Pt-based metallization of PMOS devices for the fabrication of monolithic semiconducting/YBa2Cu3O7-d superconducting devices on silicon

Mo, Pt, Pt/Mo and Pt/Ti thin films have been deposited onto Si and SiO2 substrates by RF sputtering and annealed in the YBa2Cu3O7-d growth conditions. The effect of annealing on the sheet resitance of unpatterned layers was measured. A Pt-based multilayered metallization for the PMOS devices was proposed and tested for the monolithic integration of PMOS devices and YBCO sensors on the same silicon substrate. The best results were obtained with a Pt/Ti/Mo-silicide structure showing (0.472 \Omega_{\Box}) interconnect sheet resistivity and $2 \times 10^{-4} \Omega \cdot cm^{2}$ specific contact resistivity after annealing for (60) minutes at (700^{\circ})C in (0.5) mbar O(_{2}) pressure.Comment: 6 pages, accepted for Microelectronic Engineering, elsevie

Fusion individuelle de données cérébrales multimodales : informations issues d'images numériques et connaissances expertes

National audienceL'étude de l'activité fonctionnelle cérébrale à partir d'images TEP est difficile à cause de la résolution spatiale limitée et du faible rapport signal sur bruit de celles-ci. Cette étude nécessite l'utilisation conjointe et la fusion d'informations provenant de différentes modalités d'images numériques et de connaissances expertes modélisées dans des atlas. Ces derniers se rapportant à une anatomie standard, il est fondamental de les adapter auparavant à la morphologie spécifique du patient concerné. Pour résoudre au mieux les problèmes rencontrés depuis l'acquisition de l'image à l'identification des différentes zones, nous proposons dans cet article une méthodologie pour obtenir des données individualisées et pour les fusionner. La première étape fait intervenir un processus automatique de recalage de l'image TEP avec une image RM, via une radiographie par Rayons X, par l'introduction d'informations a priori extraites d'un atlas. La seconde étape vise à individualiser les atlas anatomiques pour que la superposition avec les images TEP soit plus précise. Dans cette optique, une méthode d'identification des sillons du cortex d'un patient sur une image RM 3D est présentée. L'accent est mis sur la généralité de la démarche, sur l'explicitation des connaissances et des mécanismes de fusion, et sur l'évaluation des résultats en fonction des images traitées

Fuzzy knowledge-based recognition of internal structures of the head

Nous proposons une méthode basée sur la connaissance a priori pour la segmentation et la reconnaissance des formes des structures internes du cerveau en IRM. Les connaissances sur les formes des structures et les distances entre elles, provenant de l'atlas de Talairach, sont modélisées par un champ flou en utilisant une analogie avec la distribution du potentiel d'électrostatique. Une sur-segmentation est d'abord effectuée sur le cerveau pour obtenir des régions homogènes. La reconnaissance des structures est ensuite obtenue par la classification des régions utilisant un algorithme génétique, suivie par un affinement au niveau du pixel. Les connaissances floues modélisées sont utilisées dans ces deux étapes. La performance de la méthode proposée est validée par référence aux résultats manuels en utilisant 4 indices de quantification

Automatic morphological sieving: comparison between different methods, application to DNA ploidy measurements

The aim of the present study is to propose alternative automatic methods to time consuming interactive sorting of elements for DNA ploidy measurements. One archival brain tumour and two archival breast carcinoma were studied, corresponding to 7120 elements (3764 nuclei, 3356 debris and aggregates). Three automatic classification methods were tested to eliminate debris and aggregates from DNA ploidy measurements (mathematical morphology (MM), multiparametric analysis (MA) and neural network (NN)). Performances were evaluated by reference to interactive sorting. The results obtained for the three methods concerning the percentage of debris and aggregates automatically removed reach 63, 75 and 85% for MM, MA and NN methods, respectively, with false positive rates of 6, 21 and 25%. In-* Corresponding author. formation about DNA ploidy abnormalities were globally preserved after automatic elimination of debris and aggregates by MM and MA methods as opposed to NN method, showing that automatic classification methods can offer alternatives to tedious interactive elimination of debris and aggregates, for DNA ploidy measurements of archival tumours

SARS-CoV-2 uses CD4 to infect T helper lymphocytes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

SARS-CoV-2 uses CD4 to infect T helper lymphocytes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

Reconstruction d'images à haute résolution à partir de multiples clichés à basse résolution (application à la détection de pixels défectueux sur des écrans plats de définition très supérieure à celle du système d'acquisition)

CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

PRETRAITEMENT ET SEGMENTATION D'IMAGES PAR MISE EN UVRE DE TECHNIQUES BASEES SUR LES EQUATIONS AUX DERIVEES PARTIELLES (APPLICATION EN IMAGERIE MICROSCOPIQUE BIOMEDICALE)

CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

Contribution à la mise en oeuvre d'une micro-sonde de Hall associée à une antenne ferromagnétique (bruit et résolution spatiale)

CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

MRF models and multifractal analysis for MRI segmentation

International audienceWe demonstrate the interest of the multifractal analysis for removing the ambiguities due to the intensity overlap, and we propose a brain tissue segmentation method from MRI images, which is based on Markov random field (MRF) models. The brain segmentation consists of separating the encephalon into the three main brain tissues: gray matter, white matter and cerebrospinal fluid (CSF). The classical MRF model uses the intensity and the neighborhood information, which is not robust enough to solve problems, such as partial volume effects. Therefore, we propose to use the multifractal analysis, which can provide the intensity variations, to describe brain tissues. The value of the Holder exponent α is calculated, and the corresponding multifractal spectrum f(α) is defined. The α priori knowledge about (α,f(α)) is modeled and then incorporated into an MRF model. This technique has been successfully applied to real MRI images. The contribution of the multifractal analysis is shown