Dragon09/odtu_chatbot huggingface dataset:
application of a new kinetic microbial theorymicrobial transition state on enriched nitrifying activated sludge model

traditional engineering models known as activated sludge model asm are commonly used for operation design and optimization of wastewater treatment plants asm predict microbial growth with simulation and calibration of model parameters which are bound to a range of experimental conditions recently microbial transition state mts approach was proposed which is a statisticalphysics based theory of microbial growth linking thermodynamic balances and growth rates a growth equation was derived from these first principles which links a flux the growth of microbes to a force ie the gibbs free potential energy density in medium contained in randomly distributed substrate molecules mts has not been fully verified with experimental data on mixed microbial cultures such as sustained in wastewater treatment systems as the theory is developed for describing standard microbial growth conditions such as inhibition of growth by any pollutant compound has not been consideredapplied in real systems the aim of the research is to investigate growth kinetics of two different nitrifying bacteria individually ammonia and nitrite oxidizers with mts modeling approach and identify nitirification growth kinetics inhibition of bacterial growth with different types of inhibitors will also be investigated in order to evaluate and verify application of mts model for different guilds in mixed microbial cultures under different environmental conditions the outcomes of the study will contribute to understanding and control of microbial growth processes in engineered and natural treatment systems identification of inhibition kinetics will be used for mitigation of pollution generated from industrial processes as well as theoretical development of mts theory to be applied in more complex biological process applications the project is an intersection of environmental engineering molecular biology and modeling disciplines which will contribute green deal strategy of eu
catalysation of institutional transformations of higher education institutions through the adoption of acceleration services

the overall aim of catalisi is to help and support higher education institutions to successfully implement a strategy and individual pathway for institutional transformation through the adoption of acceleration services these are designed to facilitate and accelerate institutional transformations in the field of research and innovation which will strengthen european universities collaborations and alliances as lighthouses of european values catalisi will analyse how the governance of higher education institutions can be changed considering the governance as a way in which societal and state actors intentionally interact in order to transform science technology and innovation systems by regulating issues of societal concern wp1 will ensure the codesign and implementation of actingll throughout the project the knowledge and information produced in the wp1 will serve as a basis for the creation of the knowledge sharing and mutual learning programme in the framework of wp2 the knowledge and information produced in wp1 will also support the activities of wp3 that will guarantee the design the implementation and the sustainability of the institutional transformations the whole process will be constantly monitored and assessed through wp4 that will develop monitoring and assessment toolkits to keep track of the progress related to the institutional transformations through the adoption of acceleration services along the 3 domains defined by catalisi wp4 will also provide strategic information and guidance through policy recommendations about the structural and institutional changes that are needed to maximize the value of research and its impact within the european regionwp5 and wp6 are crosscutting and linked to all other wps activities communication and management catalisi will be carried out by a consortium integrating 11 partners from 8 eu member states covering different geographical areas of europe 3 of them from widening countries
values and dependence of society on pollinators

valor is a multiactor project that will develop a comprehensive systems based approach to develop a deeper understanding of the cascading impacts of pollinator shifts from flower to fork and beyond our work spans seven localised focal regions across europe where we will explore the impacts of pollinator shifts on ecosystems farm businesses and local communities through primary research and modelling these will be supported by broader synthesis and modelling tasks to determine the impacts of pollinators on value chains international trade and nutrition and highlight the risks to ecosystems and natural capital across europe under different codeveloped future scenarios valor aims to empower actors to better understand their relationship with pollinators and will produce a number of codeveloped tools for landowners businesses and policymakers to better understand these risks and easily undertake their own studies by replicating our methods and employing our models throughout the project valor will work through our existing networks of policy and business collaborators to maximise the engagement with our outcomes datasets and tools build synergies with the wider pollinator research community and effectively mainstream pollinators into decisionmaking at all levels throughout europe
transcriptional adaptation during vertebrate development at the singlecell level

the development of a multicellular organism requires the precise control of gene expression in space and time so that cells adopt their correct identity however genetic mutations can alter this complex process recently transcriptional adaptation ta has been uncovered as one of the mechanisms underlying genetic compensation in zebrafish mouse cells in culture and caenorhabditis elegans ta refers to the phenomenon by which mutated genes often with mrnadestabilizing mutations trigger the transcriptional modulation of related genes called adapting genes however little is known about the spatial and temporal characteristics of adapting gene regulation and particularly during the zygotic genome activation this project aims to decipher when and where ta occurs during early zebrafish development using genome engineering followed by live imaging highresolution microscopy and quantitative analysis i will test the hypothesis that ta is regulated in a temporal manner during zygotic genome activation and that there is a specific mode of transcription during the modulation of the adapting genes ie lineardiscontinuous furthermore i will investigate the subcellular localization of mutant mrna degradation as well as the heterogeneity of the ta response between embryonic cells finally i will implement the live imaging of translation in zebrafish embryo to decipher whether the dynamics of translation is involved during the tagenetic compensation processuntil now ta has been mostly investigated on pooled populations of cells therefore we lack the understanding of this phenomenon at the single cell level this project aims to fill this gap and obtain a better understanding of the spatiotemporal characteristics of genetic compensation which aid in the robustness of vertebrate development
graphenebased alloptical technology platform for secure internet of things

the need for a nextgeneration computing platform becomes clear from iot and 5g6g and their high performance and low power requirements now graphene and 2d materials 2dm offer the unique ability to enable highly confined nonlinear interactions of light at low powers and at extremely low response times in the femtosecond range however it must be integrated with cmos lowloss silicon nitride sin platform that facilitates the possibility to create circuits for fast low power high bandwidth general purpose computing and memory completely in the optical domain as the most important challenge comes from the maturity of the graphene processes with standard cmos environments the main goal of gatepost is to fabricate and demonstrate a radically new graphenebased alloptical data processing platform integrated and tested in a real cmos pilot line as a user case we focus on a network security device for distributed denial of service ddos detection and network packet inspection even though on average 170 cyberattacks are performed per iot device per day there is still a huge lack of security due to the added power latency operating costs and bandwidth limitations involved this is unacceptable especially considering that the cybercrimes topped an estimated 318 billion in 2021 alone with our graphenebased computing platform we will show how lowpower lowlatency and high bandwidth network security is ready for the iot and 5g6g future the full system showcases the unique expertise of each consortium member in alloptical digital logic neuromorphic computing memory and ultrafast clock generation these components are realized in the 2dexperimental pilot line at ihp allowing for scalable fabrication and strengthening the eus supplychain in highperformance computing in the future the developed platform can be deployed for applications in ai autonomous driving and more paving the way for computing beyond von neumann and moores law
highthroughput quantum chip diagnostics

computing power has grown exponentially for decades closely following moores law but progress is slowing down and it is time for new ideas quantum computers based on qubits can extend the realm of possibilities unavailable to conventional computers based on bits the market opportunity for quantum computers is huge but the tools to transition from academic research to industrygrade quantum chip development are lacking for example diagnosing the quality of a quantum chip requires months of work by highly specialized quantum engineers in a full cryogenic lab we are revolutionizing this practice with our orange quantum diagnostics systems once a quantum chip is fabricated you just insert it and the system will run a comprehensive set of proprietary diagnostic protocols to ensure deployment readiness our solution is fully automated and orders of magnitude faster than standard practices with the eic support we will trigger moores law scaling for quantum computers
geometric methods in inverse problems for partial differential equations

inverse problems are a research field at the intersection of pure and applied mathematics the goal in inverse problems is to recover information from indirect incomplete or noisy observations the problems arise in medical and seismic imaging where measurements made on the exterior of a body are used to deduce the properties of the inaccessible interior we use mathematical methods ranging from microlocal analysis of partial differential equations and metric geometry to stochastics and computational methods to solve these problemsthe focus of the project are the inverse problems for nonlinear partial differential equations we attack these problems using a recent method that we developed originally for the geometric wave equation this method uses the nonlinear interaction of waves as a beneficial tool using it we have been able to solve inverse problems for nonlinear equations for which the corresponding problem for linear equations is still unsolved we study the determination of a lorentzian spacetime from scattering measurements and the lens rigidity conjecture we use geometric methods originally developed for general relativity to analyze waves in a moving medium and to develop methods for medical imaging by applying riemannian geometry and our results in invisibility cloaking we study counterexamples for nonlinear inverse problems and use transformation optics to construct scatterers with exotic propertieswe also consider solution algorithms that combine the techniques used to prove uniqueness results for inverse problems manifold learning and operator recurrent networks applications include new virus imaging methods using electron microscopy and the imaging of brains practical algorithms based on the results of the research will be developed in collaboration with scientists working in medical imaging optics and earth sciences
mechanoredoxbiocatalysis functional enzyme supports that harvest vibrational energy to power redox biocatalysis

redox enzymes are a diverse enzyme class with significant industrial potential improving sustainability in food fuel and co2 conversion however redox enzymes need a source of energy electrons to power their important reactions while nature employs electrons transferred from cofactors eg nadph to drive redox biocatalysis industry translation requires the addition of sacrificial chemicals which increases cost waste and purification and impedes scalability this project aims to develop a new method to power redox enzymes using mechanical energy and piezoelectric materials establishing a unique research field in mechanoredox biocatalysis inspired by natural mechanotransduction where living systems convert mechanical stimuli into electrochemical activity i will employ mechanoredox materials to transform ubiquitous vibrational energy from the environment into a sustainable supply of electrons to power redox biocatalysis i will demonstrate this technology by coupling scalable piezoelectricpolymer composites with formate dehydrogenase fdh as a model enzyme for vibrationpowered co2 reduction first i will design construct and optimise piezopolymer beads and films that generate a mechanoredox potential matched to redox enzymes next i will couple these materials with fdh to catalyse co2 reduction using vibrations from pumping as mechanical stimulus two routes will be explored namely mediated and direct energy transfer met and det from the stimulated mechanoredox materials culminating in a platform technology for exploiting redox enzymes in industry i will gain extensive scientific and transferable skills from the team of prof anne meyer at dtu and my industry partner novozymes to support my career development including enzyme production and immobilization and commercialisation vibrozyme embodies a new strategy to enhance the scalability sustainability and productivity of redox biomanufacturing with immense commercial potential
ai to predict cancer metastasis using ultraechosono imaging

cancer metastasis is the leading cause of death within tumor disease and is currently an unmet clinical need this dramatic clinical evidence requires immediate action in terms of early diagnosis and then preemptive therapies not only for the primary tumor but especially at the first stages of metastasis formation metastasis is the result of a complex mechanism still under study which involves the trafficking of biological materials in particular extracellular vesicles evs from the primary tumor site towards other tissuesthe project ai cures aims to propose novel methodologies for early diagnosis of metastasis by gaining new knowledge at the early stage of their formation with a realtime and predictive approach this work has the future vision of predicting possible patients outcomes and helping to set timely preemptive therapies i believe that this is possible by exploiting the evs specifically produced by the primary tumor here by colorectal cancer crc in ai cures such evs will be tagged by specific nanosizedcontrast agents and their trafficking in biological media reported by an innovative multimodal bioimaging technology having high resolution deep tissue penetration realtime and molecular characteristics the collected images of evs trafficking towards recipient cells or tissues will be refined and further elaborated by artificial intelligence algorithms with the aim to predict the possible metastasis development from a starting crc tumor massai cures will prove the efficacy of this approach by enabling as expected outcomes deep scientific knowledge on metastasis formation mechanisms and demonstrating a breakthrough innovation in bioimaging technologies and digital tools for health monitoring and diagnosis of oncological patients the results can be also useful in other tumors leading to metastasis and to empower europe towards digital tools for predictive diagnosis
improving waste management through an aipowered detection system of batteries utilising data from xray detectors and pickandplace robots

the weee waste from electrical and electronic equipment management chain has realised an explosion of fire incidences caused by carelessly discarded batteries into either recycling bins or black rubbish bags where they are easily damaged by sorting equipment and start to burn endangering human lives disrupting waste services and causing millions of euros of damage from 190000 up to 13m per fire incidentthe grinner project aims at commercialising an autonomous aienabled robotic sorting system capable of detecting and removing waste containing batteries from current waste streams before they enter inhospitabletobattery machines that crush and consolidate waste the system will comprise i the fastest energyresolved xray detectors in the market ii an mlenabled software module that will analyse xray data and effectively detect waste containing batteries while passing through the waste flow and visionbased pickandplace robots that will remove the flagged weeeproject objectives build an xray data set of weee scrap customisation of the xray system develop the ai software module for detection of batteries within weee using xray data deploy a visionbased robotic solution capable of pickingandplacing weee develop integrate and install a prototype system in a weee facility environment to conduct live trials and validate grinner explore the potential exploitation of grinner as an economically viable standalone product for recycling facilities
the missing mathematical story of bayesian uncertainty quantification for big data

recent years have seen a rapid increase in available information this has created an urgent need for fast statistical and machine learning methods that can scale up to big data sets standard approaches including the now routinely used bayesian methods are becoming computationally infeasible especially in complex models with many parameters and large data sizes a variety of algorithms have been proposed to speed up these procedures but these are typically black box methods with very limited theoretical support in fact empirical evidence shows the potentially bad performance of such methods this is especially concerning in realworld applications eg in medicine in this project i shall open up the black box and provide a theory for scalable bayesian methods combining recent stateoftheart techniques from bayesian nonparametrics empirical process theory and machine learning i focus on two very important classes of scalable techniques variational and distributed bayes i shall establish guarantees but also limitations of these procedures for estimating the parameter of interest and for quantifying the corresponding uncertainty within a framework that will also convince outside of the bayesian paradigm as a result scalable bayesian techniques will have more accurate performance and also better acceptance by a wider community of scientists and practitioners the proposed research although motivated by real world problems is of a mathematical nature in the analysis i consider mathematical models which are routinely used in various fields eg highdimensional linear and logistic regressions are the work horses in econometrics or genetics my theoretical results will provide principled new insights that can be used for instance in multiple specific applications i am involved in including developing novel statistical methods for understanding fundamental questions in cosmology and the early detection of dementia using multiple data sources
clinical impact through aiassisted ms care

multiple sclerosis ms is a devastating immunemediated disorder of the central nervous system since there is no cure available yet for ms the primary therapeutic goal for ms patients is to slow down disability progression and to reduce relapses at early stages of the disease despite 19 available disease modifying treatments dmts the large heterogeneity of the disease further complicated by the high prevalance of comorbidities and multipharmacy and the limited understanding of the working mechanisms dmt thereon results in a poor and variable treatment response in a realworld setting the claims project is a publicprivate partnership aiming to address this and make precision medicine for ms patients a reality through datadriven prognosis and treatment advice in order to better slow down disease progression and eventually conversion to progressive ms we will develop validate and submit for regulatory approval a companion diagnostic platform which offers the ms care team a holistic view of the patient through the visualization of the clinical and subclinical biomarkers and the prediction of the expected disease trajectory under different treatments for biomarker extraction and treatment prediction stateoftheart technologies that allow a reliable and scalable implementation across the world will be used we believe this platform will initiate the paradigm shift from a trialanderror experiencedbased treatment of ms patients to a firsttimeright valuebased holistic treatment management and will hence improve patient outcomes at a lower total cost of care enhance patient experience and improve the wellbeing of the care team
actomyosin driven forcegeneration at the egg surface during fertilization

the fusion of two specialized gametes represents the starting point of life in sexually reproductive organisms despite this fundamental role in the creation of life the molecular and biophysical mechanisms underlying this process remain largely elusive it is evident that the dynamic modulation of the egg actomyosin cortex plays a central role during different steps of fertilization including cortical granule exocytosis ca2 signaling and sperm uptake how this high plasticity of the actin cortex is regulated during fertilization has not been studied in detail in the past research of this dynamic process has been limited by livecell microscopy techniques lacking the needed spatiotemporal resolution gamete interaction is not only a very fast process in mammals it also occurs synchronously with gamete activation to overcome these limitations we propose utilizing eggs isolated from zebrafish that can be activated independently of interaction with sperm specifically we will comparatively characterize the dynamic egg actomyosin cortex properties during fertilization using advanced singlemolecule imaging techniques and biophysical tools like optical tweezers we will specifically address whether the egg actin cortex has mechanosensitive properties that might facilitate gamete fusion as has been shown for other cellcell fusion events it is our goal to identify the basic molecular and biophysical mechanisms underlying fertilization and the comprehensive quantitative study of actin structures within the egg cortex will establish an essential step in addressing this question
documentation of intergenerational transmission in zoroastrian womens silk weaving

zoroastrian womens silk weaving has all the characteristics of intangible cultural heritage