Project acronym 3D-OA-HISTO
Project Development of 3D Histopathological Grading of Osteoarthritis
Researcher (PI) Simo Jaakko Saarakkala
Host Institution (HI) OULUN YLIOPISTO
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 Brain Health Toolbox
Project The Brain Health Toolbox: Facilitating personalized decision-making for effective dementia prevention
Researcher (PI) Alina Gabriela SOLOMON
Host Institution (HI) ITA-SUOMEN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2018-STG
Summary Preventing dementia and Alzheimer disease (AD) is a global priority. Previous single-intervention failures stress the critical need for a new multimodal preventive approach in these complex multifactorial conditions. The Brain Health Toolbox is designed to create a seamless continuum from accurate dementia prediction to effective prevention by i) developing the missing disease models and prediction tools for multimodal prevention; ii) testing them in actual multimodal prevention trials; and iii) bridging the gap between non-pharmacological and pharmacological approaches by designing a combined multimodal prevention trial based on a new European adaptive trial platform. Disease models and prediction tools will be multi-dimensional, i.e. a broad range of risk factors and biomarker types, including novel markers. An innovative machine learning method will be used for pattern identification and risk profiling to highlight most important contributors to an individual’s overall risk level. This is crucial for early identification of individuals with high dementia risk and/or high likelihood of specific brain pathologies, quantifying an individual’s prevention potential, and longitudinal risk and disease monitoring, also beyond trial duration. Three Toolbox test scenarios are considered: use for selecting target populations, assessing heterogeneity of intervention effects, and use as trial outcome. The project is based on a unique set-up aligning several new multimodal lifestyle trials aiming to adapt and test non-pharmacological interventions to different geographic, economic and cultural settings, with two reference libraries (observational - large datasets; and interventional - four recently completed pioneering multimodal lifestyle prevention trials). The Brain Health Toolbox covers the entire continuum from general populations to patients with preclinical/prodromal disease stages, and will provide tools for personalized decision-making for dementia prevention.
Summary
Preventing dementia and Alzheimer disease (AD) is a global priority. Previous single-intervention failures stress the critical need for a new multimodal preventive approach in these complex multifactorial conditions. The Brain Health Toolbox is designed to create a seamless continuum from accurate dementia prediction to effective prevention by i) developing the missing disease models and prediction tools for multimodal prevention; ii) testing them in actual multimodal prevention trials; and iii) bridging the gap between non-pharmacological and pharmacological approaches by designing a combined multimodal prevention trial based on a new European adaptive trial platform. Disease models and prediction tools will be multi-dimensional, i.e. a broad range of risk factors and biomarker types, including novel markers. An innovative machine learning method will be used for pattern identification and risk profiling to highlight most important contributors to an individual’s overall risk level. This is crucial for early identification of individuals with high dementia risk and/or high likelihood of specific brain pathologies, quantifying an individual’s prevention potential, and longitudinal risk and disease monitoring, also beyond trial duration. Three Toolbox test scenarios are considered: use for selecting target populations, assessing heterogeneity of intervention effects, and use as trial outcome. The project is based on a unique set-up aligning several new multimodal lifestyle trials aiming to adapt and test non-pharmacological interventions to different geographic, economic and cultural settings, with two reference libraries (observational - large datasets; and interventional - four recently completed pioneering multimodal lifestyle prevention trials). The Brain Health Toolbox covers the entire continuum from general populations to patients with preclinical/prodromal disease stages, and will provide tools for personalized decision-making for dementia prevention.
Max ERC Funding
1 498 268 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym DrugComb
Project Informatics approaches for the rational selection of personalized cancer drug combinations
Researcher (PI) Jing TANG
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2016-STG
Summary Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. This project will develop mathematical and computational tools to identify drug combinations that can be used to provide personalized and more effective therapeutic strategies that may prevent acquired resistance. Utilizing molecular profiling and pharmacological screening data from patient-derived leukaemia and ovarian cancer samples, I will develop model-based clustering methods for identification of patient subgroups that are differentially responsive to first-line chemotherapy. For patients resistant to chemotherapy, I will develop network modelling approaches to predict the most potential drug combinations by understanding the underlying drug target interactions. The drug combination prediction will be made for each patient and will be validated using a preclinical drug testing platform on patient samples. I will explore the drug combination screen data to identify significant synergy at the therapeutically relevant doses. The drug combination hits will be mapped into signalling networks to infer their mechanisms. Drug combinations with selective efficacy in individual patient samples or in sample subgroups will be further translated into in treatment options by clinical collaborators. This will lead to novel and personalized strategies to treat cancer patients.
Summary
Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. This project will develop mathematical and computational tools to identify drug combinations that can be used to provide personalized and more effective therapeutic strategies that may prevent acquired resistance. Utilizing molecular profiling and pharmacological screening data from patient-derived leukaemia and ovarian cancer samples, I will develop model-based clustering methods for identification of patient subgroups that are differentially responsive to first-line chemotherapy. For patients resistant to chemotherapy, I will develop network modelling approaches to predict the most potential drug combinations by understanding the underlying drug target interactions. The drug combination prediction will be made for each patient and will be validated using a preclinical drug testing platform on patient samples. I will explore the drug combination screen data to identify significant synergy at the therapeutically relevant doses. The drug combination hits will be mapped into signalling networks to infer their mechanisms. Drug combinations with selective efficacy in individual patient samples or in sample subgroups will be further translated into in treatment options by clinical collaborators. This will lead to novel and personalized strategies to treat cancer patients.
Max ERC Funding
1 500 000 €
Duration
Start date: 2017-06-01, End date: 2022-05-31
Project acronym DynaOmics
Project From longitudinal proteomics to dynamic individualized diagnostics
Researcher (PI) Laura Linnea Maria Elo-Uhlgren
Host Institution (HI) TURUN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2015-STG
Summary Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:
1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples.
2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.
3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.
The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.
Summary
Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:
1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples.
2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.
3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.
The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.
Max ERC Funding
1 499 869 €
Duration
Start date: 2016-06-01, End date: 2021-05-31
Project acronym EarlyDev
Project Brain networks for processing social signals of emotions: early development and the emergence of individual differences
Researcher (PI) Jukka Matias Leppänen
Host Institution (HI) TAMPEREEN YLIOPISTO
Call Details Starting Grant (StG), SH4, ERC-2011-StG_20101124
Summary Recent research has shown that genetic variations in central serotonin function are associated with biases in emotional information processing (heightened attention to signals of negative emotion) and that these biases contribute significantly to vulnerability to affective disorders. Here, we propose to examine a novel hypothesis that the biases in attention to emotional cues are ontogenetically primary, arise very early in development, and modulate an individual’s interaction with the environment during development. The four specific aims of the project are to 1) test the hypothesis that developmental processes resulting in increased functional connectivity of visual and emotion/attention-related neural systems (i.e., increased phase-synchrony of oscillatory activity) from 5 to 7 months of age are associated with the emergence of an overt attentional bias towards affectively salient facial expressions at 7 months of age, 2) use eye-tracking to ascertain that the attentional bias in 7-month-old infants reflects sensitivity to the emotional signal value of facial expressions instead of correlated non-emotional features, 3) test the hypothesis that increased serotonergic tone early in life (through genetic polymorphisms or exposure to serotonin enhancing drugs) is associated with reduced control of attention to affectively salient facial expressions and reduced temperamental emotion-regulation at 7, 24 and 48 months of age, and 4) examine the plasticity of the attentional bias towards emotional facial expressions in infancy, particularly whether the bias can be overridden by using positive reinforcers. The proposed studies will be the first to explicate the neural bases and nature of early-emerging cognitive deficits and biases that pose a risk for emotional dysfunction. As such, the results will be very important for developing intervention methods that benefit of the plasticity of the developing brain and skill formation to support healthy development.
Summary
Recent research has shown that genetic variations in central serotonin function are associated with biases in emotional information processing (heightened attention to signals of negative emotion) and that these biases contribute significantly to vulnerability to affective disorders. Here, we propose to examine a novel hypothesis that the biases in attention to emotional cues are ontogenetically primary, arise very early in development, and modulate an individual’s interaction with the environment during development. The four specific aims of the project are to 1) test the hypothesis that developmental processes resulting in increased functional connectivity of visual and emotion/attention-related neural systems (i.e., increased phase-synchrony of oscillatory activity) from 5 to 7 months of age are associated with the emergence of an overt attentional bias towards affectively salient facial expressions at 7 months of age, 2) use eye-tracking to ascertain that the attentional bias in 7-month-old infants reflects sensitivity to the emotional signal value of facial expressions instead of correlated non-emotional features, 3) test the hypothesis that increased serotonergic tone early in life (through genetic polymorphisms or exposure to serotonin enhancing drugs) is associated with reduced control of attention to affectively salient facial expressions and reduced temperamental emotion-regulation at 7, 24 and 48 months of age, and 4) examine the plasticity of the attentional bias towards emotional facial expressions in infancy, particularly whether the bias can be overridden by using positive reinforcers. The proposed studies will be the first to explicate the neural bases and nature of early-emerging cognitive deficits and biases that pose a risk for emotional dysfunction. As such, the results will be very important for developing intervention methods that benefit of the plasticity of the developing brain and skill formation to support healthy development.
Max ERC Funding
1 397 351 €
Duration
Start date: 2012-02-01, End date: 2017-01-31
Project acronym EpComp
Project Competence and Success in Epistemology and Beyond
Researcher (PI) Maria LASONEN-AARNIO
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), SH4, ERC-2017-STG
Summary This project formulates and defends a novel approach in epistemology, demonstrating how it solves a range of key problems in the field. New frontiers of research are opened up by applying the core lessons learnt in epistemology to the study of practical reason and ethics.
My dual evaluations approach hypothesizes that for a wide range of key evaluative notions, competence is neither necessary nor sufficient for success: there are both cases of incompetent success and of competent failure. For instance, a subject can know without exercising knowledge-conducive competence, and vice versa – and similarly for justified or rational belief. The project demonstrates how this recognition solves a cluster of key problems in the field relating to so-called higher-order evidence, and how it allows accommodating internalist evaluations in more externalist frameworks, thus bridging perhaps the most significant divide in epistemology.
What will emerge is a thorough re-structuring of the epistemological landscape. The project generalizes some of the lessons learnt to the study of structural requirements of rationality. Finally, the approach is deployed to investigate the relationship between morally right and morally worthy action. The main objectives of the project are:
(O1) To develop the theoretical foundations of the dual evaluations approach.
(O2) To put forth a novel view in epistemology that demonstrates how recognizing both cases of competent failure and incompetent success solves highly current problems and puzzles, reconciling two opposing theoretical starting points.
(O3) To investigate and ultimately reject as theoretically important the notion of structural rationality, offering an alternative, competence-based explanation of verdicts that seem to show the need for such a notion.
(O4) To explore generalizations of the results of the previous parts of the project to the practical and moral domains.
Summary
This project formulates and defends a novel approach in epistemology, demonstrating how it solves a range of key problems in the field. New frontiers of research are opened up by applying the core lessons learnt in epistemology to the study of practical reason and ethics.
My dual evaluations approach hypothesizes that for a wide range of key evaluative notions, competence is neither necessary nor sufficient for success: there are both cases of incompetent success and of competent failure. For instance, a subject can know without exercising knowledge-conducive competence, and vice versa – and similarly for justified or rational belief. The project demonstrates how this recognition solves a cluster of key problems in the field relating to so-called higher-order evidence, and how it allows accommodating internalist evaluations in more externalist frameworks, thus bridging perhaps the most significant divide in epistemology.
What will emerge is a thorough re-structuring of the epistemological landscape. The project generalizes some of the lessons learnt to the study of structural requirements of rationality. Finally, the approach is deployed to investigate the relationship between morally right and morally worthy action. The main objectives of the project are:
(O1) To develop the theoretical foundations of the dual evaluations approach.
(O2) To put forth a novel view in epistemology that demonstrates how recognizing both cases of competent failure and incompetent success solves highly current problems and puzzles, reconciling two opposing theoretical starting points.
(O3) To investigate and ultimately reject as theoretically important the notion of structural rationality, offering an alternative, competence-based explanation of verdicts that seem to show the need for such a notion.
(O4) To explore generalizations of the results of the previous parts of the project to the practical and moral domains.
Max ERC Funding
1 470 665 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym FutureTrophicFactors
Project Elucidating therapeutic effects and mode of action of future trophic factorsin ALS and Parkinson’s disease
Researcher (PI) Merja Hannele VOUTILAINEN
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2018-STG
Summary The prevalence of neurodegenerative diseases such as Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS) is growing rapidly due to an aging population and increased life expectancy. Current treatments for ALS and PD only relieve symptoms and cannot stop the progression of the disease, thus there is an urgent need for new therapies. Neurotrophic factors (NTFs) are secretary proteins that regulate the survival of neurons, neurite growth and branching. They have been explored as novel drugs for the treatment of ALS and PD but their efficacy in clinical trials is poor. CDNF is a protein with NTF properties that protects and restores the function of dopamine neurons in rodent and rhesus monkey toxin models of PD more effectively than other NTFs. CDNF is currently in phase 1/2 clinical trials on PD patients. Despite promising results with CDNF in animal models of PD, NTF and CDNF-based treatments have drawbacks. CDNF requires direct delivery to the brain through invasive surgery since, it cannot pass through the blood brain barrier (BBB). My recent discovery, however, may overcome this difficulty: I showed that a novel CDNF variant protects DA neurons in vitro and in vivo and that it efficiently enters DA neurons in culture. Furthermore, my data show the CDNF fragment can pass through the BBB as measured by 3 different methods and has a neurorestorative effect in a 6-OHDA toxin model of PD when administered subcutaneously. The ultimate goal of my research is to understand the mode of action and therapeutic effect of novel BBB penetrating CDNF-derived polypeptides in cultures of human induced pluripotent stem (iPS) cell-derived nerve cells from patients and in animal models of ALS and PD. The innovative aspect of this proposal is the new groundbreaking concept for treating neurodegenerative diseases – peripheral delivery of BBB penetrating peptides with trophic factor properties and the potential to treat non-motor and motor symptoms in ALS and PD patients.
Summary
The prevalence of neurodegenerative diseases such as Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS) is growing rapidly due to an aging population and increased life expectancy. Current treatments for ALS and PD only relieve symptoms and cannot stop the progression of the disease, thus there is an urgent need for new therapies. Neurotrophic factors (NTFs) are secretary proteins that regulate the survival of neurons, neurite growth and branching. They have been explored as novel drugs for the treatment of ALS and PD but their efficacy in clinical trials is poor. CDNF is a protein with NTF properties that protects and restores the function of dopamine neurons in rodent and rhesus monkey toxin models of PD more effectively than other NTFs. CDNF is currently in phase 1/2 clinical trials on PD patients. Despite promising results with CDNF in animal models of PD, NTF and CDNF-based treatments have drawbacks. CDNF requires direct delivery to the brain through invasive surgery since, it cannot pass through the blood brain barrier (BBB). My recent discovery, however, may overcome this difficulty: I showed that a novel CDNF variant protects DA neurons in vitro and in vivo and that it efficiently enters DA neurons in culture. Furthermore, my data show the CDNF fragment can pass through the BBB as measured by 3 different methods and has a neurorestorative effect in a 6-OHDA toxin model of PD when administered subcutaneously. The ultimate goal of my research is to understand the mode of action and therapeutic effect of novel BBB penetrating CDNF-derived polypeptides in cultures of human induced pluripotent stem (iPS) cell-derived nerve cells from patients and in animal models of ALS and PD. The innovative aspect of this proposal is the new groundbreaking concept for treating neurodegenerative diseases – peripheral delivery of BBB penetrating peptides with trophic factor properties and the potential to treat non-motor and motor symptoms in ALS and PD patients.
Max ERC Funding
1 497 597 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym GramAdapt
Project Linguistic Adaptation: Typological and Sociolinguistic Perspectives to Language Variation
Researcher (PI) Kaius Tatu-Kustaa SINNEMÄKI
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), SH4, ERC-2018-STG
Summary The GramAdapt project researches linguistic adaptation by developing a synthesis of typological and sociolinguistic approaches to language variation. This novel framework enables combining typological data with rich sociolinguistic data into the same model and evaluating their relationship statistically. The main research question is: Can I prove with typological data that language structure adapts to sociolinguistic context?
The project has 4 objectives:
- to develop a methodological approach that makes it possible to combine typological and sociolinguistic data into the same model and to statistically research their relationship,
- to understand the degree and nature of linguistic adaptation in the world's languages and whether it is independent of language-internal structural tendencies,
- to analyze 3-4 sociolinguistic factors that are likely to drive changes in linguistic structures (language contact vs. isolation, multilingualism, community size, and prestige) via a sample 150 languages,
- to analyze 3-4 broad linguistic categories that are prone to respond to changes in sociolinguistic environment (case, gender, and number) in the same set of 150 languages to support assessing linguistic adaptations.
Three key methodological innovations will be created: (i) language structures will be analyzed typologically from the perspective of how difficult they are for adult second language learners, (ii) sociolinguistic environments will be analyzed across societies via using the idea of comparative concepts from typology, (iii) a new sampling strategy will be developed to draw conclusions from both large families and language isolates. This framework enables researching linguistic adaptations typologically in a principled way and it has the potential to forge a deeper relationship between typology and sociolinguistics and thus open new domains of inquiry. The results will create a strong argument for treating language as part of the general adaptive human behavior.
Summary
The GramAdapt project researches linguistic adaptation by developing a synthesis of typological and sociolinguistic approaches to language variation. This novel framework enables combining typological data with rich sociolinguistic data into the same model and evaluating their relationship statistically. The main research question is: Can I prove with typological data that language structure adapts to sociolinguistic context?
The project has 4 objectives:
- to develop a methodological approach that makes it possible to combine typological and sociolinguistic data into the same model and to statistically research their relationship,
- to understand the degree and nature of linguistic adaptation in the world's languages and whether it is independent of language-internal structural tendencies,
- to analyze 3-4 sociolinguistic factors that are likely to drive changes in linguistic structures (language contact vs. isolation, multilingualism, community size, and prestige) via a sample 150 languages,
- to analyze 3-4 broad linguistic categories that are prone to respond to changes in sociolinguistic environment (case, gender, and number) in the same set of 150 languages to support assessing linguistic adaptations.
Three key methodological innovations will be created: (i) language structures will be analyzed typologically from the perspective of how difficult they are for adult second language learners, (ii) sociolinguistic environments will be analyzed across societies via using the idea of comparative concepts from typology, (iii) a new sampling strategy will be developed to draw conclusions from both large families and language isolates. This framework enables researching linguistic adaptations typologically in a principled way and it has the potential to forge a deeper relationship between typology and sociolinguistics and thus open new domains of inquiry. The results will create a strong argument for treating language as part of the general adaptive human behavior.
Max ERC Funding
1 491 493 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym HIVBIOCHIP
Project A POINT-OF-CARE BIOCHIP FOR HIV MONITORING IN THE DEVELOPING WORLD
Researcher (PI) Nikolaos Chronis
Host Institution (HI) "NATIONAL CENTER FOR SCIENTIFIC RESEARCH ""DEMOKRITOS"""
Call Details Starting Grant (StG), LS7, ERC-2011-StG_20101109
Summary HIV/AIDS is one of the most destructive pandemics in human history, responsible for more than 25 million deaths. More than 30 million people live with limited or no access to therapeutic treatments, mainly due to the high cost of highly active antiretroviral therapies (HAART) and current diagnostic tests as well as due to the lack of basic infrastructure (e.g. lack of electricity, no trained personnel) that can support these tests. The need for innovative, inexpensive diagnostic instrumentation technology that can be used in resource-limited settings is immediate.
While programs that offer free HAART are being implemented in resource-limited settings, no diagnostic tests are available for evaluating the efficacy of HAART provided for the reasons mentioned above. Efficient management of HAART requires monitoring the course of HIV infection over time. The World Health Organization (WHO) recommends the CD4 T-cell count test for monitoring the clinical status of HIV individuals in resource-limited settings.
We propose to develop a portable, inexpensive, MEMS (MicroElectroMechanical Systems)-based, imaging system for counting the absolute number of CD4 cells from 1 l of whole blood. We use the term ‘imaging system’ to denote the different approach we follow for counting CD4 cells: rather the reading one by one singles cells (as it is done with flow cytometry), our system can image simultaneously thousands of individual cells, pre-assembled on the surface of a biochip. Although the proposed imaging system can replace current expensive cell counting instrumentation, our goal is to develop a system that can reach the end-user wherever limited infrastructure is present and no access to a hospital or clinic is possible. Such technology will not only enable to monitor the efficacy of an individual’s HAART in the developing world, but it will make more medicines available by identifying patients who need a treatment from patients who do not need it.
Summary
HIV/AIDS is one of the most destructive pandemics in human history, responsible for more than 25 million deaths. More than 30 million people live with limited or no access to therapeutic treatments, mainly due to the high cost of highly active antiretroviral therapies (HAART) and current diagnostic tests as well as due to the lack of basic infrastructure (e.g. lack of electricity, no trained personnel) that can support these tests. The need for innovative, inexpensive diagnostic instrumentation technology that can be used in resource-limited settings is immediate.
While programs that offer free HAART are being implemented in resource-limited settings, no diagnostic tests are available for evaluating the efficacy of HAART provided for the reasons mentioned above. Efficient management of HAART requires monitoring the course of HIV infection over time. The World Health Organization (WHO) recommends the CD4 T-cell count test for monitoring the clinical status of HIV individuals in resource-limited settings.
We propose to develop a portable, inexpensive, MEMS (MicroElectroMechanical Systems)-based, imaging system for counting the absolute number of CD4 cells from 1 l of whole blood. We use the term ‘imaging system’ to denote the different approach we follow for counting CD4 cells: rather the reading one by one singles cells (as it is done with flow cytometry), our system can image simultaneously thousands of individual cells, pre-assembled on the surface of a biochip. Although the proposed imaging system can replace current expensive cell counting instrumentation, our goal is to develop a system that can reach the end-user wherever limited infrastructure is present and no access to a hospital or clinic is possible. Such technology will not only enable to monitor the efficacy of an individual’s HAART in the developing world, but it will make more medicines available by identifying patients who need a treatment from patients who do not need it.
Max ERC Funding
1 986 000 €
Duration
Start date: 2012-06-01, End date: 2017-05-31
Project acronym HRMEG
Project HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography
Researcher (PI) Lauri Tapio Parkkonen
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Call Details Starting Grant (StG), LS7, ERC-2015-STG
Summary To date, neuroimaging has provided a wealth of information on how the human brain works in health and disease. With functional magnetic resonance imaging (fMRI), we can obtain spatially precise information about long-lasting brain activations whereas electro- and magnetoencephalography (EEG/MEG) can track transient cortical responses at millisecond resolution. However, none of these methods excel in time-resolved detection of sustained cortical activations, which are typically reflected as bursts of gamma-range (30–150 Hz) oscillations, frequently present in invasive recordings in patients. Although we have recently demonstrated that in exceptional situations MEG can detect even single gamma responses, their signal-to-noise ratio is usually prohibitively low, largely due to the substantial distance (4–5 cm) between cortex and sensors. Here, I propose to exploit recent advances in a novel magnetic sensor technology—atomic magnetometry—to construct a new kind of MEG system that allows capturing cerebral magnetic fields within millimetres from the scalp. Our simulations show that this proximity leads up to a 5-fold increase in the signal amplitude and an order-of-magnitude improvement of spatial resolution compared to conventional MEG. Therefore, a high-resolution MEG (HRMEG) system based on atomic magnetometers should enable non-invasive recordings of cortical activity at unprecedented sensitivity and detail level, which I propose to capitalize on by characterizing cortical responses, particularly gamma oscillations, during complex cognitive tasks. Additionally, since atomic magnetometers can recover within milliseconds from fields of several tesla, I also propose to combine transcranial magnetic stimulation (TMS) with MEG, leveraging the reciprocity of TMS and MEG and thus allowing better-than-ever characterization of TMS-evoked responses. This proposal comprises the research towards a HRMEG system and its application to study the working human brain in a new way.
Summary
To date, neuroimaging has provided a wealth of information on how the human brain works in health and disease. With functional magnetic resonance imaging (fMRI), we can obtain spatially precise information about long-lasting brain activations whereas electro- and magnetoencephalography (EEG/MEG) can track transient cortical responses at millisecond resolution. However, none of these methods excel in time-resolved detection of sustained cortical activations, which are typically reflected as bursts of gamma-range (30–150 Hz) oscillations, frequently present in invasive recordings in patients. Although we have recently demonstrated that in exceptional situations MEG can detect even single gamma responses, their signal-to-noise ratio is usually prohibitively low, largely due to the substantial distance (4–5 cm) between cortex and sensors. Here, I propose to exploit recent advances in a novel magnetic sensor technology—atomic magnetometry—to construct a new kind of MEG system that allows capturing cerebral magnetic fields within millimetres from the scalp. Our simulations show that this proximity leads up to a 5-fold increase in the signal amplitude and an order-of-magnitude improvement of spatial resolution compared to conventional MEG. Therefore, a high-resolution MEG (HRMEG) system based on atomic magnetometers should enable non-invasive recordings of cortical activity at unprecedented sensitivity and detail level, which I propose to capitalize on by characterizing cortical responses, particularly gamma oscillations, during complex cognitive tasks. Additionally, since atomic magnetometers can recover within milliseconds from fields of several tesla, I also propose to combine transcranial magnetic stimulation (TMS) with MEG, leveraging the reciprocity of TMS and MEG and thus allowing better-than-ever characterization of TMS-evoked responses. This proposal comprises the research towards a HRMEG system and its application to study the working human brain in a new way.
Max ERC Funding
1 498 806 €
Duration
Start date: 2016-09-01, End date: 2021-08-31
