909 resultados para 750102 Changing work patterns
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Comparing the patterns of population differentiation among genetic markers with different modes of inheritance call provide insights into patterns of sex-biased dispersal and gene flow. The blue-and-yellow Macaw (Ara ararauna) is a Neotropical parrot with a broad geographic distribution ill South America. However, little is known about the natural history and current status Of remaining wild populations, including levels of genetic variability. The progressive decline and possible fragmentation of populations may endanger this species in the near future. We analyzed mitochondrial DNA (mtDNA) control-region sequences and six microsatellite 106 Of Blue-and-yellow Macaws sampled throughout their geographic range ill Brazil to describe population genetic Structure, to make inferences about historical demography and dispersal behavior, and to provide insight for conservation efforts. Analyses of population genetic structure based on mtDNA showed evidence of two major populations ill western and eastern Brazil that share a few low-frequency haplotypes. This phylogeographic pattern seems to have originated by the historical isolation of Blue-and-yellow Macaw populations similar to 374,000 years ago and has been maintained by restricted gene flow and female philopatry. By contrast, variation ill biparentally inherited microsatellites was not structured geographically, Male-biased dispersal and female philopatry best explain the different patterns observed in these two markers. Because females disperse less than males, the two regional populations with well-differentiated mtDNA haplogroups should be considered two different management units for conservation purposes. Received 4 November 2007 accepted 10 December 2008.
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Background: Macrophage migration inhibitory factor (MIF) has special pro-inflammatory roles, affecting the functions of macrophages and lymphocytes and counter-regulating the effects of glucocorticoids on the immune response. The conspicuous expression of MIF during human implantation and early embryonic development also suggests this factor acts in reproductive functions. The overall goal of this study was to evaluate Mif expression by trophoblast and embryo placental cells during mouse pregnancy. Methods: Mif was immunolocalized at implantation sites on gestation days (gd) 7.5, 10.5, 13.5 and 17.5. Ectoplacental cones and fetal placentas dissected from the maternal tissues were used for Western blotting and qRT-PCR assays on the same gestation days. Results: During the post-implantation period (gd7.5), trophoblast giant cells showed strong Mif reactivity. In later placentation phases (gds 10.5-17.5), Mif appeared to be concentrated in the junctional zone and trophoblast giant cells. Mif protein expression increased significantly from gd7.5 to 10.5 (p = 0.005) and from gd7.5 to 13.5 (p = 0.03), remaining at high concentration as gestation proceeded. Higher mRNA expression was found on gd10.5 and was significantly different from gd13.5 (p = 0.048) and 17.5 (p = 0.009). Conclusions: The up-regulation of Mif on gd10.5 coincides with the stage in which the placenta assumes its three-layered organization (giant cells, spongiotrophoblast and labyrinth zones), fetal blood circulation begins and population of uNK cells reaches high proportions at the maternal counter part of the placenta, suggesting that Mif may play a role in either the placentation or in the adaptation of the differentiated placenta to the uterus or still in gestational immunomodulatory responses. Moreover, it reinforces the possibility of specific activities for Mif at the maternal fetal interface.
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Human respiratory syncytial virus (HRSV) is the major cause of lower respiratory tract infections in children under 5 years of age and the elderly, causing annual disease outbreaks during the fall and winter. Multiple lineages of the HRSVA and HRSVB serotypes co-circulate within a single outbreak and display a strongly temporal pattern of genetic variation, with a replacement of dominant genotypes occurring during consecutive years. In the present study we utilized phylogenetic methods to detect and map sites subject to adaptive evolution in the G protein of HRSVA and HRSVB. A total of 29 and 23 amino acid sites were found to be putatively positively selected in HRSVA and HRSVB, respectively. Several of these sites defined genotypes and lineages within genotypes in both groups, and correlated well with epitopes previously described in group A. Remarkably, 18 of these positively selected tended to revert in time to a previous codon state, producing a ""flipflop'' phylogenetic pattern. Such frequent evolutionary reversals in HRSV are indicative of a combination of frequent positive selection, reflecting the changing immune status of the human population, and a limited repertoire of functionally viable amino acids at specific amino acid sites.
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A great part of the interest in complex networks has been motivated by the presence of structured, frequently nonuniform, connectivity. Because diverse connectivity patterns tend to result in distinct network dynamics, and also because they provide the means to identify and classify several types of complex network, it becomes important to obtain meaningful measurements of the local network topology. In addition to traditional features such as the node degree, clustering coefficient, and shortest path, motifs have been introduced in the literature in order to provide complementary descriptions of the network connectivity. The current work proposes a different type of motif, namely, chains of nodes, that is, sequences of connected nodes with degree 2. These chains have been subdivided into cords, tails, rings, and handles, depending on the type of their extremities (e.g., open or connected). A theoretical analysis of the density of such motifs in random and scale-free networks is described, and an algorithm for identifying these motifs in general networks is presented. The potential of considering chains for network characterization has been illustrated with respect to five categories of real-world networks including 16 cases. Several interesting findings were obtained, including the fact that several chains were observed in real-world networks, especially the world wide web, books, and the power grid. The possibility of chains resulting from incompletely sampled networks is also investigated.
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Recurrences are close returns of a given state in a time series, and can be used to identify different dynamical regimes and other related phenomena, being particularly suited for analyzing experimental data. In this work, we use recurrence quantification analysis to investigate dynamical patterns in scalar data series obtained from measurements of floating potential and ion saturation current at the plasma edge of the Tokamak Chauffage Alfveacuten Breacutesilien [R. M. O. Galva approximate to o , Plasma Phys. Controlled Fusion 43, 1181 (2001)]. We consider plasma discharges with and without the application of radial electric bias, and also with two different regimes of current ramp. Our results indicate that biasing improves confinement through destroying highly recurrent regions within the plasma column that enhance particle and heat transport.
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The existence of a reversed magnetic shear in tokamaks improves the plasma confinement through the formation of internal transport barriers that reduce radial particle and heat transport. However, the transport poloidal profile is much influenced by the presence of chaotic magnetic field lines at the plasma edge caused by external perturbations. Contrary to many expectations, it has been observed that such a chaotic region does not uniformize heat and particle deposition on the inner tokamak wall. The deposition is characterized instead by structured patterns called magnetic footprints, here investigated for a nonmonotonic analytical plasma equilibrium perturbed by an ergodic limiter. The magnetic footprints appear due to the underlying mathematical skeleton of chaotic magnetic field lines determined by the manifold tangles. For the investigated edge safety factor ranges, these effects on the wall are associated with the field line stickiness and escape channels due to internal island chains near the flux surfaces. Comparisons between magnetic footprints and escape basins from different equilibrium and ergodic limiter characteristic parameters show that highly concentrated magnetic footprints can be avoided by properly choosing these parameters. (c) 2008 American Institute of Physics.
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The Sznajd model is a sociophysics model that mimics the propagation of opinions in a closed society, where the interactions favor groups of agreeing people. It is based in the Ising and Potts ferromagnetic models and, although the original model used only linear chains, it has since been adapted to general networks. This model has a very rich transient, which has been used to model several aspects of elections, but its stationary states are always consensus states. In order to model more complex behaviors, we have, in a recent work, introduced the idea of biases and prejudices to the Sznajd model by generalizing the bounded confidence rule, which is common to many continuous opinion models, to what we called confidence rules. In that work we have found that the mean field version of this model (corresponding to a complete network) allows for stationary states where noninteracting opinions survive, but never for the coexistence of interacting opinions. In the present work, we provide networks that allow for the coexistence of interacting opinions for certain confidence rules. Moreover, we show that the model does not become inactive; that is, the opinions keep changing, even in the stationary regime. This is an important result in the context of understanding how a rule that breeds local conformity is still able to sustain global diversity while avoiding a frozen stationary state. We also provide results that give some insights on how this behavior approaches the mean field behavior as the networks are changed.
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Biological neuronal networks constitute a special class of dynamical systems, as they are formed by individual geometrical components, namely the neurons. In the existing literature, relatively little attention has been given to the influence of neuron shape on the overall connectivity and dynamics of the emerging networks. The current work addresses this issue by considering simplified neuronal shapes consisting of circular regions (soma/axons) with spokes (dendrites). Networks are grown by placing these patterns randomly in the two-dimensional (2D) plane and establishing connections whenever a piece of dendrite falls inside an axon. Several topological and dynamical properties of the resulting graph are measured, including the degree distribution, clustering coefficients, symmetry of connections, size of the largest connected component, as well as three hierarchical measurements of the local topology. By varying the number of processes of the individual basic patterns, we can quantify relationships between the individual neuronal shape and the topological and dynamical features of the networks. Integrate-and-fire dynamics on these networks is also investigated with respect to transient activation from a source node, indicating that long-range connections play an important role in the propagation of avalanches.
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We obtain the exact nonequilibrium work generating function (NEWGF) for a small system consisting of a massive Brownian particle connected to internal and external springs. The external work is provided to the system for a finite-time interval. The Jarzynski equality, obtained in this case directly from the NEWGF, is shown to be valid for the present model, in an exact way regardless of the rate of external work.
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Tropical ecosystems play a large and complex role in the global carbon cycle. Clearing of natural ecosystems for agriculture leads to large pulses of CO(2) to the atmosphere from terrestrial biomass. Concurrently, the remaining intact ecosystems, especially tropical forests, may be sequestering a large amount of carbon from the atmosphere in response to global environmental changes including climate changes and an increase in atmospheric CO(2). Here we use an approach that integrates census-based historical land use reconstructions, remote-sensing-based contemporary land use change analyses, and simulation modeling of terrestrial biogeochemistry to estimate the net carbon balance over the period 1901-2006 for the state of Mato Grosso, Brazil, which is one of the most rapidly changing agricultural frontiers in the world. By the end of this period, we estimate that of the state`s 925 225 km(2), 221 092 km(2) have been converted to pastures and 89 533 km(2) have been converted to croplands, with forest-to-pasture conversions being the dominant land use trajectory but with recent transitions to croplands increasing rapidly in the last decade. These conversions have led to a cumulative release of 4.8 Pg C to the atmosphere, with similar to 80% from forest clearing and 20% from the clearing of cerrado. Over the same period, we estimate that the residual undisturbed ecosystems accumulated 0.3 Pg C in response to CO2 fertilization. Therefore, the net emissions of carbon from Mato Grosso over this period were 4.5 Pg C. Net carbon emissions from Mato Grosso since 2000 averaged 146 Tg C/yr, on the order of Brazil`s fossil fuel emissions during this period. These emissions were associated with the expansion of croplands to grow soybeans. While alternative management regimes in croplands, including tillage, fertilization, and cropping patterns promote carbon storage in ecosystems, they remain a small portion of the net carbon balance for the region. This detailed accounting of a region`s carbon balance is the type of foundation analysis needed by the new United Nations Collaborative Programmme for Reducing Emissions from Deforestation and Forest Degradation (REDD).
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Addressing spatial variability in nitrogen (N) availability in the Central Brazilian Amazon, we hypothesized that N availability varies among white-sand vegetation types (campina and campinarana) and lowland tropical forests (dense terra-firme forests) in the Central Brazilian Amazon, under the same climate conditions. Accordingly, we measured soil and foliar N concentration and N isotope ratios (delta(15)N) throughout the campina-campinarana transect and compared to published dense terra-firme forest results. There were no differences between white-sand vegetation types in regard to soil N concentration, C:N ratio and delta(15)N across the transect. Both white-sand vegetation types showed very low foliar N concentrations and elevated foliar C:N ratios, and no significant difference between site types was observed. Foliar delta(15)N was depleted, varying from -9.6 to 1.6aEuro degrees in the white-sand vegetations. The legume Aldina heterophylla had the highest average delta(15)N values (-1.5aEuro degrees) as well as the highest foliar N concentration (2.1%) while the non-legume species had more depleted delta(15)N values and the average foliar N concentrations varied from 0.9 to 1.5% among them. Despite the high variation in foliar delta(15)N among plants, a significant and gradual (15)N-enrichment in foliar isotopic signatures throughout the campina-campinarana transect was observed. Individual plants growing in the campinarana were significantly enriched in (15)N compared to those in campina. In the white-sand N-limited ecosystems, the differentiation of N use seems to be a major cause of variations observed in foliar delta(15)N values throughout the campina-campinarana transect.
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Nitrogen variations at different spatial scales and integrated across functional groups were addressed for lowland tropical forests in the Brazilian Amazon as follows: (1) how does N availability vary across the region over different spatial scales (regional x landscape scale); ( 2) how are these variations in N availability integrated across plant functional groups ( legume 9 non-legume trees). Leaf N, P, and Ca concentrations as well the leaf N isotope ratios (delta(15)N) from a large set of legume and non-legume tree species were measured. Legumes had higher foliar N/Ca ratios than non-legumes, consistent with the high energetic costs in plant growth associated with higher foliar P/Ca ratios found in legumes than in non-legumes. At the regional level, foliar delta(15)N decreased with increasing rainfall. At the landscape level, N availability was higher in the forests on clayey soils on the plateau than in forests on sandier soils. The isotope as well as the non-isotope data relationships here documented, explain to a large extent the variation in delta(15)N signatures across gradients of rainfall and soil. Although at the regional level, the precipitation regime is a major determinant of differences in N availability, at the landscape level, under the same precipitation regime, soil type seems to be a major factor influencing the availability of N in the Brazilian Amazon forest.
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The brief interaction of precipitation with a forest canopy can create a high spatial variability of both throughfall and solute deposition. We hypothesized that (i) the variability in natural forest systems is high but depends on system-inherent stability, (ii) the spatial variability of solute deposition shows seasonal dynamics depending on the increase in rainfall frequency, and (iii) spatial patterns persist only in the short-term. The study area in the north-western Brazilian state of Rondonia is subject to a climate with a distinct wet and dry season. We collected rain and throughfall on an event basis during the early wet season (n = 14) and peak of the wet season (n = 14) and analyzed the samples for pH and concentrations of NH4+, Na+, K+, Ca2+ Mg2+,, Cl-, NO3-, SO42- and DOC. The coefficient 3 4 cient of variation for throughfall based on both sampling intervals was 29%, which is at the lower end of values reported from other tropical forest sites, but which is higher than in most temperate forests. Coefficients of variation of solute deposition ranged from 29% to 52%. This heterogeneity of solute deposition is neither particularly high nor particularly tow compared with a range of tropical and temperate forest ecosystems. We observed an increase in solute deposition variability with the progressing wet season, which was explained by a negative correlation between heterogeneity of solute deposition and antecedent dry period. The temporal stability of throughfall. patterns was Low during the early wet season, but gained in stability as the wet season progressed. We suggest that rapid plant growth at the beginning of the rainy season is responsible for the lower stability, whereas less vegetative activity during the later rainy season might favor the higher persistence of ""hot"" and ""cold"" spots of throughfall. quantities. The relatively high stability of throughfall patterns during later stages of the wet season may influence processes at the forest floor and in the soil. Solute deposition patterns showed less clear trends but all patterns displayed a short-term stability only. The weak stability of those patterns is apt to impede the formation of solute deposition -induced biochemical microhabitats in the soil. (C) 2008 Elsevier B.V. All rights reserved.
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Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.
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The Brazilian Atlantic Forest is one of the richest biodiversity hotspots of the world. Paleoclimatic models have predicted two large stability regions in its northern and central parts, whereas southern regions might have suffered strong instability during Pleistocene glaciations. Molecular phylogeographic and endemism studies show, nevertheless, contradictory results: although some results validate these predictions, other data suggest that paleoclimatic models fail to predict stable rainforest areas in the south. Most studies, however, have surveyed species with relatively high dispersal rates whereas taxa with lower dispersion capabilities should be better predictors of habitat stability. Here, we have used two land planarian species as model organisms to analyse the patterns and levels of nucleotide diversity on a locality within the Southern Atlantic Forest. We find that both species harbour high levels of genetic variability without exhibiting the molecular footprint of recent colonization or population expansions, suggesting a long-term stability scenario. The results reflect, therefore, that paleoclimatic models may fail to detect refugia in the Southern Atlantic Forest, and that model organisms with low dispersal capability can improve the resolution of these models.