Complex networks analysis of language complexity

Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability. Copyright (c) EPLA, 2012

Unveiling the relationship between complex networks metrics and word senses

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012

CLEANING ASSOCIATIONS BETWEEN BIRDS AND HERBIVOROUS MAMMALS IN BRAZIL: STRUCTURE AND COMPLEXITY

Birds that remove ectoparasites and other food material from their hosts are iconic illustrations of mutualistic-commensalistic cleaning associations. To assess the complex pattern of food resource use embedded in cleaning interactions of an assemblage of birds and their herbivorous mammal hosts in open habitats in Brazil, we used a network approach that characterized their patterns of association. Cleaning interactions showed a distinctly nested pattern, related to the number of interactions of cleaners and hosts and to the range of food types that each host species provided. Hosts that provided a wide range of food types (flies, ticks, tissue and blood, and organic debris) were attended by more species of cleaners and formed the core of the web. On the other hand, core cleaner species did not exploit the full range of available food resources, but used a variety of host species to exploit these resources instead. The structure that we found indicates that cleaners rely on cleaning interactions to obtain food types that would not be available otherwise (e.g., blood-engorged ticks or horseflies, wounded tissue). Additionally, a nested organization for the cleaner bird mammalian herbivore association means that both generalist and selective species take part in the interactions and that partners of selective species form an ordered subset of the partners of generalist species. The availability of predictable protein-rich food sources for birds provided by cleaning interactions may lead to an evolutionary pathway favoring their increased use by birds that forage opportunistically. Received 30 June 2011, accepted 10 November 2011.

Radiation of the Tnt1 retrotransposon superfamily in three Solanaceae genera

Abstract Background Tnt1 was the first active plant retrotransposon identified in tobacco after nitrate reductase gene disruption. The Tnt1 superfamily comprises elements from Nicotiana (Tnt1 and Tto1) and Lycopersicon (Retrolyc1 and Tlc1) species. The study presented here was conducted to characterise Tnt1-related sequences in 20 wild species of Solanum and five cultivars of Solanum tuberosum. Results Tnt1-related sequences were amplified from total genomic DNA using a PCR-based approach. Purified fragments were cloned and sequenced, and clustering analysis revealed three groups that differ in their U3 region. Using a network approach with a total of 453 non-redundant sequences isolated from Solanum (197), Nicotiana (140) and Lycopersicon (116) species, it is demonstrated that the Tnt1 superfamily can be treated as a population to resolve previous phylogenetic multifurcations. The resulting RNAseH network revealed that sequences group according to the Solanaceae genus, supporting a strong association with the host genome, whereas tracing the U3 region sequence association characterises the modular evolutionary pattern within the Tnt1 superfamily. Within each genus, and irrespective of species, nearly 20% of Tnt1 sequences analysed are identical, indicative of being part of an active copy. The network approach enabled the identification of putative "master" sequences and provided evidence that within a genus these master sequences are associated with distinct U3 regions. Conclusion The results presented here support the hypothesis that the Tnt1 superfamily was present early in the evolution of Solanaceae. The evidence also suggests that the RNAseH region of Tnt1 became fixed at the host genus level whereas, within each genus, propagation was ensured by the diversification of the U3 region. Different selection pressures seemed to have acted on the U3 and RNAseH modules of ancestral Tnt1 elements, probably due to the distinct functions of these regions in the retrotransposon life cycle, resulting in both co evolution and adaptation of the element population with its host.

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates

Abstract Background Prostate cancer is a leading cause of death in the male population, therefore, a comprehensive study about the genes and the molecular networks involved in the tumoral prostate process becomes necessary. In order to understand the biological process behind potential biomarkers, we have analyzed a set of 57 cDNA microarrays containing ~25,000 genes. Results Principal Component Analysis (PCA) combined with the Maximum-entropy Linear Discriminant Analysis (MLDA) were applied in order to identify genes with the most discriminative information between normal and tumoral prostatic tissues. Data analysis was carried out using three different approaches, namely: (i) differences in gene expression levels between normal and tumoral conditions from an univariate point of view; (ii) in a multivariate fashion using MLDA; and (iii) with a dependence network approach. Our results show that malignant transformation in the prostatic tissue is more related to functional connectivity changes in their dependence networks than to differential gene expression. The MYLK, KLK2, KLK3, HAN11, LTF, CSRP1 and TGM4 genes presented significant changes in their functional connectivity between normal and tumoral conditions and were also classified as the top seven most informative genes for the prostate cancer genesis process by our discriminant analysis. Moreover, among the identified genes we found classically known biomarkers and genes which are closely related to tumoral prostate, such as KLK3 and KLK2 and several other potential ones. Conclusion We have demonstrated that changes in functional connectivity may be implicit in the biological process which renders some genes more informative to discriminate between normal and tumoral conditions. Using the proposed method, namely, MLDA, in order to analyze the multivariate characteristic of genes, it was possible to capture the changes in dependence networks which are related to cell transformation.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Behavioral and EEG effects of GABAergic manipulation of the nigro-tectal pathway in the Wistar audiogenic rat (WAR) strain II: An EEG wavelet analysis and retrograde neuronal tracer approach

The role of the substantia nigra pars reticulata (SNPr) and superior colliculus (SC) network in rat strains susceptible to audiogenic seizures still remain underexplored in epileptology. In a previous study from our laboratory, the GABAergic drugs bicuculline (BIC) and muscimol (MUS) were microinjected into the deep layers of either the anterior SC (aSC) or the posterior SC (pSC) in animals of the Wistar audiogenic rat (WAR) strain submitted to acoustic stimulation, in which simultaneous electroencephalographic (EEG) recording of the aSC, pSC, SNPr and striatum was performed. Only MUS microinjected into the pSC blocked audiogenic seizures. In the present study, we expanded upon these previous results using the retrograde tracer Fluorogold (FG) microinjected into the aSC and pSC in conjunction with quantitative EEG analysis (wavelet transform), in the search for mechanisms associated with the susceptibility of this inbred strain to acoustic stimulation. Our hypothesis was that the WAR strain would have different connectivity between specific subareas of the superior colliculus and the SNPr when compared with resistant Wistar animals and that these connections would lead to altered behavior of this network during audiogenic seizures. Wavelet analysis showed that the only treatment with an anticonvulsant effect was MUS microinjected into the pSC region, and this treatment induced a sustained oscillation in the theta band only in the SNPr and in the pSC. These data suggest that in WAR animals, there are at least two subcortical loops and that the one involved in audiogenic seizure susceptibility appears to be the pSC-SNPr circuit. We also found that WARs presented an increase in the number of FG + projections from the posterior SNPr to both the aSC and pSC (primarily to the pSC), with both acting as proconvulsant nuclei when compared with Wistar rats. We concluded that these two different subcortical loops within the basal ganglia are probably a consequence of the WAR genetic background. (C) 2012 Elsevier Inc. All rights reserved.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Forum: social network for the surveillance and prevention of workplace accidents

In 2008, academic researchers and public service officials created a university extension studies platform based on online and on-site meetings denominated "Work-Related Accidents Forum: Analysis, Prevention, and Other Relevant Aspects. Its aim was to help public agents and social partners to propagate a systemic approach that would be helpful in the surveillance and prevention of work-related accidents. This article describes and analyses such a platform. Online access is free and structured to: support dissemination of updated concepts; support on-site meetings and capacity to build educational activities; and keep a permanent space for debate among the registered participants. The desired result is the propagation of a social-technical-systemic view of work-related accidents that replaces the current traditional view that emphasizes human error and results in blaming the victims. The Forum uses an educational approach known as permanent health education, which is based on the experience and needs of workers and encourages debate among participants. The forum adopts a problematizing pedagogy that starts from the requirements and experiences of the social actors and stimulates support and discussions among them in line with an ongoing health educational approach. The current challenge is to turn the platform into a social networking website in order to broaden its links with society.

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Abstract Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.

Annealed Ising model on hierarchical structures: a transfer matrix approach.

Spin systems in the presence of disorder are described by two sets of degrees of freedom, associated with orientational (spin) and disorder variables, which may be characterized by two distinct relaxation times. Disordered spin models have been mostly investigated in the quenched regime, which is the usual situation in solid state physics, and in which the relaxation time of the disorder variables is much larger than the typical measurement times. In this quenched regime, disorder variables are fixed, and only the orientational variables are duly thermalized. Recent studies in the context of lattice statistical models for the phase diagrams of nematic liquid-crystalline systems have stimulated the interest of going beyond the quenched regime. The phase diagrams predicted by these calculations for a simple Maier-Saupe model turn out to be qualitative different from the quenched case if the two sets of degrees of freedom are allowed to reach thermal equilibrium during the experimental time, which is known as the fully annealed regime. In this work, we develop a transfer matrix formalism to investigate annealed disordered Ising models on two hierarchical structures, the diamond hierarchical lattice (DHL) and the Apollonian network (AN). The calculations follow the same steps used for the analysis of simple uniform systems, which amounts to deriving proper recurrence maps for the thermodynamic and magnetic variables in terms of the generations of the construction of the hierarchical structures. In this context, we may consider different kinds of disorder, and different types of ferromagnetic and anti-ferromagnetic interactions. In the present work, we analyze the effects of dilution, which are produced by the removal of some magnetic ions. The system is treated in a “grand canonical" ensemble. The introduction of two extra fields, related to the concentration of two different types of particles, leads to higher-rank transfer matrices as compared with the formalism for the usual uniform models. Preliminary calculations on a DHL indicate that there is a phase transition for a wide range of dilution concentrations. Ising spin systems on the AN are known to be ferromagnetically ordered at all temperatures; in the presence of dilution, however, there are indications of a disordered (paramagnetic) phase at low concentrations of magnetic ions.

Too Big to Ignore: Global Partnership on Small-Scale Fisheries Research - the Latin America and Caribbean network

Too Big to Ignore (TBTI; www.toobigtoignore.net) is a research network and knowledge mobilization partnership established to elevate the profile of small-scale fisheries (SSF), to argue against their marginalization in national and international policies, and to develop research and governance capacity to address global fisheries challenges. Network participants and partners are conducting global and comparative analyses, as well as in-depth studies of SSF in the context of local complexity and dynamics, along with a thorough examination of governance challenges, to encourage careful consideration of this sector in local, regional and global policy arenas. Comprising 15 partners and 62 researchers from 27 countries, TBTI conducts activities in five regions of the world. In Latin America and the Caribbean (LAC) region, we are taking a participative approach to investigate and promote stewardship and self-governance in SSF, seeking best practices and success stories that could be replicated elsewhere. As well, the region will focus to promote sustainable livelihoods of coastal communities. Key activities include workshops and stakeholder meetings, facilitation of policy dialogue and networking, as well as assessing local capacity needs and training. Currently, LAC members are putting together publications that examine key issues concerning SSF in the region and best practices, with a first focus on ecosystem stewardship. Other planned deliverables include comparative analysis, a regional profile on the top research issues on SSF, and a synthesis of SSF knowledge in LAC