at establishing the precise set of processes and at determining the conditions of variation across maximally diverse languages. This project focuses on three processes: (i) distributional learning, (ii) generalization-based learning and (iii) interaction-based learning. To investigate these processes I will work with a sample of five clusters of languages including longitudinal data of two languages each. The clusters were determined by a clustering algorithm seeking the structurally most divergent languages in a typological database. The languages are: Cluster 1: Slavey and Cree, Cluster 2: Indonesian and Yucatec, Cluster 3: Inuktitut and Chintang, Cluster 4: Sesotho and Russian, Cluster 5: Japanese and Turkish. For all languages, corpora are available, except for Slavey where fieldwork is planned. The leading hypothesis will be tested against the acquisition of aspect and negation in each language of the sample and also against the two structures in each language that are most salient and challenging in them (e. g. complex morphology in Chintang). The acquisition processes also depend on statistical patterns in the input children receive. I will examine these patterns across the sample with respect to repetitiveness effects, applying data-mining methods and systematically comparing child-directed and child-surrounding speech."
Summary
"Children learn any language that they grow up with, adapting to any of the ca. 7000 languages of the world, no matter how divergent or complex their structures are. What cognitive processes make this extreme flexibility possible? This is one of the most burning questions in cognitive science and the ACQDIV project aims at answering it by testing and refining the following leading hypothesis: Language acquisition is flexible and adaptive to any kind of language because it relies on a small set of universal cognitive processes that variably target different structures at different times during acquisition in every language. The project aims at establishing the precise set of processes and at determining the conditions of variation across maximally diverse languages. This project focuses on three processes: (i) distributional learning, (ii) generalization-based learning and (iii) interaction-based learning. To investigate these processes I will work with a sample of five clusters of languages including longitudinal data of two languages each. The clusters were determined by a clustering algorithm seeking the structurally most divergent languages in a typological database. The languages are: Cluster 1: Slavey and Cree, Cluster 2: Indonesian and Yucatec, Cluster 3: Inuktitut and Chintang, Cluster 4: Sesotho and Russian, Cluster 5: Japanese and Turkish. For all languages, corpora are available, except for Slavey where fieldwork is planned. The leading hypothesis will be tested against the acquisition of aspect and negation in each language of the sample and also against the two structures in each language that are most salient and challenging in them (e. g. complex morphology in Chintang). The acquisition processes also depend on statistical patterns in the input children receive. I will examine these patterns across the sample with respect to repetitiveness effects, applying data-mining methods and systematically comparing child-directed and child-surrounding speech."
Max ERC Funding
1 998 438 €
Duration
Start date: 2014-09-01, End date: 2019-08-31
Project acronym ActionContraThreat
Project Action selection under threat: the complex control of human defense
Researcher (PI) Dominik BACH
Host Institution (HI) UNIVERSITAT ZURICH
Call Details Consolidator Grant (CoG), SH4, ERC-2018-COG
Summary Run away, sidestep, duck-and-cover, watch: when under threat, humans immediately choreograph a large repertoire of defensive actions. Understanding action-selection under threat is important for anybody wanting to explain why anxiety disorders imply some of these behaviours in harmless situations. Current concepts of human defensive behaviour are largely derived from rodent research and focus on a small number of broad, cross-species, action tendencies. This is likely to underestimate the complexity of the underlying action-selection mechanisms. This research programme will take decisive steps to understand these psychological mechanisms and elucidate their neural implementation.
To elicit threat-related action in the laboratory, I will use virtual reality computer games with full body motion, and track actions with motion-capture technology. Based on a cognitive-computational framework, I will systematically characterise the space of actions under threat, investigate the psychological mechanisms by which actions are selected in different scenarios, and describe them with computational algorithms that allow quantitative predictions. To independently verify their neural implementation, I will use wearable magnetoencephalography (MEG) in freely moving subjects.
This proposal fills a lacuna between defence system concepts based on rodent research, emotion psychology, and clinical accounts of anxiety disorders. By combining a stringent experimental approach with the formalism of cognitive-computational psychology, it furnishes a unique opportunity to understand the mechanisms of action-selection under threat, and how these are distinct from more general-purpose action-selection systems. Beyond its immediate scope, the proposal has a potential to lead to a better understanding of anxiety disorders, and to pave the way towards improved diagnostics and therapies.
Summary
Run away, sidestep, duck-and-cover, watch: when under threat, humans immediately choreograph a large repertoire of defensive actions. Understanding action-selection under threat is important for anybody wanting to explain why anxiety disorders imply some of these behaviours in harmless situations. Current concepts of human defensive behaviour are largely derived from rodent research and focus on a small number of broad, cross-species, action tendencies. This is likely to underestimate the complexity of the underlying action-selection mechanisms. This research programme will take decisive steps to understand these psychological mechanisms and elucidate their neural implementation.
To elicit threat-related action in the laboratory, I will use virtual reality computer games with full body motion, and track actions with motion-capture technology. Based on a cognitive-computational framework, I will systematically characterise the space of actions under threat, investigate the psychological mechanisms by which actions are selected in different scenarios, and describe them with computational algorithms that allow quantitative predictions. To independently verify their neural implementation, I will use wearable magnetoencephalography (MEG) in freely moving subjects.
This proposal fills a lacuna between defence system concepts based on rodent research, emotion psychology, and clinical accounts of anxiety disorders. By combining a stringent experimental approach with the formalism of cognitive-computational psychology, it furnishes a unique opportunity to understand the mechanisms of action-selection under threat, and how these are distinct from more general-purpose action-selection systems. Beyond its immediate scope, the proposal has a potential to lead to a better understanding of anxiety disorders, and to pave the way towards improved diagnostics and therapies.
Max ERC Funding
1 998 750 €
Duration
Start date: 2019-10-01, End date: 2024-09-30
Project acronym ADDABU
Project Automated detection of damage to buildings
Researcher (PI) Luc VAN GOOL
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Call Details Proof of Concept (PoC), ERC-2017-PoC
Summary Hail and storm damages represent the most often occurring cases for building insurance companies. Currently, the damage is estimated by an insurance expert, visiting the damaged building and drafting a report. Researchers at the Computer Vision Lab at ETH Zurich joined forces with business and sales people, spinning out the company Casalva, to strongly reduce such costs via automated image analysis. The idea is that the insurers’ clients upload photos of the damages, which will then be analyzed automatically by a computer. This involves computer vision technologies – grounded in the ERC project VarCity – to recognize the damaged building structures and to analyze the corresponding textures as to assess the extent of the damage and the estimated costs for its repair. Cutting costs is not the only consideration, as the fast assessment of damages improves customer satisfaction and prevents the occurrence of additional damages because of a delayed repair (like water leaking before repair). Such follow-on damages are estimated to be 20% of overall costs on average and are therefore far from negligible. Guaranteeing a short term response currently is a major issue, as a single storm may affect thousands of buildings. Processing times tend to stretch out due to the peak in cases following such extreme weather events. Over half of hail storm damage cases concern facade structures. The VarCity project produced methods to automatically parse facades into such structures, and to select the best way to describe their textures. These will be refined to optimally deal with the application area. The remaining technical developments and risk mitigations will be funded through other means (a Swiss project that has already been submitted), while this Proof-of-Concept project will focus on equally important aspects like market analysis, development of a corporate identity and graphical house style for the Casalva spin-off, that has been created but should now get market introduction.
Summary
Hail and storm damages represent the most often occurring cases for building insurance companies. Currently, the damage is estimated by an insurance expert, visiting the damaged building and drafting a report. Researchers at the Computer Vision Lab at ETH Zurich joined forces with business and sales people, spinning out the company Casalva, to strongly reduce such costs via automated image analysis. The idea is that the insurers’ clients upload photos of the damages, which will then be analyzed automatically by a computer. This involves computer vision technologies – grounded in the ERC project VarCity – to recognize the damaged building structures and to analyze the corresponding textures as to assess the extent of the damage and the estimated costs for its repair. Cutting costs is not the only consideration, as the fast assessment of damages improves customer satisfaction and prevents the occurrence of additional damages because of a delayed repair (like water leaking before repair). Such follow-on damages are estimated to be 20% of overall costs on average and are therefore far from negligible. Guaranteeing a short term response currently is a major issue, as a single storm may affect thousands of buildings. Processing times tend to stretch out due to the peak in cases following such extreme weather events. Over half of hail storm damage cases concern facade structures. The VarCity project produced methods to automatically parse facades into such structures, and to select the best way to describe their textures. These will be refined to optimally deal with the application area. The remaining technical developments and risk mitigations will be funded through other means (a Swiss project that has already been submitted), while this Proof-of-Concept project will focus on equally important aspects like market analysis, development of a corporate identity and graphical house style for the Casalva spin-off, that has been created but should now get market introduction.
Max ERC Funding
143 750 €
Duration
Start date: 2017-09-01, End date: 2018-08-31
Project acronym ADIPODIF
Project Adipocyte Differentiation and Metabolic Functions in Obesity and Type 2 Diabetes
Researcher (PI) Christian Wolfrum
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Call Details Starting Grant (StG), LS6, ERC-2007-StG
Summary Obesity associated disorders such as T2D, hypertension and CVD, commonly referred to as the “metabolic syndrome”, are prevalent diseases of industrialized societies. Deranged adipose tissue proliferation and differentiation contribute significantly to the development of these metabolic disorders. Comparatively little however is known, about how these processes influence the development of metabolic disorders. Using a multidisciplinary approach, I plan to elucidate molecular mechanisms underlying the altered adipocyte differentiation and maturation in different models of obesity associated metabolic disorders. Special emphasis will be given to the analysis of gene expression, postranslational modifications and lipid molecular species composition. To achieve this goal, I am establishing several novel methods to isolate pure primary preadipocytes including a new animal model that will allow me to monitor preadipocytes, in vivo and track their cellular fate in the context of a complete organism. These systems will allow, for the first time to study preadipocyte biology, in an in vivo setting. By monitoring preadipocyte differentiation in vivo, I will also be able to answer the key questions regarding the development of preadipocytes and examine signals that induce or inhibit their differentiation. Using transplantation techniques, I will elucidate the genetic and environmental contributions to the progression of obesity and its associated metabolic disorders. Furthermore, these studies will integrate a lipidomics approach to systematically analyze lipid molecular species composition in different models of metabolic disorders. My studies will provide new insights into the mechanisms and dynamics underlying adipocyte differentiation and maturation, and relate them to metabolic disorders. Detailed knowledge of these mechanisms will facilitate development of novel therapeutic approaches for the treatment of obesity and associated metabolic disorders.
Summary
Obesity associated disorders such as T2D, hypertension and CVD, commonly referred to as the “metabolic syndrome”, are prevalent diseases of industrialized societies. Deranged adipose tissue proliferation and differentiation contribute significantly to the development of these metabolic disorders. Comparatively little however is known, about how these processes influence the development of metabolic disorders. Using a multidisciplinary approach, I plan to elucidate molecular mechanisms underlying the altered adipocyte differentiation and maturation in different models of obesity associated metabolic disorders. Special emphasis will be given to the analysis of gene expression, postranslational modifications and lipid molecular species composition. To achieve this goal, I am establishing several novel methods to isolate pure primary preadipocytes including a new animal model that will allow me to monitor preadipocytes, in vivo and track their cellular fate in the context of a complete organism. These systems will allow, for the first time to study preadipocyte biology, in an in vivo setting. By monitoring preadipocyte differentiation in vivo, I will also be able to answer the key questions regarding the development of preadipocytes and examine signals that induce or inhibit their differentiation. Using transplantation techniques, I will elucidate the genetic and environmental contributions to the progression of obesity and its associated metabolic disorders. Furthermore, these studies will integrate a lipidomics approach to systematically analyze lipid molecular species composition in different models of metabolic disorders. My studies will provide new insights into the mechanisms and dynamics underlying adipocyte differentiation and maturation, and relate them to metabolic disorders. Detailed knowledge of these mechanisms will facilitate development of novel therapeutic approaches for the treatment of obesity and associated metabolic disorders.
Max ERC Funding
1 607 105 €
Duration
Start date: 2008-07-01, End date: 2013-06-30
Project acronym ADJUV-ANT VACCINES
Project Elucidating the Molecular Mechanisms of Synthetic Saponin Adjuvants and Development of Novel Self-Adjuvanting Vaccines
Researcher (PI) Alberto FERNANDEZ TEJADA
Host Institution (HI) ASOCIACION CENTRO DE INVESTIGACION COOPERATIVA EN BIOCIENCIAS
Call Details Starting Grant (StG), PE5, ERC-2016-STG
Summary The clinical success of anticancer and antiviral vaccines often requires the use of an adjuvant, a substance that helps stimulate the body’s immune response to the vaccine, making it work better. However, few adjuvants are sufficiently potent and non-toxic for clinical use; moreover, it is not really known how they work. Current vaccine approaches based on weak carbohydrate and glycopeptide antigens are not being particularly effective to induce the human immune system to mount an effective fight against cancer. Despite intensive research and several clinical trials, no such carbohydrate-based antitumor vaccine has yet been approved for public use. In this context, the proposed project has a double, ultimate goal based on applying chemistry to address the above clear gaps in the adjuvant-vaccine field. First, I will develop new improved adjuvants and novel chemical strategies towards more effective, self-adjuvanting synthetic vaccines. Second, I will probe deeply into the molecular mechanisms of the synthetic constructs by combining extensive immunological evaluations with molecular target identification and detailed conformational studies. Thus, the singularity of this multidisciplinary proposal stems from the integration of its main objectives and approaches connecting chemical synthesis and chemical/structural biology with cellular and molecular immunology. This ground-breaking project at the chemistry-biology frontier will allow me to establish my own independent research group and explore key unresolved mechanistic questions in the adjuvant/vaccine arena with extraordinary chemical precision. Therefore, with this transformative and timely research program I aim to (a) develop novel synthetic antitumor and antiviral vaccines with improved properties and efficacy for their prospective translation into the clinic and (b) gain new critical insights into the molecular basis and three-dimensional structure underlying the biological activity of these constructs.
Summary
The clinical success of anticancer and antiviral vaccines often requires the use of an adjuvant, a substance that helps stimulate the body’s immune response to the vaccine, making it work better. However, few adjuvants are sufficiently potent and non-toxic for clinical use; moreover, it is not really known how they work. Current vaccine approaches based on weak carbohydrate and glycopeptide antigens are not being particularly effective to induce the human immune system to mount an effective fight against cancer. Despite intensive research and several clinical trials, no such carbohydrate-based antitumor vaccine has yet been approved for public use. In this context, the proposed project has a double, ultimate goal based on applying chemistry to address the above clear gaps in the adjuvant-vaccine field. First, I will develop new improved adjuvants and novel chemical strategies towards more effective, self-adjuvanting synthetic vaccines. Second, I will probe deeply into the molecular mechanisms of the synthetic constructs by combining extensive immunological evaluations with molecular target identification and detailed conformational studies. Thus, the singularity of this multidisciplinary proposal stems from the integration of its main objectives and approaches connecting chemical synthesis and chemical/structural biology with cellular and molecular immunology. This ground-breaking project at the chemistry-biology frontier will allow me to establish my own independent research group and explore key unresolved mechanistic questions in the adjuvant/vaccine arena with extraordinary chemical precision. Therefore, with this transformative and timely research program I aim to (a) develop novel synthetic antitumor and antiviral vaccines with improved properties and efficacy for their prospective translation into the clinic and (b) gain new critical insights into the molecular basis and three-dimensional structure underlying the biological activity of these constructs.
Max ERC Funding
1 499 219 €
Duration
Start date: 2017-03-01, End date: 2022-02-28
Project acronym Aftermath
Project THE AFTERMATH OF THE EAST ASIAN WAR OF 1592-1598.
Researcher (PI) Rebekah CLEMENTS
Host Institution (HI) UNIVERSITAT AUTONOMA DE BARCELONA
Call Details Starting Grant (StG), SH6, ERC-2017-STG
Summary Aftermath seeks to understand the legacy of the East Asian War of 1592-1598. This conflict involved over 500,000 combatants from Japan, China, and Korea; up to 100,000 Korean civilians were abducted to Japan. The war caused momentous demographic upheaval and widespread destruction, but also had long-lasting cultural impact as a result of the removal to Japan of Korean technology and skilled labourers. The conflict and its aftermath bear striking parallels to events in East Asia during World War 2, and memories of the 16th century war remain deeply resonant in the region. However, the war and its immediate aftermath are also significant because they occurred at the juncture of periods often characterized as “medieval” and “early modern” in the East Asian case. What were the implications for the social, economic, and cultural contours of early modern East Asia? What can this conflict tell us about war “aftermath” across historical periods and about such periodization itself? There is little Western scholarship on the war and few studies in any language cross linguistic, disciplinary, and national boundaries to achieve a regional perspective that reflects the interconnected history of East Asia. Aftermath will radically alter our understanding of the region’s history by providing the first analysis of the state of East Asia as a result of the war. The focus will be on the period up to the middle of the 17th century, but not precluding ongoing effects. The team, with expertise covering Japan, Korea, and China, will investigate three themes: the movement of people and demographic change, the impact on the natural environment, and technological diffusion. The project will be the first large scale investigation to use Japanese, Korean, and Chinese sources to understand the war’s aftermath. It will broaden understandings of the early modern world, and push the boundaries of war legacy studies by exploring the meanings of “aftermath” in the early modern East Asian context.
Summary
Aftermath seeks to understand the legacy of the East Asian War of 1592-1598. This conflict involved over 500,000 combatants from Japan, China, and Korea; up to 100,000 Korean civilians were abducted to Japan. The war caused momentous demographic upheaval and widespread destruction, but also had long-lasting cultural impact as a result of the removal to Japan of Korean technology and skilled labourers. The conflict and its aftermath bear striking parallels to events in East Asia during World War 2, and memories of the 16th century war remain deeply resonant in the region. However, the war and its immediate aftermath are also significant because they occurred at the juncture of periods often characterized as “medieval” and “early modern” in the East Asian case. What were the implications for the social, economic, and cultural contours of early modern East Asia? What can this conflict tell us about war “aftermath” across historical periods and about such periodization itself? There is little Western scholarship on the war and few studies in any language cross linguistic, disciplinary, and national boundaries to achieve a regional perspective that reflects the interconnected history of East Asia. Aftermath will radically alter our understanding of the region’s history by providing the first analysis of the state of East Asia as a result of the war. The focus will be on the period up to the middle of the 17th century, but not precluding ongoing effects. The team, with expertise covering Japan, Korea, and China, will investigate three themes: the movement of people and demographic change, the impact on the natural environment, and technological diffusion. The project will be the first large scale investigation to use Japanese, Korean, and Chinese sources to understand the war’s aftermath. It will broaden understandings of the early modern world, and push the boundaries of war legacy studies by exploring the meanings of “aftermath” in the early modern East Asian context.
Max ERC Funding
1 444 980 €
Duration
Start date: 2018-11-01, End date: 2023-10-31
Project acronym AGRISCENTS
Project Scents and sensibility in agriculture: exploiting specificity in herbivore- and pathogen-induced plant volatiles for real-time crop monitoring
Researcher (PI) Theodoor Turlings
Host Institution (HI) UNIVERSITE DE NEUCHATEL
Call Details Advanced Grant (AdG), LS9, ERC-2017-ADG
Summary Plants typically release large quantities of volatiles in response to attack by herbivores or pathogens. I may claim to have contributed to various breakthroughs in this research field, including the discovery that the volatile blends induced by different attackers are astonishingly specific, resulting in characteristic, readily distinguishable odour blends. Using maize as our model plant, I wish to take several leaps forward in our understanding of this signal specificity and use this knowledge to develop sensors for the real-time detection of crop pests and diseases. For this, three interconnected work-packages will aim to:
• Develop chemical analytical techniques and statistical models to decipher the odorous vocabulary of plants, and to create a complete inventory of “odour-prints” for a wide range of herbivore-plant and pathogen-plant combinations, including simultaneous infestations.
• Develop and optimize nano-mechanical sensors for the detection of specific plant volatile mixtures. For this, we will initially adapt a prototype sensor that has been successfully developed for the detection of cancer-related volatiles in human breath.
• Genetically manipulate maize plants to release a unique blend of root-produced volatiles upon herbivory. For this, we will engineer gene cassettes that combine recently identified P450 (CYP) genes from poplar with inducible, root-specific promoters from maize. This will result in maize plants that, in response to pest attack, release easy-to-detect aldoximes and nitriles from their roots.
In short, by investigating and manipulating the specificity of inducible odour blends we will generate the necessary knowhow to develop a novel odour-detection device. The envisioned sensor technology will permit real-time monitoring of the pests and enable farmers to apply crop protection treatments at the right time and in the right place.
Summary
Plants typically release large quantities of volatiles in response to attack by herbivores or pathogens. I may claim to have contributed to various breakthroughs in this research field, including the discovery that the volatile blends induced by different attackers are astonishingly specific, resulting in characteristic, readily distinguishable odour blends. Using maize as our model plant, I wish to take several leaps forward in our understanding of this signal specificity and use this knowledge to develop sensors for the real-time detection of crop pests and diseases. For this, three interconnected work-packages will aim to:
• Develop chemical analytical techniques and statistical models to decipher the odorous vocabulary of plants, and to create a complete inventory of “odour-prints” for a wide range of herbivore-plant and pathogen-plant combinations, including simultaneous infestations.
• Develop and optimize nano-mechanical sensors for the detection of specific plant volatile mixtures. For this, we will initially adapt a prototype sensor that has been successfully developed for the detection of cancer-related volatiles in human breath.
• Genetically manipulate maize plants to release a unique blend of root-produced volatiles upon herbivory. For this, we will engineer gene cassettes that combine recently identified P450 (CYP) genes from poplar with inducible, root-specific promoters from maize. This will result in maize plants that, in response to pest attack, release easy-to-detect aldoximes and nitriles from their roots.
In short, by investigating and manipulating the specificity of inducible odour blends we will generate the necessary knowhow to develop a novel odour-detection device. The envisioned sensor technology will permit real-time monitoring of the pests and enable farmers to apply crop protection treatments at the right time and in the right place.
Max ERC Funding
2 498 086 €
Duration
Start date: 2018-09-01, End date: 2023-08-31
Project acronym AGRIWESTMED
Project Origins and spread of agriculture in the south-western Mediterranean region
Researcher (PI) Maria Leonor Peña Chocarro
Host Institution (HI) AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS
Call Details Advanced Grant (AdG), SH6, ERC-2008-AdG
Summary This project focuses on one of the most fascinating events of the long history of the human species: the origins and spread of agriculture. Research over the past 40 years has provided an invaluable dataset on crop domestication and the spread of agriculture into Europe. However, despite the enormous advances in research there are important areas that remain almost unexplored, some of immense interest. This is the case of the western Mediterranean region from where our knowledge is still limited (Iberian Peninsula) or almost inexistent (northern Morocco). The last few years have witnessed a considerable increase in archaeobotany and the effort of a group of Spanish researchers working together in different aspects of agriculture has started to produce the first results. My proposal will approach the study of the arrival of agriculture to the western Mediterranean by exploring different interrelated research areas. The project involves the
application of different techniques (analysis of charred plant remains, pollen and non-pollen microfossils, phytoliths, micro-wear analyses, isotopes, soil micromorphology, genetics, and ethnoarchaeology) which will help to define the emergence and spread of agriculture in the area, its likely place of origin, its main technological attributes as well as the range crop husbandry practices carried out. The interaction between the different approaches and the methodologies involved will allow achieving a greater understanding of the type of agriculture that characterized the first farming communities in the most south-western part of Europe.
Summary
This project focuses on one of the most fascinating events of the long history of the human species: the origins and spread of agriculture. Research over the past 40 years has provided an invaluable dataset on crop domestication and the spread of agriculture into Europe. However, despite the enormous advances in research there are important areas that remain almost unexplored, some of immense interest. This is the case of the western Mediterranean region from where our knowledge is still limited (Iberian Peninsula) or almost inexistent (northern Morocco). The last few years have witnessed a considerable increase in archaeobotany and the effort of a group of Spanish researchers working together in different aspects of agriculture has started to produce the first results. My proposal will approach the study of the arrival of agriculture to the western Mediterranean by exploring different interrelated research areas. The project involves the
application of different techniques (analysis of charred plant remains, pollen and non-pollen microfossils, phytoliths, micro-wear analyses, isotopes, soil micromorphology, genetics, and ethnoarchaeology) which will help to define the emergence and spread of agriculture in the area, its likely place of origin, its main technological attributes as well as the range crop husbandry practices carried out. The interaction between the different approaches and the methodologies involved will allow achieving a greater understanding of the type of agriculture that characterized the first farming communities in the most south-western part of Europe.
Max ERC Funding
1 545 169 €
Duration
Start date: 2009-04-01, End date: 2013-03-31
Project acronym AI4REASON
Project Artificial Intelligence for Large-Scale Computer-Assisted Reasoning
Researcher (PI) Josef Urban
Host Institution (HI) CESKE VYSOKE UCENI TECHNICKE V PRAZE
Call Details Consolidator Grant (CoG), PE6, ERC-2014-CoG
Summary The goal of the AI4REASON project is a breakthrough in what is considered a very hard problem in AI and automation of reasoning, namely the problem of automatically proving theorems in large and complex theories. Such complex formal theories arise in projects aimed at verification of today's advanced mathematics such as the Formal Proof of the Kepler Conjecture (Flyspeck), verification of software and hardware designs such as the seL4 operating system kernel, and verification of other advanced systems and technologies on which today's information society critically depends.
It seems extremely complex and unlikely to design an explicitly programmed solution to the problem. However, we have recently demonstrated that the performance of existing approaches can be multiplied by data-driven AI methods that learn reasoning guidance from large proof corpora. The breakthrough will be achieved by developing such novel AI methods. First, we will devise suitable Automated Reasoning and Machine Learning methods that learn reasoning knowledge and steer the reasoning processes at various levels of granularity. Second, we will combine them into autonomous self-improving AI systems that interleave deduction and learning in positive feedback loops. Third, we will develop approaches that aggregate reasoning knowledge across many formal, semi-formal and informal corpora and deploy the methods as strong automation services for the formal proof community.
The expected outcome is our ability to prove automatically at least 50% more theorems in high-assurance projects such as Flyspeck and seL4, bringing a major breakthrough in formal reasoning and verification. As an AI effort, the project offers a unique path to large-scale semantic AI. The formal corpora concentrate centuries of deep human thinking in a computer-understandable form on which deductive and inductive AI can be combined and co-evolved, providing new insights into how humans do mathematics and science.
Summary
The goal of the AI4REASON project is a breakthrough in what is considered a very hard problem in AI and automation of reasoning, namely the problem of automatically proving theorems in large and complex theories. Such complex formal theories arise in projects aimed at verification of today's advanced mathematics such as the Formal Proof of the Kepler Conjecture (Flyspeck), verification of software and hardware designs such as the seL4 operating system kernel, and verification of other advanced systems and technologies on which today's information society critically depends.
It seems extremely complex and unlikely to design an explicitly programmed solution to the problem. However, we have recently demonstrated that the performance of existing approaches can be multiplied by data-driven AI methods that learn reasoning guidance from large proof corpora. The breakthrough will be achieved by developing such novel AI methods. First, we will devise suitable Automated Reasoning and Machine Learning methods that learn reasoning knowledge and steer the reasoning processes at various levels of granularity. Second, we will combine them into autonomous self-improving AI systems that interleave deduction and learning in positive feedback loops. Third, we will develop approaches that aggregate reasoning knowledge across many formal, semi-formal and informal corpora and deploy the methods as strong automation services for the formal proof community.
The expected outcome is our ability to prove automatically at least 50% more theorems in high-assurance projects such as Flyspeck and seL4, bringing a major breakthrough in formal reasoning and verification. As an AI effort, the project offers a unique path to large-scale semantic AI. The formal corpora concentrate centuries of deep human thinking in a computer-understandable form on which deductive and inductive AI can be combined and co-evolved, providing new insights into how humans do mathematics and science.
Max ERC Funding
1 499 500 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym AIR-NB
Project Pre-natal exposure to urban AIR pollution and pre- and post-Natal Brain development
Researcher (PI) Jordi Sunyer
Host Institution (HI) FUNDACION PRIVADA INSTITUTO DE SALUD GLOBAL BARCELONA
Call Details Advanced Grant (AdG), LS7, ERC-2017-ADG
Summary Air pollution is the main urban-related environmental hazard. It appears to affect brain development, although current evidence is inadequate given the lack of studies during the most vulnerable stages of brain development and the lack of brain anatomical structure and regional connectivity data underlying these effects. Of particular interest is the prenatal period, when brain structures are forming and growing, and when the effect of in utero exposure to environmental factors may cause permanent brain injury. I and others have conducted studies focused on effects during school age which could be less profound. I postulate that: pre-natal exposure to urban air pollution during pregnancy impairs foetal and postnatal brain development, mainly by affecting myelination; these effects are at least partially mediated by translocation of airborne particulate matter to the placenta and by placental dysfunction; and prenatal exposure to air pollution impairs post-natal brain development independently of urban context and post-natal exposure to air pollution. I aim to evaluate the effect of pre-natal exposure to urban air pollution on pre- and post-natal brain structure and function by following 900 pregnant women and their neonates with contrasting levels of pre-natal exposure to air pollutants by: i) establishing a new pregnancy cohort and evaluating brain imaging (pre-natal and neo-natal brain structure, connectivity and function), and post-natal motor and cognitive development; ii) measuring total personal exposure and inhaled dose of air pollutants during specific time-windows of gestation, noise, paternal stress and other stressors, using personal samplers and sensors; iii) detecting nanoparticles in placenta and its vascular function; iv) modelling mathematical causality and mediation, including a replication study in an external cohort. The expected results will create an impulse to implement policy interventions that genuinely protect the health of urban citizens.
Summary
Air pollution is the main urban-related environmental hazard. It appears to affect brain development, although current evidence is inadequate given the lack of studies during the most vulnerable stages of brain development and the lack of brain anatomical structure and regional connectivity data underlying these effects. Of particular interest is the prenatal period, when brain structures are forming and growing, and when the effect of in utero exposure to environmental factors may cause permanent brain injury. I and others have conducted studies focused on effects during school age which could be less profound. I postulate that: pre-natal exposure to urban air pollution during pregnancy impairs foetal and postnatal brain development, mainly by affecting myelination; these effects are at least partially mediated by translocation of airborne particulate matter to the placenta and by placental dysfunction; and prenatal exposure to air pollution impairs post-natal brain development independently of urban context and post-natal exposure to air pollution. I aim to evaluate the effect of pre-natal exposure to urban air pollution on pre- and post-natal brain structure and function by following 900 pregnant women and their neonates with contrasting levels of pre-natal exposure to air pollutants by: i) establishing a new pregnancy cohort and evaluating brain imaging (pre-natal and neo-natal brain structure, connectivity and function), and post-natal motor and cognitive development; ii) measuring total personal exposure and inhaled dose of air pollutants during specific time-windows of gestation, noise, paternal stress and other stressors, using personal samplers and sensors; iii) detecting nanoparticles in placenta and its vascular function; iv) modelling mathematical causality and mediation, including a replication study in an external cohort. The expected results will create an impulse to implement policy interventions that genuinely protect the health of urban citizens.
Max ERC Funding
2 499 992 €
Duration
Start date: 2018-09-01, End date: 2023-08-31
Project acronym ALGILE
Project Foundations of Algebraic and Dynamic Data Management Systems
Researcher (PI) Christoph Koch
Host Institution (HI) ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Call Details Starting Grant (StG), PE6, ERC-2011-StG_20101014
Summary "Contemporary database query languages are ultimately founded on logic and feature an additive operation – usually a form of (multi)set union or disjunction – that is asymmetric in that additions or updates do not always have an inverse. This asymmetry puts a greater part of the machinery of abstract algebra for equation solving outside the reach of databases. However, such equation solving would be a key functionality that problems such as query equivalence testing and data integration could be reduced to: In the current scenario of the presence of an asymmetric additive operation they are undecidable. Moreover, query languages with a symmetric additive operation (i.e., which has an inverse and is thus based on ring theory) would open up databases for a large range of new scientific and mathematical applications.
The goal of the proposed project is to reinvent database management systems with a foundation in abstract algebra and specifically in ring theory. The presence of an additive inverse allows to cleanly define differences between queries. This gives rise to a database analog of differential calculus that leads to radically new incremental and adaptive query evaluation algorithms that substantially outperform the state of the art techniques. These algorithms enable a new class of systems which I call Dynamic Data Management Systems. Such systems can maintain continuously fresh query views at extremely high update rates and have important applications in interactive Large-scale Data Analysis. There is a natural connection between differences and updates, motivating the group theoretic study of updates that will lead to better ways of creating out-of-core data processing algorithms for new storage devices. Basing queries on ring theory leads to a new class of systems, Algebraic Data Management Systems, which herald a convergence of database systems and computer algebra systems."
Summary
"Contemporary database query languages are ultimately founded on logic and feature an additive operation – usually a form of (multi)set union or disjunction – that is asymmetric in that additions or updates do not always have an inverse. This asymmetry puts a greater part of the machinery of abstract algebra for equation solving outside the reach of databases. However, such equation solving would be a key functionality that problems such as query equivalence testing and data integration could be reduced to: In the current scenario of the presence of an asymmetric additive operation they are undecidable. Moreover, query languages with a symmetric additive operation (i.e., which has an inverse and is thus based on ring theory) would open up databases for a large range of new scientific and mathematical applications.
The goal of the proposed project is to reinvent database management systems with a foundation in abstract algebra and specifically in ring theory. The presence of an additive inverse allows to cleanly define differences between queries. This gives rise to a database analog of differential calculus that leads to radically new incremental and adaptive query evaluation algorithms that substantially outperform the state of the art techniques. These algorithms enable a new class of systems which I call Dynamic Data Management Systems. Such systems can maintain continuously fresh query views at extremely high update rates and have important applications in interactive Large-scale Data Analysis. There is a natural connection between differences and updates, motivating the group theoretic study of updates that will lead to better ways of creating out-of-core data processing algorithms for new storage devices. Basing queries on ring theory leads to a new class of systems, Algebraic Data Management Systems, which herald a convergence of database systems and computer algebra systems."
Max ERC Funding
1 480 548 €
Duration
Start date: 2012-01-01, End date: 2016-12-31
Project acronym AlgoRNN
Project Recurrent Neural Networks and Related Machines That Learn Algorithms
Researcher (PI) Juergen Schmidhuber
Host Institution (HI) UNIVERSITA DELLA SVIZZERA ITALIANA
Call Details Advanced Grant (AdG), PE6, ERC-2016-ADG
Summary Recurrent neural networks (RNNs) are general parallel-sequential computers. Some learn their programs or weights. Our supervised Long Short-Term Memory (LSTM) RNNs were the first to win pattern recognition contests, and recently enabled best known results in speech and handwriting recognition, machine translation, etc. They are now available to billions of users through the world's most valuable public companies including Google and Apple. Nevertheless, in lots of real-world tasks RNNs do not yet live up to their full potential. Although universal in theory, in practice they fail to learn important types of algorithms. This ERC project will go far beyond today's best RNNs through novel RNN-like systems that address some of the biggest open RNN problems and hottest RNN research topics: (1) How can RNNs learn to control (through internal spotlights of attention) separate large short-memory structures such as sub-networks with fast weights, to improve performance on many natural short-term memory-intensive tasks which are currently hard to learn by RNNs, such as answering detailed questions on recently observed videos? (2) How can such RNN-like systems metalearn entire learning algorithms that outperform the original learning algorithms? (3) How to achieve efficient transfer learning from one RNN-learned set of problem-solving programs to new RNN programs solving new tasks? In other words, how can one RNN-like system actively learn to exploit algorithmic information contained in the programs running on another? We will test our systems existing benchmarks, and create new, more challenging multi-task benchmarks. This will be supported by a rather cheap, GPU-based mini-brain for implementing large RNNs.
Summary
Recurrent neural networks (RNNs) are general parallel-sequential computers. Some learn their programs or weights. Our supervised Long Short-Term Memory (LSTM) RNNs were the first to win pattern recognition contests, and recently enabled best known results in speech and handwriting recognition, machine translation, etc. They are now available to billions of users through the world's most valuable public companies including Google and Apple. Nevertheless, in lots of real-world tasks RNNs do not yet live up to their full potential. Although universal in theory, in practice they fail to learn important types of algorithms. This ERC project will go far beyond today's best RNNs through novel RNN-like systems that address some of the biggest open RNN problems and hottest RNN research topics: (1) How can RNNs learn to control (through internal spotlights of attention) separate large short-memory structures such as sub-networks with fast weights, to improve performance on many natural short-term memory-intensive tasks which are currently hard to learn by RNNs, such as answering detailed questions on recently observed videos? (2) How can such RNN-like systems metalearn entire learning algorithms that outperform the original learning algorithms? (3) How to achieve efficient transfer learning from one RNN-learned set of problem-solving programs to new RNN programs solving new tasks? In other words, how can one RNN-like system actively learn to exploit algorithmic information contained in the programs running on another? We will test our systems existing benchmarks, and create new, more challenging multi-task benchmarks. This will be supported by a rather cheap, GPU-based mini-brain for implementing large RNNs.
Max ERC Funding
2 500 000 €
Duration
Start date: 2017-10-01, End date: 2022-09-30
Project acronym AMADEUS
Project Advancing CO2 Capture Materials by Atomic Scale Design: the Quest for Understanding
Researcher (PI) Christoph Rüdiger MÜLLER
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Call Details Consolidator Grant (CoG), PE8, ERC-2018-COG
Summary Carbon dioxide capture and storage is a technology to mitigate climate change by removing CO2 from flue gas streams or the atmosphere and storing it in geological formations. While CO2 removal from natural gas by amine scrubbing is implemented on the large scale, the cost of such process is currently prohibitively expensive. Inexpensive alkali earth metal oxides (MgO and CaO) feature high theoretical CO2 uptakes, but suffer from poor cyclic stability and slow kinetics. Yet, the key objective of recent research on alkali earth metal oxide based CO2 sorbents has been the processing of inexpensive, naturally occurring CO2 sorbents, notably limestone and dolomite, to stabilize their modest CO2 uptake and to establish re-activation methods through engineering approaches. While this research demonstrated a landmark Megawatt (MW) scale viability of the process, our fundamental understanding of the underlying CO2 capture, regeneration and deactivation pathways did not improve. The latter knowledge is, however, vital for the rational design of improved, yet practical CaO and MgO sorbents. Hence this proposal is concerned with obtaining an understanding of the underlying mechanisms that control the ability of an alkali metal oxide to capture a large quantity of CO2 with a high rate, to regenerate and to operate with high cyclic stability. Achieving these aims relies on the ability to fabricate model structures and to characterize in great detail their surface chemistry, morphology, chemical composition and changes therein under reactive conditions. This makes the development of operando and in situ characterization tools an essential prerequisite. Advances in these areas shall allow achieving the overall goal of this project, viz. to formulate a roadmap to fabricate improved CO2 sorbents through their precisely engineered structure, composition and morphology.
Summary
Carbon dioxide capture and storage is a technology to mitigate climate change by removing CO2 from flue gas streams or the atmosphere and storing it in geological formations. While CO2 removal from natural gas by amine scrubbing is implemented on the large scale, the cost of such process is currently prohibitively expensive. Inexpensive alkali earth metal oxides (MgO and CaO) feature high theoretical CO2 uptakes, but suffer from poor cyclic stability and slow kinetics. Yet, the key objective of recent research on alkali earth metal oxide based CO2 sorbents has been the processing of inexpensive, naturally occurring CO2 sorbents, notably limestone and dolomite, to stabilize their modest CO2 uptake and to establish re-activation methods through engineering approaches. While this research demonstrated a landmark Megawatt (MW) scale viability of the process, our fundamental understanding of the underlying CO2 capture, regeneration and deactivation pathways did not improve. The latter knowledge is, however, vital for the rational design of improved, yet practical CaO and MgO sorbents. Hence this proposal is concerned with obtaining an understanding of the underlying mechanisms that control the ability of an alkali metal oxide to capture a large quantity of CO2 with a high rate, to regenerate and to operate with high cyclic stability. Achieving these aims relies on the ability to fabricate model structures and to characterize in great detail their surface chemistry, morphology, chemical composition and changes therein under reactive conditions. This makes the development of operando and in situ characterization tools an essential prerequisite. Advances in these areas shall allow achieving the overall goal of this project, viz. to formulate a roadmap to fabricate improved CO2 sorbents through their precisely engineered structure, composition and morphology.
Max ERC Funding
1 994 900 €
Duration
Start date: 2019-06-01, End date: 2024-05-31
Project acronym Amitochondriates
Project Life without mitochondrion
Researcher (PI) Vladimir HAMPL
Host Institution (HI) UNIVERZITA KARLOVA
Call Details Consolidator Grant (CoG), LS8, ERC-2017-COG
Summary Mitochondria are often referred to as the “power houses” of eukaryotic cells. All eukaryotes were thought to have mitochondria of some form until 2016, when the first eukaryote thriving without mitochondria was discovered by our laboratory – a flagellate Monocercomonoides. Understanding cellular functions of these cells, which represent a new functional type of eukaryotes, and understanding the circumstances of the unique event of mitochondrial loss are motivations for this proposal. The first objective focuses on the cell physiology. We will perform a metabolomic study revealing major metabolic pathways and concentrate further on elucidating its unique system of iron-sulphur cluster assembly. In the second objective, we will investigate in details the unique case of mitochondrial loss. We will examine two additional potentially amitochondriate lineages by means of genomics and transcriptomics, conduct experiments simulating the moments of mitochondrial loss and try to induce the mitochondrial loss in vitro by knocking out or down genes for mitochondrial biogenesis. We have chosen Giardia intestinalis and Entamoeba histolytica as models for the latter experiments, because their mitochondria are already reduced to minimalistic “mitosomes” and because some genetic tools are already available for them. Successful mitochondrial knock-outs would enable us to study mitochondrial loss in ‘real time’ and in vivo. In the third objective, we will focus on transforming Monocercomonoides into a tractable laboratory model by developing methods of axenic cultivation and genetic manipulation. This will open new possibilities in the studies of this organism and create a cell culture representing an amitochondriate model for cell biological studies enabling the dissection of mitochondrial effects from those of other compartments. The team is composed of the laboratory of PI and eight invited experts and we hope it has the ability to address these challenging questions.
Summary
Mitochondria are often referred to as the “power houses” of eukaryotic cells. All eukaryotes were thought to have mitochondria of some form until 2016, when the first eukaryote thriving without mitochondria was discovered by our laboratory – a flagellate Monocercomonoides. Understanding cellular functions of these cells, which represent a new functional type of eukaryotes, and understanding the circumstances of the unique event of mitochondrial loss are motivations for this proposal. The first objective focuses on the cell physiology. We will perform a metabolomic study revealing major metabolic pathways and concentrate further on elucidating its unique system of iron-sulphur cluster assembly. In the second objective, we will investigate in details the unique case of mitochondrial loss. We will examine two additional potentially amitochondriate lineages by means of genomics and transcriptomics, conduct experiments simulating the moments of mitochondrial loss and try to induce the mitochondrial loss in vitro by knocking out or down genes for mitochondrial biogenesis. We have chosen Giardia intestinalis and Entamoeba histolytica as models for the latter experiments, because their mitochondria are already reduced to minimalistic “mitosomes” and because some genetic tools are already available for them. Successful mitochondrial knock-outs would enable us to study mitochondrial loss in ‘real time’ and in vivo. In the third objective, we will focus on transforming Monocercomonoides into a tractable laboratory model by developing methods of axenic cultivation and genetic manipulation. This will open new possibilities in the studies of this organism and create a cell culture representing an amitochondriate model for cell biological studies enabling the dissection of mitochondrial effects from those of other compartments. The team is composed of the laboratory of PI and eight invited experts and we hope it has the ability to address these challenging questions.
Max ERC Funding
1 935 500 €
Duration
Start date: 2018-05-01, End date: 2023-04-30
Project acronym AMORE
Project A distributional MOdel of Reference to Entities
Researcher (PI) Gemma BOLEDA TORRENT
Host Institution (HI) UNIVERSIDAD POMPEU FABRA
Call Details Starting Grant (StG), SH4, ERC-2016-STG
Summary "When I asked my seven-year-old daughter ""Who is the boy in your class who was also new in school last year, like you?"", she instantly replied ""Daniel"", using the descriptive content in my utterance to identify an entity in the real world and refer to it. The ability to use language to refer to reality is crucial for humans, and yet it is very difficult to model. AMORE breaks new ground in Computational Linguistics, Linguistics, and Artificial Intelligence by developing a model of linguistic reference to entities implemented as a computational system that can learn its own representations from data.
This interdisciplinary project builds on two complementary semantic traditions: 1) Formal semantics, a symbolic approach that can delimit and track linguistic referents, but does not adequately match them with the descriptive content of linguistic expressions; 2) Distributional semantics, which can handle descriptive content but does not associate it to individuated referents. AMORE synthesizes the two approaches into a unified, scalable model of reference that operates with individuated referents and links them to referential expressions characterized by rich descriptive content. The model is a distributed (neural network) version of a formal semantic framework that is furthermore able to integrate perceptual (visual) and linguistic information about entities. We test it extensively in referential tasks that require matching noun phrases (“the Medicine student”, “the white cat”) with entity representations extracted from text and images.
AMORE advances our scientific understanding of language and its computational modeling, and contributes to the far-reaching debate between symbolic and distributed approaches to cognition with an integrative proposal. I am in a privileged position to carry out this integration, since I have contributed top research in both distributional and formal semantics.
"
Summary
"When I asked my seven-year-old daughter ""Who is the boy in your class who was also new in school last year, like you?"", she instantly replied ""Daniel"", using the descriptive content in my utterance to identify an entity in the real world and refer to it. The ability to use language to refer to reality is crucial for humans, and yet it is very difficult to model. AMORE breaks new ground in Computational Linguistics, Linguistics, and Artificial Intelligence by developing a model of linguistic reference to entities implemented as a computational system that can learn its own representations from data.
This interdisciplinary project builds on two complementary semantic traditions: 1) Formal semantics, a symbolic approach that can delimit and track linguistic referents, but does not adequately match them with the descriptive content of linguistic expressions; 2) Distributional semantics, which can handle descriptive content but does not associate it to individuated referents. AMORE synthesizes the two approaches into a unified, scalable model of reference that operates with individuated referents and links them to referential expressions characterized by rich descriptive content. The model is a distributed (neural network) version of a formal semantic framework that is furthermore able to integrate perceptual (visual) and linguistic information about entities. We test it extensively in referential tasks that require matching noun phrases (“the Medicine student”, “the white cat”) with entity representations extracted from text and images.
AMORE advances our scientific understanding of language and its computational modeling, and contributes to the far-reaching debate between symbolic and distributed approaches to cognition with an integrative proposal. I am in a privileged position to carry out this integration, since I have contributed top research in both distributional and formal semantics.
"
Max ERC Funding
1 499 805 €
Duration
Start date: 2017-02-01, End date: 2022-01-31
Project acronym AMSEL
Project Atomic Force Microscopy for Molecular Structure Elucidation
Researcher (PI) Leo Gross
Host Institution (HI) IBM RESEARCH GMBH
Call Details Consolidator Grant (CoG), PE4, ERC-2015-CoG
Summary Molecular structure elucidation is of great importance in synthetic chemistry, pharmacy, life sciences, energy and environmental sciences, and technology applications. To date structure elucidation by atomic force microscopy (AFM) has been demonstrated for a few, small and mainly planar molecules. In this project high-risk, high-impact scientific questions will be solved using structure elucidation with the AFM employing a novel tool and novel methodologies.
A combined low-temperature scanning tunneling microscope/atomic force microscope (LT-STM/AFM) with high throughput and in situ electrospray deposition method will be developed. Chemical resolution will be achieved by novel measurement techniques, in particular the usage of different and novel tip functionalizations and combination with Kelvin probe force microscopy. Elements will be identified using substructure recognition provided by a database that will be erected and by refined theory and simulations.
The developed tools and techniques will be applied to molecules of increasing fragility, complexity, size, and three-dimensionality. In particular samples that are challenging to characterize with conventional methods will be studied. Complex molecular mixtures will be investigated molecule-by-molecule taking advantage of the single-molecule sensitivity. The absolute stereochemistry of molecules will be determined, resolving molecules with multiple stereocenters. The operation of single molecular machines as nanocars and molecular gears will be investigated. Reactive intermediates generated with atomic manipulation will be characterized and their on-surface reactivity will be studied by AFM.
Summary
Molecular structure elucidation is of great importance in synthetic chemistry, pharmacy, life sciences, energy and environmental sciences, and technology applications. To date structure elucidation by atomic force microscopy (AFM) has been demonstrated for a few, small and mainly planar molecules. In this project high-risk, high-impact scientific questions will be solved using structure elucidation with the AFM employing a novel tool and novel methodologies.
A combined low-temperature scanning tunneling microscope/atomic force microscope (LT-STM/AFM) with high throughput and in situ electrospray deposition method will be developed. Chemical resolution will be achieved by novel measurement techniques, in particular the usage of different and novel tip functionalizations and combination with Kelvin probe force microscopy. Elements will be identified using substructure recognition provided by a database that will be erected and by refined theory and simulations.
The developed tools and techniques will be applied to molecules of increasing fragility, complexity, size, and three-dimensionality. In particular samples that are challenging to characterize with conventional methods will be studied. Complex molecular mixtures will be investigated molecule-by-molecule taking advantage of the single-molecule sensitivity. The absolute stereochemistry of molecules will be determined, resolving molecules with multiple stereocenters. The operation of single molecular machines as nanocars and molecular gears will be investigated. Reactive intermediates generated with atomic manipulation will be characterized and their on-surface reactivity will be studied by AFM.
Max ERC Funding
2 000 000 €
Duration
Start date: 2016-06-01, End date: 2021-05-31
Project acronym Amygdala Circuits
Project Amygdala Circuits for Appetitive Conditioning
Researcher (PI) Andreas Luthi
Host Institution (HI) FRIEDRICH MIESCHER INSTITUTE FOR BIOMEDICAL RESEARCH FONDATION
Call Details Advanced Grant (AdG), LS5, ERC-2014-ADG
Summary The project outlined here addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.
In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms at the level of neuronal circuits have focused on aversive learning, such as in Pavlovian fear conditioning. Understanding how activity in defined neuronal circuits mediates appetitive learning, as well as how these circuitries are shared and interact with aversive learning circuits, is a central question in the neuroscience of learning and memory and the focus of this grant application.
Using a multidisciplinary approach in mice, combining behavioral, in vivo and in vitro electrophysiological, imaging, optogenetic and state-of-the-art viral circuit tracing techniques, we aim at dissecting the neuronal circuitry of appetitive Pavlovian conditioning with a focus on the amygdala, a key brain region important for both aversive and appetitive learning. Ultimately, elucidating these mechanisms at the level of defined neurons and circuits is fundamental not only for an understanding of memory processes in the brain in general, but also to inform a mechanistic approach to psychiatric conditions associated with amygdala dysfunction and dysregulated emotional responses including anxiety and mood disorders.
Summary
The project outlined here addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.
In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms at the level of neuronal circuits have focused on aversive learning, such as in Pavlovian fear conditioning. Understanding how activity in defined neuronal circuits mediates appetitive learning, as well as how these circuitries are shared and interact with aversive learning circuits, is a central question in the neuroscience of learning and memory and the focus of this grant application.
Using a multidisciplinary approach in mice, combining behavioral, in vivo and in vitro electrophysiological, imaging, optogenetic and state-of-the-art viral circuit tracing techniques, we aim at dissecting the neuronal circuitry of appetitive Pavlovian conditioning with a focus on the amygdala, a key brain region important for both aversive and appetitive learning. Ultimately, elucidating these mechanisms at the level of defined neurons and circuits is fundamental not only for an understanding of memory processes in the brain in general, but also to inform a mechanistic approach to psychiatric conditions associated with amygdala dysfunction and dysregulated emotional responses including anxiety and mood disorders.
Max ERC Funding
2 497 200 €
Duration
Start date: 2016-01-01, End date: 2020-12-31
