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 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Decrease in self-esteem (SE) is found in all mood disorders during inter-episode phases. This trait was associated with relapse and suicidality but its genetic basis is still undefined, probably because SE has multiple components. The aim of the current study was to ascertain which of those components were altered in a sample of affective patients. Three hundred and thirty-one outpatients with bipolar (N=199) and major depressive MD (N=132) disorders in remission for at least three months and one hundred controls completed the Rosenberg Self-esteem Scale (RSE; [Rosenberg, M., 1965. The measurement of self-esteem, Society and the Adolescent Self-Image. Princeton University Press, pp.16-36]). Principal component analysis was performed to identify RSE factor structure. Extracted factors were compared across case and control groups in the whole sample (N=431) and in a sub-sample (N=301) with low self-esteem (RSE <20). PCA yielded a two-factor solution with self-confidence (SC) and self-deprecation (SD) that was largely consistent with the existing literature. Such factors were both associated with lower scores in affective patients than controls (SC: F=52, p<0.01; SD: F=43, p<0.01). However in the low RSE group only self-confidence was found to be decreased in subjects with mood disorders (SC: F=13.8, p<0.01; SD: F=0.05, p=0.9). These findings suggest that self-esteem deficit in affective disorders might involve specific components. Implications for research and clinical practice are discussed.
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PMID:Components of self-esteem in affective patients and non-psychiatric controls. 1604 Jan 27

Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is proposed in this paper, which consists of the following three main steps: (1) From the template of normal galaxy, the two sets of samples are simulated, one with the redshift of 0.0-0.3, the other of 0.3-0.5, then the PCA is used to extract the main components, and train samples are projected to the main component subspace to obtain characteristic spectra. (2) The characteristic spectra are used to train a Probabilistic Neural Network to obtain a Bayes classifier. (3) An unknown real NG spectrum is first inputted to this Bayes classifier to determine the possible range of redshift, then the template matching is invoked to locate the redshift value within the estimated range. Compared with the traditional template matching technique with an unconstrained range, our proposed method not only halves the computational load, but also increases the estimation accuracy. As a result, the proposed method is particularly useful for automatic spectrum processing produced from a large-scale sky survey project.
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PMID:[Using neural networks based template matching method to obtain redshifts of normal galaxies]. 1620 92

Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space. At first, the K-means clustering is used for the initialization, then based on the sum of squares errors and a gradient descent optimization technique, the number of neurons in the hidden layer is adaptively increased to improve the recognition performance; (3) The quasar spectra recognition is effectively carried out by the above trained RBFN. The author's proposed adaptive RBFN is shown to be able to not only overcome the difficulty of selecting the number of neurons in hidden layer of the traditional RBFN algorithm, but also increase the stability and accuracy of recognition of quasars. Besides, the proposed method is particularly useful for automatic voluminous spectra processing produced from a large-scale sky survey project, such as our LAMOST, due to its efficiency.
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PMID:[Automated recognition of quasars based on adaptive radial basis function neural networks]. 1682 29

Automated spectra analysis is desirable and necessary for efficiency of large sky surveys such as SDSS (Sloan digital sky survey), 2DF (2 degree fields) and LAMOST (large sky area multi-object spectroscopic telescope). In the present paper, we present a method for redshift estimation of galaxy spectra based on similarity measure. Firstly, we extract the spectral lines of the observed spectrum using the feature constrains of spectral lines; secondly, the authors determine the redshift candidates of the observed spectrum by spectral line features; then, the similarity between the observed spectrum and the template spectra shifted by each redshift candidate is measured; finally, the candidate of the highest similarity is chosen as the estimated redshift. PCA (principal component analysis) is used to build the static galaxy template spectra. The authors perform PCA for the four template spectra E, S0, Sa and Sb of the normal galaxy and the seven template spectra Sc, Sb1, Sb2, Sb3, Sb4, Sb5 and Sb6 of the starburst galaxy respectively, where the eleven template spectra are presented by Kinney & Calzetti et al. Two eigen-spectra are produced with the variance contribution rate of 99%. The authors choose the two eigen-spectra as the galaxy templates. The similarity measure proposed, which is similar to the evidence accumulation, is defined as the weighted sum of several similarity evidences. It can reduce the influence caused by some error matching. The authors divide the observed spectrum and the template spectrum respectively into several parts, and measure the correlations of the corresponding parts of them, which is chosen as the similarity evidences in the proposed similarity measure. The principle of setting the weights is that the higher the correlation, the higher the corresponding weight. The proposed approach is compared with the method based on spectral line matching and the traditional cross correlation technique by experiments, the results show that the proposed method has a higher correct rate.
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PMID:[Redshift estimation of galaxy spectra based on similarity measure]. 1842 62

In the present paper, an automatic and efficient method for searching for dwarf nova candidates is presented. The methods PCA (principal component analysis) and SVM (support vector machine) are applied in the newly released SDSS-DR9 spectra. The final dimensions of the feature space are determined by the identification accuracy of training samples with different dimensions constrained by SVM. The massive spectra are dimension reduced by PCA at first and classified by the best SVM clas sifier. The final less number of candidates can be identified manually. A total number of 276 dwarf nova candidates are selected by the method and 6 of them are new discoveries which prove that our approach to finding special celestial bodies in massive spectra data is feasible. The new discoveries of this paper are added in the current dwarf nova template library which can contribute to constructing a more accurate feature space. The method proposed in this paper can also be used for special objects searching in other sky survey telescopes like Guoshoujing (Large Sky Area Multi-Object Fiber Spectroscopic Telescope -LAMOST) telescope.
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PMID:[Searching for dwarf nova candidates with automatic methods in massive spectra]. 2461 13

The spectral reflectance function of a surface specifies the fraction of the illumination reflected by it at each wavelength. Jointly with the illumination spectral density, this function determines the apparent colour of the surface. Models for the distribution of spectral reflectance functions in the natural environment are considered. The realism of the models is assessed in terms of the individual reflectance functions they generate, and in terms of the overall distribution of colours which they give rise to. Both realism assessments are made in comparison to empirical datasets. Previously described models (PCA- and fourier-based) of reflectance function statistics are evaluated, as are improved versions; and also a novel model, which synthesizes reflectance functions as a sum of sigmoid functions. Key model features for realism are identified. The new sigmoid-sum model is shown to be the most realistic, generating reflectance functions that are hard to distinguish from real ones, and accounting for the majority of colours found in natural images with the exception of an abundance of vegetation green and sky blue.
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PMID:Reconciling the statistics of spectral reflectance and colour. 3170 60