Effet d'une intervention infirmière de soutien favorisant l'autodétermination sur la pratique des auto-soins chez des patients atteints d'insuffisance cardiaque

L’insuffisance cardiaque (IC) est une maladie chronique dont les symptômes sévères peuvent mener à des hospitalisations répétées. Pour gérer ces symptômes, le plan de traitement implique plusieurs auto-soins, par exemple une diète limitée en sel et en liquide, ce qui est parfois difficile à respecter. Le but de la présente étude pilote randomisée à deux groupes (n = 16 / groupe) était d’évaluer la faisabilité, l’acceptabilité et l’efficacité préliminaire d’une intervention infirmière favorisant la pratique des auto-soins des patients atteints d’IC. L’intervention est basée sur la théorie de l’autodétermination (TAD) qui promeut l’autonomie dans la pratique des auto-soins. L’autodétermination est favorisée par le sentiment de compétence perçue, d’autonomie et d’affiliation. Pour soutenir le besoin d’affiliation, un aidant principal participe à l’intervention qui consiste en cinq entrevues chez le groupe d’intervention (GI) dont deux durant l’hospitalisation et trois par un appel téléphonique suite au congé. Les interventions découlant de cette théorie incluent par exemple de proposer des choix plutôt que d’imposer des restrictions, d’éviter la critique, de favoriser l’empathie et le renforcement positif. Les entrevues ont servi également à guider les aidants principaux pour qu’ils soutiennent à leur tour l’autodétermination de leur parent atteint d’IC. Les résultats soutiennent la faisabilité et l’acceptabilité de l’intervention évaluée dans la présente étude et permettent d’améliorer la pratique des auto-soins avec des résultats favorables pour la majorité des hypothèses évaluées. Ces résultats prometteurs permettront de guider la pratique clinique et offrent des pistes de recherches futures.

Fluorescence and reflectance spectroscopy for identification of atherosclerosis in human carotid arteries using principal components analysis

Objectives: The aim of this work was to verify the differentiation between normal and pathological human carotid artery tissues by using fluorescence and reflectance spectroscopy in the 400- to 700-nm range and the spectral characterization by means of principal components analysis. Background Data: Atherosclerosis is the most common and serious pathology of the cardiovascular system. Principal components represent the main spectral characteristics that occur within the spectral data and could be used for tissue classification. Materials and Methods: Sixty postmortem carotid artery fragments (26 non-atherosclerotic and 34 atherosclerotic with non-calcified plaques) were studied. The excitation radiation consisted of a 488-nm argon laser. Two 600-mu m core optical fibers were used, one for excitation and one to collect the fluorescence radiation from the samples. The reflectance system was composed of a halogen lamp coupled to an excitation fiber positioned in one of the ports of an integrating sphere that delivered 5 mW to the sample. The photo-reflectance signal was coupled to a 1/4-m spectrograph via an optical fiber. Euclidean distance was then used to classify each principal component score into one of two classes, normal and atherosclerotic tissue, for both fluorescence and reflectance. Results: The principal components analysis allowed classification of the samples with 81% sensitivity and 88% specificity for fluorescence, and 81% sensitivity and 91% specificity for reflectance. Conclusions: Our results showed that principal components analysis could be applied to differentiate between normal and atherosclerotic tissue with high sensitivity and specificity.

NGC 7097: THE ACTIVE GALACTIC NUCLEUS AND ITS MIRROR, REVEALED BY PRINCIPAL COMPONENT ANALYSIS TOMOGRAPHY

Three-dimensional spectroscopy techniques are becoming more and more popular, producing an increasing number of large data cubes. The challenge of extracting information from these cubes requires the development of new techniques for data processing and analysis. We apply the recently developed technique of principal component analysis (PCA) tomography to a data cube from the center of the elliptical galaxy NGC 7097 and show that this technique is effective in decomposing the data into physically interpretable information. We find that the first five principal components of our data are associated with distinct physical characteristics. In particular, we detect a low-ionization nuclear-emitting region (LINER) with a weak broad component in the Balmer lines. Two images of the LINER are present in our data, one seen through a disk of gas and dust, and the other after scattering by free electrons and/or dust particles in the ionization cone. Furthermore, we extract the spectrum of the LINER, decontaminated from stellar and extended nebular emission, using only the technique of PCA tomography. We anticipate that the scattered image has polarized light due to its scattered nature.

Hubble parameter reconstruction from a principal component analysis: minimizing the bias

Aims. A model-independent reconstruction of the cosmic expansion rate is essential to a robust analysis of cosmological observations. Our goal is to demonstrate that current data are able to provide reasonable constraints on the behavior of the Hubble parameter with redshift, independently of any cosmological model or underlying gravity theory. Methods. Using type Ia supernova data, we show that it is possible to analytically calculate the Fisher matrix components in a Hubble parameter analysis without assumptions about the energy content of the Universe. We used a principal component analysis to reconstruct the Hubble parameter as a linear combination of the Fisher matrix eigenvectors (principal components). To suppress the bias introduced by the high redshift behavior of the components, we considered the value of the Hubble parameter at high redshift as a free parameter. We first tested our procedure using a mock sample of type Ia supernova observations, we then applied it to the real data compiled by the Sloan Digital Sky Survey (SDSS) group. Results. In the mock sample analysis, we demonstrate that it is possible to drastically suppress the bias introduced by the high redshift behavior of the principal components. Applying our procedure to the real data, we show that it allows us to determine the behavior of the Hubble parameter with reasonable uncertainty, without introducing any ad-hoc parameterizations. Beyond that, our reconstruction agrees with completely independent measurements of the Hubble parameter obtained from red-envelope galaxies.

POLAR ACTIONS ON CERTAIN PRINCIPAL BUNDLES OVER SYMMETRIC SPACES OF COMPACT TYPE

We study polar actions with horizontal sections on the total space of certain principal bundles G/K -> G/H with base a symmetric space of compact type. We classify such actions up to orbit equivalence in many cases. In particular, we exhibit examples of hyperpolar actions with cohomogeneity greater than one on locally irreducible homogeneous spaces with nonnegative curvature which are not homeomorphic to symmetric spaces.

A state-contingent production approach to principal-agent problems with an application to point-source pollution control

Two basic representations of principal-agent relationships, the 'state-space' and 'parameterized distribution' formulations, have emerged. Although the state-space formulation appears more natural, analytical studies using this formulation have had limited success. This paper develops a state-space formulation of the moral-hazard problem using a general representation of production under uncertainty. A closed-form solution for the agency-cost problem is derived. Comparative-static results are deduced. Next we solve the principal's problem of selecting the optimal output given the agency-cost function. The analysis is applied to the problem of point-source pollution control. (C) 1998 Published by Elsevier Science S.A. All rights reserved.

Morphological and electrophysiological properties of principal neurons in the rat lateral amygdala in vitro

In this study, we characterize the electrophysiological and morphological properties of spiny principal neurons in the rat lateral amygdala using whole cell recordings in acute brain slices. These neurons exhibited a range of firing properties in response to prolonged current injection. Responses varied from cells that showed full spike frequency adaptation, spiking three to five times, to those that showed no adaptation. The differences in firing patterns were largely explained by the amplitude of the afterhyperpolarization (AHP) that followed spike trains. Cells that showed full spike frequency adaptation had large amplitude slow AHPs, whereas cells that discharged tonically had slow AHPs of much smaller amplitude. During spike trains, all cells showed a similar broadening of their action potentials. Biocytin-filled neurons showed a range of pyramidal-like morphologies, differed in dendritic complexity, had spiny dendrites, and differed in the degree to which they clearly exhibited apical versus basal dendrites. Quantitative analysis revealed no association between cell morphology and firing properties. We conclude that the discharge properties of neurons in the lateral nucleus, in response to somatic current injections, are determined by the differential distribution of ionic conductances rather than through mechanisms that rely on cell morphology.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Strong Grobner bases for polynomials over a principal ideal ring

Grobner bases have been generalised to polynomials over a commutative ring A in several ways. Here we focus on strong Grobner bases, also known as D-bases. Several authors have shown that strong Grobner bases can be effectively constructed over a principal ideal domain. We show that this extends to any principal ideal ring. We characterise Grobner bases and strong Grobner bases when A is a principal ideal ring. We also give algorithms for computing Grobner bases and strong Grobner bases which generalise known algorithms to principal ideal rings. In particular, we give an algorithm for computing a strong Grobner basis over a finite-chain ring, for example a Galois ring.

CDKN2A is not the principal target of deletions on the short arm of chromosome 9 in neuroendocrine (Merkel cell) carcinoma of the skin

The majority of small-cell lung cancers (SCLCs) express p16 but not pRb, Given our previous study showing loss of pRb in Merkel cell carcinoma (MCC)/neuroendocrine carcinoma of the skin and the clinicopathological similarities between SCLC acid MCC, we wished to determine if this was also the case in MCC, Twenty-nine MCC specimens from 23 patients were examined for deletions at 10 loci on 9p and I on 9p. No loss of heterozygosity (LO H) was peen in 9 patients including 2 for which tumour and cell line DNAs were examined. Four patients had LOH for all informative loci on 9p, Ten tumours showed more limited regions of loss on 9p, and from these 2 common regions of deletion were determined, Half of all informative cases had LOH at D95168, the most telomeric marker examined, and 3 specimens showed loss of only D9S168, A second region (InFNA-D9S126) showed L0H in 10(44%) cases, and case MCC26 showed LOH for only D9S126, implicating genes centromeric of the CDKN2A locus. No mutations in the coding regions of p16 were seen in 7 cell lines tested, and reactivity to anti-p16 antibody was seen in all Il tumour specimens examined and in 6 of 7 cell lines from 6 patients. Furthermore, all cell lines examined reacted with anti-p 14' antibody, These results suggest that neither transcript of the CDKN2A locus is the target of deletions on 9p in MCC and imply the existence of tumour-suppressor genes mapping both centromeric and telomeric of this locus. (C) 2001 Wiley-Liss, Inc.

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Characterization of microsatellite loci in Anopheles flavirostris, the principal malaria vector in the Philippines

Microsatellites were isolated and characterized from Anopheles flavirostris, the principal malaria vector in the Philippines. Fifty of the 150 positive clones sequenced contained mostly dinucleotide microsatellites and only 16 had trinucleotide repeats. We designed primers from the unique sequences flanking 18 microsatellite loci. Of these, 11 loci produced successful amplification and revealed high levels of polymorphism; 86 alleles were detected with allele number ranging from 2 to 16 at each locus. The high allelic variability will make these microsatellite loci very useful for taxonomic and population genetic studies.