Sandra Jiménez

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OBJECTIVE The use of the new International Association of the Diabetes and Pregnancy Study Groups criteria (IADPSGC) for the diagnosis of gestational diabetes mellitus (GDM) results in an increased prevalence of GDM. Whether their introduction improves pregnancy outcomes has yet to be established. We sought to evaluate the cost-effectiveness of one-step(More)
About SYNTHESIs This volume is a printed version of a work that appears in the Synthesis Digital Library of Engineering and Computer Science. Synthesis Lectures provide concise, original presentations of important research and development topics, published quickly, in digital and print formats. For more information visit SYNTHESIS(More)
In an experimental model of interstitial nephritis resulting in fibrosis, lymphokines secreted by a common population of antigen-reactive T lymphocytes have been shown to exert a bidirectional influence on fibroblast proliferation and collagen synthesis. We report that these lymphokines can be chromatographically separated into two fractions. The larger(More)
The in vitro antiproliferative and antioxidant effects of different fractions of Rosa canina hips on human colon cancer cell lines (Caco-2) was studied. The compounds tested were total extract (fraction 1), vitamin C (fraction 2), neutral polyphenols (fraction 3) and acidic polyphenols (fraction 4). All the extracts showed high cytotoxicity after 72 h, both(More)
Mechanisms of human color vision are characterized by two phenomenological aspects: the system is nonlinear and adaptive to changing environments. Conventional attempts to derive these features from statistics use separate arguments for each aspect. The few statistical explanations that do consider both phenomena simultaneously follow parametric(More)
Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold learning based on the use of a sequence of Principal Polynomials that capture the eventually nonlinear(More)
Principal Component Analysis (PCA) has been widely used to identify relevant features in remote sensing data due to its good performance in reconstruction error after dimensionality reduction. Different applications include compact representation of spatio-spectral information [1], hyperspectral unmixing [2, 3], assimilation for numerical weather prediction(More)
In the satellite hyperspectral measures the contributions of light, surface, and atmosphere are mixed. Applications need separate access to the sources. Conventional inversion techniques usually take a pixel-wise, spectral-only approach. However, recent improvements in retrieving surface and atmosphere characteristics use heuristic spatial smoothness(More)
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of maximal variance by means of curves, instead of straight lines. Contrarily to previous approaches, PPA(More)
See Aubourg (doi:10.1093/awv271) for a scientific commentary on this article.X-linked adrenoleukodystrophy is caused by mutations in the ABCD1 gene leading to accumulation of very long chain fatty acids. Its most severe neurological manifestation is cerebral adrenoleukodystrophy. Here we demonstrate that progressive inflammatory demyelination in cerebral(More)