Dragon09/odtu_chatbot huggingface dataset:
overcoming pancreatic cancer immunotherapy resistance developing pyrimidine receptor antagonists

the introduction of immunotherapy revolutionized cancer treatment nevertheless pancreatic cancer is still an omen of death with a dismal 5year survival rate improvement of the current therapies for pancreatic cancer patients is thus an unmet medical need the research we performed for the erc cog immunofit identified novel metabolic pathways shaping the tumor microenvironment toward immunosuppression our latest publication in nature cancer characterized the cdaudpp2y6 axis as a major determinant of pancreatic cancer immune escape with this poc grant we want to start the valorization of our findings generating firstinclass p2y6 biological blockers characterization of the metabolic crosstalk between cancer cells and the immune infiltrate identified the udpelicited p2y6dependent pathway as the most promising target to induce strong antitumor response and immunotherapy sensitization at minimal risk of side effects and generation of resistance evaluation of the current landscape of small molecules available for p2y6 inhibition highlighted the urgent need for an outofthebox approach to generate a new class of molecules suitable to target this pyrimidine receptor in vivo here we will take advantage of the specific knowhow present in our host institute to generate in collaboration with the vib drug discovery center vhh nanobody blockers the feasibility of this ambitious project is ensured by i the strong results supporting the scientific rationale ii the compelling freedomtooperate analysis iii the keen evaluation of the landscape of competitors as well as iv the presence of an outstanding experience within our institution on gpcrtargeting nanobody generationwith this application we aim to start the generation and validation of p2y6 biological blockers and file a composition of matter patent application to foster the further clinical development of our identified lead
socioeconomic pathways adaptation and resilience to changing climate in europe

climate change brings many highly uncertain impacts onto society and economy and its risks incorporating vulnerability require more comprehensive assessments this requires an enhanced understanding of the interdependencies between climate impacts adaptation and mitigation measures at high sectoral and spatial granularity and the temporal evolution and the capacity of the broader socioeconomic context to cope with the climate challengesparccle will deliver new cuttingedge methodological capabilities by advancing and linking knowledge across research communities comprehensive assessment of risk will entail production of granular socioeconomic data and projections bottomup analyses of multidimensional climate vulnerabilities including underresearched aspects such as gender inequality highresolution probabilistic hazards damages and mitigationadaptation synergies and tradeoffs topdown integrated assessment frameworks and leading multisectoral macroeconomic models socioeconomic analyses incorporating crosssector and spillover effects and distributional implications stresstest scenarios will be deployed for an advanced understanding of the critical climate risks and how they can be managedtool and process codesign and codevelopment with private and public sector stakeholders including the jrc will enhance exploitation capacity building and bridge the sciencepolicypractice gap all underpinned by established open science data and models and disseminated through various mediums including online decisionsupport platforms and public api sparccle outputs will be guided by the ongoing climate policy ambitions such as mission adaptation and eu green deal to deliver new insights and decisionmaking support tools which inform robust climate mitigation and adaptation policy and contribute to the design of societal transformation pathways of for the eu
environmentally friendly aviation for all classes of aircraft

the efaca project consists of 6 main objectives at 3 levels level 1 consists of three trl3 demonstrations of technologies relevant to the greening of aviation wp1 bench testing of a gearbox combining input from gas turbine and electric motor for an hybrid turboelectric propulsion system for a propellerdriven regional aircraft wp2 comparative testing of fuel cells with conventional liquid and novel phase cooling to show the benefits up to 20 of the latter in higher net power reduced heat losses and smaller volume and weight also reengineering of fuel cell and structural components to increase powertoweight ratio up to 80 wp3 static ground testing of a complete liquid hydrogen fuel system from cryogenic tank to vaporization and combustion in a wide range of operating regimes and simulation of application to the speed and altitude flight envelope of jet airliners level 2 consists of two preliminary designs wp7 an 80seat 1000km range regional propeller driven aircraft including design and integration of hybrid turboelectric propulsion wp89 a 150seat 2000km range jet liner with liquid hydrogen fuel including design and integration of cryogenic tanks and fuel system at level 3 a road map wp10 for the achievement of the eu environmental targets for aviation synthetizing conclusions in four steps i current status on wp4 emissions and wp5 noise versus future targets and gap to be covered ii assessment of relevant technologies to cover the gaps including wp6 battery electric and wp9 sustainable aviation fuels besides hydrogen wp7 fuel cells and wp8 turbines iii most suitable technology for each class of aircraft light small and medium regional single and twin aisle jetliners and maturation time of the technology iv contribution of each aircraft class to co2 and non co2 global and local emissions and noise leading to wp10 a comprehensive road map of actions for carbonfree or emissionsfree flight
the evolution and function of fish trim e3 ubiquitin ligases

fishtrim aims to study the evolution and function of the trim family of e3 ubiquitin ligases in fish this family has undergone several cladespecific expansions across the fish phylogeny with singlecopy mammalian trims frequently present in large numbers in fish genomes trim e3 ligases and their direct interactions with viral proteins are key in hostpathogen relationships and several fish trims have been associated to antiviral responses additionally positive selection has been observed in fish trim protein domains involved in proteinprotein interactions and pathogen recognition this suggests that this family of fish e3 ubiquitin ligases may act as an immune mechanism against specific viral pathogens and therefore the repeated independent expansions of the trim repertoire in fish could be the result of an arms race between fish and viruses to address these questions fishtrim will focus on understanding the function of this family in fish and their potential coevolutionary interactions with viruses recent advances in the fields of functional genetics and genomics such as singlecell technologies and genome editing allow us to interrogate gene function at unprecedented scale and resolution nonetheless a phylogenomic approach will be fundamental to determine potential functional overlap across independent expansions and selection patterns in the context of trim protein domains will contribute to illuminate the function of the expanded trim repertoire fishtrim aims to deliver frontier knowledge on the biological significance of the different fish trim expansions with potential to revolutionise our understanding of fish immune systems and the evolution of the ubiquitination machinery fishtrim will also explore the role of trims in resistance to viral diseases in fish with potential transformative outcomes leading to improved fish welfare and food security
noise andor ultrafine particulate matter induced cerebral and cardiovascular damage novel insights from experimental and epidemiological brainheart axis biomarkers and computational models

air pollution especially particulate matter pm and traffic noise are major intertwined environmental risks they contribute to the incidence of cardiovascular cerebrovascular mental and metabolic socalled noncommunicable diseases ncds air pollution contributes to annual premature deaths 05 million and traffic noise to loss of 16 million healthy life years in europe critical issues are that noise and pm are underrepresented in clinical guidelines and that european legal exposure limits exceed who standards also due to a limited understanding of knowledge transfer and success metrics significant knowledge gaps are related to additive effects of pm and noise the role of ultrafine particle ufp adverse brainheart axis signaling and the consequences for vulnerable groups such as highrisk patients and the elderlywe address these critical health issues of traffic noise and air pollution pm incl ufp by a unique translational approach using experimental and computational models in clinical interventional and epidemiological studies a primary goal is to identify diseaserelevant biomarkers and understand the molecular pathways of cerebral pulmonary and cardiovascular ncds also by effective translation of animal findings to human health our bench to life approach on brainheart axis is entirely driven by profound preclinical mechanistic knowledge and will use novel multiomics methodology eg redoxphosphoproteomics spatial epigenetics allowing analysis of key pathomechanisms to be included in exposureresponse models this will improve risk assessment and allow evaluation of the effectiveness of mitigation strategies we will also consider the societal circumstances and policies at the national level and their impact on different stakeholders markopolo will advance our understanding of the complex interplay between noise air pollution and human wellbeing and provide clearer information and guidelines for various stakeholders
modelling transient granular flow

granular materials are omnipresent in our daily life the same granular material can behave like solid and fluid which poses a formidable challenge to the constitutive models and numerical methods traditionally constitutive models for the solid and fluidlike behaviour have been developed for the respective flow regimes in different engineeringscientific disciplines with hardly any intersections a single constitutive model capable of describing the transient behaviour during phase transitions in both solidlike and fluidlike regimes is a challenging task with enormous application potential motran takes on this challenge with a simple yet efficient ansatz by decomposing the stress rate into a frictional and collisional part which gives rise to an unconventional constitutive model with the 2nd order strain rate similar to the acceleration of motion it serves as an excellent classifier for steady and transient motions this constitutive model is then augmented to include a length scale in micropolar continuum for multiscale analysis based on the mixture theory the field equations are established in rate form for the first time and discretised by a multilayer sph model for polydisperse granular flow with individual large particles the sph model is coupled with own developed surface mesh represented dem to simulate particles of arbitrary shapes advanced solution techniques are developed based on multigpu acceleration for high fidelity simulation of largescale problems the constitutive model is calibrated by laboratory experiments on natural granular materials and their transparent surrogate the numerical model is validated by scaled model tests under elevated acceleration in centrifuge as well as realworld cases of our database motran is an exciting endeavour with the potential to create a new paradigm that will revolutionise the way how transient granular flow is to be modelled
doctoral network for microprocess engineering for electrosynthesis new synthesis concepts for pharmaceutical fine chemical industry

in miel 10 2 doctoral candidates will develop synthesis technology for the chemical industries of the 21st century by combining the advantages of electrochemistry micro process engineering and flowchemistry in theory electrochemical technologies offer the highest energy efficiency in production as well as microfluidics offer the highest safety and best process control in chemical processes a combination of these two technologies seems to be the logical step towards a more reliable flexible safe and sustainable chemical industry especially for the synthesis of fine chemicals or pharmaceuticals with relatively low output but specific chemistry like fluorination this route offers some advantages in production three synthesis routes 1 twophase electrosynthesis 2 aqueous and 3 nonaqueous electrolytes will be investigated these three reaction paths can be regarded as relevant model processes for pharmaceuticalfine chemical industry the ambitious research objective is to upscale these technologies using integrated cell concepts such as printed circuit board technology pcb technology with integrated process control with insitu optimized yield control the cells can be assembled in synthesis arrays for the safe flexible and sustainable synthesis of chemical products which can also be used for catalytic screening this approach will allow to find new synthetic routes for the sustainable chemical industry of the futuremiels network is embedded into a highly specialized modelling community which develops models on different length scales helping to simulate electrode structures with multiphase flow of fluids multielectron step reactions and electrochemical flow cells a tecnoeconomical investigation provides guidance of all disciplines and ensures that the outcome of the project is to define the economic and ecologic sweet spot in applied electrosynthesis
biophysical models of bacterial growth

biology operates in a dynamic changing environment with fluctuations occurring over many time and length scales microorganisms are capable of duplicating themselves accurately over a short time in this noisy environment this selfreplication known as the cell cycle must be tightly regulated in order for replication to be efficient key processes such as growth both of volume and biomass division and dna replication must be coordinated what biophysical cues are measured by the cell and what feedback is utilized to achieve this tight control is a fundamental open and inherently interdisciplinary question the goal of this proposal is to build integrated models which can account for the simultaneous regulation of multiple cellular traits and account quantitatively for the coupling between the various cellular processes we will consider coarsegrained models that operate both on long timescales the coupling of dna replication gene expression and cell division and short timescales associated with water flow and ion transport across the membrane building on our expertise in the physics of stochastic processes we will develop biophysical models that explain how microbes deal with fluctuations we will develop new analysis tools that will enable us to learn from fluctuations in particular through the powerful methodology of causal inference which has not been previously applied in this context the models will allow us to study the implications of variability on the population growth and fitness and elucidate the design principles involved taken together these models will take us toward comprehensive and predictive biophysical models of bacterial growth
offresonant parametric control over thz collective modes in correlated materials complex driving protocols nonequilibrium correlated states and applications

currently light induced states of matter focus the attention of many researchers in condensed matter physics an important limitation at the moment is the lack of efficient technology that could be used to control material properties at thz frequencies on the other hand this regime is interesting because many materials exhibit collective resonances in the thz regime some examples are the higgs modes of superconducting and charge density wave phases plasmons in two dimensional materials and magnons in antiferromagnetsthis project addresses the problem of exciting and manipulating thz collective modes in correlated electronic systems the idea is to use complex drives with a nontrivial spectral composition or polarization properties one example that will be investigated in this project are amplitude modulated signals here a high frequency signal is modulated on the thz scale under special conditions the modulation can couple to thz collective modes such as magnons and plasmons the collective modes are then parametrically excited an intriguing aspect of this scheme is that the driving is offresonant ie the driving consists of frequencies which are much higher eg by a factor of a hundred then the collective resonances this can be useful to reduce noise and heatingduring this project we will study ways to offresonantly excite low frequency collective modes through complex driving protocols our main focus will be on antiferromagnetic mott insulators with a strong hund coupling and plasmons in two dimensional gapped dirac materials upon establishing tailored driving protocols we will study the nonequilibrium states to which the systems evolve under strong driving finally applications such as time varying spintronic and plasmonic media with unusual wave propagation properties and means to create thz entangled magnons which could be useful in quantum devices will be considered
reconciling classical and modern deep machine learning for realworld applications

despite the undeniable success of machine learning in addressing a wide variety of technological and scientific challenges the current trend of training predictive models with an evergrowing number of parameters from an evergrowing amount of data is not sustainable these huge models often engineered by large corporations benefiting from huge computational resources typically require learning a billion or more of parameters they have proven to be very effective in solving prediction tasks in computer vision natural language processing and computational biology for example but they mostly remain black boxes that are hard to interpret computationally demanding and not robust to small data perturbationswith a strong emphasis on visual modeling the grand challenge of apheleia is to develop a new generation of machine learning models that are more robust interpretable and efficient and do not require massive amounts of data to produce accurate predictions to achieve this objective we will foster new interactions between classical signal processing statistics optimization and modern deep learning our goal is to reduce the need for massive data by enabling scientists and engineers to design trainable machine learning models that directly encode a priori knowledge of the task semantics and data formation process while automatically prefering simple and stable solutions over complex ones these models will be built on solid theoretical foundations with convergence and robustness guarantees which are important to make reallife trustworthy predictions in the wild we will implement these ideas in an opensource software toolbox readily applicable to visual recognition and inverse imaging problems which will also handle other modalities this will stimulate interdisciplinary collaborations with the potential to be a game changer in the way scientists and engineers design machine learning problems
mapping inequalities through the life course

the mapineq project studies the trends and drivers of intergenerational educational labour market and health inequalities over the life course during the last decades our main driving research questions are1 how do local and national opportunity structures enhance suppress or mediate inequalities2 how do changes and spillovers across the life domains and over life course contribute to inequalities3 how are inequalities influenced by policies and societal shocksopportunity structures refer to social institutions demographic and macroeconomic conditions and socioenvironmental context which we analyse across countries regions and localities we focus on societal changes influencing inequalities including those related to family diversity and complexity fertility migration and population ageing digitalization the 200708 global financial crisis and how the covid19 pandemic revealed and exacerbated inequalitieswe compile a policy database of the educational family labour market social benefits and taxrelated policies matched with subnationallevel information on social and institutional structures and physical environments this results in an easytouse open access mapineq product which consists of visualization and mapping tools all underlying data statistical programming tools and openaccess code we link the information in the database to individuallevel longitudinal and crosscountry datasets to study the dynamic interplay between the spheres of life this research is conducted under lifecourse stage specific work packages inequalities in the early childhood and families educational inequalities inequalities in schooltowork transitions unequal midcareer trajectories and labour market exits we consider how the covered societal changes are linked with the perceptions on inequalities across and within countries we cocreate solutions through discussion fora for stakeholder groups across multiple levels of governance
dynamic production sequencing to minimize patient delays for compounded medications

when standard medications are not appropriate for a patients needs eg due to their allergies retail pharmacies must rely on compounding pharmacies to blend raw ingredients and produce a personalized medication to order given the growth in demand for personalized medications and the legal mandate in some countries that compel pharmacies to provide such medications to patients the efficient operation of compounding pharmacies is critical to timely access to medications however there are complex operational dynamics when sequencing production driven by differences in medications and the need to prevent crosscontamination that lead to production delays for patients inspired by discussions with the management team of a compounding pharmacy this project aims to improve operational efficiency and reduce delays by developing a dynamic production control algorithm that sequences medication production the challenge of identifying optimal policies for multiproduct production systems in the presence of setup times has lead researchers to focus on heuristics however existing policies do not account for the sequencedependent setup times or batch processing in this setting we propose and evaluate a theorydriven heuristic based on a novel modification of an optimal control engineering technique known as the statedependent riccati equation approach the performance of this heuristic is to be evaluated both theoretically as well as numerically relative to alternative heuristic policies via simulation this contributes to the operations management and management literature through the development and analysis of an innovation in production governance a critical component of the industrial value chain in this setting moreover the proposed algorithm can be modified for use in other complex production settings where optimal policies are intractable and decisionmakers must rely on heuristics