Què saben els pares dels infants de 0-3 anys, sobre l'ús del rentat nasal i els inhaladors amb cambra?

Aquest estudi insisteix en la importància de l’educació sanitària dirigida als pares per tald’assegurar l’eficàcia del tractament a administrar i consolidar els coneixements i habilitats del progenitors per tal de que aquests puguin dur a terme les tècniques requerides de forma adequada.Destacar la necessitat de conscienciar a tots els professionals de la salut, sobretot els quepassen més temps amb els familiars, de la importància que té la seva competència comeducador en l’àmbit de la salut.Evidenciar la importància de fer una avaluació continuada dels coneixements i les habilitatsadquirides pels cuidadors principals, sobre com dur a terme les tècniques del rentat de nas iadministració de la medicació inhalada amb cambra i mascareta, per detectar-ne els possibleserrors i poder posar-hi mesures correctores per pal·liar-los.Constatar la utilitat de preguntar i esbrinar “in situ” què saben els progenitors sobre lestècniques a avaluar (rentat de nas i administració de la medicació inhalada amb cambra i mascareta), abans de donar qualsevol explicació o iniciar l’educació sanitària per detectar lesnecessitats específiques de cada pare o mare i poder individualitzar el procés educatiu

A genetic algorithm to ensemble feature selection

We are going to implement the "GA-SEFS" by Tsymbal and analyse experimentally its performance depending on the classifier algorithms used in the fitness function (NB, MNge, SMO). We are also going to study the effect of adding to the fitness function a measure to control complexity of the base classifiers.

A survey on transfer-based approaches to MT using feature structures

Aquest treball és una revisió d'alguns sistemes de Traducció Automàtica que segueixen l'estratègia de Transfer i fan servir estructures de trets com a eina de representació. El treball s'integra dins el projecte MLAP-9315, projecte que investiga la reutilització de les especificacions lingüístiques del projecte EUROTRA per estàndards industrials.

Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia; and Girona, Spain, respectively). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

Proteasome inhibition reduces proliferation, collagen expression, and inflammatory cytokine production in nasal mucosa and polyp fibroblasts

Proteasome inhibitors, used in cancer treatment for their proapoptotic effects, have anti-inflammatory and antifibrotic effects on animal models of various inflammatory and fibrotic diseases. Their effects in cells from patients affected by either inflammatory or fibrotic diseases have been poorly investigated. Nasal polyposis is a chronic inflammatory disease of the sinus mucosa characterized by tissue inflammation and remodeling. We tested the hypothesis that proteasome inhibition of nasal polyp fibroblasts might reduce their proliferation and inflammatory and fibrotic response. Accordingly, we investigated the effect of the proteasome inhibitor Z-Leu-Leu-Leu-B(OH)2 (MG262) on cell viability and proliferation and on the production of collagen and inflammatory cytokines in nasal polyp and nasal mucosa fibroblasts obtained from surgery specimens. MG262 reduced the viability of nasal mucosa and polyp fibroblasts concentration- and time-dependently, with marked effects after 48 h of treatment. The proteasome inhibitor bortezomib provoked a similar effect. MG262-induced cell death involved loss of mitochondrial membrane potential, caspase-3 and poly(ADP-ribose) polymerase activation, induction of c-Jun phosphorylation, and mitogen-activated protein kinase phosphatase-1 expression. Low concentrations of MG262 provoked growth arrest, inhibited DNA replication and retinoblastoma phosphorylation, and increased expression of the cell cycle inhibitors p21 and p27. MG262 concentration-dependently inhibited basal and transforming growth factor-β-induced collagen mRNA expression and interleukin (IL)-1β-induced production of IL-6, IL-8, monocyte chemoattractant protein-1, regulated on activation normal T cell expressed and secreted, and granulocyte/macrophage colony-stimulating factor in both fibroblast types. MG262 inhibited IL-1β/tumor necrosis factor-α-induced activation of nuclear factor-κB. We conclude that noncytotoxic treatment with MG262 reduces the proliferative, fibrotic, and inflammatory response of nasal fibroblasts, whereas high MG262 concentrations induce apoptosis.

Proteasome inhibition reduces proliferation, collagen expression, and inflammatory cytokine production in nasal mucosa and polyp fibroblasts

Proteasome inhibitors, used in cancer treatment for their proapoptotic effects, have anti-inflammatory and antifibrotic effects on animal models of various inflammatory and fibrotic diseases. Their effects in cells from patients affected by either inflammatory or fibrotic diseases have been poorly investigated. Nasal polyposis is a chronic inflammatory disease of the sinus mucosa characterized by tissue inflammation and remodeling. We tested the hypothesis that proteasome inhibition of nasal polyp fibroblasts might reduce their proliferation and inflammatory and fibrotic response. Accordingly, we investigated the effect of the proteasome inhibitor Z-Leu-Leu-Leu-B(OH)2 (MG262) on cell viability and proliferation and on the production of collagen and inflammatory cytokines in nasal polyp and nasal mucosa fibroblasts obtained from surgery specimens. MG262 reduced the viability of nasal mucosa and polyp fibroblasts concentration- and time-dependently, with marked effects after 48 h of treatment. The proteasome inhibitor bortezomib provoked a similar effect. MG262-induced cell death involved loss of mitochondrial membrane potential, caspase-3 and poly(ADP-ribose) polymerase activation, induction of c-Jun phosphorylation, and mitogen-activated protein kinase phosphatase-1 expression. Low concentrations of MG262 provoked growth arrest, inhibited DNA replication and retinoblastoma phosphorylation, and increased expression of the cell cycle inhibitors p21 and p27. MG262 concentration-dependently inhibited basal and transforming growth factor-β-induced collagen mRNA expression and interleukin (IL)-1β-induced production of IL-6, IL-8, monocyte chemoattractant protein-1, regulated on activation normal T cell expressed and secreted, and granulocyte/macrophage colony-stimulating factor in both fibroblast types. MG262 inhibited IL-1β/tumor necrosis factor-α-induced activation of nuclear factor-κB. We conclude that noncytotoxic treatment with MG262 reduces the proliferative, fibrotic, and inflammatory response of nasal fibroblasts, whereas high MG262 concentrations induce apoptosis.

Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination

Stochastic learning processes for a specific feature detector are studied. This technique is applied to nonsmooth multilayer neural networks requested to perform a discrimination task of order 3 based on the ssT-block¿ssC-block problem. Our system proves to be capable of achieving perfect generalization, after presenting finite numbers of examples, by undergoing a phase transition. The corresponding annealed theory, which involves the Ising model under external field, shows good agreement with Monte Carlo simulations.

Resurrecting Disney: Tracing Walt Disney‘s ever-present spirit in feature animated films of the Michael Eisner era (1984-2004)

El treball Ressuscitant a Disney: Rastrejant el sempre present esperit de Walt Disney en els llargmetratges animats de l'era Michael Eisner (1984-2004) pretén definir i analitzar les característiques, tant respecte al procés creatiu com en la definició de contingut, integrades en els clàssics originals de Disney per, a continuació, demostrar que aquestes van ser recuperades i implementades de nou després de la mort de Walt Disney -amb lleus adaptacions- per donar lloc a una segona edat d'or de l'animació

Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed