Project acronym 2DNANOCAPS
Project Next Generation of 2D-Nanomaterials: Enabling Supercapacitor Development
Researcher (PI) Valeria Nicolosi
Host Institution (HI) THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Country Ireland
Call Details Starting Grant (StG), PE8, ERC-2011-StG_20101014
Summary Climate change and the decreasing availability of fossil fuels require society to move towards sustainable and renewable resources. 2DNanoCaps will focus on electrochemical energy storage, specifically supercapacitors. In terms of performance supercapacitors fill up the gap between batteries and the classical capacitors. Whereas batteries possess a high energy density but low power density, supercapacitors possess high power density but low energy density. Efforts are currently dedicated to move supercapacitors towards high energy density and high power density performance. Improvements have been achieved in the last few years due to the use of new electrode nanomaterials and the design of new hybrid faradic/capacitive systems. We recognize, however, that we are reaching a newer limit beyond which we will only see small incremental improvements. The main reason for this being the intrinsic difficulty in handling and processing materials at the nano-scale and the lack of communication across different scientific disciplines. I plan to use a multidisciplinary approach, where novel nanomaterials, existing knowledge on nano-scale processing and established expertise in device fabrication and testing will be brought together to focus on creating more efficient supercapacitor technologies. 2DNanoCaps will exploit liquid phase exfoliated two-dimensional nanomaterials such as transition metal oxides, layered metal chalcogenides and graphene as electrode materials. Electrodes will be ultra-thin (capacitance and thickness of the electrodes are inversely proportional), conductive, with high dielectric constants. Intercalation of ions between the assembled 2D flakes will be also achievable, providing pseudo-capacitance. The research here proposed will be initially based on fundamental laboratory studies, recognising that this holds the key to achieving step-change in supercapacitors, but also includes scaling-up and hybridisation as final objectives.
Summary
Climate change and the decreasing availability of fossil fuels require society to move towards sustainable and renewable resources. 2DNanoCaps will focus on electrochemical energy storage, specifically supercapacitors. In terms of performance supercapacitors fill up the gap between batteries and the classical capacitors. Whereas batteries possess a high energy density but low power density, supercapacitors possess high power density but low energy density. Efforts are currently dedicated to move supercapacitors towards high energy density and high power density performance. Improvements have been achieved in the last few years due to the use of new electrode nanomaterials and the design of new hybrid faradic/capacitive systems. We recognize, however, that we are reaching a newer limit beyond which we will only see small incremental improvements. The main reason for this being the intrinsic difficulty in handling and processing materials at the nano-scale and the lack of communication across different scientific disciplines. I plan to use a multidisciplinary approach, where novel nanomaterials, existing knowledge on nano-scale processing and established expertise in device fabrication and testing will be brought together to focus on creating more efficient supercapacitor technologies. 2DNanoCaps will exploit liquid phase exfoliated two-dimensional nanomaterials such as transition metal oxides, layered metal chalcogenides and graphene as electrode materials. Electrodes will be ultra-thin (capacitance and thickness of the electrodes are inversely proportional), conductive, with high dielectric constants. Intercalation of ions between the assembled 2D flakes will be also achievable, providing pseudo-capacitance. The research here proposed will be initially based on fundamental laboratory studies, recognising that this holds the key to achieving step-change in supercapacitors, but also includes scaling-up and hybridisation as final objectives.
Max ERC Funding
1 501 296 €
Duration
Start date: 2011-10-01, End date: 2016-09-30
Project acronym 3CBIOTECH
Project Cold Carbon Catabolism of Microbial Communities underprinning a Sustainable Bioenergy and Biorefinery Economy
Researcher (PI) Gavin James Collins
Host Institution (HI) NATIONAL UNIVERSITY OF IRELAND GALWAY
Country Ireland
Call Details Starting Grant (StG), LS9, ERC-2010-StG_20091118
Summary The applicant will collaborate with Irish, European and U.S.-based colleagues to develop a sustainable biorefinery and bioenergy industry in Ireland and Europe. The focus of this ERC Starting Grant will be the application of classical microbiological, physiological and real-time polymerase chain reaction (PCR)-based assays, to qualitatively and quantitatively characterize microbial communities underpinning novel and innovative, low-temperature, anaerobic waste (and other biomass) conversion technologies, including municipal wastewater treatment and, demonstration- and full-scale biorefinery applications.
Anaerobic digestion (AD) is a naturally-occurring process, which is widely applied for the conversion of waste to methane-containing biogas. Low-temperature (<20 degrees C) AD has been applied by the applicant as a cost-effective alternative to mesophilic (c. 35C) AD for the treatment of several waste categories. However, the microbiology of low-temperature AD is poorly understood. The applicant will work with microbial consortia isolated from anaerobic bioreactors, which have been operated for long-term experiments (>3.5 years), and include organic acid-oxidizing, hydrogen-producing syntrophic microbes and hydrogen-consuming methanogens. A major focus of the project will be the ecophysiology of psychrotolerant and psychrophilic methanogens already identified and cultivated by the applicant. The project will also investigate the role(s) of poorly-understood Crenarchaeota populations and homoacetogenic bacteria, in complex consortia. The host organization is a leading player in the microbiology of waste-to-energy applications. The applicant will train a team of scientists in all aspects of the microbiology and bioengineering of biomass conversion systems.
Summary
The applicant will collaborate with Irish, European and U.S.-based colleagues to develop a sustainable biorefinery and bioenergy industry in Ireland and Europe. The focus of this ERC Starting Grant will be the application of classical microbiological, physiological and real-time polymerase chain reaction (PCR)-based assays, to qualitatively and quantitatively characterize microbial communities underpinning novel and innovative, low-temperature, anaerobic waste (and other biomass) conversion technologies, including municipal wastewater treatment and, demonstration- and full-scale biorefinery applications.
Anaerobic digestion (AD) is a naturally-occurring process, which is widely applied for the conversion of waste to methane-containing biogas. Low-temperature (<20 degrees C) AD has been applied by the applicant as a cost-effective alternative to mesophilic (c. 35C) AD for the treatment of several waste categories. However, the microbiology of low-temperature AD is poorly understood. The applicant will work with microbial consortia isolated from anaerobic bioreactors, which have been operated for long-term experiments (>3.5 years), and include organic acid-oxidizing, hydrogen-producing syntrophic microbes and hydrogen-consuming methanogens. A major focus of the project will be the ecophysiology of psychrotolerant and psychrophilic methanogens already identified and cultivated by the applicant. The project will also investigate the role(s) of poorly-understood Crenarchaeota populations and homoacetogenic bacteria, in complex consortia. The host organization is a leading player in the microbiology of waste-to-energy applications. The applicant will train a team of scientists in all aspects of the microbiology and bioengineering of biomass conversion systems.
Max ERC Funding
1 499 797 €
Duration
Start date: 2011-05-01, End date: 2016-04-30
Project acronym 3D-OA-HISTO
Project Development of 3D Histopathological Grading of Osteoarthritis
Researcher (PI) Simo Jaakko Saarakkala
Host Institution (HI) OULUN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2013-StG
Summary "Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Summary
"Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Max ERC Funding
1 500 000 €
Duration
Start date: 2014-02-01, End date: 2019-01-31
Project acronym ADNABIOARC
Project From the earliest modern humans to the onset of farming (45,000-4,500 BP): the role of climate, life-style, health, migration and selection in shaping European population history
Researcher (PI) Ron Pinhasi
Host Institution (HI) UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Country Ireland
Call Details Starting Grant (StG), SH6, ERC-2010-StG_20091209
Summary The colonisation of Europe by anatomically modern humans (AMHs) ca. 45,000 years before present (BP) and the transition to farming ca. 8,000 BP are two major events in human prehistory. Both events involved certain cultural and biological adaptations, technological innovations, and behavioural plasticity which are unique to our species. The reconstruction of these processes and the causality between them has so far remained elusive due to technological, methodological and logistical complexities. Major developments in our understanding of the anthropology of the Upper Palaeolithic, Mesolithic and Neolithic, and advances in ancient DNA (aDNA) technology and chronometric methods now allow us to assess in sufficient resolution the interface between these evolutionary processes, and changes in human culture and behaviour.
The proposed research will investigate the complex interface between the morphological, genetic, behavioural, and cultural factors that shaped the population history of European AMHs. The PI s interdisciplinary expertise in these areas, his access to and experience of relevant skeletal collections, and his ongoing European collaborations will allow significant progress in addressing these fundamental questions. The approach taken will include (a) the collection of bioarchaeological, aDNA, stable isotope (for the analysis of ancient diet) and radiometric data on 500 skeletons from key sites/phases in Europe and western Anatolia, and (b) the application of existing and novel aDNA, bioarchaeological and simulation methodologies. This research will yield results that transform our current understanding of major demographic and evolutionary processes and will place Europe at the forefront of anthropological biological and genetic research.
Summary
The colonisation of Europe by anatomically modern humans (AMHs) ca. 45,000 years before present (BP) and the transition to farming ca. 8,000 BP are two major events in human prehistory. Both events involved certain cultural and biological adaptations, technological innovations, and behavioural plasticity which are unique to our species. The reconstruction of these processes and the causality between them has so far remained elusive due to technological, methodological and logistical complexities. Major developments in our understanding of the anthropology of the Upper Palaeolithic, Mesolithic and Neolithic, and advances in ancient DNA (aDNA) technology and chronometric methods now allow us to assess in sufficient resolution the interface between these evolutionary processes, and changes in human culture and behaviour.
The proposed research will investigate the complex interface between the morphological, genetic, behavioural, and cultural factors that shaped the population history of European AMHs. The PI s interdisciplinary expertise in these areas, his access to and experience of relevant skeletal collections, and his ongoing European collaborations will allow significant progress in addressing these fundamental questions. The approach taken will include (a) the collection of bioarchaeological, aDNA, stable isotope (for the analysis of ancient diet) and radiometric data on 500 skeletons from key sites/phases in Europe and western Anatolia, and (b) the application of existing and novel aDNA, bioarchaeological and simulation methodologies. This research will yield results that transform our current understanding of major demographic and evolutionary processes and will place Europe at the forefront of anthropological biological and genetic research.
Max ERC Funding
1 088 386 €
Duration
Start date: 2011-01-01, End date: 2015-12-31
Project acronym AFFIRM
Project Analysis of Biofilm Mediated Fouling of Nanofiltration Membranes
Researcher (PI) Eoin Casey
Host Institution (HI) UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Country Ireland
Call Details Starting Grant (StG), PE8, ERC-2011-StG_20101014
Summary 1.2 billion people worldwide lack access to safe drinking water. Drinking water quality is threatened by newly emerging organic micro-pollutants (pesticides, pharmaceuticals, industrial chemicals) in source waters. Nanofiltration is a technology that is expected to play a key role in future water treatment processes due to its effectiveness in removal of micropollutants. However, the loss of membrane flux due to fouling is one of the main impediments in the development of membrane processes for use in drinking water treatment. Currently there is a wholly inadequate mechanistic understanding of the role of biofilm on the fouling of nanofiltration membranes.
Applying techniques including confocal microscopy, force spectroscopy, and infrared spectroscopy using an experimental programme informed by a technique known as scale-down together with mathematical modelling, it is confidently expected that significant advances will be gained in the mechanistic understanding of nanofiltration biofouling.
The specific objectives are 1. How is the rate of formation and extent of such biofilms influenced by the biological response to the local microenvironment? 2 Elucidate the effect of extracellular polysaccharide substances on physical properties, composition and structure of these biofilms. 3: Investigate mechanisms to enhance biofilm removal by a physical detachment process complemented by techniques that alter biofilm material properties.
A more fundamental insight into the mechanisms of nanofiltration operation will help in further development of this treatment method in future water treatment processes.
Summary
1.2 billion people worldwide lack access to safe drinking water. Drinking water quality is threatened by newly emerging organic micro-pollutants (pesticides, pharmaceuticals, industrial chemicals) in source waters. Nanofiltration is a technology that is expected to play a key role in future water treatment processes due to its effectiveness in removal of micropollutants. However, the loss of membrane flux due to fouling is one of the main impediments in the development of membrane processes for use in drinking water treatment. Currently there is a wholly inadequate mechanistic understanding of the role of biofilm on the fouling of nanofiltration membranes.
Applying techniques including confocal microscopy, force spectroscopy, and infrared spectroscopy using an experimental programme informed by a technique known as scale-down together with mathematical modelling, it is confidently expected that significant advances will be gained in the mechanistic understanding of nanofiltration biofouling.
The specific objectives are 1. How is the rate of formation and extent of such biofilms influenced by the biological response to the local microenvironment? 2 Elucidate the effect of extracellular polysaccharide substances on physical properties, composition and structure of these biofilms. 3: Investigate mechanisms to enhance biofilm removal by a physical detachment process complemented by techniques that alter biofilm material properties.
A more fundamental insight into the mechanisms of nanofiltration operation will help in further development of this treatment method in future water treatment processes.
Max ERC Funding
1 468 987 €
Duration
Start date: 2011-10-01, End date: 2016-09-30
Project acronym Age Asymmetry
Project Age-Selective Segregation of Organelles
Researcher (PI) Pekka Aleksi Katajisto
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS3, ERC-2015-STG
Summary Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage.
I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging.
We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.
Summary
Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage.
I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging.
We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.
Max ERC Funding
1 500 000 €
Duration
Start date: 2016-05-01, End date: 2021-04-30
Project acronym AGELESS
Project Comparative genomics / ‘wildlife’ transcriptomics uncovers the mechanisms of halted ageing in mammals
Researcher (PI) Emma Teeling
Host Institution (HI) UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Country Ireland
Call Details Starting Grant (StG), LS2, ERC-2012-StG_20111109
Summary "Ageing is the gradual and irreversible breakdown of living systems associated with the advancement of time, which leads to an increase in vulnerability and eventual mortality. Despite recent advances in ageing research, the intrinsic complexity of the ageing process has prevented a full understanding of this process, therefore, ageing remains a grand challenge in contemporary biology. In AGELESS, we will tackle this challenge by uncovering the molecular mechanisms of halted ageing in a unique model system, the bats. Bats are the longest-lived mammals relative to their body size, and defy the ‘rate-of-living’ theories as they use twice as much the energy as other species of considerable size, but live far longer. This suggests that bats have some underlying mechanisms that may explain their exceptional longevity. In AGELESS, we will identify the molecular mechanisms that enable mammals to achieve extraordinary longevity, using state-of-the-art comparative genomic methodologies focused on bats. We will identify, using population transcriptomics and telomere/mtDNA genomics, the molecular changes that occur in an ageing wild population of bats to uncover how bats ‘age’ so slowly compared with other mammals. In silico whole genome analyses, field based ageing transcriptomic data, mtDNA and telomeric studies will be integrated and analysed using a networks approach, to ascertain how these systems interact to halt ageing. For the first time, we will be able to utilize the diversity seen within nature to identify key molecular targets and regions that regulate and control ageing in mammals. AGELESS will provide a deeper understanding of the causal mechanisms of ageing, potentially uncovering the crucial molecular pathways that can be modified to halt, alleviate and perhaps even reverse this process in man."
Summary
"Ageing is the gradual and irreversible breakdown of living systems associated with the advancement of time, which leads to an increase in vulnerability and eventual mortality. Despite recent advances in ageing research, the intrinsic complexity of the ageing process has prevented a full understanding of this process, therefore, ageing remains a grand challenge in contemporary biology. In AGELESS, we will tackle this challenge by uncovering the molecular mechanisms of halted ageing in a unique model system, the bats. Bats are the longest-lived mammals relative to their body size, and defy the ‘rate-of-living’ theories as they use twice as much the energy as other species of considerable size, but live far longer. This suggests that bats have some underlying mechanisms that may explain their exceptional longevity. In AGELESS, we will identify the molecular mechanisms that enable mammals to achieve extraordinary longevity, using state-of-the-art comparative genomic methodologies focused on bats. We will identify, using population transcriptomics and telomere/mtDNA genomics, the molecular changes that occur in an ageing wild population of bats to uncover how bats ‘age’ so slowly compared with other mammals. In silico whole genome analyses, field based ageing transcriptomic data, mtDNA and telomeric studies will be integrated and analysed using a networks approach, to ascertain how these systems interact to halt ageing. For the first time, we will be able to utilize the diversity seen within nature to identify key molecular targets and regions that regulate and control ageing in mammals. AGELESS will provide a deeper understanding of the causal mechanisms of ageing, potentially uncovering the crucial molecular pathways that can be modified to halt, alleviate and perhaps even reverse this process in man."
Max ERC Funding
1 499 768 €
Duration
Start date: 2013-01-01, End date: 2017-12-31
Project acronym AI-DEMON
Project Artificial intelligence design of molecular nano-magnets and molecular qubits
Researcher (PI) Alessandro LUNGHI
Host Institution (HI) THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Country Ireland
Call Details Starting Grant (StG), PE4, ERC-2020-STG
Summary As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.
Summary
As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.
Max ERC Funding
1 499 786 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym AI-PREVENT
Project A nationwide artificial intelligence risk assessment for primary prevention of cardiometabolic diseases
Researcher (PI) Andrea Ganna
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives:
1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals).
2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction.
3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals.
My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.
Summary
Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives:
1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals).
2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction.
3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals.
My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.
Max ERC Funding
1 550 057 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym ALGOCom
Project Novel Algorithmic Techniques through the Lens of Combinatorics
Researcher (PI) Parinya Chalermsook
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Country Finland
Call Details Starting Grant (StG), PE6, ERC-2017-STG
Summary Real-world optimization problems pose major challenges to algorithmic research. For instance, (i) many important problems are believed to be intractable (i.e. NP-hard) and (ii) with the growth of data size, modern applications often require a decision making under {\em incomplete and dynamically changing input data}. After several decades of research, central problems in these domains have remained poorly understood (e.g. Is there an asymptotically most efficient binary search trees?) Existing algorithmic techniques either reach their limitation or are inherently tailored to special cases.
This project attempts to untangle this gap in the state of the art and seeks new interplay across multiple areas of algorithms, such as approximation algorithms, online algorithms, fixed-parameter tractable (FPT) algorithms, exponential time algorithms, and data structures. We propose new directions from the {\em structural perspectives} that connect the aforementioned algorithmic problems to basic questions in combinatorics.
Our approaches fall into one of the three broad schemes: (i) new structural theory, (ii) intermediate problems, and (iii) transfer of techniques. These directions partially build on the PI's successes in resolving more than ten classical problems in this context.
Resolving the proposed problems will likely revolutionize our understanding about algorithms and data structures and potentially unify techniques in multiple algorithmic regimes. Any progress is, in fact, already a significant contribution to the algorithms community. We suggest concrete intermediate goals that are of independent interest and have lower risks, so they are suitable for Ph.D students.
Summary
Real-world optimization problems pose major challenges to algorithmic research. For instance, (i) many important problems are believed to be intractable (i.e. NP-hard) and (ii) with the growth of data size, modern applications often require a decision making under {\em incomplete and dynamically changing input data}. After several decades of research, central problems in these domains have remained poorly understood (e.g. Is there an asymptotically most efficient binary search trees?) Existing algorithmic techniques either reach their limitation or are inherently tailored to special cases.
This project attempts to untangle this gap in the state of the art and seeks new interplay across multiple areas of algorithms, such as approximation algorithms, online algorithms, fixed-parameter tractable (FPT) algorithms, exponential time algorithms, and data structures. We propose new directions from the {\em structural perspectives} that connect the aforementioned algorithmic problems to basic questions in combinatorics.
Our approaches fall into one of the three broad schemes: (i) new structural theory, (ii) intermediate problems, and (iii) transfer of techniques. These directions partially build on the PI's successes in resolving more than ten classical problems in this context.
Resolving the proposed problems will likely revolutionize our understanding about algorithms and data structures and potentially unify techniques in multiple algorithmic regimes. Any progress is, in fact, already a significant contribution to the algorithms community. We suggest concrete intermediate goals that are of independent interest and have lower risks, so they are suitable for Ph.D students.
Max ERC Funding
1 411 258 €
Duration
Start date: 2018-02-01, End date: 2024-01-31