Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview

Background: DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes. Results: Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools. Conclusions: DAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

The role of exon shuffling in shaping protein-protein interaction networks

Background: Physical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results: We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners) in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains), self-interacting (able to interact with another copy of themselves) and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions: Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

Dissolved carbon in an urban area of a river in the Brazilian Amazon

The main objective of this study was to evaluate dissolved organic and inorganic carbon dynamics along upstream and downstream reaches of the Acre River draining the city of Rio Branco, in the state of Acre, Brazil. Dissolved organic carbon (DOC) concentrations in the Acre River were significantly higher during the wet season, ranging from 385 +/- A 160 to 430 +/- A 131 mu M among the stations, with no difference in upstream and downstream concentrations. Dissolved inorganic carbon (DIC) showed an inverse pattern, with higher concentrations in the dry season, ranging from 816 +/- A 215 to 998 +/- A 754 mu M among the stations, as well as no difference in upstream and downstream DIC concentrations. Bicarbonate was the dominant DIC fraction and was mainly observed during the dry season. Due to lower pH values during the wet season, CO(2) partial pressure (pCO(2)) in the Acre River was higher in the wet season, with values ranging from 4,567 +/- A 1,813 to 4,893 +/- A 837 ppm among the stations. Our results indicate that, although the Acre River drains a large city with significant sewage disposal into the river, seasonal hydrological processes are the main driver of dissolved carbon dynamics, even in the downstream study reach directly influenced by urbanization.

Coexistence of forest and savanna in an Amazonian area from a geological perspective

Question: How can the coexistence of savanna and forest in Amazonian areas with relatively uniform climates be explained? Location: Eastern Marajo Island, northeast Amazonia, Brazil. Methods: The study integrated floristic analysis, terrain morphology, sedimentology and delta(13)C of soil organic matter. Floristic analysis involved rapid ecological assessment of 33 sites, determination of occurrence, specific richness, hierarchical distribution and matrix of floristic similarity between paired vegetation types. Terrain characterization was based on analysis of Landsat images using 4(R), 5(G) and 7(B) composition and digital elevation model (DEM). Sedimentology involved field descriptions of surface and core sediments. Finally, radiocarbon dating and analysis of delta(13)C of soil profile organic matter and natural ecotone forest-savanna was undertaken. Results: Slight tectonic subsidence in eastern Marajo Island favours seasonal flooding, making it unsuitable for forest growth. However, this area displays slightly convex-up, sinuous morphologies related to paleochannels, covered by forest. Terra-firme lowland forests are expanding from west to east, preferentially occupying paleochannels and replacing savanna. Slack, running water during channel abandonment leads to disappearance of varzea/gallery forest at channel margins. Long-abandoned channels sustain continuous terra-firme forests, because of longer times for more species to establish. Recently abandoned channels have had less time to become sites for widespread tree development, and are either not vegetated or covered by savanna. Conclusion: Landforms in eastern Marajo Island reflect changes in the physical environment due to reactivation of tectonic faults during the latest Quaternary. This promoted a dynamic history of channel abandonment, which controlled a set of interrelated parameters (soil type, topography, hydrology) that determined species location. Inclusion of a geological perspective for paleoenvironmental reconstruction can increase understanding of plant distribution in Amazonia.

Rare canopy species in communities within the Atlantic Coastal Forest in Rio de Janeiro State, Brazil

Rare species are one of the principal components of the species richness and diversity encountered in Dense Ombrophilous Tropical Forests. This study sought to analyze the rare canopy species within the Atlantic Coastal Forest in Rio de Janeiro State, Brazil. Six different communities were examined: Dense Ombrophilous alluvial Forest; Dense sub-montane Ombrophilous Forest; Dense Montane Ombrophilous in Serra do Mar and Serra da Mantiqueira. In each area the vegetation was sampled within forty 10 x 25 m plots alternately distributed along a linear transect. All trees with DBH (1.3 m above ground level) a parts per thousand yen5 cm were sampled. The canopy was characterized using the allometric relationship between diameter and height, and included all trees with BDH a parts per thousand yen10 cm and height a parts per thousand yen10 m. A total of 64 families, 206 genera, and 542 species were sampled, of which 297 (54.8%) represented rare species (less than one individual per hectare). The percentage of rare species varied from 34 to 50% in each of the different communities sampled. A majority of these rare trees belonged to the Rosidae, and a smaller proportion to the Dilleniidae. It was concluded that there was no apparent pattern to rarity among families, that rarity was probably derived from a number of processes (such as gap formation), and that a great majority of the rare species sampled were consistently rare. This indicates that the restricted geographic distribution and high degree of endemism of many arboreal taxa justifies the conservation of even small fragments of Atlantic Forest.

Colonization and extinction of land planarians (Platyhelminthes, Tricladida) in a Brazilian Atlantic Forest regrowth remnant

Long-term assessments of species assemblages are valuable tools for detecting species ecological preferences and their dispersal tracks, as well as for assessing the possible effects of alien species on native communities. Here we report a 50-year-long study on population dynamics of the four species of land flatworms (Platyhelminthes, Tricladida, Terricola) that have colonized or become extinct in a 70-year-old Atlantic Forest regrowth remnant through the period 1955-2006. On the one hand, the two initially most abundant species, which are native to the study site, Notogynaphallia ernesti and Geoplana multicolor have declined over decades and at present do not exist in the forest remnant. The extinction of these species is most likely related with their preference for open vegetation areas, which presently do not exist in the forest remnant. On the other hand, the neotropical Geoplaninae 1 and the exotic Endeavouria septemlineata were detected in the forest only very recently. The long-term study allowed us to conclude that Geoplaninae 1 was introduced into the study area, although it is only known from the study site. Endeavouria septemlineata, an active predator of the exotic giant African snail, is originally known from Hawaii. This land flatworm species was observed repeatedly in Brazilian anthropogenic areas, and this is the first report of the species in relatively well preserved native forest, which may be evidence of an ongoing adaptive process. Monitoring of its geographic spread and its ecological role would be a good practice for preventing potential damaging effects, since it also feeds on native mollusk fauna, as we observed in lab conditions.

Successful maintenance of a stingless bee population despite a severe genetic bottleneck

Stingless bees play an important ecological role as pollinators of many wild plant species in the tropics and have significant potential for the pollination of agricultural crops. Nevertheless, conservation efforts as well as commercial breeding programmes require better guidelines on the amount of genetic variation that is needed to maintain viable populations. In this context, we carried out a long-term genetic study on the stingless bee Melipona scutellaris to evaluate the population viability consequences of prolonged breeding from a small number of founder colonies. In particular, it was artificially imposed a genetic bottleneck by setting up a population starting from only two founder colonies, and continued breeding from it for a period of over 10 years in a location outside its natural area of occurrence. We show that despite a great reduction in the number of alleles present at both neutral microsatellite loci and the sex-determining locus relative to its natural source population, and an increased frequency in the production of sterile diploid males, the genetically impoverished population could be successfully bred and maintained for at least 10 years. This shows that in stingless bees, breeding from a small stock of colonies may have less severe consequences than previously suspected. In addition, we provide a simulation model to determine the number of colonies that are needed to maintain a certain number of sex alleles in a population, thereby providing useful guidelines for stingless bee breeding and conservation efforts.

The use of contingent valuation for evaluating protected areas in the developing world: Economic valuation of Morro do Diabo State Park, Atlantic Rainforest, Sao Paulo State (Brazil)

The Brazilian Atlantic Rainforest is internationally recognised as one of the most biodiverse and threatened tropical forests in the world [Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B., Kent, J., 2000. Biodiversity hotspots for conservation priorities. Nature 403, 853-858]. The Seasonal Semi-Deciduous Forest is among the most fragmented and threatened biomes of the Atlantic Rainforest Domain. The largest remnant of this biome (35,000 ha) is protected by the Morro do Diabo State Park (MDSP), situated in the area known as the Pontal do Paranapanema, in Sao Paulo State, Brazil. Despite its environmental importance, the park is under political, economic and demographic pressure. The main aim of our research was to estimate the population`s willingness to pay (WTP) for the conservation of MDSP and for the Atlantic Rainforest`s remnants in Sao Paulo State as a whole, by means of the contingent valuation method (CVM). The results featured a high incidence of null WTP and of protest votes. Nevertheless, the population is willing to pay US$ 2,113,548.00/year (R$ 7,080,385.00/year) for the conservation of the MDSP (use and existence values), or US$ 60.39 ha/year (R$ 202.30/ha/year). The results indicate that the preservation value is strongly associated to the population`s ability to pay, increasing with income levels. Qualitative research questions showed that the population considers protected areas to be very important. Still, the valuation of MDSP revealed a gap between the government budget allotted to the park and the value assigned to the area by the public. (C) 2007 Elsevier B.V. All rights reserved.

Correlation of Modified Natural Rubber Properties by Artificial Neural Networks

Natural rubber (NR) is a raw material largely used by the modern industry; however, it is common that chemical modifications must be made to NR in order to improve properties such as hydrophobicity or mechanical resistance. This work deals with the correlation of properties of NR modified with dimethylaminoethylmethacrylate or methylmethacrylate as grafting agents. Dynamic-mechanical behavior and stress/strain relations are very important properties because they furnish essential characteristics of the material such as glass transition temperature and rupture point. These properties are concerned with different physical principles; for this reason, normally they are not related to each other. This work showed that they can be correlated by artificial neural networks (ANN). So, from one type of assay, the properties that as a rule only could be obtained from the other can be extracted by ANN correlation. POLYM. ENG. SCI., 49:499-505, 2009. (c) 2009 Society of Plastics Engineers

Optimization of the AZO dyes decoloration process through neural networks: Determination of the H2O2 addition critical point

The concentration of hydrogen peroxide is an important parameter in the azo dyes decoloration process through the utilization of advanced oxidizing processes, particularly by oxidizing via UV/H2O2. It is pointed out that, from a specific concentration, the hydrogen peroxide works as a hydroxyl radical self-consumer and thus a decrease of the system`s oxidizing power happens. The determination of the process critical point (maximum amount of hydrogen peroxide to be added) was performed through a ""thorough mapping"" or discretization of the target region, founded on the maximization of an objective function objective (constant of reaction kinetics of pseudo-first order). The discretization of the operational region occurred through a feedforward backpropagation neural model. The neural model obtained presented remarkable coefficient of correlation between real and predicted values for the absorbance variable, above 0.98. In the present work, the neural model had, as phenomenological basis the Acid Brown 75 dye decoloration process. The hydrogen peroxide addition critical point, represented by a value of mass relation (F) between the hydrogen peroxide mass and the dye mass, was established in the interval 50 < F < 60. (C) 2007 Elsevier B.V. All rights reserved.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. All rights reserved.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.