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Well hidden from passersby, caves nevertheless conceal particularly aesthetic secrets. For the past four years, Martin Vlieghe has been pursuing a PhD in geology at UNamur.  He is exploring the origin of the surprisingly varied colours of certain concretions nestled in the heart of Belgian and French caves. Together with Prof. Johan Yans and Gaëtan Rochez, he samples, observes, and analyses these magnificent objects with the aim of uncovering the mysteries they conceal.

In the coming months, the University of Namur will participate in an exceptional space mission aboard the International Space Station (ISS), alongside Belgian astronaut Raphaël Liégeois. The BeBlob project, led by researchers Boris Hespeels (ILEE Institute) and Anne-Catherine Heuskin (NARILIS Institute), aims to study the DNA repair capabilities of a fascinating organism: the blob (Physarum polycephalum).

Alison Forrester is a F.R.S.-FNRS Qualified Researcher (CQ). Her research focuses on studying compounds that can modify the efficiency of protein production processes within our cells, and thus open up new therapeutic avenues. Together with a group of top international researchers, she has published a road map article in the prestigious journal Nature Reviews Molecular Cell Biology.

Abstract Since its emergence in 1996, the Asian H5 Goose/Guangdong (Gs/Gd) lineage has circulated widely in poultry in southern China, spilling over to wild birds by 2002. Wild bird infections facilitated global dissemination via migratory waterfowl and repeated spillback into poultry, challenging the view that HPAI primarily arises from LPAI mutation. Subclade 2.3.4.4b emerged in Asia in 2013, reached Europe in 2016, caused recurrent epizootics, diversified into multiple genotypes, became dominant in wild birds, and shows zoonotic potential.This thesis investigates critical knowledge gaps regarding H5Nx subclade 2.3.4.4b in poultry: (1) early within-flock spread after punctual introduction in chickens, particularly during the first European epizootics; (2) influence of pre-existing immunity on silent circulation; (3) limitations in diagnostic throughput during epizootic peaks; (4) potential of environmental surveillance, including air and dust sampling; and (5) impact on egg contamination and the reproductive tract, relevant for food safety and zoonotic risk.Four main objectives were addressed: (1) development of an experimental model simulating punctual introductions and spread, comparing 2017 and 2020 strains and assessing pre-existing immunity; (2) enhancement of diagnostic capacity via alternative sampling, semi-automated RNA extraction, and high-throughput processing; (3) evaluation of air and dust sampling for virus monitoring under experimental and field conditions; and (4) assessment of egg contamination risk. Alternative sampling and environmental monitoring were also applied to Newcastle disease virus as a comparative notifiable pathogen. Jury Prof. Tuci Elio - University of Namur, BelgiumProf. Anthony Cleve - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Alvaro Gutierrez - Universidad Politecnica de Madrid, EspagneMr. Fabian Duchesne - Qualitics SPRLProf. Anders Lyhne Christensen - SDU, Denmark Evènement public et gratuit - Inscription obligatoire Je m'inscris

To mark International Women's Day, celebrated on March 8, we invite you to discover the portraits of seven inspiring women from the university's seven faculties. Throughout March, a series of portraits of women from the university will be on display in various areas of the campus. Conceived and produced by four UNamur students and coordinated by the University Community Life Service (VéCU), this project offers an inspiring showcase for the careers, voices, and commitments of these women who bring the institution to life on a daily basis.  

Created on the initiative of the Belgian Nuclear Research Center SCK CEN, this Chair is awarded every two years by the F.R.S.-FNRS and the FWO to recognize a leading researcher in the field of nuclear sciences and their applications. In 2025, the Chair will pay tribute to Carine Michiels' brilliant career and her outstanding contributions to radiobiology and cancer research. 

Which chatbot can we really trust? A reliable answer to this question can now be provided thanks to a unique scientific tool: the AI Score. Developed by a multidisciplinary team of researchers at the University of Namur, it provides an objective, reproducible, and transparent way of measuring the reliability of educational chatbots.

Abstract Deep learning has revolutionized computer vision in recent years and has been applied to many fields. This thesis focuses on medical endoscopy, where deep learning can assist physicians in many tasks, such as navigating the lungs during bronchoscopy, assisting in the detection of lung diseases, detecting Crohn's disease from capsule endoscopy (PillCam), or automating the detection of polyps during colonoscopy procedures.This thesis, entitled From Pixels to Practice: Deep Learning for Endoscopy, explores how modern neural networks and learning paradigms can improve visual understanding in endoscopy, with the aim of contributing to computer-aided detection (CAD) systems that can be integrated into clinical workflows.This work follows an article-based structure and links methodological advances in geometric and temporal modeling to techniques for handling data scarcity and imbalance, as well as to the practical and clinical implications of deep learning for lung tumor detection, both from a clinical and practitioner perspective. The first part of the manuscript provides a common foundation for all subsequent parts. First, we present a general introduction to the field of machine learning in Chapter 1, explaining concepts such as classification, loss functions, and artificial neural networks. Next, Chapter 2 focuses on the field of deep learning for computer vision, detailing the main vision tasks, the concept of convolutional neural networks, ResNet, and U-Net. Finally, Chapter 3 describes medical imaging, with a focus on computed tomography (CT) scans and optical imaging. The second part of the thesis focuses on learning spatio-temporal representations. In Chapter 4, we use deep neural networks combining spatial features and temporal recurrence to address the problem of detecting the bronchial carina, an anatomical landmark that helps doctors navigate the lungs. By evaluating classification (ResNet-50), segmentation (nnU-Net), and recurrent (GRU) models on a bronchoscopy dataset we created, the study highlights the benefits of combining information from segmentation masks and temporal features. Chapter 5 continues the segmentation task by analyzing the extent to which rotation-equivariant U-Nets, based on E(2)-CNNs with C4, C8, and D4 symmetry groups, can improve performance when the orientation of objects in the image is arbitrary. Together, these chapters show how temporal and geometric modeling capture complementary aspects of visual structure. They further highlight that data imbalance and scarcity are recurring problems in deep learning. The third part studies learning in situations of data scarcity and imbalance. First, Chapter 6 explores supervised contrastive pre-training [1] on large, domain-close endoscopic datasets (Hyper-Kvasir [2], LDPolyp [3]), which is then transferred to smaller, disease-specific data (Crohn-IPI [4]). This methodology performs better than pre-training on ImageNet or based on cross-entropy, highlighting the value of domain-specific contrastive representations. Next, Chapter 7 introduces Mask-Aware Cropping (MAC), a new data augmentation technique that mitigates pixel-level imbalance in segmentation. On various datasets with varying imbalance regimes (URDE [5], Kvasir-SEG [6], HAM10000 [7]), MAC consistently improves Dice and IoU metrics under conditions of extreme imbalance. Together, these methods form a data-centric framework for effective learning when annotations are scarce or unevenly distributed. The fourth part of the thesis focuses on deep learning in the operating room. Chapter 8 proposes a first model (ResNet-50) for the visual detection of lung cancer in bronchoscopy, trained on real, in-vivo data. The model outperforms junior physicians, while remaining inferior to experts. This result shows that CAD systems for lung cancer detection are promising. Chapter 9 extends this work by evaluating the usability of a CAD system based on a deep learning model. Combining probability indices, temporal graphs, and saliency map overlays, a multicenter evaluation with 10 physicians is conducted. The tool received favorable feedback, with high usability (SUS score of 80.5 [8]) and strong clinical acceptance. Beyond endoscopy, the results concerning rotation equivariance and pixel imbalance can be generalized to other fields such as microscopy, dermatology, and aerial imaging. This shows that the proposed methods are applicable to visual learning under structured variability and limited data constraints.Keywords: machine learning, computer vision, medicine, endoscopy, convolutional neural networks, segmentation, recurrent models, equivariance.  Jury Prof. Bruno Dumas - University of NamurProf. Frénay Benoit - University of NamurProf. Schobbens P-Y. - University of NamurProf. Beuls Katrien - University of Namur,Dr. Benjamin Mertens - Lys MédicalProf. Oramas Mogrojevo José Antonio - University of AntwerpDr. Mancas Matei - University of Mons Register

Are we prisoners of our genetic heritage? Can filiation be reduced to genes alone? Can we escape our destiny? Existential questions we all ask ourselves, and to which Gaëlle Pontarotti, lecturer and researcher in the Department of Sciences, Philosophies and Societies at UNamur, sheds new light in her book Par-delà les gènes. Une autre histoire de l'hérédité, published last October by Gallimard.

JuryProf. Régis HALLEZ (UNamur), PresidentProf. Jean-Yves MATROULE (UNamur), SecretaryDr. Rob VAN HOUDT (SCK CEN)Dr. Kristel MIJNENDONCKX (SCK CEN)Prof. Liselot DEWACHTER (UCLouvain)AbstractThe viable-but-nonculturable (VBNC) state is a type of bacterial dormancy triggered by sublethal stress, where cells remain intact but lose the ability to grow on standard media. This poses challenges for microbial monitoring and public health, as VBNC cells can evade detection and might regain virulence upon resuscitation. Copper compounds are explored as antimicrobial agents, however sublethal Cu concentrations were shown to induce the VBNC state in certain bacteria. This thesis investigates the Cu-induced VBNC state in Cupriavidus metallidurans, a metal-resistant betaproteobacterium, and examines the involvement of its Cu resistance determinants (CRDs). While resuscitation is usually mediated by external factors, we aimed to uncover intrinsic processes that enable spontaneous resuscitation, a rare phenotype lacking mechanistic understanding. Proteomic analysis revealed that expression of CRDs, among others, correlated with mitigated dormancy. Time-resolved profiling showed that VBNC cells exhibit highly dynamic proteomes: VBNC entry involved oxidative stress response, and resuscitation correlated with metabolic reconstitution and the strong induction of periplasmic CRDs. Temporal clustering corroborated the explored proteomic modifications. Through mutational studies we identified the plasmid-encoded copAB system as the minimal resuscitation factor, where integrity of the CopA methionine-rich domain proved critical. ICP-MS analysis indicated that detoxification relies on Cu sequestration rather than export. Altogether, this work uncovers key intrinsic factors and proposes a mechanistic basis for spontaneous resuscitation from the Cu-induced VBNC state in C. metallidurans. These insights refine our understanding of the VBNC state as a dynamic survival strategy and of bacterial Cu resistance.

JuryDr Luca FUSARO (UNamur), PresidentProf. Carmela APRILE (UNamur), SecretaryProf. Francesco GIACALONE (University of Palermo)Prof. Paolo PESCARMONA (University of Groningen)Prof. Michelangelo GRUTTADAURIA (University of Palermo)AbstractThis PhD research focused on the design, synthesis, and catalytic evaluation of novel materials based on metal phthalocyanines (MPCs) and imidazolium bromide salts. The initial materials were extensively characterized using a wide range of analytical, spectroscopic, and spectrometric techniques, including solid-state NMR, XPS, TEM, EDX, FT-IR, Raman, CHN analysis, ICP-OES, N₂ physisorption, and TGA. These systems showed remarkable performance in promoting the cycloaddition of CO₂ to epoxides to form cyclic carbonates. Building on these results, a new class of catalysts was developed by covalently anchoring metal phthalocyanines and imidazolium salts onto multi-walled carbon nanotubes (MWCNTs), yielding materials denoted as MPC@MWCNTs. This strategy enabled the creation of a versatile family of catalysts-prepared with different metal centers (Al, Mg, Fe, Ni, Co, Cu, Zn)-while maintaining a unified synthetic approach. The incorporation of MWCNTs was aimed at enhancing both catalytic activity and stability through synergistic support effects. The resulting MPC@MWCNTs were successfully applied in diverse catalytic contexts: CO₂ valorization into cyclic carbonates (Mg-, Fe-, Cu-, and Zn-based systems), nitro-reduction reactions to afford amines (Fe-based system), and electrocatalytic methanol oxidation for energy-related applications (Ni-based system). Overall, this work demonstrated the potential of MPC@MWCNT hybrid materials as robust, tunable, and multifunctional catalysts for sustainable chemical transformations. Attend the event remotely (Teams)

Alexandre Mauroy has been a professor and researcher in the Department of Mathematics for almost 10 years, working in the field of dynamical systems. He is also Director of the naXys Research Institute, which puts its expertise in complex systems at the service of UNamur researchers from all disciplines. Aware of the sometimes austere reputation of maths among the general public, Alexandre Mauroy works to demonstrate that this discipline is at the heart of today's technological and scientific challenges..