Dragon09/odtu_chatbot huggingface dataset:
5d electromechanical biointerface for neuronal tissue engineering

the cellular microenvironment is tightly regulated by biochemical and physical cues while state of the art electrical and mechanical devices can perturb the biophysical cell niche in 2d monolayers 3d tissue cultures are considered a much more comprehensive and representative model of the in vivo microenvironment however the available biomodulation toolkit does not meet the required level of complexity specificity and accuracy this limitation hinders the ability to address basic questions in brain research and to develop new nonpharmacological interventions such as next generation neuroengineeringtechnologies and biointerfaceswe propose to develop a novel biomaterial for nongenetic leadless electrical and mechanical biomodulation in 3d engineered tissues the leadless electrical biomodulation will be induced via optical illumination of semiconducting silicon micro and nanostructures which will potentially yield spatial resolution of hundreds of nanometres two orders of magnitude smaller than the current stateoftheart 3d biointerfaces the mechanical perturbation will be achieved by spatially defined iron microstructures that will be manipulated via spatially homogenous magnetic fields resulting in mechanical perturbation resolution down to few microns which is unprecedented in 3d tissue constructs lastly we will integrate the two materials into a single 3d platform to construct the 5dneuro allowing leadless electrical and mechanical bimodal perturbation simultaneously and independentlyherein we will both establish a new tool for biophysical modulation and generate new fundamental knowledge about the role of bioelectrical biomechanical and their synergistic effect on neuronal growth and regeneration in 3d models moreover such a platform lays the ground for next generation engineered tissues for applications spanning from fundamental brain developmental research and future translational clinical interventions
develop tools that use genetic data to better classify complex human diseases

many common diseases are highly heterogeneous meaning that two individuals can be diagnosed with the same disease but have very different progressions or respond very differently to the same medication these heterogeneous diseases affect a sizeable proportion of the population for example approximately one in four people will develop a heterogeneous brain disorder eg a neurological condition such as epilepsy or parkinsons disease or a psychiatric condition such as depression or schizophreniato effectively treat a patient with a heterogeneous disease it is necessary to quickly and accurately identify their subtype at present patient subtypes are decided using only clinical observations and the process is highly suboptimal for example the available subtypes are often incomplete or poorlydefined meaning that many patients are wrongly classified or can not be classified at allprevious research indicates that for many heterogeneous diseases the classification of patients can be improved by incorporating genetic information however for this to become a reality requires statistical tools that do not yet exist my project will develop novel statistical tools for classifying heterogeneous diseases based on genetic informationmy project will prioritize classification of two heterogeneous diseases epilepsy and schizophrenia however i will ensure that my new tools are general freely available and easytouse so that other groups can construct classification models for many other diseases overall my project has the potential to revolutionize how patients with heterogeneous diseases are treated and to facilitate more widespread use of precision medicine
strengthening clinical trial regulatory and ethical review oversight in east africa

there is an increase in the number of drug development clinical trials conducted in subsaharan africa ssa including vaccine development studies for povertyrelated diseases prds underway in ssa these increases have not been matched with increase in regulatory support and ethical review for such studies in ssa specific gaps exist in skills and knowledge in providing adequate regulatory oversight ethical review and pharmacovigilance for such studies efficiency of the regulatory and ethical review processes with sequential processes of review requiring a prolonged period before approvals are granted there is dissatisfaction with the the time interval from submission to decision within the research community additionally mechanisms for reporting adverse events to both the ethics committees and national regulators are not well developed the aim of accessafrica 2 is to improve ethical review and regulatory capacity for clinical trials in two subsaharan africa ssa countries with specific focus on the review and oversight of reported adverse events during clinical trials improve efficiencies of the necs and nras and equip them with skills and knowledge for oversight for novel research designsaccessafrica 2 is a embedded in the eastern africa consortium for clinical research eaccr and edct funded network and is continuation of accessafrica project which was funded by edctp we propose to address the identified gaps through the three objectives and four work packages we will establish a consensus driven consortium with partners leveraging each others strength to achieve the objectives we propose to incorporate the avaref guidelines in the regulatory and ethical review guidelines of the partners and work with the kenya pharmacy and poisons board to improve the pharmacovigilance for research capacity in the partner countries
probing the fibrillation of staphylococcal amyloids and their interactions with membranes at the nanoscale

phenolsoluble modulins 3 psm3 are functional amyloids involved in the virulence of staphylococcus aureus a multidrug resistant pathogen under the surveillance of the european union eu the project psmnano aims at unveiling the molecular mechanisms driving psm3 interactions with cell membranes which lead to their cytotoxic and proinflammatory activities with a focus on the impact of amyloid fibrillation so far poorly known dissecting those mechanisms is yet critical to efficiently target psm3 activities in turn s aureus pathogenicity and avoid the pandemics foreseen by the eu in the coming decades thanks to atomic force microscopy coupled to complementary multiscale biophysical tools psmnano will provide high spatiotemporal resolution multiparametric imaging of those interactions while quantifying their affinity and energetic landscape on both mimetic models and living cells remarkably singlemolecule experiments and cell budding will help decipher the forces driving the fpr2 receptordependent role of psm3 the expertise of the fellow dr matheliguinlet in surface chemistry and nanobiophysics related to bacterial virulence the complementary knowledge she will acquire from the supervisor pr molinari on membrane models design and spectroscopic investigations of amyloids and the cuttingedge technical platforms of the host institution cbmn university of bordeaux france will be instrumental for the success of this interdisciplinary project the fundamental insights brought by the understanding of the role of psmscell membrane interactions in s aureus pathogenesis could set the stage for the design of novel therapeutics eg inhibitors of psm3 activity psmnano could also open up avenues for standardized protocols to characterize amyloidmembrane interactions involved in degenerative diseases with valuable short and longterm outcomes this project will undoubtedly strengthen the competitiveness of the eu health program
confidential computing and privacypreserving technologies for 6g

6g infrastructures must ensure reliability trust and resilience on a globally connected continuum of heterogeneous environments supported by the convergence of networks and it systems to enable new future digital services the substantial increase of coverage and network heterogeneity raises severe concerns that 6g security and privacy can be worse than the previous generations the 6g network should be a deep integration of emerging ai tools new hardware components and accelerators compute and networking functions iot and edge nodes contemporary security is not designed for serving a massive number of connected and highmobility heterogeneous devices confidential6g emphasizes on privacy preservation and security of sensitive data by focusing on protection of datain use this is an unsolved issue with solutions just emerging with confidential computingin transit confidential6g will enhance communication protection with postquantum cryptography blockchain technologies and secure data access control and traceability platforms at the edge confidential6g will work on specifying the postquantum cryptographic approach most appropriate to cater for constrained edge and iot devices additionally in order to avoid data movement from the edge and increase trust and security federated aiml will be researchedconfidential6g will base its research on 3 pillars postquantum cryptography confidential computing and confidential communication confidential6g will test and validate the developed solutions in three use cases 1 predictive maintenance for airline consortium using blockchainbased data sharing platform and federated aiml orchestration 2 privacypreserving confidential computing platform that enables mitigation of internal threats for telecom cloud providers and 3 intelligent connected vehicle missioncritical services ota updates flml and vehicle to infrastructure communication
robotics and ai as enablers for greener dismantling remanufacturing and recycling

during the last years eu manufacturing has faced production flexibility challenges by deploying among others novel hybrid manufacturing systems involving collaborative robots and mobile manipulators combined with flexible grippers vision systems sophisticated tasksactions planning solutions and flexible integration platforms despite the importance of ai enabled flexible robotic systems several aspects settle back their wider adoption and impact on the objectives of the green deallimited cognition intelligence existing solutions support nontrivial tasks but cannot act autonomously insufficient perception and diagnostics in a circular economy there is an increased need for understanding the state of products or parts that are being handled after they have been useddecision making is restricted current decisionmaking focuses on process or line level not taking into account optimization at value chain level or per individual product small scale adaptation of ai due to small number of available data and training needed to support tailored solutions in high variability contextlack of use of explicitized knowledge in ai and robotics lifecycle data and knowledge is not used across the value chain to improve decision making after a products first life complexity in robot programming and interaction which requires the involvement of skilled engineers does not provide flexibility in executionthus rob4green aims to develop easy to use and deploy ai driven collaborative robotic systems that can reason and adapt to a variety of strategies for processing products after their first life both hardware and behavior wise improving existing skills and generating new ones working autonomously combining data and knowledge such systems will be validated at scale and in major industries showcasing optimization ranging from cell to the whole value chain towards achieving significant impact on the objectives of the green deal
control of extreme events in turbulent flows with scientific machine learning

climate change and the race to decarbonise our society is making extreme events in fluids more prevalent these are rare events where the flow suddenly takes extreme states far from its normal state these can be found in any flow systems such as in the atmosphere with atmospheric blocking causing extreme heatwaves or in our oceans with rogue waves waves of extreme heights capable of capsizing boats or in engineering flows in hydrogenbased clean combustors with flashback events where the flame suddenly moves back into the injection systemcurrently we cannot accurately predict such extreme events due to several roadblocks first the chaotic nature of these turbulent flows makes them hard to predict any infinitesimal perturbation leads to drastically different evolutions the butterfly effect second extreme events originate from complex nonlinear interactions which are very different for systems with different physical mechanisms this makes any past development difficult to generalize across different flow systems third we have very limited observations of such eventsto revolutionize how we tackle extreme events the context project will create a cuttingedge scientific machine learning framework that blends deep learning with physicsbased techniques contexts framework will provide the means to i identify precursors and mechanisms of extreme events ii forecast the flow evolution before and during extreme events and iii control the flows to prevent extreme events contexts framework will be able to handle diverse and disparate physics with this being demonstrated across different flows of increasing complexity and with different physics culminating in a demonstration of the practical impact of the framework on the engineeringrelevant multiphysics test case of a flashbacking hydrogen combustorcontext will provide a comprehensive framework to achieve the understanding prediction and prevention of extreme events in turbulent flows
an intelligent agent for generalpurpose protein engineering

proteins offer an exciting path to address a multitude of biotechnological challenges capable of working under nontoxic mild conditions and performing a myriad of functions their controllable design has been soughtafter for decades however to gain a technological advantage in a world with pressing demands in sustainability and healthcare we must accelerate the development of customtailored proficient proteins in this proposal we will develop an intelligent system capable of efficiently engineering functional proteins tailored to userdefined specificationsartificial intelligence ai advancements are promoting a fresh wave of enthusiasm across many fields providing solutions to problems that escape human intuition recently protein language models plms are showing unprecedented performance in generating novel efficient proteins we have trained three advanced plms demonstrating promising preliminary results in experimental settings in this proposal we will train an agent that will learn from combined sequence structural functional and dynamic data to perform multiple protein engineering tasks the agent will iteratively improve from experimental feedback using reinforcement learning and explainable ai will allow us to open the black box and understand its decision process a vital component of this work will be its rigorous experimental validation progressing through increasingly challenging tasks with biotechnological applications this project will deliver an intelligent agent with continuous learning capabilities accessible through userfriendly interfaces empowering researchers worldwide with an easytouse tool to design customtailored proteins in addition by incorporating explainability it will offer a novel angle to understanding complex sequencetofunction relationships lastly comprehensive experimental validation will assess the reliability and applicability of these novel approaches in realworld contexts
a new frontier in eu urban policymaking commonsinspired coproduction arrangements

a new tendency is on the rise in eu urban policymaking adopting coproduction arrangements inspired by the paradigm of the commons cac some pioneering cases stand out such as the urban commons regulation in bologna 2014 the commons transition plan in ghent 2017 and the citizen assets programme in barcelona 2017 these cities have been joined by others that either have adopted similar arrangements or are currently considering adopting them by and large these arrangements aim to redistribute decisionmaking power to citizens over services and resources that are considered as essential for urban collective wellbeing eg public buildings and spaces energy and water utilities by fostering community selfmanagement preliminary and applied research on these arrangements has been carried out mostly based on single case study analyses however scientific indepth and comparative knowledge on cac is still scarce by adopting an urban epistemology to the traditional statecentred political science field commoncity will produce key useful and timely knowledge on cac it will comparatively analyse the i policy models ii political social and administrative challenges iii impact on urban democracy of recently adopted cac in the three eu pioneering cities barcelona bologna and ghent by adopting a coproductionoriented approach to data collection and analysis that will foster citizen science it will provide unique empirical data on the varieties effective functioning and democratising effect of these arrangements the results of commoncity will contribute to the scientific debate in the broad urban study field and specifically in urban democracy urban governance urban policymaking and urban participation it will also provide policy recommendations to various levels of government in order to foster the adoption of cac and improve the functioning and democratic impact of existing and underadoption ones
beyond lowrank factorizations

lowrank matrix factorizations lrmfs such as principal component analysis nonnegative matrix factorization and sparse component analysis are linear dimensionality reduction techniques and are powerful unsupervised models to represent and analyze highdimensional data sets they are used in a wide variety of areas such as machine learning signal processing and data mining many lrmfs have been proposed in the literature in particular in the last two decades and used extensively in many applications such as recommender systems blind source separation and text mining although lrmfs have known and still know tremendous success they have several limitations two key limitations are that they are linear models and that they only learn one layer of features in this project we go beyond lrmfs considering generalized lrmfs glrmfs that overcome these limitations considering nonlinear and deep lrmfs these generalizations have been introduced more recently and they have hardly been explored compared to lrmfs in particular elinor will focus on three fundamental aspects 1 theory understand glrmfs with a focus on computational complexity and identifiability uniqueness of the decompositions 2 algorithms solve glrmfs via provably correct and heuristic algorithms and 3 models design new glrmfs for specific applications this unified approach will enable us to understand these problems better to develop and analyze algorithms and to then use them for applications the novelty and groundbreaking nature of this project also lies in the methodology exploring glrmfs from different but complementary perspectives bringing together ideas from different fields the ultimate goal of elinor is to provide practitioners with theoretical and algorithmic tools for glrmfs allowing them to decide which model and algorithm to use in which situation and what kind of outcome to expect leading to a widespread and reliable use of glrmfs
largescale subcellular ensemble dynamics in the mouse brain revealed by adaptive optics twophoton mesoscopy

higher brain functions such as decisionmaking are associated with the coordinated activity of multiple cellular structures eg cell bodies and cellular processes of neuronal and non neuronal cells distributed over multiple brain areas recent technical developments enable monitoring this ensemble activity across different brain regions with high ie cellular and subcellular resolution using large fieldofview fov twophoton 2p mesoscopy however optical aberrations mostly due to light scattering strongly limit the application of these technologies when the imaging depth within the brain increases to address this limitation i will here design develop and validate the first adaptive optics ao mesoscope combining the strength of adaptive aberration correction with the power of large fov 2p mesoscopy i will first benchmark this novel approach to image ensemble ca2 dynamics of small brain structures eg processes of neuronal and nonneuronal cells in living mice over large fov and at increasing imaging depths i will then focus on recording ca2 signals from processes of astrocytes the main nonneuronal cell type in the brain which play fundamental roles in brain physiology i will study how networks of thin astrocytic structures encode behavioural information over multiple cortical regions during a decision making task finally i will apply analytical methods based on information theory and machine learning to extract emergent properties of distributed ensembles and their correlation with behaviour in conclusion i will combine expertise in optical engineering neuroscience and computation to reveal the functional dynamics of the fine cellular structures across multiple cortical areas this project will allow extracting details about ensemble dynamics that have long remained inaccessible contributing to elucidate the contribution of coordinated activity of cellular and subcellular structures to higher brain function and behaviour
demonstration of a digitized energy system integration across sectors enhancing flexibility and resilience towards efficient sustainable costoptimised affordable secure and stable energy supply

elexia will developupgrade validated tools for planning and managing integrated energy systems in different conditions and will integrate and combine energy systems across vectors and sectors towards a costoptimised as well as flexible and resilient energy system of systems a digital services platform will host the energy management and planning services and will foster flexibility and sector coupling a system planning toolbox will be developed and deployed to support effective sector coupling at local sites considering different scenarios operational details possibly conflicting interests of multiple local actors and security of supply an energy management systems will be built and deployed for flexible costoptimised and resilient operation of sector coupled local sites including forecasting digital twins optimization control monitoring assessing operating conditions predicting anomalous operation and preventing occurrence of breakdowns elexia will demonstrate the use of planning and operational tools in a onestopshop modular and open digital platform at trl78 it will demonstrate the benefits of sector integration at local national level in three different geographical climate and economic conditions in europe in an industrial port environment in portugal in an urbancity hub environment in denmark and in an industrialurbanresidential environment in norway elexia will assess environmental economic and social sustainability will deliver a methodology for capex opex and value creation and will focus on policy and governance it will put focus on stakeholder engagement and societal acceptance and will ensure effort towards future exploitation and replication elexia will establish and demonstrate realistic and concrete pathways to ultimately achieve independence of fossil fuels by harnessing the latent flexibility of the energy system through integration dataintelligence and planning working towards the 2050 european goals