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: 2023-01-31
Project acronym Bi3BoostFlowBat
Project Bioinspired, biphasic and bipolar flow batteries with boosters for sustainable large-scale energy storage
Researcher (PI) Pekka PELJO
Host Institution (HI) TURUN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), PE8, ERC-2020-STG
Summary To satisfy our growing energy demand while reducing reliance on fossil fuels, a switch to renewable energy sources is vital. The intermittent nature of the latter means innovations in energy storage technology is a key grand challenge. Cost and sustainability issues currently limit the widespread use of electrochemical energy storage technologies, such as lithium ion and redox flow batteries. As the scale for energy storage is simply enormous, the only option is to look for abundant materials. However, compounds that fulfil the extensive requirements entailed at low cost has yet to be reported. While it is possible that the holy grail of energy storage will be found, for example by advanced computational tools and machine learning to design “perfect” abundant molecules, a more flexible, innovative solution to sustainable and cost-effective large-scale energy storage is required. Bi3BoostFlowBat will develop game changing strategies to widen the choice of compounds utilizable for batteries to simultaneously satisfy the requirements for low cost, optimal redox potentials, high solubility and stability in all conditions. The aim of this project is to develop cost-efficient batteries by using solid boosters and by eliminating cross over. Two approaches will be pursued for cross-over elimination 1) bio-inspired polymer batteries, where cross-over of solubilized polymers is prevented by size-exclusion membranes and 2) biphasic emulsion flow batteries, where redox species are transferred to oil phase droplets upon charge. Third research direction focuses on systems to maintain a pH gradient, to allow operation of differential pH systems to improve the cell voltages. Limits of different approaches will be explored by taking an electrochemical engineering approach to model the performance of different systems and by validating the models experimentally. This work will chart the route towards the future third generation battery technologies for the large-scale energy storage.
Summary
To satisfy our growing energy demand while reducing reliance on fossil fuels, a switch to renewable energy sources is vital. The intermittent nature of the latter means innovations in energy storage technology is a key grand challenge. Cost and sustainability issues currently limit the widespread use of electrochemical energy storage technologies, such as lithium ion and redox flow batteries. As the scale for energy storage is simply enormous, the only option is to look for abundant materials. However, compounds that fulfil the extensive requirements entailed at low cost has yet to be reported. While it is possible that the holy grail of energy storage will be found, for example by advanced computational tools and machine learning to design “perfect” abundant molecules, a more flexible, innovative solution to sustainable and cost-effective large-scale energy storage is required. Bi3BoostFlowBat will develop game changing strategies to widen the choice of compounds utilizable for batteries to simultaneously satisfy the requirements for low cost, optimal redox potentials, high solubility and stability in all conditions. The aim of this project is to develop cost-efficient batteries by using solid boosters and by eliminating cross over. Two approaches will be pursued for cross-over elimination 1) bio-inspired polymer batteries, where cross-over of solubilized polymers is prevented by size-exclusion membranes and 2) biphasic emulsion flow batteries, where redox species are transferred to oil phase droplets upon charge. Third research direction focuses on systems to maintain a pH gradient, to allow operation of differential pH systems to improve the cell voltages. Limits of different approaches will be explored by taking an electrochemical engineering approach to model the performance of different systems and by validating the models experimentally. This work will chart the route towards the future third generation battery technologies for the large-scale energy storage.
Max ERC Funding
1 499 880 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym CapBed
Project Engineered Capillary Beds for Successful Prevascularization of Tissue Engineering Constructs
Researcher (PI) Rogerio Pedro Lemos de Sousa Pirraco
Host Institution (HI) UNIVERSIDADE DO MINHO
Country Portugal
Call Details Starting Grant (StG), PE8, ERC-2018-STG
Summary The demand for donated organs vastly outnumbers the supply, leading each year to the death of thousands of people and the suffering of millions more. Engineered tissues and organs following Tissue Engineering approaches are a possible solution to this problem. However, a prevascularization solution to irrigate complex engineered tissues and assure their survival after transplantation is currently elusive. In the human body, complex organs and tissues irrigation is achieved by a network of blood vessels termed capillary bed which suggests such a structure is needed in engineered tissues. Previous approaches to engineer capillary beds reached different levels of success but none yielded a fully functional one due to the inability in simultaneously addressing key elements such as correct angiogenic cell populations, a suitable matrix and dynamic conditions that mimic blood flow.
CapBed aims at proposing a new technology to fabricate in vitro capillary beds that include a vascular axis that can be anastomosed with a patient circulation. Such capillary beds could be used as prime tools to prevascularize in vitro engineered tissues and provide fast perfusion of those after transplantation to a patient. Cutting edge techniques will be for the first time integrated in a disruptive approach to address the requirements listed above. Angiogenic cell sheets of human Adipose-derived Stromal Vascular fraction cells will provide the cell populations that integrate the capillaries and manage its intricate formation, as well as the collagen required to build the matrix that will hold the capillary beds. Innovative fabrication technologies such as 3D printing and laser photoablation will be used for the fabrication of the micropatterned matrix that will allow fluid flow through microfluidics. The resulting functional capillary beds can be used with virtually every tissue engineering strategy rendering the proposed strategy with massive economical, scientific and medical potential
Summary
The demand for donated organs vastly outnumbers the supply, leading each year to the death of thousands of people and the suffering of millions more. Engineered tissues and organs following Tissue Engineering approaches are a possible solution to this problem. However, a prevascularization solution to irrigate complex engineered tissues and assure their survival after transplantation is currently elusive. In the human body, complex organs and tissues irrigation is achieved by a network of blood vessels termed capillary bed which suggests such a structure is needed in engineered tissues. Previous approaches to engineer capillary beds reached different levels of success but none yielded a fully functional one due to the inability in simultaneously addressing key elements such as correct angiogenic cell populations, a suitable matrix and dynamic conditions that mimic blood flow.
CapBed aims at proposing a new technology to fabricate in vitro capillary beds that include a vascular axis that can be anastomosed with a patient circulation. Such capillary beds could be used as prime tools to prevascularize in vitro engineered tissues and provide fast perfusion of those after transplantation to a patient. Cutting edge techniques will be for the first time integrated in a disruptive approach to address the requirements listed above. Angiogenic cell sheets of human Adipose-derived Stromal Vascular fraction cells will provide the cell populations that integrate the capillaries and manage its intricate formation, as well as the collagen required to build the matrix that will hold the capillary beds. Innovative fabrication technologies such as 3D printing and laser photoablation will be used for the fabrication of the micropatterned matrix that will allow fluid flow through microfluidics. The resulting functional capillary beds can be used with virtually every tissue engineering strategy rendering the proposed strategy with massive economical, scientific and medical potential
Max ERC Funding
1 499 940 €
Duration
Start date: 2018-11-01, End date: 2023-10-31
Project acronym COMPUTED
Project Computational User Interface Design
Researcher (PI) Antti Olavi Oulasvirta
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Country Finland
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary PROBLEM: Despite extensive research on human-computer interaction (HCI), no method exists that guarantees the optimal or even a provably good user interface (UI) design. The prevailing approach relies on heuristics and iteration, which can be costly and even ineffective, because UI design often involves combinatorially hard problems with immense design spaces, multiple objectives and constraints, and complex user behavior.
OBJECTIVES: COMPUTED establishes the foundations for optimizing UI designs. A design can be automatically optimized to given objectives and constraints by using combinatorial optimization methods that deploy predictive models of user behavior as objective functions. Although previous work has shown some improvements to usability, the scope has been restricted to keyboards and widgets. COMPUTED researches methods that can vastly expand the scope of optimizable problems. First, algorithmic support is developed for acquiring objective functions that cover the main human factors in a given HCI task. Second, formal analysis of decision problems in UI design allows combating a broader range of design tasks with efficient and appropriate optimization methods. Third, a novel interactive UI optimization paradigm for UI designers promotes fast convergence to good results even in the face of uncertainty and incomplete knowledge.
IMPACT: Combinatorial UI optimization offers a strong complement to the prevailing design approaches. Because the structured search process has a high chance of finding good solutions, optimization could improve the quality of interfaces used in everyday life. Optimization can also increase cost-efficiency, because reference to optimality can eliminate fruitless iteration. Moreover, because no preknowledge of UI design is required, even novices will be able to design great UIs. Even in “messy,” less well-defined problems, it may support designers by allowing them to delegate the solving of well-known sub-problems.
Summary
PROBLEM: Despite extensive research on human-computer interaction (HCI), no method exists that guarantees the optimal or even a provably good user interface (UI) design. The prevailing approach relies on heuristics and iteration, which can be costly and even ineffective, because UI design often involves combinatorially hard problems with immense design spaces, multiple objectives and constraints, and complex user behavior.
OBJECTIVES: COMPUTED establishes the foundations for optimizing UI designs. A design can be automatically optimized to given objectives and constraints by using combinatorial optimization methods that deploy predictive models of user behavior as objective functions. Although previous work has shown some improvements to usability, the scope has been restricted to keyboards and widgets. COMPUTED researches methods that can vastly expand the scope of optimizable problems. First, algorithmic support is developed for acquiring objective functions that cover the main human factors in a given HCI task. Second, formal analysis of decision problems in UI design allows combating a broader range of design tasks with efficient and appropriate optimization methods. Third, a novel interactive UI optimization paradigm for UI designers promotes fast convergence to good results even in the face of uncertainty and incomplete knowledge.
IMPACT: Combinatorial UI optimization offers a strong complement to the prevailing design approaches. Because the structured search process has a high chance of finding good solutions, optimization could improve the quality of interfaces used in everyday life. Optimization can also increase cost-efficiency, because reference to optimality can eliminate fruitless iteration. Moreover, because no preknowledge of UI design is required, even novices will be able to design great UIs. Even in “messy,” less well-defined problems, it may support designers by allowing them to delegate the solving of well-known sub-problems.
Max ERC Funding
1 499 790 €
Duration
Start date: 2015-04-01, End date: 2020-03-31
Project acronym CUMTAS
Project Customized Micro Total Analysis Systems to Study Human Phase I Metabolism
Researcher (PI) Tiina Marjukka Sikanen
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS9, ERC-2012-StG_20111109
Summary The goal of this project is to develop inexpensive, high-throughput technology to screen the thus far unexplored metabolic interactions between environmental and household chemicals and clinically relevant drugs. The main influential focus will be on human phase I metabolism (redox reactions) of common toxicants like agrochemicals and plasticizers. On the basis of their structural resemblance to pharmaceuticals and endogenous compounds, many of these chemicals are suspected to have critical effects on cytochrome P450 metabolism which is the main detoxification route of pharmaceuticals in man. However, with the current analytical instrumentation, screening of such large chemical pool would take several years, and new chemicals would be introduced faster than the old ones are screened. Thus, the main technological goal of this project is to develop novel, practically zero-cost analytical instruments that enable characterization of a compound’s metabolic profile at very high speed (<1 min/sample). This goal is achieved through miniaturization and high degree of integration of analytical instrumentation by microfabrication means, an approach often called lab(oratory)-on-a-chip. The microfabricated arrays are envisioned to incorporate all analytical key functions required (i.e., sample pretreatment, metabolic reaction, separation of the reaction products, detection) on a single chip. Thanks to the reduced dimensions, the amount of chemical waste and consumption of expensive reagents are significantly reduced. In this project, several different microfabrication techniques, from delicate cleanroom processes to extremely simple printing techniques, will be exploited to produce smart microfluidic designs and multifunctional surfaces. Towards the end of the project, more focus will be put on “printable microfluidics” which provides a truly low-cost approach for fabrication of highly customized microfluidic assays. Numerical modelling is also an integral part of the work.
Summary
The goal of this project is to develop inexpensive, high-throughput technology to screen the thus far unexplored metabolic interactions between environmental and household chemicals and clinically relevant drugs. The main influential focus will be on human phase I metabolism (redox reactions) of common toxicants like agrochemicals and plasticizers. On the basis of their structural resemblance to pharmaceuticals and endogenous compounds, many of these chemicals are suspected to have critical effects on cytochrome P450 metabolism which is the main detoxification route of pharmaceuticals in man. However, with the current analytical instrumentation, screening of such large chemical pool would take several years, and new chemicals would be introduced faster than the old ones are screened. Thus, the main technological goal of this project is to develop novel, practically zero-cost analytical instruments that enable characterization of a compound’s metabolic profile at very high speed (<1 min/sample). This goal is achieved through miniaturization and high degree of integration of analytical instrumentation by microfabrication means, an approach often called lab(oratory)-on-a-chip. The microfabricated arrays are envisioned to incorporate all analytical key functions required (i.e., sample pretreatment, metabolic reaction, separation of the reaction products, detection) on a single chip. Thanks to the reduced dimensions, the amount of chemical waste and consumption of expensive reagents are significantly reduced. In this project, several different microfabrication techniques, from delicate cleanroom processes to extremely simple printing techniques, will be exploited to produce smart microfluidic designs and multifunctional surfaces. Towards the end of the project, more focus will be put on “printable microfluidics” which provides a truly low-cost approach for fabrication of highly customized microfluidic assays. Numerical modelling is also an integral part of the work.
Max ERC Funding
1 499 668 €
Duration
Start date: 2013-05-01, End date: 2019-02-28
Project acronym DeepSPIN
Project Deep Learning for Structured Prediction in Natural Language Processing
Researcher (PI) Andre Filipe TORRES MARTINS
Host Institution (HI) INSTITUTO DE TELECOMUNICACOES
Country Portugal
Call Details Starting Grant (StG), PE6, ERC-2017-STG
Summary "Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines.
From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning.
This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a ""planning mechanism"" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data.
Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry."
Summary
"Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines.
From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning.
This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a ""planning mechanism"" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data.
Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry."
Max ERC Funding
1 436 000 €
Duration
Start date: 2018-02-01, End date: 2023-01-31
Project acronym DEPENDABLECLOUD
Project Towards the dependable cloud:
Building the foundations for tomorrow's dependable cloud computing
Researcher (PI) Rodrigo Seromenho Miragaia Rodrigues
Host Institution (HI) INESC ID - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, INVESTIGACAO E DESENVOLVIMENTO EM LISBOA
Country Portugal
Call Details Starting Grant (StG), PE6, ERC-2012-StG_20111012
Summary Cloud computing is being increasingly adopted by individuals, organizations, and governments. However, as the computations that are offloaded to the cloud expand to societal-critical services, the dependability requirements of cloud services become much higher, and we need to ensure that the infrastructure that supports these services is ready to meet these requirements. In particular, this proposal tackles the challenges that arise from two distinctive characteristic of the cloud infrastructure.
The first is that non-crash faults, despite being considered highly unlikely by the designers of traditional systems, become commonplace at the scale and complexity of the cloud infrastructure. We argue that the current ad-hoc methods for handling these faults are insufficient, and that the only principled approach of assuming Byzantine faults is too pessimistic. Therefore, we call for a new systematic approach to tolerating non-crash, non-adversarial faults. This requires the definition of a new fault model, and the construction of a series of building blocks and key protocol elements that enable the construction of fault-tolerant cloud services.
The second issue is that to meet their scalability requirements, cloud services spread their state across multiple data centers, and direct users to the closest one. This raises the issue that not all operations can be executed optimistically, without being aware of concurrent operations over the same data, and thus multiple levels of consistency must coexist. However, this puts the onus of reasoning about which behaviors are allowed under such a hybrid consistency model on the programmer of the service. We propose a systematic solution to this problem, which includes a novel consistency model that allows for developing highly scalable services that are fast when possible and consistent when necessary, and a labeling methodology to guide the programmer in deciding which operations can run at each consistency level.
Summary
Cloud computing is being increasingly adopted by individuals, organizations, and governments. However, as the computations that are offloaded to the cloud expand to societal-critical services, the dependability requirements of cloud services become much higher, and we need to ensure that the infrastructure that supports these services is ready to meet these requirements. In particular, this proposal tackles the challenges that arise from two distinctive characteristic of the cloud infrastructure.
The first is that non-crash faults, despite being considered highly unlikely by the designers of traditional systems, become commonplace at the scale and complexity of the cloud infrastructure. We argue that the current ad-hoc methods for handling these faults are insufficient, and that the only principled approach of assuming Byzantine faults is too pessimistic. Therefore, we call for a new systematic approach to tolerating non-crash, non-adversarial faults. This requires the definition of a new fault model, and the construction of a series of building blocks and key protocol elements that enable the construction of fault-tolerant cloud services.
The second issue is that to meet their scalability requirements, cloud services spread their state across multiple data centers, and direct users to the closest one. This raises the issue that not all operations can be executed optimistically, without being aware of concurrent operations over the same data, and thus multiple levels of consistency must coexist. However, this puts the onus of reasoning about which behaviors are allowed under such a hybrid consistency model on the programmer of the service. We propose a systematic solution to this problem, which includes a novel consistency model that allows for developing highly scalable services that are fast when possible and consistent when necessary, and a labeling methodology to guide the programmer in deciding which operations can run at each consistency level.
Max ERC Funding
1 076 084 €
Duration
Start date: 2012-10-01, End date: 2018-01-31
Project acronym DIADRUG
Project Insulin resistance and diabetic nephropathy - development of novel in vivo models for drug discovery
Researcher (PI) Sanna Lehtonen
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS9, ERC-2009-StG
Summary Up to one third of diabetic patients develop nephropathy, a serious complication of diabetes. Microalbuminuria is the earliest sign of the complication, which may ultimately develop to end-stage renal disease requiring dialysis or a kidney transplant. Insulin resistance and metabolic syndrome are associated with an increased risk for diabetic nephropathy. Interestingly, glomerular epithelial cells or podocytes have recently been shown to be insulin responsive. Further, nephrin, a key structural component of podocytes, is essential for insulin action in these cells. Our novel findings show that adaptor protein CD2AP, an interaction partner of nephrin, associates with regulators of insulin signaling and glucose transport in glomeruli. The results suggest that nephrin and CD2AP are involved, by association with these proteins, in the regulation of insulin signaling and glucose transport in podocytes. We hypothesize that podocytes can develop insulin resistance and that disturbances in insulin response affect podocyte function and contribute to the development of diabetic nephropathy. The aim of this project is to clarify the mechanisms leading to development of insulin resistance in podocytes and to study the association between insulin resistance and the development of diabetic nephropathy. For this we will develop transgenic zebrafish and mouse models by overexpressing/knocking down insulin signaling-associated proteins specifically in podocytes. Further, we aim to identify novel drug leads to treat insulin resistance and diabetic nephropathy by performing high-throughput small molecule library screens on the developed transgenic fish models. The ultimate goal is to find a treatment to combat the early stages of diabetic nephropathy in humans.
Summary
Up to one third of diabetic patients develop nephropathy, a serious complication of diabetes. Microalbuminuria is the earliest sign of the complication, which may ultimately develop to end-stage renal disease requiring dialysis or a kidney transplant. Insulin resistance and metabolic syndrome are associated with an increased risk for diabetic nephropathy. Interestingly, glomerular epithelial cells or podocytes have recently been shown to be insulin responsive. Further, nephrin, a key structural component of podocytes, is essential for insulin action in these cells. Our novel findings show that adaptor protein CD2AP, an interaction partner of nephrin, associates with regulators of insulin signaling and glucose transport in glomeruli. The results suggest that nephrin and CD2AP are involved, by association with these proteins, in the regulation of insulin signaling and glucose transport in podocytes. We hypothesize that podocytes can develop insulin resistance and that disturbances in insulin response affect podocyte function and contribute to the development of diabetic nephropathy. The aim of this project is to clarify the mechanisms leading to development of insulin resistance in podocytes and to study the association between insulin resistance and the development of diabetic nephropathy. For this we will develop transgenic zebrafish and mouse models by overexpressing/knocking down insulin signaling-associated proteins specifically in podocytes. Further, we aim to identify novel drug leads to treat insulin resistance and diabetic nephropathy by performing high-throughput small molecule library screens on the developed transgenic fish models. The ultimate goal is to find a treatment to combat the early stages of diabetic nephropathy in humans.
Max ERC Funding
2 000 000 €
Duration
Start date: 2009-11-01, End date: 2014-10-31
Project acronym DOGPSYCH
Project Canine models of human psychiatric disease: identifying novel anxiety genes with the help of man's best friend
Researcher (PI) Hannes Tapani Lohi
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS2, ERC-2010-StG_20091118
Summary Anxiety disorders include different forms of pathological fear and anxiety and rank among the most common health concerns in human medicine. Millions of people become affected every year, and many of them do not respond to treatments. Anxiety disorders are heritable, but genetically complex. As a result, traditional gene mapping methods in the human population with prominent locus and allelic heterogeneity have not succeeded. Similarly, rodents have provided some insights into the circuitry of anxiety, but naturally occurring versions do not exist and gene deletion studies have not provided adequate models. To break through and identify new anxiety genes, I propose a novel and unique approach that resorts to man s best friend, dog. Taking advantage of the exaggerated genetic homogeneity characteristic of purebred dogs, recent genomics tools and the existence of naturally occurring heritable behaviour disorders in dogs can remedy the current lack of a suitable animal model of human psychiatric disorders. I propose to collect and perform a genome-wide association study in four breed-specific anxiety traits in dogs representing the three major forms of human anxiety: compulsive pacing and tail-chasing, noise phobia, and shyness corresponding to human OCD, panic disorder and social phobia, respectively. Canine anxiety disorders respond to human medications and other phenomenological studies suggest a share biological mechanism in both species. The proposed research has the potential to discover new genetic risk factors, which eventually will shed light on the biological basis of common neuropsychiatric disorders in both dog and human, provide insight into etiological mechanisms, enable identification of individuals at high-risk for adverse health outcomes, and facilitate development of tailored treatments.
Summary
Anxiety disorders include different forms of pathological fear and anxiety and rank among the most common health concerns in human medicine. Millions of people become affected every year, and many of them do not respond to treatments. Anxiety disorders are heritable, but genetically complex. As a result, traditional gene mapping methods in the human population with prominent locus and allelic heterogeneity have not succeeded. Similarly, rodents have provided some insights into the circuitry of anxiety, but naturally occurring versions do not exist and gene deletion studies have not provided adequate models. To break through and identify new anxiety genes, I propose a novel and unique approach that resorts to man s best friend, dog. Taking advantage of the exaggerated genetic homogeneity characteristic of purebred dogs, recent genomics tools and the existence of naturally occurring heritable behaviour disorders in dogs can remedy the current lack of a suitable animal model of human psychiatric disorders. I propose to collect and perform a genome-wide association study in four breed-specific anxiety traits in dogs representing the three major forms of human anxiety: compulsive pacing and tail-chasing, noise phobia, and shyness corresponding to human OCD, panic disorder and social phobia, respectively. Canine anxiety disorders respond to human medications and other phenomenological studies suggest a share biological mechanism in both species. The proposed research has the potential to discover new genetic risk factors, which eventually will shed light on the biological basis of common neuropsychiatric disorders in both dog and human, provide insight into etiological mechanisms, enable identification of individuals at high-risk for adverse health outcomes, and facilitate development of tailored treatments.
Max ERC Funding
1 381 807 €
Duration
Start date: 2010-10-01, End date: 2015-09-30
Project acronym E-CONTROL
Project "Electric-Field Control of Magnetic Domain Wall Motion and Fast Magnetic Switching: Magnetoelectrics at Micro, Nano, and Atomic Length Scales"
Researcher (PI) Sebastiaan Van Dijken
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Country Finland
Call Details Starting Grant (StG), PE3, ERC-2012-StG_20111012
Summary "The aim of the proposed research is to study electric-field induced magnetic phenomena in thin-film ferromagnetic-ferroelectric heterostructures. In particular, the project addresses ferroic order competition and magnetoelectric coupling dynamics at micro, nano, and atomic length scales.
The first part of the project focuses on the dynamics of coupled ferromagnetic-ferroelectric domains and electric-field induced magnetic domain wall motion at sub-nanosecond time scales. For simultaneous imaging of both ferroic domain responses to ultra-short electric-field pulses, the construction of a time-resolved polarization microscope is proposed. The second part relates to finite-size scaling of ferroic domain correlations in continuous films and electric-field control of magnetic effects in patterned nanostructures. Here, the aim is to elucidate the competition between magnetoelectric coupling at ferromagnetic-ferroelectric interfaces and the relevant energy scales within the bulk of ferroic materials. Moreover, electric-field induced domain wall motion in magnetic nanowires is pursued as a viable low-power alternative to current-driven spin-torque effects. Finally, the third part of E-CONTROL aims at visualization of magnetoelectric coupling effects with atomic precision. For this frontier study, the development of in situ transmission electron microscopy (TEM) techniques is proposed. The new measurement method enables the application of local electric fields on cross-sectional specimen during TEM analysis and this is bound to provide unique insights in strain-mediated and charge-modulated coupling mechanisms between ferromagnetic and ferroelectric thin films."
Summary
"The aim of the proposed research is to study electric-field induced magnetic phenomena in thin-film ferromagnetic-ferroelectric heterostructures. In particular, the project addresses ferroic order competition and magnetoelectric coupling dynamics at micro, nano, and atomic length scales.
The first part of the project focuses on the dynamics of coupled ferromagnetic-ferroelectric domains and electric-field induced magnetic domain wall motion at sub-nanosecond time scales. For simultaneous imaging of both ferroic domain responses to ultra-short electric-field pulses, the construction of a time-resolved polarization microscope is proposed. The second part relates to finite-size scaling of ferroic domain correlations in continuous films and electric-field control of magnetic effects in patterned nanostructures. Here, the aim is to elucidate the competition between magnetoelectric coupling at ferromagnetic-ferroelectric interfaces and the relevant energy scales within the bulk of ferroic materials. Moreover, electric-field induced domain wall motion in magnetic nanowires is pursued as a viable low-power alternative to current-driven spin-torque effects. Finally, the third part of E-CONTROL aims at visualization of magnetoelectric coupling effects with atomic precision. For this frontier study, the development of in situ transmission electron microscopy (TEM) techniques is proposed. The new measurement method enables the application of local electric fields on cross-sectional specimen during TEM analysis and this is bound to provide unique insights in strain-mediated and charge-modulated coupling mechanisms between ferromagnetic and ferroelectric thin films."
Max ERC Funding
1 499 465 €
Duration
Start date: 2012-10-01, End date: 2017-09-30
