Dragon09/odtu_chatbot huggingface dataset:
a webplatform interfaced software for spectroscopic molecular characterization and early diagnosis of parkinsons disease

the goal of this proposal is the production of the semisoft software and an associate web platform for spectroscopic molecular characterization and early diagnosis of parkinsons disease semisoft will be designed to decipher complex irraman spectra which are currently hampering development of new clinical protocols and other technological advances this will be achieved upon implementation of the innovative semiclassically approximate quantum dynamics methods developed in the parent erccog semicomplex project which allow for a simplification of complex spectra and unprecedented quantum mechanical accuracy to further increase the level of accuracy in semisoft simulations a new machinelearning based force field for biological systems will be developed and incorporated in the softwarethe semisoft web platform will be accessible to nonexpert users from any browser without desktop installation and based on a flexible dropdown menu instructing the user step by step the user will be able to reproduce irraman spectra and identify molecular species in gas liquid or solid state the web platform will find immediate application in a freemium version thanks to an industrial partner laboratory whose goal is to find biomarkers of parkinsons disease in human saliva this pilot test will allow the team to tune the web platform for future commercialization an analysis of economic social and market prospects will be performed to define a value proposition and an appropriate business model by comparing the feasibility and relative impact of licensing paid access through a user account and spinoff the definition of an appropriate intellectual property protection strategy will be also part of the project semisoft is expected to have a longterm impact too raman and related spectroscopies are widespread in laboratories for chemical characterization and this computational tool will boost spectroscopy applications in material physics and chemical sciences
paragon novel vaccine pnv an effective protection against all serotypes of five deadly bacteria to eradicate antimicrobial resistance amr

immunethep has developed patented wo2015189422 and successfully undergone preclinical trials for a new breakthrough vaccine pnvparagon novel vaccine it protects against bacterial infections caused by 5 different bacteria s aureus s pneumoniae k pneumoniae e coli and gbs our innovation relies on the discovery of a virulence and immunosuppressive mechanism common to these pathogens the bacteria excrete a highly immunosuppressive protein designated as glyceraldehyde3phosphate dehydrogenase gapdh which disables the host immune response to infection pnv works by blocking bacterial gapdh while not reacting to human gapdh allowing the host immune system to fight infection and eliminate the bacteria preclinical trials demonstrated high levels of efficacy against any of the 5 bacteria with an excellent safety profile pnv is the worlds first vaccine not only to prevent multiple bacterial infections but for all invasive serotypes including multiresistant strains
tracer ramandriven mass spectrometry imaging tramasi a new platform for ontissue tracing of nutritional and biochemical trajectories

unlocking the mysteries surrounding nutrient fate within the human body has remained an enigmatic pursuit our organs and tissues crave specific nutrients especially in physiological and pathological conditions yet the intricate journey these nutrients embark upon within our bodies remains largely unclear the aim of my ambitious project is thus to create an innovative chemicalspatial workflow capable of 1 providing the anatomical coordinates of nutrients of interest across tissues and organs and 2 understanding the biochemical route these nutrients undergo in the respective tissue areas of the target organ for this i will use a complementary approach relying on advanced techniques such as tracer metabolomics mass spectrometry imaging msi and stimulated raman spectroscopy srs i will benchmark this technological platform onto a model of acute kidney injury aki to identify and localize the predominant biochemical pathways the amalgamation of srs and msi with tracer metabolomics is groundbreaking given the current technological landscape and provides an innovative and feasible solution to the posed challenge
prodrug technology for immunotherapypriming via patientfriendly athome metronomic dosing

immunotherapy holds great promise for curative cancer treatment while immune checkpoint inhibitors can induce complete and longterm cures the percentage of patients responding to immunotherapy remains to be low combining immunotherapy with chemotherapeutic drugs that induce immunogenic cell death icd is among the most promising strategies to potentiate antitumor immune responses chemotherapeutics are clinically typically administered via intravenous infusion once every couple of weeks at high doses metronomic dosing is based on the application of chemotherapy drugs at high frequency and low doses and it is gaining increasing interest to potentiate chemoimmunotherapy responses however highfrequency lowdose chemotherapy administration in the clinic via intravenous infusion is pragmatically undoable and economically not feasible athome administration via oral ingestion or subcutaneous selfinjection is impossible because of the side effect spectrum of chemotherapeutic drugs in the prime project we aim to establish prodrug technology for immunotherapypriming via patientfriendly athome metronomic dosing prodrugs have assisted in improving drug performance for over a century now and they are widely employed in pharmaceutical industry and clinical practice with approximately 10 of new drug approvals technically being prodrugs we will set out to establish a synthetic and formulation strategy to produce a novel immunogenic prodrug platform and upon subcutaneous metronomic dosing we will evaluate the preclinical performance of our prodrugs as monotherapy and in combination with immunotherapy in breast and prostate cancer mouse models we anticipate that exploiting the technological and socioeconomic potential of prime will unlock new avenues towards athome cancer treatment opportunities with enhanced therapeutic outcomes and improved patient qualityoflife
perception in action neural dynamics of sensorimotor interactions in touch

selftouch is perceived as less intense than physically identical touch from an external source current computational theories propose that the brain predicts and dampens the sensory consequences of actions a phenomenon referred to as sensory attenuation when prediction mechanisms fail it can have powerful consequences for perception from mild perceptual disturbances to more extreme hallucinations and delusions that are characteristic of psychosisspectrum disorders such as schizophrenia predicting touch is likely supported by specific patterns of brain oscillations subserving brain connectivity yet little is known about these mechanisms selftouch will use cuttingedge methods from magnetoencephalography to study oscillatory dynamics during the prediction generation and attenuation of selftouch the objectives are to i reveal how the brain dynamically predicts touch during the timecourse of actions ii to study how the brain treats violations of sensory predictions and iii to examine these predictive mechanisms in individuals with high schizotypy understanding the temporal dynamics of sensory prediction could reveal how perceptual disturbances manifest in psychosis and may aid development of biomarkers for early intervention of schizophrenia the project will result in a synergy between the applicants highly interdisciplinary profile and the high quality of the host institution it will be completed at a worldleading researchled institution karolinska institutet department of neuroscience within a team led by dr konstantina kilteni providing theoretical knowledge and practical expertise in sensorimotor neuroscience and sensory attenuation the applicant brings highly complementary expertise in cognitive neuroscience psychophysics and neural timeseries analyses the proposed work will expand the applicants experience research competencies and professional networks enhancing the development of his career as an independent researcher
dynamic peptidephosphatebased hydrogels for modelling diseases and therapeutic interventions

supramolecular biomaterials represent a promising solution to address complex challenges in advanced therapies and disease modelling platforms dphosmates overarching aim is to explore different formulation strategies and molecular designs based on enzymes natural phosphatecontaining units and peptide amphiphile building blocks to generate selfassembling hydrogels with timedependent mechanical and degradation properties as vehicles for the delivery of living therapeutics generated in situ and in vitro extracellular matrix models along disease progression these biocompatible hydrogels will exhibit enzymeinstructed biomechanical and chemical tuneability which represent versatile platforms that will be leveraged for two applications first for the postoperative treatment of glioblastoma multiforme the most aggressive malignant brain tumour and second for understating the molecular mechanisms responsible for ectopic calcifications the mineralisation of the extracellular matrix of soft tissues like kidneys joints heart valves and arteries among othersoutcomes from the project will benefit several communities including peptide and supramolecular chemistry biomaterials science bioengineering cancer and cardiovascular medicine among others all of this while providing advanced training and career development opportunities for a young researcher at the intersection of advanced biomaterials design peptide nanotechnology and precision medicine molecular tools provided by dphosmate will increase available cancer therapeutic choices and will contribute to improving the translation of preclinical research enhancing success rates of clinical trials with a consequent multibilliondollar impact and the life quality improvement of patients in need of innovative therapies
effects of rewilding in forests and agricultural lands on carbon sequestration and diversity

reaching net zero greenhouse gas ghg emissions by 2050 is key to limit global warming to 15 c and achieve the targets set out in the paris climate agreement mitigation approaches such as renewable energy sources improved energy efficiency and forest preservation need to be combined with active carbon dioxide co2 removal cdr lowcost naturebased solutions need to be identified assessed and promoted on a large scale for both co2 sequestration and biodiversity conservation wildcard will for the first time in europe assess the overall potential impact of natural rewilding of abandoned agricultural land and proforestation on carbon sequestration and biodiversity at multiple spatial and temporal scales combining field observations remote sensing and vegetation modelling with economic societal and political analyses wildcard will inform national and european policy makers on the contribution potential of naturebased solutions to achieve net zero emissions by 2050 the project will investigate the regulatory cultural and economic barriers to natural rewilding and proforestation and identify which social innovation mechanisms models and incentives can better support our cdr approach wildcard will use a dedicated crossscale analysis linking sitebased indepth knowledge on rewilding impacts and socioeconomic consequences to a europeanscale assessment embedded in the current eu policy context and informed by global scenarios from iams and esms the final projects aim is to offer concrete and realistic policy options aimed at enhanced uptake of rewilding as a significant solution to achieve global climate objectives
development of ultrathick laser ablation for ultrathick electrode processing

the proposal aims to improve the electrochemical power performance of nmc based generation 3b batteries through the development of ultrathick electrodes this will be done through the following objectives1 increased power performance in ultrathickfilm electrodes through the development of 3d electrode architectures2 correlation of lithiumion diffusion characteristics with 3d electrode architectures and related electrochemical performances through simple modeling via penetration depth model pdm3 establishment of nuclear magnetic resonance nmr techniques and laserinduced breakdown spectroscopy libs as complementary methods for identification of lithiumdiffusion pathways in 3d electrodes4 realization of optimized 3d electrode architectures for anodes and cathodes prepared with environmentally friendly waterbased slurriesthis will be done through a multidiscipline approach that involves the following techniques and principles laser based ablation ablated materials recycling water based formulation nuclear magnetic resonance imaging and penetration depth model laser ablation will be used to fabricate 3d microstructures in ultrathick nmc nickelmanganesecobalt oxide based electrodes to increase the electrode porosity and help attain optimum cell power density of upto 209 whkg at 2c cycling the optimization of the 3d ablation patterns will be done through the correlating the electrodes power performance with its diffusion coefficient determined via nmr imaging electrode parameters and effective porosity via the pdm water based slurry formulation and ablated materials recycling will be done in conjunction to decrease production cost and allow laser ablation processing for electrodes closer to demonstration pilot level trl 56 upon success of the project nmc based generation 3b batteries can be realized for electric vehicle applications and we could reach on of the goals stated in horizon 2021s work program
deep cognition learning for language generation

in recent years transformerbased deep learning models such as bert or gpt3 have led to impressive results in many natural language processing nlp tasks exhibiting transfer and fewshot learning capabilitieshowever despite faring well in benchmarks current deep learning models for nlp often fail badly in the wild they are bad at outofdomain generalization they do not exploit contextual information they are poorly calibrated and their memory is not traceable these limitations stem from their monolithic architectures which are good for perception but unsuitable for tasks requiring higherlevel cognition in this project i attack these fundamental problems by bringing together tools and ideas from machine learning sparse modeling information theory and cognitive science in an interdisciplinary approach first i will use uncertainty and quality estimates for utilityguided controlled generation combining this control mechanism with the efficient encoding of contextual information and integration of multiple modalities second i will develop sparse and structured memory models together with attention descriptive representations towards conscious processing third i will build mathematical models for sparse communication reconciling discrete and continuous domains supporting endtoend differentiability and enabling a shared workspace where multiple modules and agents can communicate i will apply the innovations above to highly challenging language generation tasks including machine translation open dialogue and story generation to reinforce interdisciplinarity and maximize technological impact collaborations are planned with cognitive scientists and with a scaleup company in the crowdsourcing translation industry
coproduced transformative knowledge to accelerate change for biodiversity

food and biomass production systems are among the most prominent drivers of biodiversity loss worldwide halting and reversing the loss of biodiversity therefore requires transformative change of food and biomass systems addressing the nexus of agricultural production processing and transport retailing consumer preferences and diets as well as investment climate action and ecosystem conservation and restoration the rainforest project will contribute to enabling upscaling and accelerating transformative change to reduce biodiversity impacts of major food and biomass value chains together with stakeholders we will codevelop and evaluate just and viable transformative change pathways and interventions we will identify stakeholder preferences for a range of policy and technologybased solutions as well as governance enablers for more sustainable food and biomass value chains we will then evaluate these pathways and solutions using a novel combination of integrated assessment modeling inputoutput modeling and life cycle assessment based on case studies in various stages of the nexus at different spatial scales and organizational levels this coproduction approach enables the identification and evaluation of just and viable transformative change leverage points levers and their impacts for conserving biodiversity sdgs 12 1415 that minimize tradeoffs with targets related to climate sdg13 and socioeconomic developments sdgs 13 we will elucidate leverage points impacts and obstacles for transformative change and provide concrete and actionable recommendations for transformative change for consumers producers investors and policymakers
biophotonic nanoparticleenabled laser blood test for early detection of pancreatic cancer

pancreatic ductal adenocarcinoma pdac is a deadly form of cancer that is on the rise due to various factors including an aging population and unhealthy lifestyles regrettably pdac is among the top cancer killers with a dismal fiveyear survival rate of less than 10 the lack of adequate screening programs is a significant factor contributing to this dire statistic however the identification of pdac in its early stages could help reduce the mortality rate by as much as 80 the project laserblood biophotonic nanoparticleenabled laser blood test for early detection of pancreatic cancer aims to develop an in vitro diagnostic test based on the fluorescence lifetime fingerprint of the personalized protein corona offering critical information at every stage of pdac progression the protein corona pc is a coating of biomolecular substances surrounding nanoparticles when exposed to biofluids it is both personalized and diseasespecific making it an ideal marker to monitor the variation of nanoparticle pc and correlate it to the development of pdac the analysis will use fluorescence lifetime fl analysis a noninvasive reactantfree and realtime technique in the initial phase the consortium will utilize a mouse model mkc to identify the fl fingerprint of protein corona at each stage of pdac development the mkc mouse model is genetically engineered to be bioluminescent and develop pdac in a controlled manner by linking the development of pdac observed through bioluminescent imaging to the fl response of pc in blood samples the consortium will provide an unprecedented fingerprint of the diseases progression from its first occurrence at a second stage the project will validate on humans the use of the fl fingerprint of protein corona as a tool for the early diagnosis of pdac the findings will provide the scientific and technological foundation for the development of an in vitro pdac test for large scale screening of the population
industrial selective plating for solar heterojunction

despite the boom in the photovoltaics industry there are still barriers to solar cell deployment costly and cumbersome manufacturing processes emitting high levels of ghg are a major hindrance a key step in cell manufacture is metallisation representing over 30 of the cost of manufacture currently heterojunction hjt cell metallisation utilises silver paste which has major disadvantages in terms of cost efficiency and environmental impact alternative metals such as copper can overcome these challenges however efficient process technology has not yet been brought to market the isplash project will cause a paradigm shift in hjt cell metallisation our technology will be the only processing technology on the market to cost effectively exploit the low price of copper and facilitate the reliable and precise fine line deposition of copper onto hjt cells completely eliminating the use of silver isplash technology will reduce metallisation costs by 90 and carbon emissions
explainable ai for molecules aichemist

optimising biological activity and physicochemical properties while minimising their toxicity are objectives when developing new compounds in chemical industries advanced machine learning ai methods are indispensable to this process they are also increasingly used in environmental chemistry to identify compounds damaging to the environment and humans traditional machine learning ml methods provide reliable predictions though only for compounds similar to the training set thus defining their applicability domain ad emerging representation learning approaches can efficiently approximate the physical interactions of molecules with an accuracy comparable to physicsbased methods in only fractions of time models based on these representations should have much larger ad due to pretraining on large chemical sets of theoretical values here we will develop and benchmark representation learning approaches addressing their accuracy and ads using public and inhouse data for endpoints ranging from chemical reactions to toxicity while explainable ai xai methods are actively developing in the ml community there is a gap with their use in chemistry ie there is a need to translate their results to the end users chemists and regulatory bodies since the research program is tightly coupled with the target users large companies regulatory agencies and smes it provides a clear path for technology transfer from academia to industry aichemist will provide structured training to its fellows through a combination of online courses and schools strengthening european innovation capacity in the education of specialists in ai methods the fellows will receive comprehensive training in transferable skills the complementary expertise and strong commitment of the partners make this ambitious innovative research program realistic via the proper allocation of individual tasks and resources as described below