Mast cells act as pro-angiogenic and pro-tumorigenic players in pituitary gonadotroph tumors

International audienceAbstract Background The tumor microenvironment (TME) represents a promising avenue to understand gonadotroph tumors and develop therapeutic tools. Here, we aimed to gain insight into the tumorigenesis mechanisms driven by the gonadotoph TME. Methods Single-cell and spatial-omics were combined with histological analysis. Mice engrafted with tumor cells were used for functional validation. Results using single-cell and spatial transcriptomic data from gonadotroph tumors and normal tissues, we identified mast cells in the microenvironment of gonadotroph tumors and confirmed their physical and functional interaction with endothelial cells. Quantification of mast cells in 40 patients suggested their pro-tumoral role as tumors relapsing after surgery harbored more mast cells. More interestingly, the distribution of mast cells was associated with the presence of a higher number of blood vessels, with an increased microvessel density (MVD), and with blood vessels with thicker walls. Ligand-receptor network analysis highlighted VEGFA as a modulator of mast/endothelial cell communication, a result confirmed by the identification of intratumoral mast cells expressing VEGFA in mouse and human gonadotroph tumors. Finally, using mice engrafted with gonadotroph tumor cells, we demonstrated that the depletion of mast cells reduces tumor volume through increased apoptosis. These observations were associated with increased hemorrhagic areas and a significant reduction of the number of blood vessels and MVD as evidenced in human gonadotroph tumors. Conclusion we demonstrate that mast cells represent a new actor of the gonadotroph TME, and highlight their pro-angiogenic and pro-tumorigenic roles as potential targets for the therapeutic treatment of gonadotroph tumors

MILo: Mesh-In-the-Loop Gaussian Splatting for Detailed and Efficient Surface Reconstruction

International audienceWhile recent advances in Gaussian Splatting have enabled fast reconstruction of high-quality 3D scenes from images, extracting accurate surface meshes remains a challenge. Current approaches extract the surface through costly post-processing steps, resulting in the loss of fine geometric details or requiring significant time and leading to very dense meshes with millions of vertices. More fundamentally, the a posteriori conversion from a volumetric to a surface representation limits the ability of the final mesh to preserve all geometric structures captured during training.We present MILo, a novel Gaussian Splatting framework that bridges the gap between volumetric and surface representations by differentiably extracting a mesh from the 3D Gaussians. We design a fully differentiable procedure that constructs the mesh-including both vertex locations and connectivity-at every iteration directly from the parameters of the Gaussians, which are the only quantities optimized during training.Our method introduces three key technical contributions: (1) a bidirectional consistency framework ensuring both representations-Gaussians and the extracted mesh-capture the same underlying geometry during training; (2) an adaptive mesh extraction process performed at each training iteration, which uses Gaussians as differentiable pivots for Delaunay triangulation; (3) a novel method for computing signed distance values from the 3D Gaussians that enables precise surface extraction while avoiding geometric erosion. Our approach can reconstruct complete scenes, including backgrounds, with state-of-the-art quality while requiring an order of magnitude fewer mesh vertices than previous methods. Due to their light weight and empty interior, our meshes are well suited for downstream applications such as physics simulations and animation.The code for our approach and an online gallery are available at https://anttwo.github.io/milo/

Predicting Non-Response to Cystic Fibrosis Modulators from Microbiota Using Standard and Custom Machine Learning

National audienceAbstract Background: Predictive biomarkers for non-response to modulators in cystic fibrosis are needed. The microbiota is a promising candidate but challenging for standard machine learning. Results: Random Forests, Lasso, FLORAL, and Graph Neural Networks showed varying performance, with differences in taxa identification. Conclusions: Non-linear methods showed the best AUC and consistent results.Background Cystic fibrosis (CF) Cystic fibrosis (CF) results from CF transmembrane conductance regulator (CFTR) gene mutations. CFTR modulators have revolutionized patient outcomes, but response varies, highlighting the need for predictive biomarkers. The airway microbiota, key in disease progression, is a promising candidate. Microbiome-based outcome prediction is common but faces challenges like compositionality, phylogeny, high dimensionality, and sparsity. We applied standard random forests and lasso with various data transformations [1], FLORAL (pairwise log-ratio Lasso), and a Graph Neural Network (GNN) embedding microbiome counts in a phylogenetic tree to predict modulator nonresponse (no BMI z-score improvement after one year) from lung bacterial relative abundances. We assessed performance via ROC-AUC, PR-AUC, and interpretability.Results We analyzed 16S data from 73 pediatric patients (26% non-responders, 64 bacterial OTUs) The bestperforming models were GNN, Lasso regression, and Random Forest, with ROC-AUC above 0.65 and PR-AUC above 0.50. Random Forest and Lasso regression offered interpretability, while FLORAL identified distinct taxa. The GNN performed well; however, its interpretability was limited. Conclusion Different ML methods produced varying results with different advantages and disadvantages. In this study, the bacteria identified by the ML models aligned with CF microbiome literature. References [1] Karwowska Z, Aasmets O, Estonian Biobank RT, Kosciolek T, Org E. Effects of data transformationand model selection on feature importance in microbiome classification data. Microbiome. 2025</div

Towards a verified compiler for Distributed PlusCal

International audienceFormal verification techniques are frequently used to ensure correctness properties of distributed systems and algorithms. However, languages used for formal modeling and verification are often substantially different from languages used in software development, and verifying an abstract representation of an algorithm does not ensure that its handwritten implementation will be correct. This paper presents work in progress on a verified compiler for an extension of Lamport's PlusCal with threads and communication channels. Its syntax and semantics are formalized in the Lean 4 proof assistant and the passes compiling PlusCal algorithms into Go code are implemented in the underlying Lean 4 programming language. This paper gives formal semantics for the first pass of the compiler and outlines its mechanically verified correctness proof.</div

Averaging principle for jump processes depending on fast ergodic dynamics

We consider a slow-fast stochastic process where the slow component is a jump process on a measurable index set whose transition rates depend on the position of the fast component. Between the jumps, the fast component evolves according to an ergodic dynamic in a state space determined by the index process. We prove that, when the ergodic dynamics are accelerated, the slow index process converges to an autonomous pure jump process on the index set.We apply our results to prove the convergence of a typed branching process toward a continuous-time Galton-Watson process, and of an epidemic model with fast viral loads dynamics to a standard contact process.</div

Variability in brain imaging studies across different analysis pipelines

KeynoteInternational audienceWhen a change in analysis methods leads to different results, what does it mean for our research findings? In this presentation, I will discuss reproducibility in the field of brain imaging (also known as neuroimaging). Neuroimaging studies are characterized by a very large analysis space, and practitioners usually have to choose between different software, software versions, algorithms, parameters, etc. For many years, these choices have been regarded as implementation details, but it is becoming increasingly clear that the exact choices of analytical strategy can lead to different and sometimes contradictory results. I will review our recent efforts to better understand and manage the different sources of this analytical variability

Quantitative susceptibility mapping of the cervical spinal cord for MS monitoring

International audienceObjectives:Quantitative susceptibility mapping (QSM) [1] is a promising MRI technique that can be used in multiple sclerosis (MS) to characterize lesions and serve as a biomarker of chronic inflammation in white matter lesions. While QSM has been widely used for brain applications, its feasibility in the spinal cord (SC) has not been demonstrated. Yet, SC lesions are seen in up to 80% of people with MS and have a strong prognostic value. Moreover, a histopathologic study [2] found a high prevalence of 41% chronic active SC lesions among lesions of 119 MS patients. Thus, a QSM tool capable of quantifying the inflammatory status of SC lesions could be of great value in better understanding MS and tailoring treatments.Materials and Methods:The presence of fat in the spinal cord prompted us to use a water-fat separation IDEAL technique [3] (implemented in python) to estimate the total field before computing the QSM map using the algorithm MEDI [1] (available in the open-source software Sepia [4]). For initialization of IDEAL, we designed a 2-sequence MRI protocol that provides in-phase (IP) and out-of-phase (OOP) SC QSM data. Images from 8 healthy controls (HC) and 5 MS patients were acquired on a 3T Prisma scanner (clinical trials ID: NCT05622643; NCT05107232) in the axial plane between C3 and C5 with high spatial resolution (0.4x0.4x1mm3, TE1 IP 2.64ms, TE1 OOP 3.69ms, ΔTE=2.46ms, TR 33ms, 12 echoes each).Results:We obtain encouraging SC QSM maps where it is possible to distinguish hyperintense gray matter and QSM signal variations in lesions, as shown below in a HC (fig. 1) and an MS patient (fig2.). The T2* axial patient imagewas used to segment lesions (in green on QSM).Conclusion:We showed that SC QSM with high spatial resolution is feasible and allows to detect QSM signal variations in lesions. Future prospects include increasing the number of patients, classifying SC lesions according to QSM maps, and a reproducibility study.References :[1] Wang Y et al. MRM 2015;[2] Waldman A, et al. Acta Neuropathol 2024;[3] Guo et al. NMR Biomed 2019;[4] Chan et al. Neuroimage, 2021.Acknowledgments:This work is supported by the RHU PRIMUS, FLI RE4 QSM-SPICO and France Sclérose en Plaques

Any theory that admits a Wigner's Friend type multi-agent paradox is logically contextual

39+16 pages. Both authors contributed equally to this work. Initial versions of some of these results were included in NN's PhD thesis (ETH Zurich, 2023)Wigner's Friend scenarios push the boundaries of quantum theory by modeling agents, along with their memories storing measurement outcomes, as physical quantum systems. Extending these ideas beyond quantum theory, we ask: in which physical theories, and under what assumptions, can agents who are reasoning logically about each other's measurement outcomes encounter apparent paradoxes? To address this, we prove a link between Wigner's Friend type multi-agent paradoxes and contextuality in general theories: if agents who are modeled within a physical theory come to a contradiction when reasoning using that theory (under certain assumptions on how they reason and describe measurements), then the theory must admit contextual correlations of a logical form. This also yields a link between the distinct fundamental concepts of Heisenberg cuts and measurement contexts in general theories, and in particular, implies that the quantum Frauchiger-Renner paradox is a proof of logical contextuality. Moreover, we identify structural properties of such paradoxes in general theories and specific to quantum theory. For instance, we demonstrate that theories admitting behaviors corresponding to extremal vertices of n-cycle contextuality scenarios admit Wigner's Friend type paradoxes without post-selection, and that any quantum Wigner's Friend paradox based on the n-cycle scenario must necessarily involve post-selection. Further, we construct a multi-agent paradox based on a genuine contextuality scenario involving sequential measurements on a single system, showing that Bell non-local correlations between distinct subsystems are not necessary for Wigner's Friend paradoxes. Our work offers an approach to investigate the structure of physical theories and their information-theoretic resources by means of deconstructing the assumptions underlying multi-agent physical paradoxes

Soft-ECM : une extension de l'algorithme Evidentiel C-Means pour des données complexes

International audienceDans cet article, nous reformulons le problème de l'Evidential C-Means (ECM) pour le clustering de données complexes. Nous proposons un nouvel algorithme, Soft-ECM, qui positionne de manière cohérente les centres des clusters imprécis en se basant sur une semi-métrique. Nos expérimentations montrent que Soft-ECM obtient des résultats comparables aux approches usuelles de clustering floue sur des données dans un espace euclidien et nous montrons sur des données catégorielles et des séries temporelles l'intérêt de combiner le clustering floue et l'utilisation de semi-métriques

Enhancing Fluorescence Correlation Spectroscopy with machine learning to infer anomalous molecular motion

International audienceThe random motion of molecules in living cells has consistently been reported to deviate from standard Brownian motion, a behavior coined as ``anomalous diffusion''. To study this phenomenon in living cells, Fluorescence Correlation Spectroscopy (FCS) and Single-Particle Tracking (SPT) are the two main methods of reference. In opposition to SPT, FCS with its classical analysis methodology cannot consider models of motion for which no analytical expression of the auto-correlation function is known. This excludes for instance anomalous Continuous-Time Random Walks (CTRW) and Random Walk on fractal (RWf). Moreover, the whole acquisition sequence of the classical FCS methodology takes several tens of minutes. Here, we propose a new analysis approach that frees FCS of these limitations. Our approach associates each individual FCS recording with a vector of features based on an estimator of the auto-correlation function and uses machine learning to infer the underlying model of motion and to estimate the values of the motion parameters. Using simulated recordings, we show that this approach endows FCS with the capacity to distinguish between a range of standard and anomalous random motions, including CTRW and RWf. Our approach exhibits performances comparable to the best-in-class state-of-the-art algorithms for SPT and can be used with a range of FCS setup parameters. Since it can be applied on individual recordings of short duration, we show that with our method, FCS can be used to monitor rapid changes of the motion parameters. Finally, we apply our method on experimental FCS recordings of calibrated fluorescent beads in increasing concentrations of glycerol in water. Our results accurately predict that the beads follow Brownian motion with a diffusion coefficient and anomalous exponent which agree with classical predictions from Stokes-Einstein law even at large glycerol concentrations. Taken together, our approach significantly augments the analysis power of FCS to capacities that are similar to state-of-the-art SPT approaches