No abstract

Context Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL] and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension. Objective Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability. Design Retrospective analyses of PHT and EHT patients from a European multicentre study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a “classical approach” (CA) (performing a series of univariate and multivariate analyses) and a “machine learning approach” (MLA) (using Random Forest) were used. Patients The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n=59) and EHT (n=223 with 40 CS, 107 PA and 76 PPGL), respectively. Results From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 15 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The ROC curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83). Conclusions TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

Abstract-Transmitting texture and depth maps from one or more reference views enables a user to freely choose virtual viewpoints from which to synthesize images for observation via depth-image-based rendering (DIBR). In each DIBR-synthesized image, however, there remain disocclusion holes with missing pixels corresponding to spatial regions occluded from view in the reference images. To complete these holes, unlike previous schemes that rely heavily (and unrealistically) on the availability of a high-quality depth map in the virtual view for inpainting of the corresponding texture map, in this paper a new Joint TextureDepth Inpainting (JTDI) algorithm is proposed that simultaneously fill in missing texture and depth pixels. Specifically, we first use available partial depth information to compute priority terms to identify the next target pixel patch in a disocclusion hole for inpainting. Then, after identifying the best-matched texture patch in the known pixel region via template matching for texture inpainting, the variance of the corresponding depth patch is copied to the target depth patch for depth inpainting. Experimental results show that JTDI outperforms two previous inpainting schemes that either does not use available depth information during inpainting, or depends on the availability of a good depth map at the virtual view for good inpainting performance.