54 research outputs found

    Exploring application of AI technologies for engineering education in systematic invention and innovation

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    The increasing diffusion of rapidly developing AI technologies led to the idea of the experiment to combine TRIZ-based automated idea generation with the natural language processing tool ChatGPT, using the chatbot to interpret the automatically generated elementary solution principles. The article explores the opportunities and benefits of a novel AI-enhanced approach to teaching systematic innovation, analyses the learning experience, identifies the factors that affect students' innovation and problem-solving performance, and highlights the main difficulties students face, especially in interdisciplinary problems

    Oscillations in the expression of a self-repressed gene induced by a slow transcriptional dynamics

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    We revisit the dynamics of a gene repressed by its own protein in the case where the transcription rate does not adapt instantaneously to protein concentration but is a dynamical variable. We derive analytical criteria for the appearance of sustained oscillations and find that they require degradation mechanisms much less nonlinear than for infinitely fast regulation. Deterministic predictions are also compared with stochastic simulations of this minimal genetic oscillator

    Oscillations in the expression of a self-repressed gene induced by a slow transcriptional dynamics

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    We revisit the dynamics of a gene repressed by its own protein in the case where the transcription rate does not adapt instantaneously to protein concentration but is a dynamical variable. We derive analytical criteria for the appearance of sustained oscillations and find that they require degradation mechanisms much less nonlinear than for infinitely fast regulation. Deterministic predictions are also compared with stochastic simulations of this minimal genetic oscillator

    Energy Resolution Performance of the CMS Electromagnetic Calorimeter

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    The energy resolution performance of the CMS lead tungstate crystal electromagnetic calorimeter is presented. Measurements were made with an electron beam using a fully equipped supermodule of the calorimeter barrel. Results are given both for electrons incident on the centre of crystals and for electrons distributed uniformly over the calorimeter surface. The electron energy is reconstructed in matrices of 3 times 3 or 5 times 5 crystals centred on the crystal containing the maximum energy. Corrections for variations in the shower containment are applied in the case of uniform incidence. The resolution measured is consistent with the design goals

    Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

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    Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

    Study of microRNAs in microglial extracellular vesicles : signatures and neuroprotection

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    Dans le SystĂšme Nerveux Central (SNC), les cellules gliales influencent les activitĂ©s neuronales. Les cellules microgliales, cellules immunitaires rĂ©sidentes du SNC, contrĂŽlent grandement l’état neuroinflammatoire. Ce contrĂŽle est particuliĂšrement important dans les fonctions physiologiques et s’avĂšre souvent dĂ©fectueux dans les neuropathologies. Les cellules microgliales sont en relation avec le microenvironnement cĂ©rĂ©bral et communiquent avec les autres types cellulaires (astrocytes, oligodendrocytes et neurones) afin de contrĂŽler l’état neuroinflammatoire. Parmi les diffĂ©rents modes de communication intercellulaire au sein du SNC, les vĂ©sicules extracellulaires (VEs) interviennent largement dans les processus physiologiques (dĂ©veloppement, homĂ©ostasie
) et pathologiques (maladies neurodĂ©gĂ©nĂ©ratives
). C’est pourquoi, ce mode de communication a Ă©tĂ© Ă©tudiĂ© dans le dialogue entre la microglie et les neurones chez la sangsue Hirudo medicinalis. Cet annĂ©lide est un modĂšle intĂ©ressant de neurobiologie grĂące Ă  la structure linĂ©aire de son systĂšme nerveux et Ă  l’organisation de ses types cellulaires. Il permet l’étude du dialogue entre les cellules microgliales et les neurones au niveau d’une lĂ©sion expĂ©rimentale. Dans un premier temps, les rĂ©sultats ont montrĂ© que les cellules microgliales interagissent avec les neurones lors d’une lĂ©sion du SNC et que des VEs sont libĂ©rĂ©es au niveau de cette lĂ©sion. De plus, les cellules microgliales produisent des VEs qui interagissent avec les neurones et dĂ©livrent un effet neurotrophique in vitro sur des neurones de sangsue et de rat. Dans un deuxiĂšme temps, la complexitĂ© des composĂ©s vĂ©siculaires ainsi que des impĂ©ratifs d’efficacitĂ© liĂ©s aux mĂ©thodes d’isolement nous ont conduits Ă  dĂ©velopper l’analyse protĂ©omique non ciblĂ©e et Ă  grande Ă©chelle afin de valider les fractions positives en VEs mais aussi identifier leurs signatures protĂ©iques biologiquement actives. Dans une derniĂšre partie, nous nous sommes intĂ©ressĂ©s aux microARNs (miARNs) contenus dans les VEs microgliales. Les rĂ©sultats ont permis l’identification de 6 miARNs dans les VEs microgliales, dont un seul, miR-146a, est dĂ©crit Ă  ce jour dans le SNC chez les mammifĂšres. Dans un contexte de dialogue neuroprotecteur entre VEs microgliales et neurones, les analyses neuronales ont prĂ©dit des ARNm potentiellement rĂ©gulĂ©s par les miARNs contenus dans les VEs. Ces 6 miARNs ont Ă©galement Ă©tĂ© identifiĂ©s dans les VEs issues de microglie de souris, de rat et humaine. Dans leur ensemble, les rĂ©sultats montrent que les cellules microgliales chez la sangsue produisent des VEs, ayant un effet neurotrophique sur les neurones, y compris des neurones de rat. L’identification des molĂ©cules prĂ©sentes dans ces VEs (protĂ©ines et miARNs) a permis de soulever des perspectives sur les mĂ©canismes neuroprotecteurs supportant ce dialogue microglie-neurone qu’il sera intĂ©ressant d’examiner chez les mammifĂšres dans un contexte de lĂ©sion nerveuse.In the Central Nervous System (CNS), the glial cells influence neuronal activities. The microglial cells, resident immune cells of the CNS, greatly control the neuroinflammatory state. This control is particularly important in physiological functions and is often defective in neuropathologies. The microglial cell activities depend on the brain microenvironment and they communicate with other cell types (astrocytes, oligodendrocytes and neurons) to control the neuroinflammatory state. Among the different mechanisms of intercellular communication within the CNS, extracellular vesicles (EVs) play a major role in physiological processes (development, homeostasis, etc.) and pathological processes (neurodegenerative diseases, etc.). Therefore, this mode of communication was studied in the dialogue between microglia and neurons in the leech Hirudo medicinalis. This annelid is an interesting model of neurobiology thanks to the linear structure of its nervous system and the organization of its cell types. It allows the study of the dialogue between microglial cells and neurons at the level of an experimental lesion. At first, the results showed that microglial cells interact with neurons during CNS injury and that EVs are released at the level of this lesion. In addition, microglial cells produce EVs that interact with neurons and deliver a neurotrophic effect in vitro on leech and rat neurons. In a second step, the complexity of the vesicular compounds as well as efficiency requirements related to the isolation methods led us to develop the non-targeted proteomic analysis on a large scale in order to validate the positive EV fractions but also to identify their biologically active protein signatures. In a last part, we were interested in the microRNAs (miRNAs) contained in microglial EVs. The results allowed the identification of 6 miRNAs in microglial EVs, of which only one, miR-146a, is described to date in the mammalian CNS. In a context of neuroprotective dialogue between microglial EVs and neurons, the analysis of neuronal protein signatures predicted mRNAs potentially regulated by miRNAs contained in EVs. These 6 miRNAs were also identified in EVs derived from mouse, rat and human microglia. Overall, the results show that microglial cells in the leech produce EVs, exerting a neurotrophic effect on neurons, including rat neurons. The identification of the molecules present in these microglial EVs (proteins and miRNAs) made it possible to raise perspectives on the neuroprotective mechanisms supporting this microglia-neuron dialogue that will be interesting to examine in mammals in a context of nerve injury

    Sequence Models for Speech and Music Detection in Radio Broadcast

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    Speech and Music detection is an important meta-data extraction step for radio broadcasters. It provides them with a good time-stamping of the audio, including parts where speech and music overlap. This task has important applications in royalty collection in broadcast audio for instance, which is the use case for this particular study. The study is focused on deep neural network architectures made to process sequential data such as recurrent neural networks or convolutional architectures for sequential learning. Different architectures that have not yet been applied for this task are evaluated and compared with a state-of-the-art architecture (Bidirectional Long Short-Term Memory). Moreover, different strategies to take advantage of both low and high-quality datasets are evaluated. The study shows that Temporal Convolution Network (TCN) architectures can outperform state-of-the-art architectures, and that especially non-causal TCNs lead to a significant improvement in the accuracy. The code used for this study has been made available on GitHub.Taloch musikdetektion Àr ett viktigt steg för att extrahera metadata för radiobolag. Det ger dem en bra tidsstÀmpling av ljudet inklusive de delar dÀr tal och musik överlappar varandra. TillÀmpningen Àr viktig vid insamling av royalties för radiosÀndningar vilket Àr anvÀndningsfallet för den hÀr studien. Studien Àr inriktad pÄ djupa neurala nÀtverksarkitekturer, Deep Neural Networks (DNN), gjorda för att behandla sekventiell data som Recurrent Neural Networks (RNN) eller faltningsarkitekturer för sekventiell inlÀrning. Olika arkitekturer som Ànnu inte har tillÀmpats för denna uppgift utvÀrderas och jÀmförs med en state-of-the-art-arkitektur (Bidirectional Long Short-Term Memory). Dessutom utvÀrderas olika strategier för att utnyttja bÄde lÄgoch högkvalitativa dataset. Studien visar att arkitekturerna för Temporal Convolution Network (TCN) kan övertrÀffa state-of-the-art-arkitekturer, och att speciellt icke-kausala TCN leder till en signifikant förbÀttring av noggrannheten. Koden som anvÀnds för denna studie finns tillgÀnglig pÄ GitHub

    Sequence Models for Speech and Music Detection in Radio Broadcast

    No full text
    Speech and Music detection is an important meta-data extraction step for radio broadcasters. It provides them with a good time-stamping of the audio, including parts where speech and music overlap. This task has important applications in royalty collection in broadcast audio for instance, which is the use case for this particular study. The study is focused on deep neural network architectures made to process sequential data such as recurrent neural networks or convolutional architectures for sequential learning. Different architectures that have not yet been applied for this task are evaluated and compared with a state-of-the-art architecture (Bidirectional Long Short-Term Memory). Moreover, different strategies to take advantage of both low and high-quality datasets are evaluated. The study shows that Temporal Convolution Network (TCN) architectures can outperform state-of-the-art architectures, and that especially non-causal TCNs lead to a significant improvement in the accuracy. The code used for this study has been made available on GitHub.Taloch musikdetektion Àr ett viktigt steg för att extrahera metadata för radiobolag. Det ger dem en bra tidsstÀmpling av ljudet inklusive de delar dÀr tal och musik överlappar varandra. TillÀmpningen Àr viktig vid insamling av royalties för radiosÀndningar vilket Àr anvÀndningsfallet för den hÀr studien. Studien Àr inriktad pÄ djupa neurala nÀtverksarkitekturer, Deep Neural Networks (DNN), gjorda för att behandla sekventiell data som Recurrent Neural Networks (RNN) eller faltningsarkitekturer för sekventiell inlÀrning. Olika arkitekturer som Ànnu inte har tillÀmpats för denna uppgift utvÀrderas och jÀmförs med en state-of-the-art-arkitektur (Bidirectional Long Short-Term Memory). Dessutom utvÀrderas olika strategier för att utnyttja bÄde lÄgoch högkvalitativa dataset. Studien visar att arkitekturerna för Temporal Convolution Network (TCN) kan övertrÀffa state-of-the-art-arkitekturer, och att speciellt icke-kausala TCN leder till en signifikant förbÀttring av noggrannheten. Koden som anvÀnds för denna studie finns tillgÀnglig pÄ GitHub

    Sequence Models for Speech and Music Detection in Radio Broadcast

    No full text
    Speech and Music detection is an important meta-data extraction step for radio broadcasters. It provides them with a good time-stamping of the audio, including parts where speech and music overlap. This task has important applications in royalty collection in broadcast audio for instance, which is the use case for this particular study. The study is focused on deep neural network architectures made to process sequential data such as recurrent neural networks or convolutional architectures for sequential learning. Different architectures that have not yet been applied for this task are evaluated and compared with a state-of-the-art architecture (Bidirectional Long Short-Term Memory). Moreover, different strategies to take advantage of both low and high-quality datasets are evaluated. The study shows that Temporal Convolution Network (TCN) architectures can outperform state-of-the-art architectures, and that especially non-causal TCNs lead to a significant improvement in the accuracy. The code used for this study has been made available on GitHub.Taloch musikdetektion Àr ett viktigt steg för att extrahera metadata för radiobolag. Det ger dem en bra tidsstÀmpling av ljudet inklusive de delar dÀr tal och musik överlappar varandra. TillÀmpningen Àr viktig vid insamling av royalties för radiosÀndningar vilket Àr anvÀndningsfallet för den hÀr studien. Studien Àr inriktad pÄ djupa neurala nÀtverksarkitekturer, Deep Neural Networks (DNN), gjorda för att behandla sekventiell data som Recurrent Neural Networks (RNN) eller faltningsarkitekturer för sekventiell inlÀrning. Olika arkitekturer som Ànnu inte har tillÀmpats för denna uppgift utvÀrderas och jÀmförs med en state-of-the-art-arkitektur (Bidirectional Long Short-Term Memory). Dessutom utvÀrderas olika strategier för att utnyttja bÄde lÄgoch högkvalitativa dataset. Studien visar att arkitekturerna för Temporal Convolution Network (TCN) kan övertrÀffa state-of-the-art-arkitekturer, och att speciellt icke-kausala TCN leder till en signifikant förbÀttring av noggrannheten. Koden som anvÀnds för denna studie finns tillgÀnglig pÄ GitHub

    Analyse de l’impact des fusions-acquisitions sur les performances boursiĂšres et Ă©conomiques des sociĂ©tĂ©s acquĂ©reuses belges

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    Les fusions-acquisitions sont des opĂ©rations de regroupement externe ayant une grande implication dans le monde actuel de la finance. Ces derniĂšres annĂ©es, une hausse de ce type d’opĂ©ration a Ă©tĂ© constatĂ©e. Diverses Ă©tudes ont Ă©tĂ© rĂ©alisĂ©es Ă  ce sujet Ă  travers le monde. Or les opinions sur les performances des entreprises Ă  la suite d’une fusion-acquisition diffĂšrent fortement entre les auteurs. De plus, la littĂ©rature relative aux performances des fusions-acquisitions des entitĂ©s belges est rare. Tentant de rĂ©pondre Ă  ce gap dans la littĂ©rature, ce travail a pour objectif d’étudier les performances boursiĂšres et Ă©conomiques de sociĂ©tĂ©s cotĂ©es belges Ă  la suite d’une fusion-acquisition. Notre Ă©tude se divise en deux grandes parties. La premiĂšre se focalise sur les performances boursiĂšres des fusions-acquisitions. Dans cette partie, l’impact de l’annonce d’un regroupement externe sur le marchĂ© boursier est analysĂ© Ă  court terme et Ă  long terme, en Ă©tudiant les rendements anormaux. La seconde partie s’intĂ©resse quant Ă  elle aux performances Ă©conomiques de sociĂ©tĂ©s acquĂ©reuses belges qui ont clĂŽturĂ© une fusion-acquisition. Ces performances sont Ă©tudiĂ©es Ă  travers divers indicateurs financiers. L’objectif est de comparer les indicateurs financiers entre la pĂ©riode prĂ©-opĂ©ration et la pĂ©riode post-opĂ©ration et de comparer les performances obtenues avec celles d’un groupe de contrĂŽle n’ayant pas rĂ©alisĂ© d’opĂ©ration de regroupement. L’étude empirique rĂ©alisĂ©e se base sur un Ă©chantillon de 40 entitĂ©s belges cotĂ©es. Au niveau de l’analyse boursiĂšre, les rĂ©sultats obtenus montrent des performances diffĂ©rentes en fonction de l’horizon temporel. En effet, une hausse du cours boursier est constatĂ©e Ă  court terme lors de l’annonce du regroupement externe alors qu’en revanche, une baisse des performances boursiĂšres est observĂ©e sur le long terme. Quant aux performances Ă©conomiques, les rĂ©sultats des sociĂ©tĂ©s acquĂ©reuses belges, ajustĂ©s au groupe de contrĂŽle, tendent Ă  indiquer une baisse des performances entre les pĂ©riodes prĂ©-opĂ©ration et post-opĂ©ration.Master [120] en sciences de gestion, UniversitĂ© catholique de Louvain, 201
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