Dragon09/odtu_chatbot huggingface dataset:
transforming european taxonomy through training research and innovations

accurate taxonomic knowledge and tools are needed to understand the drivers and impact of biodiversity decline however the field of taxonomy is severely hampered by a continuous decrease in capacity with tettris we envision a transformative change in the field of taxonomy to build and sustain taxonomic research capacity through increasing knowledge and developing systems tettris will achieve this aim by creating joint knowledge in reference collections training frameworks and with innovative tools as well as by developing centralized resources providing access to an expertise marketplace the taxonomic knowledge platform and career paths the core methodology for reaching these objectives includes cocreation with citizen scientists and professionals in biodiversity hotspots the openaccess knowledge and systems built into tettris together with citizen scientists will accelerate the integration and expansion of taxonomy in education governance and multidisciplinary research this will ensure the longterm relevance of taxonomy as an instrumental science necessary to halt european and global biodiversity loss and ensuring ecosystems and their services are preserved and sustainably restored on land inland water and at seatettris builds taxonomic research capacity near biodiversity hotspots by networking natural history museums and other taxonomic facilities through bottomup cocreation between 17 partners the consortium includes the european citizen science association and several of europes leading natural history museums botanic gardens and universities unified under cetaf the leading european voice for taxonomy and systematic biology impact throughout the eu and beyond is secured through involvement of associated initiatives such as dissco and dest partners in third party projects and key tettris dissemination activities towards a new generation of taxonomists citizen scientists users in need of taxonomic knowledge and decision makers
a machine learning conservation approach to evaluate extinction risk in freshwater biodiversity

accurate assessments of species contemporaneous extinction risk cer are vital to quantifying the current biodiversity crisis and prioritising conservation efforts however the most comprehensive global dataset of cer the iucn red list of threatened species is taxonomically biased due to the lengthy assessment process leaving understudied taxa such as those in freshwaters under no formal protection predictionbased models based on novel machine learning methods enable largescale automated assessments of cer reducing data deficits rapidly the main goal of this project is to identify predictors of cer in freshwater habitats focusing on the largest family of freshwater gastropods the hydrobiidae first we will use a deeplearning approach to automatically predict the red list status of hundreds of hydrobiid species from multiple regions and ecosystems that have not been evaluated yet basing the predictions on ecological and macroevolutionary data second highthroughput sequencing methods will be conducted for the first time in this taxon to compare microevolutionary diversity with population trends derived from longterm field surveys last by establishing a multifactorial predictionbased method the project will identify which features ecological macro microevolutionary or all are meaningful to inferring cer in freshwater organisms the implications of this proposal are threefold and relevant to scientific technological and societal concerns our findings may provide a basis for comparing predictors of cer across taxa they will also open up a more integrative framework for conservation actions moving beyond speciesbyspecies categorisation focussing on the natural resources agriculture environment area from horizon 20212027 this project addresses knowledge gaps in species threats and safeguards freshwater resources illustrating this with understudied taxa
contribution of lipid droplets to the pathogenesis and chemoresistance of acute myeloid leukemia

acute myeloid leukemia aml results from the uncontrolled expansion of differentiationdefective hematopoietic stemprogenitor cells or immature myeloid cells blasts in the bone marrow bm aml remains a highrisk disease mainly due to treatment refractoriness which is dependent on leukemiabm stroma interactions unfortunately treatments have only experienced minor developments over the last decades on the other hand emerging evidence in cancer metabolism has identified lipid droplets lds cytosolic fat storage organelles as key regulators of lipid metabolism and signalling in cells exposed to stress such as hypoxia chemotherapy and nutrient deprivation however the precise role of lds in the pathogenesis and chemoresistance of aml remains poorly investigated here we aim to i characterize the accumulation and role of lds in aml ii assess the functional impact of blasts and bm stromalds on aml pathogenesis and chemoresistance and iii investigate the involvement of lds in the progression and refractoriness of aml cuttingedge in vitro aml primary cells bmstroma cells and in vivo assays patientderived xenografts will be used to provide evidence of lds as a key player in leukemogenesis our project is based on solid preliminary single cellrna sequencing data indicating that lds are highly accumulated in aml blasts and that inhibition of lds is more effective than the standardofcare chemotherapy in impairing aml cell proliferation our proposal represents an innovative approach achievable thanks to the complementary knowledge and expertise of two topnotch laboratories in acute leukemia supervisor and lds collaborator results from this study will open new avenues to explore the therapeutic opportunity of ld inhibitors as antileukemic coadjuvant this fellowship will enable me to acquire transformational knowledge in leukemia metabolism and will constitute a major first step towards initiating my independent academic career
prediction error computation in visual perception

prior knowledge plays a central role in shaping our perception and interactions with the world predictive coding theories represent an influential framework formalizing the neural computations that underlie the integration of prior knowledge into perception one central tenet of predictive coding is the computation of prediction errors which signify the discrepancy between predictions derived from our prior knowledge and incoming sensory input despite ample empirical evidence supporting the existence of prediction errors such as heightened sensory responses to unexpected compared to expected stimuli several unresolved questions persistone pivotal question pertains to the nature of the surprise encoded in sensory prediction errors across the visual cortical hierarchy does the computation of prediction errors reflect local surprise specific to each level in the visual hierarchy eg surprising orientations in primary visual cortex alternatively is the coding of surprise inherited topdown aligning with the notion of predictive coding that predictions are relayed from higher to lower areas furthermore it remains unclear whether the computation of surprise dynamically adapts to our goals or remains a static feature of cortical tuning lastly the networks and temporal dynamics that underlie prediction and prediction error computations are poorly understoodwithin the scope of the prevision project my objective is to address these critical knowledge gaps through the integration of advanced neuroimaging techniques and artificial intelligence methodologies thereby shedding light on the fundamental neurocomputational mechanisms that drive perception consequently this project represents an excellent opportunity for me to engage in innovative and pioneering research investigating pressing questions within contemporary cognitive neuroscience and significantly advance my scientific career
naming the world semantic associations and formmeaning mappings in the mental lexicon across sign languages

words in sign languages are rich in visual meaning they contain shapes movements relations in space etc that depict objects and actions as symbolic metaphors eg the action of pulling words out of the head in one language means to ponder yet signs are also encoded into units of form that are articulated in simultaneous constructions unlike sequences of consonants and vowels in spoken words how then do signers store richly symbolic words that occur in highly simultaneous forms in their mental lexicons at present insight into these mental mappings remains highly occluded not only at the level of behavioral and neural phenomena but in terms of linguistic analysis as well what indeed is morphology in sign languages when even the smallest units of formlike hooked fingers or a location at the throatcan carry meaning what is the nature of these units do they vary across sign languages or are the iconic roots of formmeaning mappings so powerful that the same ones reoccur across unrelated sign languages answering these questions is urgently needed to create better sign language resources for teaching and learning and to advance language technologies the semasign project proposes a groundbreaking approach to these questions by locating formmeaning correspondences in sign languages through computational means while creating new empiricallyrobust datasets to reveal how signs are organized in the mental lexicon semantic networks are created for sign languages from germany kenya and guinea bissau on the basis of word association responses in which a signer sees a sign from their language and responds with the first three signs that come to mind this will establish an objective measure of semantic relatedness enabling computational means to locate clusters of signs unusually close in both form and meaning as the language in guinea bissau was formed only 15 years ago we will also discover how lexicons emerge and grow at a very early stage
quantifying the landscape of fear collaborative camera trap networks to assess wildlife responses to activities of humans and freeranging domestic species across europe

standardized largescale monitoring programs are critical to track progress toward the biodiversity and sustainable goals identified by several international initiatives including the european green deal yet these programs are hard to establish often because of limited resources international collaborations centered on data and knowledge sharing can fill this gap the future fellow will gather and collate camera trap data collected across europe in the past decade to quantify the impact on wildlife of the presence of humans and their domestic species in natural environments at a fine temporal scale and for the first time across europe the mere presence of human and freeranging dogs cats and livestock in natural environments might be detrimental to wildlife populations with relatively unknown consequences on species persistence this action aims at 1 quantifying the effects of human presence on mammals occupancy and activity 2 assessing distribution and cooccurrence of freeranging domestic animals and wildlife populations 3 quantifying the effects of freeranging domestic cats on urban and suburban mammal and bird communities along with leveraging publicly shared datasets this action will build on and cement emerging european camera trap collaborations and thus help establish the longterm largescale monitoring program that is paramount to tracking the status of terrestrial communities across europe and informing european conservation policies the future fellow will be hosted at the max planck institute of animal behavior a leading actor in the study of animal behavior and a strong promotor of european largescale camera trap efforts the future supervisor prof martin wikelski is a pioneer in leveraging international collaborations and technology to advance our understanding of animal decisionmaking processes across large scales this action will greatly advance the fellows expertise and establish her as an independent researcher
data aware efficient models of the urban microclimate

the share of the worlds population living in cities is rapidly increasing and it is expected to rise to 80 by 2050 it is therefore crucial to develop new efficient and reliable methods to model the urban microclimate in fact these models can support urban planners and policymakers to create more comfortable and sustainable cities high computational requirements limit existing numerical methodologies and dante fits in this context and aims to create a new paradigm for fast and reliable numerical simulations bridging the fields of model order reduction machine learning and data assimilation the idea is to create a research team to answer many unresolved questions in model order reduction for complex and reallife urban microclimate simulations particular emphasis will be given to advanced machine learning tools which incorporate physics knowledge aiming to improve the accuracy interpretability and reliability of predictive models the identified tasks cover a wide range of different topics dimensionality reduction of the solution manifold in problems governed by complex physical principles uncertainty quantification data assimilation and inverse modeling the new tools will have the agility of datadriven methods in complex nonlinear settings and the physical rigor of projectionbased methods with quantified errors the developed methods will significantly impact digital transformation enabling digital twins of urban environments possible applications include but are not limited to urban air pollution heat island modeling wind loads on buildings and inverse modeling approaches
millimetrewave superconducting quantum circuits

i propose an experimental program to investigate quantumcoherent properties of superconducting circuits at frequencies one order of magnitude larger than those demonstrated until now my idea is to develop a new generation of superconducting qubits with significantly increased energy level separation between their ground and the first excited states pushing the operation frequency of superconducting qubits up offers a number of potential technological advantages due to the increased level separation such novel millimetrewave quantum processors could be operated at much higher temperatures than their present counterparts even at millikelvin temperatures the higher qubit resonance frequency will offer better protection from nonthermal noise furthermore qubit logic gates can be performed faster at higher frequencies quantum circuit components can be reduced in size due to smaller wavelength at higher frequencies thus allowing for a smaller footprint denser packaging and better integration these numerous potential advantages face nevertheless a number of challenges and pose open questions that will be addressed and are aimed to be answered in the proposed project the goal is to develop prototype qubits for the 100 ghz frequency range and to demonstrate their manipulation and quantum state tomography this challenging project will unearth fundamental knowledge about decoherence in this yet unexplored frequency range we will study dielectric loss and other decoherence sources as functions of frequency and temperature once successful this approach will open a new way of building a superconducting quantum computer
revolutionizing remote care of chronic disease patients an interoperable and smart monitoring platform

the growing demand for proper monitoring and treatment of chronic disease patients is unsustainable for european healthcare systems despite the recent advancements remote patient monitoring rpm systems have not achieved their potential due their inability to integrate accurate data from any given medical device to the electronic health records ehrs to cater to the urgent need of innovative monitoring systems we deliver the humanitcare platform an aipowered platform which can collect data from any given device adapt itself for the needs of the patients and enables integration into the ehrs humanitcare reduces 40 of total visits and generates smart alarms and reports to inform patients and caregivers hence contributing significantly to clinical and selfcare with our innovative software architecture we enable security interoperability sustainability and scalability the superior features of our platform will enable us to be the leaders of the industry in europe
business intelligence engine for things in motion

navalgo is building a business intelligence engine for things in motion tailored for transport logistics it allows users to extract valuable insights from the growing amounts of spatiotemporal data that is being generated from iot devices but not only to improve operations reduce costs meet the challenges of the digital sustainability transitionsthe solution a horizontal b2b cloudnative application enables companies to face the data deluge they experience to make a strategic use of their data with a very short timetomarket by turning raw unqueriable often inconsistent data on assets movement transport parameters into a reliable vision of their processfocus their attention on actionable insights related to their supply chain processes covering anomaly detection forecasts and risks of delay or damage the solution responds to the growing business intelligence needs of companies working with sources of complex data with a special focus on data originating from moving assets including their location it is a complete technology stack which upon deployment is meant to be accessible to users without technical skills eg sales business teams the solution allows clients to retain full control of their data its main societal and economic impact lies in helping to achieve more resilient optimized and environmentally friendly supply chains navalgo is a womenled company both the ceo and coo identify as women a deeptech project whose development started in early 2020 this application aims to reinforce aspects of gotomarket including ip strategy customer validation for the primary vertical transport logistics market analysis for further verticals as well as input to the funding strategy
high resolution dual comb spectroscopy and ranging

spectroscopy and metrology are amongst the most important applications of electromagnetic fieldsthe very fast oscillations of light up to 200 thz in the infrared permit very precise distance measurements as well as broadband analysis of radiations emitted and absorbed by matterthe technologies have evolved tremendously over the years and at the turn of the century a groundbreaking technology was proposed by using two sources of optical pulse trains corresponding to optical frequency combs in the spectral domain no moving parts would be required and much faster measurements could be performed socalled dual comb spectroscopy and ranging have had a very significant impact on their respective fields and have established capabilities in terms of speed and accuracy that are still relevant todayyet some important limitations remain most importantly there is a tradeoff between resolution an acquisition time which is currently treated as a fundamental limit of dual comb systems it prevents these systems from being used for high resolution spectroscopy or longdistance ranging which are critical for some applications such as gas sensing or largescale metrologyin highres i propose a novel technique which permits overcoming this fundamental limit i expect it will improve the current state of the art by as much as three orders of magnitude in terms of resolution or distancewe will start by a theoretical analysis of the general principle we propose we will then build a dual comb source with unprecedented characteristics based on soliton formation in driven fiber resonators finally using this novel source we will experimentally demonstrate two proof of principle applications which would greatly benefit from this novel technique namely thz spectroscopy and very longdistance ranging
magnetoionic devices based on oxygenion solid state intercalation

the digitalization of the world and the rise of the bigdata era continuously push towards improved computing capabilities able to sustain new and complex functionalities traditional computer architectures based on the von neumann model present limitations when dealing with rapidly growing technologies such artificial intelligence since to low access speed between the separated memory and cpu inevitably leads to low processing capabilities and high energy consumptions a possible solution to overcome these limitations is the development of nonvolatile memories nvms as element with both storage and logic capabilities enabling the computation of tasks directly on the memory unit inmemory computing magnoxy aims at developing a novel solid state batterylike nvm based on the control of magnetic properties of functional oxide thin films through a fast deintercalation of oxygen ions the control of magnetism through the reversible deintercalation of ions into a target material mediated by the application of an electrical bias through an ionic electrolyte ie magnetoionics mi is the most recent approach for controlling magnetism by voltage mibased magnetoelectric randomaccess memories meram have the potential to deliver robust fast and energy efficient nvms for inmemory computing however mi control is still at an early stage of the rd process and many aspects still needs to be improved to deliver a competitive device in essence magnoxy will provide i a solid state thin film device based on the electrochemical insertion of oxygen ions at rt ii a large and analogically tuneable magnetic response of the cell ii a fast 100 ms magnetic switching capabilities through the implementation of nanoionics concept for boosting ionic motion iii an improved understanding of the oxygenion intercalation mechanisms in oxide perovskite thin films iv device scalability and miniaturization
targeted respiratory drug delivery by tribo charged sprays

the main purpose of this proposal is to develop a radically new technology for very precisely delivering medicine to the human body targeted deposition of drug formulations to the lungs exhibits great promise in several treatments eg sirna treatment against lung cancer or mrna vaccination against covid19 this requires oral and nasal inhalation for which spray pumps and nebulizers are widely used to transform an aqueous formulation into a soft mist spray however producing the necessary drop size is hampered by droplet coalescence making drops too large and polydisperse because of this they deposit in the wrong places large droplets between 10 and 50 micrometers deposit mostly in the upper respiratory tract mouth throat or nasal cavity through ballistic impact on the mucosal tissue whereas small droplets between 5 and 10 micrometers can be lifted by the airstreams and deposit mostly inthe lower respiratory tract bronchi and lungs our innovation relies on the charging of drops through the friction with the nozzle material the charged drops repel and do not coalesce allowing precise control of their size to target eg the lungs this allows to achieve safe and effective targeted deposition of drugs and or vaccines with hitherto unequaled precision