Armando Blanco

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OBJECTIVE To assess the usefulness of the "Candida score" (CS) for discriminating between Candida species colonization and invasive candidiasis (IC) in non-neutropenic critically ill patients. A rate of IC <5% in patients with CS <3 was the primary end point. DESIGN Prospective, cohort, observational study. SETTING Thirty-six medical-surgical intensive(More)
The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven(More)
Although the extraction of symbolic knowledge from trained feedforward neural netŽ . works has been widely studied, research in recurrent neural networks RNN has been more neglected, even though it performs better in areas such as control, speech recognition, time series prediction, etc. Nowadays, a subject of particular interest is Ž . crisprfuzzy(More)
Results on the monitoring of strong African dust outbreaks at Lecce in the southeastern corner of Italy (40 degrees 20' N, 18 degrees 6' E) during May 2001 are presented. This activity has been performed in the framework of the European Aerosol Research Lidar Network (EARLINET). The lidar station of Lecce is located on a flat rural area that is(More)
To assess the value of (1→3)-β-d-glucan (BDG), Candida albicans germ tube antibody (CAGTA), C-reactive protein (CRP), and procalcitonin (PCT) levels for the diagnosis of invasive candidiasis (IC) and for differentiating Candida spp. colonization from infection in ICU patients with severe abdominal conditions (SAC). Prospective study of 176 non-neutropenic(More)
BACKGROUND Infections occurring among outpatients having recent contact with the health-care system have been recently classified as health-care-associated infections to distinguish them from hospital- and community-acquired infections. Patients with bloodstream infections (BSIs) were studied to assess health-care-associated infections at admission in the(More)
Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially(More)
BACKGROUND Children born small for gestational age (SGA) experience higher rates of morbidity and mortality than those born appropriate for gestational age. In Latin America, identification and optimal management of children born SGA is a critical issue. Leading experts in pediatric endocrinology throughout Latin America established working groups in order(More)
Biclustering has emerged as an important method for analyzing gene expression data from microarray technology. It allows to identify groups of genes which behave similarly under a subset of conditions. As a gene may play more than one biological role in conjunction with distinct groups of genes, non-exclusive biclustering algorithms are required. In this(More)
It has been shown that neural networks are able to infer regular crisp grammars from positive and negative examples. The fuzzy grammatical inference (FGI) problem however has received considerably less attention. In this paper we show that a suitable two-layer neural network model is able to infer fuzzy regular grammars from a set of fuzzy examples(More)