Polytechnic University of Valencia

About: Polytechnic University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Catalysis & Population. The organization has 16282 authors who have published 40162 publications receiving 850234 citations.

Diffusion Weighted Image Denoising Using Overcomplete Local PCA

Abstract: Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.

Uniform ripening encodes a Golden 2-like transcription factor regulating tomato fruit chloroplast development.

Abstract: Modern tomato (Solanum lycopersicum) varieties are bred for uniform ripening (u) light green fruit phenotypes to facilitate harvests of evenly ripened fruit. U encodes a Golden 2-like (GLK) transcription factor, SlGLK2, which determines chlorophyll accumulation and distribution in developing fruit. In tomato, two GLKs--SlGLK1 and SlGLK2--are expressed in leaves, but only SlGLK2 is expressed in fruit. Expressing GLKs increased the chlorophyll content of fruit, whereas SlGLK2 suppression recapitulated the u mutant phenotype. GLK overexpression enhanced fruit photosynthesis gene expression and chloroplast development, leading to elevated carbohydrates and carotenoids in ripe fruit. SlGLK2 influences photosynthesis in developing fruit, contributing to mature fruit characteristics and suggesting that selection of u inadvertently compromised ripe fruit quality in exchange for desirable production traits.

Learning Decision Trees Using the Area Under the ROC Curve

Abstract: ROC analysis is increasingly being recognised as an important tool for evaluation and comparison of classifiers when the operating characteristics (i.e. class distribution and cost parameters) are not known at training time. Usually, each classifier is characterised by its estimated true and false positive rates and is represented by a single point in the ROC diagram. In this paper, we show how a single decision tree can represent a set of classifiers by choosing different labellings of its leaves, or equivalently, an ordering on the leaves. In this setting, rather than estimating the accuracy of a single tree, it makes more sense to use the area under the ROC curve (AUC) as a quality metric. We also propose a novel splitting criterion which chooses the split with the highest local AUC. To the best of our knowledge, this is the first probabilistic splitting criterion that is not based on weighted average impurity. We present experiments suggesting that the AUC splitting criterion leads to trees with equal or better AUC value, without sacrificing accuracy if a single labelling is chosen.