Spreading model for wall films generated by high-pressure sprays

[EN] This paper presents a new model developed to predict the area of wall films that may develop in gasoline direct injection engines (GDI). In a always more restrictive legislation on gas emissions the injection process in internal combustion (IC) engines has been highlighted as a domain of great concern in order to satisfy these requirements. Many spray wall interactions models exist in literature and are included in different CFD tools. Most often they are based on the sum of single drop-wall impacts. The specificity of the present model lies in its simplicity and the way the film is treated globally. Here its propagation is predicted using a balance between the momentum given by the spray and the viscous shear stress. Jointly with the theoretical model, an experimental set-up has been built up, an optical measurement technique called Refractive Index Matching method is used to follow the development of the wall film. It has been found that the area of the wall film is proportional to the duration of injection, while the distance between the injector and the wall has not shown many influence on the evolution of area. The influence of the injection pressure has also been identified, when the pressure is doubled the radius of the film is multiplied by √3 2. Eventually the model predicts that film thickness decreases as fuel pressure rises.ANR and ANRT are acknowledged for their financial support.Lamiel, Q.; Lamarque, N.; Hélie, J.; Legendre, D. (2017). Spreading model for wall films generated by high-pressure sprays. En Ilass Europe. 28th european conference on Liquid Atomization and Spray Systems. Editorial Universitat Politècnica de València. 138-145. https://doi.org/10.4995/ILASS2017.2017.4999OCS13814

3D surface reconstruction using dense optical flow combined to feature matching: Application to endoscopy

International audienceIn structure from motion (SfM) algorithms, the surface reconstruction performance strongly depends on the quality of the determination of homologous points between images. Classical feature matching-based methods as integrated in the state-of the-art SfM-algorithms are often inoperative for scenes including weak structures and textures (e.g., as those in medical endoscopic videos). This contribution introduces an effective solution based on the combination of dense optical flow and feature matching. The accuracy and robustness of the proposed method were validated using results obtained for a phantom with known dimensions and with patient data, respectively. Apart from the high performance obtained for cystoscopy and gastroscopy, the proposed solution has a high potential in other medical and non-medical scenes.Dans les algorithmes de structures à partir du mouvement (SfM), la performance de la reconstruction des surfaces dépend fortement de la qualité de la détermination des points homologues entre images. Les méthodes SfM de référence sont souvent inopérantes pour les scènes avec peu de structures et textures faiblement contrastées car elles reposent uniquement sur l'appariement de caractéristiques. Cette contribution présente une solution associant un flot optique dense à la mise en correspondance de caractéristiques. La précision et la robustesse de la reconstruction ont été validées via des résultats obtenus pour un fantôme avec des dimensions connues et avec des données patient en cystoscopie et en gastroscopie, respectivement. Plus généralement, cette approche a un fort potentiel pour toute scène peu constrastée, médicales ou non

Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging

Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs. However, endoscopic images are often affected by illumination artefacts: image parts may be over- or underexposed according to the light source pose and the tissue orientation. These artifacts have a strong negative impact on the performance of computer vision or AI-based diagnosis tools. Although endoscopic image enhancement methods are greatly required, little effort has been devoted to over- and under-exposition enhancement in real-time. This contribution presents an extension to the objective function of LMSPEC, a method originally introduced to enhance images from natural scenes. It is used here for the exposure correction in endoscopic imaging and the preservation of structural information. To the best of our knowledge, this contribution is the first one that addresses the enhancement of endoscopic images using deep learning (DL) methods. Tested on the Endo4IE dataset, the proposed implementation has yielded a significant improvement over LMSPEC reaching a SSIM increase of 4.40% and 4.21% for over- and underexposed images, respectively.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Secretory IgA mediates retrotranscytosis of intact gliadin peptides via the transferrin receptor in celiac disease

Celiac disease (CD) is an enteropathy resulting from an abnormal immune response to gluten-derived peptides in genetically susceptible individuals. This immune response is initiated by intestinal transport of intact peptide 31-49 (p31-49) and 33-mer gliadin peptides through an unknown mechanism. We show that the transferrin receptor CD71 is responsible for apical to basal retrotranscytosis of gliadin peptides, a process during which p31-49 and 33-mer peptides are protected from degradation. In patients with active CD, CD71 is overexpressed in the intestinal epithelium and colocalizes with immunoglobulin (Ig) A. Intestinal transport of intact p31-49 and 33-mer peptides was blocked by polymeric and secretory IgA (SIgA) and by soluble CD71 receptors, pointing to a role of SIgA–gliadin complexes in this abnormal intestinal transport. This retrotranscytosis of SIgA–gliadin complexes may promote the entry of harmful gliadin peptides into the intestinal mucosa, thereby triggering an immune response and perpetuating intestinal inflammation. Our findings strongly implicate CD71 in the pathogenesis of CD

From array-based hybridization of Helicobacter pylori isolates to the complete genome sequence of an isolate associated with MALT lymphoma

<p>Abstract</p> <p>Background</p> <p><it>elicobacter pylori </it>infection is associated with several gastro-duodenal inflammatory diseases of various levels of severity. To determine whether certain combinations of genetic markers can be used to predict the clinical source of the infection, we analyzed well documented and geographically homogenous clinical isolates using a comparative genomics approach.</p> <p>Results</p> <p>A set of 254 <it>H. pylori </it>genes was used to perform array-based comparative genomic hybridization among 120 French <it>H. pylori </it>strains associated with chronic gastritis (n = 33), duodenal ulcers (n = 27), intestinal metaplasia (n = 17) or gastric extra-nodal marginal zone B-cell MALT lymphoma (n = 43). Hierarchical cluster analyses of the DNA hybridization values allowed us to identify a homogeneous subpopulation of strains that clustered exclusively with <it>cag</it>PAI minus MALT lymphoma isolates. The genome sequence of B38, a representative of this MALT lymphoma strain-cluster, was completed, fully annotated, and compared with the six previously released <it>H. pylori </it>genomes (i.e. J99, 26695, HPAG1, P12, G27 and Shi470). B38 has the smallest <it>H. pylori </it>genome described thus far (1,576,758 base pairs containing 1,528 CDSs); it contains the <it>vacA</it>s2m2 allele and lacks the genes encoding the major virulence factors (absence of <it>cag</it>PAI, <it>bab</it>B, <it>bab</it>C, <it>sab</it>B, and <it>hom</it>B). Comparative genomics led to the identification of very few sequences that are unique to the B38 strain (9 intact CDSs and 7 pseudogenes). Pair-wise genomic synteny comparisons between B38 and the 6 <it>H. pylori </it>sequenced genomes revealed an almost complete co-linearity, never seen before between the genomes of strain Shi470 (a Peruvian isolate) and B38.</p> <p>Conclusion</p> <p>These isolates are deprived of the main <it>H. pylori </it>virulence factors characterized previously, but are nonetheless associated with gastric neoplasia.</p

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures

The C-Terminus of Toxoplasma RON2 Provides the Crucial Link between AMA1 and the Host-Associated Invasion Complex

Host cell invasion by apicomplexan parasites requires formation of the moving junction (MJ), a ring-like apposition between the parasite and host plasma membranes that the parasite migrates through during entry. The Toxoplasma MJ is a secreted complex including TgAMA1, a transmembrane protein on the parasite surface, and a complex of rhoptry neck proteins (TgRON2/4/5/8) described as host cell-associated. How these proteins connect the parasite and host cell has not previously been described. Here we show that TgRON2 localizes to the MJ and that two short segments flanking a hydrophobic stretch near its C-terminus (D3 and D4) independently associate with the ectodomain of TgAMA1. Pre-incubation of parasites with D3 (fused to glutathione S-transferase) dramatically reduces invasion but does not prevent injection of rhoptry bulb proteins. Hence, the entire C-terminal region of TgRON2 forms the crucial bridge between TgAMA1 and the rest of the MJ complex but this association is not required for rhoptry protein injection