The dynamical state of galaxy groups and their luminosity content

We analyse the dependence of the luminosity function (LF) of galaxies in groups on group dynamical state. We use the Gaussianity of the velocity distribution of galaxy members as a measurement of the dynamical equilibrium of groups identified in the Sloan Digital Sky Survey Data Release 7 by Zandivarez & Martinez. We apply the Anderson-Darling goodness-of-fit test to distinguish between groups according to whether they have Gaussian or non-Gaussian velocity distributions, i.e. whether they are relaxed or not. For these two subsamples, we compute the (0.1)r-band LF as a function of group virial mass and group total luminosity. For massive groups, , we find statistically significant differences between the LF of the two subsamples: the LFs of groups that have Gaussian velocity distributions have a brighter characteristic absolute magnitude (similar to 0.3 mag) and a steeper faint-end slope (similar to 0.25). We detect a similar effect when comparing the LF of bright [M-0.1r(group) - 5log(h) < -23.5] Gaussian and non-Gaussian groups. Our results indicate that, for massive/luminous groups, the dynamical state of the system is directly related to the luminosity of its galaxy members.

Using complex networks to quantify consistency in the use of words

In this paper we have quantified the consistency of word usage in written texts represented by complex networks, where words were taken as nodes, by measuring the degree of preservation of the node neighborhood. Words were considered highly consistent if the authors used them with the same neighborhood. When ranked according to the consistency of use, the words obeyed a log-normal distribution, in contrast to Zipf's law that applies to the frequency of use. Consistency correlated positively with the familiarity and frequency of use, and negatively with ambiguity and age of acquisition. An inspection of some highly consistent words confirmed that they are used in very limited semantic contexts. A comparison of consistency indices for eight authors indicated that these indices may be employed for author recognition. Indeed, as expected, authors of novels could be distinguished from those who wrote scientific texts. Our analysis demonstrated the suitability of the consistency indices, which can now be applied in other tasks, such as emotion recognition.

Liquid-Liquid Equilibrium Data for the System Lard plus Oleic Acid plus Ethanol + Water at 318.2 K: Cholesterol Distribution Coefficients

This research reports liquid liquid equilibrium data for the system lard (swine fat), cis-9-octadecenoic acid (oleic acid), ethanol, and water at 318.2 K, as well as their correlation with the nonrandom two-liquid (NRTL) and universal quasichemical activity coefficient (UNIQUAC) thermodynamic equations, which have provided global deviations of 0.41 % and 0.53 %, respectively. Additional equilibrium experiments were also performed to obtain cholesterol partition (or distribution) coefficients to verify the availability of the use of ethanol plus water to reduce the cholesterol content in lard. The partition experiments were performed with concentrations of free fatty acids (commercial oleic acid) that varied from (0 to 20) mass % and of water in the solvent that varied from (0 to 18) mass %. The percentage of free fatty acids initially present in lard had a slight effect on the distribution of cholesterol between the phases. Furthermore, the distribution coefficients decreased by adding water in the ethanol; specifically, it resulted in a diminution of the capability of the solvent to remove the cholesterol.

Soil sulfur fractions dynamics and distribution in a tropical grass pasture amended with nitrogen and sulfur fertilizers

Soil sulfur (S) partitioning among the various pools and changes in tropical pasture ecosystems remain poorly understood. Our study aimed to investigate the dynamics and distribution of soil S fractions in an 8-year-old signal grass (Brachiaria decumbens Stapf.) pasture fertilized with nitrogen (N) and S. A factorial combination of two N rates (0 and 600?kg N ha1 y1, as NH4NO3) and two S rates (0 and 60?kg S ha1 y1, as gypsum) were applied to signal grass pastures during 2 y. Cattle grazing was controlled during the experimental period. Organic S was the major S pool found in the tropical pasture soil, and represented 97% to 99% of total S content. Among the organic S fractions, residual S was the most abundant (42% to 67% of total S), followed by ester-bonded S (19% to 42%), and C-bonded S (11% to 19%). Plant-available inorganic SO4-S concentrations were very low, even for the treatments receiving S fertilizers. Low inorganic SO4-S stocks suggest that S losses may play a major role in S dynamics of sandy tropical soils. Nitrogen and S additions affected forage yield, S plant uptake, and organic S fractions in the soil. Among the various soil fractions, residual S showed the greatest changes in response to N and S fertilization. Soil organic S increased in plots fertilized with S following the residual S fraction increment (16.6% to 34.8%). Soils cultivated without N and S fertilization showed a decrease in all soil organic S fractions.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index

Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely "graphenes" and "complex networks", thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two - in-degree (i.e. citation counts) and age of publication - had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness. (C) 2012 Elsevier Ltd. All rights reserved.

Determination of the critical coupling of explosive synchronization transitions in scale-free networks by mean-field approximations

An explosive synchronization can be observed in scale-free networks when Kuramoto oscillators have natural frequencies equal to their number of connections. The present paper reports on mean-field approximations to determine the critical coupling of such explosive synchronization. It has been verified that the equation obtained for the critical coupling has an inverse dependence on the network average degree. This expression differs from those whose frequency distributions are unimodal and even. In this case, the critical coupling depends on the ratio between the first and second statistical moments of the degree distribution. Numerical simulations were also conducted to verify our analytical results.

Effects of myenteric denervation on extracellular matrix fibers and mast cell distribution in normal stomach and gastric lesions

Abstract Background In this study the effect of myenteric denervation induced by benzalconium chloride (BAC) on distribution of fibrillar components of extracellular matrix (ECM) and inflammatory cells was investigated in gastric carcinogenesis induced by N-methyl-N'-nitro-N-nitrosoguanidine (MNNG). Rats were divided in four experimental groups: non-denervated (I) and denervated stomach (II) without MNNG treatment; non-denervated (III) and denervated stomachs (IV) treated with MNNG. For histopathological, histochemical and stereological analysis, sections of gastric fragments were stained with Hematoxylin-Eosin, Picrosirius-Hematoxylin, Gomori reticulin, Weigert's Resorcin-Fuchsin, Toluidine Blue and Alcian-Blue/Safranin (AB-SAF). Results BAC denervation causes an increase in the frequency of reticular and elastic fibers in the denervated (group II) compared to the non-denervated stomachs (group I). The treatment of the animals with MNNG induced the development of adenocarcinomas in non-denervated and denervated stomachs (groups III and IV, respectively) with a notable increase in the relative volume of the stroma, the frequency of reticular fibers and the inflammatory infiltrate that was more intense in group IV. An increase in the frequency of elastic fibers was observed in adenocarcinomas of denervated (group IV) compared to the non-denervated stomachs (group III) that showed degradation of these fibers. The development of lesions (groups III and IV) was also associated with an increase in the mast cell population, especially AB and AB-SAF positives, the latter mainly in the denervated group IV. Conclusions The results show a strong association in the morphological alteration of the ECM fibrillar components, the increased density of mast cells and the development of tumors induced by MNNG in the non-denervated rat stomach or denervated by BAC. This suggests that the study of extracellular and intracellular components of tumor microenvironment contributes to understanding of tumor biology by action of myenteric denervation.

Hormone-regulated expression and distribution of versican in mouse uterine tissues

Abstract Background Remodeling of the extracellular matrix is one of the most striking features observed in the uterus during the estrous cycle and after hormone replacement. Versican (VER) is a hyaluronan-binding proteoglycan that undergoes RNA alternative splicing, generating four distinct isoforms. This study analyzed the synthesis and distribution of VER in mouse uterine tissues during the estrous cycle, in ovariectomized (OVX) animals and after 17beta-estradiol (E2) and medroxyprogesterone (MPA) treatments, either alone or in combination. Methods Uteri from mice in all phases of the estrous cycle, and animals subjected to ovariectomy and hormone replacement were collected for immunoperoxidase staining for versican, as well as PCR and quantitative Real Time PCR. Results In diestrus and proestrus, VER was exclusively expressed in the endometrial stroma. In estrus and metaestrus, VER was present in both endometrial stroma and myometrium. In OVX mice, VER immunoreaction was abolished in all uterine tissues. VER expression was restored by E2, MPA and E2+MPA treatments. Real Time PCR analysis showed that VER expression increases considerably in the MPA-treated group. Analysis of mRNA identified isoforms V0, V1 and V3 in the mouse uterus. Conclusion These results show that the expression of versican in uterine tissues is modulated by ovarian steroid hormones, in a tissue-specific manner. VER is induced in the myometrium exclusively by E2, whereas MPA induces VER deposition only in the endometrial stroma.

Tissue distribution of a plasmid DNA encoding Hsp65 gene is dependent on the dose administered through intramuscular delivery

In order to assess a new strategy of DNA vaccine for a more complete understanding of its action in immune response, it is important to determine the in vivo biodistribution fate and antigen expression. In previous studies, our group focused on the prophylactic and therapeutic use of a plasmid DNA encoding the Mycobacterium leprae 65-kDa heat shock protein (Hsp65) and achieved an efficient immune response induction as well as protection against virulent M. tuberculosis challenge. In the present study, we examined in vivo tissue distribution of naked DNA-Hsp65 vaccine, the Hsp65 message, genome integration and methylation status of plasmid DNA. The DNA-Hsp65 was detectable in several tissue types, indicating that DNA-Hsp65 disseminates widely throughout the body. The biodistribution was dose-dependent. In contrast, RT-PCR detected the Hsp65 message for at least 15 days in muscle or liver tissue from immunized mice. We also analyzed the methylation status and integration of the injected plasmid DNA into the host cellular genome. The bacterial methylation pattern persisted for at least 6 months, indicating that the plasmid DNA-Hsp65 does not replicate in mammalian tissue, and Southern blot analysis showed that plasmid DNA was not integrated. These results have important implications for the use of DNA-Hsp65 vaccine in a clinical setting and open new perspectives for DNA vaccines and new considerations about the inoculation site and delivery system.

Tissue distribution of DNA-Hsp65/TDM-loaded PLGA microspheres and uptake by phagocytic cells

This study aimed to demonstrate that microspheres, used as delivery vehicle of DNA-Hsp65/TDM [plasmid DNA encoding heat shock protein 65 (Hsp65) coencapsulated with trehalose dimycolate (TDM) into PLGA microspheres], are widely spread among several organs after intramuscular administration in BALB/c mice. In general, we showed that these particles were phagocytosed by antigen presenting cells, such as macrophages and dendritic cells. Besides, it was demonstrated herein that draining lymph node cells presented a significant increase in the number of cells expressing costimulatory molecules (CD80 and CD86) and MHC class II, and also that the administration of the DNA-Hsp65/TDM and vector/TDM formulations resulted in the up-regulation of CD80, CD86 and MHC class II expression when compared to control formulations (vector/TDM and empty). Regarding the intracellular trafficking we observed that following phagocytosis, the microspheres were not found in the late endosomes and/or lysosomes, until 15 days after internalization, and we suggest that these constructions were hydrolysed in early compartments. Overall, these data expand our knowledge on PLGA [poly (lactic-co- glycolic acid)] microspheres as gene carriers in vaccination strategies, as well as open perspectives for their potential use in clinical practice.

Distribution of hepatitis c virus (hcv) genotypes in patients with chronic infection from Rondônia, Brazil

Abstract Background Hepatitis C virus (HCV) is an important human pathogen affecting around 3% of the human population. In Brazil, it is estimated that there are approximately 2 to 3 million HCV chronic carriers. There are few reports of HCV prevalence in Rondônia State (RO), but it was estimated in 9.7% from 1999 to 2005. The aim of this study was to characterize HCV genotypes in 58 chronic HCV infected patients from Porto Velho, Rondônia (RO), Brazil. Methods A fragment of 380 bp of NS5B region was amplified by nested PCR for genotyping analysis. Viral sequences were characterized by phylogenetic analysis using reference sequences obtained from the GenBank (n = 173). Sequences were aligned using Muscle software and edited in the SE-AL software. Phylogenetic analyses were conducted using Bayesian Markov chain Monte Carlo simulation (MCMC) to obtain the MCC tree using BEAST v.1.5.3. Results From 58 anti-HCV positive samples, 22 were positive to the NS5B fragment and successfully sequenced. Genotype 1b was the most prevalent in this population (50%), followed by 1a (27.2%), 2b (13.6%) and 3a (9.0%). Conclusions This study is the first report of HCV genotypes from Rondônia State and subtype 1b was found to be the most prevalent. This subtype is mostly found among people who have a previous history of blood transfusion but more detailed studies with a larger number of patients are necessary to understand the HCV dynamics in the population of Rondônia State, Brazil.

Predicting the connectivity of primate cortical networks from topological and spatial node properties

Abstract Background The organization of the connectivity between mammalian cortical areas has become a major subject of study, because of its important role in scaffolding the macroscopic aspects of animal behavior and intelligence. In this study we present a computational reconstruction approach to the problem of network organization, by considering the topological and spatial features of each area in the primate cerebral cortex as subsidy for the reconstruction of the global cortical network connectivity. Starting with all areas being disconnected, pairs of areas with similar sets of features are linked together, in an attempt to recover the original network structure. Results Inferring primate cortical connectivity from the properties of the nodes, remarkably good reconstructions of the global network organization could be obtained, with the topological features allowing slightly superior accuracy to the spatial ones. Analogous reconstruction attempts for the C. elegans neuronal network resulted in substantially poorer recovery, indicating that cortical area interconnections are relatively stronger related to the considered topological and spatial properties than neuronal projections in the nematode. Conclusion The close relationship between area-based features and global connectivity may hint on developmental rules and constraints for cortical networks. Particularly, differences between the predictions from topological and spatial properties, together with the poorer recovery resulting from spatial properties, indicate that the organization of cortical networks is not entirely determined by spatial constraints.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.