Hyperbolic Wavelet-Fisz denoising for a model arising in Ultrasound Imaging

International audienceWe present an algorithm and its fully data-driven extension for noise reduction in ultrasound imaging. Our proposed method computes the hyperbolic wavelet transform of the image, before applying a multiscale variance stabilization technique, via a Fisz transformation. This adapts the wavelet coefficients statistics to the wavelet thresholding paradigm. The aim of the hyperbolic setting is to recover the image while respecting the anisotropic nature of structural details. The data-driven extension removes the need for any prior knowledge of the noise model parameters by estimating the noise variance using an isotonic Nadaraya-Watson estimator. Experiments on synthetic and real data, and comparisons with other noise reduction methods demonstrate the potential of our method at recovering ultrasound images while preserving tissue details. Finally, we emphasize the noise model we consider by applying our variance estimation procedure on real images

Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging

International audienceFluorescence imaging in diffusive media is an emerging imaging modality for medical applications that uses injected fluorescent markers that bind to specific targets, e.g., carcinoma. The region of interest is illuminated with near-IR light and the emitted back fluorescence is analyzed to localize the fluorescence sources. To investigate a thick medium, as the fluorescence signal decreases with the light travel distance, any disturbing signal, such as biological tissues intrinsic fluorescence (called autofluorescence) is a limiting factor. Several specific markers may also be simultaneously injected to bind to different molecules, and one may want to isolate each specific fluorescent signal from the others. To remove the unwanted fluorescence contributions or separate different specific markers, a spectroscopic approach is explored. The nonnegative matrix factorization (NMF) is the blind positive source separation method we chose. We run an original regularized NMF algorithm we developed on experimental data, and successfully obtain separated in vivo fluorescence spectra

In vivo fluorescence spectra unmixing and autofluorescence removal by sparse Non-negative Matrix Factorization

International audienceFluorescence imaging locates fluorescent markers that specifically bind to targets, as tumors: markers are injected to a patient, optimally excited with near infrared light, and located thanks to emitted back fluorescence analysis. To investigate thick and diffusive media, as the fluorescence signal decreases with the light travel distance, the autofluorescence of biological tissues comes to be a limiting factor. To remove autofluorescence and isolate specific fluorescence, a spectroscopic approach, based on Non-negative Matrix Factorization (NMF), is explored. To improve results on spatially sparse markers detection, we suggest a new constrained NMF algorithm which takes sparsity constraints into account. A comparative study between both algorithms is proposed on simulated and in vivo data

Application de la Factorisation en Matrices Non-négatives pour l'amélioration de la localisation de tumeurs en tomographie optique diffusive de fluorescence

National audienceL'imagerie optique de fluorescence permet de localiser des marqueurs fluorescents spécifiques injectés au patient qui s'accumulent autour de tumeurs cancéreuses. Une fois les régions dŠintérêt illuminées, un signal de fluorescence est émis par les marqueurs mais également par les tissus sains environnants. Lors de l'analyse de tissus épais, alors que le signal de fluorescence décroit avec le parcours de la lumière, l'autofluorescence des tissus prévient la détection des marqueurs profonds. Un approche spectroscopique basée sur la Factorisation en Matrices Non-négatives (FMN) est proposée pour séparer les spectres de fluorescence et éliminer l'autofluorescence des tissus. Afin de limiter le problème de non-unicité de la décomposition, l'ajout d'a priori à la méthode classique développée par Lee et Seung est proposé; la pertinence de ces contraintes est illustrée sur des exemples d'acquisitions de fluorescence in vivo

Non-negative Matrix Factorization under sparsity constraints to unmix in vivo spectrally resolved acquisitions

International audienceFluorescence imaging in diffusive media is an emerging imaging modality for medical applications which uses injected fluorescent markers (several ones may be simultaneously injected) that bind to specific targets, as tumors. The region of interest is illuminated with near infrared light and the emitted back fluorescence is analyzed to localize the fluorescence sources. To investigate thick medium, as the fluorescence signal decreases with the light travel distance, any disturbing signal, such as biological tissues intrinsic fluorescence - called autofluorescence -, is a limiting factor. To remove autofluorescence and isolate each specific fluorescent signal from the others, a spectroscopic approach, based on Non-negative Matrix Factorization, is explored. We ran an NMF algorithm with sparsity constraints on experimental data, and successfully obtained separated in vivo fluorescence spectra

Regularized non negative matrix factorization for autofluorescence removal in fluorescence optical imaging

International audienceFluorescence imaging in diffusive media locates cancers thanks to injected fluorescent markers specific to the tumors. The region of interest is illuminated with red light and the emitted back fluorescence is analyzed to locate the fluorescence sources. To detect accurately the markers signal and be able to explore thick media (breast, prostate), autofluorescence emitted by biological tissues has to be removed. We propose a spectroscopic approach, based on Non-negative Matrix Factorization (NMF) method, and present interest of regularized NMF algorithms on unmixing results. In vivo autofluorescence removal and tumor detection enhancement results on mice will be presented

: a tutorial for psychology students and other beginners

Version françaçise de Navarro, D. J., & Foxcroft, D. R. (2019). Learning statistics with Jamovi: a tutorial for psychology students and other beginners. (Version 0.70). Consulté à l’adresse http://www.learnstatswithJamovi.comL'apprentissage des statistiques avec Jamovi couvre le contenu d'un cours d'introduction à la statistique, tel qu'il est généralement enseigné aux étudiants de premier cycle en psychologie. Le livre aborde la façon de commencer dans Jamovi et donne une introduction à la manipulation des données. D'un point de vue statistique, l'ouvrage traite d'abord des statistiques descriptives et la représentation graphique, puis de la théorie des probabilités, de l'échantillonnage et de l'estimation et de la vérification des hypothèses nulles. Après avoir présenté la théorie, le livre couvre l'analyse des tableaux de contingence, la corrélation, les tests t, la régression, l'ANOVA et l'analyse factorielle. Les statistiques bayésiennes sont présentées à la fin du livre

Follicular lymphoma-like B cells in healthy individuals: a novel intermediate step in early lymphomagenesis

Follicular lymphoma is one of the most common adult lymphoma, and remains virtually incurable despite its relatively indolent nature. t(14;18)(q32;q21) translocation, the genetic hallmark and early initiating event of follicular lymphoma (FL) pathogenesis, is also present at low frequency in the peripheral blood of healthy individuals. It has long been assumed that in healthy individuals t(14;18) is carried by circulating quiescent naive B cells, where its oncogenic potential would be restrained. Here, we question this current view and demonstrate that in healthy individuals, t(14;18) is actually carried by an expanding population of atypical B cells issued from germinal centers, displaying genotypic and phenotypic features of FL, and prone to constitute potent premalignant FL niches. These findings strongly impact both on the current understanding of disease progression and on the proper handling of t(14;18) frequency in blood as a potential early biomarker for lymphoma

In Vivo Reinsertion of Excised Episomes by the V(D)J Recombinase: A Potential Threat to Genomic Stability

It has long been thought that signal joints, the byproducts of V(D)J recombination, are not involved in the dynamics of the rearrangement process. Evidence has now started to accumulate that this is not the case, and that signal joints play unsuspected roles in events that might compromise genomic integrity. Here we show both ex vivo and in vivo that the episomal circles excised during the normal process of receptor gene rearrangement may be reintegrated into the genome through trans-V(D)J recombination occurring between the episomal signal joint and an immunoglobulin/T-cell receptor target. We further demonstrate that cryptic recombination sites involved in T-cell acute lymphoblastic leukemia–associated chromosomal translocations constitute hotspots of insertion. Eventually, the identification of two in vivo cases associating episomal reintegration and chromosomal translocation suggests that reintegration events are linked to genomic instability. Altogether, our data suggest that V(D)J-mediated reintegration of episomal circles, an event likely eluding classical cytogenetic screenings, might represent an additional potent source of genomic instability and lymphoid cancer