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)

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. 

Ce 10 mai 2025, la Vice-Rectrice Carine Michiels et la professeure Anne-Catherine Heuskin ont remis le chèque de l’UNamur lors de la grande soirée de clôture de l’opération Télévie qui a permis cette année de récolter un chiffre record de 13 351 977 € au profit du Fonds National de la Recherche Scientifique. Les fonds Télévie sont intégralement destinés au financement de projets de recherche contre le cancer dans les universités en Fédération Wallonie-Bruxelles et au Grand-Duché de Luxembourg.  

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

Our guest lecturer will be Xavier Coumoul, Professor of Toxicology and Biochemistry at Université Paris Cité.Programme09:00 - 09:30 Welcome coffee09:30 - 10:30  LECTURE | Science on the Edge: The Perils and Ethics of Research Integrity10:45 - 12:15   WORKSHOP | Science Under Scrutiny: The Dos and Don’ts of Ethical ResearchThis workshop explores the fundamental principles of scientific integrity and research ethics, addressing key challenges and best practices to ensure ethical research conduct. The Key Topics that may be covered:Defining Ethics & Integrity – Understanding their role in scientific research. Equality vs. Equity – Addressing fairness in academia. The "Publish or Perish" Culture – How pressure influences research misconduct. The Business of Scientific Publishing – Predatory journals and conferences. Scientific Fraud: The Big Three (FFP) – Fabrication, Falsification, Plagiarism and their impact. Detecting Misconduct – Case studies on data manipulation and image forensics (e.g., Elisabeth Bik, PubPeer). Consequences of Fraud – How misconduct affects careers, credibility, and public trust. Fostering Ethical Research – Tools and best practices to uphold integrity. With an interactive approach, the session encourages participants to critically analyze scientific integrity and develop strategies to maintain ethical standards in their work.Participation is free, but registration is mandatory. Registration deadline : April 15, 2025.PhD students will receive certificates of attendance to validate doctoral training credits. Register here