Résultats de recherche

Abstract Radiotherapy is a cornerstone of cancer treatment and is currently administered to approximately half of all cancer patients. However, the cytotoxic effects of ionizing radiation on normal tissues represent a major limitation, as they restrict the dose that can be safely delivered to patients and, consequently, reduce the likelihood of effective tumor control. In this context, delivering radiation at ultra-high dose rates (UHDR, > 40 Gy/s) is gaining increasing attention due to its potential to spare healthy tissues surrounding the tumor and to prevent radiation-induced side effects, as compared to conventional dose rates (CONV, on the order of Gy/min).The mechanism underlying this protective effect—termed the FLASH effect—remains elusive, driving intensive research to elucidate the biological processes triggered by this type of irradiation.In vitro models offer a valuable tool to support this research, allowing for the efficient screening of various beam parameters and biological responses in a time- and cost-effective manner. In this study, multicellular tumor spheroids and normal cells were exposed to proton irradiation at UHDR to evaluate its effectiveness in controlling tumor growth and its cytotoxic impact on healthy tissues, respectively.We report that UHDR and CONV irradiation induced a comparable growth delay in 3D tumor spheroids, suggesting similar efficacy in tumor control. In normal cells, both dose rates induced similar levels of senescence; however, UHDR irradiation led to lower apoptosis induction at clinically relevant doses and early time points post-irradiation.Taken together, these findings further highlight the potential of UHDR irradiation to modulate the response of normal tissues while maintaining comparable tumor control.JuryProf. Thomas BALLIGAND (UNamur), PrésidentProf. Stéphane LUCAS (UNamur), SecrétaireProf. Carine MICHIELS (UNamur)Dr Sébastien PENNINCKX (Hôpital Universitaire de Bruxelles)Prof. Cristian FERNANDEZ (Université de Bern)Dr Rudi LABARBE (IBA)

La deadline d'inscription et de soumission pour les abstracts : 20 août 2025.  Plus d'infos sur le site internet de l'Institut NARILIS

Abstract Detecting semantic code clones in logic programs is a longstanding challenge, due to the lack of a unified definition of semantic similarity and the diversity of syntactic expressions that can represent similar behaviours. This thesis introduces a formal and flexible framework for semantic clone detection based on Constrained Horn Clauses (CHC). The approach considers two predicates as semantic clones if they can be independently transformed, via semantics-preserving program transformations, into a common third predicate. At the core of the method lies anti-unification, a process that computes the most specific generalisation of two predicates by identifying their shared structural patterns. The framework is parametric in regard with the allowed program transformations, the notion of generality, and the so-called quality estimators that steer the anti-unification process. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Katrien Beuls - University of Namur, BelgiumProf. Jean-Marie Jacquet - University of Namur, BelgiumProf. Temur Kutsia - Johannes Kepler University, AustriaProf. Frédéric Mesnard - University of the Reunion, Reunion IslandProf. Paul Van Eecke - Free University of Brussels, Belgium

Le carcinome hépatocellulaire est le cancer primitif du foie le plus fréquent. Malheureusement, cette tumeur présente toujours un haut taux de mortalité en raison de l’absence de traitements efficaces contre ses formes les plus avancées ou mal localisées. Dans le cadre d’un partenariat avec le CHU UCL Namur - site de Godinne et avec le soutien de l’entreprise Roche Belgique, les chercheurs et les chercheuses du Département des sciences biomédicales de la Faculté de médecine tentent de comprendre pourquoi les cellules tumorales du foie sont si résistantes aux traitements et d’identifier des alternatives thérapeutiques pour mieux les cibler. 

Abstract In recent decades, the volume of data generated worldwide has grown exponentially, significantly accelerating advancements in machine learning. This explosion of data has led to an increased need for effective data exploration techniques, giving rise to a specialized field known as dimensionality reduction. Dimensionality reduction methods are used to transform high-dimensional data into a low-dimensional space (typically 2D or 3D), so that it can be easily visualized and understood by humans. Algorithms such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE) have become essential tools for visualizing complex datasets. These techniques play a critical role in exploratory data analysis and in interpreting complex models like Convolutional Neural Networks (CNNs). Despite their widespread adoption, dimensionality reduction techniques, particularly non-linear ones, often lack interpretability. This opacity makes it difficult for users to understand the meaning of the visualizations or the rationale behind specific low-dimensional representations. In contrast, the field of supervised machine learning has seen significant progress in explainable AI (XAI), which aims to clarify model decisions, especially in high-stakes scenarios. While many post-hoc explanation tools have been developed to interpret the outputs of supervised models, there is still a notable gap in methods for explaining the results of dimensionality reduction techniques. This research investigates how post-hoc explanation techniques can be integrated into dimensionality reduction algorithms to improve user understanding of the resulting visualizations. Specifically, it explores how interpretability methods originally developed for supervised learning can be adapted to explain the behavior of non-linear dimensionality reduction algorithms. Additionally, this work examines whether the integration of post-hoc explanations can enhance the overall effectiveness of data exploration. As these tools are intended for end-users, we also design and evaluate an interactive system that incorporates explanatory mechanisms. We argue that combining interpretability with interactivity significantly improves users' understanding of embeddings produced by non-linear dimensionality reduction techniques. In this research, we propose enhancements to an existing post-hoc explanation method that adapts LIME for t-SNE. We introduce a globally-local framework for fast and scalable explanations of t-SNE embeddings. Furthermore, we present a completely new approach that adapts saliency map-based explanations to locally interpret non-linear dimensionality reduction results. Lastly, we introduce our interactive tool, Insight-SNE, which integrates our gradient-based explanation method and enables users to explore low-dimensional embeddings through direct interaction with the explanations. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Bruno Dumas - University of Namur, BelgiumProf. John Lee - University of Louvain, BelgiumProf. Luis Galarraga - University of Rennes, France

Troisième et dernier focus de l’été sur le centre de recherche NaDI-CeRCLe, qui s’est démarqué à l’international ces dernières semaines grâce aux reconnaissances obtenues par trois jeunes chercheurs en management des services. Après Floriane Goosse et Victor Sluÿters, nous vous proposons de découvrir le travail de Florence Nizette, jeune chercheuse travaillant sur les technologies d’Intelligence artificielle.

Souvent détecté trop tard, le cancer du pancréas est l’un des plus agressifs, avec moins de 10 % de survie à cinq ans. À l’Université de Namur, une équipe de chercheurs s’attaque à cette pathologie en étudiant les premiers changements cellulaires liés à la maladie. Objectifs : ouvrir la voie à un dépistage précoce et à de nouvelles pistes thérapeutiques.

Des chercheuses et chercheurs des universités de Namur (UNamur), Bruxelles (ULB) et Liège (ULiège) viennent de franchir une étape clé dans la compréhension des mécanismes viraux. Leur étude, publiée dans la revue scientifique internationale PLOS Pathogens, s’intéresse à un type particulier de molécules produites par les virus, les ARN circulaires, et présente un outil bio-informatique innovant capable de mieux les identifier. 

Programme16:00-18:00 Focus sur sept projets de recherche cliniqueDécouvrez sept projets innovants portés par des chercheurs namurois :Alexandre Goussens | Biofabrication de greffons œsophagiens biocompatiblesMorgane Canonne | Thérapie innovante pour la leucémie myéloïde aiguëMélanie Lefebvre | IA et diabète : vers une autonomie accrue des patients diabétiquesEmma Calluy | Exploration des biomarqueurs sanguins liés à la démenceJonathan Douxfils | Le test nAPCsr : prévenir les risques de thrombose associés aux contraceptifs orauxDelphine Bourmorck | Le rôle des services d’urgence dans les soins palliatifs aux patients âgésEric Mormont | Suicide chez les patients atteints de démence: une revue systématique18:00-20:00 Session posters et Walking dinnerUn moment convivial d’échanges autour des posters et d’un walking dinner. L'événement se clôturera à 20h par l'annonce des meilleurs présentations poster.Appel à postersTous les (cliniciens)-chercheurs actifs dans la recherche clinique sont invités à soumettre un abstract pour une présentation sous forme de poster. Des prix seront décernés aux meilleures présentations!InscriptionsLes inscriptions, avec ou sans soumission d'abstract, sont ouvertes jusqu'au 26 septembre via un formulaire unique.Des attestations de présence seront délivrées à tous les participants et permettront aux doctorants de faire valider des crédits de formation doctorale.Une accréditation INAMI est demandée pour les médecins.Comité organisateurProf. Marie de Saint-Hubert, CHU UCL Namur, Service de gériatrieProf. Charlotte Beaudart, UNamur, Département des sciences biomédicales, URPC, Public Health Aging Research & Epidemiology (PHARE) Group  Je m'inscris

Abstract Primarily described as an alarmone, secondary messenger (p)ppGpp, when accumulated, binds to many targets involved in DNA replication, translation, and transcription. In the asymmetrically-dividing a-proteobacterium Caulobacter crescentus, (p)ppGpp has been shown to strongly impact cell cycle progression and differentiation, promoting the non-replicating G1/swarmer phase. Mutations in the major subunits of transcriptional complex, b or b’ subunits, were able to display the (p)ppGpp-related phenotypes even in the absence of the alarmone. We identified that the transcriptional holo-enzyme, RNA polymerase (RNAP) is a primary target of (p)ppGpp in C. crescentus. Furthermore, mutations that inactivate (p)ppGpp binding to RNAP annihilated the (p)ppGpp-related phenotypes and phenocopied a (p)ppGpp0 strain. Our RNAseq analysis further elucidated the changes in the transcriptional landscape of C. crescentus cells displaying different (p)ppGpp levels and expressing RNAP mutants. Since the DNA replication initiation protein DnaA is required to exit the G1 phase, we observed that it was significantly less abundant in cells accumulating (p)ppGpp. We further explored its proteolysis under the influence of (p)ppGpp. Our work suggests that (p)ppGpp regulates cell cycle and differentiation in C. crescentus by reprogramming transcription and triggering proteolytic degradation of key cell cycle regulators by yet unknown mechanisms. In Part II, we identified two σ factors belonging to the ECF family that might be involved in this (p)ppGpp-accompanied phenotypes. In Part III, we propose an overlapping role of the ω subunit, RpoZ, and the heat shock subunit, RpoH, in carbon metabolism.JuryProf. Gipsi LIMA MENDEZ (UNamur), PresidentProf Régis HALLEZ (UNamur), SecretaryDr Emanuele BIONDI (CNRS-Université Paris-Saclay)Prof. Justine COLLIER (University of Lausanne)Dr Marie DELABY (Université de Montréal)

Abstract Estrogens originating from human and animal excretion, as well as from anthropogenic sources such as cosmetics, plastics, pesticides, detergents, and pharmaceuticals, are among the most concerning endocrine-disrupting compounds in aquatic environments due to their potent estrogenic activity. While their effects on fish reproduction are well documented, their impact on development, particularly metamorphosis, remains poorly studied. This hormonal transition, mainly controlled by the thyroid axis, is essential for the shift from the larval to the juvenile stage in teleosts.The effects of two contraceptive estrogens on zebrafish (Danio rerio) metamorphosis were evaluated: 17α-ethinylestradiol (EE2), a synthetic reference estrogen, and estetrol (E4), a natural estrogen recently introduced in a new combined oral contraceptive formulation. Continuous exposure from fertilization to the end of metamorphosis allowed the assessment of morphological changes, disruptions of the thyroid axis, and modifications of additional molecular pathways potentially involved in metamorphic regulation.EE2 induced significant delays and disturbances in metamorphosis, affecting both internal and external morphological traits, confirming its role as an endocrine disruptor of concern. In contrast, E4 did not cause any detectable morphological alterations even at concentrations far exceeding those expected in the environment, indicating a limited ecotoxicological risk. Molecular analyses showed that EE2 strongly affected thyroid signaling and energy metabolism during metamorphosis, whereas E4 induced only minor transcriptional and proteomic changes.This study provides the first evidence that EE2 can disrupt zebrafish metamorphosis and highlights the importance of including this developmental stage in ecotoxicological assessments. The results also suggest a larger environmental safety margin for E4, although further research is needed to clarify the mechanisms linking estrogen exposure to metamorphic regulation.JuryProf. Frederik DE LAENDER (UNamur), PrésidentProf. Patrick KESTEMONT (UNamur), SecrétaireDr Sébastien BAEKELANDT (UNamur)Dr Valérie CORNET (UNamur)Prof. Jean-Baptiste FINI (Muséum National d’Histoire Naturelle de Paris)Dr Marc MULLER (ULiège)Prof. Veerle DARRAS (KULeuven) 

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin’ichi Satoh - National Institute of Informatics, Japon