An improved continuous medium technique for structural frame analysis

This paper presents an improved constitutive equation of frame in the context of continuous medium technique. This improved constitutive equation, which is a consistent formulation of column global bending, is applicable to a complete class of frameworks including the ideal shear frame panel, for which the beams are assumed to be rigid, and the associated column system, for which the rigidity of beams is negligible. Global buckling and second-order effects of the frame structure are discussed. The main results can be extended to other types of lateral stiffening elements as built-up columns. A worked example is presented in order to compare the main results with those obtained by the classic matrix method. Copyright (C) 2007 John Wiley & Sons, Ltd.

Biomarkers of selenium status in the amazonian context: Blood, urine and sequential hair segments

Selenium (Se) is an essential element and deficit or excess of dietary Se is associated with health disorders. Relatively elevated Se levels have been reported in the Brazilian Amazon, where there are also important annual variations in the availability of different foods. The present study was conducted among six riparian communities of the Tapajos River to evaluate seasonal variations in blood and sequential hair cm Se concentrations, and to examine the relationships between Se in blood and hair, and blood and urine. Two cross-sectional studies were conducted, at the descending water (DWS, n = 259) and the rising water (RWS, n = 137) seasons, with repeated measures for a subgroup (n = 112). Blood Se (B-Se), hair Se (H-Se) and urine Se (U-Se) were determined. Match-paired analyses were used for seasonal comparisons and the method of best fit was used to describe the relationships between biomarkers. B-Se levels presented a very large range (142-2447 mu g/l) with no overall seasonal variation (median 284 and 292 mu g/l, respectively). Sequential analysis of 13 cm hair strands showed significant variations over time: Se concentrations at the DWS were significantly lower compared with the rising water season (medians: 0.7 and 0.9 mu g/g; ranges: 0.2-4.3 mu g/g and 0.2-5.4 mu g/g, respectively). At both seasons, the relationships between B-Se and H-Se were linear and highly significant (r(2) = 67.9 and 63.6, respectively), while the relationship between B-Se and U-Se was best described by a sigmoid curve. Gender, age, education and smoking did not influence Se status or biomarker relationships. Variations in H-Se suggest that there may be seasonal availability of Se sources in local food. For populations presenting a large range and/or elevated Se exposure, sequential analyses of H-Se may provide a good reflection of variations in Se status.

Neighborhood and Efficiency in Manufacturing in Brazilian Regions: A Spatial Markov Chain Analysis

This paper analyzes the geography of regional competitiveness in manufacturing in Brazil. The authors estimate stochastic frontiers to calculate regional efficiency of representative firms in 137 regions in the period 2000-2006, in four sectors defined by technological intensity. The efficiency results are analyzed using Markov Spatial Transition Matrices to provide insights into the transition of regions between efficiency levels, considering their local spatial context. The results indicate that geography plays an important role in manufacturing competitiveness. In particular, regions with more competitive neighbors are more likely to improve their relative efficiency (pull effect) over time, and regions with less competitive neighbors are more likely to lose relative efficiency (drag effect). The authors find that the pull effect is stronger than the drag effect.

Mutational analysis of the necdin gene in patients with congenital isolated hypogonadotropic hypogonadism

Context: Necdin activates GNRH gene expression and is fundamental for the development, migration, and axonal extension of murine GNRH neurons. In humans, necdin plays a potential role in the hypogonadotropic hypogonadism phenotype in patients with Prader-Willi syndrome. Aim: To investigate necdin gene (NDN) variants in patients with isolated hypogonadotropic hypogonadism (IHH). Patients and methods: We studied 160 Brazilian patients with IHH, which includes 92 with Kallmann syndrome and 68 with normosmic IHH. Genomic DNA was extracted and the single NDN exon was amplified and sequenced. To measure GNRH transcriptional activity, luciferase reporter plasmids containing GNRH regulatory regions were transiently transfected into GT1-7 cells in the presence and absence of overexpressed wild-type or mutant necdin. Results: A heterozygous variant of necdin, p.V318A, was identified in a 23-year-old male with Kallmann syndrome. The p.V318A was also present in affected aunt and his father and was absent in 100 Brazilian control subjects. Previous FGFR1 gene analysis revealed a missense mutation (p.P366L) in this family. Functional studies revealed a minor difference in the activation of GNRH transcription by mutant protein compared with wild type in that a significant impairment of the necdin protein activity threshold was observed. Conclusion: A rare variant of necdin (p.V318A) was described in a family with Kallmann syndrome associated with a FGFR1 mutation. Familial segregation and in vitro analysis suggested that this non-synonymous variant did not have a direct causative role in the hypogonadism phenotype. NDN mutations are not a frequent cause of congenital IHH.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Further psychometric study of the Beck Anxiety Inventory including factorial analysis and social anxiety disorder screening

Objective. To analyze the psychometric properties of the Brazilian Portuguese version of the Beck Anxiety Inventory (BAI) in terms of its internal consistency, scores distribution, concurrent and discriminant validity, and factorial analysis in a sample of university students and social anxiety disorder (SAD) cases and non-cases. Methods. A sample of Brazilian university students from the general population (N = 2314) and a sample of university students identified as cases (N = 88) and non-cases (N = 90) of SAD were assessed, using as a parameter the Structured Clinical Interview for the DSM-IV. The different instruments were completed individually in the presence of an experienced rater. Results. The BAI showed adequate internal consistency (0.88-0.92) and discriminant validity, with 0.74 sensitivity and 0.71 specificity for a cut-off score of 10. The factorial analysis suggested a three-factor solution to be the most adequate. Conclusions. The version of the BAI studied is quite adequate to be used in the context of Brazilian university students, identifying the presence of anxiety indicators. However, its usefulness to screen for SAD seems limited.

Paleoamerican Morphology in the Context of European and East Asian Late Pleistocene Variation: Implications for Human Dispersion Into the New World

Early American crania show a different morphological pattern from the one shared by late Native Americans. Although the origin of the diachronic morphological diversity seen on the continents is still debated, the distinct morphology of early Americans is well documented and widely dispersed. This morphology has been described extensively for South America, where larger samples are available. Here we test the hypotheses that the morphology of Early Americans results from retention of the morphological pattern of Late Pleistocene modern humans and that the occupation of the New World precedes the morphological differentiation that gave rise to recent Eurasian and American morphology. We compare Early American samples with European Upper Paleolithic skulls, the East Asian Zhoukoudian Upper Cave specimens and a series of 20 modern human reference crania. Canonical Analysis and Minimum Spanning Tree were used to assess the morphological affinities among the series, while Mantel and Dow-Cheverud tests based on Mahalanobis Squared Distances were used to test different evolutionary scenarios. Our results show strong morphological affinities among the early series irrespective of geographical origin, which together with the matrix analyses results favor the scenario of a late morphological differentiation of modern humans. We conclude that the geographic differentiation of modern human morphology is a late phenomenon that occurred after the initial settlement of the Americas. Am J Phys Anthropol 144:442-453, 2011. (c) 2010 Wiley-Liss, Inc.

Coalescent analysis of mtDNA indicates Pleistocene divergence among three species of howler monkey (Alouatta spp.) and population subdivision within the Atlantic Coastal Forest species, A. guariba

We have used coalescent analysis of mtDNA cytochrome b (cyt b) sequences to estimate times of divergence of three species of Alouatta-A. caraya, A. belzebul, and A. guariba-which are in close geographic proximity. A. caraya is inferred to have diverged from the A. guariba/A. belzebul clade approximately 3.83 million years ago (MYA), with the later pair diverging approximately 1.55 MYA. These dates are much more recent than previous dates based on molecular-clock methods. In addition, analyses of new sequences from the Atlantic Coastal Forest species A. guariba indicate the presence of two distinct haplogroups corresponding to northern and southern populations with both haplogroups occurring in sympatry within Sao Paulo state. The time of divergence of these two haplogroups is estimated to be 1.2 MYA and so follows quite closely after the divergence of A. guariba and A. belzebul. These more recent dates point to the importance of Pleistocene environmental events as important factors in the diversification of A. belzebul and A. guariba. We discuss the diversification of the three Alouatta species in the context of recent models of climatic change and with regard to recent molecular phylogeographic analyses of other animal groups distributed in Brazil.

Coding behavioural data for cladistic analysis: using dynamic homology without parsimony

Many of the controversies around the concept of homology rest on the subjectivity inherent to primary homology propositions. Dynamic homology partially solves this problem, but there has been up to now scant application of it outside of the molecular domain. This is probably because morphological and behavioural characters are rich in properties, connections and qualities, so that there is less space for conflicting character delimitations. Here we present a new method for the direct optimization of behavioural data, a method that relies on the richness of this database to delimit the characters, and on dynamic procedures to establish character state identity. We use between-species congruence in the data matrix and topological stability to choose the best cladogram. We test the methodology using sequences of predatory behaviour in a group of spiders that evolved the highly modified predatory technique of spitting glue onto prey. The cladogram recovered is fully compatible with previous analyses in the literature, and thus the method seems consistent. Besides the advantage of enhanced objectivity in character proposition, the new procedure allows the use of complex, context-dependent behavioural characters in an evolutionary framework, an important step towards the practical integration of the evolutionary and ecological perspectives on diversity. (C) The Willi Hennig Society 2010.

AMYGDALAR ROLES DURING EXPOSURE TO A LIVE PREDATOR AND TO A PREDATOR-ASSOCIATED CONTEXT

The amygdala plays a critical role in determining the emotional significance of sensory stimuli and the production of fear-related responses. Large amygdalar lesions have been shown to practically abolish innate defensiveness to a predator; however, it is not clear how the different amygdalar systems participate in the defensive response to a live predator. Our first aim was to provide a comprehensive analysis of the amygdalar activation pattern during exposure to a live cat and to a predator-associated context. Accordingly, exposure to a live predator up-regulated Fos expression in the medial amygdalar nucleus (MEA) and in the lateral and posterior basomedial nuclei, the former responding to predator-related pheromonal information and the latter two nuclei likely to integrate a wider array of predatory sensory information, ranging from olfactory to non-olfactory ones, such as visual and auditory sensory inputs. Next, we tested how the amygdalar nuclei most responsive to predator exposure (i.e. the medial, posterior basomedial and lateral amygdalar nuclei) and the central amygdalar nucleus (CEA) influence both unconditioned and contextual conditioned anti-predatory defensive behavior. Medial amygdalar nucleus lesions practically abolished defensive responses during cat exposure, whereas lesions of the posterior basomedial or lateral amygdalar nuclei reduced freezing and increased risk assessment displays (i.e. crouch sniff and stretch postures), a pattern of responses compatible with decreased defensiveness to predator stimuli. Moreover, the present findings suggest a role for the posterior basomedial and lateral amygdalar nuclei in the conditioning responses to a predator-related context. We have further shown that the CEA does not seem to be involved in either unconditioned or contextual conditioned anti-predatory responses. Overall, the present results help to clarify the amygdalar systems involved in processing predator-related sensory stimuli and how they influence the expression of unconditioned and contextual conditioned anti-predatory responses. (C) 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

On comparing two sequences of numbers and its applications to clustering analysis

A conceptual problem that appears in different contexts of clustering analysis is that of measuring the degree of compatibility between two sequences of numbers. This problem is usually addressed by means of numerical indexes referred to as sequence correlation indexes. This paper elaborates on why some specific sequence correlation indexes may not be good choices depending on the application scenario in hand. A variant of the Product-Moment correlation coefficient and a weighted formulation for the Goodman-Kruskal and Kendall`s indexes are derived that may be more appropriate for some particular application scenarios. The proposed and existing indexes are analyzed from different perspectives, such as their sensitivity to the ranks and magnitudes of the sequences under evaluation, among other relevant aspects of the problem. The results help suggesting scenarios within the context of clustering analysis that are possibly more appropriate for the application of each index. (C) 2008 Elsevier Inc. All rights reserved.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

Surface roughness is an important geomorphological variable which has been used in the Earth and planetary sciences to infer material properties, current/past processes, and the time elapsed since formation. No single definition exists; however, within the context of geomorphometry, we use surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter`s behavior at different spatial scales and data-set resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenization of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography highlighted local features and did not detect regional relief. Standard deviation of elevation correctly identified breaks of slope and was good at detecting regional relief. Standard deviation of slope (SD(slope)) also correctly identified smooth sloping areas and breaks of slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks of slope, although not as strongly as SD(slope), and it is sensitive to noise and spurious data. In general, SD(slope) offered good performance at a variety of scales, while the simplicity of calculation is perhaps its single greatest benefit.