Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Interaction among nitric oxide (NO)-related genes in migraine susceptibility

The pathogenic mechanisms involved in migraine are complex and not completely clarified. Because there is evidence for the involvement of nitric oxide (NO) in migraine pathophysiology, candidate gene approaches focusing on genes affecting the endothelial function have been studied including the genes encoding endothelial NO synthase (eNOS), inducible NO synthase (iNOS), and vascular endothelial growth factor (VEGF). However, investigations on gene-gene interactions are warranted to better elucidate the genetic basis of migraine. This study aimed at characterizing interactions among nine clinically relevant polymorphisms in eNOS (T-786C/rs2070744, the 27 bp VNTR in intron 4, the Glu298Asp/rs1799983, and two additional tagSNPs rs3918226 and rs743506), iNOS (C(-1026)A/rs2779249 and G2087A/rs2297518), and VEGF (C(-2578)A/rs699947 and G(-634)C/rs2010963) in migraine patients and control group. Genotypes were determined by real-time polymerase chain reaction using the Taqman(A (R)) allele discrimination assays or PCR and fragment separation by electrophoresis in 99 healthy women without migraine (control group) and in 150 women with migraine divided into two groups: 107 with migraine without aura and 43 with aura. The multifactor dimensionality reduction method was used to detect and characterize gene-gene interactions. We found a significant interaction between eNOS rs743506 and iNOS 2087G/A polymorphisms in migraine patients compared to control group (P < 0.05), suggesting that this combination affect the susceptibility to migraine. Further studies are needed to determine the molecular mechanisms explaining this interaction.

Epistasis among eNOS, MMP-9 and VEGF maternal genotypes in hypertensive disorders of pregnancy

Polymorphisms of the endothelial nitric oxide synthase (eNOS), matrix metalloproteinase-9 (MMP-9) and vascular endothelial growth factor (VEGF) genes were shown to be associated with hypertensive disorders of pregnancy. However, epistasis is suggested to be an important component of the genetic susceptibility to preeclampsia (PE). The aim of this study was to characterize the interactions among these genes in PE and gestational hypertension (GH). Seven clinically relevant polymorphisms of eNOS (T-786C, rs2070744, a variable number of tandem repeats in intron 4 and Glu298Asp, rs1799983), MMP-9 (C-1562T, rs3918242 and -90(CA)(13-25), rs2234681) and VEGF (C-2578A, rs699947 and G-634C, rs2010963) were genotyped by TaqMan allelic discrimination assays or PCR and fragment separation by electrophoresis in 122 patients with PE, 107 patients with GH and a control group of 102 normotensive pregnant (NP) women. A robust multifactor dimensionality reduction analysis was used to characterize gene-gene interactions. Although no significant genotype combinations were observed for the comparison between the GH and NP groups (P>0.05), the combination of MMP-9-1562CC with VEGF-634GG was more frequent in NP women than in women with PE (P<0.05). Moreover, the combination of MMP-9-1562CC with VEGF-634CC or MMP-9-1562CT with VEGF-634CC or-634GG was more frequent in women with PE than in NP women (P<0.05). These results are obscured when single polymorphisms in these genes are considered and suggest that specific genotype combinations of MMP-9 and VEGF contribute to PE susceptibility. Hypertension Research (2012) 35, 917-921; doi:10.1038/hr.2012.60; published online 10 May 2012

Effect of external fields in Axelrod's model of social dynamics

The study of the effects of spatially uniform fields on the steady-state properties of Axelrod's model has yielded plenty of counterintuitive results. Here, we reexamine the impact of this type of field for a selection of parameters such that the field-free steady state of the model is heterogeneous or multicultural. Analyses of both one- and two-dimensional versions of Axelrod's model indicate that the steady state remains heterogeneous regardless of the value of the field strength. Turning on the field leads to a discontinuous decrease on the number of cultural domains, which we argue is due to the instability of zero-field heterogeneous absorbing configurations. We find, however, that spatially nonuniform fields that implement a consensus rule among the neighborhood of the agents enforce homogenization. Although the overall effects of the fields are essentially the same irrespective of the dimensionality of the model, we argue that the dimensionality has a significant impact on the stability of the field-free homogeneous steady state.

Cross-cultural adaptation and psychometric properties of the Brazilian-Portuguese version of the Duke Anticoagulation Satisfaction Scale

Aim. The aim of this study was to evaluate the internal reliability and validity of the BrazilianPortuguese version of Duke Anticoagulation Satisfaction Scale (DASS) among cardiovascular patients. Background. Oral anticoagulation is widely used to prevent and treat thromboembolic events in several conditions, especially in cardiovascular diseases; however, this therapy can induce dissatisfaction and reduce the quality of life. Design. Methodological and cross-sectional research design. Methods. The cultural adaptation of the DASS included the translation and back-translation, discussions with healthcare professionals and patients to ensure conceptual equivalence, semantic evaluation and instrument pretest. The BrazilianPortuguese version of the DASS was tested among subjects followed in a university hospital anticoagulation outpatient clinic. The psychometric properties were assessed by construct validity (convergent, known groups and dimensionality) and internal consistency/reliability (Cronbachs alpha). Results. A total of 180 subjects under oral anticoagulation formed the baseline validation population. DASS total score and SF-36 domain correlations were moderate for General health (r = -0.47, p < 0.01), Vitality (r = -0.44, p < 0.01) and Mental health (r = -0.42, p < 0.01) (convergent). Age and length on oral anticoagulation therapy (in years) were weakly correlated with total DASS score and most of the subscales, except Limitation (r = -0.375, p < 0.01) (Known groups). The Cronbachs alpha coefficient was 0.79 for the total scale, and it ranged from 0.76 (hassles and burdens)0.46 (psychological impact) among the domains, confirming the internal consistency reliability. Conclusions. The BrazilianPortuguese version of the DASS has shown levels of reliability and validity comparable with the original English version. Relevance to clinical practice. Healthcare practitioners and researchers need internationally validated measurement tools to compare outcomes of interventions in clinical management and research tools in oral anticoagulation therapy.

A comparative study on multiscale fractal dimension descriptors

Fractal theory presents a large number of applications to image and signal analysis. Although the fractal dimension can be used as an image object descriptor, a multiscale approach, such as multiscale fractal dimension (MFD), increases the amount of information extracted from an object. MFD provides a curve which describes object complexity along the scale. However, this curve presents much redundant information, which could be discarded without loss in performance. Thus, it is necessary the use of a descriptor technique to analyze this curve and also to reduce the dimensionality of these data by selecting its meaningful descriptors. This paper shows a comparative study among different techniques for MFD descriptors generation. It compares the use of well-known and state-of-the-art descriptors, such as Fourier, Wavelet, Polynomial Approximation (PA), Functional Data Analysis (FDA), Principal Component Analysis (PCA), Symbolic Aggregate Approximation (SAX), kernel PCA, Independent Component Analysis (ICA), geometrical and statistical features. The descriptors are evaluated in a classification experiment using Linear Discriminant Analysis over the descriptors computed from MFD curves from two data sets: generic shapes and rotated fish contours. Results indicate that PCA, FDA, PA and Wavelet Approximation provide the best MFD descriptors for recognition and classification tasks. (C) 2012 Elsevier B.V. All rights reserved.

Psychometric properties of the Spanish version of the mindful attention awareness scale (MAAS) in patients with fibromyalgia

Background Mindful-based interventions improve functioning and quality of life in fibromyalgia (FM) patients. The aim of the study is to perform a psychometric analysis of the Spanish version of the Mindful Attention Awareness Scale (MAAS) in a sample of patients diagnosed with FM. Methods The following measures were administered to 251 Spanish patients with FM: the Spanish version of MAAS, the Chronic Pain Acceptance Questionnaire, the Pain Catastrophising Scale, the Injustice Experience Questionnaire, the Psychological Inflexibility in Pain Scale, the Fibromyalgia Impact Questionnaire and the Euroqol. Factorial structure was analysed using Confirmatory Factor Analyses (CFA). Cronbach's α coefficient was calculated to examine internal consistency, and the intraclass correlation coefficient (ICC) was calculated to assess the test-retest reliability of the measures. Pearson’s correlation tests were run to evaluate univariate relationships between scores on the MAAS and criterion variables. Results The MAAS scores in our sample were low (M = 56.7; SD = 17.5). CFA confirmed a two-factor structure, with the following fit indices [sbX2 = 172.34 (p < 0.001), CFI = 0.95, GFI = 0.90, SRMR = 0.05, RMSEA = 0.06. MAAS was found to have high internal consistency (Cronbach’s α = 0.90) and adequate test-retest reliability at a 1–2 week interval (ICC = 0.90). It showed significant and expected correlations with the criterion measures with the exception of the Euroqol (Pearson = 0.15). Conclusion Psychometric properties of the Spanish version of the MAAS in patients with FM are adequate. The dimensionality of the MAAS found in this sample and directions for future research are discussed.