Dragon09/odtu_chatbot huggingface dataset:
integrated mechanistic modelling and analysis of largescale biomedical data

modern cancer therapeutics target signalling processes within the cancer cells and the interaction of cancer and immune cells a comprehensive understanding of these signalling processes is therefore essential to identify drug targets plan clinical trials and to select suitable drugs drug combinations and drug dosages for a specific patient yet most of the available mathematical models capture only a small number of molecular species and pathways thereby ignoring important crosstalk and feedback loops furthermore these models are usually based on experimental data for cell lines which behave differently from complex cancer tissuesin integrate i will develop computational methods for the full process of datadriven modelling of signalling processes in cancer ranging from model development to parameterisation all the way to uncertainty analysis to this end i will combine methods from the fields of mathematical modelling machine learning and signal processing with established approaches in systems biology the model development will employ natural language processing and an automatic testing framework for federated model inference i will develop scalable minibatch optimisation and marginalisation based uncertainty quantification to refine models i will exploit tools from signal processing such as blind identification of latent variables i will apply the developed scalable mechanistic modelling approach to integrate largescale biomedical data for molecular phenotyping studies and clinical trials across sites this will provide mechanistic models reconciling the available datathe study will for the first time combine mechanistic modelling and machine learning for the integrated analysis of patientderived omics and phenotypic data by linking these data sources integrate will deepen our understanding of biological signal processing and provide the basis for the development of digital twins
modern approach to ocular disease treatment smart lipidbased nanoparticle systems for the delivery of mrna to the ocular tissues

many severe ocular diseases lead to visual impairment and blindness in millions of patients worldwide and the number is rapidly growing in aging populations most ocular diseases are still without drug treatment and the current treatments are based on the use of small molecules and protein drugs however poor ocular absorption and rapid elimination restrict their development and use in ophthalmologytechnology for mrna transfer into the cornea and retina and subsequent expression of encoded proteins may open widely applicable possibilities for the treatment of ocular diseases eg various retinal degenerations uveitis as topical eye drops or intravitreal injections however clinical application of mrnas is limited by their poor in vivo stability and low cellular entry therefore efficient and safe delivery systems for ocular mrna transfer are urgently neededour research program aims to develop lipidbased nanoparticle lnp systems that are specifically tailored for mrna delivery into the corneal conjunctival and retinal cells the project will address the critical anatomical and physiological barriers of ocular mrna delivery topically and intravitreally chemical structure and composition of lnps will be carefully modified to optimize mrna delivery across ocular barriers smart phsensitive lnps with eye specific surface moieties will be used to target ocular cells and trigger mrna release and cytosolic delivery moreover eye drop formulations will be mucoadhesive increasing precorneal residence time whereas intravitreal injectables will be capable of permeating in the vitreous and inner limiting membrane into the retinal cells the representative in vitro and ex vivo test models will be used to select the most promising lnps for versatile animal experiments finally in vivo pharmacokinetics and mrna mediated antivegf responses of the delivery systems will be investigated to understand their translational potential towards clinical use
robust learning and reasoning for complex event forecasting

a growing number of applications rely on aibased solutions to carryout missioncritical tasks many of which are of temporal nature dealing with everevolving flows of information crucial for mitigating threats and taking advantage of opportunities in such domains is the ability to forecast imminent situations and critical complex events ahead of time evenflow will develop hybrid learning techniques for complex event forecasting which combine deep learning with logicbased learning and reasoning into neurosymbolic forecasting models the envisioned methods will combine i neural representation learning techniques capable of constructing eventbased features from streams of perceptionlevel data with ii powerful symbolic learning and reasoning tools that utilize such features to synthesize highlevel interpretable patterns of critical situations to be forecast crucial in the evenflow approach is the online nature of the learning methods which makes them applicable to evolving data flows and allows to utilize rich domain knowledge that is becoming available progressively over time to deal with the brittleness of neural predictors and the high volumevelocity of temporal data flows the evenflow techniques will rely on novel formal verification techniques for machine learning in addition to a suite of scalability algorithms for federated training and incremental model construction the learnt forecasters will be interpretable and scalable allowing for fully explainable insights delivered in a timely fashion and enabling proactive decision making evenflow will be evaluated on three challenging use cases related to oncological forecasting in precision medicine safe and efficient behavior of autonomous transportation robots in smart factories and reliable life cycle assessment of critical infrastructure
codesigning holistic forestbased policy pathways for climate change mitigation

the eu target to significantly reduce its emissions by 2030 and become climate neutral by 2050 requires new mitigation measures within all sectors clear policy pathways are needed that outline alternative trajectories for european forests and the forestbased sector towards a climateneutral and resilient society and economy forestpaths will codesign quantify and evaluate holistic forestbased policy pathways to optimize the contribution of forests and the forestbased sector to climate change mitigation while considering the need to adapt forests to climate change conserve biodiversity and sustain forest ecosystem services provisioning forestpaths goes beyond the stateoftheart by finding feasible climate biodiversitysmart cbs options across europe suggesting effective mitigation actions and analysing their cobenefits and tradeoffs with biodiversity and ecosystem services forestpaths engages with policymakers forest practitioners regional and national authorities forestbased sector certification bodies researchers and civil society in a codesign process to develop policy pathways and cbs options and maximize their practical usability transparency and trustworthiness openly available through an online policy support platform pathways will be quantified based on a nextgeneration assessment framework of models tools and data this framework will consider forest growth climate change risks as well as interactions between forests and the forestbased sector and economic system supported by stateofthe art monitoring of forest disturbances composition and structure the multidisciplinary consortium consists of research institutes universities and smes from across europe with leading expertise on climate energy and biodiversity policies complex system modelling lifecycle analysis remote sensing disturbance ecology forest and ghg inventories social sciences stakeholder process and communications
development of a novel machine learningbased model for multiphase flows

multiphase flow mf is the simultaneous flow of materials with two or more thermodynamic phases mf occurs in numerous settings bioengineering conventional and nuclear power plants oil and gas production and transport pharmaceutical industry combustion engines chemical industry flows inside the human body biological industry and process technology to name a few researchers use experimental and theoretical techniques to study mf experimental techniques are usually restricted to smaller domains or laboratory scales due to very high costs in addition experiments in realistic conditions are very difficult to manage on the other hand theory usually requires numerical computation which is very timeconsuming for realistic mf problemsthe objective of this study is to develop a novel machine learning mlbased hidden fluid dynamics approach for mf this innovative method combines ml and fluid dynamics by means of information accessible from photographs of flow visualizations and governing equations the developed approach will considerably reduce the cost and time required to analyse mf systems it will unlock many opportunities to improve the design and efficiency of existing and future mf systems thus lowering designoperational coststhis will be the first study to develop a mlbased model for mf the novelty of the proposal is that it will open the opportunity to explore via a relatively cheap and fast computation method the vast number of design parameter variations normally needed for the optimisation of mf systems eg efficiency pressure drop etc this contrasts with expensive and timeconsuming experiments and timeinefficient parameter studies involving traditional computational fluid dynamics the proposal will be carried out in three stages 1 to develop a mlbased model for mf 2 to test the model with several benchmark mf problems 3 to use the model to improve design and reduce cost in an actual practical environment
wireless magnetothermal enteromodulation

gastrointestinal gi disorders affect over 40 of the world population pharmacological agents that modulate cell function and subsequently impact processes involved in such disorders suffer from limited spatiotemporal specificity and accompanying side effects despite the advances in the field of bioelectronic medicine which have enabled localized modulation of electrogenic cells it still requires invasive hardware impeding their use in soft and continuously moving organs we recently showed that magnetic nanoparticles injected into a deep solid organ and exposed to alternating magnetic fields amfs dissipate heat that can trigger localized calcium signaling mediated by endogenously expressed heatsensitive ion channels however penetrating the sensitive and hollow intestine tissue is challenging this proposal aims to develop a novel approach for remote activation of the intestine via adhesive materials to achieve this goal an implantable biocompatible and stretchable magnetic gel will be designed with controlled adhesiveness allowing for its effective adherence to tissues and detachment the magnetic gel can be remotely controlled to dissipate heat under amfs prior to attempting thermal stimulation of the intestine a proofofprinciple demonstration will be performed in the adrenal gland a solid organ which expresses heatsensitive ion channels next the gel properties will be tailored to the intestinal tissue and the activation of heatsensitive ion channels in the colon will be thoroughly characterized ex vivo finally in vivo enteromodulation will be demonstrated while addressing barriers associated with use of magnetic nanomaterials in in vivo models and modulating parameters to achieve short latency between stimulation onset and cell response ultimately i propose to achieve wireless ondemand control of calcium influx and neuropeptide release in the colon to address gastric symptoms of disturbed motility and inflammatory response
development of advanced thermometric nanomotors

temperature is a central concept in thermodynamics and is one of the most crucial parameters to be described in scientific and technological achievements due to the high importance of measuring this property thermometers are accounted for 80 of the sensor market and could reach 686 billion usd in 2023 according to a study done by grand view research inc since technology is advancing in miniaturizing devices and scientific interest in nanoscale structures and phenomena is increasing the need for new thermometric sensor platforms is surging since regular thermometers are limited to tens of micrometers size range additionally a remote way to control their position has not been realized therefore the development of a thermometer working on the nanoscale with a precise position control is highly desired nanomicromotors are particles capable of realizing motion through external stimuli and they can be spatially and temporally controlled remotely thus opening new possibilities for sensor technology in thermotors we propose combining the selfpropulsion of photoactive nanomotors with the thermometric properties of lanthanoiddoped nanocrystals to obtain advanced functional nanodevices that can move when excited by an uv source while ir excitation will allow for measuring temperature in the surroundings in this way a great scientific development will be made in allowing temperature to be measured remotely in a small size scale and at the same time allow the precise control of the region in which the temperature will be measured
is there a perennial future of agriculture

perennial will investigate whether a shift from annual to perennial grain crops as the basis for food production is possible and identify the major opportunities and obstacles for such a perennial revolution since the neolithic revolution the world food system has been dominated by annual crops cereals legumes and oil seeds covering 70 of global croplands the use of annual plants disrupts agroecological processes every season resulting in soil erosion nutrient losses and greenhouse gas emissions from machinery and soils the costly practices of managing annuals including a strong dependence on herbicides and mineral fertilisers partly explain the high indebtedness and low returns in agriculture a shift from annual to perennial grains that are planted once and harvested for many consecutive years could revolutionise agriculture and revitalise rural society it could solve problems associated with annuals and increase the potential for adaptation to climate change advances in plant breeding have shown that a shift to perennials is possible within a few decades but it would challenge powerful vested interests to investigate the prospects of a perennial revolution we will a conduct a systematic critique of the current political economy of agriculture b suggest desirable viable and achievable perennial alternatives to annual monocultures and c develop a strategy for change we complement social science expertise with that of plant breeding agroecology soil and plant science social fields theory is applied to study the political economy of agriculture including the politics of seeds and agrochemicals and soil health theory to suggest alternatives we then incorporate ecological feedbacks to develop an agricultural treadmill theory and apply transition theory to study social change using mixed methods we will conduct discourse analysis network analysis and modelling at multiple scales including farms river basins and jurisdictions
trustworthy ai tools for the prediction of obesity related vascular diseases

weight problems and obesity are increasing at a rapid rate already concerning more than 436mio people in european countries obese persons have a 50 higher risk of cardiovascular disease cvd mortality and treatment costs result in a total economic burden of over 210 billion euro per year to date the prediction of the individual risk for major adverse cvd events in the obese patient population is a challenge current risk scores are not sufficiently accurate and there is no implementation of scores into user friendly solutions the aipod project aims to reduce the number of cvd related deaths in europe by developing an aibased risk prediction score to support clinical decision making and by equipping obese persons with trustworthy ai tools ai tools will integrate clinical laboratory and imaging data to translate disease risk into actionable health information to guide diagnostic steps and treatment recommendations the tools will be validated in six clinical sites on cvd and serve as the basis for a lasting interdisciplinary platform for distributed learning in other vascular territories aipod will push the boundaries of clinical insight in cvd in obese persons including its treatment and risk management aipod main outcomes are 1 a novel imagingbased aibased risk score and clinical decision support system cdss for the risk assessment and prediction of obesityrelated cvd and associated complications as a prerequiste for further aibased prevention and treatment management 2 an innovative easytouse mobile app for citizens citizen app that interacts with the cdss empowering obese people to better monitor and manage their own health physicians will benefit from more efficient workflows and in consequence public health budgets will be unburdened by reducing morbidity and mortality of obese indiviudals
aiops framework for automated intelligent and reliable dataai pipelines lifecycle with humansintheloop and coupling of hybrid scienceguided and ai models

aidapt brings forward a datacentric mentality in ai that is effectively fused with a modelcentric scienceguided approach across the complete lifecycle of aiops by introducing endtoend automation and aibased systematic methods to support the design the execution the observability and the lifecycle management of robust intelligent and scalable dataai pipelines that continuously learn and adapt based on their context aidapt will design a novel aiops intelligent pipeline lifecycle framework crosscutting the different business legalethics data ai logicmodels and system requirements while always ensuring a humanintheloop hitl approach across five axis data design for ai data nurturning for ai data generation for ai model delivery for ai datamodel optimization for ai aidapt will contribute to the current research and advance the stateoftheart techniques and technologies across a number of research paths including sophisticated explainable ai xaidriven data operations from purposing harvestingmining exploration documentation and valuation to interoperability annotation cleaning augmentation and bias detection collaborative feature engineering minimizing the data where appropriate adaptive ai for model retraining purposesoverall aidapt aims at reinstating the pure datarelated work in its rightful place in ai and at reinforcing the generalizability reliability trustworthiness and fairness of al solutionsin order to demonstrate the actual innovation and added value that can be derived through the aidapt scientific advancements the aidapt results will be validated in two interlinked axesi through their actual application to address reallife problems in four 4 representative industries health robotics energy and manufacturing ii through their integration in different ai solutions either open source or commercial that are currently available in the market
computational approaches to reveal the role of astrocytes in information processing and neural networks dynamics

information about the external world is encoded in the brain through the dynamical activity of neuronal ensembles socalled neural population codes neurons however are not the only cell types making up the nervous tissue astrocytes interact with neurons at the synaptic and network level astrocytic intracellular calcium dynamics integrates and modulates surrounding neural activity over a wide range of spatial and temporal scales recent studies showed that these calcium transients encode information about external stimuli and that this information is complementary to that encoded in neural activity suggesting that astrocytes may play an active role in computations carried out in neural circuits here i will investigate the role of astrocytes in shaping neural population focusing on representations of space in the mouse the hippocampus during navigation by using biophysically plausible network modeling and information theory i will characterize how the dynamics and information coding properties of networks of neurons are enhanced when they interact with astrocytes the model will be constrained and guided by analysis of simultaneous dualimaging recordings of neurons and astrocytes then i will consider new data provided by the secondment lab of longitudinal neural recordings during which astrocytic calcium dynamics was chemogenetically manipulated during the exploration of familiar and new environments over days i will determine the causal role of astrocytes in neural information processing by using information theory and machine learning to analyze neural information coding when astrocytes are or are not perturbed then i will use the data to construct biophysically plausible models of astrocytes contribute to the evolution of neural codes across days these major advances in modelling and understanding how astrocytes support an complement neural networks will contribute to understanding how glial cells contribute to healthy brain function
infrastructure for marine and inland water research

the overall objective of the project is to develop a virtual environment equipped with fair multidisciplinary data and services to support marine and freshwater scientists and stakeholders restoring healthy oceans seas coastal and inland waters the aquainfra virtual environment will enable the target stakeholders to store share access analyse and process research data and other research digital objects from their own discipline across research infrastructures disciplines and national borders leveraging on eosc and the other existing operational dataspaces besides supporting the ongoing development of the eosc as an overarching research infrastructure aquainfra is addressing the specific need for enabling researchers from the marine and freshwater communities to work and collaborate across those two domains a specific goal of aquainfra will be to develop an eosc based research infrastructure combining the marine and freshwater domains which will include the development of a cross domain and crosscountry search and discovery mechanism as well as building services for spatiotemporal analysis and modelling through virtual research environments a set of strategic use cases including a paneuropean use case as well as more focused use cases in the baltic sea and the north sea will provide the setting for codesigning and testing services in the targeted research communities the aquainfra project results are expected to contribute to the utilisation of eosc as an overarching research infrastructure enabling collaboration across the domains of marine and freshwater scientists and stakeholders working on restoring of healthy oceans seas coastal and inland waters