Dragon09/odtu_chatbot huggingface dataset:
transitions in rubblepile asteroid chaotic environment and granular structures

we are now living exciting times for asteroid exploration the increasing availability of insitu observation data providing unprecedented level of detail makes the study of asteroids an exciting and living frontier asteroids are rubble piles ie gravitational aggregates of loosely consolidated material however no direct measurements of asteroids interior exist and little is known about the mechanisms governing their formation and evolution not only limited by a lack of data the understanding of asteroids properties is challenged at afundamental level by their rubblepile nature this makes their dynamics subject to the laws of granular mechanics one of the major unsolved problems in physicstraces enables a new paradigm for the characterization of granular systems in asteroidrelated scenarios the ambition is to demonstrate that the macroscopic behaviour of granular media in asteroid environment can be inferred from local properties of the grain the methodology lays its foundation on a cuttingedge simulation tool able to resolve the dynamics of grains to particlescale precision and a theoretical framework able to decode the chaotic nature of particlescale dynamicstraces hypothesis is validated through theoretical numerical and experimental work the ability of the methodology to characterize and identify transitions between dynamical regimes of granular media is tested gradually for increasing levels of realism ranging between proofofconcept laboratory scenarios involving experiments in vacuumlowg and fullscale scenarios involving asteroid mission dataif successful traces will enable the characterization of surface and internal properties of asteroids with limited observation data this will play a crucial role to enable the next breakthrough in asteroid science as well as efficientcosteffective design of the next generation of space missions to explore and exploit asteroids including planetary defence applications
secrecypreserving proofs with solid foundations

the traditional notion of a proof offers no secrecy proving the validity of a blockchain transaction would reveal its details proving qualification for a loan would reveal private financial information and proving that a system has been hacked could reveal sensitive details about the system remarkably using cryptography this problem can be solved secrecypreserving proofs are a class of protocols allowing to prove assertions about secret information without actually revealing the information the most prominent notion of such a proof is that of zero knowledge proofs which reveal no information at all recent years have seen zero knowledge proofs transition from theory to practice with major investment from industry and governments they are now being deployed and standardized driven by largescale applications such as blockchains deployment efforts have put special stress on efficiency often compromising on the core principal of rigorous security analysis based on solid hardness assumptions at the same time the nearing possibility of new threats such as quantum attacks only requires stronger securitythe goal of the project is secrecypreserving proofs that meet present day challenges without compromising on the gold standard of cryptographic security we envision a world where secrecypreserving proofs are reliable enough to be used in highstake applications and efficient enough to be used in largescale applications the project will apply foundational theoretical research to overcome existing barriers and challenges toward achieving this goal
implementation of new machine learning algorithms for the optimisation of drug formulations

correctly developing and predicting crystalline forms with specific physicochemical properties is essential to the pharmaceutical industry the main challenge this industry faces is the fact that most active pharmaceutical ingredients in most drugs can interconvert into a different usually more stable polymorph potentially reducing the solubility of the drug slowing down the release of the api and affecting the pharmacokinetics bioavailability and efficacy of the drug for instance due to the complex interplay between thermodynamics and kinetics it often happens that unexpected polymorphs emerge either in development best case scenario or long after the drug has been approved for market worst case scenario a previously known stable form that disappears or the sudden appearance of an even more stable form can have grave consequences for instance the new form may have new properties that are not suitable for the intended purpose of the drug leading to significant economic and public health repercussions this erc proof of concept project aims to implement new machine learning approaches that would allow to accelerate the process of predicting crystal structures by a factor of 100 thereby making it sustainable and enabling industry to investigate other crystal structures of the same drug to find the most suitable formulation eg hydrates salts cocrystals etc beyond pharma which is our target application for machinedrug polymorphism of chemical structures has significant importance across many other different industries for instance the polymorphism of a pigment can generate a different colour or the polymorphism of a chemical structure can lead to a material with significantly different properties thermal plastic etc as such machinedrug is a lean targeted project with a clear scope but its potential applications are limitless
nav15 regulation finetuning as a therapy for cardiac conduction and arrhythmic diseases at risk of sudden death

the voltagegated sodium channel nachannel nav15 is a central component of cardiac electrogenesis its dysfunction can lead to ventricular fibrillation and sudden cardiac death while nav15 represents a highly relevant therapeutic target for prevention of lifethreatening cardiac arrhythmias therapies that target the expression or function of this channel are nonexistent such a therapy would provide for the first time an alternative for the invasive and costly cardioverter defibrillator or pacemaker therapies currently used nav15cared consortium proposes to capitalize on their largest worldwide cohorts of patients with inherited cardiac electrical and conduction defects to uncover regulatory regions and proteins that modulate nav15 expression and function with the goal to develop and validate innovative therapies to restore the function of nav15 we have three objectives 1 predict the risk of fatal arrhythmia and conduction defect at the individual level by developing personalized polygenic risk score prs 2 characterize the molecular mechanism associating regulatory dna regions and genes with the cardiac diseases to uncover new therapeutic targets 3 and develop new candidates for therapeutic intervention able to restore nav15 loss of function after databases harmonization we will conduct whole genome genetic studies to identify variants associated with ventricular arrhythmia and conduction defects and generate prs relevant to stratify the risk of arrhythmia and degree of conduction defect genetic studies will also be used to identify new therapeutic that will be evaluated in dedicated and highthroughput human cardiomyocytes derived induced pluripotent cell models
investigating quantum phases at extreme charge doping limit by scanning tunneling microscopy

tailoring electronic properties of quantum matter is of immense current interest in the condensed matter physics community the ability to control quantum states benefits both fundamental understanding of the underlying physics and the advancement of nextgeneration quantum techniques recently ionic gating has emerged as a powerful tool in manipulating electronic states by achieving ultrahigh doping levels however this technique has not been applied to scanning tunneling microscopy stm studies which are crucial in accessing the local density of electronic states and exploring correlated phases i will establish a new stm technique that integrates ionic gating sigstm to manipulate quantum states through ultrahigh carrier density doping sigstm will open a completely new window to study highly doped correlated materials at the atomic level 1 i will fabricate the nanodevices that can reach ultrahigh doping levels through ionic gating and are compatible with lowtemperature stm 2 i will carry out stm studies on the devices to demonstrate the technique of sigstm 3 i will combine sigstm and molecular beam epitaxy to study versatile and lowdimension materials this project combines the strengths of both the experienced researcher and the host group our new sigstm technique combines our expertise in the fabrication of highquality devices from myself and stm measurements from the host group sigstm will be a breakthrough in condensed matter physics that can contribute to longlasting problems such as the origin of hightemperature superconductivity in addition sigstm has great potential to expand understanding of new functional materials such as dissipationless materials and advanced electronics in quantum technologies with strong societal and potentially economic impact
listening to silence the first blood test for risk of individuals at prophylactic time windows to develop type 2 diabetes mellitus

type 2 diabetes mellitus t2dm is a heterogeneous disease associated with mortality and severe morbidity and affecting hundreds of millions of people around the globe despite some misconception accepted in the public current models to predict the onset of t2dm have insufficient power and are generally limited to individuals that already progressed into prediabetes states at which the assignment of diseasepreventing treatments is largely ineffective clinicallevel prediction power auc09 among nondiabetic individuals would enable effective preemptive strategies but the way to produce such predictions remined unknown based on a new understanding of enhancersilencer regulatory networks i have developed a new class of supreme epigenetic biomarkers diseaseprediction models that built on the basis of these novel understanding and biomarkers significantly overpower the best currentlyavailable prediction models importantly these pioneering models can identify the people at risk well before the appearance of any clinical symptoms strong preliminary evidence signified the potential to develop the first clinical test to identify the risk to develop t2dm and associated diseases among nonprediabetic individuals at the required prediction power and in time to deliver effective diseasepreventing treatments validation of these results and demonstration of the effectiveness of the new clinicallevel test will allows the directions of the effective though expensive preventing treatments to the people at risk thus dramatically improve the way we are dealing with this harmful disease
hard work plastic flow a datacentric approach to dislocationbased plasticity

metals as structural materials are at the core of our society almost everything we physically interact with includes some form of metal manufactured to specific properties and formed into a desired shape consequently the understanding and design of the balance between ductility and strength of metals are one of the primary disciplines of materials science on a fundamental level this is the description of crystalline line defects called dislocations at the atomic scale the current understanding of dislocations is often on the level of individual dislocation properties at the component scale collective behavior is commonly formulated in continuum variables with the drawback of limited applicability over a wide range of possible scenarios our current understanding still shows a gap in how individual dislocation properties translate into their collective behavior to address this longstanding question i propose a datacentric approach first a comprehensive dataset of dislocation ensemble trajectories for various loading and initial conditions is created using discrete dislocation dynamics as well as molecular dynamics simulations and iteratively extended the trajectories are subsequently analyzed with tools borrowed from graph theory and timeseries analysis to capture the network character of dislocation structures subsequently a novel class of plasticity models is developed instead of humanderived state variables i will let the data speak for itself to bridge the gap between individual and collective dislocation behavior the project solves two timely challenges in materials science one is the described gap and the other is a demonstration of effective research data management of complex materials data providing solutions to data generation storage accessibility data fusion reuse and analysis using the example of dislocation trajectories
impact of exposure to chemicals to the onset and progression of lewy body dementia

with aging the incidence of neurodegenerative diseases is increasing clinical manifestations of neurodegenerative disease often result in dementia a group of symptoms including loss of memory and impaired cognitive functions that have serious consequences for the wellbeing of aging individuals and society dementia is a syndrome of many diseases including lewy body dementia lbd whilst the genetic factors contributing to the development of lbd have been investigated links between exposure to chemicals and lbd have not been sufficiently studied the frankie project aims to comprehensively investigate the chemical exposome of patients with lbd and elucidate underlying mechanisms of exposurerelated neurotoxicity bloodbased chemical exposure agents and metabolites of lbd patients will be measured via a unique dual liquid chromatography coupled to highresolution mass spectrometry platform advanced statistical and data mining methods will be employed to associate chemical exposures with dysregulated metabolic pathways to identify biomarkers of effect furthermore by comparison of metabolic profiles of healthy people people with early lbd and lbd patients novel prodromal and clinical blood biomarkers will be identified the proposed interdisciplinary datadriven study is a big step towards understanding the influence of everyday chemical exposure on the onset and progression of neurodegenerative disease the frankie project will produce mechanistic insights of chemical exposurerelated neurotoxicity providing the opportunity to identify prevention measures and potentially reduce the incidence of neurodegenerative diseases to improve the quality of life of the elderly and aging populations and contribute towards a healthier society
taming controlling and harnessing quantum complexity

quantum processes hold tremendous potential for the development of novel technologies as epitomized by the recent proliferation of commercially available quantum devices to date most research and innovation in the field has focused on spatial quantum effects most prominently entanglement leaving quantum effects in time an asofyet untapped resource progress in understanding such effects has been hindered by the inherent complexity of quantum memory and the lack of an adequate conceptual framework this in turn has prevented the development of tools to probe predict and compress general quantum processes this action aims to overcome these hurdles and answer the following fundamental questions what kinds of quantum memory effects are physically possible how can they be efficiently probed how can predictive models for quantum processes be constructed answers to these questions will be obtained by combining a novel fullygeneral approach to the dynamics of open quantum systems with a proven systematic construction of memoryminimal models of classical phenomena the ensuing framework and tools will engender bounds on the strength and distribution of quantum memory enable the construction of predictive models for complex quantum processes and render their characterization experimentally tractable these crucial innovations will pave the way to a reconceptualization of quantum information processing that provides the means to harness the full potential of quantum processes both in space and time as a result the outcomes of the action are expected to find longterm application in a wide array of fields ranging from quantum information to complexity theory and biochemical processesthe research will be carried out at trinity college dublin in the perfectly suited group of felix binder who is a leading expert working at the interface of quantum thermodynamics classical complexity theory and quantum information theory
bioelectrochemical systems with immobilized redox mediators for powertomethane conversion

biomethane is a gas consisting mainly of methane ch4 that can be produced from agricultural and food waste or other organic materials at a low or even negative price since biomethane is almost equivalent to natural gas it is seen as a possible functional substitute biomethane can be used an energy carrier for excess renewable electricity and as a feedstock for chemical synthesis it is possible to produce biomethane by means of various biochemical and bioelectrochemical reactors in which the main role is played by methanogenic microorganisms that convert co2 or other organic compounds into ch4 this project is dedicated to the development of two bioelectrochemical reactors based on a tricklebed reactor using raw biogas from an industrial anaerobic digester and hydrogen from an alkaline electrolysis for the synthesis of ch4 the main fundamental novelty of this project is the use of immobilized redox mediators anthraquinone derivatives which promote the rate of methanogenesis and thus increase the performance of the whole setup in the case of the bioreactor carbon particles with immobilized redox mediators are used as the biofilm support material in the case of the bioelectrochemical reactor the same materials also act as colloidal electrodes this approach is expected to significantly improve key characteristics such as methane conversion rate and power density which will enhance the economic attractiveness of the technology also the project will use zerogap flow electrochemical cells for the first time to accelerate the screening of promising materials
groundbreaking tools and models to reduce air pollution in urban areas

modelair proposes a combination of theoretical experimental numerical and datadriven science that will simulate control and design new disruptive technologies for future sustainable cities and will provide specialized training to 10 doctoral candidates dcs to make these new technologies available to city councils and relevant industrial sectors modelair contributes to eu mission climateneutral and smart cities reducing gas emissions and offering cleaner air to citizensmodelair will develop an artificial intelligence ai based tool to help make informed and sensible decisions to control air pollution in urban areas for such aim modelair will develop novel analysis tools and new reduced order models roms based on both nonintrusive sensing and innovative data sources assessed by the industry and city councils from bristol brussels and madrid modelair will improve the current stateoftheart modeling capability taking into account the influence that buildings roadways and other structures have on the flow and dispersion of air pollution the limits of applicability of the novel aibased tool will be tested in three specific problems related to air pollution in urban areas by i studying the influence of the urban topology on air pollution ii characterizing the ixelles district brusselsbe to develop a realtime decisionmaking tool that will assess about the disposition and maintenance of the sensor network to provide a highquality air pollution monitoring service and iii investigating the influence of the pollutant emission source strength and location to optimize traffic routes to reduce air pollution the final aim is to obtain transferable outputs using novel tools and methodology approaches addressing three different challenges to improve air quality dispersion models that will serve to city councils and industry to develop new regulations to control air pollution
deciphering cotranslational protein folding assembly and quality control pathways in health and disease

the majority of cellular proteins do not function alone rather acting together to achieve concerted functions despite the prevalence of protein complexes little is known of the mechanisms that ensure their correct folding and assembly in the crowded cytoplasm the importance of the folding challenge is underscored by the growing number of misfolding diseases often characterized by aggregation of lonely unassembled proteinsubunits at the critical intersection of synthesis and folding the ribosome is emerging as a hub guiding the polypeptidechain interactions with targeting factors modifying enzymes and folding chaperones we have recently discovered that even the final step of folding the assembly into complexes is coordinated with translation to capture cotranslational events in vivo we developed a ribosome profiling approach this approach revealed that emerging polypeptidechains are constantly engaged by their partner subunits protecting them from misfolding shiber et al nature 2018 however the mechanisms regulating cotranslational assembly pathways remain largely obscure in this proposal we aim to elucidate cotranslational protein folding and degradation mechanisms in health and disease we will i identify and characterize novel cotranslational degradation pathways by targeting ribosomes synthesizing misfoldingprone subunits ii elucidate the conformational energetic and kinetic parameters directing folding and assembly at the atomic level by molecular dynamics iii develop ribofriend a singlemolecule in vivo approach to elucidate the interplay of chaperones degradation and assembly factors during synthesis our collective preliminary results strongly support the feasibility of this proposal the ability to capture cotranslational folding and misfolding pathways in singlemolecule resolution can revolutionize our understanding of conformational diseases and the aging process opening new horizons for therapy