970 resultados para hierarchical classification structures
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This study aimed to analyze the social representations in the professionals of technical staff, who work with children at USP daycare centers. Eight professionals of the nursing field underwent a semi-structured interview. The interviews were recorded and transcribed in their entirety and the content of the discourse was subjected to thematic-categorical analysis. The categories were transformed into variables and processed by the software Classification Hiérarchique Classificatoire et Cohésitive (CHIC®) and analyzed by the hierarchical similarity tree. The results indicate that actions to promote health are reported as educational and transformative, in which health care gains new meaning through contextualized conceptions in the field of child education. We conclude that professionals attribute new meanings to their practices in the health care environment of daycare centers as their representations shifts from the logic of the biomedical field to a logic of educational care. In this sense, they perceive themselves as being challenged to establish an interaction with the children in terms of their activities related to the promotion of health and in an educational act.
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OBJECTIVE To validate terms of nursing language especially for physical-motor rehabilitation and map them to the terms of ICNP® 2.0. METHOD A methodology research based on document analysis, with collection and analysis of terms from 1,425 records. RESULTS 825 terms were obtained after the methodological procedure, of which 226 had still not been included in the ICNP® 2.0. These terms were distributed as follows: 47 on the Focus axis; 15 on the Judgment axis; 31 on the Action axis; 25 on the Location axis; 102 on the Means axis; three on the Time axis; and three on the Client axis. All non-constant terms in ICNP® have been validated by experts, having reached an agreement index ≥0.80. CONCLUSION The ICNP® is applicable and used in nursing care for physical-motor rehabilitation.
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The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.
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AbstractOBJECTIVETo describe the pressure ulcer healing process in critically ill patients treated with conventional dressing therapy plus low-intensity laser therapy evaluated by the Pressure Ulcer Scale for Healing (PUSH) and the result of Wound Healing: Secondary Intention, according to the Nursing Outcomes Classification (NOC).METHODCase report study according to nursing process conducted with an Intensive Care Unit patient. Data were collected with an instrument containing the PUSH and the result of the NOC. In the analysis we used descriptive statistics, considering the scores obtained on the instrument.RESULTSA reduction in the size of lesions of 7cm to 1.5cm of length and 6cm to 1.1cm width, in addition to the increase of epithelial tissue and granulation, decreased secretion and odor.CONCLUSIONThere was improvement in the healing process of the lesion treated with adjuvant therapy and the use of NOC allowed a more detailed and accurate assessment than the PUSH.
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The purpose of this article is to analyse the conditions under which referendum campaigns have an impact on voting choices. Based on a model of opinion formation that integrates both campaign effects and partisan effects, we argue that campaign effects vary according to the context of the popular vote (size and type of conflict among the party elite and intensity and direction of the referendum campaign). We test our hypotheses with two-step estimations for hierarchical models on data covering 25 popular votes on foreign, European and immigration policy in Switzerland. Our results show strong campaign effects and they suggest that their strength and nature are indeed highly conditional on the context of the vote: the type of party coalition pre-structures the patterns of individual voting choices, campaign effects are higher when the campaign is highly intense and they are more symmetric when it is balanced.
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Using data for all the fixtures for the seasons from 1972-73 to 2002-03, we estimate a dynamic model of demand for football pools in Spain paying attention to whether their main economic explanatory variable is the effective price of a ticket or the jackpot. Additionally, we evaluate the importance of the composition of the list of games in terms of whether First Division matches are included or not. Results show that the jackpot model is preferred to the effective price model, having important implications in terms of how the structure of the game should be changed in order to increase demand.
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The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.
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BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.
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Using one male-inherited and eight biparentally inherited microsatellite markers, we investigate the population genetic structure of the Valais chromosome race of the common shrew (Sorex araneus) in the Central Alps of Europe. Unexpectedly, the Y-chromosome microsatellite suggests nearly complete absence of male gene flow among populations from the St-Bernard and Simplon regions (Switzerland). Autosomal markers also show significant genetic structuring among these two geographical areas. Isolation by distance is significant and possible barriers to gene flow exist in the study area. Two different approaches are used to better understand the geographical patterns and the causes of this structuring. Using a principal component analysis for which testing procedure exists, and partial Mantel tests, we show that the St-Bernard pass does not represent a significant barrier to gene flow although it culminates at 2469 m, close to the highest altitudinal record for this species. Similar results are found for the Simplon pass, indicating that both passes represented potential postglacial recolonization routes into Switzerland from Italian refugia after the last Pleistocene glaciations. In contrast with the weak effect of these mountain passes, the Rhône valley lowlands significantly reduce gene flow in this species. Natural obstacles (the large Rhône river) and unsuitable habitats (dry slopes) are both present in the valley. Moreover, anthropogenic changes to landscape structures are likely to have strongly reduced available habitats for this shrew in the lowlands, thereby promoting genetic differentiation of populations found on opposite sides of the Rhône valley.
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Structural equation models are widely used in economic, socialand behavioral studies to analyze linear interrelationships amongvariables, some of which may be unobservable or subject to measurementerror. Alternative estimation methods that exploit different distributionalassumptions are now available. The present paper deals with issues ofasymptotic statistical inferences, such as the evaluation of standarderrors of estimates and chi--square goodness--of--fit statistics,in the general context of mean and covariance structures. The emphasisis on drawing correct statistical inferences regardless of thedistribution of the data and the method of estimation employed. A(distribution--free) consistent estimate of $\Gamma$, the matrix ofasymptotic variances of the vector of sample second--order moments,will be used to compute robust standard errors and a robust chi--squaregoodness--of--fit squares. Simple modifications of the usual estimateof $\Gamma$ will also permit correct inferences in the case of multi--stage complex samples. We will also discuss the conditions under which,regardless of the distribution of the data, one can rely on the usual(non--robust) inferential statistics. Finally, a multivariate regressionmodel with errors--in--variables will be used to illustrate, by meansof simulated data, various theoretical aspects of the paper.