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 ABIONYS
Project Artificial Enzyme Modules as Tools in a Tailor-made Biosynthesis
Researcher (PI) Jan DESKA
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Country Finland
Call Details Consolidator Grant (CoG), PE5, ERC-2019-COG
Summary In order to tackle some of the prime societal challenges of this century, science has to urgently provide effective tools addressing the redesign of chemical value chains through the exploitation of novel, bio-based raw materials, and the discovery and implementation of more resource-efficient production platforms. Nature will inevitably play a pivotal role in the imminent transformation of industrial strategies, and the recent bioeconomy approaches can only be regarded as initial step towards a sustainable future. Operating at the interface between chemistry and life sciences, my ABIONYS will fundamentally challenge the widely held distinction separating chemical from biosynthesis, and will deliver the first proof-of-concept where abiotic reactions act as productive puzzle pieces in biosynthetic arrangements. On the basis of our previous ground-breaking discoveries on artificial enzyme functions, I will create a significantly extended toolbox of biocatalysis modules by applying protein-based interpretations of synthetically crucial but non-natural reactions i.e. transformations that are in no way biosynthetically encoded in living organisms. My research will exploit these tools in multi-enzyme cascades for the preparation of complex organic target structures, not only to highlight the great synthetic potential of these approaches, but also to lay the groundwork for in vivo implementations. Eventually, the knowledge gathered from enzyme discovery and cascade design will enable to create an unprecedented class of bioproduction systems, where the genetic incorporation of artificial enzyme functions into recombinant microbial host organisms will yield tailor-made cellular factories. Combining classical organic synthesis strategies with the power of modern biotechnology, ABIONYS is going to transform the way we synthesize complex and functional building blocks by allowing us to encode organic chemistry thinking into living production platforms.
Summary
In order to tackle some of the prime societal challenges of this century, science has to urgently provide effective tools addressing the redesign of chemical value chains through the exploitation of novel, bio-based raw materials, and the discovery and implementation of more resource-efficient production platforms. Nature will inevitably play a pivotal role in the imminent transformation of industrial strategies, and the recent bioeconomy approaches can only be regarded as initial step towards a sustainable future. Operating at the interface between chemistry and life sciences, my ABIONYS will fundamentally challenge the widely held distinction separating chemical from biosynthesis, and will deliver the first proof-of-concept where abiotic reactions act as productive puzzle pieces in biosynthetic arrangements. On the basis of our previous ground-breaking discoveries on artificial enzyme functions, I will create a significantly extended toolbox of biocatalysis modules by applying protein-based interpretations of synthetically crucial but non-natural reactions i.e. transformations that are in no way biosynthetically encoded in living organisms. My research will exploit these tools in multi-enzyme cascades for the preparation of complex organic target structures, not only to highlight the great synthetic potential of these approaches, but also to lay the groundwork for in vivo implementations. Eventually, the knowledge gathered from enzyme discovery and cascade design will enable to create an unprecedented class of bioproduction systems, where the genetic incorporation of artificial enzyme functions into recombinant microbial host organisms will yield tailor-made cellular factories. Combining classical organic synthesis strategies with the power of modern biotechnology, ABIONYS is going to transform the way we synthesize complex and functional building blocks by allowing us to encode organic chemistry thinking into living production platforms.
Max ERC Funding
1 995 707 €
Duration
Start date: 2020-11-01, End date: 2025-10-31
Project acronym ADAPT
Project Autoxidation of Anthropogenic Volatile Organic Compounds (AVOC) as a Source of Urban Air Pollution
Researcher (PI) Matti Rissanen
Host Institution (HI) TAMPEREEN KORKEAKOULUSAATIO SR
Country Finland
Call Details Consolidator Grant (CoG), PE10, ERC-2020-COG
Summary Previous efforts to raise living standards have been based on relentlessly increasing combustion, causing environmental destruction at all scales. In addition to climate-warming CO2, fossil fuel combustion also produces a large number of organic compounds and particulate matter, which deteriorate air quality.
The atmosphere is cleansed from such pollutants by gas-phase oxidation reactions, which are invariably mediated by peroxy radicals (RO2). Oxidation transforms initially volatile and water-insoluble hydrocarbons into water-soluble forms (ultimately CO2), enabling scavenging by liquid droplets. A minor but crucially important alternative oxidation pathway leads to oxidative molecular growth, and formation of atmospheric aerosols. Aerosols impart a huge influence on the atmosphere, from local air quality issues to global climate forcing, yet their formation mechanisms and structures of organic aerosol precursors remains elusive.
In a paradigm change, RO2 was recently found to undergo autoxidation, enabling rapid aerosol precursor formation even at sub-second time-scales – in stark contrast to the long processing times (days - weeks) previously assumed to be necessary. We have shown how abundant biogenic hydrocarbons (BVOC) autoxidize, but due to key structural differences, the same pathways are not available for anthropogenic hydrocarbons (AVOC), and thus they were not expected to autoxidize. My preliminary experiments reveal that AVOCs do autoxidize, but the mechanism enabling this remain unknown. Crucially, the co-reactants shown to inhibit BVOC seem to enforce AVOC autoxidation – potentially explaining the recent mysterious discovery of new-particle formation in polluted megacities. In ADAPT, I will use a combination of novel mass spectrometric detection methods fortified by theoretical calculations, to solve the mechanism of AVOC autoxidation. This will directly assist both air quality management, and the design of cleaner fuels and engines.
Summary
Previous efforts to raise living standards have been based on relentlessly increasing combustion, causing environmental destruction at all scales. In addition to climate-warming CO2, fossil fuel combustion also produces a large number of organic compounds and particulate matter, which deteriorate air quality.
The atmosphere is cleansed from such pollutants by gas-phase oxidation reactions, which are invariably mediated by peroxy radicals (RO2). Oxidation transforms initially volatile and water-insoluble hydrocarbons into water-soluble forms (ultimately CO2), enabling scavenging by liquid droplets. A minor but crucially important alternative oxidation pathway leads to oxidative molecular growth, and formation of atmospheric aerosols. Aerosols impart a huge influence on the atmosphere, from local air quality issues to global climate forcing, yet their formation mechanisms and structures of organic aerosol precursors remains elusive.
In a paradigm change, RO2 was recently found to undergo autoxidation, enabling rapid aerosol precursor formation even at sub-second time-scales – in stark contrast to the long processing times (days - weeks) previously assumed to be necessary. We have shown how abundant biogenic hydrocarbons (BVOC) autoxidize, but due to key structural differences, the same pathways are not available for anthropogenic hydrocarbons (AVOC), and thus they were not expected to autoxidize. My preliminary experiments reveal that AVOCs do autoxidize, but the mechanism enabling this remain unknown. Crucially, the co-reactants shown to inhibit BVOC seem to enforce AVOC autoxidation – potentially explaining the recent mysterious discovery of new-particle formation in polluted megacities. In ADAPT, I will use a combination of novel mass spectrometric detection methods fortified by theoretical calculations, to solve the mechanism of AVOC autoxidation. This will directly assist both air quality management, and the design of cleaner fuels and engines.
Max ERC Funding
2 689 147 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym ADHESWITCHES
Project Adhesion switches in cancer and development: from in vivo to synthetic biology
Researcher (PI) Mari Johanna Ivaska
Host Institution (HI) TURUN YLIOPISTO
Country Finland
Call Details Consolidator Grant (CoG), LS3, ERC-2013-CoG
Summary Integrins are transmembrane cell adhesion receptors controlling cell proliferation and migration. Our objective is to gain fundamentally novel mechanistic insight into the emerging new roles of integrins in cancer and to generate a road map of integrin dependent pathways critical in mammary gland development and integrin signalling thus opening new targets for therapeutic interventions. We will combine an in vivo based translational approach with cell and molecular biological studies aiming to identify entirely novel concepts in integrin function using cutting edge techniques and synthetic-biology tools.
The specific objectives are:
1) Integrin inactivation in branching morphogenesis and cancer invasion. Integrins regulate mammary gland development and cancer invasion but the role of integrin inactivating proteins in these processes is currently completely unknown. We will investigate this using genetically modified mice, ex-vivo organoid models and human tissues with the aim to identify beneficial combinational treatments against cancer invasion.
2) Endosomal adhesomes – cross-talk between integrin activity and integrin “inside-in signaling”. We hypothesize that endocytosed active integrins engage in specialized endosomal signaling that governs cell survival especially in cancer. RNAi cell arrays, super-resolution STED imaging and endosomal proteomics will be used to investigate integrin signaling in endosomes.
3) Spatio-temporal co-ordination of adhesion and endocytosis. Several cytosolic proteins compete for integrin binding to regulate activation, endocytosis and recycling. Photoactivatable protein-traps and predefined matrix micropatterns will be employed to mechanistically dissect the spatio-temporal dynamics and hierarchy of their recruitment.
We will employ innovative and unconventional techniques to address three major unanswered questions in the field and significantly advance our understanding of integrin function in development and cancer.
Summary
Integrins are transmembrane cell adhesion receptors controlling cell proliferation and migration. Our objective is to gain fundamentally novel mechanistic insight into the emerging new roles of integrins in cancer and to generate a road map of integrin dependent pathways critical in mammary gland development and integrin signalling thus opening new targets for therapeutic interventions. We will combine an in vivo based translational approach with cell and molecular biological studies aiming to identify entirely novel concepts in integrin function using cutting edge techniques and synthetic-biology tools.
The specific objectives are:
1) Integrin inactivation in branching morphogenesis and cancer invasion. Integrins regulate mammary gland development and cancer invasion but the role of integrin inactivating proteins in these processes is currently completely unknown. We will investigate this using genetically modified mice, ex-vivo organoid models and human tissues with the aim to identify beneficial combinational treatments against cancer invasion.
2) Endosomal adhesomes – cross-talk between integrin activity and integrin “inside-in signaling”. We hypothesize that endocytosed active integrins engage in specialized endosomal signaling that governs cell survival especially in cancer. RNAi cell arrays, super-resolution STED imaging and endosomal proteomics will be used to investigate integrin signaling in endosomes.
3) Spatio-temporal co-ordination of adhesion and endocytosis. Several cytosolic proteins compete for integrin binding to regulate activation, endocytosis and recycling. Photoactivatable protein-traps and predefined matrix micropatterns will be employed to mechanistically dissect the spatio-temporal dynamics and hierarchy of their recruitment.
We will employ innovative and unconventional techniques to address three major unanswered questions in the field and significantly advance our understanding of integrin function in development and cancer.
Max ERC Funding
1 887 910 €
Duration
Start date: 2014-05-01, End date: 2019-04-30
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 AGNES
Project ACTIVE AGEING – RESILIENCE AND EXTERNAL SUPPORT AS MODIFIERS OF THE DISABLEMENT OUTCOME
Researcher (PI) Taina Tuulikki RANTANEN
Host Institution (HI) JYVASKYLAN YLIOPISTO
Country Finland
Call Details Advanced Grant (AdG), SH3, ERC-2015-AdG
Summary The goals are 1. To develop a scale assessing the diversity of active ageing with four dimensions that are ability (what people can do), activity (what people do do), ambition (what are the valued activities that people want to do), and autonomy (how satisfied people are with the opportunity to do valued activities); 2. To examine health and physical and psychological functioning as the determinants and social and build environment, resilience and personal skills as modifiers of active ageing; 3. To develop a multicomponent sustainable intervention aiming to promote active ageing (methods: counselling, information technology, help from volunteers); 4. To test the feasibility and effectiveness on the intervention; and 5. To study cohort effects on the phenotypes on the pathway to active ageing.
“If You Can Measure It, You Can Change It.” Active ageing assessment needs conceptual progress, which I propose to do. A quantifiable scale will be developed that captures the diversity of active ageing stemming from the WHO definition of active ageing as the process of optimizing opportunities for health and participation in the society for all people in line with their needs, goals and capacities as they age. I will collect cross-sectional data (N=1000, ages 75, 80 and 85 years) and model the pathway to active ageing with state-of-the art statistical methods. By doing this I will create novel knowledge on preconditions for active ageing. The collected cohort data will be compared to a pre-existing cohort data that was collected 25 years ago to obtain knowledge about changes over time in functioning of older people. A randomized controlled trial (N=200) will be conducted to assess the effectiveness of the envisioned intervention promoting active ageing through participation. The project will regenerate ageing research by launching a novel scale, by training young scientists, by creating new concepts and theory development and by producing evidence for active ageing promotion
Summary
The goals are 1. To develop a scale assessing the diversity of active ageing with four dimensions that are ability (what people can do), activity (what people do do), ambition (what are the valued activities that people want to do), and autonomy (how satisfied people are with the opportunity to do valued activities); 2. To examine health and physical and psychological functioning as the determinants and social and build environment, resilience and personal skills as modifiers of active ageing; 3. To develop a multicomponent sustainable intervention aiming to promote active ageing (methods: counselling, information technology, help from volunteers); 4. To test the feasibility and effectiveness on the intervention; and 5. To study cohort effects on the phenotypes on the pathway to active ageing.
“If You Can Measure It, You Can Change It.” Active ageing assessment needs conceptual progress, which I propose to do. A quantifiable scale will be developed that captures the diversity of active ageing stemming from the WHO definition of active ageing as the process of optimizing opportunities for health and participation in the society for all people in line with their needs, goals and capacities as they age. I will collect cross-sectional data (N=1000, ages 75, 80 and 85 years) and model the pathway to active ageing with state-of-the art statistical methods. By doing this I will create novel knowledge on preconditions for active ageing. The collected cohort data will be compared to a pre-existing cohort data that was collected 25 years ago to obtain knowledge about changes over time in functioning of older people. A randomized controlled trial (N=200) will be conducted to assess the effectiveness of the envisioned intervention promoting active ageing through participation. The project will regenerate ageing research by launching a novel scale, by training young scientists, by creating new concepts and theory development and by producing evidence for active ageing promotion
Max ERC Funding
2 044 364 €
Duration
Start date: 2016-09-01, End date: 2021-08-31
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 ALEM
Project ADDITIONAL LOSSES IN ELECTRICAL MACHINES
Researcher (PI) Matti Antero Arkkio
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Country Finland
Call Details Advanced Grant (AdG), PE8, ERC-2013-ADG
Summary "Electrical motors consume about 40 % of the electrical energy produced in the European Union. About 90 % of this energy is converted to mechanical work. However, 0.5-2.5 % of it goes to so called additional load losses whose exact origins are unknown. Our ambitious aim is to reveal the origins of these losses, build up numerical tools for modeling them and optimize electrical motors to minimize the losses.
As the hypothesis of the research, we assume that the additional losses mainly result from the deterioration of the core materials during the manufacturing process of the machine. By calorimetric measurements, we have found that the core losses of electrical machines may be twice as large as comprehensive loss models predict. The electrical steel sheets are punched, welded together and shrink fit to the frame. This causes residual strains in the core sheets deteriorating their magnetic characteristics. The cutting burrs make galvanic contacts between the sheets and form paths for inter-lamination currents. Another potential source of additional losses are the circulating currents between the parallel strands of random-wound armature windings. The stochastic nature of these potential sources of additional losses puts more challenge on the research.
We shall develop a physical loss model that couples the mechanical strains and electromagnetic losses in electrical steel sheets and apply the new model for comprehensive loss analysis of electrical machines. The stochastic variables related to the core losses and circulating-current losses will be discretized together with the temporal and spatial discretization of the electromechanical field variables. The numerical stochastic loss model will be used to search for such machine constructions that are insensitive to the manufacturing defects. We shall validate the new numerical loss models by electromechanical and calorimetric measurements."
Summary
"Electrical motors consume about 40 % of the electrical energy produced in the European Union. About 90 % of this energy is converted to mechanical work. However, 0.5-2.5 % of it goes to so called additional load losses whose exact origins are unknown. Our ambitious aim is to reveal the origins of these losses, build up numerical tools for modeling them and optimize electrical motors to minimize the losses.
As the hypothesis of the research, we assume that the additional losses mainly result from the deterioration of the core materials during the manufacturing process of the machine. By calorimetric measurements, we have found that the core losses of electrical machines may be twice as large as comprehensive loss models predict. The electrical steel sheets are punched, welded together and shrink fit to the frame. This causes residual strains in the core sheets deteriorating their magnetic characteristics. The cutting burrs make galvanic contacts between the sheets and form paths for inter-lamination currents. Another potential source of additional losses are the circulating currents between the parallel strands of random-wound armature windings. The stochastic nature of these potential sources of additional losses puts more challenge on the research.
We shall develop a physical loss model that couples the mechanical strains and electromagnetic losses in electrical steel sheets and apply the new model for comprehensive loss analysis of electrical machines. The stochastic variables related to the core losses and circulating-current losses will be discretized together with the temporal and spatial discretization of the electromechanical field variables. The numerical stochastic loss model will be used to search for such machine constructions that are insensitive to the manufacturing defects. We shall validate the new numerical loss models by electromechanical and calorimetric measurements."
Max ERC Funding
2 489 949 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
Project acronym ALGOA
Project Novel algorithm for treatment planning of patients with osteoarthritis
Researcher (PI) Rami Kristian KORHONEN
Host Institution (HI) ITA-SUOMEN YLIOPISTO
Country Finland
Call Details Proof of Concept (PoC), PC1, ERC-2016-PoC
Summary Osteoarthritis (OA) is a common joint disease affecting over 40 million Europeans. Most common consequences of OA are pain, disability and social isolation. What is alarming, the number of patients will increase 50% in developed countries during the next 20 years. Moreover, the economic costs of OA are considerable since 1) direct healthcare (hospital admissions, medical examinations, drug therapy, etc.) and 2) productivity costs due to reduced performance while at work and absence from work have been estimated to be between 1% and 2.5% of the gross domestic product (GDP) in Western countries.
We have developed an algorithm that is able to predict the progression of OA for overweight subjects while healthy subjects do not develop OA. When employed in clinical use, preventive and personalised treatments can be started before clinically significant symptoms are observed. This marks a major breakthrough in improving the life quality of OA patients and patients prone to OA. Our discovery will directly lead to longer working careers and lesser absence from work, and will result subsequently increased productivity. Moreover, the patients are expected to live longer due to reduced disability and social isolation.
Moreover, the discovery provides economic long-term relief for the health care system, which is burdened by increasing geriatric population and stringent economic environment. With our tool, as an example, by eliminating 25% of medical examinations annually due to overweight or obesity in Finland (150.000 patients), we estimate to decrease annual direct costs by 140M€ and indirect costs by 185M€.
In the PoC project we will carry out technical proof-of-concept and perform pre-commercialisation actions to shorten the time to market. The ultimate goal after the project is to develop our innovation towards a software product, aiding the OA diagnostics in hospitals and having commercialisation potential amongst medical device companies.
Summary
Osteoarthritis (OA) is a common joint disease affecting over 40 million Europeans. Most common consequences of OA are pain, disability and social isolation. What is alarming, the number of patients will increase 50% in developed countries during the next 20 years. Moreover, the economic costs of OA are considerable since 1) direct healthcare (hospital admissions, medical examinations, drug therapy, etc.) and 2) productivity costs due to reduced performance while at work and absence from work have been estimated to be between 1% and 2.5% of the gross domestic product (GDP) in Western countries.
We have developed an algorithm that is able to predict the progression of OA for overweight subjects while healthy subjects do not develop OA. When employed in clinical use, preventive and personalised treatments can be started before clinically significant symptoms are observed. This marks a major breakthrough in improving the life quality of OA patients and patients prone to OA. Our discovery will directly lead to longer working careers and lesser absence from work, and will result subsequently increased productivity. Moreover, the patients are expected to live longer due to reduced disability and social isolation.
Moreover, the discovery provides economic long-term relief for the health care system, which is burdened by increasing geriatric population and stringent economic environment. With our tool, as an example, by eliminating 25% of medical examinations annually due to overweight or obesity in Finland (150.000 patients), we estimate to decrease annual direct costs by 140M€ and indirect costs by 185M€.
In the PoC project we will carry out technical proof-of-concept and perform pre-commercialisation actions to shorten the time to market. The ultimate goal after the project is to develop our innovation towards a software product, aiding the OA diagnostics in hospitals and having commercialisation potential amongst medical device companies.
Max ERC Funding
150 000 €
Duration
Start date: 2018-01-01, End date: 2019-06-30
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